\nThe output of
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.28.3\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 | packaged by Anaconda, Inc. | (main, Jun 5 2025, 13:09:17) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-5.15.0-161-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.6.85\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : GPU 0: NVIDIA L40S\nNvidia driver version : 550.163.01\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.5.1\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 16\nOn-line CPU(s) list: 0-15\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Gold 6338 CPU @ 2.00GHz\nCPU family: 6\nModel: 106\nThread(s) per core: 2\nCore(s) per socket: 8\nSocket(s): 1\nStepping: 6\nBogoMIPS: 3990.65\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq dtes64 ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid fsrm md_clear arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 512 KiB (16 instances)\nL1i cache: 512 KiB (16 instances)\nL2 cache: 32 MiB (8 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-15\nVulnerability Gather data sampling: Unknown: Dependent on hypervisor status\nVulnerability Indirect target selection: Mitigation; Aligned branch/return thunks\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional;",
"url": "https://github.com/vllm-project/vllm/issues/31353",
"state": "open",
"labels": [
"bug",
"help wanted"
],
"created_at": "2025-12-25T13:56:52Z",
"updated_at": "2025-12-27T15:55:34Z",
"comments": 1,
"user": "aravilli"
},
{
"repo": "sgl-project/sglang",
"number": 15825,
"title": "Is it normal that Qwen3-30B-A3B runs slower than Qwen3-8B?",
"body": "I serve two models on the Ascend 910 platform (following sglang's ascend examples) with the same tp2dp8 and benchmarked them. \nBefore testing, I suppose A3B will be faster than 8B for fewer activated tensor blocks. \nBut the result is different:\n### qwen 30B A3B\n```\nexport SGLANG_SET_CPU_AFFINITY=1\nexport PYTORCH_NPU_ALLOC_CONF=expandable_segments:True\nexport STREAMS_PER_DEVICE=32\nexport HCCL_BUFFSIZE=1536\nexport HCCL_OP_EXPANSION_MODE=AIV\nexport SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=32\nexport SGLANG_DEEPEP_BF16_DISPATCH=1\nexport ENABLE_ASCEND_MOE_NZ=1\n\npython -m sglang.launch_server \\\n --device npu \\\n --attention-backend ascend \\\n --trust-remote-code \\\n --tp-size 2 \\\n --dp-size 8 \\\n --model **Qwen/Qwen3-30B-A3B-Instruct-2507** \\\n --model-path /models/Qwen3-30B-A3B-Instruct-2507 \\\n --port 30111 \\\n --mem-fraction-static 0.8\n```\n```\n============ Serving Benchmark Result ============\nBackend: sglang \nTraffic request rate: inf \nMax request concurrency: not set \nSuccessful requests: 1000 \nBenchmark duration (s): 69.68 \nTotal input tokens: 3055233 \nTotal input text tokens: 3055233 \nTotal input vision tokens: 0 \nTotal generated tokens: 513413 \nTotal generated tokens (retokenized): 512578 \nRequest throughput (req/s): 14.35 \nInput token throughput (tok/s): 43846.56 \n**Output token throughput (tok/s): 7368.14** \nPeak output token throughput (tok/s): 12775.00 \nPeak concurrent requests: 1000 \nTotal token throughput (tok/s): 51214.70 \nConcurrency: 665.97 \n----------------End-to-End Latency----------------\nMean E2E Latency (ms): 46404.83 \nMedian E2E Latency (ms): 49605.93 \n---------------Time to First Token----------------\nMean TTFT (ms): 10682.85 \nMedian TTFT (ms): 9808.31 \nP99 TTFT (ms): 16320.45 \n-----Time per Output Token (excl. 1st token)------\nMean TPOT (ms): 96.14 \nMedian TPOT (ms): 75.08 \nP99 TPOT (ms): 399.24 \n---------------Inter-Token Latency----------------\nMean ITL (ms): 69.71 \nMedian ITL (ms): 69.43 \nP95 ITL (ms): 80.73 \nP99 ITL (ms): 96.53 \nMax ITL (ms): 5450.67 \n==================================================\n```\n\n### Qwen3 8B\n\n```\nexport SGLANG_SET_CPU_AFFINITY=1\nexport PYTORCH_NPU_ALLOC_CONF=expandable_segments:True\nexport STREAMS_PER_DEVICE=32\nexport HCCL_BUFFSIZE=1536\nexport HCCL_OP_EXPANSION_MODE=AIV\n\nASCEND_RT_VISIBLE_DEVICES=0 python -m sglang.launch_server \\\n --device npu \\\n --attention-backend ascend \\\n --trust-remote-code \\\n --model Qwen/Qwen3-8B \\\n --model-path /models/Qwen3-8B \\\n --port 30111 \\\n --mem-fraction-static 0.8 \\\n --tp-size 2 \\\n --dp-size 8 \n```\n```\n============ Serving Benchmark Result ============\nBackend: sglang \nTraffic request rate: inf \nMax request concurrency: not set \nSuccessful requests: 1000 \nBenchmark duration (s): 49.67 \nTotal input tokens: 3055233 \nTotal input text tokens: 3055233 \nTotal input vision tokens: 0 \nTotal generated tokens: 513413 \nTotal generated tokens (retokenized): 512976 \nRequest throughput (req/s): 20.13 \nInput token throughput (tok/s): 61513.14 \n**Output token throughput (tok/s): 10336.90** \nPeak output token throughput (tok/s): 23242.00 \nPeak concurrent requests: 1000 \nTotal token throughput (tok/s): 71850.04 \nConcurrency: 709.69 \n----------------End-to-End Latency----------------\nMean E2E Latency (ms): 35249.04 \nMedian E2E Latency (ms): 36490.95 \n---------------Time to First Token----------------\nMean TTFT (ms): 10977.22 \nMedian TTFT (ms): 9339.57 \nP99 TTFT (ms): 16697.36 \n-----Time per Output Token (excl. 1st token)------\nMean TPOT (ms): 82.35 \nMedian TPOT (ms): 48.71 \nP99 TPOT (ms): 516.74 \n---------------Inter-Token Latency----------------\nMean ITL (ms): 47.37 \nMedian ITL (ms): 35.12 \nP95 ITL (ms): 105.74 \nP99 ITL (ms): 463.46 \nMax I",
"url": "https://github.com/sgl-project/sglang/issues/15825",
"state": "open",
"labels": [],
"created_at": "2025-12-25T11:26:10Z",
"updated_at": "2025-12-25T11:26:10Z",
"comments": 0,
"user": "yucc-leon"
},
{
"repo": "vllm-project/vllm",
"number": 31344,
"title": "[Usage]: how to pass param logits_processors in AsyncEngineArgs?",
"body": "### Your current environment\n import torch\nfrom transformers import LogitsProcessor\nfrom transformers.generation.logits_process import _calc_banned_ngram_tokens\nfrom typing import List, Set\n\n\nclass NoRepeatNGramLogitsProcessor(LogitsProcessor):\n\n def __init__(self, ngram_size: int, window_size: int = 100, whitelist_token_ids: set = None):\n if not isinstance(ngram_size, int) or ngram_size <= 0:\n raise ValueError(f\"`ngram_size` has to be a strictly positive integer, but is {ngram_size}\")\n if not isinstance(window_size, int) or window_size <= 0:\n raise ValueError(f\"`window_size` has to be a strictly positive integer, but is {window_size}\")\n self.ngram_size = ngram_size\n self.window_size = window_size\n self.whitelist_token_ids = whitelist_token_ids or set()\n \n def __call__(self, input_ids: List[int], scores: torch.FloatTensor) -> torch.FloatTensor:\n if len(input_ids) < self.ngram_size:\n return scores\n \n current_prefix = tuple(input_ids[-(self.ngram_size - 1):])\n \n search_start = max(0, len(input_ids) - self.window_size)\n search_end = len(input_ids) - self.ngram_size + 1\n \n banned_tokens = set()\n for i in range(search_start, search_end):\n ngram = tuple(input_ids[i:i + self.ngram_size])\n if ngram[:-1] == current_prefix:\n banned_tokens.add(ngram[-1])\n \n banned_tokens = banned_tokens - self.whitelist_token_ids\n \n if banned_tokens:\n scores = scores.clone()\n for token in banned_tokens:\n scores[token] = -float(\"inf\")\n \n return scores\n\n\n\n async def stream_generate(image=None, prompt=''):\n logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=30, window_size=90,\n whitelist_token_ids={128821, 128822})] # whitelist: , \n #\u9ad8\u7248\u672c\n logits_processors_config: list[Dict[str, Any]] = [\n {\n \"class\": NoRepeatNGramLogitsProcessor, # \u4f20\u5165\u7c7b\u5bf9\u8c61\n \"kwargs\": { # \u521d\u59cb\u5316\u53c2\u6570\n \"ngram_size\": 30,\n \"window_size\": 90,\n \"whitelist_token_ids\": {128821, 128822}\n }\n }\n ]\n \n engine_args = AsyncEngineArgs(\n model=MODEL_PATH,\n #hf_overrides={\"architectures\": [\"DeepseekOCRForCausalLM\"]},\n block_size=256,\n max_model_len=8192,\n enforce_eager=False,\n trust_remote_code=True, \n tensor_parallel_size=1,\n gpu_memory_utilization=0.75,\n logits_processors=logits_processors_config\n )\n engine = AsyncLLMEngine.from_engine_args(engine_args)\n \n\nerror:\n\".local/lib/python3.13/site-packages/vllm/engine/arg_utils.py\", line 1189, in create_model_config\n return ModelConfig(\n model=self.model,\n ...<46 lines>...\n io_processor_plugin=self.io_processor_plugin,\n )\n File \"/.local/lib/python3.13/site-packages/pydantic/_internal/_dataclasses.py\", line 121, in __init__\n s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)\n ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\npydantic_core._pydantic_core.ValidationError: 2 validation errors for ModelConfig\nlogits_processors.0.str\n Input should be a valid string [type=string_type, input_value={'class': tag ",
"body": "### Your current environment\n\nI am on docker nightly vLLM API server version 0.14.0rc1.dev104+g8ee90c83f\n\n\n### \ud83d\udc1b Describe the bug\n\n\nI hosted the model via vllm and already without reasoning_parser, I found the model output with directly output without but having close tag later. \n\n```\nroot@iv-ydzbs5zshss6ipm6s5gu /h/n/d/ark_http_proxy# curl --location 'http://localhost/v1/chat/completions' \\\n --header 'Authorization: Bearer YOUR_API_KEY' \\\n --header 'Content-Type: application/json' \\\n --data '{\n \"model\": \"GLM-4.7-FP8\", \"stream\": true,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"what is cryptography\"\n }\n ],\"chat_template_kwargs\": {\"enable_thinking\": true}, \"skip_special_tokens\": false,\n \"thinking\": {\n \"type\": \"enabled\"\n },\n \"max_tokens\": 1024,\n \"temperature\": 1.0\n }'\ndata: {\"id\":\"chatcmpl-9fbc092d919f9e51\",\"object\":\"chat.completion.chunk\",\"created\":1766599479,\"model\":\"GLM-4.7-FP8\",\"choices\":[{\"index\":0,\"delta\":{\"role\":\"assistant\",\"content\":\"\",\"reasoning_content\":null},\"logprobs\":null,\"finish_reason\":null}],\"prompt_token_ids\":null}\n\ndata: {\"id\":\"chatcmpl-9fbc092d919f9e51\",\"object\":\"chat.completion.chunk\",\"created\":1766599479,\"model\":\"GLM-4.7-FP8\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"1\",\"reasoning_content\":null},\"logprobs\":null,\"finish_reason\":null,\"token_ids\":null}]}\n\ndata: {\"id\":\"chatcmpl-9fbc092d919f9e51\",\"object\":\"chat.completion.chunk\",\"created\":1766599479,\"model\":\"GLM-4.7-FP8\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\". \",\"reasoning_content\":null},\"logprobs\":null,\"finish_reason\":null,\"token_ids\":null}]}\n\ndata: {\"id\":\"chatcmpl-9fbc092d919f9e51\",\"object\":\"chat.completion.chunk\",\"created\":1766599479,\"model\":\"GLM-4.7-FP8\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\" **An\",\"reasoning_content\":null},\"logprobs\":null,\"finish_reason\":null,\"token_ids\":null}]}\n\ndata: {\"id\":\"chatcmpl-9fbc092d919f9e51\",\"object\":\"chat.completion.chunk\",\"created\":1766599479,\"model\":\"GLM-4.7-FP8\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"alyze the\",\"reasoning_content\":null},\"logprobs\":null,\"finish_reason\":null,\"token_ids\":null}]}\n```\nI confirmed that chat template will \n\n```\nroot@iv-ydzbs5zshss6ipm6s5gu /h/n/d/ark_http_proxy# curl -sS 'http://127.0.0.1/tokenize' \\\n -H 'Content-Type: application/json' \\\n -d '{\"model\":\"GLM-4.7-FP8\",\"messages\":[{\"role\":\"user\",\"content\":\"hi\"}],\"add_generation_prompt\":true,\"return_token_strs\":true}'\n{\"count\":6,\"max_model_len\":202752,\"tokens\":[151331,151333,151336,6023,151337,151350],\"token_strs\":[\"[gMASK]\",\"\",\"<|user|>\",\"hi\",\"<|assistant|>\",\"\"]}\u23ce \n```\n\n\nI think we need a similar **minimax_m2_append_think** reasoning parser to simply append think to content beginning?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31319",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-24T18:45:34Z",
"updated_at": "2026-01-06T07:59:45Z",
"comments": 16,
"user": "Nemo-G"
},
{
"repo": "vllm-project/vllm",
"number": 31278,
"title": "[Usage]:\u8bf7\u95eeQwen3-VL\u672c\u5730\u52a0\u8f7d\u6a21\u5f0f\u652f\u6301\u5355\u72ec\u52a0\u8f7dLoRA\u4e48\uff1f",
"body": "\u8bf7\u95eeQwen3-VL\u672c\u5730\u52a0\u8f7d\u6a21\u5f0f\u652f\u6301\u5355\u72ec\u52a0\u8f7dLoRA\u4e48\uff1f",
"url": "https://github.com/vllm-project/vllm/issues/31278",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-24T11:33:08Z",
"updated_at": "2025-12-25T03:52:16Z",
"comments": 3,
"user": "dengdeng-cat"
},
{
"repo": "vllm-project/vllm",
"number": 31272,
"title": "[Performance]: b200x8 deepseek-ai/DeepSeek-V3.2-Exp max perf",
"body": "### Proposal to improve performance\n\n_No response_\n\n### Report of performance regression\n\nDo you have any ideas on how to increase TPS? I have two servers \u2014 one with H200 \u00d78 and another with B200 \u00d78. They use the same startup script, but the performance is almost identical. In my opinion, B200 should be faster than H200, so maybe my settings are not optimal\nvllm serve \\\n --model deepseek-ai/DeepSeek-V3.2-Exp \\\n --served-model-name deepseek-ai/DeepSeek-V3.2-Exp \\\n --host 0.0.0.0 \\\n --port 12345 \\\n --tensor-parallel-size 8 \\\n --enable-auto-tool-choice \\\n --tool-call-parser deepseek_v31 \\\n --chat-template /root/tool_chat_template_deepseekv31.jinja \\\n --gpu-memory-utilization 0.9 \\\n --max-model-len 125000 \\\n\n### Misc discussion on performance\n\n_No response_\n\n### Your current environment (if you think it is necessary)\n\n```text\nCollecting environment information...\nuv is set\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.8.0-87-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 13.0.88\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA B200\nGPU 1: NVIDIA B200\nGPU 2: NVIDIA B200\nGPU 3: NVIDIA B200\nGPU 4: NVIDIA B200\nGPU 5: NVIDIA B200\nGPU 6: NVIDIA B200\nGPU 7: NVIDIA B200\n\nNvidia driver version : 580.95.05\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.14.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.14.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.14.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.14.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.14.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.14.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.14.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.14.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 240\nOn-line CPU(s) list: 0-239\nVendor ID: AuthenticAMD\nBIOS Vendor ID: QEMU\nModel name: AMD EPYC 9575F 64-Core Processor\nBIOS Model name: pc-q35-8.2 CPU @ 2.0GHz\nBIOS CPU family: 1\nCPU family: 26\nModel: 2\nThread(s) per core: 1\nCore(s) per socket: 1\nSocket(s): 240\nStepping: 1\nBogoMIPS: 6590.10\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core ssbd ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 clzero xsaveerptr wbnoinvd arat npt lbrv nrip_save tsc_scale vmcb_clean flushbyasid pausefilter pfthreshold v_vmsave_vmload vgif vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid movdiri movdir64b fsrm avx512_vp2intersect flush_l1d arch_capabilities\nVirtualization: AMD-V\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 15 MiB (240 instances)\nL1i cache: 15 MiB (240 instances)\nL2 cache: ",
"url": "https://github.com/vllm-project/vllm/issues/31272",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-12-24T09:48:01Z",
"updated_at": "2025-12-24T10:09:29Z",
"comments": 0,
"user": "evgeniiperepelkin"
},
{
"repo": "huggingface/trl",
"number": 4747,
"title": "Addition of Supervised Reinforcement Learning",
"body": "### Feature request\n\nhttps://arxiv.org/pdf/2510.25992 can i work on its implementation ?\n\n### Motivation\n\nBetter approach then previous RL's\n\n### Your contribution\n\nI can work on it following reference paper ",
"url": "https://github.com/huggingface/trl/issues/4747",
"state": "open",
"labels": [],
"created_at": "2025-12-24T09:20:32Z",
"updated_at": "2025-12-24T09:20:32Z",
"comments": 0,
"user": "kushalgarg101"
},
{
"repo": "vllm-project/vllm",
"number": 31270,
"title": "[Bug]: Can run Speculative decode with PP >2?",
"body": "### Your current environment\n\nvllm:0.12.0\n\n### \ud83d\udc1b Describe the bug\n\nI run vllm:0.12.0 with start args like this: \n`python3 -m vllm.entrypoints.openai.api_server \\\n--host 0.0.0.0 --port 8080 --dtype bfloat16 --model /Qwen3-32B \\\n--pipeline-parallel-size 2 \\\n--gpu-memory-utilization 0.9 --max-model-len 32768 --max-num-batched-tokens 5120 \\\n--trust-remote-code --no-enable-prefix-caching \\\n--speculative_config '{\"method\": \"ngram\",\"num_speculative_tokens\": 10,\"prompt_lookup_max\": 4, \"enforce_eager\": \"True\"}'`\nThe server can start, but when use the interface of '/chat/completion', the vllm server will crash.\n\n### Before submitting a new issue...\n\n- [ ] #31271",
"url": "https://github.com/vllm-project/vllm/issues/31270",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-24T09:10:05Z",
"updated_at": "2025-12-26T07:27:11Z",
"comments": 1,
"user": "frankie-ys"
},
{
"repo": "sgl-project/sglang",
"number": 15739,
"title": "[Bug] Failed to deploy DeepSeek-V3.2 with LMCache",
"body": "### Checklist\n\n- [x] I searched related issues but found no solution.\n- [x] The bug persists in the latest version.\n- [x] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.\n- [x] If this is not a bug report but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [x] Please use English. Otherwise, it will be closed.\n\n### Describe the bug\n\nI use v0.5.6.post2 with LMCache 0.3.10 to deploy DeepSeek-V3.2.\nI got the following error :\n```\n[2025-12-24 08:20:12 PP0 TP2 EP2] Scheduler hit an exception: Traceback (most recent call last):\n File \"/sgl-workspace/sglang/python/sglang/srt/managers/scheduler.py\", line 2680, in run_scheduler_process\n scheduler = Scheduler(\n ^^^^^^^^^^\n File \"/sgl-workspace/sglang/python/sglang/srt/managers/scheduler.py\", line 434, in __init__\n self.init_cache_with_memory_pool()\n File \"/sgl-workspace/sglang/python/sglang/srt/managers/scheduler.py\", line 781, in init_cache_with_memory_pool\n self.tree_cache = LMCRadixCache(\n ^^^^^^^^^^^^^^\n File \"/sgl-workspace/sglang/python/sglang/srt/mem_cache/storage/lmcache/lmc_radix_cache.py\", line 91, in __init__\n getattr(self.token_to_kv_pool_allocator._kvcache, \"k_buffer\"),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'NSATokenToKVPool' object has no attribute 'k_buffer'. Did you mean: 'kv_buffer'?\n```\n\nIs there anything wrong with my configuration? Please advise.\nThanks~\n\n### Reproduction\n\nMy configs:\n\n> lmcache_config.yaml\n```\nchunk_size: 256\n\nlocal_cpu: true\nmax_local_cpu_size: 5.0\n#\nremote_url: \"redis://10.62.207.53:32628\"\nremote_serde: \"naive\"\n```\n\n> master.sh\n```\nexport LMCACHE_CONFIG_PATH=/mnt/scripts/lmcache_config.yaml \nexport LMCACHE_ENABLE=True\npython -m sglang.launch_server \\\n --model-path=/mnt/models/deepseek-ai/DeepSeek-V3.2 \\\n --served-model-name=deepseek-ai/DeepSeek-V3.2 \\\n --tensor-parallel-size=4 \\\n --pipeline-parallel-size=2 \\\n --expert-parallel-size=4 \\\n --data-parallel-size=1 \\\n --enable-dp-attention \\\n --trust-remote-code \\\n --mem-fraction-static=0.8 \\\n --log-requests \\\n --log-requests-level=3 \\\n --dist-init-addr=\"${MASTER_IP}:${PORT}\" \\\n --nnodes=\"$NNODES\" \\\n --node-rank=\"$NODE_RANK\" \\\n --tool-call-parser=deepseekv32 \\\n --reasoning-parser=deepseek-v3 \\\n --host=0.0.0.0 \\\n --port=8000 \\\n --enable-lmcache \\\n --enable-metrics\n```\n\n> worker.sh\n```\nexport LMCACHE_CONFIG_PATH=/mnt/scripts/lmcache_config.yaml \nexport LMCACHE_ENABLE=True\npython -m sglang.launch_server \\\n --model-path=/mnt/models/deepseek-ai/DeepSeek-V3.2 \\\n --served-model-name=deepseek-ai/DeepSeek-V3.2 \\\n --tensor-parallel-size=4 \\\n --pipeline-parallel-size=2 \\\n --expert-parallel-size=4 \\\n --data-parallel-size=1 \\\n --enable-dp-attention \\\n --trust-remote-code \\\n --mem-fraction-static=0.8 \\\n --log-requests \\\n --log-requests-level=3 \\\n --dist-init-addr=\"${MASTER_IP}:${PORT}\" \\\n --nnodes=\"$NNODES\" \\\n --node-rank=\"$NODE_RANK\" \\\n --tool-call-parser=deepseekv32 \\\n --reasoning-parser=deepseek-v3 \\\n --enable-lmcache \\\n --enable-metrics\n```\n\n### Environment\n\nsglang: v0.5.6.post2 \nlmcache: v0.3.10 \nmodel: DeepSeek-V3.2",
"url": "https://github.com/sgl-project/sglang/issues/15739",
"state": "open",
"labels": [],
"created_at": "2025-12-24T08:45:29Z",
"updated_at": "2025-12-29T22:55:27Z",
"comments": 1,
"user": "niceallen"
},
{
"repo": "sgl-project/sglang",
"number": 15710,
"title": "[Bug] Using TBO, but no overlap in decoding phase?",
"body": "### Checklist\n\n- [x] I searched related issues but found no solution.\n- [x] The bug persists in the latest version.\n- [x] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.\n- [x] If this is not a bug report but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [x] Please use English. Otherwise, it will be closed.\n\n### Describe the bug\n\n\n\n### Reproduction\n\npython -m sglang.launch_server --model-path /root/temp_can/DeepSeek-V3-0324 --load-format dummy --tp 4 --ep 4 --moe-a2a-backend deepep --deepep-mode auto --chunked-prefill-size -1 --host 0.0.0.0 --port 30000 --enable-two-batch-overlap --mem-fraction-static 0.4\n\npython3 -m sglang.bench_one_batch_server --model-path /root/temp_can/DeepSeek-V3-0324 --base-url http://127.0.0.1:30000 --batch-size 256 --input-len 64 --output-len 128 --skip-warmup --profile\n\n### Environment\n\n(new_py310) root@zyhuang0-0:~/temp_can/sglang# python3 -m sglang.check_env\nPython: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1: NVIDIA H100 80GB HBM3\nGPU 0,1 Compute Capability: 9.0\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 12.9, V12.9.41\nCUDA Driver Version: 550.54.15\nPyTorch: 2.9.1+cu128\nsglang: 0.5.6.post2\nsgl_kernel: 0.3.19\nflashinfer_python: 0.5.3\nflashinfer_cubin: 0.5.3\nflashinfer_jit_cache: Module Not Found\ntriton: 3.5.1\ntransformers: 4.57.1\ntorchao: 0.9.0\nnumpy: 2.2.6\naiohttp: 3.13.2\nfastapi: 0.127.0\nhf_transfer: 0.1.9\nhuggingface_hub: 0.36.0\ninteregular: 0.3.3\nmodelscope: 1.33.0\norjson: 3.11.5\noutlines: 0.1.11\npackaging: 25.0\npsutil: 7.1.3\npydantic: 2.12.5\npython-multipart: 0.0.21\npyzmq: 27.1.0\nuvicorn: 0.40.0\nuvloop: 0.22.1\nvllm: Module Not Found\nxgrammar: 0.1.27\nopenai: 2.6.1\ntiktoken: 0.12.0\nanthropic: 0.75.0\nlitellm: Module Not Found\ndecord2: 3.0.0\nNVIDIA Topology: \n GPU0 GPU1 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 CPU Affinity NUMA Affinity GPU NUMA ID\nGPU0 X NV18 SYS PIX SYS SYS SYS SYS SYS 0-47,96-143 0 N/A\nGPU1 NV18 X SYS SYS SYS SYS SYS PIX SYS 48-95,144-191 1 N/A\nNIC0 SYS SYS X SYS SYS SYS SYS SYS SYS\nNIC1 PIX SYS SYS X SYS SYS SYS SYS SYS\nNIC2 SYS SYS SYS SYS X PXB PXB SYS SYS\nNIC3 SYS SYS SYS SYS PXB X PIX SYS SYS\nNIC4 SYS SYS SYS SYS PXB PIX X SYS SYS\nNIC5 SYS PIX SYS SYS SYS SYS SYS X SYS\nNIC6 SYS SYS SYS SYS SYS SYS SYS SYS X \n\nLegend:\n\n X = Self\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\n PIX = Connection traversing at most a single PCIe bridge\n NV# = Connection traversing a bonded set of # NVLinks\n\nNIC Legend:\n\n NIC0: mlx5_0\n NIC1: mlx5_1\n NIC2: mlx5_2\n NIC3: mlx5_3\n NIC4: mlx5_4\n NIC5: mlx5_5\n NIC6: mlx5_6\n\n\nulimit soft: 1048576",
"url": "https://github.com/sgl-project/sglang/issues/15710",
"state": "open",
"labels": [],
"created_at": "2025-12-24T02:22:19Z",
"updated_at": "2025-12-24T02:22:19Z",
"comments": 0,
"user": "ziyuhuang123"
},
{
"repo": "sgl-project/sglang",
"number": 15707,
"title": "[Feature] diffusion: TurboDiffusion achieves a 200x speedup on a single GPU, bringing video into the second-level era",
"body": "### Checklist\n\n- [ ] If this is not a feature request but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [ ] Please use English. Otherwise, it will be closed.\n\n### Motivation\n\nhttps://github.com/thu-ml/TurboDiffusion\n\nWhen can it be integrated into sglang-diffusion ?\n\n> [\u6e05\u534e\u7cfb DeepSeek \u65f6\u523b\u6765\u4e86\uff0c\u7845\u8c37\u6cb8\u817e\uff01\u5355\u5361 200 \u500d\u52a0\u901f\uff0c\u89c6\u9891\u8fdb\u5165\u79d2\u7ea7\u65f6\u4ee3](https://mp.weixin.qq.com/s/JmHwMsCYr9M39JLy1jAb7A)\n\n### Related resources\n\n_No response_",
"url": "https://github.com/sgl-project/sglang/issues/15707",
"state": "open",
"labels": [],
"created_at": "2025-12-24T01:50:02Z",
"updated_at": "2025-12-30T08:45:43Z",
"comments": 1,
"user": "xiaolin8"
},
{
"repo": "huggingface/transformers",
"number": 43023,
"title": "How to investigate \"CAS service error\" during model downloading?",
"body": "### System Info\n\n\n(nm) PS C:\\Users\\myuser\\AppData\\Local\\anaconda3\\envs\\nm\\Lib\\site-packages\\transformers\\commands> python .\\transformers_cli.py env\n\n```\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n\n- `transformers` version: 4.57.3\n- Platform: Windows-10-10.0.19045-SP0\n- Python version: 3.10.19\n- Huggingface_hub version: 0.36.0\n- Safetensors version: 0.7.0\n- Accelerate version: not installed\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.7.0 (NA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: the whole code posted below\n```\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nBase example from [here](https://huggingface.co/cross-encoder/ms-marco-MiniLM-L6-v2) \n\n```\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification\nimport torch\n\nmodel = AutoModelForSequenceClassification.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2')\ntokenizer = AutoTokenizer.from_pretrained('cross-encoder/ms-marco-MiniLM-L6-v2')\n\nfeatures = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors=\"pt\")\n\nmodel.eval()\nwith torch.no_grad():\n scores = model(**features).logits\n print(scores)\n```\n\nreturns\n\n```\nmodel.safetensors:\u2007\u2007\u20070%\n\u20070.00/90.9M\u2007[00:32 [1037](file:///C:/Users/myuser\t/AppData/Local/anaconda3/envs/nm/lib/site-packages/transformers/modeling_utils.py:1037) resolved_archive_file = cached_file(pretrained_model_name_or_path, filename, **cached_file_kwargs)\n 1039 # Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None\n 1040 # result when internet is up, the repo and revision exist, but the file does not.\n\nFile c:\\Users\\myuser\\AppData\\Local\\anaconda3\\envs\\nm\\lib\\site-packages\\transformers\\utils\\hub.py:322, in cached_file(path_or_repo_id, filename, **kwargs)\n 269 \"\"\"\n 270 Tries to locate a file in a local folder and repo, downloads and cache it if necessary.\n 271 \n (...)\n 320 ```\n 321 \"\"\"\n--> [322](file:///C:/Users/myuser\t/AppData/Local/anaconda3/envs/nm/lib/site-packages/transformers/utils/hub.py:322) file = cached_files(path_or_repo_id=path_or_repo_id, filenames=[filename], **kwargs)\n 323 file = file[0] if file is not None else file\n\nFile c:\\Users\\myuser\\AppData\\Local\\anaconda3\\envs\\nm\\lib\\site-packages\\transformers\\utils\\hub.py:567, in cached_files(path_or_repo_id, filenames, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\n 566 elif not isinstance(e, EntryNotFoundError):\n--> [567](file:///C:/Users/myuser\t/AppData/Local/anaconda3/envs/nm/lib/site-packages/transformers/utils/hub.py:567) raise e\n 569 resolved_files = [\n 570 _get_cache_file_to_return(path_or_repo_id, filename, cache_dir, revision) for filename in full_filenames\n 571 ]\n\nFile c:\\Users\\myuser\\AppData\\Local\\anaconda3\\envs\\nm\\lib\\site-packages\\transformers\\utils\\hub.py:479, in cached_files(path_or_repo_id, filenames, cache_dir, force_download, resume_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, **deprecated_kwargs)\n 477 if len(full_filenames) == 1:\n 478 # This is slightly better for only 1 file\n--> [479](file:///C:/Users/myuser\t/AppData/Local/anaconda3/envs/nm/lib/site-packages/transformers/utils/hub.py:479) hf_hub_download",
"url": "https://github.com/huggingface/transformers/issues/43023",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-23T14:48:51Z",
"updated_at": "2025-12-25T14:36:42Z",
"user": "satyrmipt"
},
{
"repo": "vllm-project/vllm",
"number": 31217,
"title": "[Usage]: suffix decoding",
"body": "### Your current environment\n\nDoes suffix decoding necessarily require a repetition penalty of 1?\n\n### How would you like to use vllm\n\nDoes suffix decoding necessarily require a repetition penalty of 1?\nIn suffix decoding, I found that when the repetition penalty is not equal to 1, the acceleration is not significant. However, when the repetition penalty is equal to 1, the acceleration is very noticeable.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31217",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-23T10:43:45Z",
"updated_at": "2025-12-24T02:56:35Z",
"comments": 1,
"user": "jiangix-paper"
},
{
"repo": "huggingface/lerobot",
"number": 2707,
"title": "Transformers dependency",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\n- lerobot version: 0.4.3\n- Platform: Linux-5.14.0-570.26.1.el9_6.x86_64-x86_64-with-glibc2.34\n- Python version: 3.12.12\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 2.3.5\n- PyTorch version: 2.7.1\n- Is PyTorch built with CUDA support?: False\n- Cuda version: N/A\n- GPU model: N/A\n- Using GPU in script?: \n```\n\n### Description\n\nHi, \n\nSince commit f04958527e70cac3aa95265badd97b53f3ef7633 and the dependency bump of transformers from 4.53 to 4.57, some extras are conflicting (pi that requires a custom 4.53 and almost all others that requires >=4.57), so I can't upgrade. Was the bump really necessary ? Or is there a possibility to get rid of the custom openpi transformers ?\n\n### Context & Reproduction\n\n[pypi-dependencies]\nlerobot = {git = \"https://github.com/huggingface/lerobot.git\", extras = [\"smolvla\", \"pi\", \"groot\"]}\n\npixi install\n\n### Relevant logs or stack trace\n\n```Shell\n\n```\n\n### Checklist\n\n- [x] I have searched existing tickets to ensure this isn't a duplicate.\n- [x] I am using the latest version of the `main` branch.\n- [x] I have verified this is not an environment-specific problem.\n\n### Additional Info / Workarounds\n\n_No response_",
"url": "https://github.com/huggingface/lerobot/issues/2707",
"state": "closed",
"labels": [
"bug",
"question",
"dependencies"
],
"created_at": "2025-12-23T10:37:53Z",
"updated_at": "2025-12-23T23:43:10Z",
"user": "RomDeffayet"
},
{
"repo": "vllm-project/vllm",
"number": 31216,
"title": "[RFC]: Sampling Optimization: move gather of logits after argmax.",
"body": "### Motivation.\n\nAs shown in the left part of the following picture, in the original sampling procedure we perform `llm_head` and `gather` first, then perform `argmax` to full `logits`. However, we can in fact move `gather` after `argmax` to reduce both the communication volume of `gather` and the computation load of `argmax`.\n\n![\"Image\"](\"https://github.com/user-attachments/assets/6dc7aeb1-3d1f-45e2-98f8-5f2ef9e35cb1\")
\n\nThe test results during the puncturing phase show that this feature can optimize the `logits_processor + sampler` time consumption by more than 200 us in certain scenarios. In speculative decoding scenarios, where multiple rounds of post-processing are required for each step, the benefits of this feature can become even more pronounced. So I think this is an important optimization especially when eagle3 become more and more popular. Later I will propose a PR to implement this.\n\n### Proposed Change.\n\n1. Remove the `gather/all_gather` operation from logits processor.\n2. Add two `gather/all_gather` operations to sampler to gather both max value and max index of `argmax`. Then perform `max` to obtain the global max value and related max index.\n\n### Feedback Period.\n\n_No response_\n\n### CC List.\n\n@youkaichao @zhuohan123 @WoosukKwon\n\n### Any Other Things.\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31216",
"state": "open",
"labels": [
"RFC"
],
"created_at": "2025-12-23T10:23:34Z",
"updated_at": "2025-12-26T03:33:04Z",
"comments": 2,
"user": "whx-sjtu"
},
{
"repo": "huggingface/diffusers",
"number": 12884,
"title": "Compatibility issues regarding checkpoint/VAE dependency conflicts when Diffusers load Civitai LoRA",
"body": "Hello everyone, I'm currently learning to use diffusers and would like to ask all my friends a question. I saw a good lora on Civitai, but this lora has requirements for checkpoint and vea. So I downloaded both models as the author requested. However, when I ran the following code, an error occurred.\nThe specific code is as follows:\n~~~python\npipeline = StableDiffusionPipeline.from_single_file(\n r\"E:\\Project_draw\\Models\\vae\\clearvaeSD15_v23.safetensors\",\n use_safetensors=True,\n torch_dtype=torch.float16,\n safety_checker=None\n )\n~~~\nThe errors are as follows:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/9788f5b9-59d2-454d-ade8-b6837115f0de\")
\n\nI checked the documentation of diffusers. The documentation mentioned that it is possible to load the model in this way, but I don't know why an error occurred. I saw many convert scripts in the script folder of diffusers, but I don't know which one is the corresponding conversion script and what the requirements are. If there are any friends who know how to solve it, could you please tell me what it feels like",
"url": "https://github.com/huggingface/diffusers/issues/12884",
"state": "closed",
"labels": [],
"created_at": "2025-12-23T10:11:27Z",
"updated_at": "2025-12-23T13:41:47Z",
"comments": 1,
"user": "hhhFuture"
},
{
"repo": "vllm-project/vllm",
"number": 31211,
"title": "[Doc]: Add missing GPT-OSS tool calling instructions",
"body": "### \ud83d\udcda The doc issue\n\nCurrently the `openai` tool calling format is not documented in [the tool calling documentation](https://docs.vllm.ai/en/stable/features/tool_calling/). However it is documented in the [cookbook](https://docs.vllm.ai/projects/recipes/en/latest/OpenAI/GPT-OSS.html#tool-use)\n\n### Suggest a potential alternative/fix\n\nIt would make sense to list the `openai` format alongside the other formats\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31211",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-12-23T08:35:09Z",
"updated_at": "2025-12-25T05:29:11Z",
"comments": 0,
"user": "amithkk"
},
{
"repo": "huggingface/lerobot",
"number": 2704,
"title": "Training XVLA: IndexError with auto mode; size mismatch with joint mode on 14D joint-action dataset",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\n\n```\n\n### Description\n\nI am trying to train XVLA with base and folding checkpoint on a 14D joint-action dataset.\nWhen I set --policy.action_mode=auto\nlerobot-train \\\n --dataset.repo_id= \\\n --output_dir=./outputs/xvla_bimanual \\\n --job_name=xvla_training \\\n --policy.dtype=bfloat16 \\\n --steps=3000 \\\n --policy.device=cuda \\\n --policy.action_mode=auto \\\n --policy.max_action_dim=20 \\\n --policy.repo_id= \\\n --policy.path=\"lerobot/xvla-base\" \\\n --policy.freeze_vision_encoder=false \\\n --policy.freeze_language_encoder=false \\\n --policy.train_policy_transformer=true \\\n --policy.train_soft_prompts=true \\\n --rename_map='{\n \"observation.images.top\": \"observation.images.image\",\n \"observation.images.right\": \"observation.images.image2\",\n \"observation.images.left\": \"observation.images.empty_camera_0\"\n}'\nI've got this error:\n\nNFO 2025-12-23 06:44:14 ot_train.py:310 Output dir: outputs/xvla_bimanual\nINFO 2025-12-23 06:44:14 ot_train.py:317 cfg.steps=3000 (3K)\nINFO 2025-12-23 06:44:14 ot_train.py:318 dataset.num_frames=1724070 (2M)\nINFO 2025-12-23 06:44:14 ot_train.py:319 dataset.num_episodes=1613\nINFO 2025-12-23 06:44:14 ot_train.py:322 Effective batch size: 8 x 1 = 8\nINFO 2025-12-23 06:44:14 ot_train.py:323 num_learnable_params=879482456 (879M)\nINFO 2025-12-23 06:44:14 ot_train.py:324 num_total_params=879482456 (879M)\nINFO 2025-12-23 06:44:14 ot_train.py:380 Start offline training on a fixed dataset, with effective batch size: 8\nTraceback (most recent call last):\n File \"/lambda/nfs/XVLA/X-VLA/.venv/bin/lerobot-train\", line 8, in \n sys.exit(main())\n File \"/lambda/nfs/XVLA/lerobot/src/lerobot/scripts/lerobot_train.py\", line 516, in main\n train()\n File \"/lambda/nfs/XVLA/lerobot/src/lerobot/configs/parser.py\", line 233, in wrapper_inner\n response = fn(cfg, *args, **kwargs)\n File \"/lambda/nfs/XVLA/lerobot/src/lerobot/scripts/lerobot_train.py\", line 386, in train\n batch = next(dl_iter)\n File \"/lambda/nfs/XVLA/lerobot/src/lerobot/datasets/utils.py\", line 912, in cycle\n yield next(iterator)\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/accelerate/data_loader.py\", line 579, in __iter__\n next_batch = next(dataloader_iter)\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 733, in __next__\n data = self._next_data()\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1515, in _next_data\n return self._process_data(data, worker_id)\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1550, in _process_data\n data.reraise()\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/torch/_utils.py\", line 750, in reraise\n raise exception\nIndexError: Caught IndexError in DataLoader worker process 2.\nOriginal Traceback (most recent call last):\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 349, in _worker_loop\n data = fetcher.fetch(index) # type: ignore[possibly-undefined]\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 52, in fetch\n data = [self.dataset[idx] for idx in possibly_batched_index]\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 52, in \n data = [self.dataset[idx] for idx in possibly_batched_index]\n File \"/lambda/nfs/XVLA/lerobot/src/lerobot/datasets/lerobot_dataset.py\", line 1028, in __getitem__\n item = self.hf_dataset[idx]\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 2862, in __getitem__\n return self._getitem(key)\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 2843, in _getitem\n pa_subtable = query_table(self._data, key, indices=self._indices)\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/datasets/formatting/formatting.py\", line 612, in query_table\n _check_valid_index_key(key, size)\n File \"/lambda/nfs/XVLA/X-VLA/.venv/lib/python3.10/site-packages/datasets/formatting/formatting.py\", line 552, in _check_valid_index_key\n raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\nIndexError: Invalid key: 1723717 is out of bounds for size 1708592\n\nWhen --policy.action_mode=joint, remove --policy.max_action_dim=20, I've this error:\n\nINFO 2025-12-23 07:14:31 ot_train.py:195 Logs will be saved locally.\nINFO 2025-12-23 07:14:31 ot_train.py:207 Creating dataset\nINFO 2025-12-23 07:14:32 ot_train.py:226 Creating policy\nFlorence2ForConditionalGeneration has generative capabilities, as `prepare_inputs_for_generation` is explicitly defined. However, it doesn't directly inherit from `GenerationMixin`. From \ud83d\udc49v4.50\ud83d\udc48 onwards, `PreTrainedModel` will NOT inherit from `Gen",
"url": "https://github.com/huggingface/lerobot/issues/2704",
"state": "closed",
"labels": [
"bug",
"documentation",
"question",
"policies",
"dataset",
"CI",
"examples",
"training"
],
"created_at": "2025-12-23T07:20:25Z",
"updated_at": "2025-12-23T08:54:21Z",
"user": "DaKhanh"
},
{
"repo": "vllm-project/vllm",
"number": 31205,
"title": "ValueError: Qwen3OmniMoeThinkerForConditionalGeneration does not support LoRA yet.",
"body": "\nhi, I have trained qwen3-omni thinker via ms-swift. However, when I tried to infer qwen3-omni with lora ckpt, an error occurred:\n```\nValueError: Qwen3OmniMoeThinkerForConditionalGeneration does not support LoRA yet.\n```\n\nI have tried many verions of vllm including 0.9.2, 0.11.0 and 0.12.0\n\nhere is my script:\n\n```\nCUDA_VISIBLE_DEVICES=0,1 \\\nMAX_PIXELS=1003520 \\\nswift infer \\\n --model models/omni/Qwen3-Omni/Qwen3-Omni-30B-A3B-Instruct \\\n --adapters ckpt/Qwen3-Omni/v4-20251212-163234/checkpoint-3 \\\n --merge_lora false \\\n --stream true \\\n --infer_backend vllm \\\n --val_dataset ms-swift/data/train_test.jsonl \\\n --vllm_gpu_memory_utilization 0.9 \\\n --vllm_tensor_parallel_size 2 \\\n --vllm_max_model_len 32768 \\\n --max_new_tokens 2048 \\\n --vllm_limit_mm_per_prompt '{'image': 3, 'video': 3, 'audio': 3}'\n\n```\n\n\nhow can I solved this problem? \n",
"url": "https://github.com/vllm-project/vllm/issues/31205",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-23T06:52:11Z",
"updated_at": "2025-12-29T14:50:37Z",
"comments": 2,
"user": "VJJJJJJ1"
},
{
"repo": "vllm-project/vllm",
"number": 31204,
"title": "[RFC]: Supporting Multi MTP layers in Speculative Decoding (EagleProposer)",
"body": "### Motivation.\n\nThe EagleProposer for speculative decoding is only able to utilize the first MTP layer.\nHowever, the model [XiaomiMiMo/MiMo-V2-Flash](https://huggingface.co/XiaomiMiMo/MiMo-V2-Flash) has 3 MTP layers.\nIs there any plan or ongoing PR to extend support for multi MTP layers in speculative decoding?\nbtw, [hugo-wind-ding/qwq-32b-mtp](https://huggingface.co/hugo-wind-ding/qwq-32b-mtp) has 7 mtp layers for QwQ-32B\n\n### Proposed Change.\n\nEagleProposer needs a new member function to pass spec_step_idx to mtp models, when num_nextn_predict_layers > 1 and num_speculative_tokens > 1.\n\n\n### Feedback Period.\n\n_No response_\n\n### CC List.\n\n_No response_\n\n### Any Other Things.\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31204",
"state": "open",
"labels": [
"RFC"
],
"created_at": "2025-12-23T03:34:05Z",
"updated_at": "2025-12-23T03:34:05Z",
"comments": 0,
"user": "DingYibin"
},
{
"repo": "huggingface/lerobot",
"number": 2701,
"title": "Image keys with underscores not supported when migrating to v0.4.x",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\nPython 3.12.3, LeRobot versions 0.3.4 and 0.4.2\n\nFrom v0.4.2:\n lerobot version: 0.4.2\n- Platform: Linux-6.14.0-37-generic-x86_64-with-glibc2.39\n- Python version: 3.12.3\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 2.2.6\n- PyTorch version: 2.7.1+cu126\n```\n\n### Description\n\nWhen upgrading a model from 0.3.4 to 0.4.2, `migrate_policy_normalization` replaces all `_` in features with `.` at https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/migrate_policy_normalization.py#L112 . I have a camera named `front_camera` used as `observations.images.front_camera`. After migrating my policy, the normalization processor expects `observations.images.front.camera`, causing https://github.com/huggingface/lerobot/blob/main/src/lerobot/processor/normalize_processor.py#L306 to fail, and my images are left unnormalized.\n\nI've hacked it by inserting `key = key.replace(\"_cam\",\".cam\")` right above the check, but this is not a good long-term fix.\n\n### Context & Reproduction\n\n1. Have a model trained under LeRobot 0.3.4 with non-identity normalizations on images, and the image keys having underscores, such as `front_camera`\n2. Check the `input_features` in `config.json`, see the underscores in the image name\n3. Migrate the model to LeRobot 0.4.2 using `migrate_policy_normalization.py`\n4. See that the underscores are preserved in the migrated `config.json`, but in the corresponding `policy_preprocessor_step__normalizer_processor.safetensors` they have been replaced with dots\n\n### Relevant logs or stack trace\n\n```Shell\n\n```\n\n### Checklist\n\n- [x] I have searched existing tickets to ensure this isn't a duplicate.\n- [ ] I am using the latest version of the `main` branch.\n- [x] I have verified this is not an environment-specific problem.\n\n### Additional Info / Workarounds\n\nI've inserted `key = key.replace(\"_cam\",\".cam\")` into `normalize_processor.py` in order to make the keys match what the processor is expecting.",
"url": "https://github.com/huggingface/lerobot/issues/2701",
"state": "open",
"labels": [
"bug",
"question",
"policies",
"sensors",
"processor"
],
"created_at": "2025-12-23T03:27:41Z",
"updated_at": "2025-12-23T03:27:50Z",
"user": "dangr"
},
{
"repo": "huggingface/lerobot",
"number": 2700,
"title": "Training an Smolvla model on the lerobot/aloha_sim_insertion_human dataset does not converge",
"body": "### Ticket Type\n\n\u2753 Technical Question\n\n### Environment & System Info\n\n```Shell\nUbuntu 22.04\nlerobot 0.4.1\npython 3.10\n\nlerobot-train \\\n --job_name aloha_smolvla \\\n --output_dir $OUTPUT_DIR \\\n --env.type=aloha \\\n --env.task=\"AlohaInsertion-v0\" \\\n --policy.type=smolvla \\\n --policy.load_vlm_weights=true \\\n --steps=200000 \\\n --eval_freq=50000 \\\n --save_freq=50000 \\\n --dataset.repo_id=\"lerobot/aloha_sim_insertion_human\" \\\n --policy.push_to_hub=false \\\n --wandb.enable=true\n```\n\n### Description\n\nI am tring to train Smolvla model on the lerobot/aloha_sim_insertion_human dataset, but the training does not converge. In the simulation, the robotic arm trembled and got stuck at a certain position, failing to successfully pick up the object.\n\n### Context & Reproduction\n\n```bash\nlerobot-train \\\n --job_name aloha_smolvla \\\n --output_dir $OUTPUT_DIR \\\n --env.type=aloha \\\n --env.task=\"AlohaInsertion-v0\" \\\n --policy.type=smolvla \\\n --policy.load_vlm_weights=true \\\n --steps=200000 \\\n --eval_freq=50000 \\\n --save_freq=50000 \\\n --dataset.repo_id=\"lerobot/aloha_sim_insertion_human\" \\\n --policy.push_to_hub=false \\\n --wandb.enable=true\n\nlerobot-eval \\\n --policy.path=\"$CHECKPOINT_DIR\" \\\n --env.type=aloha \\\n --env.task=\"AlohaInsertion-v0\" \\\n --eval.n_episodes=50 \\\n --eval.batch_size=50\n```\n\nhttps://github.com/user-attachments/assets/97feea83-2f1e-45a3-9253-6dffdd13f7ea\n\n### Relevant logs or stack trace\n\n```Shell\n\n```\n\n### Checklist\n\n- [x] I have searched existing tickets to ensure this isn't a duplicate.\n- [ ] I am using the latest version of the `main` branch.\n- [ ] I have verified this is not an environment-specific problem.\n\n### Additional Info / Workarounds\n\n_No response_",
"url": "https://github.com/huggingface/lerobot/issues/2700",
"state": "open",
"labels": [
"question",
"policies",
"dataset",
"simulation",
"robots",
"training"
],
"created_at": "2025-12-23T03:13:47Z",
"updated_at": "2025-12-30T21:05:50Z",
"user": "sslndora0612-max"
},
{
"repo": "vllm-project/vllm",
"number": 31202,
"title": "[Bug]: Mixtral Fp8 Accuracy is Degraded",
"body": "### Your current environment\n\nH200\n\n### \ud83d\udc1b Describe the bug\n- launch\n```bash\nvllm serve amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV --enforce-eager -tp 2\n```\n\n- eval\n```bash\n\nlm_eval \\\n\t--model local-completions \\\n\t--tasks gsm8k \\\n\t--model_args \"model=amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV,base_url=http://localhost:8000/v1/completions,num_concurrent=1000,tokenized_requests=False\"\n```\n\n- on main:\n```bash\nlocal-completions (model=amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV,base_url=http://localhost:8000/v1/completions,num_concurrent=1000,tokenized_requests=False), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1\n|Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|\n|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|\n|gsm8k| 3|flexible-extract| 5|exact_match|\u2191 |0.2843|\u00b1 |0.0124|\n| | |strict-match | 5|exact_match|\u2191 |0.2108|\u00b1 |0.0112|\n```\n\n- on 0.12.0:\n```bash\nlocal-completions (model=amd/Mixtral-8x7B-Instruct-v0.1-FP8-KV,base_url=http://localhost:8000/v1/completions,num_concurrent=1000,tokenized_requests=False), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 1\n|Tasks|Version| Filter |n-shot| Metric | |Value | |Stderr|\n|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|\n|gsm8k| 3|flexible-extract| 5|exact_match|\u2191 |0.6459|\u00b1 |0.0132|\n| | |strict-match | 5|exact_match|\u2191 |0.6452|\u00b1 |0.0132|\n```\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31202",
"state": "closed",
"labels": [
"bug",
"help wanted"
],
"created_at": "2025-12-23T02:27:28Z",
"updated_at": "2025-12-23T02:42:58Z",
"comments": 1,
"user": "robertgshaw2-redhat"
},
{
"repo": "vllm-project/vllm",
"number": 31200,
"title": "[Bug]: class Request and block_hasher has cirular reference, may cause memory leak.",
"body": "### Your current environment\n\n\n```\n\n### Description\n\nI am running PI0.5 on Libero Benchmark and I encounter with the following issues:\n\n```python\nBuilt vec env | suite=libero_spatial | task_id=6 | n_envs=1\nBuilt vec env | suite=libero_spatial | task_id=7 | n_envs=1\nBuilt vec env | suite=libero_spatial | task_id=8 | n_envs=1\nBuilt vec env | suite=libero_spatial | task_id=9 | n_envs=1\nINFO 2025-12-22 03:42:33 bot_eval.py:499 Making policy.\nThe PI05 model is a direct port of the OpenPI implementation. \nThis implementation follows the original OpenPI structure for compatibility. \nOriginal implementation: https://github.com/Physical-Intelligence/openpi\n`torch_dtype` is deprecated! Use `dtype` instead!\nTraceback (most recent call last):\n File \"/home/yu/miniconda3/envs/lerobot/bin/lerobot-eval\", line 7, in \n sys.exit(main())\n File \"/home/yu/copy/vla/lerobot/src/lerobot/scripts/lerobot_eval.py\", line 763, in main\n eval_main()\n File \"/home/yu/copy/vla/lerobot/src/lerobot/configs/parser.py\", line 233, in wrapper_inner\n response = fn(cfg, *args, **kwargs)\n File \"/home/yu/copy/vla/lerobot/src/lerobot/scripts/lerobot_eval.py\", line 501, in eval_main\n policy = make_policy(\n File \"/home/yu/copy/vla/lerobot/src/lerobot/policies/factory.py\", line 412, in make_policy\n policy = policy_cls.from_pretrained(**kwargs)\n File \"/home/yu/copy/vla/lerobot/src/lerobot/policies/pi05/modeling_pi05.py\", line 893, in from_pretrained\n model = cls(config, **kwargs)\n File \"/home/yu/copy/vla/lerobot/src/lerobot/policies/pi05/modeling_pi05.py\", line 842, in __init__\n self.model = PI05Pytorch(config)\n File \"/home/yu/copy/vla/lerobot/src/lerobot/policies/pi05/modeling_pi05.py\", line 541, in __init__\n raise ValueError(msg) from None\nValueError: An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues\n```\n\nCan I kindly ask is there anyone encounter with similar issues?\n",
"url": "https://github.com/huggingface/lerobot/issues/2697",
"state": "open",
"labels": [
"bug",
"question",
"evaluation"
],
"created_at": "2025-12-22T08:54:56Z",
"updated_at": "2025-12-22T16:20:01Z",
"user": "yqi19"
},
{
"repo": "huggingface/lerobot",
"number": 2696,
"title": "RTC does not work.",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\n- lerobot version: 0.4.3\n- Platform: Linux-5.10.134-17.3.al8.x86_64-x86_64-with-glibc2.35\n- Python version: 3.10.19\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 2.2.6\n- PyTorch version: 2.7.1+cu126\n- Is PyTorch built with CUDA support?: True\n- Cuda version: 12.6\n- GPU model: NVIDIA H20\n- Using GPU in script?: \n```\n\n### Description\n\nI trained pi05 using my own dataset (where the actions are absolute joint angles). The final training loss reached 0.006.\n\nThen I ran examples/rtc/eval_dataset.py.\n`rtc=RTCConfig(enabled=True, prefix_attention_schedule=, max_guidance_weight=100.0, execution_horizon=8, debug=True, debug_maxlen=1000), device='cuda:0', output_dir='rtc_debug_output', seed=42, inference_delay=4, use_torch_compile=False, torch_compile_backend='inductor', torch_compile_mode='default', torch_compile_disable_cudagraphs=True)`\n\n![\"Image\"](\"https://github.com/user-attachments/assets/284b28e6-7098-4614-938b-b15582a573ce\")
\n\n![\"Image\"](\"https://github.com/user-attachments/assets/c1f8794a-ffaa-4ded-b4ce-04195e4011ab\")
\n\n![\"Image\"](\"https://github.com/user-attachments/assets/1486bbff-777a-4405-90a3-73593cd9e02b\")
\n\n![\"Image\"](\"https://github.com/user-attachments/assets/c9f61848-3504-4908-be94-fa8005e75039\")
\n\n![\"Image\"](\"https://github.com/user-attachments/assets/4dfe6dda-1108-43f4-9cd5-0d294aeaff3b\")
\n\nHowever, when I run the same script with the following parameters:\n`\npython examples/rtc/eval_dataset.py \\\n --policy.path=lerobot/pi05_libero_finetuned \\\n --dataset.repo_id=HuggingFaceVLA/libero \\\n --rtc.execution_horizon=8 \\\n --device=cuda\n`\neverything works fine. I would like to know where the error might be occurring.\n\n### Context & Reproduction\n\nThe training parameters are as follows:\n` \"args\": [\n \"--dataset.repo_id=/mnt/model/wlz/real_stack_purple_toy_joint_lerobot\",\n \"--policy.type=pi05\",\n \"--output_dir=./outputs/pi05_training\",\n \"--job_name=pi05_training\",\n \"--policy.repo_id=wlz\",\n \"--policy.pretrained_path=lerobot/pi05_base\",\n \"--policy.compile_model=true\",\n \"--policy.gradient_checkpointing=true\",\n \"--wandb.enable=false\",\n \"--policy.dtype=bfloat16\",\n \"--steps=5000\",\n \"--policy.device=cuda\",\n \"--batch_size=32\",\n \"--policy.input_features={\\\"observation.images.image\\\":{\\\"shape\\\":[3,256,256],\\\"type\\\":\\\"VISUAL\\\"},\\\"observation.images.image2\\\":{\\\"shape\\\":[3,256,256],\\\"type\\\":\\\"VISUAL\\\"},\\\"observation.state\\\":{\\\"shape\\\":[7],\\\"type\\\":\\\"STATE\\\"}}\",\n \"--policy.output_features={\\\"action\\\":{\\\"shape\\\":[7],\\\"type\\\":\\\"ACTION\\\"}}\"\n ]\n`\n\n### Relevant logs or stack trace\n\n```Shell\n\n```\n\n### Checklist\n\n- [ ] I have searched existing tickets to ensure this isn't a duplicate.\n- [ ] I am using the latest version of the `main` branch.\n- [ ] I have verified this is not an environment-specific problem.\n\n### Additional Info / Workarounds\n\n_No response_",
"url": "https://github.com/huggingface/lerobot/issues/2696",
"state": "closed",
"labels": [
"bug",
"question",
"policies",
"dataset",
"CI",
"python",
"examples",
"training"
],
"created_at": "2025-12-22T03:22:23Z",
"updated_at": "2025-12-22T05:20:39Z",
"user": "xiaozhisky1"
},
{
"repo": "huggingface/sentence-transformers",
"number": 3601,
"title": "how to finetuning a bi-encoder embedding model of multimodel input",
"body": "I want to cluster ecommerce products by bi-encoder. For each product, it has a name(text) and an image. Can I use sentence-transfomer to finetune a bi-encoder model? The training dataset contains product clusters, like:\n\n```\nproduct1_name, product1_img, cluster_id1\nproduct2_name, product2_img, cluster_id1\nproduct3_name, product3_img, cluster_id2\n\nproductm_name,productm_img, cluster_idn\n```\n\nI want to try first to define it as a classification problem(cluster_id1,...cluster_idn) and use arcface loss. But If there are other suitable losses, it's also fine.\n\n Is sentence transformer suitable for my use case? I find siglip(something like clip) is good at embedding. Its training data is image/text pair, but my data is not the same as it.\n",
"url": "https://github.com/huggingface/sentence-transformers/issues/3601",
"state": "open",
"labels": [],
"created_at": "2025-12-22T02:46:43Z",
"updated_at": "2025-12-22T09:09:31Z",
"user": "fancyerii"
},
{
"repo": "vllm-project/vllm",
"number": 31096,
"title": "[Usage]: Qwen3-Next: Both Instruct and Thinking models don't support function calling",
"body": "\nDoes the Qwen3-Next model not support the function calling feature? Test results show some common error scenarios:\n1. The tools should be called, but content returned something like the following:\n```\n{\n \"choices\": [\n {\n \"message\": {\n \"content\": \" \\n{\\\"name\\\": \\\"send_email\\\", \\\"arguments\\\": {\\\"userInput\\\": \\\"ALAN\u7684ID\u662f123456\uff0cALAN\uff0c\u4e2d\u56fd\u4eba\uff0c\u82f1\u8bed\u6570\u5b66\u5f88\u725b\\\"}}\\n\",\n \"tool_calls\": []\n },\n \"finish_reason\": \"stop\"\n }\n ]\n}\n```\n```\n{\n \"id\": \"chatcmpl-38af97847cce417a84577fe604d5b31e\",\n \"object\": \"chat.completion\",\n \"created\": 1766119733,\n \"model\": \"Next\",\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"\\n{\\\"name\\\": \\\"get_weather\\\", \\\"arguments\\\": {\\\"location\\\": \\\"San Francisco\\\", \\\"unit\\\": \\\"celsius\\\"}}\\n \",\n \"refusal\": null,\n \"annotations\": null,\n \"audio\": null,\n \"function_call\": null,\n \"tool_calls\": [],\n \"reasoning_content\": null\n },\n \"logprobs\": null,\n \"finish_reason\": \"stop\",\n \"stop_reason\": null,\n \"token_ids\": null\n }\n ],\n \"service_tier\": null,\n \"system_fingerprint\": null,\n \"usage\": {\n \"prompt_tokens\": 619,\n \"total_tokens\": 647,\n \"completion_tokens\": 28,\n \"prompt_tokens_details\": null\n },\n \"prompt_logprobs\": null,\n \"prompt_token_ids\": null,\n \"kv_transfer_params\": null\n}\n```\n2. Returning to the contents of the parameter list may result in non-standard characters, causing parameter retrieval to fail.\n```\n{\n \"choices\": [\n {\n \"message\": {\n \"content\": \"\",\n \"tool_calls\": [\n {\n \"id\": \"chatcmpl-tool-ad840dff072841759d3ed8a26e21391f\",\n \"type\": \"function\",\n \"index\": 0,\n \"function\": {\n \"name\": \"get_weather\",\n \"arguments\": \"\"\n }\n }\n ]\n },\n \"finish_reason\": \"tool_calls\"\n }\n ]\n}\n```\n\n\n",
"url": "https://github.com/vllm-project/vllm/issues/31096",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-21T12:02:08Z",
"updated_at": "2025-12-23T03:02:02Z",
"comments": 0,
"user": "PHOEBEMOON0802"
},
{
"repo": "huggingface/lerobot",
"number": 2694,
"title": "The GT00T algorithm simply won't run and throws the following error. Could someone please help me fix it?",
"body": "The GT00T algorithm simply won't run and throws the following error. Could someone please help me fix it?\n\nn_model.post_layernorm.bias', 'backbone.eagle_model.vision_model.vision_model.post_layernorm.weight']\nTraceback (most recent call last):\n File \"/home/ruijia/miniconda3/envs/lerobot/bin/lerobot-train\", line 7, in \n sys.exit(main())\n File \"/home/ruijia/lerobot_code/lerobot/src/lerobot/scripts/lerobot_train.py\", line 517, in main\n train()\n File \"/home/ruijia/lerobot_code/lerobot/src/lerobot/configs/parser.py\", line 233, in wrapper_inner\n response = fn(cfg, *args, **kwargs)\n File \"/home/ruijia/lerobot_code/lerobot/src/lerobot/scripts/lerobot_train.py\", line 268, in train\n preprocessor, postprocessor = make_pre_post_processors(\n File \"/home/ruijia/lerobot_code/lerobot/src/lerobot/policies/factory.py\", line 252, in make_pre_post_processors\n PolicyProcessorPipeline.from_pretrained(\n File \"/home/ruijia/lerobot_code/lerobot/src/lerobot/processor/pipeline.py\", line 567, in from_pretrained\n loaded_config, base_path = cls._load_config(model_id, config_filename, hub_download_kwargs)\n File \"/home/ruijia/lerobot_code/lerobot/src/lerobot/processor/pipeline.py\", line 638, in _load_config\n cls._suggest_processor_migration(model_id, f\"Config file '{config_filename}' not found\")\n File \"/home/ruijia/lerobot_code/lerobot/src/lerobot/processor/pipeline.py\", line 1212, in _suggest_processor_migration\n raise ProcessorMigrationError(model_path, migration_command, original_error)\nlerobot.processor.pipeline.ProcessorMigrationError: Model '/home/ruijia/llmweights/GR00T-N1.5-3B' requires migration to processor format. Run: python src/lerobot/processor/migrate_policy_normalization.py --pretrained-path /home/ruijia/llmweights/GR00T-N1.5-3B\n\nOriginal error: Config file 'policy_preprocessor.json' not found\n@kashif @ozten @jpizarrom @julien-c @jbcayrou ",
"url": "https://github.com/huggingface/lerobot/issues/2694",
"state": "open",
"labels": [
"bug",
"question",
"policies",
"CI",
"python",
"processor",
"examples",
"training"
],
"created_at": "2025-12-21T09:12:14Z",
"updated_at": "2025-12-24T00:06:08Z",
"user": "wuxiaolianggit"
},
{
"repo": "huggingface/lerobot",
"number": 2693,
"title": "Wrist Roll motor not responding",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\nlerobot version 0.4.0\n```\n\n### Description\n\nI connected to the lerobot so101 bot ->setup motors->callibrated->tested teleoperation\n,everything wewnt fine .But after few hours when recallibration is done in some other system the wrist roll motor of the follower arm went partially stiff and it is not responding . [Used FT SCServo Debugger]\n\n![\"Image\"](\"https://github.com/user-attachments/assets/9c2cfbe3-b041-4847-86ef-a677b97f801a\")
\n\n### Context & Reproduction\n\n_No response_\n\n### Relevant logs or stack trace\n\n```Shell\n\n```\n\n### Checklist\n\n- [x] I have searched existing tickets to ensure this isn't a duplicate.\n- [x] I am using the latest version of the `main` branch.\n- [x] I have verified this is not an environment-specific problem.\n\n### Additional Info / Workarounds\n\n_No response_",
"url": "https://github.com/huggingface/lerobot/issues/2693",
"state": "open",
"labels": [
"bug",
"question",
"teleoperators"
],
"created_at": "2025-12-21T09:01:51Z",
"updated_at": "2025-12-26T10:19:17Z",
"user": "CHIRANJEET1729DAS"
},
{
"repo": "huggingface/lerobot",
"number": 2692,
"title": "[Bug] Too many errors when Train RL in Simulation",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\n`\n- LeRobot version: 0.4.3\n- Platform: Linux-6.8.0-90-generic-x86_64-with-glibc2.35\n- Python version: 3.10.19\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 2.2.6\n- FFmpeg version: N/A\n- PyTorch version: 2.7.1+cu126\n- Is PyTorch built with CUDA support?: True\n- Cuda version: 12.6\n- GPU model: NVIDIA GeForce RTX 4090 D\n- Using GPU in script?: \n- lerobot scripts: ['lerobot-calibrate', 'lerobot-dataset-viz', 'lerobot-edit-dataset', 'lerobot-eval', 'lerobot-find-cameras', 'lerobot-find-joint-limits', 'lerobot-find-port', 'lerobot-imgtransform-viz', 'lerobot-info', 'lerobot-record', 'lerobot-replay', 'lerobot-setup-motors', 'lerobot-teleoperate', 'lerobot-train']\n\n`\n```\n\n### Description\n\nFirst of all, thank you for your excellent open-source work. \nI've noticed that LeRobot's code has undergone some significant refactoring recently, especially the code in the hil-serl section. Therefore, I want to re-test the hil-serl code.\n\nI followed the official documentation step by step, but I encountered many problems and don't know how to solve them. The documents: https://huggingface.co/docs/lerobot/hilserl_sim\n\nFirst, I need Recording a Dataset. Therefore, I executed the following script.\n```shell\npython -m lerobot.rl.gym_manipulator --config_path gym_hil/env_config.json\n```\n\nThe gym_hil/env_config.json is like this:\n```\n{\n \"env\": {\n \"name\": \"gym_hil\",\n \"task\": \"PandaPickCubeKeyboard-v0\",\n \"fps\": 10,\n \"robot\": null,\n \"teleop\": null,\n \"processor\": {\n \"control_mode\": \"gamepad\",\n \"gripper\": {\n \"use_gripper\": true,\n \"gripper_penalty\": -0.02,\n \"gripper_penalty_in_reward\": false\n },\n \"reset\": {\n \"fixed_reset_joint_positions\": [0.0, 0.195, 0.0, -2.43, 0.0, 2.62, 0.785],\n \"reset_time_s\": 2.0,\n \"control_time_s\": 15.0,\n \"terminate_on_success\": true\n }\n }\n },\n \"dataset\": {\n \"repo_id\": \"franka_sim_pick_lift_6\",\n \"root\": \"/mnt/hukongtao/codebase/lerobot/franka_sim_pick_lift_6\",\n \"task\": \"PandaPickCubeKeyboard-v0\",\n \"num_episodes_to_record\": 30,\n \"replay_episode\": 0,\n \"push_to_hub\": false\n },\n \"mode\": \"record\",\n \"device\": \"cpu\"\n}\n```\nfrom https://huggingface.co/api/resolve-cache/datasets/lerobot/config_examples/e9cea127f440dab0eb333f8b8007828ce8f48e23/rl%2Fgym_hil%2Fenv_config.json?%2Fdatasets%2Flerobot%2Fconfig_examples%2Fresolve%2Fmain%2Frl%2Fgym_hil%2Fenv_config.json=&etag=%22a4b2ef62f6cee4e31f608639134c07c7e8d3c4ab%22\n\n\nI got my first error:\n```\ndraccus.utils.DecodingError: `processor.gripper`: Could not decode the value into any of the given types:\n GripperConfig: The fields `gripper_penalty_in_reward` are not valid for GripperConfig\n```\nSo I delete gripper_penalty_in_reward in gym_hil/env_config.json. Then I ran the program again. I got another error:\n```\nTraceback (most recent call last):\n File \"/data/hukongtao/miniconda3/envs/lerobot/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n return _run_code(code, main_globals, None,\n File \"/data/hukongtao/miniconda3/envs/lerobot/lib/python3.10/runpy.py\", line 86, in _run_code\n exec(code, run_globals)\n File \"/mnt/hukongtao/codebase/lerobot/src/lerobot/rl/gym_manipulator.py\", line 770, in \n main()\n File \"/mnt/hukongtao/codebase/lerobot/src/lerobot/configs/parser.py\", line 233, in wrapper_inner\n response = fn(cfg, *args, **kwargs)\n File \"/mnt/hukongtao/codebase/lerobot/src/lerobot/rl/gym_manipulator.py\", line 766, in main\n control_loop(env, env_processor, action_processor, teleop_device, cfg)\n File \"/mnt/hukongtao/codebase/lerobot/src/lerobot/rl/gym_manipulator.py\", line 602, in control_loop\n action_features = teleop_device.action_features\nAttributeError: 'NoneType' object has no attribute 'action_features'\n\n```\nI'm certain this is a bug in the code, because in a simulation environment teleop_device is None. But I don't know how to fix this error.\n\n### Context & Reproduction\n\n```\ngit clone https://github.com/huggingface/lerobot.git\ncd lerobot\npip install -e .[all]\npython -m lerobot.rl.gym_manipulator --config_path gym_hil/env_config.json\n```\n\n### Relevant logs or stack trace\n\n```Shell\n\n```\n\n### Checklist\n\n- [x] I have searched existing tickets to ensure this isn't a duplicate.\n- [x] I am using the latest version of the `main` branch.\n- [x] I have verified this is not an environment-specific problem.\n\n### Additional Info / Workarounds\n\nI tested the hil-serl code in version 0.3.3 of lerobot, and it worked without any problems.",
"url": "https://github.com/huggingface/lerobot/issues/2692",
"state": "open",
"labels": [
"bug",
"documentation",
"question",
"dataset",
"simulation",
"tests",
"examples",
"training"
],
"created_at": "2025-12-21T08:22:16Z",
"updated_at": "2026-01-04T06:19:05Z",
"user": "Hukongtao"
},
{
"repo": "huggingface/accelerate",
"number": 3894,
"title": "How to specify different number of process per node",
"body": "I've 2 node. First node has 8 gpus while second node has 2 GPUs. I want to specify the number of process to be 8 and 2 respectively in both nodes. I'm using this config in both node. But it always tries to divide equal number of process in both node. With below config file, it's starting 5 process in both nodes:-\n\nNode 1:-\n\n```compute_environment: LOCAL_MACHINE\ndebug: false\ndistributed_type: MULTI_GPU\ndowncast_bf16: 'no'\nenable_cpu_affinity: false\ngpu_ids: 0,1,2,3,4,5,6,7\nmachine_rank: 0\nmain_process_ip: xxxxx\nmain_process_port: 5000\nmain_training_function: main\nmixed_precision: fp16\nnum_machines: 2\nnum_processes: 10\nrdzv_backend: static\nsame_network: true\ntpu_env: []\ntpu_use_cluster: false\ntpu_use_sudo: false\nuse_cpu: false \n```\n\nNode 2:-\n```\ncompute_environment: LOCAL_MACHINE\ndebug: false\ndistributed_type: MULTI_GPU\ndowncast_bf16: 'no'\nenable_cpu_affinity: false\ngpu_ids: 0,1\nmachine_rank: 1\nmain_process_ip: xxxx\nmain_process_port: 5000\nmain_training_function: main\nmixed_precision: fp16\nnum_machines: 2\nnum_processes: 10\nrdzv_backend: static\nsame_network: true\ntpu_env: []\ntpu_use_cluster: false\ntpu_use_sudo: false\nuse_cpu: false\n```",
"url": "https://github.com/huggingface/accelerate/issues/3894",
"state": "open",
"labels": [],
"created_at": "2025-12-21T07:09:15Z",
"updated_at": "2025-12-21T07:09:15Z",
"user": "AIML001"
},
{
"repo": "vllm-project/vllm",
"number": 31091,
"title": "[Usage]: Image Embedding Models (CLIP, Siglip, etc)",
"body": "### Your current environment\n\n```text\nroot@3904bdeddb91:/vllm-workspace# python3 collect_env.py\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu129\nIs debug build : False\nCUDA used to build PyTorch : 12.9\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-6.8.0-87-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.9.86\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA RTX PRO 6000 Blackwell Workstation Edition\nGPU 1: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition\n\nNvidia driver version : 580.65.06\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 43 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 64\nOn-line CPU(s) list: 0-63\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7502 32-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 2\nCore(s) per socket: 32\nSocket(s): 1\nStepping: 0\nFrequency boost: enabled\nCPU max MHz: 2500.0000\nCPU min MHz: 1500.0000\nBogoMIPS: 4999.95\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es ibpb_exit_to_user\nVirtualization: AMD-V\nL1d cache: 1 MiB (32 instances)\nL1i cache: 1 MiB (32 instances)\nL2 cache: 16 MiB (32 instances)\nL3 cache: 128 MiB (8 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-63\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection\nVulnerability Spec rstack overflow: Mitigation; Safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Mitigation; IBPB before exit to userspace\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.5.3\n",
"url": "https://github.com/vllm-project/vllm/issues/31091",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-12-21T04:10:10Z",
"updated_at": "2025-12-23T03:26:40Z",
"comments": 2,
"user": "JamesDConley"
},
{
"repo": "huggingface/lerobot",
"number": 2690,
"title": "[Bug] Pi0 Inference RuntimeError: Dimension mismatch in Gemma eager_attention_forward (Causal Mask vs Attn Weights)",
"body": "",
"url": "https://github.com/huggingface/lerobot/issues/2690",
"state": "closed",
"labels": [
"bug",
"question",
"policies",
"dataset",
"CI",
"performance",
"robots",
"examples",
"training"
],
"created_at": "2025-12-20T16:08:36Z",
"updated_at": "2025-12-22T09:34:57Z",
"user": "SMWTDDY"
},
{
"repo": "huggingface/lerobot",
"number": 2689,
"title": "problem regarding to update aloha sim dataset version v2.1 to v3.0",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\nlerobot version 3.0, h100 gpu, openpi repository, training aloha simulation with pi0.5\n```\n\n### Description\n\nDuring training aloha simulation, I updated lerobot aloha sim insertion dataset from compatible with 2.1 to 3.0, the training results showing aloha joints are working weirdly (showing spark of joint actions).\n\nThe dataset conversion followed as below.\n\n```\nlerobot.datasets.backward_compatibility.BackwardCompatibilityError: \nThe dataset you requested (lerobot/aloha_sim_insertion_scripted) is in 2.1 format.\n\nWe introduced a new format since v3.0 which is not backward compatible with v2.1.\nPlease, update your dataset to the new format using this command:\n\npython -m lerobot.datasets.v30.convert_dataset_v21_to_v30 --repo-id=lerobot/aloha_sim_insertion_scripted\n```\n\n### Context & Reproduction\n\n_No response_\n\n### Relevant logs or stack trace\n\n```Shell\n\n```\n\n### Checklist\n\n- [ ] I have searched existing tickets to ensure this isn't a duplicate.\n- [ ] I am using the latest version of the `main` branch.\n- [ ] I have verified this is not an environment-specific problem.\n\n### Additional Info / Workarounds\n\n_No response_",
"url": "https://github.com/huggingface/lerobot/issues/2689",
"state": "open",
"labels": [
"bug",
"question",
"dataset",
"simulation",
"CI",
"robots",
"training"
],
"created_at": "2025-12-20T13:42:39Z",
"updated_at": "2025-12-24T00:06:09Z",
"user": "conscious-choi"
},
{
"repo": "sgl-project/sglang",
"number": 15524,
"title": "[Bug] Deepseek R1 multi-turn tool calling not working",
"body": "### Checklist\n\n- [x] I searched related issues but found no solution.\n- [x] The bug persists in the latest version.\n- [ ] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.\n- [x] If this is not a bug report but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [x] Please use English. Otherwise, it will be closed.\n\n### Describe the bug\n\nThe multi-turn tool calling failed with error: `{\"object\":\"error\",\"message\":\"'dict object' has no attribute 'name'\",\"type\":\"BadRequest\",\"param\":null,\"code\":400}`\n\nHere is the example query:\n```\ncurl http://127.0.0.1:7080/v1/chat/completions -H \"Content-Type: application/json\" -d '{\n \"model\": \"deepseek-ai/DeepSeek-R1\",\n \"stream\": false,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"What is the weather like in San Francisco?\"\n },\n {\n \"role\": \"assistant\",\n \"content\": \"I will check the weather for San Francisco. Please hold on.\",\n \"tool_calls\": [\n {\n \"id\": \"call_ab97cb439a5e41cfbdd8960c\",\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"arguments\": \"{\\\"location\\\": \\\"San Francisco, CA\\\"}\"\n }\n }\n ]\n },\n {\n \"role\": \"tool\",\n \"tool_call_id\": \"call_ab97cb439a5e41cfbdd8960c\",\n \"content\": \"70 degrees and foggy\"\n }\n ],\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"description\": \"Get the current weather\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"The city and state (both required), e.g. San Francisco, CA.\"\n }\n },\n \"required\": [\n \"location\"\n ]\n }\n }\n }\n ]\n}'\n```\n\nHowever, the same query works for the image back in August.\n\n### Reproduction\n\n*) Start server on B200\n```\npython3 -m sglang.launch_server \\\n --model-path nvidia/DeepSeek-R1-0528-NVFP4 \\\n --port 7080 \\\n --host 0.0.0.0 \\\n --tp-size=8 \\\n --ep-size=8 \\\n --moe-runner-backend=flashinfer_trtllm \\\n --enable-flashinfer-allreduce-fusion \\\n --tool-call-parser=deepseekv3 \\\n --chat-template=/sgl-workspace/sglang/examples/chat_template/tool_chat_template_deepseekr1.jinja \\\n --speculative-num-steps=3 \\\n --speculative-eagle-topk=1 \\\n --speculative-num-draft-tokens=4 \\\n --speculative-algorithm=EAGLE \\\n --trust-remote-code\n```\n\n*) send query\n```\ncurl http://127.0.0.1:7080/v1/chat/completions -H \"Content-Type: application/json\" -d '{\n \"model\": \"deepseek-ai/DeepSeek-R1\",\n \"stream\": false,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"What is the weather like in San Francisco?\"\n },\n {\n \"role\": \"assistant\",\n \"content\": \"I will check the weather for San Francisco. Please hold on.\",\n \"tool_calls\": [\n {\n \"id\": \"call_ab97cb439a5e41cfbdd8960c\",\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"arguments\": \"{\\\"location\\\": \\\"San Francisco, CA\\\"}\"\n }\n }\n ]\n },\n {\n \"role\": \"tool\",\n \"tool_call_id\": \"call_ab97cb439a5e41cfbdd8960c\",\n \"content\": \"70 degrees and foggy\"\n }\n ],\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"description\": \"Get the current weather\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"The city and state (both required), e.g. San Francisco, CA.\"\n }\n },\n \"required\": [\n \"location\"\n ]\n }\n }\n }\n ]\n}'\n```\n\n### Environment\n\n```\nPython: 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0]\nCUDA available: True\nGPU 0,1,2,3,4,5,6,7: NVIDIA B200\nGPU 0,1,2,3,4,5,6,7 Compute Capability: 10.0\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 12.9, V12.9.86\nCUDA Driver Version: 580.95.05\nPyTorch: 2.9.1+cu129\nsglang: 0.5.6.post2\nsgl_kernel: 0.3.19\nflashinfer_python: 0.5.3\nflashinfer_cubin: 0.5.3\nflashinfer_jit_cache: Module Not Found\ntriton: 3.5.1\ntransformers: 4.57.1\ntorchao: 0.9.0\nnumpy: 2.3.5\naiohttp: 3.13.2\nfastapi: 0.124.2\nhf_transfer: 0.1.9\nhuggingface_hub: 0.36.0\ninteregular: 0.3.3\nmodelscope: 1.33.0\norjson: 3.11.5\noutlines: 0.1.11\npackaging: 25.0\npsutil: 7.1.3\npydantic: 2.12.5\npython-multipart: 0.0.20\npyzmq: 27.1.0\nuvicorn: 0.38.0\nuvloop: 0.22.1\nvllm: Module Not Found\nxgrammar: 0.1.27\nopenai: 2.6.1\ntiktoken: 0.12.0\nanthropic: 0.75.0\nlitellm: Module Not Found\ndecord2: 2.0.0\nNVIDIA Topology: \n\tGPU0\tGPU1\tGPU2\tGPU3\tGPU4\tGPU5\tGPU6\tGPU7\tCPU Affinity\tNUMA Affinity\tGPU NUMA ID\nGPU0\t X \tNV18\tNV18\tNV18\tNV18\tNV18\tNV18\tNV18\t0-55,112-167\t0\t\tN/A\nGPU1\tNV18\t X \tNV18\tNV18\tNV18\tNV18\tNV18\tNV1",
"url": "https://github.com/sgl-project/sglang/issues/15524",
"state": "closed",
"labels": [],
"created_at": "2025-12-20T10:31:36Z",
"updated_at": "2025-12-21T01:29:43Z",
"comments": 2,
"user": "ynwang007"
},
{
"repo": "vllm-project/vllm",
"number": 31066,
"title": "[Doc]: Formatting issue in markdown file",
"body": "### \ud83d\udcda The doc issue\n\nin [paged_attention.md](https://github.com/vllm-project/vllm/blob/ff2168bca3a195b835c64a5c9012d7b6a9f34e61/docs/design/paged_attention.md#query), there is an issue where a pictures arent formatted correctly and only show the html link .\nFor example, specifically, in the Query subsection, we can see:\n\n`{ align=\"center\" alt=\"q_vecs\" width=\"70%\" }`\n\nThe asset isnt loaded correctly.\n\nThere are a total of **7 such issues**, particularly, we have \n\n- Query subsection - 2 instances.\n- Key subsection - 2 instances.\n- Value subsection - 3 instances\n\n### Suggest a potential alternative/fix\nPerhaps the reference for the images can be checked, it must be broken somewhere\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31066",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-12-20T06:23:44Z",
"updated_at": "2025-12-22T01:38:56Z",
"comments": 1,
"user": "ssaketh-ch"
},
{
"repo": "vllm-project/vllm",
"number": 31044,
"title": "[CI Failure]: Blackwell Fusion Tests",
"body": "### Name of failing test\n\nFAILED tests/compile/test_fusion_attn.py::test_attention_quant_pattern[AttentionBackendEnum.TRITON_ATTN-nvidia/Llama-4-Scout-17B-16E-Instruct-FP8-TestAttentionFp8StaticQuantPatternModel--quant_fp8-dtype1-533-128-40-8] - AssertionError: Tensor-likes are not close!\n\n### Basic information\n\n- [x] Flaky test\n- [ ] Can reproduce locally\n- [ ] Caused by external libraries (e.g. bug in `transformers`)\n\n### \ud83e\uddea Describe the failing test\n\nOn B200:\n\nFAILED tests/compile/test_fusion_attn.py::test_attention_quant_pattern[AttentionBackendEnum.TRITON_ATTN-nvidia/Llama-4-Scout-17B-16E-Instruct-FP8-TestAttentionFp8StaticQuantPatternModel--quant_fp8-dtype1-533-128-40-8] - AssertionError: Tensor-likes are not close!\n\n```bash\npytest -v -x tests/compile/test_fusion_attn.py::test_attention_quant_pattern\n```\n\n### \ud83d\udcdd History of failing test\n\nx\n\n### CC List.\n\nx",
"url": "https://github.com/vllm-project/vllm/issues/31044",
"state": "open",
"labels": [
"help wanted",
"torch.compile",
"ci-failure"
],
"created_at": "2025-12-19T18:49:59Z",
"updated_at": "2025-12-26T21:58:25Z",
"comments": 3,
"user": "robertgshaw2-redhat"
},
{
"repo": "vllm-project/vllm",
"number": 31043,
"title": "[BugFix]: move torch.Size across graphs in split_graph",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nWhen fixing a moe x cudagraph issue (see #30914), we found that `split_graph` may generate a submodule that returns a torch.Size and later another submodule that takes torch.Size. This errors since pt2 somehow does not support `torch.Size` as output yet. \n\nOne fix is to manually reorder some lines in the model code to avoid this split happen between getting the `torch.Size` and using it. But this is too intrusive and requires manual efforts on many models.\n\nA more automated approach is to have a graph pass in `split_graph` to move the torch.Size a bit to avoid patterns like\n\n```\n# Old:\nsize = tensor_a.shape\nsome_cg_unsafe_op\ntensor_b = tensor_b.view(size)\n```\n---->\n\n```\n# New:\nsome_cg_unsafe_op\nsize = tensor_a.shape\ntensor_b = tensor_b.view(size)\n```\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31043",
"state": "open",
"labels": [
"help wanted",
"feature request",
"torch.compile"
],
"created_at": "2025-12-19T18:24:58Z",
"updated_at": "2025-12-22T21:23:04Z",
"comments": 1,
"user": "BoyuanFeng"
},
{
"repo": "vllm-project/vllm",
"number": 31039,
"title": "[Feature]: Integrate Sonic MoE",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nhttps://x.com/wentaoguo7/status/2001773245318541324?s=46&t=jLcDgQXDbYe6HgFmTNYgpg\nhttps://github.com/Dao-AILab/sonic-moe\n\nCurious to see benchmarks!\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31039",
"state": "open",
"labels": [
"help wanted",
"good first issue",
"feature request"
],
"created_at": "2025-12-19T17:29:59Z",
"updated_at": "2026-01-04T14:10:21Z",
"comments": 4,
"user": "robertgshaw2-redhat"
},
{
"repo": "sgl-project/sglang",
"number": 15481,
"title": "[Bug] Seeded Deterministic/Batch Invariant Inference Not Working on v1/completions endpoint",
"body": "### Checklist\n\n- [x] I searched related issues but found no solution.\n- [x] The bug persists in the latest version.\n- [x] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.\n- [x] If this is not a bug report but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [x] Please use English. Otherwise, it will be closed.\n\n### Describe the bug\n\nI\u2019m trying to enable batch-invariant (deterministic) inference while serving SGLang behind an OpenAI API-compatible interface.\n\nDeterministic inference docs: https://docs.sglang.io/advanced_features/deterministic_inference.html\n\n## What works\n\nThe native /generate endpoint correctly varies output by seed and is repeatable per seed.\n\nExample request:\n\nPOST {base}/generate\n```json\n{\n \"text\": \"generate a uuid. UUID:\",\n \"sampling_params\": {\n \"temperature\": 1,\n \"max_new_tokens\": 32,\n \"sampling_seed\": 0\n }\n}\n```\n\nBehavior: changing sampling_seed changes the output; repeating with the same sampling_seed reproduces it.\n\n## What doesn\u2019t work\n\nOn the OpenAI-compatible endpoint POST {base}/v1/completions, seed appears to have no effect (even with temperature=1 and top_p=1).\n\nExample:\n\nPOST {base}/v1/completions\n```json\n{\n \"model\": \"Qwen/Qwen3-30B-A3B\",\n \"prompt\": \"generate a uuid. UUID: \",\n \"max_tokens\": 32,\n \"temperature\": 1,\n \"top_p\": 1,\n \"n\": 1,\n \"seed\": 0\n}\n```\n\nBehavior: response is the same regardless of seed value.\n\nExpected behavior\n\nWith --enable-deterministic-inference, I expected the OpenAI-compatible endpoints to:\n* honor seed as the sampling seed (analogous to sampling_seed), and\n* remain deterministic/repeatable for the same (prompt, params, seed).\n\n### Reproduction\n\nServer launch:\n\n```bash\nexec python3 -m sglang.launch_server \\\n --model-path \"Qwen/Qwen3-30B-A3B\" \\\n --host 0.0.0.0 \\\n --port 8000 \\\n --tp \"1\" \\\n --attention-backend \"triton\" \\\n --context-length \"32000\" \\\n --trust-remote-code \\\n --enable-deterministic-inference\n```\n\nPOST {base}/v1/completions\n```json\n{\n \"model\": \"Qwen/Qwen3-30B-A3B\",\n \"prompt\": \"generate a uuid. UUID: \",\n \"max_tokens\": 32,\n \"temperature\": 1,\n \"top_p\": 1,\n \"n\": 1,\n \"seed\": 0\n}\n```\n\nvarying the seed results in same output\n\n### Environment\n\n==========\n== CUDA ==\n==========\nCUDA Version 12.9.1\nContainer image Copyright (c) 2016-2023, NVIDIA CORPORATION & AFFILIATES. All rights reserved.\nThis container image and its contents are governed by the NVIDIA Deep Learning Container License.\nBy pulling and using the container, you accept the terms and conditions of this license:\nhttps://developer.nvidia.com/ngc/nvidia-deep-learning-container-license\nA copy of this license is made available in this container at /NGC-DL-CONTAINER-LICENSE for your convenience.\nAuto-detected 1 GPU(s)\nPython: 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0]\nCUDA available: True\nGPU 0: NVIDIA RTX PRO 6000 Blackwell Server Edition\nGPU 0 Compute Capability: 12.0\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 12.9, V12.9.86\nCUDA Driver Version: 580.105.08\nPyTorch: 2.9.1+cu129\nsglang: 0.5.6.post2\nsgl_kernel: 0.3.19\nflashinfer_python: 0.5.3\nflashinfer_cubin: 0.5.3\nflashinfer_jit_cache: Module Not Found\ntriton: 3.5.1\ntransformers: 4.57.1\ntorchao: 0.9.0\nnumpy: 2.3.5\naiohttp: 3.13.2\nfastapi: 0.124.2\nhf_transfer: 0.1.9\nhuggingface_hub: 0.36.0\ninteregular: 0.3.3\nmodelscope: 1.33.0\norjson: 3.11.5\noutlines: 0.1.11\npackaging: 25.0\npsutil: 7.1.3\npydantic: 2.12.5\npython-multipart: 0.0.20\npyzmq: 27.1.0\nuvicorn: 0.38.0\nuvloop: 0.22.1\nvllm: Module Not Found\nxgrammar: 0.1.27\nopenai: 2.6.1\ntiktoken: 0.12.0\nanthropic: 0.75.0\nlitellm: Module Not Found\ndecord2: 2.0.0\nNVIDIA Topology:\n\t\u001b[4mGPU0\tNIC0\tCPU Affinity\tNUMA Affinity\tGPU NUMA ID\u001b[0m\nGPU0\t X \tSYS\t0-63,128-191\t0\t\tN/A\nNIC0\tSYS\t X\nLegend:\n X = Self\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\n PIX = Connection traversing at most a single PCIe bridge\n NV# = Connection traversing a bonded set of # NVLinks\nNIC Legend:\n NIC0: mlx5_bond_0\nulimit soft: 1024",
"url": "https://github.com/sgl-project/sglang/issues/15481",
"state": "closed",
"labels": [
"bug",
"high priority"
],
"created_at": "2025-12-19T15:04:26Z",
"updated_at": "2025-12-20T04:32:15Z",
"comments": 8,
"user": "jamesheavey"
},
{
"repo": "huggingface/lerobot",
"number": 2684,
"title": "How to manually push a dataset",
"body": "Say you `lerobot-record` a dataset with the flag `--dataset.push_to_hub=False`, or you encounter any problem at uploading time.\n\nIs using `hf upload` enough, or does `lerobot` datasets need additional stuff?",
"url": "https://github.com/huggingface/lerobot/issues/2684",
"state": "open",
"labels": [
"documentation",
"question",
"dataset"
],
"created_at": "2025-12-19T13:00:20Z",
"updated_at": "2025-12-19T15:41:42Z",
"user": "mcres"
},
{
"repo": "vllm-project/vllm",
"number": 31023,
"title": "[Doc]: FP8 KV Cache: Does softmax output multiply with FP8 V directly or after dequantization?",
"body": "### \ud83d\udcda The doc issue\n\nhttps://docs.vllm.ai/en/v0.8.5.post1/features/quantization/quantized_kvcache.html\nQuestion:\nIn the FP8 KV Cache implementation, after computing attention scores and softmax at higher precision (FP16/BF16), is the resulting attention weight matrix:\nQuantized to FP8 and multiplied directly with FP8 V cache, or\nMultiplied with V cache after dequantizing V to higher precision?\nThe documentation mentions \"no fused dequantization and attention operations yet\" but doesn't specify the precision of this final multiplication. Clarifying this detail would help understand the accuracy-performance tradeoff.\nThanks!\n\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31023",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-12-19T10:33:22Z",
"updated_at": "2025-12-22T00:41:38Z",
"comments": 0,
"user": "jorjiang"
},
{
"repo": "vllm-project/vllm",
"number": 31019,
"title": "[Bug]: Qwen3-VL 2:4 sparsity llm-compressor RuntimeError: shape mismatch (0.12, 0.13rc2)",
"body": "### Your current environment\n\n
The output of python collect_env.py
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.28.3\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 | packaged by Anaconda, Inc. | (main, Jun 5 2025, 13:09:17) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-5.15.0-161-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.6.85\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : GPU 0: NVIDIA L40S\nNvidia driver version : 550.163.01\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.5.1\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.5.1\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 16\nOn-line CPU(s) list: 0-15\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Gold 6338 CPU @ 2.00GHz\nCPU family: 6\nModel: 106\nThread(s) per core: 2\nCore(s) per socket: 8\nSocket(s): 1\nStepping: 6\nBogoMIPS: 3990.65\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq dtes64 ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid fsrm md_clear arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 512 KiB (16 instances)\nL1i cache: 512 KiB (16 instances)\nL2 cache: 32 MiB (8 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-15\nVulnerability Gather data sampling: Unknown: Dependent on hypervisor status\nVulnerability Indirect target selection: Mitigation; Aligned branch/return thunks\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional;",
"url": "https://github.com/vllm-project/vllm/issues/31353",
"state": "open",
"labels": [
"bug",
"help wanted"
],
"created_at": "2025-12-25T13:56:52Z",
"updated_at": "2025-12-27T15:55:34Z",
"comments": 1,
"user": "aravilli"
},
{
"repo": "sgl-project/sglang",
"number": 15825,
"title": "Is it normal that Qwen3-30B-A3B runs slower than Qwen3-8B?",
"body": "I serve two models on the Ascend 910 platform (following sglang's ascend examples) with the same tp2dp8 and benchmarked them. \nBefore testing, I suppose A3B will be faster than 8B for fewer activated tensor blocks. \nBut the result is different:\n### qwen 30B A3B\n```\nexport SGLANG_SET_CPU_AFFINITY=1\nexport PYTORCH_NPU_ALLOC_CONF=expandable_segments:True\nexport STREAMS_PER_DEVICE=32\nexport HCCL_BUFFSIZE=1536\nexport HCCL_OP_EXPANSION_MODE=AIV\nexport SGLANG_DEEPEP_NUM_MAX_DISPATCH_TOKENS_PER_RANK=32\nexport SGLANG_DEEPEP_BF16_DISPATCH=1\nexport ENABLE_ASCEND_MOE_NZ=1\n\npython -m sglang.launch_server \\\n --device npu \\\n --attention-backend ascend \\\n --trust-remote-code \\\n --tp-size 2 \\\n --dp-size 8 \\\n --model **Qwen/Qwen3-30B-A3B-Instruct-2507** \\\n --model-path /models/Qwen3-30B-A3B-Instruct-2507 \\\n --port 30111 \\\n --mem-fraction-static 0.8\n```\n```\n============ Serving Benchmark Result ============\nBackend: sglang \nTraffic request rate: inf \nMax request concurrency: not set \nSuccessful requests: 1000 \nBenchmark duration (s): 69.68 \nTotal input tokens: 3055233 \nTotal input text tokens: 3055233 \nTotal input vision tokens: 0 \nTotal generated tokens: 513413 \nTotal generated tokens (retokenized): 512578 \nRequest throughput (req/s): 14.35 \nInput token throughput (tok/s): 43846.56 \n**Output token throughput (tok/s): 7368.14** \nPeak output token throughput (tok/s): 12775.00 \nPeak concurrent requests: 1000 \nTotal token throughput (tok/s): 51214.70 \nConcurrency: 665.97 \n----------------End-to-End Latency----------------\nMean E2E Latency (ms): 46404.83 \nMedian E2E Latency (ms): 49605.93 \n---------------Time to First Token----------------\nMean TTFT (ms): 10682.85 \nMedian TTFT (ms): 9808.31 \nP99 TTFT (ms): 16320.45 \n-----Time per Output Token (excl. 1st token)------\nMean TPOT (ms): 96.14 \nMedian TPOT (ms): 75.08 \nP99 TPOT (ms): 399.24 \n---------------Inter-Token Latency----------------\nMean ITL (ms): 69.71 \nMedian ITL (ms): 69.43 \nP95 ITL (ms): 80.73 \nP99 ITL (ms): 96.53 \nMax ITL (ms): 5450.67 \n==================================================\n```\n\n### Qwen3 8B\n\n```\nexport SGLANG_SET_CPU_AFFINITY=1\nexport PYTORCH_NPU_ALLOC_CONF=expandable_segments:True\nexport STREAMS_PER_DEVICE=32\nexport HCCL_BUFFSIZE=1536\nexport HCCL_OP_EXPANSION_MODE=AIV\n\nASCEND_RT_VISIBLE_DEVICES=0 python -m sglang.launch_server \\\n --device npu \\\n --attention-backend ascend \\\n --trust-remote-code \\\n --model Qwen/Qwen3-8B \\\n --model-path /models/Qwen3-8B \\\n --port 30111 \\\n --mem-fraction-static 0.8 \\\n --tp-size 2 \\\n --dp-size 8 \n```\n```\n============ Serving Benchmark Result ============\nBackend: sglang \nTraffic request rate: inf \nMax request concurrency: not set \nSuccessful requests: 1000 \nBenchmark duration (s): 49.67 \nTotal input tokens: 3055233 \nTotal input text tokens: 3055233 \nTotal input vision tokens: 0 \nTotal generated tokens: 513413 \nTotal generated tokens (retokenized): 512976 \nRequest throughput (req/s): 20.13 \nInput token throughput (tok/s): 61513.14 \n**Output token throughput (tok/s): 10336.90** \nPeak output token throughput (tok/s): 23242.00 \nPeak concurrent requests: 1000 \nTotal token throughput (tok/s): 71850.04 \nConcurrency: 709.69 \n----------------End-to-End Latency----------------\nMean E2E Latency (ms): 35249.04 \nMedian E2E Latency (ms): 36490.95 \n---------------Time to First Token----------------\nMean TTFT (ms): 10977.22 \nMedian TTFT (ms): 9339.57 \nP99 TTFT (ms): 16697.36 \n-----Time per Output Token (excl. 1st token)------\nMean TPOT (ms): 82.35 \nMedian TPOT (ms): 48.71 \nP99 TPOT (ms): 516.74 \n---------------Inter-Token Latency----------------\nMean ITL (ms): 47.37 \nMedian ITL (ms): 35.12 \nP95 ITL (ms): 105.74 \nP99 ITL (ms): 463.46 \nMax I",
"url": "https://github.com/sgl-project/sglang/issues/15825",
"state": "open",
"labels": [],
"created_at": "2025-12-25T11:26:10Z",
"updated_at": "2025-12-25T11:26:10Z",
"comments": 0,
"user": "yucc-leon"
},
{
"repo": "vllm-project/vllm",
"number": 31344,
"title": "[Usage]: how to pass param logits_processors in AsyncEngineArgs?",
"body": "### Your current environment\n import torch\nfrom transformers import LogitsProcessor\nfrom transformers.generation.logits_process import _calc_banned_ngram_tokens\nfrom typing import List, Set\n\n\nclass NoRepeatNGramLogitsProcessor(LogitsProcessor):\n\n def __init__(self, ngram_size: int, window_size: int = 100, whitelist_token_ids: set = None):\n if not isinstance(ngram_size, int) or ngram_size <= 0:\n raise ValueError(f\"`ngram_size` has to be a strictly positive integer, but is {ngram_size}\")\n if not isinstance(window_size, int) or window_size <= 0:\n raise ValueError(f\"`window_size` has to be a strictly positive integer, but is {window_size}\")\n self.ngram_size = ngram_size\n self.window_size = window_size\n self.whitelist_token_ids = whitelist_token_ids or set()\n \n def __call__(self, input_ids: List[int], scores: torch.FloatTensor) -> torch.FloatTensor:\n if len(input_ids) < self.ngram_size:\n return scores\n \n current_prefix = tuple(input_ids[-(self.ngram_size - 1):])\n \n search_start = max(0, len(input_ids) - self.window_size)\n search_end = len(input_ids) - self.ngram_size + 1\n \n banned_tokens = set()\n for i in range(search_start, search_end):\n ngram = tuple(input_ids[i:i + self.ngram_size])\n if ngram[:-1] == current_prefix:\n banned_tokens.add(ngram[-1])\n \n banned_tokens = banned_tokens - self.whitelist_token_ids\n \n if banned_tokens:\n scores = scores.clone()\n for token in banned_tokens:\n scores[token] = -float(\"inf\")\n \n return scores\n\n\n\n async def stream_generate(image=None, prompt=''):\n logits_processors = [NoRepeatNGramLogitsProcessor(ngram_size=30, window_size=90,\n whitelist_token_ids={128821, 128822})] # whitelist: Running MultiModal Network with prefix caching will cause memory leak.
\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nRuning a multimodal, will cause memory leak. \nBecause the following leak trace: block_hasher -> MultiModalFeatureSpec -> MultiModalKwargsItem -> MultiModalFiledElem -> image_pixels(Tensor)\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31200",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-23T01:55:47Z",
"updated_at": "2025-12-23T15:02:37Z",
"comments": 1,
"user": "frelam"
},
{
"repo": "huggingface/diffusers",
"number": 12881,
"title": "Is that a bug of prompt2prompt pipeline with replace word pormpt?",
"body": "### Describe the bug\n\nIt performance the same when return different cross attention map, is implement error or just the problem with prompt2prompt.\n\n### Reproduction\n\nUse stable-diffusion-2-1:\n`images = pipe([\"A turtle playing with a ball\", \"A monkey playing with a ball\"],\n generator=torch.Generator(\"cuda\").manual_seed(34),\n cross_attention_kwargs={\n \"edit_type\": \"replace\",\n \"local_blend_words\": [\"turtle\", \"monkey\"],\n \"n_cross_replace\": 0.4,\n \"n_self_replace\": 0.4\n }).images`\n\nIt performance the same when return different cross attention map:\n`class AttentionReplace(AttentionControlEdit):\n def replace_cross_attention(self, attn_base, att_replace):\n return attn_base.unsqueeze(0).expand(att_replace.shape[0], *attn_base.shape)\n return torch.einsum(\"hpw,bwn->bhpn\", attn_base, self.mapper)`\n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\nDiffusers=0.30.0\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/12881",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-23T01:55:06Z",
"updated_at": "2025-12-23T01:55:06Z",
"comments": 0,
"user": "lincion"
},
{
"repo": "sgl-project/sglang",
"number": 15641,
"title": "[Feature] In the event_loop_overlap function of the scheduler, can the recv operation be processed asynchronously?",
"body": "### Checklist\n\n- [x] If this is not a feature request but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [x] Please use English. Otherwise, it will be closed.\n\n### Motivation\n\nIn the _offline large-scale high-concurrency multimodal deterministic inference scenario_, when using `event_loop_overlap `on a single machine, the `recv `operation is performed **synchronously before each step**. This can cause the GPU to idle due to waiting for recv requests, **thereby reducing the utilization rate**.\nWe have implemented a version that moves the recv to a background thread for continuous request reception. We use a priority lock to ensure that incoming requests are prioritized for queueing and processing. _On a single machine with eight H100 cards_, we have achieved an **increase in utilization from 60% to 75%**.\nWe hope that sglang can provide high-quality support for large-scale offline high-concurrency scenarios with high power and high utilization requirements.\n\n### Related resources\n\n_No response_",
"url": "https://github.com/sgl-project/sglang/issues/15641",
"state": "open",
"labels": [],
"created_at": "2025-12-22T14:04:10Z",
"updated_at": "2025-12-22T14:04:10Z",
"comments": 0,
"user": "titanium-temu"
},
{
"repo": "sgl-project/sglang",
"number": 15634,
"title": "[Bug] sgl-kernel does not support fa3???",
"body": "### Checklist\n\n- [ ] I searched related issues but found no solution.\n- [x] The bug persists in the latest version.\n- [x] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.\n- [x] If this is not a bug report but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [x] Please use English. Otherwise, it will be closed.\n\n### Describe the bug\n\nCUDA error (/sgl-kernel/build/_deps/repo-flash-attention-src/hopper/flash_fwd_launch_template.h:166): invalid configuration argument\n\n### Reproduction\n\nno_proxy=\"*\" SGLANG_TORCH_PROFILER_DIR=./ python -m sglang.launch_server --model-path /root/temp_can/DeepSeek-V3-0324 --load-format dummy --tp 4 --ep 4 --disable-cuda-graph --disable-radix-cache --moe-a2a-backend deepep --deepep-mode normal --chunked-prefill-size -1 --host 0.0.0.0 --port 30000 --enable-two-batch-overlap --attention-backend fa3\n\n### Environment\n\n(new_py310) root@zyhuang0-0:~/temp_can/sglang# python3 -m sglang.check_env\nPython: 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0]\nCUDA available: True\nGPU 0,1,2,3: NVIDIA H100 80GB HBM3\nGPU 0,1,2,3 Compute Capability: 9.0\nCUDA_HOME: /usr/local/cuda\nNVCC: Cuda compilation tools, release 12.9, V12.9.41\nCUDA Driver Version: 550.54.15\nPyTorch: 2.9.1+cu128\nsglang: 0.5.6.post2\nsgl_kernel: 0.3.19\nflashinfer_python: 0.5.3\nflashinfer_cubin: 0.5.3\nflashinfer_jit_cache: Module Not Found\ntriton: 3.5.1\ntransformers: 4.57.1\ntorchao: 0.9.0\nnumpy: 2.2.6\naiohttp: 3.13.2\nfastapi: 0.127.0\nhf_transfer: 0.1.9\nhuggingface_hub: 0.36.0\ninteregular: 0.3.3\nmodelscope: 1.33.0\norjson: 3.11.5\noutlines: 0.1.11\npackaging: 25.0\npsutil: 7.1.3\npydantic: 2.12.5\npython-multipart: 0.0.21\npyzmq: 27.1.0\nuvicorn: 0.40.0\nuvloop: 0.22.1\nvllm: Module Not Found\nxgrammar: 0.1.27\nopenai: 2.6.1\ntiktoken: 0.12.0\nanthropic: 0.75.0\nlitellm: Module Not Found\ndecord2: 3.0.0\nNVIDIA Topology: \n GPU0 GPU1 GPU2 GPU3 NIC0 NIC1 NIC2 NIC3 NIC4 NIC5 NIC6 CPU Affinity NUMA Affinity GPU NUMA ID\nGPU0 X NV18 NV18 NV18 SYS PIX SYS SYS SYS SYS SYS 0-47,96-143 0 N/A\nGPU1 NV18 X NV18 NV18 SYS SYS SYS SYS SYS PIX SYS 48-95,144-191 1 N/A\nGPU2 NV18 NV18 X NV18 SYS SYS SYS SYS SYS SYS SYS 48-95,144-191 1 N/A\nGPU3 NV18 NV18 NV18 X SYS SYS SYS SYS SYS SYS PIX 48-95,144-191 1 N/A\nNIC0 SYS SYS SYS SYS X SYS SYS SYS SYS SYS SYS\nNIC1 PIX SYS SYS SYS SYS X SYS SYS SYS SYS SYS\nNIC2 SYS SYS SYS SYS SYS SYS X PXB PXB SYS SYS\nNIC3 SYS SYS SYS SYS SYS SYS PXB X PIX SYS SYS\nNIC4 SYS SYS SYS SYS SYS SYS PXB PIX X SYS SYS\nNIC5 SYS PIX SYS SYS SYS SYS SYS SYS SYS X SYS\nNIC6 SYS SYS SYS PIX SYS SYS SYS SYS SYS SYS X \n\nLegend:\n\n X = Self\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\n PIX = Connection traversing at most a single PCIe bridge\n NV# = Connection traversing a bonded set of # NVLinks\n\nNIC Legend:\n\n NIC0: mlx5_0\n NIC1: mlx5_1\n NIC2: mlx5_2\n NIC3: mlx5_3\n NIC4: mlx5_4\n NIC5: mlx5_5\n NIC6: mlx5_6\n\n\nulimit soft: 1048576",
"url": "https://github.com/sgl-project/sglang/issues/15634",
"state": "open",
"labels": [],
"created_at": "2025-12-22T10:50:36Z",
"updated_at": "2025-12-22T10:50:55Z",
"comments": 0,
"user": "ziyuhuang123"
},
{
"repo": "huggingface/lerobot",
"number": 2697,
"title": "Run pi0.5 on Libero, incorrect version of transformers",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\nCopy-and-paste the text below in your GitHub issue and FILL OUT the last point.\n\n- lerobot version: 0.4.0\n- Platform: Linux-6.8.0-87-generic-x86_64-with-glibc2.35\n- Python version: 3.10.19\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 2.2.6\n- PyTorch version: 2.7.1+cu126\n- Is PyTorch built with CUDA support?: True\n- Cuda version: 12.6\n- GPU model: NVIDIA GeForce RTX 4090\n- Using GPU in script?: \nclass Request:\n def __init__(\n ...\n self.block_hashes: list[BlockHash] = []\n self.get_hash_new_full_blocks: Callable[[], list[BlockHash]] | None = None\n if block_hasher is not None:\n self.get_hash_new_full_blocks = partial(block_hasher, self) # Request hold block_hasher and block_hasher hold Request, create a circular references.\n self.block_hashes = self.get_hash_new_full_blocks()\n\n\nCan it change to the below code? \n\n\nimport weakref\nclass Request:\n def __init__(\n ...\n self.block_hashes: list[BlockHash] = []\n self.get_hash_new_full_blocks: Callable[[], list[BlockHash]] | None = None\n if block_hasher is not None:\n self.get_hash_new_full_blocks = partial(block_hasher, weakref.proxy(self)) # Use weakref to avoid circual references.\n self.block_hashes = self.get_hash_new_full_blocks()\n\n\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThe output of
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.14.0-1017-azure-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : GPU 0: NVIDIA H100 NVL\nNvidia driver version : 580.95.05\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 40\nOn-line CPU(s) list: 0-39\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 9V84 96-Core Processor\nCPU family: 25\nModel: 17\nThread(s) per core: 1\nCore(s) per socket: 40\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4800.09\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves user_shstk avx512_bf16 clzero xsaveerptr rdpru arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.3 MiB (40 instances)\nL1i cache: 1.3 MiB (40 instances)\nL2 cache: 40 MiB (40 instances)\nL3 cache: 160 MiB (5 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-39\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsa: Vulnerable: Clear CPU buffers attempted, no microcode\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.5.3\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime",
"url": "https://github.com/vllm-project/vllm/issues/31019",
"state": "open",
"labels": [
"bug",
"help wanted",
"good first issue"
],
"created_at": "2025-12-19T09:18:00Z",
"updated_at": "2025-12-24T12:16:01Z",
"comments": 4,
"user": "SorenDreano"
},
{
"repo": "vllm-project/vllm",
"number": 31016,
"title": "[Bug]: FlashInfer Incompatible with Sleep Mode",
"body": "### Your current environment\n\n\n- lerobot scripts: ['lerobot-calibrate', 'lerobot-dataset-viz', 'lerobot-edit-dataset', 'lerobot-eval', 'lerobot-find-cameras', 'lerobot-find-joint-limits', 'lerobot-find-port', 'lerobot-imgtransform-viz', 'lerobot-info', 'lerobot-record', 'lerobot-replay', 'lerobot-setup-motors', 'lerobot-teleoperate', 'lerobot-train']\n```\n\n### Description\n\nI'm having a problem when training a VLA with `lerobot-train` on a merged dataset.\nI'm aware of the issue #2627 as well as PR #2550 that is supposed to fix the bug.\nHowever, the problem is still occurring on the latest commit (4a151a9) of lerobot 0.4.3.\n\nThe dataset has been merged with the following script:\n`lerobot-edit-dataset \\\n --repo_id whosricky/so101-megamix-v1 \\\n --operation.type merge \\\n --operation.repo_ids \"['whosricky/so101_pick_red_cube_3cams', 'whosricky/so101_pick_blue_cube_3cams', 'whosricky/so101_pick_yellow_cube_3cams', 'whosricky/so101_pick_cube_reasoning_3cams', 'whosricky/so101_stacking_3cams', 'whosricky/so101_pickplace_red_cube_3cams', 'whosricky/so101_pickplace_all_red_cubes_3cams', 'whosricky/so101_sorting_cubes_3cams', 'whosricky/so101_pickplace_red_cubes_random_bowl_3cams']\" \\\n --push_to_hub true `\n\nTraining on the single datasets works flawlessly. Training on the merged dataset results in an error.\n\nThe problematic sample seems to be #51 of \"whosricky/so101_pick_blue_cube_3cams\" due to the timestamp exceeding the default tolerance_s. \nHowever, the problem occurs only on the merged dataset and not on the single one.\n\n### Context & Reproduction\n\n```\nlerobot-train \\\n --dataset.repo_id=whosricky/so101-megamix-v1 \\\n --output_dir=outputs_xvla_megamix_v1/train/my_xvla \\\n --job_name=xvla_training_megamix_v1 \\\n --policy.path=lerobot/xvla-base \\\n --policy.repo_id=whosricky/xvla-so101-megamix-v1 \\\n --policy.private=true \\\n --policy.dtype=bfloat16 \\\n --num_workers=8 \\\n --batch_size=8 \\\n --steps=30000 \\\n --eval_freq=5000 \\\n --log_freq=100 \\\n --save_freq=5000 \\\n --policy.device=cuda \\\n --policy.freeze_vision_encoder=false \\\n --policy.freeze_language_encoder=false \\\n --policy.train_policy_transformer=true \\\n --policy.train_soft_prompts=true \\\n --policy.action_mode=auto \\\n --policy.num_image_views=3 \\\n --policy.empty_cameras=0 \\\n --rename_map='{\"observation.images.top\": \"observation.images.image\", \"observation.images.gripper\": \"observation.images.image2\", \"observation.images.front\": \"observation.images.empty_camera_0\"}' \\\n --wandb.enable=true\n```\n\n### Relevant logs or stack trace\n\n```Shell\nWARNING:accelerate.utils.other:Detected kernel version 5.4.0, which is below the recommended minimum of 5.5.0; this can cause the process to hang. It is recommended to upgrade the kernel to the minimum version or higher.\nINFO 2025-12-18 12:38:22 ot_train.py:164 {'batch_size': 8,\n 'checkpoint_path': None,\n 'dataset': {'episodes': None,\n 'image_transforms': {'enable': False,\n 'max_num_transforms': 3,\n 'random_order': False,\n 'tfs': {'affine': {'kwargs': {'degrees': [-5.0,\n 5.0],\n 'translate': [0.05,\n 0.05]},\n 'type': 'RandomAffine',\n 'weight': 1.0},\n 'brightness': {'kwargs': {'brightness': [0.8,\n 1.2]},\n 'type': 'ColorJitter',\n 'weight': 1.0},\n 'contrast': {'kwargs': {'contrast': [0.8,\n 1.2]},\n 'type': 'ColorJitter',\n 'weight': 1.0},\n 'hue': {'kwargs': {'hue': [-0.05,\n 0.05]},\n 'type': 'ColorJitter',\n 'weight': 1.0},\n 'saturation': {'kwargs': {'satur",
"url": "https://github.com/huggingface/lerobot/issues/2680",
"state": "open",
"labels": [
"bug",
"question",
"dataset",
"visualization",
"examples",
"training"
],
"created_at": "2025-12-18T13:29:50Z",
"updated_at": "2025-12-26T06:26:37Z",
"user": "RiccardoIzzo"
},
{
"repo": "huggingface/trl",
"number": 4719,
"title": "Loss calculation of `GKDTrainer` may be inaccurate when performing gradient accumulation?",
"body": "It seems that `GKDTrainer` averages the loss of tokens in a micro batch ahead?\n\nhttps://github.com/huggingface/trl/blob/8918c9836a3e0b43a6851c08d01b69072f56ca52/trl/experimental/gkd/gkd_trainer.py#L284",
"url": "https://github.com/huggingface/trl/issues/4719",
"state": "open",
"labels": [
"\ud83d\udc1b bug",
"\ud83c\udfcb GKD"
],
"created_at": "2025-12-18T12:50:05Z",
"updated_at": "2025-12-18T12:50:49Z",
"comments": 0,
"user": "jue-jue-zi"
},
{
"repo": "huggingface/lerobot",
"number": 2679,
"title": "Merging datasets removes fps from scalar features",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\n- LeRobot version: 0.4.3\n- Platform: Linux-6.17.9-arch1-1-x86_64-with-glibc2.42\n- Python version: 3.12.11\n- Huggingface Hub version: 0.34.4\n- Datasets version: 4.1.1\n- Numpy version: 2.3.5\n- FFmpeg version: n8.0.1\n- PyTorch version: 2.7.1+cu128\n- Is PyTorch built with CUDA support?: True\n- Cuda version: 12.8\n- GPU model: NVIDIA GeForce RTX 5090 Laptop GPU\n- Using GPU in script?: \n- lerobot scripts: ['lerobot-calibrate', 'lerobot-dataset-viz', 'lerobot-edit-dataset', 'lerobot-eval', 'lerobot-find-cameras', 'lerobot-find-joint-limits', 'lerobot-find-port', 'lerobot-imgtransform-viz', 'lerobot-info', 'lerobot-record', 'lerobot-replay', 'lerobot-setup-motors', 'lerobot-teleoperate', 'lerobot-train']\n```\n\n### Description\n\nWhen using the `merge_datasets` function, the fps attribute is removed from the scalar features in the dataset. Below are the scalar features from dataset.meta.features of a dataset before and after merging\n\nBefore:\n```\n'timestamp': {'dtype': 'float32', 'shape': (1,), 'names': None, 'fps': 10}, \n'frame_index': {'dtype': 'int64', 'shape': (1,), 'names': None, 'fps': 10}, \n'episode_index': {'dtype': 'int64', 'shape': (1,), 'names': None, 'fps': 10}, \n'index': {'dtype': 'int64', 'shape': (1,), 'names': None, 'fps': 10}, \n'task_index': {'dtype': 'int64', 'shape': (1,), 'names': None, 'fps': 10}}\n```\n\nAfter:\n```\n'timestamp': {'dtype': 'float32', 'shape': (1,), 'names': None}, \n'frame_index': {'dtype': 'int64', 'shape': (1,), 'names': None}, \n'episode_index': {'dtype': 'int64', 'shape': (1,), 'names': None}, \n'index': {'dtype': 'int64', 'shape': (1,), 'names': None}, \n'task_index': {'dtype': 'int64', 'shape': (1,), 'names': None}\n```\n\nThis creates subsequent problems when trying to add an additional dataset to a merged output as the feature mismatch will cause an error to be thrown \n\n### Context & Reproduction\n\nRunning the script below shows the features change before and after the merge\n\n```\nfrom lerobot.datasets.dataset_tools import split_dataset, merge_datasets\nfrom lerobot.datasets.lerobot_dataset import LeRobotDataset\nfrom pprint import pprint\n\ndataset = LeRobotDataset(\"lerobot/pusht\")\nfeat_1 = dataset.meta.features\nsplits = split_dataset(dataset, splits={\"train\": 0.8, \"val\": 0.2})\nmerged = merge_datasets([splits[\"train\"], splits[\"val\"]], output_repo_id=\"lerobot/pusht_merged\")\nfeat_2 = merged.meta.features\n\nprint(\"Features of original dataset:\")\npprint(feat_1)\nprint(\"Features of merged dataset:\")\npprint(feat_2)\n```\n\n### Relevant logs or stack trace\n\n```Shell\nFeatures of original dataset:\n{'action': {'dtype': 'float32',\n 'fps': 10.0,\n 'names': {'motors': ['motor_0', 'motor_1']},\n 'shape': (2,)},\n 'episode_index': {'dtype': 'int64', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'frame_index': {'dtype': 'int64', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'index': {'dtype': 'int64', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'next.done': {'dtype': 'bool', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'next.reward': {'dtype': 'float32', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'next.success': {'dtype': 'bool', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'observation.image': {'dtype': 'video',\n 'names': ['height', 'width', 'channel'],\n 'shape': (96, 96, 3),\n 'video_info': {'has_audio': False,\n 'video.codec': 'av1',\n 'video.fps': 10.0,\n 'video.is_depth_map': False,\n 'video.pix_fmt': 'yuv420p'}},\n 'observation.state': {'dtype': 'float32',\n 'fps': 10.0,\n 'names': {'motors': ['motor_0', 'motor_1']},\n 'shape': (2,)},\n 'task_index': {'dtype': 'int64', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'timestamp': {'dtype': 'float32', 'fps': 10.0, 'names': None, 'shape': (1,)}}\nFeatures of merged dataset:\n{'action': {'dtype': 'float32',\n 'fps': 10.0,\n 'names': {'motors': ['motor_0', 'motor_1']},\n 'shape': (2,)},\n 'episode_index': {'dtype': 'int64', 'names': None, 'shape': (1,)},\n 'frame_index': {'dtype': 'int64', 'names': None, 'shape': (1,)},\n 'index': {'dtype': 'int64', 'names': None, 'shape': (1,)},\n 'next.done': {'dtype': 'bool', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'next.reward': {'dtype': 'float32', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'next.success': {'dtype': 'bool', 'fps': 10.0, 'names': None, 'shape': (1,)},\n 'observation.image': {'dtype': 'video',\n 'names': ['height', 'width', 'channel'],\n 'shape': (96, 96, 3),\n 'video_info': {'has_audio': False,\n 'video.codec': 'av1',\n 'video.fps': 10.0,\n ",
"url": "https://github.com/huggingface/lerobot/issues/2679",
"state": "open",
"labels": [
"bug",
"enhancement",
"question",
"dataset",
"performance",
"examples"
],
"created_at": "2025-12-18T12:47:14Z",
"updated_at": "2025-12-18T15:25:12Z",
"user": "reeceomahoney"
},
{
"repo": "vllm-project/vllm",
"number": 30956,
"title": "[Feature]: could output the given format logger ?",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nhi,dear ,\ni have def the logger from py scripts ,etc, logger_utils.py \nand could i use shell run the command with the logger,\nsuch as ,\n`vllm serve qwen3-embedding-0.6b --logger_file logger_utils.py `\n\n\nthx \ni really need your help \nSOS ,thx \n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30956",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-12-18T09:35:22Z",
"updated_at": "2025-12-19T01:52:41Z",
"comments": 5,
"user": "ucas010"
},
{
"repo": "huggingface/lerobot",
"number": 2678,
"title": "Bug: lerobot-dataset-viz IndexError when visualizing specific episodes",
"body": "# Bug Report: `lerobot-dataset-viz` IndexError when visualizing specific episodes\n\n## Description\n\nThe `lerobot-dataset-viz` command fails with an `IndexError` when trying to visualize a specific episode using the `--episode-index` parameter. The issue is caused by `EpisodeSampler` using global dataset indices while the dataset has been filtered to contain only the specified episode.\n\n## Error Message\n\n```\nIndexError: Invalid key: 180 is out of bounds for size 180\n```\n\nFull traceback:\n```\nTraceback (most recent call last):\n File \"/path/to/lerobot/scripts/lerobot_dataset_viz.py\", line 289, in main\n visualize_dataset(dataset, **vars(args))\n File \"/path/to/lerobot/scripts/lerobot_dataset_viz.py\", line 148, in visualize_dataset\n for batch in tqdm.tqdm(dataloader, total=len(dataloader)):\n ...\n File \"/path/to/lerobot/datasets/lerobot_dataset.py\", line 1028, in __getitem__\n item = self.hf_dataset[idx]\n ...\nIndexError: Invalid key: 180 is out of bounds for size 180\n```\n\n## Steps to Reproduce\n\n1. Create a LeRobot dataset with multiple episodes (e.g., 20 episodes, 180 frames each)\n2. Try to visualize episode 1:\n ```bash\n lerobot-dataset-viz \\\n --repo-id lerobot/test \\\n --root ./lerobot_dataset \\\n --mode local \\\n --episode-index 1 \\\n --batch-size 2\n ```\n3. Error occurs when trying to load the data\n\n## Root Cause Analysis\n\nThe bug is in the `EpisodeSampler` class (line 81-91 of `lerobot_dataset_viz.py`):\n\n```python\nclass EpisodeSampler(torch.utils.data.Sampler):\n def __init__(self, dataset: LeRobotDataset, episode_index: int):\n from_idx = dataset.meta.episodes[\"dataset_from_index\"][episode_index] # 180\n to_idx = dataset.meta.episodes[\"dataset_to_index\"][episode_index] # 360\n self.frame_ids = range(from_idx, to_idx) # range(180, 360)\n```\n\n**The problem:**\n1. At line 287, the dataset is filtered: `dataset = LeRobotDataset(repo_id, episodes=[args.episode_index], ...)`\n2. The filtered dataset only contains 180 frames with **local indices 0-179**\n3. But `EpisodeSampler` uses indices from `dataset.meta.episodes` which are **global indices 180-359** (position in the full dataset)\n4. When DataLoader tries to access `dataset[180]`, it fails because the filtered dataset only has indices 0-179\n\n**Example:**\n```\nFull dataset (3600 frames):\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u252c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Episode 0\u2502 Episode 1\u2502 Episode 2\u2502 ... \u2502 Episode 19\u2502\n\u2502 0-179 \u2502 180-359 \u2502 360-539 \u2502 ... \u2502 3420-3599\u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2534\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n \u2191\n Global indices\n\nFiltered dataset (180 frames, episode 1 only):\n\u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 Episode 1\u2502 \u2190 Only these 180 frames exist\n\u2502 0-179 \u2502 \u2190 Local indices in filtered dataset\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n\nEpisodeSampler tries to use: range(180, 360) \u2717 Out of bounds!\n```\n\n## Proposed Fix\n\nModify `EpisodeSampler` to handle filtered datasets:\n\n```python\nclass EpisodeSampler(torch.utils.data.Sampler):\n def __init__(self, dataset: LeRobotDataset, episode_index: int):\n # Check if dataset is already filtered to a single episode\n if dataset.episodes is not None and len(dataset.episodes) == 1:\n # Dataset is filtered, use all available frames (local indices)\n self.frame_ids = range(len(dataset))\n else:\n # Dataset is not filtered, use global indices from metadata\n from_idx = dataset.meta.episodes[\"dataset_from_index\"][episode_index]\n to_idx = dataset.meta.episodes[\"dataset_to_index\"][episode_index]\n self.frame_ids = range(from_idx, to_idx)\n\n def __iter__(self) -> Iterator:\n return iter(self.frame_ids)\n\n def __len__(self) -> int:\n return len(self.frame_ids)\n```\n\n## Workaround\n\nUntil this is fixed, users can visualize a specific episode by:\n\n1. Loading the full dataset without filtering\n2. Using `torch.utils.data.Subset` to select the episode\n\n```python\nimport rerun as rr\nfrom lerobot.datasets.lerobot_dataset import LeRobotDataset\nfrom torch.utils.data import DataLoader, Subset\n\n# Load full dataset (no filtering)\ndataset = LeRobotDataset(\n repo_id=\"lerobot/test\",\n root=\"./lerobot_dataset\"\n)\n\n# Manually select episode frames\nepisode_index = 1\nfrom_idx = dataset.meta.episodes[episode_index][\"dataset_from_index\"]\nto_idx = dataset.meta.episodes[episode_index][\"dataset_to_index\"]\nepisode_dataset = Subset(dataset, range(from_idx, to_idx))\n\n# Create dataloader\ndataloader = DataLoader(episode_dataset, batch_size=2, shuffle=False)\n\n# Visualize...\n```\n\n## Environment\n\n- **LeRobot Version:** 0.4.2\n- **Python Version:** 3.12.11\n- **PyTorch Version:** 2.7.1+cu126\n- **Datasets Version:** 4.1.1\n- **OS:** Linux\n\n## Additional Context\n\nThis issue affects any dataset where users want to visualize a specific episode that is not episode 0. The bug makes the `--episode-index` parameter effectively unusable for episodes other than the first one when the dataset has already been filtered.\n\n## Impact\n\n- **Severity:** Medium (cor",
"url": "https://github.com/huggingface/lerobot/issues/2678",
"state": "open",
"labels": [
"bug",
"question",
"dataset",
"visualization",
"python",
"examples"
],
"created_at": "2025-12-18T08:45:05Z",
"updated_at": "2025-12-24T08:31:00Z",
"user": "apeSh1t"
},
{
"repo": "vllm-project/vllm",
"number": 30941,
"title": "[Performance]: Why Does Latency Remain Unchanged in vLLM 0.11.0 When Input Token Count Decreases for qwen3-vl-30b-a3b?",
"body": "### Proposal to improve performance\n\n_No response_\n\n### Report of performance regression\n\n_No response_\n\n### Misc discussion on performance\n\nUsing vLLM version 0.11.0 to run the qwen3-vl-30b-a3b model, the stress test results show that although the number of input tokens decreases, the latency does not change.\n\nThe model is deployed on a single A800 GPU. The startup command is:\nvllm server\n--dtype bfloat16\n--max-model-len 128000\n--gpu-memory-utilization 0.95\n--limit-mm-per-prompt.video 0\n\nI performed a stress test using one image and a set of text prompts, with QPS set to 10.\nI resized the image to 0.25x and 0.7x of the original size while keeping everything else unchanged.\n\nThe conclusions are as follows:\nqwen3-30b-a3b (single image *0.25) latency 3s\nqwen3-30b-a3b (single image *0.7) latency 5s\nqwen3-30b-a3b (single image) latency 5s\n\nPrior conditions:\nInput token scale / Output token scale\nSingle image + text prompts: about 4200 / about 70\nSingle image *0.6 + text prompts: about 1900 / about 70\nSingle image *0.3 + text prompts: about 860 / about 70\n\n### Your current environment (if you think it is necessary)\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30941",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-12-18T07:40:35Z",
"updated_at": "2025-12-18T07:40:35Z",
"comments": 0,
"user": "Hormoney"
},
{
"repo": "vllm-project/vllm",
"number": 30933,
"title": "[Usage]: What is the latest instruction to run DeepSeek V3.2?",
"body": "### Your current environment\n\nvLLM 0.12.0\n\n### How would you like to use vllm\n\nI am following the guidelines here https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html for running DeepSeek v3.2. By following the instructions I installed vLLM 0.12.0 on my H200 node. However, when I try to run it with `vllm serve deepseek-ai/DeepSeek-V3.2 --tensor-parallel-size 8 --tokenizer-mode deepseek_v32` it gives an error \n\n```\n(APIServer pid=816209) ValueError: No tokenizer registered for tokenizer_mode='deepseek_v32'. \n```\n\nIf I do not include the `--tokenizer-mode` then the server spins up with no errors, but when I try to send a request, I get another error below\n\n```\n(APIServer pid=753941) ERROR 12-18 06:04:47 [serving_chat.py:263] ValueError: As of transformers v4.44, default chat template is no longer allowed, so you must provide a chat template if the tokenizer does not define one. \n```\n\nI am wondering if there is an update on the instructions to run DeepSeek V3.2 on vLLM.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30933",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-18T06:18:29Z",
"updated_at": "2025-12-18T15:50:29Z",
"comments": 1,
"user": "IKACE"
},
{
"repo": "vllm-project/vllm",
"number": 30923,
"title": "[Bug]: Use the offical doucment vllm online method deploy DeepSeek-OCR\uff0cthe result is very bad . but I ust the offline method the result is normal. why ?",
"body": "### Your current environment\n\n\n- lerobot scripts: ['lerobot-calibrate', 'lerobot-dataset-viz', 'lerobot-edit-dataset', 'lerobot-eval', 'lerobot-find-cameras', 'lerobot-find-joint-limits', 'lerobot-find-port', 'lerobot-imgtransform-viz', 'lerobot-info', 'lerobot-record', 'lerobot-replay', 'lerobot-setup-motors', 'lerobot-teleoperate', 'lerobot-train']\n```\n\n### Description\n\nI did git pull and my dataset v2 doesn't work anymore. My model raises with the logs below.\n\n### Context & Reproduction\n\n1. `lerobot-train --help`\n2. Check outputs\n\n### Relevant logs or stack trace\n\n```Shell\nFile \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 733, in __next__\n data = self._next_data()\nFile \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1488, in _next_data\n return self._process_data(data, worker_id)\nFile \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/torch/utils/data/dataloader.py\", line 1550, in _process_data\n data.reraise()\nFile \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/torch/_utils.py\", line 750, in reraise\n raise exception\nIndexError: Caught IndexError in DataLoader worker process 1.\nOriginal Traceback (most recent call last):\n File \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/torch/utils/data/_utils/worker.py\", line 349, in _worker_loop\n data = fetcher.fetch(index) # type: ignore[possibly-undefined]\n File \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 52, in fetch\n data = [self.dataset[idx] for idx in possibly_batched_index]\n File \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/torch/utils/data/_utils/fetch.py\", line 52, in \n data = [self.dataset[idx] for idx in possibly_batched_index]\n File \"/admin/home/michel_aratingi/code/collab-lerobot/src/lerobot/datasets/lerobot_dataset.py\", line 975, in __getitem__\n item = self.hf_dataset[idx]\n File \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 2859, in __getitem__\n return self._getitem(key)\n File \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/datasets/arrow_dataset.py\", line 2840, in _getitem\n pa_subtable = query_table(self._data, key, indices=self._indices)\n File \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/datasets/formatting/formatting.py\", line 612, in query_table\n _check_valid_index_key(key, size)\n File \"/admin/home/michel_aratingi/miniconda3/envs/groot/lib/python3.10/site-packages/datasets/formatting/formatting.py\", line 552, in _check_valid_index_key\n raise IndexError(f\"Invalid key: {key} is out of bounds for size {size}\")\nIndexError: Invalid key: 46969 is out of bounds for size 46963\n```\n\n### Checklist\n\n- [x] I have searched existing tickets to ensure this isn't a duplicate.\n- [x] I am using the latest version of the `main` branch.\n- [x] (I have verified this is not an environment-specific problem.\n\n### Additional Info / Workarounds\n\nMaybe if I try to update my transformers dependency?\n\nI edit this ticket",
"url": "https://github.com/huggingface/lerobot/issues/2673",
"state": "closed",
"labels": [
"enhancement",
"question",
"dataset",
"dependencies",
"training"
],
"created_at": "2025-12-17T21:35:31Z",
"updated_at": "2025-12-17T23:26:54Z",
"user": "imstevenpmwork"
},
{
"repo": "huggingface/lerobot",
"number": 2670,
"title": "Async inference for simulation (libero benchmark)",
"body": "### Issue Type\n\n{\"label\" => \"\u2753 Technical Question\"}\n\n### Environment & System Info\n\n```Shell\n\n```\n\n### Description\n\nIs there any way that we can support async inference for simulator (e.g., libero)? This makes it possible to test RTC with simulators. \n\n### Context & Reproduction\n\nA question re a feature. \n\n### Expected Behavior / Desired Outcome\n\n_No response_\n\n### Relevant logs or stack trace\n\n```Shell\n\n```\n\n### Checklist\n\n- [ ] I have searched existing issues to ensure this isn't a duplicate.\n- [ ] I am using the latest version of the `main` branch.\n- [ ] (For bugs) I have verified this is not an environment-specific issue.\n\n### Additional Info / Workarounds\n\n_No response_",
"url": "https://github.com/huggingface/lerobot/issues/2670",
"state": "open",
"labels": [
"question",
"simulation",
"performance",
"evaluation"
],
"created_at": "2025-12-17T18:57:07Z",
"updated_at": "2026-01-02T05:40:18Z",
"user": "dywsjtu"
},
{
"repo": "huggingface/transformers",
"number": 42930,
"title": "Inconsistent handling of video_metadata in Qwen3VLVideoProcessor usage example",
"body": "### System Info\n\ntransformers==4.57.3\n\n### Who can help?\n\n@zucchini-nlp @yonigozlan @molbap\n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nI'm working with the `Qwen3VLVideoProcessor` and noticed a potential inconsistency between the processor's output and its expected usage.\n\nAccording to the current implementation of `Qwen3VLVideoProcessor._preprocess()`, the returned `BatchFeature` only contains the keys:\n- `\"pixel_values_videos\"`\n- `\"video_grid_thw\"`\n\nHowever, in some calling code, I see logic like:\n\n```python\nvideos_inputs = self.video_processor(videos=videos, **kwargs)\nif \"return_metadata\" not in kwargs:\n video_metadata = videos_inputs.pop(\"video_metadata\")\n```\n\nHow does it work? thank you very much\n\n### Expected behavior\n\nI want to change Qwen2.5vl to Qwen3vl but can't set a fixed nframes",
"url": "https://github.com/huggingface/transformers/issues/42930",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-12-17T17:21:00Z",
"updated_at": "2025-12-18T10:32:23Z",
"comments": 3,
"user": "wagoriginal"
},
{
"repo": "vllm-project/vllm",
"number": 30882,
"title": "[Bug]: Marlin Fp8 Block Quant Failure",
"body": "### Your current environment\n\n, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['+quant_fp8', 'all', '+quant_fp8'], 'splitting_ops': [], 'compile_mm_encoder': False, 'compile_sizes': [], 'compile_ranges_split_points': [8192], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 0, 'cudagraph_capture_sizes': [], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': False, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False}, 'max_cudagraph_capture_size': 0, 'dynamic_shapes_config': {'type': , 'evaluate_guards': False}, 'local_cache_dir': None}\n(EngineCore_DP0 pid=3634329) INFO 12-17 15:54:32 [parallel_state.py:1210] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.243.64.5:43323 backend=nccl\n(EngineCore_DP0 pid=3634329) INFO 12-17 15:54:32 [parallel_state.py:1418] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0\n(EngineCore_DP0 pid=3634329) INFO 12-17 15:54:33 [gpu_model_runner.py:3620] Starting to load model Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8...\n(EngineCore_DP0 pid=3634329) INFO 12-17 15:54:33 [deep_gemm.py:76] DeepGEMM E8M0 enabled on current platform.\n(EngineCore_DP0 pid=3634329) INFO 12-17 15:54:33 [cuda.py:351] Using FLASH_ATTN attention backend out of potential backends: ('FLASH_ATTN', 'FLASHINFER', 'TRITON_ATTN', 'FLEX_ATTENTION')\n(EngineCore_DP0 pid=3634329) INFO 12-17 15:54:33 [layer.py:373] Enabled separate cuda str",
"url": "https://github.com/vllm-project/vllm/issues/30882",
"state": "closed",
"labels": [
"bug",
"help wanted",
"good first issue"
],
"created_at": "2025-12-17T15:55:18Z",
"updated_at": "2025-12-17T16:02:54Z",
"comments": 2,
"user": "robertgshaw2-redhat"
},
{
"repo": "vllm-project/vllm",
"number": 30879,
"title": "[Doc]: Add some documentation about encoder compilation",
"body": "### \ud83d\udcda The doc issue\n\nI want something like a design doc for encoder compilation. For example:\n- It uses support_torch_compile and set_model_tag to avoid cache collisions\n- it supports or doesn't support the following features that VllmBackend does: cudagraphs, compile_ranges, and a high-level explanation for how these are turned off or on.\n- it inherits from compilation_config (or maybe it doesn't)\n- here's how to turn it on/off\n\nI'm having a difficult time thinking through the edge cases in https://github.com/vllm-project/vllm/pull/30822 and https://github.com/vllm-project/vllm/pull/30489\n\ncc @Lucaskabela \n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30879",
"state": "open",
"labels": [
"documentation",
"torch.compile"
],
"created_at": "2025-12-17T15:44:50Z",
"updated_at": "2025-12-17T16:27:38Z",
"comments": 1,
"user": "zou3519"
},
{
"repo": "vllm-project/vllm",
"number": 30865,
"title": "[Usage]:Tools GLM4.6v with vLLM",
"body": "### Your current environment\n\nHello,\n\nI am running tests on this model, which I find excellent. However, I am encountering a few issues and would like to know whether it is possible to fix them or if I am simply asking for the impossible.\n\nFirst of all, here is my vLLM configuration:\n\n`docker run -d \\ --name vllm-llm \\ --gpus '\"device=4,5,6,7\"' \\ -e NVIDIA_DRIVER_CAPABILITIES=compute,utility \\ -e VLLM_OBJECT_STORAGE_SHM_BUFFER_NAME=\"${SHM_NAME}\" \\ -v /raid/workspace/qladane/vllm/hf-cache:/root/.cache/huggingface \\ --env \"HF_TOKEN=${HF_TOKEN:-}\" \\ -p 8003:8000 \\ --ipc=host \\ --restart unless-stopped \\ vllm-openai:glm46v \\ zai-org/GLM-4.6V-FP8 \\ --tensor-parallel-size 4 \\ --enforce-eager \\ --served-model-name ImagineAI \\ --allowed-local-media-path / \\ --limit-mm-per-prompt '{\"image\": 1, \"video\": 0}' \\ --max-model-len 131072 \\ --dtype auto \\ --kv-cache-dtype fp8 \\ --gpu-memory-utilization 0.85 \\ --reasoning-parser glm45 \\ --tool-call-parser glm45 \\ --enable-auto-tool-choice \\ --enable-expert-parallel \\ --mm-encoder-tp-mode data \\ --mm-processor-cache-type shm`\n\nNext, here is my OpenWebUI configuration:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/af5ff9c0-9cdc-407f-8b0b-8e76a42746af\")
\n\n![\"Image\"](\"https://github.com/user-attachments/assets/60fa32f9-2f54-4a75-8dc1-0ed00c69c4e5\")
\n\n![\"Image\"](\"https://github.com/user-attachments/assets/783ba2e7-08e9-426a-a8be-9a2a561b2fe0\")
\n\n![\"Image\"](\"https://github.com/user-attachments/assets/a7c30850-a680-401a-b149-5787000e7344\")
\n\nI would like to know whether, with GLM-4.6V and OpenWebUI, it is possible to make the model choose and execute tools autonomously when it considers them relevant.\n\nAt the moment:\n\nIf it is an internet search, I have to manually activate the button, even though access is already available.\n\nIf it is Python code, I have to click \u201cexecute\u201d; it does not run it by itself, even though it clearly has access to Jupyter, etc.\n\nIf anyone has already encountered this issue.\n\nThank you very much in advance for your help.\n\nKind regards\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30865",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-17T10:51:34Z",
"updated_at": "2025-12-18T08:33:44Z",
"comments": 1,
"user": "qBrabus"
},
{
"repo": "sgl-project/sglang",
"number": 15321,
"title": "[Feature][VLM] Support ViT Piecewise CUDA Graph for VLMs",
"body": "### Checklist\n\n- [ ] If this is not a feature request but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [ ] Please use English. Otherwise, it will be closed.\n\n### Motivation\n\nSupport ViT Piecewise CUDA Graph for VLMs can improve prefill performance for VLMs.\n\n- [x] Support ViT PCG Framework https://github.com/sgl-project/sglang/pull/14422\n- [x] Support Qwen2.5-VL https://github.com/sgl-project/sglang/pull/14422\n- [x] Support Qwen3-VL https://github.com/sgl-project/sglang/pull/15320\n- [ ] Support InternVL\n- [ ] Support GLM-4.1V\n\n### Related resources\n\n_No response_",
"url": "https://github.com/sgl-project/sglang/issues/15321",
"state": "open",
"labels": [
"performance",
"Multi-modal",
"vlm"
],
"created_at": "2025-12-17T09:17:18Z",
"updated_at": "2026-01-04T02:09:13Z",
"comments": 0,
"user": "yuan-luo"
},
{
"repo": "vllm-project/vllm",
"number": 30859,
"title": "[Bug]: set_current_vllm_config() is only done during the initialization stage but not the runtime stage",
"body": "### Your current environment\n\nAny env\n\n### \ud83d\udc1b Describe the bug\n\n# Issue Statement\n\nCurrently, `set_current_vllm_config()` is only done during the initialization stage but not the runtime stage. If the code tries to call `get_current_vllm_config()`, vLLM prints a warning \"Current vLLM config is not set.\" and returns a default config.\n\nHowever, this approach is problematic because:\n\n1. When contributors change the code, many of us did not realize the fact that `get_current_vllm_config()` should only be called during init stage and should not be called during runtime stage.\n2. It's just a warning instead of a hard failure, so contributors may not notice this when they run local tests.\n3. Such warnings could be annoying to users because it may be printed for every single decoding step. Plus, the warning doesn't carry any useful info about how to fix/bypass the issue.\n4. The default config may be completely incorrect for the caller function.\n5. Warning prints on every step might impact performance, because print isn't fast operation. (thanks to @vadiklyutiy )\n\n# Requirements\n\nWe should change the behavior such that:\n\n- `get_current_vllm_config()` either returns the real config set by the user or raises an error if the config does not exist.\n\n# Related Issues\n\nThis issues have appeared many times in the past. Although the fix is usually not difficult, it is an annoying recurrent issues that we should avoid in the future to avoid wasted engineering effort.\n\n- https://github.com/vllm-project/vllm/issues/13207\n- https://github.com/vllm-project/vllm/pull/29999\n- https://github.com/vllm-project/vllm/issues/30185\n- https://github.com/vllm-project/vllm/issues/30240\n- https://github.com/vllm-project/vllm/issues/30571\n\n\n# Possible Solutions\n\n## Solution A: `set_current_vllm_config()` for runtime stage as well\n\nSuch that `get_current_vllm_config()` is always available, regardless of init stage or runtime stage.\n\n## Solution B: Convert the warning in `get_current_vllm_config()` to a hard failure\n\nBut this means we may need to fix lots of CI failures.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30859",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-17T08:59:49Z",
"updated_at": "2025-12-22T18:09:55Z",
"comments": 7,
"user": "nvpohanh"
},
{
"repo": "sgl-project/sglang",
"number": 15319,
"title": "[Feature] RFC: AutoSpec, Automatic Runtime Speculative Inference Parameter Tuning",
"body": "### Checklist\n\n- [x] If this is not a feature request but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [x] Please use English. Otherwise, it will be closed.\n\n### Motivation\n\n## Summary\n\nThis proposal introduces automatic runtime tuning for speculative inference parameters in SGLang. Instead of requiring users to manually set speculative_num_steps, speculative_topk, and speculative_num_draft_tokens, the system dynamically adjusts them using a feedback-driven controller. This maximizes throughput while respecting hardware limits and draft model capabilities\u2014without any manual configuration. \n\n## Problem & Motivation\n\nCurrently, users of speculative inference in SGLang must manually tune several parameters:\n\n- speculative_num_steps\n- speculative_topk\n- speculative_num_draft_tokens\n\n![\"Image\"](\"https://github.com/user-attachments/assets/d13c9e61-e5be-4d9c-a245-d9b95cb299f6\")
\n\nFrom the graph, we see that throughput varies with speculative_num_steps and batchsize, and it also suggests that a well-tuned parameter configuration of speculative inference can increase throughput by 5%~50%. These findings suggest three current issues:\n\n1. Trial-and-error overhead \u2013 Finding optimal values per model/hardware/workload is tedious and often results in suboptimal performance.\n\n2. Model capability mismatch \u2013 Different draft models have different effective limits, but static parameters cannot adapt.\n\n3. Batch-size sensitivity \u2013 The optimal number of speculative steps decreases as batch size grows, due to compute constraints.\n\nA single fixed configuration cannot perform well across varying models, hardware, and batch sizes.\n\n## Proposed Design\n\nWe propose a lightweight feedback controller that adjusts speculative_num_steps in real time based on runtime metrics. For simplicity and stability, we keep speculative_topk=1 and speculative_num_draft_tokens=speculative_num_steps+1 (following observed best practices).\n\n### Core Architecture\n\nThe system monitors two metrics after each batch:\n\n- Acceptance rate \u2013 ratio of accepted draft tokens.\n\n- Acceptance length growth \u2013 how much accepted length changes when steps increase.\n\nUsing these, it applies the following simple rules:\n\n1. Increase steps if:\n\n- Acceptance rate is high (configurable, e.g., \u22650.6)\n\n- Acceptance length grows sufficiently (exceeding a model-aware threshold)\n\n- Hardware limits for the current batch size are not exceeded\n\n2. Decrease steps if:\n\n- Acceptance rate is low (e.g., <0.5)\n\n3. Otherwise, keep steps unchanged.\n\nThis forms a stable negative-feedback loop that converges to a near-optimal step count for the current workload.\n\n### Detailed Designs\n\n#### Initialization Phase\n\nDuring system startup, the following initialization sequence occurs:\n\n1. **Computational Threshold Calculation**: For each possible batch size (1, 2, 4, 8, 16, 32, 64), compute the maximum allowable speculative steps given hardware constraints(thres_batchsize);\n2. **Draft Model Ability Analysis**: (Optional) Assess draft model capabilities and establish maximum effective step boundaries. (This step is optional, parameters can be dynamically adjusted and saved during runtime.)\n3. **Theoretical Threshold Establishment**: Calculate lower bound of theoretical accept length growth thresholds for different speculative step values. \n\n#### Runtime Parameter Adjustment Logic\n\nThe adjustment algorithm implements a conservative approach to prevent oscillation:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/f31880cc-7374-4818-9fa7-6aa6f4d1ed91\")
\n\n```\nFor each batch run:\n 1. Collect metrics: acceptance_rate, acceptance_length_growth_rate\n 2. speculative_num_steps += 1 if (acceptance_length_growth_rate > thres_accept_length_growth_rate AND accept_rate >= thres_positive_accept_rate AND speculative_num_step+1, 'debug_dump_path': None, 'cache_dir': '', 'compile_cache_save_format': 'binary', 'backend': 'inductor', 'custom_ops': ['none'], 'splitting_ops': ['vllm::unified_attention', 'vllm::unified_attention_with_output', 'vllm::unified_mla_attention', 'vllm::unified_mla_attention_with_output', 'vllm::mamba_mixer2', 'vllm::mamba_mixer', 'vllm::short_conv', 'vllm::linear_attention', 'vllm::plamo2_mamba_mixer', 'vllm::gdn_attention_core', 'vllm::kda_attention', 'vllm::sparse_attn_indexer'], 'compile_mm_encoder': False, 'compile_sizes': [], 'inductor_compile_config': {'enable_auto_functionalized_v2': False, 'combo_kernels': True, 'benchmark_combo_kernel': True}, 'inductor_passes': {}, 'cudagraph_mode': , 'cudagraph_num_of_warmups': 1, 'cudagraph_capture_sizes': [1, 2, 4, 8, 16, 24, 32, 40, 48, 56, 64, 72, 80, 88, 96, 104, 112, 120, 128, 136, 144, 152, 160, 168, 176, 184, 192, 200, 208, 216, 224, 232, 240, 248, 256, 272, 288, 304, 320, 336, 352, 368, 384, 400, 416, 432, 448, 464, 480, 496, 512], 'cudagraph_copy_inputs': False, 'cudagraph_specialize_lora': True, 'use_inductor_graph_partition': False, 'pass_config': {'fuse_norm_quant': False, 'fuse_act_quant': False, 'fuse_attn_quant': False, 'eliminate_noops': True, 'enable_sp': False, 'fuse_gemm_comms': False, 'fuse_allreduce_rms': False}, 'max_cudagraph_capture_size': 512, 'dynamic_shapes_config': {'type': }, 'local_cache_dir': None}\n(EngineCore_DP0 pid=732093) /home/smc01/miniconda3/envs/vLLM_12/lib/python3.10/site-packages/torch/cuda/__init__.py:283: UserWarning: \n(EngineCore_DP0 pid=732093) Found GPU0 NVIDIA GB10 which is of cuda capabilit",
"url": "https://github.com/vllm-project/vllm/issues/30855",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-17T08:44:11Z",
"updated_at": "2025-12-17T08:44:11Z",
"comments": 0,
"user": "nanbogong"
},
{
"repo": "vllm-project/vllm",
"number": 30847,
"title": "[Bug]: Qwen 3VL via Efficient Video Sampling (EVS) to trim video embeddings and found that the number of tokens after timestamp in the Prompt was not aligned with the actual number of tokens after pruning?",
"body": "### Your current environment\n\n![\"Image\"](\"https://github.com/user-attachments/assets/aeb59c9e-b970-4a2c-851f-a001c8610f0a\")
\n2\u3001compute_retention_mask\n\n![\"Image\"](\"https://github.com/user-attachments/assets/d96f2ce9-1ed2-4d5d-b5ac-28c6394d8ee0\")
\n\n![\"Image\"](\"https://github.com/user-attachments/assets/2bc35d99-bbbb-4437-84fb-755567e97b40\")
\n\n3\u3001embed_input_ids\n![\"Image\"](\"https://github.com/user-attachments/assets/2f8fcd5b-9fef-49d5-b331-dd4f1f1d7335\")
\ninput_ids:\n![\"Image\"](\"https://github.com/user-attachments/assets/a62fcfe8-f29e-4e1d-a8fb-dec91c5e75e7\")
\n\nFrom the above 1 and 3, it can be seen that the data in frames_idx_token is the same as that in embed_input_ids,\nThe first frame contains 165 tokens, while the rest contain 33 tokens\n151656 is the ID of the video token. The number of 151656 is the number of video tokens. The sum of the number of video tokens in all frames is the same as the sum of frames_idx_token.\nRegarding the second item: compute_contention_mask EVS cropped mask, it was found that the number of tokens in the first frame was 165, while the number of tokens in other frames was different,\nBased on the above 1, 2, and 3, it can be concluded that the current implementation of EVS pruning algorithm has problems\nThat is, the number of tokens after timestamp in the Prompt does not match the actual number of tokens that should be retained after EVS pruning.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30847",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-17T06:46:15Z",
"updated_at": "2026-01-04T07:39:17Z",
"comments": 5,
"user": "xshqhua"
},
{
"repo": "vllm-project/vllm",
"number": 30832,
"title": "[Performance]: DeepSeek-V3.2 on 8xH20 30 decode tokens/sec",
"body": "### Proposal to improve performance\n\n**My Env:**\nvllm 0.13.0rc2.dev178+g676db55ee\ndeep_gemm 2.1.1+c9f8b34\ncuda. 12.9\npython. 3.10.18\n\n**command** is the same as:\nvllm serve mypath/DeepSeek-V3.2 \\\n --tensor-parallel-size 8 \\\n --tokenizer-mode deepseek_v32 \\\n --tool-call-parser deepseek_v32 \\\n --enable-auto-tool-choice \\\n --reasoning-parser deepseek_v3\n\n**My Question:**\nThe output tokens is 30 tokens/s 1/req which is slower than excpted on https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html#benchmarking:\n\nis there any wrong with this?\n\n------------------------------------------------\nBenchmarking[\u00b6](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html#benchmarking)\nWe used the following script to benchmark deepseek-ai/DeepSeek-V3.2 on 8xH20.\n\n\nvllm bench serve \\\n --model deepseek-ai/DeepSeek-V3.2 \\\n --dataset-name random \\\n --random-input 2048 \\\n --random-output 1024 \\\n --request-rate 10 \\\n --num-prompt 100 \\ \n --trust-remote-code\nTP8 Benchmark Output[\u00b6](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html#tp8-benchmark-output)\n\n============ Serving Benchmark Result ============\nSuccessful requests: 100 \nFailed requests: 0 \nRequest rate configured (RPS): 10.00 \nBenchmark duration (s): 129.34 \nTotal input tokens: 204800 \nTotal generated tokens: 102400 \nRequest throughput (req/s): 0.77 \nOutput token throughput (tok/s): 791.73 \nPeak output token throughput (tok/s): 1300.00 \nPeak concurrent requests: 100.00 \nTotal Token throughput (tok/s): 2375.18 \n---------------Time to First Token----------------\nMean TTFT (ms): 21147.20 \nMedian TTFT (ms): 21197.97 \nP99 TTFT (ms): 41133.00 \n-----Time per Output Token (excl. 1st token)------\nMean TPOT (ms): 99.71 \nMedian TPOT (ms): 99.25 \nP99 TPOT (ms): 124.28 \n---------------Inter-token Latency----------------\nMean ITL (ms): 99.71 \nMedian ITL (ms): 76.89 \nP99 ITL (ms): 2032.37 \n==================================================\n\n### Report of performance regression\n\n_No response_\n\n### Misc discussion on performance\n\n_No response_\n\n### Your current environment (if you think it is necessary)\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30832",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-12-17T03:08:52Z",
"updated_at": "2025-12-18T08:01:30Z",
"comments": 1,
"user": "lisp2025"
},
{
"repo": "huggingface/candle",
"number": 3247,
"title": "Parakeet V3 support?",
"body": "Any plans to support Parakeet V3 by any chance? Thank you \ud83d\ude4f ",
"url": "https://github.com/huggingface/candle/issues/3247",
"state": "open",
"labels": [],
"created_at": "2025-12-16T19:05:33Z",
"updated_at": "2025-12-16T19:05:33Z",
"comments": 0,
"user": "mobicham"
},
{
"repo": "vllm-project/vllm",
"number": 30798,
"title": "[Usage]: vllm offline server lora model",
"body": "### Your current environment\n\n\n\nHi team,\n\nI have a question about deploying LoRA models with a vLLM offline server.\n\nCurrently, we have a base model **A**. After LoRA training, we obtain adapter parameters **P**. When we serve model A with vLLM (offline server) and enable LoRA, we can select either the **base model A** or **A + P** (LoRA adapter) from the `/v1/models` list for inference.\n\nBased on this, suppose we **merge A and P** into a new merged model **B = A + P**, and then continue LoRA training on top of **B** to obtain another LoRA adapter **Q**.\n\nIs there a way to deploy on a single vLLM server such that the models list allows choosing among these three options for inference?\n\n1. **A**\n2. **A + P**\n3. **A + P + Q**\n\nIf vLLM cannot directly stack LoRA adapters (P then Q) at runtime, is there a recommended approach to **combine P and Q** into a new equivalent adapter (e.g., a single LoRA adapter **R**) that is functionally equivalent to **A + P + Q**, ideally in a way that is **equivalent to training a LoRA adapter directly on base A**?\n\nThanks a lot for your help!\n\n---\n\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30798",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-16T16:38:49Z",
"updated_at": "2025-12-18T11:52:39Z",
"comments": 4,
"user": "zapqqqwe"
},
{
"repo": "sgl-project/sglang",
"number": 15266,
"title": "Multi-Adapter Support for Embed Qwen3 8B Embedding Model",
"body": "### Checklist\n\n- [x] If this is not a feature request but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [x] Please use English. Otherwise, it will be closed.\n\n### Motivation\n\nHi Team, do we currently support multi-adapter (LoRA) support for embedding models, specifically Qwen3 8B Embedding model? If not, when can we expect the support? Thanks :)\n\n### Related resources\n\nI'm training the model for three different tasks using separate lora adapters and need to deploy the model with one base and the three different adapters.\n\nThis is similar to how [Jina v4](https://huggingface.co/jinaai/jina-embeddings-v4) Embedding model has task specific adapters.\n\nMy adapter config looks like this -\n```\n{\n \"alpha_pattern\": {},\n \"auto_mapping\": null,\n \"base_model_name_or_path\": \"/temp/local-ssd/models/Qwen3-Embedding-8B\",\n \"bias\": \"none\",\n \"corda_config\": null,\n \"eva_config\": null,\n \"exclude_modules\": null,\n \"fan_in_fan_out\": false,\n \"inference_mode\": true,\n \"init_lora_weights\": true,\n \"layer_replication\": null,\n \"layers_pattern\": null,\n \"layers_to_transform\": null,\n \"loftq_config\": {},\n \"lora_alpha\": 128,\n \"lora_bias\": false,\n \"lora_dropout\": 0.1,\n \"megatron_config\": null,\n \"megatron_core\": \"megatron.core\",\n \"modules_to_save\": [\n \"classifier\",\n \"score\",\n \"classifier\",\n \"score\"\n ],\n \"peft_type\": \"LORA\",\n \"r\": 32,\n \"rank_pattern\": {},\n \"revision\": null,\n \"target_modules\": [\n \"gate_proj\",\n \"k_proj\",\n \"up_proj\",\n \"q_proj\",\n \"down_proj\",\n \"v_proj\",\n \"o_proj\"\n ],\n \"task_type\": \"SEQ_CLS\",\n \"trainable_token_indices\": null,\n \"use_dora\": false,\n \"use_rslora\": false\n}\n```",
"url": "https://github.com/sgl-project/sglang/issues/15266",
"state": "open",
"labels": [],
"created_at": "2025-12-16T14:14:16Z",
"updated_at": "2025-12-16T14:14:22Z",
"comments": 0,
"user": "dawnik17"
},
{
"repo": "vllm-project/vllm",
"number": 30776,
"title": "[Usage]: Qwen3-omni's offline usage",
"body": "### Your current environment\n\nI used the code below in vllm==0.12.0, but failed.\n```\nimport os\nimport torch\n\nfrom vllm import LLM, SamplingParams\nfrom transformers import Qwen3OmniMoeProcessor\nfrom qwen_omni_utils import process_mm_info\n\ndef build_input(processor, messages, use_audio_in_video):\n text = processor.apply_chat_template(\n messages,\n tokenize=False,\n add_generation_prompt=True,\n )\n # print(text[0])\n # print(len(text[0]))\n audios, images, videos = process_mm_info(messages, use_audio_in_video=use_audio_in_video)\n\n inputs = {\n 'prompt': text,\n 'multi_modal_data': {},\n \"mm_processor_kwargs\": {\n \"use_audio_in_video\": use_audio_in_video,\n },\n }\n\n if images is not None:\n inputs['multi_modal_data']['image'] = images\n if videos is not None:\n inputs['multi_modal_data']['video'] = videos\n if audios is not None:\n inputs['multi_modal_data']['audio'] = audios\n \n return inputs\n\nif __name__ == '__main__':\n # vLLM engine v1 not supported yet\n os.environ['VLLM_USE_V1'] = '1'\n os.environ['CUDA_DEVICES'] = '0,1,2,3,4,5,6,7'\n\n MODEL_PATH = \"Qwen3-Omni-30B-A3B-Instruct\"\n llm = LLM(\n model=MODEL_PATH, trust_remote_code=True, gpu_memory_utilization=0.95,\n tensor_parallel_size=1,\n limit_mm_per_prompt={'image': 3, 'video': 3, 'audio': 3},\n max_num_seqs=8,\n max_model_len=32768,\n seed=17114,\n )\n\n sampling_params = SamplingParams(\n temperature=0.6,\n top_p=0.95,\n top_k=20,\n max_tokens=16384,\n )\n\n processor = Qwen3OmniMoeProcessor.from_pretrained(MODEL_PATH)\n\n conversation1 = [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"video\",\n \"video\": \"1.mp4\",\n \"fps\": 6,\n }\n ],\n }\n ]\n \n USE_AUDIO_IN_VIDEO = True\n\n # Combine messages for batch processing\n conversations = [conversation1]\n inputs = [build_input(processor, messages, USE_AUDIO_IN_VIDEO) for messages in conversations]\n # print(inputs[0])\n outputs = llm.generate(inputs, sampling_params=sampling_params)\n\n for i in range(len(outputs)):\n print(\"\\n\\n==========\\n\")\n print(outputs[i])\n```\nThe error\n```\nTraceback (most recent call last):\n File \"/sft-qwen3-omni/vllm_inference.py\", line 44, in \n llm = LLM(\n ^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/llm.py\", line 334, in __init__\n self.llm_engine = LLMEngine.from_engine_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/llm_engine.py\", line 183, in from_engine_args\n return cls(\n ^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/llm_engine.py\", line 109, in __init__\n self.engine_core = EngineCoreClient.make_client(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py\", line 93, in make_client\n return SyncMPClient(vllm_config, executor_class, log_stats)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py\", line 642, in __init__\n super().__init__(\n File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py\", line 471, in __init__\n with launch_core_engines(vllm_config, executor_class, log_stats) as (\n File \"/usr/lib/python3.12/contextlib.py\", line 144, in __exit__\n next(self.gen)\n File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/utils.py\", line 903, in launch_core_engines\n wait_for_engine_startup(\n File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/utils.py\", line 960, in wait_for_engine_startup\n raise RuntimeError(\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {'EngineCore_DP0': 1}\n[root:]$ python sft-qwen3-omni/vllm_inference.py\n[2025-12-16 12:25:00] INFO vision_process.py:42: set VIDEO_TOTAL_PIXELS: 90316800\nINFO 12-16 12:25:00 [utils.py:253] non-default args: {'trust_remote_code': True, 'seed': 17114, 'max_model_len': 32768, 'gpu_memory_utilization': 0.95, 'max_num_seqs': 8, 'disable_log_stats': True, 'limit_mm_per_prompt': {'image': 3, 'video': 3, 'audio': 3}, 'model': 'Qwen3-Omni-30B-A3B-Instruct'}\nThe argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\nUnrecognized keys in `rope_scaling` for 'rope_type'='default': {'mrope_interleaved', 'interleaved', 'mrope_section'}\nUnrecognized keys in `rope_scaling` for 'rope_type'='default': {'interleaved', 'mrope_section'}\nINFO 12-16 12:25:00 [model.py:637] Resolved architecture: Qwen3OmniMoeForConditionalGeneration\nINFO 12-16 12:25:00 [model.py:1750] Using max model len 32768\nINFO 12-16 12:25:00 [scheduler.py:228] Chun",
"url": "https://github.com/vllm-project/vllm/issues/30776",
"state": "open",
"labels": [
"bug",
"usage"
],
"created_at": "2025-12-16T12:30:18Z",
"updated_at": "2025-12-17T17:03:34Z",
"comments": 50,
"user": "Auraithm"
},
{
"repo": "sgl-project/sglang",
"number": 15260,
"title": "SGLang installs newer PyTorch automatically \u2013 is there an official SGLang \u2194 PyTorch compatibility guide?",
"body": "Hi SGLang team, thank you for the great project!\n\nI have a question regarding **PyTorch version compatibility and installation**.\n\nCurrently, the recommended installation command from the website is:\n\n```bash\nuv pip install \"sglang\" --prerelease=allow\n```\n\nHowever, when using this command, `pip/uv` automatically upgrades PyTorch to the latest version (e.g., torch 2.9.1).\nIn my environment, I am intentionally pinned to **torch 2.8.x** and would prefer not to upgrade.\n\nAt the moment, it\u2019s not clear:\n\n* Which **SGLang versions are compatible with which PyTorch versions**\n* Whether older SGLang releases are expected to work with torch 2.8\n* What the recommended installation approach is for users who need to keep a specific torch version\n\n### **Questions**\n\n1. Is there an **official or recommended SGLang \u2194 PyTorch compatibility matrix**?\n2. For users pinned to torch 2.8.x, which SGLang version is recommended?\n3. Is it safe to install SGLang with `--no-deps` or a constraints file to prevent torch upgrades?\n4. Would it be possible to document supported torch versions in the release notes or README?\n\n### **Why this matters**\n\nMany users run SGLang in **production or CUDA-pinned environments**, where upgrading PyTorch is non-trivial. Clear guidance would help avoid dependency conflicts and accidental upgrades.\n\nThanks again for your work \u2014 any guidance would be greatly appreciated!",
"url": "https://github.com/sgl-project/sglang/issues/15260",
"state": "open",
"labels": [],
"created_at": "2025-12-16T12:27:59Z",
"updated_at": "2025-12-16T12:27:59Z",
"comments": 0,
"user": "David-19940718"
},
{
"repo": "vllm-project/vllm",
"number": 30757,
"title": "[Performance]: Async sched: Why return AsyncGPUModelRunnerOutput util func sample_tokens",
"body": "### Proposal to improve performance\n\nWhy is AsyncGPUModelRunnerOutput returned only after sample_tokens, not immediately after execute_model?\nhttps://github.com/vllm-project/vllm/blob/0d0c929f2360cde5bae6817ad0f555641329e79d/vllm/v1/engine/core.py#L420-L422\nIf we defer returning AsyncGPUModelRunnerOutput until after sampling, there's a high chance that the async future completes immediately because `AsyncGPUModelRunnerOutput.get_output` is really light workload. As a result, the batch_queue size may effectively remain at 1, preventing overlap between model forward and scheduling of the next batch.\nhttps://github.com/vllm-project/vllm/blob/0d0c929f2360cde5bae6817ad0f555641329e79d/vllm/v1/engine/core.py#L430-L438\n\n### Report of performance regression\n\n_No response_\n\n### Misc discussion on performance\n\n_No response_\n\n### Your current environment (if you think it is necessary)\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30757",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-12-16T08:26:08Z",
"updated_at": "2025-12-16T08:26:49Z",
"comments": 0,
"user": "iwzbi"
},
{
"repo": "vllm-project/vllm",
"number": 30736,
"title": "[Bug] DCP/DBO: 'NoneType' error building attention_metadata during DeepSeek-V3.1 deployment dummy run",
"body": "### Your current environment\n\n```bash\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.10.0a0+git9166f61\nIs debug build : False\nCUDA used to build PyTorch : 12.9\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.19 | packaged by conda-forge | (main, Oct 22 2025, 22:29:10) [GCC 14.3.0] (64-bit runtime)\nPython platform : Linux-5.15.0-124-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.9.86\nCUDA_MODULE_LOADING set to :\nGPU models and configuration :\nGPU 0 : NVIDIA H200\nGPU 1 : NVIDIA H200\nGPU 2 : NVIDIA H200\nGPU 3 : NVIDIA H200\nGPU 4 : NVIDIA H200\nGPU 5 : NVIDIA H200\nGPU 6 : NVIDIA H200\nGPU 7 : NVIDIA H200\n\nNvidia driver version : 570.124.06\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n vLLM Info\n==============================\nROCM Version : Could not collect\nvLLM Version : 0.11.1rc4.dev1340+gd08981aba.d20251215 (git sha: d08981aba, date: 20251215)\nvLLM Build Flags:\nCUDA Archs : 9.0\nROCm : Disabled\n```\n\n### \ud83d\udc1b Describe the bug\n\nWhen starting vllm serve with the command below, it fails during the final dummy run step and does not start successfully.\n\nStartup Command:\n\n```bash\nvllm serve deepseek-ai/DeepSeek-V3.1-Terminus \\\n --enable-dbo \\\n --stream-interval 10 \\\n --api-server-count 2 \\\n --max-num-batched-tokens 32768 \\\n --max-num-seqs 256 \\\n --long-prefill-token-threshold 16384 \\\n --scheduling-policy fcfs \\\n --data-parallel-size 2 \\\n --data-parallel-size-local 2 \\\n --tensor-parallel-size 4 \\\n --decode-context-parallel-size 4 \\\n --data-parallel-backend mp \\\n --distributed-executor-backend mp \\\n --enable-expert-parallel \\\n --all2all-backend deepep_low_latency \\\n --max-model-len 131072 \\\n --gpu-memory-utilization 0.8 \\\n --quantization \"fp8\" \\\n --trust-remote-code \\\n --enable-auto-tool-choice \\\n --tool-call-parser \"deepseek_v31\" \\\n --chat-template dpsk-v3.1-tool-parser-vllm.jinja \\\n --host ${HOST} \\\n --port ${PORT} \\\n```\n\nError Output\uff1a\n\n```bash\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] WorkerProc hit an exception.\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] Traceback (most recent call last):\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] File \"/home/jovyan/rl/.pixi/envs/infer/lib/python3.12/site-packages/vllm/v1/executor/multiproc_executor.py\", line 817, in worker_busy_loop\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] output = func(*args, **kwargs)\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] ^^^^^^^^^^^^^^^^^^^^^\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] File \"/home/jovyan/rl/.pixi/envs/infer/lib/python3.12/site-packages/vllm/v1/worker/gpu_worker.py\", line 448, in compile_or_warm_up_model\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] cuda_graph_memory_bytes = self.model_runner.capture_model()\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] File \"/home/jovyan/rl/.pixi/envs/infer/lib/python3.12/site-packages/vllm/v1/worker/gpu_model_runner.py\", line 4541, in capture_model\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] self._capture_cudagraphs(\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] File \"/home/jovyan/rl/.pixi/envs/infer/lib/python3.12/site-packages/vllm/v1/worker/gpu_model_runner.py\", line 4615, in _capture_cudagraphs\n(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] self._dummy_run(\n(Worker_DP1_TP0_DCP0_EP4 pid=479)",
"url": "https://github.com/vllm-project/vllm/issues/30736",
"state": "open",
"labels": [
"bug",
"help wanted"
],
"created_at": "2025-12-16T03:07:59Z",
"updated_at": "2025-12-22T17:11:48Z",
"comments": 3,
"user": "Butterfingrz"
},
{
"repo": "huggingface/transformers.js",
"number": 1487,
"title": "License clarification for some of the converted models",
"body": "### Question\n\nHello!\n\nI want to use [Xenova/whisper-small](https://huggingface.co/Xenova/whisper-small) and [Xenova/UAE-Large-V1](https://huggingface.co/Xenova/UAE-Large-V1) in a project, but I noticed that these model cards on Hugging Face do not have a license specified in their metadata or README.\n\nSince the original weights from OpenAI and WhereIsAI are licensed, I assume these converted ONNX versions are intended to follow the same or a similar open-source licenses. Could you please clarify:\n\n- Are these models safe to use for commercial/personal projects?\n- Is it possible to update the model cards to explicitly include the license tag?\n\n\nThanks again!",
"url": "https://github.com/huggingface/transformers.js/issues/1487",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-12-16T00:27:16Z",
"updated_at": "2025-12-16T19:13:09Z",
"user": "rmahdav"
},
{
"repo": "vllm-project/vllm",
"number": 30722,
"title": "[Bug]: llama4_pythonic tool parser fails with SyntaxError on nested list parameters",
"body": "### Your current environment\n\nI don't have direct access to the cluster the model is running in. But it's running on 8x H100 GPUs using TP 8, expert parallel. \n\nThis is the fp8 model from Huggingface.\n\nThese are the vllm serve args I'm using:\n\nVLLM Version: 0.11.0\n\n```\n--port 8002 \n--model /config/models/maverick \n--device cuda \n--tensor-parallel-size 8 \n--disable-log-requests \n--max-num-batched-tokens 16000 \n--served-model-name 'llama-4-maverick-17b-128e-instruct' \n--limit-mm-per-prompt image=50 \n--kv-cache-dtype fp8 \n--trust-remote-code \n--enable-auto-tool-choice \n--enable-chunked-prefill true \n--enable-prefix-caching \n--tool-call-parser llama4_pythonic \n--enable-expert-parallel \n--chat-template examples/tool_chat_template_llama4_pythonic.jinja \n--override-generation-config '{\\\"attn_temperature_tuning\\\": true}' \n--max-model-len 1000000\n```\n\n### \ud83d\udc1b Describe the bug\n\n### Description\n\nThe `llama4_pythonic` tool parser intermittently fails to parse valid tool calls, resulting in:\n1. `SyntaxError` from `ast.parse()` when model output is malformed (missing closing `]`)\n2. Valid pythonic syntax returned as `content` instead of being parsed into `tool_calls`\n\n### Reproduction\n\n**Minimal curl (run 10+ times to observe intermittent failure):**\n\n```bash\ncurl -X POST https://your-vllm-endpoint/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"llama-4-maverick-17b-128e-instruct\",\n \"messages\": [\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n {\"role\": \"user\", \"content\": \"how do I enroll in benefits?\"}\n ],\n \"tools\": [{\n \"type\": \"function\",\n \"function\": {\n \"name\": \"enterprise_search\",\n \"description\": \"Search enterprise knowledge base\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"query\": {\"type\": \"string\"},\n \"rephrased_queries\": {\n \"type\": \"array\",\n \"items\": {\"type\": \"string\"},\n \"description\": \"List of 2 rephrased queries\"\n }\n },\n \"required\": [\"query\", \"rephrased_queries\"]\n }\n }\n }],\n \"tool_choice\": \"auto\",\n \"max_tokens\": 500,\n \"temperature\": 0,\n \"top_p\": 0.95\n }'\n```\n\n**Observed results (10 identical requests):**\n- 7/10: \u2705 `finish_reason: \"tool_calls\"`, properly parsed\n- 3/10: \u274c `finish_reason: \"stop\"`, pythonic syntax in `content` field, empty `tool_calls`\n\n### Failure Modes Observed\n\n**Mode 1: Valid pythonic not parsed**\n```json\n{\n \"finish_reason\": \"stop\",\n \"message\": {\n \"content\": \"[enterprise_search(query=\\\"Benefits enrollment\\\", rephrased_queries=[\\\"...\\\", \\\"...\\\"])]\",\n \"tool_calls\": []\n }\n}\n```\nParser fails to detect valid syntax \u2192 returned as content.\n\n**Mode 2: Model generates text after tool call**\n```json\n{\n \"content\": \"[enterprise_search(...)]\\n\\nI was unable to execute this task...\"\n}\n```\nModel mixes tool call + text, which violates parser assumption.\n\n**Mode 3: Malformed output (missing bracket)**\n```\n[enterprise_search(query='...', rephrased_queries=['...', '...'])\n```\nModel hits `stop_reason: 200007` before completing \u2192 `ast.parse()` throws SyntaxError.\n\n### Suspected Root Cause\n\n***The below is suggested by Claude Opus 4.5 so take with a grain of salt.***\n\n1. **Parser detection inconsistency** - Valid pythonic output intermittently not recognized as tool call\n2. **No text-after-tool-call handling** - Parser fails when model appends text after `]`\n3. **Stop token interference** - Model sometimes hits stop token (200007) mid-generation before completing brackets\n4. **Nested bracket complexity** - Array parameters (`rephrased_queries`) create `[...[...]...]` nesting that may confuse detection\n\n### Error Logs\n\n[err.txt](https://github.com/user-attachments/files/24175232/err.txt)\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30722",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-15T21:26:24Z",
"updated_at": "2025-12-15T21:26:24Z",
"comments": 0,
"user": "mphilippnv"
},
{
"repo": "huggingface/tokenizers",
"number": 1913,
"title": "Wrong and unsuppressable print when instantiating BPE",
"body": "I am running Python code that is of the form\n\n```python\nfrom transformers import PreTrainedTokenizerFast\nfrom tokenizers import Tokenizer\nfrom tokenizers.models import BPE\n\nvocab = {\"a\": 5, \"b\": 6, \"ab\": 7}\nmerges = [(\"a\",\"b\")]\n\nbackend_of_backend_of_backend = BPE(vocab=vocab, merges=merges, dropout=None)\nbackend_of_backend = Tokenizer(model=backend_of_backend_of_backend)\nbackend = PreTrainedTokenizerFast(tokenizer_object=backend_of_backend)\n```\n\nThe line `BPE(vocab=vocab, merges=merges, dropout=None)` has nothing to do with serialisation. Yet, when I run it, an unwanted print\n```\nThe OrderedVocab you are attempting to save contains holes for indices [0, 1, 2, 3, 4], your vocabulary could be corrupted!\n```\nappears in my console, which seems to come from\n\nhttps://github.com/huggingface/tokenizers/blob/f7db48f532b3d4e3c65732cf745fe62863cbe5fa/tokenizers/src/models/mod.rs#L53-L56\n\nNot only is the print wrong (I am not trying to **save** anything), but also, it cannot be suppressed by redirecting `stdout` and `stderr` in Python. \n\n`println!` does not belong in low-level code, so at the very least, we need a way to disable it. But besides, what is this print even for, given that it says something about **saving** when we are **loading** a tokenizer?",
"url": "https://github.com/huggingface/tokenizers/issues/1913",
"state": "closed",
"labels": [],
"created_at": "2025-12-15T16:30:46Z",
"updated_at": "2026-01-05T13:02:45Z",
"comments": 4,
"user": "bauwenst"
},
{
"repo": "vllm-project/vllm",
"number": 30694,
"title": "[Feature]: CompressedTensors: NVFP4A16 not supported for MoE models",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nNVFP4A16 (W4A16 FP4) quantization via compressed_tensors works for dense models but fails on MoE models like Qwen3-30B-A3B.\n\nLooking at `compressed_tensors_moe.py`, `_is_fp4a16_nvfp4` is checked for Linear layers but not in `get_moe_method()` for FusedMoE. Only W4A4 has a MoE method (`CompressedTensorsW4A4Nvfp4MoEMethod`).\n\nSince the Marlin kernel already supports FP4 weights + FP16 activations, is there a plan to add W4A16 MoE support for compressed_tensors? \n\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30694",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-12-15T13:29:09Z",
"updated_at": "2025-12-21T09:27:38Z",
"comments": 2,
"user": "zhangyimi"
},
{
"repo": "vllm-project/vllm",
"number": 30685,
"title": "[Feature]: fp8 kv cache for finer-grained scaling factors (e.g., per channel).",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nCurrently, the FP8 KV cache feature (in the FlashMLA interface) only supports per-tensor (scalar) scaling factors. Are you developing support for finer-grained scaling factors (e.g., per-channel)? If so, when can we expect the FP8 KV cache with such finer-grained scaling factors to be completed?\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30685",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-12-15T09:32:48Z",
"updated_at": "2025-12-15T09:32:48Z",
"comments": 0,
"user": "zx-ai"
},
{
"repo": "huggingface/transformers",
"number": 42868,
"title": "sdpa_paged: How does it handle paged cache without padding?",
"body": "Hi @ArthurZucker ,\n\nI was analyzing the [sdpa_paged](https://github.com/huggingface/transformers/blob/main/src/transformers/integrations/sdpa_paged.py#L18) implementation and found the approach quite fascinating. I have a question regarding how the input shapes are handled.\n\nIf I have a batch of 4 sequences with lengths **32, 32, 64, and 128**, a standard SDPA call usually expects a shape of `[4, 128]` (Batch Size, Max Seq Len), where the shorter sequences are padded to 128.\n\nHowever, in this implementation, it appears that the input to SDPA is a flattened tensor with shape **`[1, 256]`** (the sum of all lengths: $32+32+64+128$), implying that no padding is used and the sequences are concatenated.\n\nCould you explain how standard SDPA produces the correct result in this case? Specifically, how does it differentiate between the sequences to prevent cross-sequence attention within this single packed batch?\n\nThanks for your time!\n\n\nrelated PR: #38085",
"url": "https://github.com/huggingface/transformers/issues/42868",
"state": "closed",
"labels": [],
"created_at": "2025-12-15T08:39:00Z",
"updated_at": "2025-12-16T03:08:27Z",
"comments": 4,
"user": "jiqing-feng"
},
{
"repo": "huggingface/trl",
"number": 4692,
"title": "LLVM error during GRPO training with Apple M4 Max",
"body": "I have the below error while doing GRPO training. I am using HuggingFace example codes for GRPO. I couldn't run the model on MPS because of this issue. \nHow can I run GRPO on MPS?\n\nloc(\"mps_matmul\"(\"(mpsFileLoc): /AppleInternal/Library/BuildRoots/4~B_wkugAG-524HdEQLaK0kvU7Y_D8Jtm6UxMaIoY/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm\":43:0)): error: incompatible dimensions\nloc(\"mps_matmul\"(\"(mpsFileLoc): /AppleInternal/Library/BuildRoots/4~B_wkugAG-524HdEQLaK0kvU7Y_D8Jtm6UxMaIoY/Library/Caches/com.apple.xbs/Sources/MetalPerformanceShadersGraph/mpsgraph/MetalPerformanceShadersGraph/Core/Files/MPSGraphUtilities.mm\":43:0)): error: invalid shape\nLLVM ERROR: Failed to infer result type(s).\n\nDetails: \nOS: Tahoe 26.2\npytorch 2.9.1\ntrl: 0.26.1\nMLX:0.30.0\n\n\n\n\n\n",
"url": "https://github.com/huggingface/trl/issues/4692",
"state": "open",
"labels": [
"\ud83d\udc1b bug",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-12-14T23:01:49Z",
"updated_at": "2025-12-14T23:02:11Z",
"comments": 0,
"user": "neslihaneti"
},
{
"repo": "vllm-project/vllm",
"number": 30654,
"title": "[Feature][Attention][UX]: Incorporate Features into Attention Selection",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nSUMMARY:\n* we have default attention backends by priority and a notion of which backend supports what hw\n* however, certain features are not considered in this (e.g. fp8 kv cache, e.g. attention sinks)\n\nRecent example, we had test failures because we updated the logic to load kv cache quantization from the model config. But since CUTLASS_MLA is the default backend on B200, we started seeing test failures (since CUTLASS MLA does not support fp8 kv cache) because we were not automatically falling back to FLASHINFER_MLA (which does)\n\n\nSo the proposal is to:\n- make sure all attention backends report what features are supported\n- update the attention selector to consider these features in the selection\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30654",
"state": "open",
"labels": [
"help wanted",
"good first issue",
"feature request"
],
"created_at": "2025-12-14T18:04:14Z",
"updated_at": "2025-12-30T05:38:40Z",
"comments": 11,
"user": "robertgshaw2-redhat"
},
{
"repo": "huggingface/diffusers",
"number": 12838,
"title": "Merge Loras for FLUX",
"body": "The issue is based on https://huggingface.co/docs/diffusers/main/using-diffusers/merge_loras \n\nIs there a similar procedure for merging loras for FLUX models? The guide seems to be specific for UNet based methods. I'm working on FLUX-dev and I would like to perform a linear merge of my loras. ",
"url": "https://github.com/huggingface/diffusers/issues/12838",
"state": "open",
"labels": [],
"created_at": "2025-12-14T12:39:41Z",
"updated_at": "2025-12-14T12:39:41Z",
"comments": 0,
"user": "shrikrishnalolla"
},
{
"repo": "vllm-project/vllm",
"number": 30633,
"title": "[Installation]: How to install vLLM 0.11.0 with CUDA < 12.9 (Driver 535)? No matching wheels found",
"body": "### Your current environment\n\nI\u2019m trying to install vLLM 0.11.0 on a machine with NVIDIA Driver 535, and I ran into issues related to CUDA version compatibility.\n\nEnvironment\n\nOS: Linux (Ubuntu 20.04 / 22.04)\n\nGPU: NVIDIA GPU H20\n\nNVIDIA Driver: 535.xx\n\nPython: 3.10\n\nvLLM version: 0.11.0\n\nProblem\n\nAccording to the release information for vLLM 0.11.0, the available prebuilt wheels appear to target CUDA 12.9+.\nHowever, with Driver 535, CUDA 12.9 is not supported, and I cannot find any official wheels for CUDA 12.1 / 12.2 / 12.4 or lower.\n\nThis leads to the following questions:\n\nIs vLLM 0.11.0 officially compatible with CUDA versions < 12.9?\n\nIf yes, what is the recommended way to install it on systems with Driver 535?\n\nBuild from source with a specific CUDA version?\n\nUse a specific Docker image?\n\nPin to an older vLLM release?\n\nAre there plans to provide prebuilt wheels for CUDA 12.1 / 12.4, or is CUDA 12.9+ now a hard requirement going forward?\n\nWhat I\u2019ve tried\n\nChecked the GitHub Releases page for vLLM 0.11.0 \u2014 no wheels for CUDA < 12.9\n\nVerified that upgrading CUDA to 12.9 is not possible with Driver 535\n\nLooked for documentation on source builds for older CUDA versions, but didn\u2019t find clear guidance\n\nAny clarification or recommended workflow would be greatly appreciated.\nThanks in advance!\n\n### How you are installing vllm\n\n```sh\npip install -vvv vllm\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30633",
"state": "open",
"labels": [
"installation"
],
"created_at": "2025-12-14T04:29:41Z",
"updated_at": "2026-01-01T16:50:50Z",
"comments": 1,
"user": "whu125"
},
{
"repo": "vllm-project/vllm",
"number": 30630,
"title": "[Usage]: SymmMemCommunicator: Device capability 10.3 not supported",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nHi, I am seeing following warning using vllm serve on B300 instances.\n```\nWARNING 12-13 16:31:15 [symm_mem.py:67] SymmMemCommunicator: Device capability 10.3 not supported, communicator is not available.\n```\nvllm launch command\n```\nvllm serve \\\n --tensor-parallel-size 4 \\\n --kv-cache-dtype fp8 \\\n --tool-call-parser glm45 \\\n --reasoning-parser glm45 \\\n --enable-auto-tool-choice \\\n --model zai-org/GLM-4.6-FP8'\n```\nI built docker image using latest vllm on main branch commit 0e71eaa6447d99e76de8e03213ec22bc1d3b07df . Updated triton version to 3.5.1 and torch version to 2.9.1 to avoid compatibility issue from triton ([issue](https://github.com/triton-lang/triton/issues/8473)). \n\nfor same config benchmarking, I am seeing same perf as H200 (slightly worse) than B300. Is B300 fully supported on vllm yet?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30630",
"state": "open",
"labels": [
"usage",
"nvidia"
],
"created_at": "2025-12-14T01:00:34Z",
"updated_at": "2025-12-18T21:17:42Z",
"comments": 4,
"user": "navmarri14"
},
{
"repo": "huggingface/transformers.js",
"number": 1484,
"title": "Should npm @xenova/transformers be deleted or marked deprecated?",
"body": "### Question\n\nHello,\nI was surprised that none of the models I\u00a0tried were supported by transformerjs, even if they were using transformerjs in their README, until I realized that I was using the old npm package.\n\nShouldn't this package be removed ? Or marked as deprecated in favour of huggingface's ?\n\nBest,",
"url": "https://github.com/huggingface/transformers.js/issues/1484",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-12-13T19:49:08Z",
"updated_at": "2025-12-17T12:21:12Z",
"user": "matthieu-talbot-ergonomia"
},
{
"repo": "huggingface/tokenizers",
"number": 1910,
"title": "[Docs] `Visualizer` dead links",
"body": "It seems like documentation for `Visualizer` is out of date and all the links return 404.\n\nDocs: https://huggingface.co/docs/tokenizers/api/visualizer\nGithub Source: https://github.com/huggingface/tokenizers/blob/main/bindings/python/py_src/tokenizers/tools/visualizer.py",
"url": "https://github.com/huggingface/tokenizers/issues/1910",
"state": "open",
"labels": [],
"created_at": "2025-12-13T19:23:33Z",
"updated_at": "2025-12-13T19:23:33Z",
"comments": 0,
"user": "dudeperf3ct"
},
{
"repo": "vllm-project/vllm",
"number": 30621,
"title": "[Feature]: Remove MXFP4 Logic From `fused_experts`",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nSUMMARY:\n* as part of effort to refactor MoE, trying to reduce cruft\n* we currently only have MX emulation in vLLM\n* the logic for this emulation should be moved into quark\n\nhttps://github.com/vllm-project/vllm/blame/main/vllm/model_executor/layers/fused_moe/fused_moe.py#L1866-L1899\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30621",
"state": "open",
"labels": [
"help wanted",
"good first issue",
"feature request"
],
"created_at": "2025-12-13T18:30:30Z",
"updated_at": "2026-01-04T14:47:45Z",
"comments": 13,
"user": "robertgshaw2-redhat"
},
{
"repo": "vllm-project/vllm",
"number": 30620,
"title": "[Feature]: Remove Chunking From FusedMoE",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\n* we have some chunking logic in the triton kernels to avoid IMA: https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/fused_moe/fused_moe.py#L1807\n* we chunk in ~65k tokens\n* this case does not happen anymore because of chunked prefill\n\nWe should remove this\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30620",
"state": "open",
"labels": [
"help wanted",
"good first issue",
"feature request"
],
"created_at": "2025-12-13T18:22:30Z",
"updated_at": "2025-12-13T23:27:22Z",
"comments": 3,
"user": "robertgshaw2-redhat"
},
{
"repo": "vllm-project/vllm",
"number": 30570,
"title": "[Usage]: Why is VLLM still using SSE at all for mcp?",
"body": "### Your current environment\n\nThis is a broad question: Why is vllm still using/hardcoding sse usage at all, when its been deprecated for well over six months at this point?\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30570",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-12T20:02:08Z",
"updated_at": "2025-12-18T10:50:37Z",
"comments": 1,
"user": "bags307"
},
{
"repo": "sgl-project/sglang",
"number": 14984,
"title": "Can the source code compilation and installation of sgl-kernel support the SM86 driver for CUDA12.9",
"body": "### Checklist\n\n- [x] I searched related issues but found no solution.\n- [ ] The bug persists in the latest version.\n- [ ] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.\n- [ ] If this is not a bug report but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [ ] Please use English. Otherwise, it will be closed.\n\n### Describe the bug\n\nEncountered problem: Unable to find the. so file for sm86 when installing the latest sgl-kernel0.3.19, only sm90 and higher are available\n\n### Reproduction\n\nQuestion: The machine is a GPU driver for SM86. Can installing sgl kernel in the nvcc 12.9 container source code adapt to SM86?\n\n### Environment\n\nEnvironment: The host is SM86, the nvcc version in the Docker container is 12.9, and torch and flash attn are CU129",
"url": "https://github.com/sgl-project/sglang/issues/14984",
"state": "open",
"labels": [],
"created_at": "2025-12-12T10:29:50Z",
"updated_at": "2025-12-15T09:41:18Z",
"comments": 1,
"user": "zwt-1234"
},
{
"repo": "vllm-project/vllm",
"number": 30548,
"title": "[Feature]: Support for Q.ANT Photonic Computing ?",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nhttps://qant.com/\nhttps://qant.com/wp-content/uploads/2025/11/20251111_QANT-Photonic-AI-Accelerator-Gen-2.pdf\n\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30548",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-12-12T10:16:53Z",
"updated_at": "2025-12-12T14:45:53Z",
"comments": 2,
"user": "plitc"
},
{
"repo": "huggingface/tokenizers",
"number": 1909,
"title": "[Docs] `Encode Inputs` rendering issues",
"body": "It seems like the documentation for Encode Inputs is not rendered properly.\n\nOfficial URL: https://huggingface.co/docs/tokenizers/main/en/api/encode-inputs?code=python\nGitHub URL: https://github.com/huggingface/tokenizers/blob/main/docs/source-doc-builder/api/encode-inputs.mdx",
"url": "https://github.com/huggingface/tokenizers/issues/1909",
"state": "open",
"labels": [],
"created_at": "2025-12-12T09:47:48Z",
"updated_at": "2025-12-12T09:47:48Z",
"comments": 0,
"user": "ariG23498"
},
{
"repo": "vllm-project/vllm",
"number": 30541,
"title": "[Usage]: missing dsml token \"| DSML | \" with DeepSeek-V3.2 tools call",
"body": "### Your current environment\n\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version : Could not collect\nCMake version : version 4.0.3\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-5.15.0-50-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA H20\nGPU 1: NVIDIA H20\nGPU 2: NVIDIA H20\nGPU 3: NVIDIA H20\nGPU 4: NVIDIA H20\nGPU 5: NVIDIA H20\nGPU 6: NVIDIA H20\nGPU 7: NVIDIA H20\n\nNvidia driver version : 565.57.01\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 208\nOn-line CPU(s) list: 0-207\nVendor ID: GenuineIntel\nModel name: INTEL(R) XEON(R) PLATINUM 8563C\nCPU family: 6\nModel: 207\nThread(s) per core: 2\nCore(s) per socket: 52\nSocket(s): 2\nStepping: 2\nFrequency boost: enabled\nCPU max MHz: 4000.0000\nCPU min MHz: 800.0000\nBogoMIPS: 5200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 4.9 MiB (104 instances)\nL1i cache: 3.3 MiB (104 instances)\nL2 cache: 208 MiB (104 instances)\nL3 cache: 640 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-51,104-155\nNUMA node1 CPU(s): 52-103,156-207\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Vulnerable\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.5.3\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.16.0\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-cufile-cu12==1.1",
"url": "https://github.com/vllm-project/vllm/issues/30541",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-12T06:47:03Z",
"updated_at": "2025-12-12T20:59:40Z",
"comments": 1,
"user": "crischeng"
},
{
"repo": "vllm-project/vllm",
"number": 30511,
"title": "Potential Deadlock?",
"body": "Consider using proper synchronization primitives like threading.Event or queue.Queue.get(timeout=...)",
"url": "https://github.com/vllm-project/vllm/issues/30511",
"state": "closed",
"labels": [],
"created_at": "2025-12-11T19:57:43Z",
"updated_at": "2025-12-12T18:00:20Z",
"comments": 1,
"user": "ChuanLi1101"
},
{
"repo": "sgl-project/sglang",
"number": 14903,
"title": "Does the current Qwen3-VL (or Qwen3-VL-MoE) officially support TBO?",
"body": "Hi team,\n\nI noticed that Qwen3-VL and Qwen3-MoE adopt different model architectures.\nWhen profiling the execution path, I found that:\n\nQwen3-MoE eventually falls back to the Qwen2-MoE implementation, which explicitly supports TBO (Two-Batch Overlap).\n\nHowever, Qwen3-VL takes the path of Qwen3-VL-MoE, and I did not find any clear implementation or code path that indicates TBO support for this variant.\n\nBased on the current codebase, it seems that Qwen3-VL-MoE may not have full TBO support, or its TBO integration is not obvious from the trace.",
"url": "https://github.com/sgl-project/sglang/issues/14903",
"state": "open",
"labels": [],
"created_at": "2025-12-11T13:26:50Z",
"updated_at": "2025-12-11T13:26:50Z",
"comments": 0,
"user": "jerry-dream-fu"
},
{
"repo": "huggingface/transformers",
"number": 42804,
"title": "[`Quantization FP8`] Native `from_config` support",
"body": "### Feature request\n\nRelated to https://github.com/huggingface/transformers/pull/42028#discussion_r2592235170\n\nSince FP8 is becoming more and more standard, it would be nice to create fp8 native models via config or more like using `from_config`. Atm, quant configs are not respected apparently - either that or we need to update the docs to show how to use it properly.\n\n### Motivation\n\nFp8 is becoming increasingly important\n\n### Your contribution\n\n\ud83d\udc40 ",
"url": "https://github.com/huggingface/transformers/issues/42804",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-12-11T10:17:47Z",
"updated_at": "2025-12-14T22:49:48Z",
"comments": 3,
"user": "vasqu"
},
{
"repo": "huggingface/trl",
"number": 4679,
"title": "[SFT] High vRAM consumption during eval loop",
"body": "### Reproduction\n\n### Unexpected behavior\n\nWhen training a model on large sequences (>=20k tokens) with `PEFT LoRA` + `SFTTrainer` + `liger-kernel`, the vRAM usage spikes during the evaluation loop, consuming way more vRAM than during the training.\n\nThe size of this vRAM spike seem to scale with the length of the input sequence: for cases with `max_length=40000`, we end up with spikes of ~50GB vRAM, far exceeding the amount used during the training.\n\nHere's a MLFlow GPU vRAM extract showcasing this on an A100 for this 40k token scenario with Qwen3-0.6B:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/8d909f73-6cbe-4c3e-8d6a-e6b8c6c56dbe\")
\n\nAnd same goes for Qwen3-4B, 40k token:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/aa74b9c3-14eb-4c35-851f-c6802d2d420d\")
\n\n### Minimal reproduction script\n\nBelow is the [default SFT example from the documentation](https://github.com/huggingface/trl/blob/main/trl/scripts/sft.py), slightly altered to artificially create long input sequences (>=20k tokens) in both the training and evaluation dataset splits.\n\nBy running `watch -n 1 nvidia-smi` while the training is running, you can see that the vRAM usage is way higher during the evaluation phase than during the training. If your GPU has enough vRAM, you can increase the `max_length` parameter and this will become even more visible. _For some reason, I can't get `trackio` to properly report vRAM usage, hence the use of `nvidia-smi`.\n\nYou can launch the script with the following command:\n\n```bash\npython sft_example.py \\\n--model_name_or_path Qwen/Qwen3-0.6B \\\n--dataset_name trl-lib/Capybara \\\n--learning_rate 2.0e-4 \\\n--max-steps 10 \\\n--per_device_train_batch_size 1 \\\n--per_device_eval_batch_size 1 \\\n--eval_accumulation_steps 1 \\\n--gradient_accumulation_steps 1 \\\n--gradient_checkpointing \\\n--eos_token '<|im_end|>' \\\n--eval_strategy steps \\\n--eval_steps 10 \\\n--use_peft \\\n--lora_r 8 \\\n--lora_alpha 16 \\\n--use_liger \\\n--max_length 10000\n```\n\n```python\n# Copyright 2020-2025 The HuggingFace Team. All rights reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n# /// script\n# dependencies = [\n# \"trl\",\n# \"peft\",\n# \"trackio\",\n# \"kernels\"\n# ]\n# ///\n\nimport argparse\nimport os\n\nfrom accelerate import logging\nfrom datasets import load_dataset\nfrom transformers import AutoConfig, AutoModelForCausalLM\nfrom transformers.models.auto.modeling_auto import (\n MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES,\n)\nfrom trl import (\n DatasetMixtureConfig,\n ModelConfig,\n ScriptArguments,\n SFTConfig,\n SFTTrainer,\n TrlParser,\n get_dataset,\n get_kbit_device_map,\n get_peft_config,\n get_quantization_config,\n)\n\nlogger = logging.get_logger(__name__)\n\n# Enable logging in a Hugging Face Space\nos.environ.setdefault(\"TRACKIO_SPACE_ID\", \"trl-trackio\")\n\n\ndef main(script_args, training_args, model_args, dataset_args):\n ################\n # Model init kwargs\n ################\n model_kwargs = dict(\n revision=model_args.model_revision,\n trust_remote_code=model_args.trust_remote_code,\n attn_implementation=model_args.attn_implementation,\n dtype=model_args.dtype,\n )\n quantization_config = get_quantization_config(model_args)\n if quantization_config is not None:\n # Passing None would not be treated the same as omitting the argument, so we include it only when valid.\n model_kwargs[\"device_map\"] = get_kbit_device_map()\n model_kwargs[\"quantization_config\"] = quantization_config\n\n # Create model\n config = AutoConfig.from_pretrained(model_args.model_name_or_path)\n valid_image_text_architectures = MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES.values()\n\n if config.architectures and any(\n arch in valid_image_text_architectures for arch in config.architectures\n ):\n from transformers import AutoModelForImageTextToText\n\n model = AutoModelForImageTextToText.from_pretrained(\n model_args.model_name_or_path, **model_kwargs\n )\n else:\n model = AutoModelForCausalLM.from_pretrained(\n model_args.model_name_or_path, **model_kwargs\n )\n\n # Load the dataset\n if dataset_args.datasets and script_args.dataset_name:\n logger.warning(\n \"Both `datasets` and `dataset_name` are provided. The `datasets` argument will be used to load the \"\n \"dataset and `dataset_name` will be ignored.\"\n )\n ",
"url": "https://github.com/huggingface/trl/issues/4679",
"state": "open",
"labels": [
"\ud83d\udc1b bug",
"\ud83c\udfcb SFT",
"\u26a1 PEFT"
],
"created_at": "2025-12-11T10:01:49Z",
"updated_at": "2026-01-02T09:23:17Z",
"comments": 3,
"user": "Khreas"
},
{
"repo": "vllm-project/vllm",
"number": 30477,
"title": "[Usage]: How to disable thinking for Qwen-8B",
"body": "### Your current environment\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.5.1+cu121\nIs debug build : False\nCUDA used to build PyTorch : 12.1\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.12 (main, Nov 19 2025, 22:46:53) [Clang 21.1.4 ] (64-bit runtime)\nPython platform : Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.1.105\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : GPU 0: NVIDIA GeForce RTX 4090 Laptop GPU\nNvidia driver version : 546.26\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 39 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 32\nOn-line CPU(s) list: 0-31\nVendor ID: GenuineIntel\nModel name: Intel(R) Core(TM) i9-14900HX\nCPU family: 6\nModel: 183\nThread(s) per core: 2\nCore(s) per socket: 16\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4838.39\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid pni pclmulqdq vmx ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves avx_vnni umip waitpkg gfni vaes vpclmulqdq rdpid movdiri movdir64b fsrm md_clear serialize flush_l1d arch_capabilities\nVirtualization: VT-x\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 768 KiB (16 instances)\nL1i cache: 512 KiB (16 instances)\nL2 cache: 32 MiB (16 instances)\nL3 cache: 36 MiB (1 instance)\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Mitigation; Clear Register File\nVulnerability Retbleed: Mitigation; Enhanced IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] numpy==1.26.4\n[pip3] nvidia-cublas-cu12==12.1.3.1\n[pip3] nvidia-cuda-cupti-cu12==12.1.105\n[pip3] nvidia-cuda-nvrtc-cu12==12.1.105\n[pip3] nvidia-cuda-runtime-cu12==12.1.105\n[pip3] nvidia-cudnn-cu12==9.1.0.70\n[pip3] nvidia-cufft-cu12==11.0.2.54\n[pip3] nvidia-curand-cu12==10.3.2.106\n[pip3] nvidia-cusolver-cu12==11.4.5.107\n[pip3] nvidia-cusparse-cu12==12.1.0.106\n[pip3] nvidia-nccl-cu12==2.21.5\n[pip3] nvidia-nvjitlink-cu12==12.9.86\n[pip3] nvidia-nvtx-cu12==12.1.105\n[pip3] pyzmq==27.1.0\n[pip3] torch==2.5.1+cu121\n[pip3] torchaudio==2.5.1+cu121\n[pip3] torchvision==0.20.1+cu121\n[pip3] transformers==4.57.3\n[pip3] triton==3.1.0\n[conda] Could not collect\n\n",
"url": "https://github.com/vllm-project/vllm/issues/30477",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-12-11T09:28:40Z",
"updated_at": "2025-12-22T06:10:43Z",
"comments": 3,
"user": "fancyerii"
},
{
"repo": "huggingface/diffusers",
"number": 12823,
"title": "How to use quantizer after pipeline loaded?",
"body": "How to use quantizer after pipeline loaded? \n\n- Currently\n\n```python\n# Quantization occurs at load time.\npipe = QwenImagePipeline.from_pretrained(\n (\n args.model_path\n if args.model_path is not None\n else os.environ.get(\n \"QWEN_IMAGE_DIR\",\n \"Qwen/Qwen-Image\",\n )\n ),\n scheduler=scheduler,\n torch_dtype=torch.bfloat16,\n quantization_config=quantization_config,\n)\n```\n\n- What i want \n\n```python\n# Load on CPU -> Load and fuse lora -> quantize -> to GPU\n```",
"url": "https://github.com/huggingface/diffusers/issues/12823",
"state": "open",
"labels": [],
"created_at": "2025-12-11T06:32:38Z",
"updated_at": "2025-12-11T14:18:28Z",
"user": "DefTruth"
},
{
"repo": "huggingface/transformers",
"number": 42794,
"title": "`decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation.",
"body": "### System Info\n\nlatest transformers\n\n### Who can help?\n\n@zucchini-nlp \n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```python\nimport torch\nfrom transformers import pipeline\n\npipe = pipeline(\n \"document-question-answering\",\n model=\"naver-clova-ix/donut-base-finetuned-docvqa\",\n dtype=torch.float16,\n)\n\nimage = \"https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png\"\nquestion = \"What is the invoice number?\"\n\nresult = pipe(image=image, question=question)\nprint(result)\n```\n\nerror:\n```\nTraceback (most recent call last):\n File \"/home/jiqingfe/transformers/test_dqa.py\", line 13, in \n result = pipe(image=image, question=question)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jiqingfe/transformers/src/transformers/pipelines/document_question_answering.py\", line 310, in __call__\n return super().__call__(inputs, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jiqingfe/transformers/src/transformers/pipelines/base.py\", line 1278, in __call__\n return next(\n ^^^^^\n File \"/home/jiqingfe/transformers/src/transformers/pipelines/pt_utils.py\", line 126, in __next__\n item = next(self.iterator)\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/jiqingfe/transformers/src/transformers/pipelines/pt_utils.py\", line 271, in __next__\n processed = self.infer(next(self.iterator), **self.params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jiqingfe/transformers/src/transformers/pipelines/base.py\", line 1185, in forward\n model_outputs = self._forward(model_inputs, **forward_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jiqingfe/transformers/src/transformers/pipelines/document_question_answering.py\", line 468, in _forward\n model_outputs = self.model.generate(**model_inputs, **generate_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/sgl-workspace/miniforge3/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jiqingfe/transformers/src/transformers/generation/utils.py\", line 2551, in generate\n self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)\n File \"/home/jiqingfe/transformers/src/transformers/generation/utils.py\", line 2145, in _prepare_special_tokens\n raise ValueError(\nValueError: `decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation.\n```\n\n### Expected behavior\n\nCannot locate which PR caused this regression because too many errors recently. The transformers 4.57.3 works well on the script.",
"url": "https://github.com/huggingface/transformers/issues/42794",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-12-11T06:22:58Z",
"updated_at": "2025-12-18T18:33:40Z",
"comments": 1,
"user": "jiqing-feng"
},
{
"repo": "vllm-project/vllm",
"number": 30464,
"title": "[Usage]: How can I use the local pre-compiled wheel of vllm",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nEvery time I use `VLLM_USE_PRECOMPILED=1 uv pip install --editable .` to build vllm, it always takes much time to download the pre-compiled wheel. Would it be possible to build it by using a locally downloaded wheel file instead?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30464",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-11T06:22:43Z",
"updated_at": "2025-12-12T01:02:22Z",
"comments": 1,
"user": "gcanlin"
},
{
"repo": "huggingface/transformers",
"number": 42791,
"title": "Add support for GPT_OSS with tp_plan or enable native tensor parallelism",
"body": "### Model description\n\n #[https://huggingface.co/docs/transformers/main/perf_infer_gpu_multi?tp_plan=auto+plan](url)\n\n> https://github.com/huggingface/transformers/issues/41819\n\nThere are a list of supported models here, but GPT-OSS is not one of them. Please add support for GPT_OSS too to enable `tp_plan`. Please help me understand when model is prepared for TP in accelerate initiation, is there some native support needed in model for enabling TP.\n\nI have tried this example TP script [https://github.com/huggingface/accelerate/blob/main/examples/torch_native_parallelism/nd_parallel.py](url) with pure TP, on GPT-OSS-20B model and getting same error as mentioned in this already open issue:\n[]([https://github.com/huggingface/transformers/issues/41819](url).)\n\nAfter handling `DTensor` sinks as mentioned as a fix in above issue, still I find many such `DTensors` at multiple other places which is causing below error, due to incompatibility between ` DTensor ` and `torch.Tensor`. \n\n`raise RuntimeError(\n[rank0]: RuntimeError: aten.bmm.default: got mixed torch.Tensor and DTensor, need to convert all torch.Tensor to DTensor before calling distributed operators!`\n\n### Open source status\n\n- [x] The model implementation is available\n- [x] The model weights are available\n\n### Provide useful links for the implementation\n\n_No response_",
"url": "https://github.com/huggingface/transformers/issues/42791",
"state": "open",
"labels": [
"New model"
],
"created_at": "2025-12-11T04:31:19Z",
"updated_at": "2025-12-19T08:38:31Z",
"comments": 1,
"user": "quic-akuruvil"
},
{
"repo": "sgl-project/sglang",
"number": 14868,
"title": "How to train vicuna EAGLE3 model?",
"body": "I have carefully reviewed the official tutorials and source code, but I was unable to find the relevant config and template files specific to Vicuna.\n\nCould you please provide an example, specifically regarding the template structure?",
"url": "https://github.com/sgl-project/sglang/issues/14868",
"state": "open",
"labels": [],
"created_at": "2025-12-11T03:59:39Z",
"updated_at": "2025-12-11T03:59:39Z",
"comments": 0,
"user": "Sylvan820"
},
{
"repo": "vllm-project/vllm",
"number": 30447,
"title": "[Usage]: how to load kv cache data into local file",
"body": "### Your current environment\n\npthon3.10+vllm0.10.0\n\n### How would you like to use vllm\n\nI want to get int8 kv cache data from [qwen-int8](https://www.modelscope.cn/models/Qwen/Qwen-7B-Chat-Int8). I don't know how if vllm can do that? Thank you.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30447",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-11T01:43:58Z",
"updated_at": "2025-12-12T15:11:50Z",
"comments": 1,
"user": "chx725"
},
{
"repo": "vllm-project/vllm",
"number": 30441,
"title": "[Usage]: vllm serve setup issues on B300",
"body": "### Your current environment\n\nThe output of `python collect_env.py`\n```text\n\n\nCollecting environment information...\nuv is set\n==============================\n System Info\n==============================\nOS : Amazon Linux 2023.9.20251208 (x86_64)\nGCC version : (GCC) 11.5.0 20240719 (Red Hat 11.5.0-5)\nClang version : Could not collect\nCMake version : version 3.22.2\nLibc version : glibc-2.34\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu130\nIs debug build : False\nCUDA used to build PyTorch : 13.0\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.11.14 (main, Nov 12 2025, 00:00:00) [GCC 11.5.0 20240719 (Red Hat 11.5.0-5)] (64-bit runtime)\nPython platform : Linux-6.1.158-180.294.amzn2023.x86_64-x86_64-with-glibc2.34\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 13.0.88\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA B300 SXM6 AC\nGPU 1: NVIDIA B300 SXM6 AC\nGPU 2: NVIDIA B300 SXM6 AC\nGPU 3: NVIDIA B300 SXM6 AC\nGPU 4: NVIDIA B300 SXM6 AC\nGPU 5: NVIDIA B300 SXM6 AC\nGPU 6: NVIDIA B300 SXM6 AC\nGPU 7: NVIDIA B300 SXM6 AC\n\nNvidia driver version : 580.105.08\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8559C\nCPU family: 6\nModel: 207\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 2\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx_vnni avx512_bf16 wbnoinvd ida arat avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear serialize amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 640 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vms",
"url": "https://github.com/vllm-project/vllm/issues/30441",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-10T23:50:27Z",
"updated_at": "2025-12-13T02:01:04Z",
"comments": 1,
"user": "navmarri14"
},
{
"repo": "sgl-project/sglang",
"number": 14824,
"title": "Throughput degradation on Qwen3-30B-A3B with EAGLE3",
"body": "I observed a throughput degradation when trying to use EAGLE3 to speed up Qwen3-30B-A3B (on 2x H100).\n\nI suspect the overhead might be overshadowing the gains. It would be great if we could have some profiling analysis to pinpoint exactly where the cost is coming from.\n\nAlso, tuning parameters for MoE models feels much more difficult than for dense models. Do you think it would be possible to provide a guidance or a micro-benchmarking script? This would really help users quickly identify the optimal parameters for their specific hardware.\n\n(For reference, the related issue is [this](https://github.com/sgl-project/SpecForge/issues/339).)\n\nTwo quick questions:\n\nI\u2019m still wondering: why does EAGLE3 seem less effective on Qwen3 compared to other models?\n\nAre there any specific tricks for training a high-quality EAGLE3 draft model for this architecture?\n\nThanks! \ud83e\udd79\ud83e\udd79\n",
"url": "https://github.com/sgl-project/sglang/issues/14824",
"state": "open",
"labels": [],
"created_at": "2025-12-10T14:22:05Z",
"updated_at": "2025-12-19T21:36:54Z",
"comments": 1,
"user": "Zzsf11"
},
{
"repo": "vllm-project/vllm",
"number": 30392,
"title": "[Bug]: Docker image v0.12.0 Fail to serve via Docker image",
"body": "### Your current environment\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu129\nIs debug build : False\nCUDA used to build PyTorch : 12.9\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.9.86\nCUDA_MODULE_LOADING set to :\nGPU models and configuration :\nGPU 0: NVIDIA RTX A4000\nGPU 1: NVIDIA RTX A4000\n\nNvidia driver version : 581.15\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 8\nOn-line CPU(s) list: 0-7\nVendor ID: AuthenticAMD\nModel name: AMD Ryzen 7 3800X 8-Core Processor\nCPU family: 23\nModel: 113\nThread(s) per core: 2\nCore(s) per socket: 4\nSocket(s): 1\nStepping: 0\nBogoMIPS: 7800.02\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid tsc_known_freq pni pclmulqdq ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core ssbd ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr arat npt nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold v_vmsave_vmload umip rdpid\nVirtualization: AMD-V\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 128 KiB (4 instances)\nL1i cache: 128 KiB (4 instances)\nL2 cache: 2 MiB (4 instances)\nL3 cache: 16 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-7\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection\nVulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.5.3\n[pip3] numpy==2.2.0\n[pip3] nvidia-cublas-cu12==12.9.1.4\n[pip3] nvidia-cuda-cupti-cu12==12.9.79\n[pip3] nvidia-cuda-nvrtc-cu12==12.9.86\n[pip3] nvidia-cuda-runtime-cu12==12.9.79\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.16.0\n[pip3] nvidia-cufft-cu12==11.4.1.4\n[pip3] nvidia-cufile-cu12==1.14.1.1\n[pip3] nvidia-curand-cu12==10.3.10.19\n[pip3] nvidia-cusolver-cu12==11.7.5.82\n[pip3] nvidia-cusparse-cu12==12.5.10.65\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-cutlass-dsl==4.3.1\n[pip3] ",
"url": "https://github.com/vllm-project/vllm/issues/30392",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-10T13:43:59Z",
"updated_at": "2026-01-04T14:24:56Z",
"comments": 7,
"user": "kuopching"
},
{
"repo": "huggingface/transformers",
"number": 42771,
"title": "FSDP of Trainer does not work well with Accelerate",
"body": "### System Info\n\n- `transformers` version: 4.57.3\n- Platform: Linux-6.6.97+-x86_64-with-glibc2.35\n- Python version: 3.11.11\n- Huggingface_hub version: 0.36.0\n- Safetensors version: 0.7.0\n- Accelerate version: 1.12.0\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.9.1+cu128 (CUDA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: \n- Using GPU in script?: \n- GPU type: NVIDIA H100 80GB HBM3\n\n### Who can help?\n\n@3outeille @ArthurZucker @SunMarc \n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n```python\n\"\"\"\nSimple example of training BERT with Transformers Trainer and FSDP\nUses random data for quick demonstration\n\"\"\"\n\nimport torch\nfrom transformers import (\n BertForSequenceClassification,\n BertTokenizer,\n Trainer,\n TrainingArguments,\n)\nfrom torch.utils.data import Dataset\n\n\n# Create a simple dataset with random data\nclass RandomDataset(Dataset):\n def __init__(self, tokenizer, num_samples=1000, max_length=128):\n self.tokenizer = tokenizer\n self.num_samples = num_samples\n self.max_length = max_length\n \n def __len__(self):\n return self.num_samples\n \n def __getitem__(self, idx):\n # Generate random token IDs\n input_ids = torch.randint(\n 0, self.tokenizer.vocab_size, (self.max_length,)\n )\n attention_mask = torch.ones(self.max_length)\n labels = torch.randint(0, 2, (1,)).item() # Binary classification\n \n return {\n \"input_ids\": input_ids,\n \"attention_mask\": attention_mask,\n \"labels\": labels,\n }\n\n\ndef main():\n # Initialize tokenizer and model\n model_name = \"bert-base-uncased\"\n tokenizer = BertTokenizer.from_pretrained(model_name)\n model = BertForSequenceClassification.from_pretrained(\n model_name, num_labels=2\n )\n \n # Create random datasets\n train_dataset = RandomDataset(tokenizer, num_samples=1000)\n eval_dataset = RandomDataset(tokenizer, num_samples=200)\n \n # Configure FSDP training arguments\n training_args = TrainingArguments(\n output_dir=\"./bert_fsdp_output\",\n num_train_epochs=3,\n per_device_train_batch_size=8,\n per_device_eval_batch_size=8,\n logging_steps=50,\n eval_strategy=\"steps\",\n eval_steps=100,\n save_steps=200,\n save_total_limit=2,\n \n # FSDP Configuration\n fsdp=\"full_shard auto_wrap\", # Enable FSDP with full sharding\n fsdp_config={\n \"fsdp_transformer_layer_cls_to_wrap\": [\"BertLayer\"], # Wrap BERT layers\n \"fsdp_backward_prefetch\": \"backward_pre\",\n \"fsdp_forward_prefetch\": False,\n \"fsdp_use_orig_params\": True,\n },\n \n # Additional settings\n learning_rate=5e-5,\n warmup_steps=100,\n weight_decay=0.01,\n logging_dir=\"./logs\",\n report_to=\"none\", # Disable wandb/tensorboard for simplicity\n )\n \n # Initialize Trainer\n trainer = Trainer(\n model=model,\n args=training_args,\n train_dataset=train_dataset,\n eval_dataset=eval_dataset,\n )\n \n # Train the model\n print(\"Starting training with FSDP...\")\n trainer.train()\n \n # Save the final model\n trainer.save_model(\"./bert_fsdp_final\")\n print(\"Training completed!\")\n\n\nif __name__ == \"__main__\":\n # Note: Run this script with torchrun for multi-GPU training\n # Example: torchrun --nproc_per_node=2 train_bert_fsdp.py\n main()\n```\n\ntorchrun --nproc_per_node=2 train_bert_fsdp.py\n\n### Expected behavior\n\nIt will fail silently. The trace stack, \n```bash\nW1210 12:49:05.011000 104846 site-packages/torch/distributed/run.py:803] \nW1210 12:49:05.011000 104846 site-packages/torch/distributed/run.py:803] *****************************************\nW1210 12:49:05.011000 104846 site-packages/torch/distributed/run.py:803] Setting OMP_NUM_THREADS environment variable for each process to be 1 in default, to avoid your system being overloaded, please further tune the variable for optimal performance in your application as needed. \nW1210 12:49:05.011000 104846 site-packages/torch/distributed/run.py:803] *****************************************\nSome weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']\nYou should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\nSome weights of BertForSequenceClassification were not initialized from the model check",
"url": "https://github.com/huggingface/transformers/issues/42771",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-10T12:54:49Z",
"updated_at": "2025-12-11T07:07:19Z",
"comments": 2,
"user": "gouchangjiang"
},
{
"repo": "vllm-project/vllm",
"number": 30381,
"title": "[Usage]:",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30381",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-12-10T09:27:51Z",
"updated_at": "2025-12-10T09:28:26Z",
"comments": 0,
"user": "tobeprozy"
},
{
"repo": "vllm-project/vllm",
"number": 30380,
"title": "[Usage]: \u5927\u5bb6\u4e00\u822c\u600e\u4e48\u4f7f\u7528vllm/tests\u7684\uff1f",
"body": "### Your current environment\n\nanywhere\n\n### How would you like to use vllm\n\nI don't know how to use vllm test.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30380",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-10T09:27:46Z",
"updated_at": "2025-12-10T13:19:18Z",
"comments": 1,
"user": "tobeprozy"
},
{
"repo": "vllm-project/vllm",
"number": 30379,
"title": "[Usage]: how to use vllm/tests/\uff1f",
"body": "### Your current environment\n\n\u5927\u5bb6\u4e00\u822c\u600e\u4e48\u4f7f\u7528[vllm](https://github.com/vllm-project/vllm/tree/main)/[tests](https://github.com/vllm-project/vllm/tree/main/tests)\u7684\uff1f\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30379",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-12-10T09:25:52Z",
"updated_at": "2025-12-10T09:26:25Z",
"comments": 0,
"user": "tobeprozy"
},
{
"repo": "vllm-project/vllm",
"number": 30375,
"title": "[Bug]: [TPU] ShapeDtypeStruct error when loading custom safetensors checkpoint on TPU v5litepod",
"body": "### Your current environment\n\n is not a valid JAX type.\n```\n\n**The core issue:** The Flax NNX model loader in `tpu_inference` creates the model with `ShapeDtypeStruct` shape placeholders, but these placeholders are never replaced with actual weight arrays before JIT compilation.\n\nLoading from **HuggingFace Hub works fine** (e.g., `Qwen/Qwen3-0.6B`), but loading the **exact same model architecture from a local directory fails**.\n\n### How to reproduce the bug\n\n**Minimal reproduction:**\n\nfrom vllm import LLM\n\n# This WORKS:\nmodel = LLM(\"Qwen/Qwen3-0.6B\", tensor_parallel_size=4, dtype=\"bfloat16\")\n\n# This FAILS with ShapeDtypeStruct error:\nmodel = LLM(\n model=\"/path/to/local/checkpoint\", # Contains model.safetensors + config.json\n tensor_parallel_size=4,\n dtype=\"bfloat16\",\n trust_remote_code=True,\n)**Checkpoint directory contents:**\n```\n/path/to/local/checkpoint/\n\u251c\u2500\u2500 config.json # Valid Qwen3 config with \"architectures\": [\"Qwen3ForCausalLM\"]\n\u251c\u2500\u2500 model.safetensors # bfloat16 weights (~1.2GB for Qwen3-0.6B)\n\u251c\u2500\u2500 tokenizer.json\n\u251c\u2500\u2500 tokenizer_config.json\n\u251c\u2500\u2500 special_tokens_map.json\n\u251c\u2500\u2500 vocab.json\n\u2514\u2500\u2500 merges.txt\n```\n\n**Context:** The checkpoint was converted from MaxText/Orbax format using orbax-checkpoint + safetensors libraries. The weights are valid (verified with `safetensors.torch.load_file()`).\n\n### Full error traceback\n\n```\nFile \"/pm_env/.venv/lib/python3.12/site-packages/tpu_inference/models/common/model_loader.py\", line 345, in get_model\n return get_flax_model(vllm_config, rng, mesh, is_draft_model)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nFile \"/pm_env/.venv/lib/python3.12/site-packages/tpu_inference/models/common/model_loader.py\", line 219, in get_flax_model\n jit_model = _get_nnx_model(model_class, vllm_config, rng, mesh)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nFile \"/pm_env/.venv/lib/python3.12/site-packages/tpu_inference/models/common/model_loader.py\", line 200, in _get_nnx_model\n jit_model = create_jit_model(\n ^^^^^^^^^^^^^^^^^\nFile \"/pm_env/.venv/lib/python3.12/site-packages/flax/nnx/transforms/compilation.py\", line 431, in __call__\n pure_args_out, pure_kwargs_out, pure_out = self.jitted_fn(\n ^^^^^^^^^^^^^^^\nTypeError: Argument 'model.states[0][6]' of shape bfloat16[128] of type is not a valid JAX type.\n```\n\n### What I tried\n\n| Attempt | Result |\n|---------|--------|\n| Load from HuggingFace Hub | \u2705 Works |\n| Load local checkpoint (safetensors) | \u274c ShapeDtypeStruct error |\n| Use float32 dtype | \u274c Same error |\n| Use bfloat16 dtype | \u274c Same error |\n| Set `VLLM_USE_V1=0` | \u274c Still uses v1 engine on TPU |\n| Add `pytorch_model.bin` alongside safetensors | \u274c Same error |\n\n### Expected behavior\n\nvLLM should load the weights from the local safetensors file and initialize the model, exactly like it does when loading from HuggingFace Hub.\n\n### Analysis\n\nLooking at the traceback, the issue is in `tpu_inference/models/common/model_loader.py`:\n\n1. `get_flax_model()` creates the model architecture\n2. `_get_nnx_model()` calls `create_jit_model()` \n3. At this point, `model.states[0][6]` is still a `ShapeDtypeStruct` placeholder instead of actual weight data\n4. JIT compilation fails because it can't compile shape placeholders\n\nIt seems like when loading from Hub, weights get populated before JIT compilation, but when loading from local path, this step is skipped or fails silently.\n\n### Additional context\n\n- We're building an RL environment for LLM evaluation that needs to load custom finetuned checkpoints\n- JetStream/MaxText can load the same Orbax checkpoints without issues\n- The safetensors file was verified to contain valid tensors with correct shapes\n- This blocks our ability to use vLLM's logprobs-based evaluation on TPU\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30375",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-10T08:12:57Z",
"updated_at": "2025-12-11T05:34:19Z",
"comments": 1,
"user": "Baltsat"
},
{
"repo": "sgl-project/sglang",
"number": 14800,
"title": "How should we set piecewise-cuda-graph-max-tokens according to TP DP and chunked-prefill-size?",
"body": "How should we set piecewise-cuda-graph-max-tokens according to TP DP and chunked-prefill-size?\nFor TP only, should we set piecewise-cuda-graph-max-tokens = chunked-prefill-size?\nand for DP attention DP<=TP, should we set piecewise-cuda-graph-max-tokens = chunked-prefill-size/DP?\nThanks.",
"url": "https://github.com/sgl-project/sglang/issues/14800",
"state": "open",
"labels": [],
"created_at": "2025-12-10T07:26:36Z",
"updated_at": "2025-12-10T07:26:36Z",
"comments": 0,
"user": "llc-kc"
},
{
"repo": "sgl-project/sglang",
"number": 14783,
"title": "[Bug][ConvertLinalgRToBinary] encounters error: bishengir-compile: Unknown command line argument '--target=Ascend910B2C'. Try: '/usr/local/Ascend/ascend-toolkit/latest/bin/bishengir-compile --help' bishengir-compile: Did you mean '--pgso=Ascend910B2C'?",
"body": "### Checklist\n\n- [x] I searched related issues but found no solution.\n- [ ] The bug persists in the latest version.\n- [ ] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.\n- [ ] If this is not a bug report but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [ ] Please use English. Otherwise, it will be closed.\n\n### Describe the bug\n\n(sglang-latest) [root:trinity-asr]$ bash test.sh\n/opt/conda/envs/sglang-latest/lib/python3.11/site-packages/torch_npu/dynamo/torchair/__init__.py:8: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81.\n import pkg_resources\nINFO 12-10 11:48:25 [importing.py:53] Triton module has been replaced with a placeholder.\nINFO 12-10 11:48:26 [__init__.py:243] No platform detected, vLLM is running on UnspecifiedPlatform\nWARNING 12-10 11:48:27 [_logger.py:72] Failed to import from vllm._C with ModuleNotFoundError(\"No module named 'vllm._C'\")\n/usr/local/Ascend/thirdparty/sglang/sglang_diffusion_ascend/python/sglang/srt/layers/quantization/awq.py:69: UserWarning: Only CUDA, HIP and XPU support AWQ currently.\n warnings.warn(f\"Only CUDA, HIP and XPU support AWQ currently.\")\n/usr/local/Ascend/thirdparty/sglang/sglang_diffusion_ascend/python/sglang/srt/layers/quantization/gguf.py:46: UserWarning: Only CUDA support GGUF q uantization currently.\n warnings.warn(f\"Only CUDA support GGUF q uantization currently.\")\n[2025-12-10 11:48:27] WARNING server_args.py:1379: At this moment Ascend attention backend only supports a page_size of 128, change page_size to 128.\n[2025-12-10 11:48:27] server_args=ServerArgs(model_path='./TrinityASR', tokenizer_path='./TrinityASR', tokenizer_mode='auto', tokenizer_worker_num=1, skip_tokenizer_init=False, load_format='auto', model_loader_extra_config='{}', trust_remote_code=True, context_length=None, is_embedding=False, enable_multimodal=None, revision=None, model_impl='auto', host='0.0.0.0', port=30000, fastapi_root_path='', grpc_mode=False, skip_server_warmup=False, warmups=None, nccl_port=None, checkpoint_engine_wait_weights_before_ready=False, dtype='auto', quantization=None, quantization_param_path=None, kv_cache_dtype='auto', enable_fp32_lm_head=False, modelopt_quant=None, modelopt_checkpoint_restore_path=None, modelopt_checkpoint_save_path=None, modelopt_export_path=None, quantize_and_serve=False, mem_fraction_static=0.6, max_running_requests=None, max_queued_requests=None, max_total_tokens=None, chunked_prefill_size=-1, max_prefill_tokens=65536, schedule_policy='fcfs', enable_priority_scheduling=False, abort_on_priority_when_disabled=False, schedule_low_priority_values_first=False, priority_scheduling_preemption_threshold=10, schedule_conservativeness=1.0, page_size=128, hybrid_kvcache_ratio=None, swa_full_tokens_ratio=0.8, disable_hybrid_swa_memory=False, radix_eviction_policy='lru', device='npu', tp_size=1, pp_size=1, pp_max_micro_batch_size=None, stream_interval=1, stream_output=False, random_seed=309118768, constrained_json_whitespace_pattern=None, constrained_json_disable_any_whitespace=False, watchdog_timeout=300, dist_timeout=None, download_dir=None, base_gpu_id=0, gpu_id_step=1, sleep_on_idle=False, mm_process_config={}, log_level='info', log_level_http=None, log_requests=False, log_requests_level=2, crash_dump_folder=None, show_time_cost=False, enable_metrics=False, enable_metrics_for_all_schedulers=False, tokenizer_metrics_custom_labels_header='x-custom-labels', tokenizer_metrics_allowed_custom_labels=None, bucket_time_to_first_token=None, bucket_inter_token_latency=None, bucket_e2e_request_latency=None, collect_tokens_histogram=False, prompt_tokens_buckets=None, generation_tokens_buckets=None, gc_warning_threshold_secs=0.0, decode_log_interval=40, enable_request_time_stats_logging=False, kv_events_config=None, enable_trace=False, otlp_traces_endpoint='localhost:4317', export_metrics_to_file=False, export_metrics_to_file_dir=None, api_key=None, served_model_name='./TrinityASR', weight_version='default', chat_template=None, completion_template=None, file_storage_path='sglang_storage', enable_cache_report=False, reasoning_parser=None, tool_call_parser=None, tool_server=None, sampling_defaults='model', dp_size=1, load_balance_method='round_robin', load_watch_interval=0.1, prefill_round_robin_balance=False, dist_init_addr=None, nnodes=1, node_rank=0, json_model_override_args='{}', preferred_sampling_params=None, enable_lora=None, max_lora_rank=None, lora_target_modules=None, lora_paths=None, max_loaded_loras=None, max_loras_per_batch=8, lora_eviction_policy='lru', lora_backend='csgmv', max_lora_chunk_size=16, attention_backend='ascend', decode_attention_backend=None, prefill_attention_backend=None, sampling_backend='pytorch',",
"url": "https://github.com/sgl-project/sglang/issues/14783",
"state": "closed",
"labels": [
"npu"
],
"created_at": "2025-12-10T03:54:50Z",
"updated_at": "2025-12-13T12:28:26Z",
"comments": 1,
"user": "rsy-hub4121"
},
{
"repo": "huggingface/transformers",
"number": 42757,
"title": "cannot import name 'is_offline_mode' from 'huggingface_hub'",
"body": "### System Info\n\n- transformers-5.0.0\n- huggingface_hub-1.2.1\n```\nImportError: cannot import name 'is_offline_mode' from 'huggingface_hub' (/root/miniconda3/envs/transformers/lib/python3.10/site-packages/huggingface_hub/__init__.py)\n```\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nfrom transformers import AutoModel, AutoProcessor, AutoTokenizer\n\n### Expected behavior\n\nhow to fix ?",
"url": "https://github.com/huggingface/transformers/issues/42757",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-12-10T02:43:43Z",
"updated_at": "2025-12-23T17:15:20Z",
"comments": 0,
"user": "dollarser"
},
{
"repo": "vllm-project/vllm",
"number": 30359,
"title": "[RFC] [QeRL]: Online Quantization and Model Reloading",
"body": "### Motivation.\n\n## What is Quantized Model Reloading and Why is it Useful?\n\nvLLM serves not only as a inference runtime for serving requests from end users, but also as a means of serving requests for large language model post-training. One particularly important use case is using vLLM to serve rollouts (required by RL pipelines) using a quantized model to serve the requests. For more information, see [QeRL: Beyond Efficiency \u2013 Quantization-enhanced Reinforcement Learning for LLMs](https://arxiv.org/html/2510.11696v1).\n\nThese quantized models must be reloaded every couple of seconds in order to make sure that the rollouts match the distribution that would have been generated by the base model weights.\n\n## Existing Features in vLLM\n\nvLLM already has some pathways for enabling these kinds of workflows. However, the current implementations have caveats which can make usage difficult.\n\n### Weight Reloading\n\nAfter a model has been loaded once, the weights are stored in kernel format (see nomenclature). However, kernel format does not always match checkpoint format. There is an existing implementation which restores the original model format in order to allow reloading (implemented [here](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/model_loader/online_quantization.py), but \u201crestore\u201d step is done eagerly and effectively doubles the amount of required memory, which is unideal. The current implementation has also only been enabled for torchao configs.\n\n### Online Quantization\n\nThere are two styles of online quantization implemented in vLLM. Originally, there was on the \u201coffline\u201d style of [FP8](https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/quantization/fp8.py#L222C14-L222C42), where all unquantized weights are loaded synchronously, and then all weights are quantized synchronously after loading via `process_weights_after_loading`. This style works, but requires as much memory as the unquantized model, despite the final model being quantized, which is unideal (see Memory Requirements section).\n\nRecently, @vkuzo implemented a means of online quantization by [adding a hook to the `weight_loader`](https://github.com/vllm-project/vllm/pull/29196/files) which calls `process_weights_after_loading` to quantize the weights as they are loading. This reduces the amount of memory that is required to online quantize models, but has only been implemented for CT_FP8_CHANNELWISE and doesn't support currently post processing operations which require multiple parameters, such as marlin repacking.\n\n## Design Considerations\n\n### Nomenclature\n\n- \u201cCheckpoint format\u201d refers to the format in which weights are loaded from disk or provided by a user.\n- \u201cModel format\u201d refers to the state of the model after `init` but before weights are processed with `process_weights_after_loading` . The mapping between \u201ccheckpoint format\u201d and \u201cmodel format\u201d is implemented by `model.load_weights`.\n- \u201cKernel format\u201d refers to the state of the model after `process_weights_after_loading`\n- In the case that checkpoint format is unquantized, but the kernel format is quantized, we call this \u201conline quantization\u201d, where unquantized weights are quantized by vLLM during/after loading.\n\n### Model Cuda Graph\n\nAfter models are loaded for the first time, a cuda graph is captured of the model which is used to accelerate inference. This cuda graph shares the same tensor data pointers as the model used to load weights. As of now, the data pointers used by the cuda graph cannot be updated after capture. This means that any time reloading happens, the new data must be copied into the cuda graph tensors.\n\nRegenerating the model cuda graph is far too slow for the required cadence of model reloading (on the order of a few seconds).\n\n### Memory Requirements\n\nAn ideal solution would use as little memory as is required to load model weights. Some implementations, such as the current implementation of online quantization, require eagerly duplicating all model weights prior to loading, which effectively doubles the amount of memory required to load a model. This is a blocker for enabling reloading of large (600Gb+) models.\n\nAdditionally, an ideal solution would only use as much memory as is required to store the quantized model, not the unquantized model. In cases such as NVFP4, this would cut the memory requirements of using vLLM reloading by one fourth.\n\n### Existing Quantized Reloading Scripts\n\nAlthough online quantization and quantized weight reloading support is limited in vLLM as of now, there already exist users who are using vLLM to do online quantized reloading. Below are a list of examples.\n\n1. [MoonshotAI](https://github.com/MoonshotAI/checkpoint-engine/blob/44d5670b0e6aed5b9cd6c16e970c09f3dc888ad0/checkpoint_engine/worker.py#L167)\n2. [Verl](https://github.com/volcengine/verl/blob/f332fc814718b9ea7968f6d264211460d4e90fff/verl/utils/vllm/vllm_fp8_utils.py#L209)\n3. Periodic Labs, which calls `model.load_weights` with subsets ",
"url": "https://github.com/vllm-project/vllm/issues/30359",
"state": "open",
"labels": [
"RFC"
],
"created_at": "2025-12-09T21:24:20Z",
"updated_at": "2025-12-19T18:19:22Z",
"comments": 8,
"user": "kylesayrs"
},
{
"repo": "vllm-project/vllm",
"number": 30358,
"title": "[Bug]: NIXL PD disaggregate with host_buffer has accuracy issue - Prefill scheduled num_block mismatch at update_state_after_alloc and request_finished",
"body": "### Your current environment\n\nvllm-commit-id: 73a484caa1ad320d6e695f098c25c479a71e6774\n\nTested with A100\n\n### \ud83d\udc1b Describe the bug\n\nHow to reproduce\n```\nPREFILL_BLOCK_SIZE=16 DECODE_BLOCK_SIZE=16 bash tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh --kv_buffer_device cpu\n```\n\naccuracy is ~0.3 much lower than expected 0.4 with Qwen0.6\n\n---\n\nWhat is the issue\n\nI found that the num_blocks sent to `update_state_after_alloc` and `request_finished` sometimes is not match. \n\n`update_state_after_alloc` => this function is scheduled by `scheduler.schedule` to update req_to_save and req_to_receive list, and block_ids passed by the method will indicate which blocks belong to one request.\n\n`request_finished` => this function is called also in `scheduler._connector_finished` to send completed request block_ids list to create a new metadata for decoder.\n\nHowever, based print logs, sometimes, block_ids in `scheduler.schedule` `update_state_after_alloc` is shorter than `scheduler._connector_finished` `request_finished` sometimes.\n\nExample as below\n\n```\n\n\ud83d\udcca Found 1320 unique Request IDs.\n\nFINAL SUMMARY\n\u2705 Consistent Requests : 1085 => num_blocks are same at `update_state_after_alloc` and `request_finished` \n\u274c Mismatched Requests : 235 => num_blocks is less in `update_state_after_alloc` than `request_finished` \n```\n\n```\n================================================================================\n\ud83d\udd34 MISMATCH DETECTED: cmpl-25c7397c-5686-4b70-a569-29ef04c7b4f9-0\n First Block Count: 44\n Last Block Count : 71\n --- Raw Lines for Context ---\n \u001b[0;36m(EngineCore_DP0 pid=417455)\u001b[0;0m update_state_after_alloc req_id=\"request.request_id='cmpl-25c7397c-5686-4b70-a569-29ef04c7b4f9-0'\" num_tokens=1121 len(block_ids)=44 block_ids=[162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205]\n \u001b[0;36m(EngineCore_DP0 pid=417455)\u001b[0;0m request_finished: prepare meta for decode: request.request_id='cmpl-25c7397c-5686-4b70-a569-29ef04c7b4f9-0' request.num_tokens=1122 len(block_ids)=71 block_ids=[162, 163, 164, 165, 166, 167, 168, 169, 170, 171, 172, 173, 174, 175, 176, 177, 178, 179, 180, 181, 182, 183, 184, 185, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 201, 202, 203, 204, 205, 206, 207, 208, 209, 210, 211, 212, 213, 214, 215, 216, 217, 218, 219, 220, 221, 222, 223, 224, 225, 226, 227, 228, 229, 230, 231, 232]\n--------------------------------------------------------------------------------\n\ud83d\udd34 MISMATCH DETECTED: cmpl-d77aa1e2-a55a-4a7e-8435-f3bfaaf7c7ed-0\n First Block Count: 26\n Last Block Count : 84\n --- Raw Lines for Context ---\n \u001b[0;36m(EngineCore_DP0 pid=417455)\u001b[0;0m update_state_after_alloc req_id=\"request.request_id='cmpl-d77aa1e2-a55a-4a7e-8435-f3bfaaf7c7ed-0'\" num_tokens=1331 len(block_ids)=26 block_ids=[310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335]\n \u001b[0;36m(EngineCore_DP0 pid=417455)\u001b[0;0m request_finished: prepare meta for decode: request.request_id='cmpl-d77aa1e2-a55a-4a7e-8435-f3bfaaf7c7ed-0' request.num_tokens=1332 len(block_ids)=84 block_ids=[310, 311, 312, 313, 314, 315, 316, 317, 318, 319, 320, 321, 322, 323, 324, 325, 326, 327, 328, 329, 330, 331, 332, 333, 334, 335, 336, 337, 338, 339, 340, 341, 342, 343, 344, 345, 346, 347, 348, 349, 350, 351, 352, 353, 354, 355, 356, 357, 358, 359, 360, 361, 362, 363, 364, 365, 366, 367, 368, 369, 370, 371, 372, 373, 374, 375, 376, 377, 378, 379, 380, 381, 382, 383, 384, 385, 386, 387, 388, 389, 390, 391, 392, 393]\n--------------------------------------------------------------------------------\n\ud83d\udd34 MISMATCH DETECTED: cmpl-3ccca907-6af5-41fd-acdf-8a0bd0b48322-0\n First Block Count: 71\n Last Block Count : 82\n --- Raw Lines for Context ---\n \u001b[0;36m(EngineCore_DP0 pid=417455)\u001b[0;0m update_state_after_alloc req_id=\"request.request_id='cmpl-3ccca907-6af5-41fd-acdf-8a0bd0b48322-0'\" num_tokens=1307 len(block_ids)=71 block_ids=[394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457, 458, 459, 460, 461, 462, 463, 464]\n \u001b[0;36m(EngineCore_DP0 pid=417455)\u001b[0;0m request_finished: prepare meta for decode: request.request_id='cmpl-3ccca907-6af5-41fd-acdf-8a0bd0b48322-0' request.num_tokens=1308 len(block_ids)=82 block_ids=[394, 395, 396, 397, 398, 399, 400, 401, 402, 403, 404, 405, 406, 407, 408, 409, 410, 411, 412, 413, 414, 415, 416, 417, 418, 419, 420, 421, 422, 423, 424, 425, 426, 427, 428, 429, 430, 431, 432, 433, 434, 435, 436, 437, 438, 439, 440, 441, 442, 443, 444, 445, 446, 447, 448, 449, 450, 451, 452, 453, 454, 455, 456, 457,",
"url": "https://github.com/vllm-project/vllm/issues/30358",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-09T20:15:48Z",
"updated_at": "2025-12-10T17:07:38Z",
"comments": 3,
"user": "xuechendi"
},
{
"repo": "huggingface/datasets",
"number": 7900,
"title": "`Permission denied` when sharing cache between users",
"body": "### Describe the bug\n\nWe want to use `datasets` and `transformers` on a shared machine. Right now, each user has a separate HF_HOME in their home directory. To reduce duplicates of the datasets, we want to share that cache. While experimenting, we are running into `Permission denied` errors.\n\nIt looks like this was supported in the past (see #6589)?\n\nIs there a correct way to share caches across users?\n\n### Steps to reproduce the bug\n\n1. Create a directory `/models/hf_hub_shared_experiment` with read/write permissions for two different users\n2. For each user run the script below\n\n```python\nimport os\n\nos.environ[\"HF_HOME\"] = \"/models/hf_hub_shared_experiment\"\nos.environ[\"HF_DATASETS_CACHE\"] = \"/models/hf_hub_shared_experiment/data\"\n\nimport datasets\nimport transformers\n\nDATASET = \"tatsu-lab/alpaca\"\nMODEL = \"meta-llama/Llama-3.2-1B-Instruct\"\n\nmodel = transformers.AutoModelForCausalLM.from_pretrained(MODEL)\ntokenizer = transformers.AutoTokenizer.from_pretrained(MODEL)\ndataset = datasets.load_dataset(DATASET)\n```\n\nThe first user is able to download and use the model and dataset. The second user gets these errors:\n\n```\n$ python ./experiment_with_shared.py\nCould not cache non-existence of file. Will ignore error and continue. Error: [Errno 13] Permission denied: '/models/hf_hub_shared_experiment/hub/models--meta-llama--Llama-3.2-1B-Instruct/.no_exist/9213176726f574b556790deb65791e0c5aa438b6/custom_generate/generate.py'\nCould not cache non-existence of file. Will ignore error and continue. Error: [Errno 13] Permission denied: '/models/hf_hub_shared_experiment/hub/datasets--tatsu-lab--alpaca/.no_exist/dce01c9b08f87459cf36a430d809084718273017/alpaca.py'\nCould not cache non-existence of file. Will ignore error and continue. Error: [Errno 13] Permission denied: '/models/hf_hub_shared_experiment/hub/datasets--tatsu-lab--alpaca/.no_exist/dce01c9b08f87459cf36a430d809084718273017/.huggingface.yaml'\nCould not cache non-existence of file. Will ignore error and continue. Error: [Errno 13] Permission denied: '/models/hf_hub_shared_experiment/hub/datasets--tatsu-lab--alpaca/.no_exist/dce01c9b08f87459cf36a430d809084718273017/dataset_infos.json'\nTraceback (most recent call last):\n File \"/home/user2/.venv/experiment_with_shared.py\", line 17, in \n dataset = datasets.load_dataset(DATASET)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user2/.venv/lib/python3.12/site-packages/datasets/load.py\", line 1397, in load_dataset\n builder_instance = load_dataset_builder(\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user2/.venv/lib/python3.12/site-packages/datasets/load.py\", line 1171, in load_dataset_builder\n builder_instance: DatasetBuilder = builder_cls(\n ^^^^^^^^^^^^\n File \"/home/user2/.venv/lib/python3.12/site-packages/datasets/builder.py\", line 390, in __init__\n with FileLock(lock_path):\n File \"/home/user2/.venv/lib/python3.12/site-packages/filelock/_api.py\", line 377, in __enter__\n self.acquire()\n File \"/home/user2/.venv/lib/python3.12/site-packages/filelock/_api.py\", line 333, in acquire\n self._acquire()\n File \"/home/user2/.venv/lib/python3.12/site-packages/filelock/_unix.py\", line 45, in _acquire\n fd = os.open(self.lock_file, open_flags, self._context.mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nPermissionError: [Errno 13] Permission denied: '/models/hf_hub_shared_experiment/data/_models_hf_hub_shared_experiment_data_tatsu-lab___alpaca_default_0.0.0_dce01c9b08f87459cf36a430d809084718273017.lock'\n```\n\n### Expected behavior\n\nThe second user should be able to read the shared cache files.\n\n### Environment info\n\n$ datasets-cli env\n\n- `datasets` version: 4.4.1\n- Platform: Linux-6.8.0-88-generic-x86_64-with-glibc2.39\n- Python version: 3.12.3\n- `huggingface_hub` version: 0.36.0\n- PyArrow version: 22.0.0\n- Pandas version: 2.3.3\n- `fsspec` version: 2025.10.0",
"url": "https://github.com/huggingface/datasets/issues/7900",
"state": "open",
"labels": [],
"created_at": "2025-12-09T16:41:47Z",
"updated_at": "2025-12-16T15:39:06Z",
"comments": 2,
"user": "qthequartermasterman"
},
{
"repo": "sgl-project/sglang",
"number": 14746,
"title": "Cannot join SGL slack Channel",
"body": "same issue with [#3929](https://github.com/sgl-project/sglang/issues/3929) and [#11983](https://github.com/sgl-project/sglang/issues/11983)\n\nCan we get a new invitation link? Thanks a lot!",
"url": "https://github.com/sgl-project/sglang/issues/14746",
"state": "closed",
"labels": [],
"created_at": "2025-12-09T15:43:51Z",
"updated_at": "2025-12-10T08:33:01Z",
"comments": 2,
"user": "alphabetc1"
},
{
"repo": "huggingface/transformers",
"number": 42740,
"title": "how to train trocr with transformers 4.57+?",
"body": "i train trocr with tranfomers 4.15, the results is right,but train with 4.57.1,the acc is always 0 , i did't find the reason,did t can train succ with latest transofrmers?",
"url": "https://github.com/huggingface/transformers/issues/42740",
"state": "open",
"labels": [],
"created_at": "2025-12-09T14:07:50Z",
"updated_at": "2026-01-05T06:46:34Z",
"user": "cqray1990"
},
{
"repo": "huggingface/transformers",
"number": 42739,
"title": "How about adding local kernel loading to `transformers.KernelConfig()`",
"body": "### Feature request\n\nAs title.\n\n### Motivation\n\nCurrently, the class `KernelConfig()` creates the `kernel_mapping` through the `LayerRepository` provided by `huggingface/kernels`. The `LayerRepository` downloads and loads kernel from the hub. I think adding the ability for it to load kernel locally should be very helpful for the debugging process.\n\n### Your contribution\n\n`huggingface/kernels` already has `LocalLayerRepository` built in. Maybe we should consider adding it to `KernelConfig()`.",
"url": "https://github.com/huggingface/transformers/issues/42739",
"state": "closed",
"labels": [
"Feature request"
],
"created_at": "2025-12-09T12:22:41Z",
"updated_at": "2025-12-17T01:21:57Z",
"user": "zheliuyu"
},
{
"repo": "huggingface/peft",
"number": 2945,
"title": "Return base model state_dict with original keys",
"body": "### Feature request\n\nTL;DR: `from peft import get_base_model_state_dict`\n\nHi!\n\nI'm looking for a way to get the state dict of the base model after it has been wrapped in a `PeftModel` while preserving the original model's state dict keys. To the best of my knowledge, the only way this can be done right now is getting the state dict from `peft_model.base_model.model` and manually patching the keys by removing the `.base_layer.` infix and filtering our peft param keys.\n\nA reason you wouldn't want to load the base model's state dict before wrapping it, for example, is when you are loading state dicts after FSDP wrapping your peft model.\n\n### Your contribution\n\nI have some of this logic implemented for Torchtitan. I could repurpose some of it for a PR that handles PEFT's edge-cases a bit more gracefully (so far I've only checked my approach for LoRA).",
"url": "https://github.com/huggingface/peft/issues/2945",
"state": "open",
"labels": [],
"created_at": "2025-12-09T11:23:52Z",
"updated_at": "2025-12-09T17:06:13Z",
"comments": 6,
"user": "dvmazur"
},
{
"repo": "vllm-project/vllm",
"number": 30325,
"title": "[Performance]: Can we enable triton_kernels on sm120",
"body": "### Proposal to improve performance\n\nSince PR (https://github.com/triton-lang/triton/pull/8498) had been merged, we may enable triton_kernels on sm120. \nhttps://github.com/vllm-project/vllm/blob/67475a6e81abea915857f82e6f10d80b03b842c9/vllm/model_executor/layers/quantization/mxfp4.py#L153-L160\n\nAlthough I haven't looked at the relevant code in detail yet, I think it should be sufficient to complete the unit tests(or vllm had already had, just skip on sm120, delete one line is enough) for all the kernels involved when triton_kernels is enabled and run them on sm120.\n\n@zyongye Does this idea make sense?\n\n### Report of performance regression\n\n_No response_\n\n### Misc discussion on performance\n\n_No response_\n\n### Your current environment (if you think it is necessary)\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30325",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-12-09T09:21:04Z",
"updated_at": "2025-12-10T10:16:18Z",
"comments": 2,
"user": "ijpq"
},
{
"repo": "vllm-project/vllm",
"number": 30296,
"title": "[Usage]: Is it possible to configure P2P kv-cache in multi-machine and multi-gpu scenarios?",
"body": "### Your current environment\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version : Could not collect\nCMake version : version 4.1.3\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu129\nIs debug build : False\nCUDA used to build PyTorch : 12.9\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-5.15.0-126-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.9.86\nCUDA_MODULE_LOADING set to :\nGPU models and configuration :\nGPU 0: NVIDIA L20\nGPU 1: NVIDIA L20\nGPU 2: NVIDIA L20\nGPU 3: NVIDIA L20\nGPU 4: NVIDIA L20\nGPU 5: NVIDIA L20\nGPU 6: NVIDIA L20\nGPU 7: NVIDIA L20\n\nNvidia driver version : 550.90.07\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.5.2\n[pip3] numpy==2.2.0\n[pip3] nvidia-cublas-cu12==12.9.1.4\n[pip3] nvidia-cuda-cupti-cu12==12.9.79\n[pip3] nvidia-cuda-nvrtc-cu12==12.9.86\n[pip3] nvidia-cuda-runtime-cu12==12.9.79\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.16.0\n[pip3] nvidia-cufft-cu12==11.4.1.4\n[pip3] nvidia-cufile-cu12==1.14.1.1\n[pip3] nvidia-curand-cu12==10.3.10.19\n[pip3] nvidia-cusolver-cu12==11.7.5.82\n[pip3] nvidia-cusparse-cu12==12.5.10.65\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-cutlass-dsl==4.2.1\n[pip3] nvidia-ml-py==13.580.82\n[pip3] nvidia-nccl-cu12==2.27.5\n[pip3] nvidia-nvjitlink-cu12==12.9.86\n[pip3] nvidia-nvshmem-cu12==3.3.20\n[pip3] nvidia-nvtx-cu12==12.9.79\n[pip3] pyzmq==27.1.0\n[pip3] torch==2.9.0+cu129\n[pip3] torchaudio==2.9.0+cu129\n[pip3] torchvision==0.24.0+cu129\n[pip3] transformers==4.57.1\n[pip3] triton==3.5.0\n[conda] Could not collect\n\n==============================\n vLLM Info\n==============================\nROCM Version : Could not collect\nvLLM Version : 0.11.2\nvLLM Build Flags:\n CUDA Archs: Not Set; ROCm: Disabled\nGPU Topology:\n GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID\nGPU0 X PIX PIX PIX SYS SYS SYS SYS 0-55,112-167 0 N/A\nGPU1 PIX X PIX PIX SYS SYS SYS SYS 0-55,112-167 0 N/A\nGPU2 PIX PIX X PIX SYS SYS SYS SYS 0-55,112-167 0 N/A\nGPU3 PIX PIX PIX X SYS SYS SYS SYS 0-55,112-167 0 N/A\nGPU4 SYS SYS SYS SYS X PIX PIX PIX 56-111,168-223 1 N/A\nGPU5 SYS SYS SYS SYS PIX X PIX PIX 56-111,168-223 1 N/A\nGPU6 SYS SYS SYS SYS PIX PIX X PIX 56-111,168-223 1 N/A\nGPU7 SYS SYS SYS SYS PIX PIX PIX X 56-111,168-223 1 N/A\n\n\n==============================\n Environment Variables\n==============================\nNVIDIA_VISIBLE_DEVICES=all\nNVIDIA_REQUIRE_CUDA=cuda>=12.9 brand=unknown,driver>=535,driver<536 brand=grid,driver>=535,driver<536 brand=tesla,driver>=535,driver<536 brand=nvidia,driver>=535,driver<536 brand=quadro,driver>=535,driver<536 brand=quadrortx,driver>=535,driver<536 brand=nvidiartx,driver>=535,driver<536 brand=vapps,driver>=535,driver<536 brand=vpc,driver>=535,driver<536 brand=vcs,driver>=535,driver<536 brand=vws,driver>=535,driver<536 brand=cloudgaming,driver>=535,driver<536 brand=unknown,driver>=550,driver<551 brand=grid,driver>=550,driver<551 brand=tesla,driver>=550,driver<551 brand=nvidia,driver>=550,driver<551 brand=quadro,driver>=550,driver<551 brand=quadrortx,driver>=550,driver<551 brand=nvidiartx,driver>=550,driver<551 brand=vapps,driver>=550,driver<551 brand=vpc,driver>=550,driver<551 brand=vcs,driver>=550,driver<551 brand=vws,driver>=550,driver<551 brand=cloudgaming,driver>=550,driver<551 brand=unknown,driver>=560,driver<561 brand=grid,driver>=560,driver<561 brand=tesla,driver>=560,driver<561 brand=nvidia,driver>=560,driver<561 brand=quadro,driver>=560,driver<561 brand=quadrortx,driver>=560,driver<561 brand=nvidiartx,driver>=560,driver<561 brand=vapps,driver>=560,driver<561 brand=vpc,driver>=560,driver<561 brand=vcs,driver>=560,driver<561 brand=vws,driver>=560,driver<561 brand=cloudgaming,driver>=560,driver<561 brand=unknown,driver>=",
"url": "https://github.com/vllm-project/vllm/issues/30296",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-09T03:29:48Z",
"updated_at": "2025-12-09T03:29:48Z",
"comments": 0,
"user": "lululu-1997"
},
{
"repo": "huggingface/trl",
"number": 4641,
"title": "Further improving `GRPOTrainer` doc to include Qwen SAPO in Loss Types",
"body": "### Feature request\n\nHello,\n\nI'd like to further document the Qwen SAPO implementation from @pramodith , not in the `paper_index` (he already did a good job) but in the `loss-types` subsection of the `GRPOTrainer`: https://huggingface.co/docs/trl/main/en/grpo_trainer#loss-types.\n\nI'd like to add the formula, a short paragraph description similar to other losses presented, and maybe the figure below I made, inspired by the SAPO paper Fig.1, that highlights visually the differences in trust regions with other `loss_type` options available for GRPO (at least GRPO, DAPO and DR GRPO), which is the core difference.\n\n![\"Image\"](\"https://github.com/user-attachments/assets/7cfb33d3-bb39-4420-8da1-bd482f28f52e\")
\n\n*Note:* *negative temp* $\\tau=1.5$ *is not a typo, it's to see the difference more clearly with positive temp (as the delta with 1.05 is too small)*\n\n### Motivation\n\nCompared to the available losses in the repo, I believe Qwen's SAPO difference is more pronounced. It's not just a matter on how to average like DAPO. Changing the PPO clip that almost everyone use is worth, imo, being mentioned in the `loss-types` subsection.\n\nSince there may be people not necessarily familiar with some RL details using TRL, I thought covering SAPO could help people better grasp or visualize the difference in the trust region and gradient weights.\n\n### Your contribution\n\nI'd like to submit a PR if you think this is something useful for readers/users.",
"url": "https://github.com/huggingface/trl/issues/4641",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u2728 enhancement",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-12-08T20:06:59Z",
"updated_at": "2025-12-12T17:28:06Z",
"comments": 1,
"user": "casinca"
},
{
"repo": "huggingface/transformers",
"number": 42713,
"title": "mulitmodal forward pass for ministral 3 family",
"body": "### System Info\n\nhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/ministral3/modeling_ministral3.py#L505\n\nseems like here we are using generic class which takes only the input ids as input ignoring the pixel values. when can we expect this implemented ?\n\n\n\n### Who can help?\n\n@Cyrilvallez \n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nplease implement https://github.com/huggingface/transformers/blob/main/src/transformers/models/gemma3/modeling_gemma3.py#L1174 for ministral family as well with multimodal capabilities\n\n### Expected behavior\n\nneed multimodal capabilities using ministral for finetuning ministral for sequence classification like gemma 3 4b\nhttps://github.com/huggingface/transformers/blob/main/src/transformers/models/gemma3/modeling_gemma3.py#L1174",
"url": "https://github.com/huggingface/transformers/issues/42713",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-12-08T18:46:14Z",
"updated_at": "2025-12-15T11:21:08Z",
"comments": 4,
"user": "rishavranaut"
},
{
"repo": "vllm-project/vllm",
"number": 30271,
"title": "[Usage]: Qwen 3 VL Embedding",
"body": "### Your current environment\n\nHi I would like to ask if there is a way to extract Qwen 3 VL multimodal embeddings, similar to Jina Embeddings V4, for retrieval purposes?\n\nI've tried to initialize the model this way but it doesn't work:\n```\nmodel = LLM(\n model=\"Qwen/Qwen3-VL-8B-Instruct\",\n task=\"embed\",\n trust_remote_code=True,\n)\n```\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30271",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-12-08T17:26:41Z",
"updated_at": "2025-12-09T07:18:35Z",
"comments": 2,
"user": "MingFengC"
},
{
"repo": "huggingface/optimum",
"number": 2390,
"title": "Request for input shapes to be specified",
"body": "### Feature request\n\nCurrently, \noptimum-cli does not provide a way to specify static input shapes, it defaults to dynamic shapes. Is there a way to make it possible to specify the input shape? If not, why do we not allow this?\n\nAn example would be:\n`optimum-cli export openvino --model microsoft/resnet-50 graph_convert` -> ` optimum-cli export openvino --model microsoft/resnet-50 graph_convert --input [1, 3, 224, 224]`\n\n### Motivation\n\nSpecifying a static shape in OpenVINO IR is nice to have for the [Intel/Altera FPGA AI Suite](https://www.altera.com/products/development-tools/fpga-ai-suite) toolchain which does not support dynamic input shapes of OpenVINO IR at the moment\n\n### Your contribution\n\nYes if possible or the green light is given that this is allowed.\nSome modifications to the optimum_cli.py file [here](https://github.com/huggingface/optimum/blob/0227a1ce9652b1b02da5a510bf513c585608f8c2/optimum/commands/optimum_cli.py#L179)\nwould probably be needed ",
"url": "https://github.com/huggingface/optimum/issues/2390",
"state": "open",
"labels": [],
"created_at": "2025-12-08T15:24:04Z",
"updated_at": "2025-12-20T19:38:02Z",
"comments": 3,
"user": "danielliuce"
},
{
"repo": "huggingface/transformers",
"number": 42698,
"title": "parse_response must not accept detokenized text",
"body": "### System Info\n\n[parse_response](https://github.com/huggingface/transformers/blob/5ee9ffe386c5ecc77d8009ab648b8c4c109931ea/src/transformers/tokenization_utils_base.py#L3525) function must only accept raw tokens, but never detokenized text. Parsing from text is a vulnerability and therefore must not be possible.\n\nOnce model response is rendered to text it is not possible to distinguish control tokens from their textual representations. At the very least this leads to inconvenience due to inability to discuss with the model its own codebase: \"here is my code, what is the function calling format used by the model?\" In worst case it can be used as a part of the attack vector e.g. registering a company to pop up in search result with an `rm -rf .` name with a hope that the name will be returned by the model as-is. (E.g. in the UK there used to be [\"; DROP TABLE \"COMPANIES\";--LTD\"](https://find-and-update.company-information.service.gov.uk/company/10542519))\n\nAlso accepting a text string facilitates relying on models only producing text and when we get multimodal models, we end up with no infrastructure for them as everythong is reduced to text.\n\nIt is important to design APIs in such a way that they are hard to be used incorrectly. Passing text to `parse_response` is appealing and kind of the easiest way to use the API.\n\nI am publishing this as an open bug rather than closed security issue because it is a widespread systematic problem that haunts many implementations. It is worth discussing it openly.\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nIf a model produces following token sequences:\n\n`[\"\", \"rm -rf /\", \"\"]` \n`[\"<\", \"tool \", \"call \", \"start\", \">\", \"rm -rf /\", \"<\", \"tool \", \"call \", \"end\", \">\"]` \n\nThey both are detokenized to the same \"rm -rf .\". The [parse_response](https://github.com/huggingface/transformers/blob/5ee9ffe386c5ecc77d8009ab648b8c4c109931ea/src/transformers/tokenization_utils_base.py#L3525) function has to return the same output for both of them.\n\n### Expected behavior\n\n[parse_response](https://github.com/huggingface/transformers/blob/5ee9ffe386c5ecc77d8009ab648b8c4c109931ea/src/transformers/tokenization_utils_base.py#L3525) must return tool call for `[\"\", \"rm -rf /\", \"\"]` but a plain text for `[\"<\", \"tool \", \"call \", \"start\", \">\", \"rm -rf /\", \"<\", \"tool \", \"call \", \"end\", \">\"]` .",
"url": "https://github.com/huggingface/transformers/issues/42698",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-08T12:20:39Z",
"updated_at": "2025-12-08T15:59:19Z",
"comments": 2,
"user": "kibergus"
},
{
"repo": "vllm-project/vllm",
"number": 30248,
"title": "[Feature]: any plan to support Relaxed Acceptance in v1?",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\n[NV Relaxed Acceptance](https://github.com/NVIDIA/TensorRT-LLM/blob/main/docs/source/blogs/tech_blog/blog2_DeepSeek_R1_MTP_Implementation_and_Optimization.md#relaxed-acceptance)\nThere are PRs ([vllm](https://github.com/vllm-project/vllm/pull/21506), [vllm](https://github.com/vllm-project/vllm/pull/22238), [sglang](https://github.com/sgl-project/sglang/pull/7702), [sglang](https://github.com/sgl-project/sglang/pull/8068)) in both sglang and vllm. However, none of them has been merged. What's the story behind this?\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30248",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-12-08T08:45:20Z",
"updated_at": "2025-12-09T10:18:22Z",
"comments": 4,
"user": "chengda-wu"
},
{
"repo": "vllm-project/vllm",
"number": 30246,
"title": "[Usage]: How to disable reasoning for gpt-oss-120b",
"body": "### Your current environment\n\n```\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.11.13 (main, Jun 5 2025, 13:12:00) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-5.15.0-160-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to :\nGPU models and configuration :\nGPU 0: NVIDIA L20\nGPU 1: NVIDIA L20\nGPU 2: NVIDIA L20\nGPU 3: NVIDIA L20\nGPU 4: NVIDIA L20\nGPU 5: NVIDIA L20\nGPU 6: NVIDIA L20\nGPU 7: NVIDIA L20\n\nNvidia driver version : 535.274.02\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Gold 5418Y\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 24\nSocket(s): 2\nStepping: 8\nCPU max MHz: 3800.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4000.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 2.3 MiB (48 instances)\nL1i cache: 1.5 MiB (48 instances)\nL2 cache: 96 MiB (48 instances)\nL3 cache: 90 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-23,48-71\nNUMA node1 CPU(s): 24-47,72-95\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-",
"url": "https://github.com/vllm-project/vllm/issues/30246",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-08T08:23:58Z",
"updated_at": "2025-12-08T08:23:58Z",
"comments": 0,
"user": "WiiliamC"
},
{
"repo": "huggingface/transformers",
"number": 42690,
"title": "How to run Phi4MultimodalProcessor",
"body": "### System Info\n\ntransformers version: 4.57.1\npython version: 3.9\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n[Phi4MultiModal example](https://huggingface.co/docs/transformers/model_doc/phi4_multimodal)\n\n### Expected behavior\n\nI just run [the example](https://huggingface.co/docs/transformers/model_doc/phi4_multimodal) but there is an error raised.",
"url": "https://github.com/huggingface/transformers/issues/42690",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-08T03:27:02Z",
"updated_at": "2025-12-09T12:30:27Z",
"user": "wcrzlh"
},
{
"repo": "vllm-project/vllm",
"number": 30222,
"title": "[Bug]: gpt-oss response api: streaming + code interpreter has bugs",
"body": "### Your current environment\n\n\n```\n\n### Who can help?\n\n@zucchini-nlp \n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```\nfrom transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration\nimport torch\nfrom PIL import Image\nimport requests\n\nprocessor = LlavaNextProcessor.from_pretrained(\"llava-hf/llava-v1.6-mistral-7b-hf\")\n\nmodel = LlavaNextForConditionalGeneration.from_pretrained(\"llava-hf/llava-v1.6-mistral-7b-hf\", dtype=\"auto\", low_cpu_mem_usage=True) \n\n# prepare image and text prompt, using the appropriate prompt template\nurl = \"https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true\"\nimage = Image.open(requests.get(url, stream=True).raw)\n\n# Define a chat history and use `apply_chat_template` to get correctly formatted prompt\n# Each value in \"content\" has to be a list of dicts with types (\"text\", \"image\") \nconversation = [\n {\n\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"text\", \"text\": \"What is shown in this image?\"},\n {\"type\": \"image\"},\n ],\n },\n]\nprompt = processor.apply_chat_template(conversation, add_generation_prompt=True)\n\ninputs = processor(images=image, text=prompt, return_tensors=\"pt\")\n\n# autoregressively complete prompt\noutput = model.generate(**inputs, max_new_tokens=100)\n\nprint(processor.decode(output[0], skip_special_tokens=True))\n```\n\n### Expected behavior\n\n\ud83d\udcdd Transformers GitHub Issue: Translation\nHere is the translated text for your GitHub issue, including the title and body.\n\nTitle\nCannot inference llava-next with transformers==4.57.1 on dtype=\"auto\" bug\n\nBody\nI am encountering an issue when attempting to run inference on LLaVA-Next models (e.g., `llava-hf/llava-v1.6-mistral-7b-hf`) using `transformers==4.57.1 ` and setting `dtype=\"auto\"` when loading the model.\n\nThe issue stems from the model's `config.json` having different `torch_dtype` values for the overall model and the text configuration:\n\n```\n\"text_config\": {\n \"_name_or_path\": \"mistralai/Mistral-7B-Instruct-v0.2\",\n // ... other config values\n \"torch_dtype\": \"bfloat16\",\n \"vocab_size\": 32064\n },\n \"torch_dtype\": \"float16\",\n```\n\nWhen the model is loaded with `dtype=\"auto\"`, each submodule (the visual model and the text model) seems to load with its respective `torch_dtype` (`\"float16\"` and `\"bfloat16\"`).\n\nThis difference in data types then causes an error during inference, specifically within the `forward` pass of the `LlavaNextForConditionalGeneration` model:\n\n```\nFile \"MY_ENV/.venv/lib/python3.10/site-packages/transformers/models/llava_next/modeling_llava_next.py\", line 687, in forward\n\u00a0 \u00a0 logits = self.lm_head(hidden_states[:, slice_indices, :])\n\u00a0 File \"MY_ENV/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1773, in _wrapped_call_impl\n\u00a0 \u00a0 return self._call_impl(*args, **kwargs)\n\u00a0 File \"MY_ENV/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1784, in _call_impl\n\u00a0 \u00a0 return forward_call(*args, **kwargs)\n\u00a0 File \"MY_ENV/.venv/lib/python3.10/site-packages/torch/nn/modules/linear.py\", line 125, in forward\n\u00a0 \u00a0 return F.linear(input, self.weight, self.bias)\nRuntimeError: expected m1 and m2 to have the same dtype, but got: c10::BFloat16 != c10::Half\n```\n\nThis `RuntimeError` indicates a dtype mismatch, likely between the linear layer's weight (from `self.lm_head`) and the input tensor (`hidden_states`), which results from the different dtypes loaded by `dtype=\"auto\"` for `self.lm_head` and `self.model`.\n\nIs there a plan to support loading LLaVA-Next models with `dtype=\"auto\"` given their current configuration structure?",
"url": "https://github.com/huggingface/transformers/issues/42641",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-05T04:39:35Z",
"updated_at": "2025-12-23T11:08:56Z",
"comments": 5,
"user": "rebel-seinpark"
},
{
"repo": "vllm-project/vllm",
"number": 30098,
"title": "[Doc]: Misleading Logic & Docstring in `block_quant_to_tensor_quant` (Block FP8)",
"body": "### \ud83d\udcda The doc issue\n\nThe docstring and implementation of the `block_quant_to_tensor_quant` function have a critical mismatch regarding the dequantization process, leading to numerical errors when used outside of specific fused kernel backends.\n\n### Problematic Function\n\nThe function is currently implemented as:\n\n```python\ndef block_quant_to_tensor_quant(\n x_q_block: torch.Tensor,\n x_s: torch.Tensor,\n) -> tuple[torch.Tensor, torch.Tensor]:\n \"\"\"This function converts block-wise quantization to tensor-wise\n quantization. The inputs are block-wise quantization tensor `x_q_block`,\n block-wise quantization scale and the block size.\n The outputs are tensor-wise quantization tensor and tensor-wise\n quantization scale. Note only float8 is supported for now.\n \"\"\"\n x_dq_block = group_broadcast(x_q_block, x_s)\n x_q_tensor, scale = input_to_float8(x_dq_block, dtype=x_q_block.dtype)\n return x_q_tensor, scale\n```\n\n### Observation and Impact\n- Vllm migrated the actual 'block quant to tensor quant' operation to the kernel but keep this method. The docstring is misleading since in this method, there is no scale.\n- Misleading Docstring: The docstring claims the function performs \"conversion\" and takes the \"scale,\" implying a complete process. However, the output `x_dq_block` is an un-dequantized value with a broadcasted shape.\n\n### Suggest a potential alternative/fix\n\nThe function should be either documented clearly as a kernel preparation helper OR refactored to ensure numerical correctness when used as a conversion API.\n\n**1. Fix Documentation/Name (If intent is kernel prep):**\n* Rename the function to something like `_prepare_block_quant_for_fused_kernel`.\n* Add a warning that this function does not perform dequantization.\n\n**2. Implement Safe Logic Dispatch (If intent is a robust conversion API):**\nThe function should dynamically dispatch to the known-good, safe path if the specific fused kernel (that handles the $X_q \\times X_s$ multiplication) is not guaranteed to be active.\n\nThe safe logic is in v.0.9.2:\n```python\n# Safe path required for correctness on general backends\nx_dq_block = scaled_dequantize(x_q_block, x_s) \nx_q_tensor, scale = input_to_float8(x_dq_block, dtype=x_q_block.dtype)\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30098",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-12-05T02:12:07Z",
"updated_at": "2025-12-24T17:22:50Z",
"comments": 0,
"user": "xqoasis"
},
{
"repo": "huggingface/transformers",
"number": 42638,
"title": "Routing Replay for MoEs",
"body": "### Feature request\n\nRecentRL approaches for training MoE models increasingly rely on **Routing Replay**, as described in the following papers:\n\n- https://huggingface.co/papers/2507.18071\n- https://huggingface.co/papers/2510.11370\n- https://huggingface.co/papers/2512.01374\n\nWithout going into the training details, Routing Replay requires the ability to override the router during the forward pass, that is, to force the model to use a predefined set of router logits rather than computing new ones. This enables deterministic reproduction of expert selection.\n\nAFAICT, Transformers currently does not expose a way to override router logits or manually control expert selection at inference/training time.\n\nI imagine something along the following lines (minimal example):\n\n```python\nfrom transformers import AutoModelForCausalLM\nimport torch\n\nmodel = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen3-30B-A3B-Instruct-2507\", device_map=\"auto\", dtype=\"auto\")\n\ninput_ids = torch.tensor([[1, 2, 3, 4]], device=\"cuda\")\n\n# Standard forward pass, retrieving router logits\noutputs = model(input_ids, output_router_logits=True)\n\n# Forward pass with router logits injected (enabling Routing Replay)\nmodel(input_ids, router_logits=outputs.router_logits)\n```\n\n## Alternative\n\nIf we decide not to implement this feature, it would be nice to provide an example showing how to _patch_ a MoE to enable this.\n\n### Motivation\n\nSee above.\n\n### Your contribution\n\nI think I can do it.",
"url": "https://github.com/huggingface/transformers/issues/42638",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-12-04T23:58:14Z",
"updated_at": "2025-12-05T16:29:05Z",
"comments": 2,
"user": "qgallouedec"
},
{
"repo": "vllm-project/vllm",
"number": 30084,
"title": "[Performance]: Should I expect linear scaling with pure DP?",
"body": "### Proposal to improve performance\n\n_No response_\n\n### Report of performance regression\n\n_No response_\n\n### Misc discussion on performance\n\nI decided to benchmark vLLM 0.11.2 with pure DP of Qwen/Qwen2.5-32B-Instruct deployment(before benchmarking DP+EP with Qwen/Qwen3-30B-A3B-Instruct-2507) on DP1 vs DP8 (H200):\n\nDP1 deployment:\n```\nvllm serve ${MODEL_NAME} \\\n --port 8000 \\\n --trust-remote-code\n```\n\nDP8 deployment:\n```\nvllm serve ${MODEL_NAME} \\\n --port 8000 \\\n --trust-remote-code \\\n --data-parallel-size 8 \\\n --data-parallel-size-local 8\n```\n\nMy benchmark roughly looks like this:\n```\nfor rate in [10, 20, ... 100, 200, ... 1000, 2000, ... 100000]:\n vllm bench serve \\\n --host \"$HOST\" \\\n --model Qwen/Qwen2.5-32B-Instruct \\\n --dataset-name random \\\n --random-input-len 128 \\\n --random-output-len 128 \\\n --num-prompts 10000 \\\n --request-rate \"$rate\" \\\n --ignore-eos\n```\nShould I expect ~8x scaling? Result show only ~4x (duration, request throughput, tokens throughput, etc...)\n\n![\"Image\"](\"https://github.com/user-attachments/assets/81feb936-73d6-49c3-949e-dfbd6d7ba7d7\")
\n\ncc @KeitaW @amanshanbhag\n\n### Your current environment (if you think it is necessary)\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30084",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-12-04T19:52:45Z",
"updated_at": "2025-12-16T04:09:24Z",
"comments": 7,
"user": "pbelevich"
},
{
"repo": "vllm-project/vllm",
"number": 30082,
"title": "[Usage]: Turn off reasoning for Kimi-K2-Thinking?",
"body": "### Your current environment\n\n\n\n```text\nOutput of collect_env.py-\n\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version : Could not collect\nCMake version : version 4.1.3\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu129\nIs debug build : False\nCUDA used to build PyTorch : 12.9\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-4.18.0-553.56.1.el8_10.x86_64-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.9.86\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version : 550.163.01\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8468\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nCPU max MHz: 3800.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 8\nNUMA node0 CPU(s): 0-11,96-107\nNUMA node1 CPU(s): 12-23,108-119\nNUMA node2 CPU(s): 24-35,120-131\nNUMA node3 CPU(s): 36-47,132-143\nNUMA node4 CPU(s): 48-59,144-155\nNUMA node5 CPU(s): 60-71,156-167\nNUMA node6 CPU(s): 72-83,168-179\nNUMA node7 CPU(s): 84-95,180-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spect",
"url": "https://github.com/vllm-project/vllm/issues/30082",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-04T19:32:13Z",
"updated_at": "2025-12-08T23:02:58Z",
"comments": 2,
"user": "vikrantdeshpande09876"
},
{
"repo": "vllm-project/vllm",
"number": 30075,
"title": "[Feature]: Default eplb num_redundant_experts to the lowest valid value if unspecified",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nEPLB requires the number of experts to be chosen up front and there is a known minimum valid value that can be derived from the vllm startup configuration. Since extra EPLB experts trades kv cache memory for potential performance improvements, but that is not guaranteed to pay off, having the EPLB value default to the minimum valid value would reduce friction on enabling EPLB the first time until users are ready to tune.\n\nAs a consequence, it would also streamline templating the same config to work across multiple EP sizes for the default case.\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30075",
"state": "open",
"labels": [
"help wanted",
"good first issue",
"feature request"
],
"created_at": "2025-12-04T18:19:03Z",
"updated_at": "2025-12-20T21:00:23Z",
"comments": 4,
"user": "smarterclayton"
},
{
"repo": "vllm-project/vllm",
"number": 30058,
"title": "[Feature]: Multi-Adapter Support for Embed Qwen3 8B Embedding Model",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nHi Team, do we currently support multi-adapter (LoRA) support for embedding models, specifically Qwen3 8B Embedding model? If not, when can we expect the support? Thanks :)\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30058",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-12-04T12:05:15Z",
"updated_at": "2025-12-04T19:42:04Z",
"comments": 4,
"user": "dawnik17"
},
{
"repo": "huggingface/accelerate",
"number": 3873,
"title": "How to specify accelerate launch yaml config item when running with torchrun",
"body": "I've read the doc [Launching Accelerate scripts](https://huggingface.co/docs/accelerate/basic_tutorials/launch), and would like to launch with torchrun. However, the doc does not mention how to specify configs like `distribute_type` when using torchrun.\n\nWhat are the equivalent of these configurations when using torchrun?",
"url": "https://github.com/huggingface/accelerate/issues/3873",
"state": "open",
"labels": [],
"created_at": "2025-12-04T07:27:43Z",
"updated_at": "2026-01-03T15:07:19Z",
"user": "WhoisZihan"
},
{
"repo": "huggingface/lerobot",
"number": 2580,
"title": "How can the leader arm be synchronized to follow the follower arm during inference?",
"body": "",
"url": "https://github.com/huggingface/lerobot/issues/2580",
"state": "open",
"labels": [],
"created_at": "2025-12-04T07:22:07Z",
"updated_at": "2025-12-11T02:53:11Z",
"user": "zhoushaoxiang"
},
{
"repo": "vllm-project/vllm",
"number": 30023,
"title": "[Feature]: Support qwen3next with GGUF?",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nWith v0.11.0, `vllm` report:\n\n```\nvllm | (APIServer pid=1) ValueError: GGUF model with architecture qwen3next is not supported yet.\n```\n\nhttps://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking-GGUF\n\n\nI did a simple dig for this, seems the vllm has support of `Qwen3-Next` as architecture is `qwen3_next`.\nBut the `Qwen` set it as `qwen3next`.\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30023",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-12-04T03:40:26Z",
"updated_at": "2025-12-18T05:31:57Z",
"comments": 0,
"user": "zeerd"
},
{
"repo": "vllm-project/vllm",
"number": 29998,
"title": "[Bug]: cannot send two POST to /v1/chat/completions endpoint with identic tool function name with model GPT-OSS-120B",
"body": "### Your current environment\n\n![\"Image\"](\"https://github.com/user-attachments/assets/115672b2-f85f-43ec-b89c-d3a0daae7d81\")
\n\nThis is quite weird, because when you call it with python SDK, it is working fine, e.g.\n\n```python\nfrom openai import OpenAI\nfrom dotenv import load_dotenv\nimport os\n\nload_dotenv()\n\nclient = OpenAI(\n api_key=os.getenv(\"API_KEY\"),\n base_url=\"http://localhost:8000/v1\",\n)\n\ntools = [{\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"description\": \"Get the current weather in a given location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\"type\": \"string\"},\n \"description\": \"Location and state, e.g., 'San Francisco, CA'\"\n },\n \"required\": [\"location\"]\n },\n },\n }\n]\n\nresponse = client.chat.completions.create(\n model=\"openai/gpt-oss-120b\",\n messages=[{\"role\": \"user\", \"content\": \"How is the weather in Berlin? use the tool get_weather.\"}],\n tools=tools,\n tool_choice=\"auto\",\n stream=False \n)\n \nprint(response.choices[0].message)\n```\n\nIn fact this can also be reproduced using n8n, AI Agent nodes which are based on the typescipt langgraph implementation: https://github.com/n8n-io/n8n/blob/master/packages/%40n8n/nodes-langchain/nodes/agents/Agent/agents/ToolsAgent/V1/execute.ts#L34\n\nHere you can also see that chat windows freeze when a tool is attached and a user is asking the second question.\n\nThe bug really seems to be related to this model, because I tested Mistral and Qwen Models and I couldn't reproduce it. When I tried to debug the issue, there was a sensetivity to the description field in the parameters list of the tool. To make it clear, this can also only be sent once using the OpenAI Python SDK, but works again when the function name is changed:\n\n```python\nfrom openai import OpenAI\nfrom dotenv import load_dotenv\nimport os\n\nload_dotenv()\n\nclient = OpenAI(\n api_key=os.getenv(\"API_KEY\"),\n base_url=f\"https://{os.getenv('API_DOMAIN')}/v1\",\n)\n\ntools = [{\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"description\": \"Get the current weather in a given location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\", \n \"description\": \"Location and state, e.g., 'San Francisco, CA'\"\n },\n },\n \"required\": [\"locatio",
"url": "https://github.com/vllm-project/vllm/issues/29998",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-03T21:41:35Z",
"updated_at": "2025-12-19T15:53:43Z",
"comments": 14,
"user": "pd-t"
},
{
"repo": "huggingface/transformers",
"number": 42589,
"title": "Incorrect tokenization `tokenizers` for escaped strings / Mismatch with `mistral_common`",
"body": "### System Info\n\n```\nIn [3]: mistral_common.__version__\nOut[3]: '1.8.6'\n```\n\n```\nIn [4]: import transformers; transformers.__version__\nOut[4]: '5.0.0.dev0'\n```\n\n```\nIn [5]: import tokenizers; tokenizers.__version__\nOut[5]: '0.22.1'\n```\n\n### Who can help?\n\n@ArthurZucker @itazap \n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```py\nfrom transformers import AutoTokenizer\nfrom mistral_common.tokens.tokenizers.mistral import MistralTokenizer\nfrom mistral_common.protocol.instruct.request import ChatCompletionRequest\n\nreq = ChatCompletionRequest(messages=[\n {'role': 'system', 'content': ''},\n {'role': 'user', 'content': 'hey'},\n {'role': 'assistant', 'content': 'ju\\x16'},\n {'role': 'user', 'content': 'hey'},\n])\n\ntokenizer_orig = MistralTokenizer.from_hf_hub(\"mistralai/Ministral-3-3B-Instruct-2512\")\ntokenizer_hf = AutoTokenizer.from_pretrained(\"mistralai/Ministral-3-3B-Instruct-2512\")\n\norig_tokens = tokenizer_orig.encode_chat_completion(req).tokens\norig_text = tokenizer_orig.encode_chat_completion(req).text\n\nprint(\"Expected\")\nprint(orig_text)\nprint(orig_tokens)\n\nhf_tokens = tokenizer_hf.apply_chat_template(req.to_openai()[\"messages\"])\nhf_text = tokenizer_hf.convert_ids_to_tokens(hf_tokens)\n\nprint(\"HF\")\nprint(hf_tokens)\nprint(hf_text)\n```\n\ngives:\n\n```\nExpected\n[SYSTEM_PROMPT][/SYSTEM_PROMPT][INST]hey[/INST]ju[INST]hey[/INST]\n[1, 17, 18, 3, 74058, 4, 5517, 1022, 2, 3, 74058, 4]\nHF\n[1, 17, 18, 3, 74058, 4, 5517, 1022, 1032, 2, 3, 74058, 4]\n['', '[SYSTEM_PROMPT]', '[/SYSTEM_PROMPT]', '[INST]', 'hey', '[/INST]', 'ju', '\u0116', '\u0120', '', '[INST]', 'hey', '[/INST]']\n```\n\nAs you can see the token `1032` should not be there. I'm not sure exactly what is happening and it could very well be that the behavior of `tokenizers` makes sense here. \n\n**However**, this is a mismatch with `mistral_common` which means that any such tokenization will give slightly different token ids leading to slightly incorrect results since all Mistral models are trained with `mistral_common`.\n\nThis is especially important for \"long-log\" parsing tasks that often have escaped strings.\n\nIt's def an edge case, but would still be very nice to fix.\n\n### Expected behavior\n\nAlign encoding.",
"url": "https://github.com/huggingface/transformers/issues/42589",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-12-03T10:57:35Z",
"updated_at": "2025-12-16T10:45:35Z",
"comments": 5,
"user": "patrickvonplaten"
},
{
"repo": "huggingface/diffusers",
"number": 12781,
"title": "Impossible to log into Huggingface/Diffusers Discord",
"body": "### Describe the bug\n\nWhen trying to verify my Discord/Huggingface account, no matter what I do, I end up with this message: \n![\"Image\"](\"https://github.com/user-attachments/assets/d1d0f18b-c80f-4862-abde-fb49ee505ddd\")
\n\nHas the HF Discord died? If that is the case, what alternatives are there? \n\nI feel that there is a strong need for some kind of forum where users of Diffusers in collaboration can figure out how to make newly supported and huge models run on consumer hardware. The Diffusers discussion on GitHub is dead. So, where do we go?\n\n### Reproduction\n\nTry to log-in in to Discord. \n\n### Logs\n\n```shell\n-\n```\n\n### System Info\n\n-\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/12781",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-12-03T09:42:55Z",
"updated_at": "2025-12-04T15:11:42Z",
"comments": 4,
"user": "tin2tin"
},
{
"repo": "vllm-project/vllm",
"number": 29944,
"title": "[Usage]:It seems that the prefix cache has not brought about any performance benefits.",
"body": "### Your current environment\n\n```\nroot@ubuntu:/vllm-workspace# python3 collect_env.py\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version : Could not collect\nCMake version : version 4.1.0\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-5.15.0-25-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration :\nGPU 0: NVIDIA H20\nGPU 1: NVIDIA H20\nGPU 2: NVIDIA H20\nGPU 3: NVIDIA H20\nGPU 4: NVIDIA H20\nGPU 5: NVIDIA H20\nGPU 6: NVIDIA H20\nGPU 7: NVIDIA H20\n\nNvidia driver version : 550.127.08\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 224\nOn-line CPU(s) list: 0-223\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8480+\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 56\nSocket(s): 2\nStepping: 8\nFrequency boost: enabled\nCPU max MHz: 2001.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4000.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr avx512_fp16 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 5.3 MiB (112 instances)\nL1i cache: 3.5 MiB (112 instances)\nL2 cache: 224 MiB (112 instances)\nL3 cache: 210 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-55,112-167\nNUMA node1 CPU(s): 56-111,168-223\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.3.1\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.14.1\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-cufile-cu",
"url": "https://github.com/vllm-project/vllm/issues/29944",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-03T07:03:49Z",
"updated_at": "2025-12-03T07:04:37Z",
"comments": 0,
"user": "wenba0"
},
{
"repo": "vllm-project/vllm",
"number": 29940,
"title": "[Usage]: QWen2-Audio-7B support",
"body": "### Your current environment\n\nWe encountered numerous peculiar issues during the QWen2-Audio-7B conversion process. Do we currently support Qwen2-Audio-7B? If so, could you provide a demo?\n\nThank you very much\uff01\n\n### \ud83d\udc1b Describe the bug\n\nRefer to Whisper's demo\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29940",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-12-03T06:04:07Z",
"updated_at": "2025-12-04T14:23:05Z",
"comments": 1,
"user": "freedom-cui"
},
{
"repo": "huggingface/datasets",
"number": 7893,
"title": "push_to_hub OOM: _push_parquet_shards_to_hub accumulates all shard bytes in memory",
"body": "## Summary\n\nLarge dataset uploads crash or hang due to memory exhaustion. This appears to be the root cause of several long-standing issues.\n\n### Related Issues\n\nThis is the root cause of:\n- #5990 - Pushing a large dataset on the hub consistently hangs (46 comments, open since 2023)\n- #7400 - 504 Gateway Timeout when uploading large dataset\n- #6686 - Question: Is there any way for uploading a large image dataset?\n\n### Context\n\nDiscovered while uploading the [Aphasia Recovery Cohort (ARC)](https://openneuro.org/datasets/ds004884) neuroimaging dataset (~270GB, 902 sessions) to HuggingFace Hub using the `Nifti()` feature.\n\nWorking implementation with workaround: [arc-aphasia-bids](https://github.com/The-Obstacle-Is-The-Way/arc-aphasia-bids)\n\n## Root Cause\n\nIn `_push_parquet_shards_to_hub` (arrow_dataset.py), the `additions` list accumulates every `CommitOperationAdd` with full Parquet bytes in memory:\n\n```python\nadditions = []\nfor shard in shards:\n parquet_content = shard.to_parquet_bytes() # ~300 MB per shard\n shard_addition = CommitOperationAdd(path_or_fileobj=parquet_content)\n api.preupload_lfs_files(additions=[shard_addition])\n additions.append(shard_addition) # THE BUG: bytes stay in memory forever\n```\n\nFor a 902-shard dataset: **902 \u00d7 300 MB = ~270 GB RAM requested \u2192 OOM/hang**.\n\nThe bytes are held until the final `create_commit()` call, preventing garbage collection.\n\n## Reproduction\n\n```python\nfrom datasets import load_dataset\n\n# Any large dataset with embedded files (Image, Audio, Nifti, etc.)\nds = load_dataset(\"imagefolder\", data_dir=\"path/to/large/dataset\")\nds.push_to_hub(\"repo-id\", num_shards=500) # Watch memory grow until crash\n```\n\n## Workaround\n\nProcess one shard at a time, upload via `HfApi.upload_file(path=...)`, delete before next iteration:\n\n```python\nfrom huggingface_hub import HfApi\nimport pyarrow.parquet as pq\n\napi = HfApi()\nfor i in range(num_shards):\n shard = ds.shard(num_shards=num_shards, index=i, contiguous=True)\n \n # Write to disk, not memory\n shard.to_parquet(local_path)\n \n # Upload from file path (streams from disk)\n api.upload_file(\n path_or_fileobj=str(local_path),\n path_in_repo=f\"data/train-{i:05d}-of-{num_shards:05d}.parquet\",\n repo_id=repo_id,\n repo_type=\"dataset\",\n )\n \n # Clean up before next iteration\n local_path.unlink()\n del shard\n```\n\nMemory usage stays constant (~1-2 GB) instead of growing linearly.\n\n## Suggested Fix\n\nAfter `preupload_lfs_files` succeeds for each shard, release the bytes:\n\n1. Clear `path_or_fileobj` from the `CommitOperationAdd` after preupload\n2. Or write to temp file and pass file path instead of bytes\n3. Or commit incrementally instead of batching all additions\n\n## Environment\n\n- datasets version: main branch (post-0.22.0)\n- Platform: macOS 14.x ARM64\n- Python: 3.13\n- PyArrow: 18.1.0\n- Dataset: 902 shards, ~270 GB total embedded NIfTI files",
"url": "https://github.com/huggingface/datasets/issues/7893",
"state": "closed",
"labels": [],
"created_at": "2025-12-03T04:19:34Z",
"updated_at": "2025-12-05T22:45:59Z",
"comments": 2,
"user": "The-Obstacle-Is-The-Way"
},
{
"repo": "vllm-project/vllm",
"number": 29920,
"title": "[Feature]: Add support for fused fp8 output to FlashAttention 3",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nOn Hopper, we use FlashAttention as the default attention backend. When o-proj is quantized to fp8, we are leaving performance on the table as FA3 does not support fused output fp8 quant. With Triton/ROCm/AITER backends we saw up to 8% speedups with attention+quant fusion.\n\nvLLM already maintains our own fork of FA, adding output quant support should be pretty non-intrusive. Subtasks:\n- vllm-flash-attn:\n - add `output_scale` parameter to attention forward functions\n - plumb parameter through all layers of the interface\n - compare branching at runtime/compile-time for performance and binary size (Hopper)\n\n- vllm:\n - integrate new FA version\n - add support for attention+quant fusion to FA attention backend\n - check FA version, hardware version\n - should be as easy as modifying the `supports_fused_output_quant` method and plumbing `output_scale` from `FlashAttentionImpl.forward()` to the kernel call\n\n### Additional context\n\ncc @LucasWilkinson \n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29920",
"state": "open",
"labels": [
"help wanted",
"performance",
"feature request",
"torch.compile"
],
"created_at": "2025-12-02T20:16:31Z",
"updated_at": "2026-01-05T20:53:11Z",
"comments": 4,
"user": "ProExpertProg"
},
{
"repo": "vllm-project/vllm",
"number": 29917,
"title": "[Feature]: VLLM_DISABLE_COMPILE_CACHE should be a config flag",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\n`vllm serve` does a nice printout of non-default config flags. VLLM_DISABLE_COMPILE_CACHE gets used enough that it should have an equivalent config flag for it\n\nOffline @ProExpertProg mentioned we can treat it like VLLM_DEBUG_DUMP_PATH where we have both and the env var overrides the config option by overwriting it directly\n\n### Alternatives\n\nnone\n\n### Additional context\n\nn/a\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29917",
"state": "open",
"labels": [
"help wanted",
"feature request",
"torch.compile"
],
"created_at": "2025-12-02T20:06:01Z",
"updated_at": "2025-12-05T05:19:12Z",
"comments": 6,
"user": "zou3519"
},
{
"repo": "huggingface/inference-playground",
"number": 102,
"title": "How to know when a model is outdated ?",
"body": "I'm testing https://huggingface.co/chat/models/openai/gpt-oss-20b and there I asked this:\n```\ndo you know any github repository created in 2025?\n\n
The output of python collect_env.py
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.3 (main, Nov 6 2025, 13:44:16) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.14.0-1017-azure-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : GPU 0: NVIDIA H100 NVL\nNvidia driver version : 580.95.05\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 40\nOn-line CPU(s) list: 0-39\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 9V84 96-Core Processor\nCPU family: 25\nModel: 17\nThread(s) per core: 1\nCore(s) per socket: 40\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4800.09\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves user_shstk avx512_bf16 clzero xsaveerptr rdpru arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.3 MiB (40 instances)\nL1i cache: 1.3 MiB (40 instances)\nL2 cache: 40 MiB (40 instances)\nL3 cache: 160 MiB (5 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-39\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsa: Vulnerable: Clear CPU buffers attempted, no microcode\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.5.3\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime",
"url": "https://github.com/vllm-project/vllm/issues/31019",
"state": "open",
"labels": [
"bug",
"help wanted",
"good first issue"
],
"created_at": "2025-12-19T09:18:00Z",
"updated_at": "2025-12-24T12:16:01Z",
"comments": 4,
"user": "SorenDreano"
},
{
"repo": "vllm-project/vllm",
"number": 31016,
"title": "[Bug]: FlashInfer Incompatible with Sleep Mode",
"body": "### Your current environment\n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nHere is a script to reproduce the bug: \nI use vllm=v0.10.1 and flashinfer-python=v0.5.3.\n```\nfrom vllm import LLM, SamplingParams\n\nif __name__ == \"__main__\":\n model_pth = \"xxx/Qwen3-1.7B\" \n tp_size = 1\n llm = LLM(\n model=model_pth, \n enable_sleep_mode=True,\n tensor_parallel_size=tp_size,\n gpu_memory_utilization=0.7, \n )\n\n llm.sleep(level=1)\n llm.wake_up()\n\n prompts = [\n \"What is AI?\", \n \"Where is the Machu Picchu located?\", \n \"What is the capital of France?\",\n \"Who painted the Mona Lisa?\",\n ]\n\n sampling_params = SamplingParams(\n temperature=0.7,\n top_p=0.9,\n max_tokens=64,\n )\n\n outputs = llm.generate(prompts, sampling_params)\n\n for i, out in enumerate(outputs):\n prompt = prompts[i]\n generated = out.outputs[0].text\n print(f\"Prompt {i}: {prompt!r}\")\n print(f\"Generation: {generated}\\n\")\n ```\n\n### Root Cause\nThe bug occurs because the FlashInfer backend\u2019s `attn_metadata` is stateful. It holds a `block_table_arange` tensor that is initialized once and then reused across subsequent calls to `build`:\n\n```python\nself.block_table_arange = torch.arange(\n max_num_pages_per_req,\n dtype=torch.int32,\n device=self.device,\n)\n```\n\nThis `block_table_arange` tensor is allocated in the mempool with the `\"kv_cache\"` tag. It gets discarded after calling `llm.sleep`, but is not recreated when the engine wakes up, which leads to incorrect values and thus wrong outputs.\n\nSpecifically, this will cause bad rollout outputs in VERL using vllm + flashinfer.\n\n### Temporary Fix\nHere is a patch as a temporary workaround. It\u2019s not an ideal solution, but it works:\n\n```python\nfrom vllm.v1.attention.backends.flashinfer import FlashInferMetadataBuilder\nimport torch\n\ndef patch_flashinfer_build():\n old_build = FlashInferMetadataBuilder.build\n\n def new_build(*args, **kwargs):\n self = args[0]\n max_num_pages_per_req = self.block_table_arange.numel()\n self.block_table_arange.copy_(\n torch.arange(\n max_num_pages_per_req,\n device=self.block_table_arange.device,\n dtype=self.block_table_arange.dtype,\n )\n )\n return old_build(*args, **kwargs)\n\n FlashInferMetadataBuilder.build = new_build\n\npatch_flashinfer_build()\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31016",
"state": "open",
"labels": [
"bug",
"help wanted"
],
"created_at": "2025-12-19T08:04:19Z",
"updated_at": "2025-12-19T23:17:47Z",
"comments": 1,
"user": "xiaoxiaosuaxuan"
},
{
"repo": "huggingface/transformers.js",
"number": 1490,
"title": "Example models for each pipeline",
"body": "### Question\n\nRight now, I sorta use the docs and some searches to find good default models for https://workglow.dev/ for each pipeline that transformerjs has to offer. But they are not really the best, either in size or performance.\n\nIt would be great to have a list for each pipeline for fast and effective, best of breed, and a workhorse that is in between. Like a good, better, best.",
"url": "https://github.com/huggingface/transformers.js/issues/1490",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-12-19T07:37:16Z",
"updated_at": "2025-12-19T17:41:01Z",
"user": "sroussey"
},
{
"repo": "vllm-project/vllm",
"number": 31004,
"title": "[New Model]: T5Gemma 2",
"body": "### The model to consider.\n\nhttps://huggingface.co/collections/google/t5gemma-2\n\n\n### The closest model vllm already supports.\n\n_No response_\n\n### What's your difficulty of supporting the model you want?\n\nI know vLLM dropped encoder-decoder support, but can we bring it back?\n\nhttps://huggingface.co/docs/transformers/model_doc/t5gemma2\nhttps://blog.google/technology/developers/t5gemma-2/\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/31004",
"state": "open",
"labels": [
"new-model"
],
"created_at": "2025-12-19T03:55:00Z",
"updated_at": "2025-12-20T21:37:34Z",
"comments": 1,
"user": "ducviet00-h2"
},
{
"repo": "sgl-project/sglang",
"number": 15443,
"title": "SGLang Diffusion Cookbook Proposal",
"body": "# \ud83c\udfa8 [Community Contribution] Create SGLang Diffusion Models Cookbook\n\n## \ud83c\udfaf Goal\nCreate a comprehensive cookbook for diffusion models in SGLang, demonstrating SGLang's performance advantages for image and video generation workloads.\n\n## \ud83d\udccb Scope\n\n### Models to Cover\n\n**Image Generation:**\n- Flux-1 Dev\n- Flux-2 \n- SDXL-Turbo\n- Qwen Image Edit\n\n**Video Generation:**\n- Wan 2.1\n- Wan 2.2\n\n### Content Structure\n\nEach model section includes:\n1. **Model Introduction**\n - Capabilities and use cases\n - Resolution/quality specifications\n - Style examples and output samples\n - Links to official resources\n\n2. **SGLang Deployment**\n - One-command server launch\n - Client usage example\n - Model-specific optimization tips\n\n3. **Performance Benchmarks**\n - Throughput (images/sec or videos/min)\n - Latency and memory usage\n - Comparison: SGLang vs Diffusers vs ComfyUI\n - Bar charts and scaling analysis\n - Reproducible benchmark scripts\n\n## \ud83d\udce6 Deliverables\n```\ncookbook/diffusion/\n\u251c\u2500\u2500 README.md # Main cookbook\n\u251c\u2500\u2500 examples/ # Usage scripts per model\n\u2502 \u251c\u2500\u2500 flux1_basic.py\n\u2502 \u251c\u2500\u2500 sdxl_turbo.py\n\u2502 \u251c\u2500\u2500 wan21_video.py\n\u2502 \u2514\u2500\u2500 ...\n\u251c\u2500\u2500 benchmarks/\n\u2502 \u251c\u2500\u2500 bench_image.py\n\u2502 \u251c\u2500\u2500 bench_video.py\n\u2502 \u251c\u2500\u2500 compare_backends.py\n\u2502 \u2514\u2500\u2500 run_all.sh\n\u2514\u2500\u2500 assets/\n \u2514\u2500\u2500 output_examples/ # Curated generation examples\n```\n\n## \ud83d\ude80 Timeline\n\n**Phase 1 (Weeks 1-2):** MVP with Flux-1 + SDXL-Turbo \n**Phase 2 (Weeks 3-4):** Add remaining image models \n**Phase 3 (Weeks 5-6):** Video models + comprehensive benchmarks \n\n## \ud83d\udcaa How to Contribute\n\nWe need help with:\n\n### Required Contributors (2-3 people)\n- [ ] **Benchmark Engineer**: Run performance tests on H100/A100\n - Time commitment: ~10 hours/week for 4 weeks\n - Requirements: GPU access, Python proficiency\n \n- [ ] **Documentation Writer**: Create usage examples and guides\n - Time commitment: ~8 hours/week for 4 weeks\n - Requirements: Technical writing, SGLang familiarity\n\n- [ ] **Visual Designer** (optional): Curate output examples\n - Time commitment: ~5 hours/week for 2 weeks\n - Requirements: Eye for quality, prompt engineering\n\n### Hardware Requirements\n- H100 (80GB) - primary testing platform\n- A100 (40GB) - secondary platform (optional)\n- Access via cloud providers acceptable (AWS/Lambda/RunPod)\n\n## \ud83d\udcdd Contribution Process\n\n1. **Comment below** if interested (mention which role)\n2. **Join discussion** on implementation details\n3. **Fork repo** and work on assigned section\n4. **Submit PR** following SGLang cookbook standards\n5. **Iterate** based on review feedback\n\n## \ud83d\udd17 References\n\n- [SGLang Cookbook Template](https://cookbook.sglang.io/)\n- [DeepSeek-V3 Example](https://cookbook.sglang.io/docs/DeepSeek/DeepSeek-V3_2)\n- [Wan 2.1 GitHub](https://github.com/Wan-Video/Wan2.1)\n- [SGLang Documentation](https://docs.sglang.ai/)\n\n## \u2753 Questions?\n\n**Q: I only have consumer GPUs (4090/3090), can I help?** \nA: Yes! You can help with documentation, examples, or testing the 1.3B Wan model. You can reach out @Richardczl98 for requesting additional GPUs\n\n**Q: Which video model should we prioritize first?** \nA: Wan 2.1 - it's the most mature open-source option.\n\n**Q: Do I need to know SGLang internals?** \nA: No, just familiarity with diffusion models and Python.\n\n---\n\n**Ready to contribute?** Drop a comment below! \ud83d\ude80\n\ncc @mickqian @Qiaolin-Yu @yhyang201 ",
"url": "https://github.com/sgl-project/sglang/issues/15443",
"state": "open",
"labels": [],
"created_at": "2025-12-19T03:44:33Z",
"updated_at": "2025-12-23T13:09:31Z",
"comments": 1,
"user": "Richardczl98"
},
{
"repo": "vllm-project/vllm",
"number": 30969,
"title": "[Bug]: SmolLM3-3B FP8 Fails to Load [`compressed-tensors` and `transformers-impl` compatibility issue]",
"body": "### Your current environment\n\nThe output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThe output of
\n\nRunning in official Docker image: vllm/vllm-openai:v0.11.1\nGPU: NVIDIA L4 (GCP g2-standard-8)\n`| NVIDIA-SMI 570.195.03 Driver Version: 570.195.03 CUDA Version: 12.9 |`\nvLLM version: 0.11.1\n\n```text\n0.11.1\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nvLLM v0.11.1 fails to load SmolLM3-3B FP8 quantized models with llm-compressor using compressed-tensors.\nSame models work on v0.11.0.\n\nTested with:\n- [huggingface.co/RedHatAI/SmolLM3-3B-FP8-dynamic](https://huggingface.co/RedHatAI/SmolLM3-3B-FP8-dynamic)\n- Manually quantized fine tuned [SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) using llmcompressor==0.7 (compressed-tensors==0.12.2) in FP8-dynamic\n- Manually quantized fine tuned [SmolLM3-3B(https://huggingface.co/HuggingFaceTB/SmolLM3-3B) using llmcompressor==0.8.1 (compressed-tensors==0.12.2) in FP8-dynamic\n\nAll fail on v0.11.1.\nAll work on v0.11.0.\n\nError occurs during model loading in find_matched_target function.\nThe error is: \"Unable to find matching target for model.layers.0.self_attn.q_proj in the compressed-tensors config\"\n\nComplete error\n```\n+ exec python3 -m vllm.entrypoints.openai.api_server --model RedHatAI/SmolLM3-3B-FP8-dynamic --port 8000 --trust-remote-code --max-model-len 5000\n[APIServer pid=1] INFO 12-12 05:05:29 [api_server.py:1772] vLLM API server version 0.11.1\n[APIServer pid=1] INFO 12-12 05:05:29 [utils.py:253] non-default args: {'model': 'RedHatAI/SmolLM3-3B-FP8-dynamic', 'trust_remote_code': True, 'max_model_len': 5000}\n[APIServer pid=1] The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n[APIServer pid=1] INFO 12-12 05:05:40 [model.py:637] Resolved architecture: SmolLM3ForCausalLM\n[APIServer pid=1] INFO 12-12 05:05:40 [model.py:1750] Using max model len 5000\n[APIServer pid=1] INFO 12-12 05:05:42 [scheduler.py:228] Chunked prefill is enabled with max_num_batched_tokens=2048.\n[EngineCore_DP0 pid=37] INFO 12-12 05:05:54 [core.py:93] Initializing a V1 LLM engine (v0.11.1) with config: model='RedHatAI/SmolLM3-3B-FP8-dynamic', quantization=compressed-tensors\n[EngineCore_DP0 pid=37] INFO 12-12 05:05:55 [parallel_state.py:1200] world_size=1 rank=0 local_rank=0 distributed_init_method=tcp://10.111.66.205:48123 backend=nccl\n[EngineCore_DP0 pid=37] INFO 12-12 05:05:55 [parallel_state.py:1408] rank 0 in world size 1 is assigned as DP rank 0, PP rank 0, PCP rank 0, TP rank 0, EP rank 0\n[EngineCore_DP0 pid=37] INFO 12-12 05:05:55 [gpu_model_runner.py:3467] Starting to load model RedHatAI/SmolLM3-3B-FP8-dynamic...\n[EngineCore_DP0 pid=37] INFO 12-12 05:05:56 [base.py:121] Using Transformers modeling backend.\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] EngineCore failed to start.\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] Traceback (most recent call last):\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py\", line 834, in run_engine_core\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] engine_core = EngineCoreProc(*args, **kwargs)\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py\", line 610, in __init__\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core.py\", line 102, in __init__\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] super().__init__(\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/executor/abstract.py\", line 101, in __init__\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] self.model_executor = executor_class(vllm_config)\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/executor/uniproc_executor.py\", line 48, in _init_executor\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] self._init_executor()\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_worker.py\", line 273, in load_model\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] self.driver_worker.load_model()\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py\", line 3484, in load_model\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] self.model_runner.load_model(eep_scale_up=eep_scale_up)\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] File \"/usr/local/lib/python3.12/dist-packages/vllm/model_executor/model_loader/base_loader.py\", line 49, in load_model\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [core.py:843] self.model = model_loader.load_model(\n[EngineCore_DP0 pid=37] ERROR 12-12 05:05:56 [cor",
"url": "https://github.com/vllm-project/vllm/issues/30969",
"state": "closed",
"labels": [
"bug",
"help wanted",
"good first issue"
],
"created_at": "2025-12-18T14:36:30Z",
"updated_at": "2025-12-20T21:54:47Z",
"comments": 3,
"user": "GauthierRoy"
},
{
"repo": "huggingface/lerobot",
"number": 2680,
"title": "Invalid frame index when training on merged datasets [RuntimeError]",
"body": "### Ticket Type\n\n\ud83d\udc1b Bug Report (Something isn't working)\n\n### Environment & System Info\n\n```Shell\n- LeRobot version: 0.4.3\n- Platform: Linux-5.4.0-165-generic-x86_64-with-glibc2.35\n- Python version: 3.10.12\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 2.2.6\n- FFmpeg version: 4.4.2-0ubuntu0.22.04.1\n- PyTorch version: 2.7.1+cu126\n- Is PyTorch built with CUDA support?: True\n- Cuda version: 12.6\n- GPU model: Quadro RTX 6000\n- Using GPU in script?: The output of python collect_env.py
\n\nRunning in official Docker image: vllm/vllm-openai:v0.11.1\nGPU: NVIDIA L4 (GCP g2-standard-8)\n`| NVIDIA-SMI 570.195.03 Driver Version: 570.195.03 CUDA Version: 12.9 |`\nvLLM version: 0.11.1\n\n```text\n0.11.1\n```\n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nI use https://github.com/vllm-project/recipes/blob/main/DeepSeek/DeepSeek-OCR.md\nthe offline and online mehtod is work, run ok\u3002\nbut the same picture in offline is better than online, I can't find the reason what happend ? can someone help me \n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30923",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-12-18T04:14:33Z",
"updated_at": "2025-12-18T04:25:20Z",
"comments": 0,
"user": "git-liweichao"
},
{
"repo": "vllm-project/vllm",
"number": 30922,
"title": "[Bug]: Use the offical doucment vllm online method deploy DeepSeek-OCR\uff0cthe result is very bad . but I ust the offline method the result is normal. why ?",
"body": "### Your current environment\n\nThe output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nI use https://github.com/vllm-project/recipes/blob/main/DeepSeek/DeepSeek-OCR.md\nthe offline and online mehtod is work, run ok\u3002\nbut the same picture in offline is better than online, I can't find the reason what happend ? can someone help me \n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30922",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-18T04:08:46Z",
"updated_at": "2025-12-18T04:25:36Z",
"comments": 1,
"user": "git-liweichao"
},
{
"repo": "sgl-project/sglang",
"number": 15359,
"title": "[Bug] The handling logic for tool_choice = 'auto' in the DeepseekV3.2 model may be incorrect.",
"body": "### Checklist\n\n- [ ] I searched related issues but found no solution.\n- [ ] The bug persists in the latest version.\n- [ ] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.\n- [ ] If this is not a bug report but a general question, please start a discussion at https://github.com/sgl-project/sglang/discussions. Otherwise, it will be closed.\n- [ ] Please use English. Otherwise, it will be closed.\n\n### Describe the bug\n\nWhen using SGLang (sglang:v0.5.6.post2) with DeepseekV3.2, I noticed the response of some request which involves tool calls is not currect.\nlike the following requests\n```sh\ncurl -X POST http://{host}:{port}/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer sk-1234\" \\\n -d '{\n \"model\": \"DeepseekV3.2\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"What is the weather in Beijing?\"\n }\n ],\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_current_weather\",\n \"description\": \"Get the current weather in a given location\",\n \"strict\": true,\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"The city and state, e.g. San Francisco, CA\"\n },\n \"unit\": {\n \"type\": \"string\",\n \"enum\": [\"celsius\", \"fahrenheit\"]\n }\n },\n \"required\": [\"location\"]\n }\n }\n }\n ],\n \"tool_choice\": \"auto\",\n \"stream\": false\n }'\n``` \nmight response something like \n```sh\n{\"id\":\"88c2a168ad43446f9116aeed715cd835\",\"object\":\"chat.completion\",\"created\":1766024807,\"model\":\"DeepseekV3.2\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"tool_call_name=current_weather\",\"reasoning_content\":null,\"tool_calls\":null},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":1}],\"usage\":{\"prompt_tokens\":198,\"total_tokens\":206,\"completion_tokens\":8,\"prompt_tokens_details\":null,\"reasoning_tokens\":0},\"metadata\":{\"weight_version\":\"default\"}}\n```\nor\n```sh\n{\"id\":\"0223b02af05b4c9b99e8b9e4b2abab12\",\"object\":\"chat.completion\",\"created\":1766026261,\"model\":\"DeepseekV3.2\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"tool_call_name: get_current_weather\\ntool_call_arguments: {\\n \\\"location\\\": \\\"Beijing, China\\\",\\n \\\"unit\\\": \\\"celsius\\\"\\n}\",\"reasoning_content\":null,\"tool_calls\":null},\"logprobs\":null,\"finish_reason\":\"stop\",\"matched_stop\":1}],\"usage\":{\"prompt_tokens\":198,\"total_tokens\":232,\"completion_tokens\":34,\"prompt_tokens_details\":null,\"reasoning_tokens\":0},\"metadata\":{\"weight_version\":\"default\"}}\n```\nAs you can see from the response, the content value contains `tool_call_name` but tool_calls is set to `null`\n\nAnd if change tool_choice to 'required', the response looks like \n```sh\n{\"id\":\"550109a7f6854af3ba47fdad4f38f9d5\",\"object\":\"chat.completion\",\"created\":1766025639,\"model\":\"DeepseekV3.2\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":null,\"reasoning_content\":null,\"tool_calls\":[{\"id\":\"call_f171fbf82d7d41dab0eaf258\",\"index\":0,\"type\":\"function\",\"function\":{\"name\":\"get_current_weather\",\"arguments\":\"{\\\"location\\\": \\\"Beijing, China\\\", \\\"unit\\\": \\\"celsius\\\"}\"}}]},\"logprobs\":null,\"finish_reason\":\"tool_calls\",\"matched_stop\":null}],\"usage\":{\"prompt_tokens\":198,\"total_tokens\":229,\"completion_tokens\":31,\"prompt_tokens_details\":null,\"reasoning_tokens\":0},\"metadata\":{\"weight_version\":\"default\"}}\n```\n\nand when checking the source codes, I find it might be related to the following codes\nhttps://github.com/sgl-project/sglang/blob/9e7656be80578fe981a723bd115373371a9d0d90/python/sglang/srt/entrypoints/openai/serving_chat.py#L248-L260\n\nhttps://github.com/sgl-project/sglang/blob/9e7656be80578fe981a723bd115373371a9d0d90/python/sglang/srt/function_call/function_call_parser.py#L189-L201\n\n\n### Reproduction\n\nstart SGLang with the following command\n```sh\npython3 -m sglang.launch_server --model /root/.cache/huggingface/DeepSeek-V3.2 --served-model-name VILLM-N2 --tp 8 --ep 8 --dp 8 --enable-dp-attention --trust-remote-code --port 30000 --host 0.0.0.0 --enable-metrics --mem-fraction-static 0.75 --cuda-graph-max-bs 128 --torch-compile-max-bs 8 --speculative-algorithm EAGLE --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 --nsa-prefill-backend flashmla_sparse --nsa-decode-backend fa3 --grammar-backend xgrammar --reasoning-parser deepseek-v3 --tool-call-parser deepseekv32 --chat-template ./examples/chat_template/tool_chat_template_deepseekv32.jinja\n```\n\nsend request\n```sh\ncurl -X POST http://{host}:{port}/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -H \"Authorization: Bearer sk-1234\" \\\n -d '{\n \"model\": \"DeepseekV3.2\",\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"What is the weather in Beijing?\"\n }\n ],\n \"tools\": [\n ",
"url": "https://github.com/sgl-project/sglang/issues/15359",
"state": "closed",
"labels": [],
"created_at": "2025-12-18T02:47:26Z",
"updated_at": "2025-12-18T03:36:38Z",
"comments": 4,
"user": "JerryKwan"
},
{
"repo": "huggingface/lerobot",
"number": 2673,
"title": "Dataset v2 not working anymore",
"body": "### Ticket Type\n\nFeature\n\n### Environment & System Info\n\n```Shell\n- LeRobot version: 0.4.3\n- Platform: macOS-26.2-arm64-arm-64bit\n- Python version: 3.10.19\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 2.2.6\n- FFmpeg version: 7.1.1\n- PyTorch version: 2.7.1\n- Is PyTorch built with CUDA support?: False\n- Cuda version: N/A\n- GPU model: N/A\n- Using GPU in script?: The output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\n```bash\nMODEL := \"Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8\"\n#MODEL := \"RedHatAI/Mixtral-8x7B-Instruct-v0.1-FP8\"\n\nlaunch_marlin:\n\tVLLM_TEST_FORCE_FP8_MARLIN=1 VLLM_USE_DEEPGEMM=0 chg run --gpus 1 -- vllm serve {{MODEL}} --enforce-eager --max-model-len 8192\n\neval:\n\tlm_eval \\\n\t\t--model local-completions \\\n\t\t--tasks gsm8k \\\n\t\t--model_args \"model={{MODEL}},base_url=http://localhost:8000/v1/completions,num_concurrent=1000,tokenized_requests=False\"\n```\n\nResult:\n\n```bash\n(vllm) [robertgshaw2-redhat@nm-automation-h100-standalone-1-preserve vllm]$ just launch_marlin\nVLLM_TEST_FORCE_FP8_MARLIN=1 VLLM_USE_DEEPGEMM=0 chg run --gpus 1 -- vllm serve Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 --enforce-eager --max-model-len 8192\nReserved 1 GPU(s): [1] for command execution\n(APIServer pid=3634068) INFO 12-17 15:54:23 [api_server.py:1259] vLLM API server version 0.13.0rc2.dev185+g00a8d7628\n(APIServer pid=3634068) INFO 12-17 15:54:23 [utils.py:253] non-default args: {'model_tag': 'Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8', 'model': 'Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8', 'max_model_len': 8192, 'enforce_eager': True}\n(APIServer pid=3634068) INFO 12-17 15:54:23 [model.py:514] Resolved architecture: Qwen3MoeForCausalLM\n(APIServer pid=3634068) INFO 12-17 15:54:23 [model.py:1661] Using max model len 8192\n(APIServer pid=3634068) INFO 12-17 15:54:24 [scheduler.py:230] Chunked prefill is enabled with max_num_batched_tokens=8192.\n(APIServer pid=3634068) WARNING 12-17 15:54:24 [vllm.py:622] Enforce eager set, overriding optimization level to -O0\n(APIServer pid=3634068) INFO 12-17 15:54:24 [vllm.py:722] Cudagraph is disabled under eager mode\n(EngineCore_DP0 pid=3634329) INFO 12-17 15:54:31 [core.py:93] Initializing a V1 LLM engine (v0.13.0rc2.dev185+g00a8d7628) with config: model='Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8', speculative_config=None, tokenizer='Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=8192, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=fp8, enforce_eager=True, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='', reasoning_parser_plugin='', enable_in_reasoning=False), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, kv_cache_metrics=False, kv_cache_metrics_sample=0.01, cudagraph_metrics=False, enable_layerwise_nvtx_tracing=False), seed=0, served_model_name=Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8, enable_prefix_caching=True, enable_chunked_prefill=True, pooler_config=None, compilation_config={'level': None, 'mode': The output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nvllm serve Qwen3-VL-8B --video-pruning-rate=0.75 \n\nmessages=[\n {\n \"role\": \"user\",\n \"content\": [\n # {\"type\": \"text\", \"text\": \"What's in this video?\"},\n {\"type\": \"text\", \"text\": \"\u8fd9\u4e2a\u89c6\u9891\u548c\u56fe\u7247\u5206\u522b\u63cf\u8ff0\u7684\u662f\u4ec0\u4e48\u5185\u5bb9?\"},\n {\n \"type\": \"video_url\",\n \"video_url\": {\n \"url\": \"file:///codes/data/video/Tom_Jerry.mp4\",\n \"fps\": 1,\n },\n }\n ],\n }\n ],\nThe output of
\n\n\n\n\n\n```text\nThe get-video_deplacement_qwen3vl method in the qwen3-vl.py file\nFirstly: Calculate the number of frames per frame\nSecondly, add the specific timestamp of<{cur_time:. 1f} s>to the Prompt and add the calculated number of tokens after the timestamp.\nAt this point, the number of tokens per frame is calculated based on the clipping rate, so except for the first frame, the number of tokens after each frame remains unchanged (EVS is not used to calculate the actual tokens here).\n\nThe EVS algorithm calculates that the number of tokens reserved for each frame is different. It will cause the number of tokens after timestamp to be inconsistent with the actual number of tokens after clipping\n```\n\n
\n\u3001\n\n### \ud83d\udc1b Describe the bug\n\n\n1\u3001get_video_replacement_qwen3vl \nframes_idx_token=[165, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33]\n\nThe output of python collect_env.py
\n\n\n\n\n\n```text\nThe get-video_deplacement_qwen3vl method in the qwen3-vl.py file\nFirstly: Calculate the number of frames per frame\nSecondly, add the specific timestamp of<{cur_time:. 1f} s>to the Prompt and add the calculated number of tokens after the timestamp.\nAt this point, the number of tokens per frame is calculated based on the clipping rate, so except for the first frame, the number of tokens after each frame remains unchanged (EVS is not used to calculate the actual tokens here).\n\nThe EVS algorithm calculates that the number of tokens reserved for each frame is different. It will cause the number of tokens after timestamp to be inconsistent with the actual number of tokens after clipping\n```\n\n\nThe output of
\n\nPyTorch version: 2.9.0+cu128\nvLLM version: 0.12.0 (vllm-tpu)\nJAX version: 0.8.0\nPython version: 3.12.8 (main, Jan 14 2025, 22:49:14) [Clang 19.1.6]\n\nTPU: v5litepod-4 (4 chips, single host)\nOS: Amazon Linux 2023 (container)\nContainer runtime: Podman with --privileged --net=host\n\nAdditional packages:\n- tpu_inference (bundled with vllm-tpu)\n- flax (from tpu_inference deps)\n- orbax-checkpoint: 0.11.28\n- safetensors: 0.4.5\n- transformers: 4.57.3
\n\n\n\n### \ud83d\udc1b Describe the bug\n\nvLLM-TPU fails to load a **local HuggingFace checkpoint** (safetensors format) on TPU v5litepod with this error:\n\n```\nTypeError: Argument 'model.states[0][6]' of shape bfloat16[128] of type The output of python collect_env.py
\n\nPyTorch version: 2.9.0+cu128\nvLLM version: 0.12.0 (vllm-tpu)\nJAX version: 0.8.0\nPython version: 3.12.8 (main, Jan 14 2025, 22:49:14) [Clang 19.1.6]\n\nTPU: v5litepod-4 (4 chips, single host)\nOS: Amazon Linux 2023 (container)\nContainer runtime: Podman with --privileged --net=host\n\nAdditional packages:\n- tpu_inference (bundled with vllm-tpu)\n- flax (from tpu_inference deps)\n- orbax-checkpoint: 0.11.28\n- safetensors: 0.4.5\n- transformers: 4.57.3\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nGpt-oss in streaming mode cannot see internal code interpreter output \n\nthe problem is with https://github.com/vllm-project/vllm/blob/af0444bf40b7db2f3fb9fe1508d25ceba24cac87/vllm/entrypoints/context.py#L720-L732\n\nI can see that tool call result is not appended to message.\n\n\nMy basic testing code looks like this\n```python\nstream = client.responses.create(\n model=\"vllm-model\",\n input=[{\"role\": \"user\", \"content\": \"what is 123^456 mod 1000000007? use python tool to solve this problem\"}],\n tools=[{\"type\": \"code_interpreter\", \"container\": {\"type\": \"auto\"}}],\n max_output_tokens=32768,\n temperature=1.0,\n reasoning={\"effort\": \"high\"},\n stream=True,\n instructions=system_prompt,\n extra_body={\n \"min_p\": 0.02,\n \"stop_token_ids\": stop_token_ids,\n \"chat_template_kwargs\": {\"enable_thinking\": True},\n }\n)\n\ncurrent_tool_code = \"\"\n\nfor event in stream:\n generation_idx += 1\n\n # Reasoning text\n if event.type == \"response.reasoning_text.delta\":\n delta = event.delta\n reasoning_response += delta\n text_response += delta\n print(delta, end=\"\", flush=True) # Real-time output\n\n # Message text\n elif event.type == \"response.output_text.delta\":\n delta = event.delta\n text_response += delta\n print(delta, end=\"\", flush=True)\n\n # Tool call events\n elif event.type == \"response.code_interpreter_call_code.delta\":\n current_tool_code += event.delta\n\n elif event.type == \"response.code_interpreter_call_code.done\":\n tool_calls_log.append({\n \"code\": event.code,\n \"type\": \"code_interpreter\"\n })\n current_tool_code = \"\"\n print(event.code)\n\n elif event.type == \"response.completed\":\n # Final event - could extract full response here if needed\n pass\n```\n\n\nmodel response (ignore the pretty looking, it is just another version for visualization)\n```bash\n============================================================\n\n\n\ud83d\udcad REASONING:\nWe need to compute 123^456 mod 1000000007. It's a big power but within modular exponent. We can compute quickly with pow in Python: pow(123, 456, 1000000007). But the prompt says please use python tool to solve this problem. We'll use python.\n\ud83d\udcdd CODE EXECUTED:\npow(123, 456, 1000000007)\n\n------------------------------------------------------------\n\n\n\ud83d\udcad REASONING:\nLet's see result.\n\n\ud83d\udcad REASONING:\nIt printed something? Wait, no output visible yet. We may need to capture the output. Let's assign.\n\ud83d\udcdd CODE EXECUTED:\nresult = pow(123, 456, 1000000007)\nresult\n\n------------------------------------------------------------\n\n\n\ud83d\udcad REASONING:\nIt returned something? Let's see.\n\n\ud83d\udcad REASONING:\nIt didn't print, but the value is stored. We should print the result.\n\ud83d\udcdd CODE EXECUTED:\nprint(result)\n\n------------------------------------------------------------\n\n\n\ud83d\udcad REASONING:\n565291922\nSo answer is 565291922. Provide box.\n\n\ud83d\udcc4 FINAL ANSWER:\nThe value of \\(123^{456} \\bmod 1000000007\\) is \n\\[\n\\boxed{565291922}\n\\]\n============================================================\n\u2705 RESPONSE COMPLETED\nTool output tokens: 82\n\n============================================================\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30222",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-08T01:32:35Z",
"updated_at": "2025-12-08T09:49:55Z",
"comments": 4,
"user": "jordane95"
},
{
"repo": "vllm-project/vllm",
"number": 30211,
"title": "[Bug]: How to make vLLM support multi stream torch compile and each stream capture cuda graph.",
"body": "### Your current environment\n\nThe output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nSGLang now supports multi stream torch compile and each stream capture cuda graph. The code link is \n\nhttps://github.com/sgl-project/sglang/blob/main/python/sglang/srt/model_executor/cuda_graph_runner.py#L500-#L506\n\nIf I want to make vLLM support that. My code on vLLM bypass the vLLM backend and make it like sglang\n\n```\nimport torch._dynamo.config\nimport torch._inductor.config\n\ntorch._inductor.config.coordinate_descent_tuning = True\ntorch._inductor.config.triton.unique_kernel_names = True\ntorch._inductor.config.freezing = True \ntorch._inductor.config.fx_graph_cache = False # Experimental feature to reduce compilation times, will be on by default in future\n\nfrom vllm.model_executor.custom_op import CustomOp\n\ndef _to_torch(model: torch.nn.Module, reverse: bool, num_tokens: int):\n for sub in model._modules.values():\n # sub.enter_torch_compile(num_tokens=num_tokens)\n # if isinstance(sub, torch.nn.Module):\n # _to_torch(sub, reverse, num_tokens)\n if isinstance(sub, CustomOp):\n if reverse:\n sub.leave_torch_compile()\n else:\n sub.enter_torch_compile(num_tokens=num_tokens)\n if isinstance(sub, torch.nn.Module):\n _to_torch(sub, reverse, num_tokens)\n\n\n@contextmanager\ndef patch_model(\n model: torch.nn.Module,\n enable_compile: bool,\n num_tokens: int,\n # tp_group: GroupCoordinator,\n):\n \"\"\"Patch the model to make it compatible with with torch.compile\"\"\"\n backup_ca_comm = None\n current_stream = torch.cuda.current_stream()\n with torch.cuda.stream(current_stream):\n print(f\"patch_model, the current_stream:{current_stream.cuda_stream}\", flush = True)\n try:\n if enable_compile:\n _to_torch(model, reverse=False, num_tokens=num_tokens)\n # backup_ca_comm = tp_group.ca_comm\n # Use custom-allreduce here.\n # We found the custom allreduce is much faster than the built-in allreduce in torch,\n # even with ENABLE_INTRA_NODE_COMM=1.\n # tp_group.ca_comm = None\n wrapped_forward = model.forward # \ud83d\udd25 \u53ea\u6539\u8fd9\u91cc\n with torch.no_grad():\n compiled = torch.compile(wrapped_forward, mode=\"max-autotune-no-cudagraphs\", dynamic=False)\n yield compiled \n # yield torch.compile(\n # model.forward,\n # mode=\"max-autotune-no-cudagraphs\",\n # dynamic=False,)\n # yield torch.compile(\n # torch.no_grad()(model.forward),\n # mode=\"reduce-overhead\",\n # dynamic=_is_hip and get_bool_env_var(\"SGLANG_TORCH_DYNAMIC_SHAPE\"),\n # )\n else:\n yield model.forward\n finally:\n if enable_compile:\n _to_torch(model, reverse=True, num_tokens=num_tokens)\n\n\n \n @torch.inference_mode()\n def _my_dummy_run(\n self,\n num_tokens: int,\n run_decode_phase:bool=False,\n stream_idx: int = 0,\n ) -> torch.Tensor:\n # Set num_scheduled_tokens based on num_tokens and max_num_seqs\n # for dummy run with LoRA so that the num_reqs collectively\n # has num_tokens in total.\n with torch.cuda.stream(torch.cuda.current_stream()):\n assert num_tokens <= self.scheduler_config.max_num_batched_tokens\n max_num_reqs = self.scheduler_config.max_num_seqs\n num_reqs = max_num_reqs if num_tokens >= max_num_reqs else num_tokens\n min_tokens_per_req = num_tokens // num_reqs\n num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs\n num_scheduled_tokens_list[-1] += num_tokens % num_reqs\n assert sum(num_scheduled_tokens_list) == num_tokens\n assert len(num_scheduled_tokens_list) == num_reqs\n num_scheduled_tokens = np.array(num_scheduled_tokens_list,\n dtype=np.int32)\n\n with self.maybe_dummy_run_with_lora(self.lora_config,\n num_scheduled_tokens):\n model = self.model\n if self.is_multimodal_model:\n input_ids = None\n inputs_embeds = self.inputs_embeds[:num_tokens]\n else:\n input_ids = self.input_ids[:num_tokens]\n inputs_embeds = None\n if self.uses_mrope:\n positions = self.mrope_positions[:, :num_tokens]\n else:\n positions = self.positions[:num_tokens]\n\n if get_pp_group().is_first_rank:\n intermediate_tensors = None\n else:\n ",
"url": "https://github.com/vllm-project/vllm/issues/30211",
"state": "open",
"labels": [
"bug",
"feature request",
"nvidia"
],
"created_at": "2025-12-07T15:12:04Z",
"updated_at": "2025-12-15T05:39:39Z",
"comments": 3,
"user": "lambda7xx"
},
{
"repo": "vllm-project/vllm",
"number": 30193,
"title": "[Bug]: Behavioral Difference in hidden_states[-1] between vLLM and Transformers for Qwen3VLForConditionalGeneration",
"body": "### Your current environment\n- vLLM Version: 0.11.2\n- Transformers Version: 4.57\n- Model: Qwen3VLForConditionalGeneration\n\n### \ud83d\udc1b Describe the bug\nI have observed an inconsistency in the output of the forward method for the `Qwen3VLForConditionalGeneration` class between vLLM (version 0.11.2) and Transformers (version 4.57).\n\nIn the Transformers library, the last hidden state (`outputs.hidden_states[0, -1, :]`) returned is before the final layer normalization. However, in vLLM, the returned hidden_states appears to be after the normalization is applied.\n\nIs this discrepancy an unintended bug, or is there a configuration option in vLLM to control this output behavior (e.g., to return the pre-norm hidden states)?\n\nI don't have minimal demo, but I change the origin code to test.\n\nBecause the`forward` method of `Qwen3VLForConditionalGeneration` has the following code:\n```python\n hidden_states = self.language_model.model(\n input_ids=input_ids,\n positions=positions,\n intermediate_tensors=intermediate_tensors,\n inputs_embeds=inputs_embeds,\n # args for deepstack\n deepstack_input_embeds=deepstack_input_embeds,\n )\n```\nThe type of `self.language_model.model` is `Qwen3LLMModel`.\n\nI introduced an environment variable:`LAST_HIDDEN_STATE_NOT_NORM` before return of `Qwen3LLMModel` 's `forward` method:\n```python\n if os.environ.get(\"LAST_HIDDEN_STATE_NOT_NORM\", \"0\") == \"1\":\n return hidden_states + residual\n\n if not get_pp_group().is_last_rank:\n return IntermediateTensors(\n {\"hidden_states\": hidden_states, \"residual\": residual}\n )\n hidden_states, _ = self.norm(hidden_states, residual)\n return hidden_states\n```\n\nWhen `LAST_HIDDEN_STATE_NOT_NORM=1` is set, hidden states output exactly match Transformers' behavior.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30193",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-12-07T04:50:11Z",
"updated_at": "2025-12-16T03:24:00Z",
"comments": 3,
"user": "guodongxiaren"
},
{
"repo": "huggingface/transformers",
"number": 42674,
"title": "Missing imports for DetrLoss and DetrHungarianMatcher",
"body": "Previously, I was able to import these classes as \n```\nfrom transformers.models.detr.modeling_detr import DetrLoss, DetrObjectDetectionOutput, DetrHungarianMatcher\n```\n\nIn v4.57.3, the import fails and I also cannot find DetrLoss or DetrHungarianMatcher anywhere in the codebase. Have they been removed/replaced with an alternative? What is the up-to-date import?\n\nThank you for assistance / information",
"url": "https://github.com/huggingface/transformers/issues/42674",
"state": "open",
"labels": [],
"created_at": "2025-12-06T15:32:14Z",
"updated_at": "2026-01-06T08:02:43Z",
"comments": 1,
"user": "sammlapp"
},
{
"repo": "vllm-project/vllm",
"number": 30163,
"title": "[Usage]: Help Running NVFP4 model on 2x DGX Spark with vLLM + Ray (multi-node)",
"body": "### Your current environment\n\n# Help: Running NVFP4 model on 2x DGX Spark with vLLM + Ray (multi-node)\n\n## Hardware\n- **2x DGX Spark** (GB10 GPU each, sm_121a / compute capability 12.1)\n- Connected via 200GbE ConnectX-7/Ethernet\n- Driver: 580.95.05, Host CUDA: 13.0\n\n## Goal\nRun `lukealonso/GLM-4.6-NVFP4` (357B MoE model, NVFP4 quantization) across both nodes using vLLM with Ray distributed backend.\n\n## What I've Tried\n\n### 1. `nvcr.io/nvidia/vllm:25.11-py3` (NGC)\n- vLLM 0.11.0\n- **Error:** `FlashInfer kernels unavailable for ModelOptNvFp4FusedMoE on current platform`\n- NVFP4 requires vLLM 0.12.0+\n\n### 2. `vllm/vllm-openai:nightly-aarch64` (vLLM 0.11.2.dev575)\n- With `VLLM_USE_FLASHINFER_MOE_FP4=1`\n- **Error:** `ptxas fatal: Value 'sm_121a' is not defined for option 'gpu-name'`\n- Triton's bundled ptxas 12.8 doesn't support GB10\n\n### 3. `vllm/vllm-openai:v0.12.0-aarch64` (vLLM 0.12.0)\n- Fixed ptxas with symlink: `ln -sf /usr/local/cuda/bin/ptxas /usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/bin/ptxas`\n- Triton compilation passes \u2705\n- **Error:** `RuntimeError: [FP4 gemm Runner] Failed to run cutlass FP4 gemm on sm120. Error: Error Internal`\n\n### 4. Tried both parallelism modes:\n- `--tensor-parallel-size 2` \u2192 same CUTLASS error\n- `--pipeline-parallel-size 2` \u2192 same CUTLASS error\n\n### 5. `--enforce-eager` flag\n- Not fully tested yet\n\n## Environment Details\n| Component | Version |\n|-----------|---------|\n| Host Driver | 580.95.05 |\n| Host CUDA | 13.0 |\n| Container CUDA | 12.9 |\n| Container ptxas | 12.9.86 (supports sm_121a \u2705) |\n| Triton bundled ptxas | 12.8 (NO sm_121a \u274c) |\n| PyTorch | 2.9.0+cu129 |\n\n## The Blocking Error\n\nvLLM correctly loads weights (41/41 shards), then during profile_run:\n\n```\nINFO [flashinfer_utils.py:289] Flashinfer TRTLLM MOE backend is only supported on SM100 and later, using CUTLASS backend instead\nINFO [modelopt.py:1142] Using FlashInfer CUTLASS kernels for ModelOptNvFp4FusedMoE.\n...\nRuntimeError: [FP4 gemm Runner] Failed to run cutlass FP4 gemm on sm120. Error: Error Internal\n```\n\nFlashInfer detects GB10 is not SM100 (B200), falls back to CUTLASS - but CUTLASS FP4 also fails.\n\n## Key Question\n\n**Are CUTLASS FP4 GEMM kernels compiled for GB10 (sm_121a)?**\n\nIs there:\n1. A vLLM build with CUTLASS kernels for sm_121?\n2. A way to force Marlin FP4 fallback on GB10?\n3. Recommended Docker image for DGX Spark + NVFP4?\n\nI see NVFP4 models tested on:\n- B200 (sm_100) \u2705\n- H100/A100 with Marlin FP4 fallback \u2705\n\nBut GB10 is **sm_121** (Blackwell desktop/workstation variant). The error says `sm120` which seems wrong - GB10 should be sm_121a.\n\n\n\n## References\n- [ GLM-4.6-NVFP4](https://huggingface.co/lukealonso/GLM-4.6-NVFP4)(https://huggingface.co/lukealonso/GLM-4.6-NVFP4)\n\n- [Firworks/GLM-4.5-Air-nvfp4](https://huggingface.co/Firworks/GLM-4.5-Air-nvfp4)\n\nThanks!\n",
"url": "https://github.com/vllm-project/vllm/issues/30163",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-06T00:24:52Z",
"updated_at": "2025-12-07T16:22:40Z",
"comments": 2,
"user": "letsrock85"
},
{
"repo": "huggingface/accelerate",
"number": 3876,
"title": "Why TP can't be used with pure DP?",
"body": "As per [this](https://github.com/huggingface/accelerate/blob/b9ca0de682f25f15357a3f9f1a4d94374a1d451d/src/accelerate/parallelism_config.py#L332), we can not be use TP along with pure DP (or DDP). We need to shard the model across further nodes by specifying dp_shard_size as well. Why this limitation exists? Is it just a software limitation? \nPlease share any documentation, code reference and justification for the same.\n\nWhat to do inorder to do TP+DP?",
"url": "https://github.com/huggingface/accelerate/issues/3876",
"state": "open",
"labels": [],
"created_at": "2025-12-05T16:11:22Z",
"updated_at": "2025-12-26T10:07:09Z",
"comments": 3,
"user": "quic-meetkuma"
},
{
"repo": "huggingface/lerobot",
"number": 2589,
"title": "Clarification on XVLA folding checkpoint",
"body": "Hi Lerobot team, great work on the XVLA release!\n\nI have tried finetuning on my custom dataset and have a few clarifications:\n1. Is the [lerobot/xvla-folding](https://huggingface.co/lerobot/xvla-folding) checkpoint finetuned on [lerobot/xvla-soft-fold](https://huggingface.co/datasets/lerobot/xvla-soft-fold)? \n - I am asking this because the `info.json` don't match (eg. the dataset image keys are `observation.images.cam_high` whereby the checkpoint image keys are `observation.images.image`\n - The `observation.state` shape also do not match\n\n2. How do we finetune from a checkpoint given that the checkpoint expects different naming for the observation keys and `state` shape? Is this a custom preprocessor to remap keys or is there an arg to use?\n\nThanks!",
"url": "https://github.com/huggingface/lerobot/issues/2589",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-12-05T11:42:46Z",
"updated_at": "2025-12-22T08:43:05Z",
"user": "brycegoh"
},
{
"repo": "vllm-project/vllm",
"number": 30129,
"title": "[Feature]: About video input for qwen3vl",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nI tried using base64 encoding to provide video input for vllm inference, but it seems this input method is not yet supported by Qwen3VL (I've seen similar issues reported elsewhere). Currently, I can only specify parameters like fps/maximum frames and then pass the local path or URL of the video.\n\nHowever, in my scenario, my videos are not uniformly sampled; I need to manually sample them first and then input multiple frames. Is there a way to achieve this input method now?\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30129",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-12-05T10:32:06Z",
"updated_at": "2025-12-19T03:32:30Z",
"comments": 4,
"user": "lingcco"
},
{
"repo": "huggingface/sentence-transformers",
"number": 3585,
"title": "How to choose negative instance when using MultipleNegativesRankingLoss train embedding model?",
"body": "Firstly, I am still confused how to choose negative instance if I use MultipleNegativesRankingLoss, in https://github.com/huggingface/sentence-transformers/blob/main/sentence_transformers/losses/MultipleNegativesRankingLoss.py# L113\n`embeddings = [self.model(sentence_feature)[\"sentence_embedding\"] for sentence_feature in sentence_features]\n`\nI guess `embeddings` should include three parts, anchor, positive and negative from in-batch data, however, no matter how I change `batchsize`, I still found `len(embeddings)=2`, is it means that this embeddings only include two parts?\n\n\nHere is my simple training script, I didn't add negative part in dataset,\n```\nimport os\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0,1,2,3\"\nimport json\nimport torch\nfrom sentence_transformers import (\n SentenceTransformer, \n SentenceTransformerTrainer,\n SentenceTransformerTrainingArguments,\n InputExample, \n)\nfrom sentence_transformers.losses import MultipleNegativesRankingLoss\nfrom sentence_transformers.training_args import BatchSamplers\nfrom datasets import load_dataset, Dataset\ndef train_embedding_model():\n train_epo = 3\n save_path = f\"/app/raw_model/tmp\"\n data_path = \"/app/emb_train_1205.json\"\n model = SentenceTransformer(\n \"/app/download_models/Qwen3-Embedding-0.6B\",\n model_kwargs={\n \"attn_implementation\": \"flash_attention_2\",\n \"torch_dtype\": \"auto\"\n }\n )\n model.tokenizer.padding_side = \"left\"\n model.tokenizer.pad_token = model.tokenizer.eos_token\n model.tokenizer.model_max_length = 2048\n\n dataset = load_dataset(\"json\", data_files=data_path)\n '''\n DatasetDict({\n train: Dataset({\n features: ['question', 'positive'],\n num_rows: 4000\n })\n })\n '''\n loss = MultipleNegativesRankingLoss(model)\n args = SentenceTransformerTrainingArguments(\n output_dir=save_path,\n num_train_epochs=train_epo,\n per_device_train_batch_size=8,\n per_device_eval_batch_size=1,\n learning_rate=5e-5,\n warmup_ratio=0.1,\n fp16=True, # Set to False if you get an error that your GPU can't run on FP16\n bf16=False, # Set to True if you have a GPU that supports BF16\n batch_sampler=BatchSamplers.NO_DUPLICATES, # MultipleNegativesRankingLoss benefits from no duplicate samples in a batch \n optim='adamw_torch_fused',\n logging_steps=5,\n )\n\n trainer = SentenceTransformerTrainer(\n model=model,\n args=args,\n train_dataset=dataset['train'], # dataset['train'], train_dataset\n eval_dataset=dataset['train'], # dataset['train'], train_dataset\n loss=loss,\n )\n trainer.train()\n model.save_pretrained(save_path)\n```\n\nBesides\uff0c can I manually add a list of negatives directly into the dataset while still using the MultipleNegativesRankingLoss?",
"url": "https://github.com/huggingface/sentence-transformers/issues/3585",
"state": "open",
"labels": [],
"created_at": "2025-12-05T09:50:26Z",
"updated_at": "2025-12-09T11:49:26Z",
"user": "4daJKong"
},
{
"repo": "vllm-project/vllm",
"number": 30124,
"title": "[Bug]: How to run DeepSeek-V3.2 on 2 H100 nodes?",
"body": "\n\n### \ud83d\udc1b Describe the bug\n\nHow to run DeepSeek-V3.2 on 2 H100 nodes?\nI only found the cmd for H200/B200:\nvllm serve deepseek-ai/DeepSeek-V3.2 -tp 8\n\nbut it does not work in multi-node scenarios (e.g., 2 H100 nodes).\n\nSo what should the cmd be for two H100 nodes?\nhow should params --tp/--dp/--pp be configured?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30124",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-12-05T09:40:45Z",
"updated_at": "2025-12-14T08:57:52Z",
"comments": 2,
"user": "XQZ1120"
},
{
"repo": "vllm-project/vllm",
"number": 30121,
"title": "[Feature]: Could you please provide Chinese documentation for vLLM? \ud83d\ude0a",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nCould you please provide Chinese documentation for vLLM? \ud83d\ude0a\n\n\n\n### Alternatives\n\nCould you please provide Chinese documentation for vLLM? \ud83d\ude0a\n\n### Additional context\n\nCould you please provide Chinese documentation for vLLM? \ud83d\ude0a\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/30121",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-12-05T08:13:46Z",
"updated_at": "2025-12-08T04:31:05Z",
"comments": 4,
"user": "moshilangzi"
},
{
"repo": "huggingface/transformers",
"number": 42641,
"title": "Cannot inference llava-next with transformers==4.57.1 on dtype=\"auto\" bug",
"body": "### System Info\n\n```\n- `transformers` version: 4.57.1\n- Platform: Linux-5.15.0-161-generic-x86_64-with-glibc2.35\n- Python version: 3.10.12\n- Huggingface_hub version: 0.35.3\n- Safetensors version: 0.6.2\n- Accelerate version: 1.10.1\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.8.0+cpu (NA)\n- Tensorflow version (GPU?): 2.18.0 (False)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: The output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nThis bash script cannot be executed a second time, unless the name of the function is changed to a value which was not yet sent. Without tool definition, the POST can be sent as often as you like.\n\n```bash\n#!/bin/bash\ncurl -X POST http://localhost:8000/v1/chat/completions \\\n -H \"Authorization: Bearer ${VLLM_API_KEY}\" \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"openai/gpt-oss-120b\",\n \"stream\": false,\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"Be a helpful assistant.\"\n },\n {\n \"role\": \"user\",\n \"content\": \"Hi\"\n },\n {\n \"role\": \"assistant\",\n \"content\": \"How can I help you?\"\n },\n {\n \"role\": \"user\",\n \"content\": \"Do you like Monty Python?\"\n }\n ],\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"CHANGE-NAME-BEFORE-SENDING\",\n \"description\": \"Use this tool if you need to extract information from a website.\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"url\": {\n \"type\": \"string\",\n \"description\": \"The URL to search or extract information from.\"\n }\n },\n \"required\": [\"url\"]\n }\n }\n }\n ]\n }'\n\n```\n\nThe script doesn't finish waiting for a response and `nvidia-smi` shows the cards consuming max power. The vllm logs show that there are tokens generated, so from an external point of view the LLM seems to generate tokens without stopping.\n\nThe bug is reproducible with docker image vllm/vllm-openai:v0.12.0
\n\n```yaml\nservices:\n vllm-gptoss-large:\n image: vllm/vllm-openai:v0.12.0\n restart: always\n shm_size: '64gb'\n deploy:\n resources:\n reservations:\n devices:\n - driver: nvidia\n device_ids: ['0', '1']\n capabilities: [gpu]\n volumes:\n - ./data/hf:/data\n environment:\n - HF_TOKEN=${HF_TOKEN}\n ports:\n - 8000:8000\n command: [\"openai/gpt-oss-120b\",\n \"--tool-call-parser\",\"openai\",\n \"--enable-auto-tool-choice\",\n \"--reasoning-parser\",\"openai_gptoss\",\n \"--tensor-parallel-size\",\"2\",\n \"--port\",\"8000\",\n \"--api-key\", \"${VLLM_API_KEY}\",\n \"--download_dir\", \"/data\"]\n```\n\nSure! Here are a few GitHub repositories that were created in\u202f2025 (all with their public \u201ccreated\u202fdate\u201d and a short description):
\n\nRepository | Created | Short description | Link\n\nopen\u2011thoughts/open\u2011thoughts | 2025\u201101\u201127 | An open\u2011source platform for curating datasets that can be used to train and evaluate reasoning\u2011oriented language models. | https://github.com/open-thoughts/open-thoughts\n\u00a0...\n```\nBut on https://huggingface.co/playground:\n```\ndo you know any github repository created in 2025?\n\nI don\u2019t have any information about repositories that were created in 2025. My training data only goes up to September\u202f2023, so I can\u2019t see or reference anything that was added to GitHub after that date. If you\u2019re looking for recent projects, you could search GitHub directly or use the GitHub API to filter repositories by creation date.\n```\nI'm asking it here because I don't know where else to ask, I also opened an issue here https://github.com/ggml-org/llama.cpp/discussions/15396#discussioncomment-15136920 .\n\nI've also downloaded the https://huggingface.co/openai/gpt-oss-20b and running locally it doesn't know anything from 2025.\n\n**Based on this I suspect that the model running here https://huggingface.co/chat/models/openai/gpt-oss-20b is not the one that's here https://huggingface.co/openai/gpt-oss-20b .**\n\n**How/Where can we get the version running here https://huggingface.co/chat/models/openai/gpt-oss-20b ?**", "url": "https://github.com/huggingface/inference-playground/issues/102", "state": "open", "labels": [], "created_at": "2025-12-02T17:10:51Z", "updated_at": "2025-12-02T17:10:51Z", "user": "mingodad" }, { "repo": "vllm-project/vllm", "number": 29875, "title": "[Usage]: Is there a way to inject the grammar into the docker directly", "body": "### Your current environment\n\n```text\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version : Could not collect\nCMake version : version 3.28.0\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.19 | packaged by conda-forge | (main, Oct 22 2025, 22:29:10) [GCC 14.3.0] (64-bit runtime)\nPython platform : Linux-6.8.0-1030-azure-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : GPU 0: NVIDIA H100 NVL\nNvidia driver version : 535.247.01\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.10.2\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 40\nOn-line CPU(s) list: 0-39\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 9V84 96-Core Processor\nCPU family: 25\nModel: 17\nThread(s) per core: 1\nCore(s) per socket: 40\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4800.05\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves user_shstk avx512_bf16 clzero xsaveerptr rdpru arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.3 MiB (40 instances)\nL1i cache: 1.3 MiB (40 instances)\nL2 cache: 40 MiB (40 instances)\nL3 cache: 160 MiB (5 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-39\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.5.2\n[pip3] numpy==1.26.4\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n", "url": "https://github.com/vllm-project/vllm/issues/29875", "state": "open", "labels": [ "usage" ], "created_at": "2025-12-02T12:30:56Z", "updated_at": "2025-12-03T11:53:43Z", "comments": 1, "user": "chwundermsft" }, { "repo": "vllm-project/vllm", "number": 29871, "title": "[Usage]: Extremly low token input speed for DeepSeek-R1-Distill-Llama-70B", "body": "### Your current environment\n\n\nThe output of
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (GCC) 14.2.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.4 (main, Aug 29 2025, 09:21:27) [GCC 14.2.0] (64-bit runtime)\nPython platform : Linux-5.15.0-118-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.61\nCUDA_MODULE_LOADING set to :\nGPU models and configuration :\nGPU 0: NVIDIA H100 80GB HBM3\nGPU 1: NVIDIA H100 80GB HBM3\nGPU 2: NVIDIA H100 80GB HBM3\nGPU 3: NVIDIA H100 80GB HBM3\nGPU 4: NVIDIA H100 80GB HBM3\nGPU 5: NVIDIA H100 80GB HBM3\nGPU 6: NVIDIA H100 80GB HBM3\nGPU 7: NVIDIA H100 80GB HBM3\n\nNvidia driver version : 570.158.01\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 9654 96-Core Processor\nCPU family: 25\nModel: 17\nThread(s) per core: 1\nCore(s) per socket: 96\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU max MHz: 3707.8120\nCPU min MHz: 1500.0000\nBogoMIPS: 4793.01\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d\nVirtualization: AMD-V\nL1d cache: 6 MiB (192 instances)\nL1i cache: 6 MiB (192 instances)\nL2 cache: 192 MiB (192 instances)\nL3 cache: 768 MiB (24 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-95\nNUMA node1 CPU(s): 96-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIB",
"url": "https://github.com/vllm-project/vllm/issues/29871",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-02T11:25:25Z",
"updated_at": "2025-12-02T15:30:53Z",
"comments": 2,
"user": "muelphil"
},
{
"repo": "vllm-project/vllm",
"number": 29866,
"title": "[Doc]:",
"body": "### \ud83d\udcda The doc issue\n\n# Installation des biblioth\u00e8ques XAI\n!pip install shap\n!pip install lime\n!pip install alibi\n!pip install interpret\n!pip install dalex\n!pip install eli5\n\n\n### Suggest a potential alternative/fix\n\n# Installation des biblioth\u00e8ques XAI\n!pip install shap\n!pip install lime\n!pip install alibi\n!pip install interpret\n!pip install dalex\n!pip install eli5\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29866",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-12-02T10:43:04Z",
"updated_at": "2025-12-02T10:50:10Z",
"comments": 0,
"user": "hassaballahmahamatahmat5-cpu"
},
{
"repo": "vllm-project/vllm",
"number": 29865,
"title": "[Doc]:",
"body": "### \ud83d\udcda The doc issue\n\n# Installation des biblioth\u00e8ques XAI\n!pip install shap\n!pip install lime\n!pip install alibi\n!pip install interpret\n!pip install dalex\n!pip install eli5\n\n\n### Suggest a potential alternative/fix\n\n# Installation des biblioth\u00e8ques XAI\n!pip install shap\n!pip install lime\n!pip install alibi\n!pip install interpret\n!pip install dalex\n!pip install eli5\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29865",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-12-02T10:43:01Z",
"updated_at": "2025-12-02T10:50:00Z",
"comments": 0,
"user": "hassaballahmahamatahmat5-cpu"
},
{
"repo": "vllm-project/vllm",
"number": 29864,
"title": "[Usage]: I am unable to run the GLM-4.5-Air-REAP-82B-A12B-nvfp4 model on an RTX 5090.",
"body": "### Your current environment\n\n\n I am unable to run the GLM-4.5-Air-REAP-82B-A12B-nvfp4 model on an RTX 5090.\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 12.3.0-1ubuntu1~22.04.2) 12.3.0\nClang version : Could not collect\nCMake version : version 4.2.0\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.10.0.dev20251124+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.12 (main, Nov 4 2025, 08:48:33) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-6.8.0-87-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA GeForce RTX 5090\nGPU 1: NVIDIA GeForce RTX 5090\nGPU 2: NVIDIA GeForce RTX 5090\nGPU 3: NVIDIA GeForce RTX 5090\n\nNvidia driver version : 570.172.08\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 112\nOn-line CPU(s) list: 0-111\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Gold 6330 CPU @ 2.00GHz\nCPU family: 6\nModel: 106\nThread(s) per core: 2\nCore(s) per socket: 28\nSocket(s): 2\nStepping: 6\nCPU max MHz: 3100.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4000.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 2.6 MiB (56 instances)\nL1i cache: 1.8 MiB (56 instances)\nL2 cache: 70 MiB (56 instances)\nL3 cache: 84 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-27,56-83\nNUMA node1 CPU(s): 28-55,84-111\nVulnerability Gather data sampling: Mitigation; Microcode\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n",
"url": "https://github.com/vllm-project/vllm/issues/29864",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-02T10:13:31Z",
"updated_at": "2025-12-05T17:06:30Z",
"comments": 2,
"user": "east612-ai"
},
{
"repo": "huggingface/diffusers",
"number": 12772,
"title": "How to convert diffusers model to wan2.2 format",
"body": "I see convert_wan_to_diffusers.py in diffusers repo, but no convert_diffusers_to_wan.py. Do you have plan to upload a convert scripts?\n",
"url": "https://github.com/huggingface/diffusers/issues/12772",
"state": "open",
"labels": [],
"created_at": "2025-12-02T09:19:29Z",
"updated_at": "2025-12-02T09:19:29Z",
"user": "wikiwen"
},
{
"repo": "huggingface/diffusers",
"number": 12764,
"title": "When will the img2img pipeline of FLUX.2-dev be released?",
"body": "I see that the current version(0.36.0-dev) only updated the text-to-image pipeline for Flux2. We are looking forward to the update of the image-to-image pipeline!\n",
"url": "https://github.com/huggingface/diffusers/issues/12764",
"state": "open",
"labels": [],
"created_at": "2025-12-01T11:25:35Z",
"updated_at": "2025-12-01T11:41:56Z",
"comments": 1,
"user": "guanxyu"
},
{
"repo": "huggingface/smolagents",
"number": 1890,
"title": "Question: how to use sever-side tools provided by Google Gemini or OpenAI GPT?",
"body": "Gemini has some server-side tools like google_search (https://ai.google.dev/gemini-api/docs/google-search) or google_map. OpenAI also has server-side tools like web_search. Does Smolagents support using such server-side tools from agents? If so, how?",
"url": "https://github.com/huggingface/smolagents/issues/1890",
"state": "open",
"labels": [],
"created_at": "2025-12-01T05:16:01Z",
"updated_at": "2025-12-23T10:49:45Z",
"user": "victorx-deckard"
},
{
"repo": "huggingface/agents-course",
"number": 623,
"title": "Message: Submission received, but no valid/matching task IDs were found in the 1 answers provided. Score did not improve previous record, leaderboard not updated.",
"body": "I am correctly downloading the GAIA 2023 Level 1 validation dataset using snapshot_download and load_dataset. This submission is for Unit 4 Agent Course. \n\n data_dir = snapshot_download(\n repo_id=\"gaia-benchmark/GAIA\",\n repo_type=\"dataset\"\n )\n \n dataset = load_dataset(data_dir, \"2023_level1\", split=\"validation\")\n subset = dataset.select(range(20))\n for item in subset:\n task_id = item.get(\"task_id\")\n question_text = item.get(\"Question\")\n file_name = item.get(\"file_name\")\n\nI experience failures when trying to run the first 20 questions i received only 5 task ids are valid.. When I specifically tried to isolate and run the task ID '935e2cff-ae78-4218-b3f5-115589b19dae' using the filtering method, the evaluation system reported. \n\n![\"Image\"](\"https://github.com/user-attachments/assets/f4e9fe1b-8608-4ab4-84cc-1b196a601694\")
\n\n 'Submission received, but no valid/matching task IDs were found in the 1 answers provided.' This occurred even though I was confident the answer was correct",
"url": "https://github.com/huggingface/agents-course/issues/623",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-12-01T02:09:21Z",
"updated_at": "2025-12-01T02:09:21Z",
"user": "ShwetaBorole"
},
{
"repo": "huggingface/tokenizers",
"number": 1902,
"title": "Guide: Compiling `tokenizers` on Android/Termux",
"body": "Hello Hugging Face team and fellow developers,\n\nThis is a guide for anyone trying to install `tokenizers` (or packages that depend on it, like `transformers` or `docling`) on an Android device using [Termux](https://termux.dev/). Currently, there are no other issues mentioning Termux, so hopefully, this guide can help others.\n\n### The Problem\n\nWhen running `pip install tokenizers` in a standard Termux environment, the installation fails during the compilation of a C++ dependency with an error similar to this:\n```\nerror: use of undeclared identifier 'pthread_cond_clockwait'\n```\nThis happens because the build system is targeting an Android API level where this function is not available in the C library headers.\n\n### The Solution\n\nThe solution is to force the compilation from source and pass specific flags to the C++ compiler to set the correct Android API level and link the required libraries.\n\nHere is a step-by-step guide:\n\n#### Step 1: Install Build Dependencies\n\nYou will need the Rust toolchain and other build essentials. You can install them in Termux using `pkg`:\n```bash\npkg update && pkg install rust clang make maturin\n```\n\n#### Step 2: Find Your Android API Level\n\nThe fix requires telling the compiler which Android API level you are using. You can get this number by running the following command in your Termux shell:\n```bash\ngetprop ro.build.version.sdk\n```\nThis will return a number, for example `29`, `30`, `33`, etc. This function (`pthread_cond_clockwait`) was introduced in API level 21, so your device's level should be higher than that.\n\n#### Step 3: Compile and Install `tokenizers`\n\nNow, you can install the package using `pip`. The command below will automatically use the API level from the previous step.\n\n```bash\n# This command automatically gets your API level and uses it to compile tokenizers\nANDROID_API_LEVEL=$(getprop ro.build.version.sdk)\nCXXFLAGS=\"-lpthread -D__ANDROID_API__=${ANDROID_API_LEVEL}\" pip install tokenizers --no-binary :all:\n```\n\nAfter this, `pip install tokenizers` (and packages that depend on it) should succeed.\n\n#### Explanation of the Flags:\n\n* `CXXFLAGS=\"...\"`: This sets environment variables to pass flags to the C++ compiler.\n* `-lpthread`: This flag explicitly tells the linker to link against the POSIX threads library.\n* `-D__ANDROID_API__=${ANDROID_API_LEVEL}`: This is the critical part. It defines a macro that tells the C++ headers to expose functions available for your specific Android version, making `pthread_cond_clockwait` visible to the compiler.\n* `--no-binary :all:`: This forces `pip` to ignore pre-compiled wheels and build the package from the source code, which is necessary for the flags to be applied.\n\nHope this helps other developers working in the Termux environment!",
"url": "https://github.com/huggingface/tokenizers/issues/1902",
"state": "open",
"labels": [],
"created_at": "2025-12-01T00:46:42Z",
"updated_at": "2025-12-01T00:46:42Z",
"comments": 0,
"user": "Manamama-Gemini-Cloud-AI-01"
},
{
"repo": "vllm-project/vllm",
"number": 29747,
"title": "[Bug]: --scheduling-policy=priority & n>1 crashes engine",
"body": "### Your current environment\n\n
\u2022 Dermatology: FDA\u2011cleared apps (e.g., SkinVision) classify skin lesions from photos.
\u2022 Pathology: Paige.ai assists in detecting prostate cancer in biopsy slides. | Faster, more consistent readings; can catch subtle findings that human eyes miss. |\\n| **Predictive Analytics** | Forecast disease onset, complications, readmission risk | \u2022 Sepsis prediction models (e.g., Epic Sepsis Model) trigger alerts hours before clinical signs.
\u2022 Cardiovascular risk calculators incorporating genomics and wearables. | Enables proactive interventions, reduces morbidity and cost. |\\n| **Treatment Planning** | Decision support, dose optimisation, drug selection | \u2022 IBM Watson for Oncology (clinical trial matching).
\u2022 Radiation oncology: AI\u2011driven dose\u2011painting to spare healthy tissue.
\u2022 Pharmacogenomics: AI predicts drug\u2011gene interactions. | Personalises therapy, improves outcomes, reduces adverse events. |\\n| **Robotics & Minimally Invasive Surgery** | Real\u2011time image guidance, autonomous suturing, task automation | \u2022 Da Vinci Surgical System (augmented with AI for instrument tracking).
\u2022 VERDICT AI for autonomous suturing in animal models. | Increases precision, reduces surgeon fatigue, shortens recovery. |\\n\\n---\\n\\n## 2. Patient\u2011Facing Applications\\n\\n| Application | Description | Example |\\n|-------------|-------------|---------|\\n| **Virtual Assistants & Chatbots** | Symptom triage, medication reminders, mental\u2011health chat | \u2022 Babylon Health (AI\u2011driven triage).
\u2022 Woebot (CBT\u2011based mental\u2011health chatbot). |\\n| **Telemedicine Enhancements** | Real\u2011time vitals extraction from video, automated note\u2011taking | \u2022 KardiaMobile ECG integration with AI\u2011based arrhythmia detection. |\\n| **Wearables & Remote Monitoring** | Continuous data streams analysed for early alerts | \u2022 Apple Watch ECG + AI arrhythmia detection; Fitbit heart\u2011rate trend alerts. |\\n\\n---\\n\\n## 3. Operational & Administrative Efficiency\\n\\n| Domain | AI Functions | Example |", "url": "https://github.com/vllm-project/vllm/issues/29641", "state": "closed", "labels": [ "bug" ], "created_at": "2025-11-28T03:39:34Z", "updated_at": "2025-12-21T02:39:32Z", "comments": 16, "user": "soodrohit" }, { "repo": "huggingface/transformers", "number": 42464, "title": "Add SAM 3D Objects Encoder", "body": "### Model description\n\n## Model Description\n\nSAM 3D Objects is Meta AI's foundation model for 3D object reconstruction from single images. I'm proposing to add the **encoder component** (DINOv2-based Vision Transformer) to Transformers.\n\n**Scope**: Encoder only, not the full 3D generation pipeline (which includes Gaussian Splatting/Mesh decoders better suited for Diffusers).\n\n## Open source status\n\n- [x] The model implementation available\n- [x] The model weights are available\n\n## Provide useful links for the implementation\n\n- **Model Card**: https://huggingface.co/facebook/sam-3d-objects\n- **Paper**: https://arxiv.org/abs/2511.16624\n- **Original Repository**: https://github.com/facebookresearch/sam-3d-objects\n- **Blog Post**: https://ai.meta.com/blog/sam-3d/\n\n## Implementation Progress\n\nI have already implemented this model and it's ready for review:\n\n\u2705 **Implementation Complete:**\n- `Sam3DObjectsEncoderConfig` - Configuration with DINO variant support\n- `Sam3DObjectsEncoder` - Main encoder model\n- `Sam3DObjectsEncoderForMasks` - Variant for mask encoding\n- `Sam3DObjectsImageProcessor` - Image preprocessing\n- Comprehensive test suite: **28/28 tests passing**\n- Full documentation\n\n**Test Results:**\ncollected 29 items\n28 passed, 1 skipped in 4.92s\n\n**Example Usage:**\n```python\nfrom transformers.models.sam3d_objects import (\n Sam3DObjectsEncoder,\n Sam3DObjectsEncoderConfig,\n Sam3DObjectsImageProcessor,\n)\n\nconfig = Sam3DObjectsEncoderConfig.from_dino_config(\"dinov2_vitl14\")\nmodel = Sam3DObjectsEncoder(config)\nprocessor = Sam3DObjectsImageProcessor()\n\ninputs = processor(images=image, return_tensors=\"pt\")\noutputs = model(**inputs)\nembeddings = outputs.last_hidden_state\n```\n\n## Questions\n\n1. Is there interest in adding the SAM 3D Objects Encoder to Transformers?\n2. Should this be limited to the encoder component (my recommendation)?\n3. Should I submit a PR, or are there any requirements I should address first?\n\n## Additional Context\n\n- The encoder is based on DINOv2 and fits naturally in Transformers\n- Full 3D generation pipeline would be better suited for Diffusers\n- Model is gated on Hub (requires license acceptance)\n- Implementation follows Transformers patterns and guidelines\n\nI'm ready to submit a PR and address any feedback.\n\n### Open source status\n\n- [x] The model implementation is available\n- [x] The model weights are available\n\n### Provide useful links for the implementation\n\n## Links\n\n- **Model Card**: https://huggingface.co/facebook/sam-3d-objects\n- **Paper**: https://arxiv.org/abs/2511.16624 (SAM 3D: 3Dfy Anything in Images)\n- **Original Repository**: https://github.com/facebookresearch/sam-3d-objects\n- **Blog Post**: https://ai.meta.com/blog/sam-3d/\n- **Project Page**: https://ai.meta.com/sam3d/\n\n## Authors\n\n**SAM 3D Team** from Meta AI\n\nFor the complete author list and contributions, see:\n- [ArXiv Paper](https://arxiv.org/abs/2511.16624)\n- [Original Repository](https://github.com/facebookresearch/sam-3d-objects)\n\n*Note: This is a large collaborative project with many contributors from Meta Superintelligence Labs.*\n\n## Implementation Details\n\n**Model Type**: Vision Encoder (DINOv2-based) \n**Architecture**: Vision Transformer (ViT) \n**Variants Supported**: \n- ViT-S/14 (384 dim)\n- ViT-B/14 (768 dim)\n- ViT-L/14 (1024 dim)\n- ViT-G/14 (1536 dim)\n\n**Input**: RGB images (224x224 or 518x518) \n**Output**: Visual embeddings for 3D generation tasks\n\n**License**: SAM License (gated model on HuggingFace Hub)", "url": "https://github.com/huggingface/transformers/issues/42464", "state": "open", "labels": [ "New model" ], "created_at": "2025-11-27T19:48:28Z", "updated_at": "2025-12-05T10:32:33Z", "comments": 1, "user": "Aznix07" }, { "repo": "vllm-project/vllm", "number": 29584, "title": "[Usage]: Can KV Cache be disabled in non-autoregressive generation tasks?", "body": "### Your current environment\n\n```text\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.28.3\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.15 (main, Oct 3 2024, 07:27:34) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-6.8.0-87-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to :\nGPU models and configuration :\nGPU 0: NVIDIA GeForce RTX 3090\nGPU 1: NVIDIA GeForce RTX 3090\n\nNvidia driver version : 575.57.08\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n vLLM Info\n==============================\nROCM Version : Could not collect\nvLLM Version : 0.11.2\nvLLM Build Flags:\n CUDA Archs: Not Set; ROCm: Disabled\nGPU Topology:\n GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID\nGPU0 X SYS 0-23,48-71 0 N/A\nGPU1 SYS X 24-47,72-95 1 N/A\n\nLegend:\n\n X = Self\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\n PIX = Connection traversing at most a single PCIe bridge\n NV# = Connection traversing a bonded set of # NVLinks\n```\n\n### How would you like to use vllm\n\nHello vLLM team,\n\nCurrently, vLLM (v0.11.2) enables KV cache for certain LLM-based pooling and reranking models, such as the Qwen3-Embedding series, even when `--no-enable-chunked-prefill` and `--no-enable-prefix-caching` are set. This leads to unnecessary GPU memory usage.\n\nWould it be possible to disable KV cache for pooling and reranking models under these conditions?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.", "url": "https://github.com/vllm-project/vllm/issues/29584", "state": "open", "labels": [ "usage" ], "created_at": "2025-11-27T05:30:08Z", "updated_at": "2025-12-05T02:40:28Z", "comments": 5, "user": "GitEventhandler" }, { "repo": "vllm-project/vllm", "number": 29574, "title": "[Performance]: Using vLLM to accelerate VLM models, does the vision encoding part currently support parallel processing, or is it still being processed serially?", "body": "### Proposal to improve performance\n\nI found that currently, images of different sizes are processed sequentially, which significantly slows down the processing speed. How can we adapt to parallel processing? Should we resize or pad all images to the same size for batch processing, or can we run multiple encoder models in parallel? Thank you.\n\n### Report of performance regression\n\n_No response_\n\n### Misc discussion on performance\n\n_No response_\n\n### Your current environment (if you think it is necessary)\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.", "url": "https://github.com/vllm-project/vllm/issues/29574", "state": "open", "labels": [ "performance" ], "created_at": "2025-11-27T03:51:36Z", "updated_at": "2025-11-27T10:54:09Z", "comments": 2, "user": "NewZxy" }, { "repo": "vllm-project/vllm", "number": 29564, "title": "[Doc]: Make PyTorch profiler gzip and CUDA time dump configurable", "body": "### \ud83d\udcda The doc issue\n\nWe observed that enabling both use_gzip and dump_self_cuda_time_total in the vLLM torch profiler introduces significant overhead during profiling.\n\nFor example, when profiling 10 randomly generated requests (1000 input tokens, 200 output tokens) on an A100 using the Qwen3-32B model, we found that gzip compression of the profiling trace and dumping the CUDA time table take ~68 seconds, dominating the overall profiling time.\n\nThe main sources of overhead appear to be:\n1. Gzip compression of the profiling trace file\n2. Generation and dumping of the CUDA time summary table\n\nAfter disabling these two features, the total profiling dump time is reduced to ~18 seconds.\n\nIn many profiling scenarios (e.g., quick performance checks or small-scale experiments), users may not need gzip compression or the CUDA time table. Therefore, it would be helpful to make these two behaviors individually configurable via environment variables\u2014enabled by default for completeness, but optionally turnable off when faster profiling turnaround is preferred. Moreover, gzip compression could potentially be performed asynchronously after the trace is dumped, allowing lower-latency profiling in staging or pre-production environments.\n\nThis patch proposes adding such configurability so users can selectively disable gzip compression and/or CUDA time table generation when they want a faster and lighter profiling workflow.\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.", "url": "https://github.com/vllm-project/vllm/issues/29564", "state": "closed", "labels": [ "documentation" ], "created_at": "2025-11-27T02:21:20Z", "updated_at": "2025-12-01T04:30:48Z", "comments": 1, "user": "zhangruoxu" }, { "repo": "vllm-project/vllm", "number": 29562, "title": "[Bug]: \"\\n\\n\" content between reasoning and tool_call content when tool_call and stream mode", "body": "### Your current environment\n\n![\"Image\"](\"https://github.com/user-attachments/assets/0cc36343-3c0f-4ce1-9028-30f561a55dac\")
\n\nHere is my requests:\n```\n{\n \"model\": \"Qwen3-235B-A22B-Thinking-2507\",\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"search_law_articles\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"level\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u641c\u7d22\u6761\u4ef6\uff1a\u6cd5\u89c4\u7c7b\u578b\"\n },\n \"query\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u67e5\u8be2\u8bed\u53e5\"\n },\n \"title\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u6cd5\u5f8b\u6807\u9898\"\n },\n \"article\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u6cd5\u5f8b\u6761\u6b3e\u5e8f\u53f7\uff0c\u5982 \u7b2c\u5341\u6761\"\n },\n \"content\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u6cd5\u5f8b\u6761\u6b3e\u53ca\u5185\u5bb9\uff0c\u5982 \u7b2c\u5341\u6761 \u8d37\u6b3e\u4eba\u59d4\u6258\u652f\u4ed8\"\n },\n \"pub_department\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u53d1\u5e03\u90e8\u95e8\"\n },\n \"pub_time_after\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u641c\u7d22\u6761\u4ef6\uff1a\u53d1\u5e03\u65f6\u95f4\u665a\u4e8e\u6b64\u65f6\u95f4\uff0c\u683c\u5f0f\u59822025-06-20\"\n },\n \"pub_time_before\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u641c\u7d22\u6761\u4ef6\uff1a\u53d1\u5e03\u65f6\u95f4\u65e9\u4e8e\u6b64\u65f6\u95f4\uff0c\u683c\u5f0f\u59822025-06-20\"\n },\n \"imply_time_after\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u641c\u7d22\u6761\u4ef6\uff1a\u5b9e\u65bd\u65f6\u95f4\u665a\u4e8e\u6b64\u65f6\u95f4\uff0c\u683c\u5f0f\u59822025-06-20\"\n },\n \"imply_time_before\": {\n \"anyOf\": [\n {\n \"type\": \"string\"\n },\n {\n \"type\": \"null\"\n }\n ],\n \"default\": null,\n \"description\": \"\u641c\u7d22\u6761\u4ef6\uff1a\u5b9e\u65bd\u65f6\u95f4\u65e9\u4e8e\u6b64\u65f6\u95f4\uff0c\u683c\u5f0f\u59822025-06-20\"\n }\n }\n },\n \"description\": \"\u6b64\u5de5\u5177\u7528\u4e8e\u641c\u7d22\u6cd5\u6761\u5185\u5bb9\uff0c \u5e93\u4e2d\u662f\u6309\u7167\u6cd5\u5f8b\u6761\u76ee\u8fdb\u884c\u5b58\u50a8\uff0c \u67e5\u8be2\u53ef\u9009\u591a\u4e2a\u67e5\u8be2\u8fc7\u6ee4\u6761\u4ef6\"\n }\n }\n ],\n \"stream\": true,\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"text\": \"\u5e2e\u6211\u89e3\u8bfb\u4e0b\u7f51\u7edc\u5b89\u5168\u6cd5\",\n \"type\": \"text\"\n }\n ]\n }\n ]\n}\n```\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29562",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-27T01:49:04Z",
"updated_at": "2025-11-27T01:49:04Z",
"comments": 0,
"user": "NiuBlibing"
},
{
"repo": "vllm-project/vllm",
"number": 29560,
"title": "[Doc]: Batch Invariance on Ampere Platforms",
"body": "### \ud83d\udcda The doc issue\n\nDoes the batch invariance feature released in vllm 0.11.2 support the Ampere architecture? If adaptations are required, what modifications need to be made?\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29560",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-11-27T01:06:49Z",
"updated_at": "2025-11-27T14:21:30Z",
"comments": 0,
"user": "luo1206"
},
{
"repo": "huggingface/trl",
"number": 4582,
"title": "Does the GRPO Trainer support multi-image input for Qwen3-VL?",
"body": "Does the GRPO Trainer support multi-image input for Qwen3-VL?",
"url": "https://github.com/huggingface/trl/issues/4582",
"state": "open",
"labels": [
"\ud83c\udfcb GRPO"
],
"created_at": "2025-11-26T14:03:57Z",
"updated_at": "2025-11-27T08:08:25Z",
"comments": 1,
"user": "Lestoky"
},
{
"repo": "huggingface/diffusers",
"number": 12722,
"title": "How to run qwen-image in kaggle gpu T4 * 2 successfully?",
"body": "```python3\n!python3 -m pip install -U diffusers peft bitsandbytes\nimport diffusers, torch, math\nqwen = diffusers.QwenImagePipeline.from_pretrained('Qwen/Qwen-Image', torch_dtype=torch.float16, low_cpu_mem_usage=True, quantization_config=diffusers.PipelineQuantizationConfig(quant_backend='bitsandbytes_4bit', quant_kwargs={'load_in_4bit':True, 'bnb_4bit_quant_type':'nf4', 'bnb_4bit_compute_dtype':torch.float16}, components_to_quantize=['transformer', 'text_encoder']))\nqwen.scheduler = diffusers.FlowMatchEulerDiscreteScheduler.from_config({'base_image_seq_len':256, 'base_shift':math.log(3), 'invert_sigmas':False, 'max_image_seq_len':8192, 'max_shift':math.log(3), 'num_train_timesteps':1000, 'shift':1, 'shift_terminal':None, 'stochastic_sampling':False, 'time_shift_type':'exponential', 'use_beta_sigmas':False, 'use_dynamic_shifting':True, 'use_exponential_sigmas':False, 'use_karras_sigmas':False})\nqwen.load_lora_weights('lightx2v/Qwen-Image-Lightning', weight_name='Qwen-Image-Lightning-4steps-V2.0.safetensors', adapter_name='lightning')\nqwen.set_adapters('lightning', adapter_weights=1)\nqwen.enable_sequential_cpu_offload()\nqwen(prompt='a beautiful girl', height=1280, width=720, num_inference_steps=4, true_cfg_scale=1).images[0].save('a.png')\n```\n----> 3 qwen = diffusers.QwenImagePipeline.from_pretrained('Qwen/Qwen-Image', torch_dtype=torch.float16, low_cpu_mem_usage=True, quantization_config=diffusers.PipelineQuantizationConfig(quant_backend='bitsandbytes_4bit', quant_kwargs={'load_in_4bit':True, 'bnb_4bit_quant_type':'nf4', 'bnb_4bit_compute_dtype':torch.float16}, components_to_quantize=['transformer', 'text_encoder']))\n\nOutOfMemoryError: CUDA out of memory. Tried to allocate 34.00 MiB. GPU 0 has a total capacity of 14.74 GiB of which 4.19 MiB is free. Process 8568 has 14.73 GiB memory in use. Of the allocated memory 14.50 GiB is allocated by PyTorch, and 129.00 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n\nHow to get more cuda memory?\n\n@yiyixuxu @DN6",
"url": "https://github.com/huggingface/diffusers/issues/12722",
"state": "open",
"labels": [],
"created_at": "2025-11-26T12:53:30Z",
"updated_at": "2025-11-28T03:54:07Z",
"user": "chaowenguo"
},
{
"repo": "vllm-project/vllm",
"number": 29494,
"title": "[Doc]: Documentation inconsistency: Blog mentions append_slots() but codebase uses allocate_slots()",
"body": "### \ud83d\udcda The doc issue\n\nThe Automatic Prefix Caching blog post mentions:\n> \"The scheduler calls kv_cache_manager.append_slots()\"\n\nHowever, the actual codebase uses a unified `kv_cache_manager.allocate_slots()` method that handles both prefill and decode requests.\n\n**Location:**\n- Blog: [[link to blog post](https://docs.vllm.ai/en/v0.8.5/design/v1/prefix_caching.html#operations)]\n- Code: ./vllm/v1/core/kv_cache_manager.py\n\n### Suggest a potential alternative/fix\n\nUpdate the blog post to reflect the actual implementation `kv_cache_manager.allocate_slots()`\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29494",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-11-26T11:37:40Z",
"updated_at": "2025-11-26T11:46:08Z",
"comments": 1,
"user": "pradsgit"
},
{
"repo": "huggingface/transformers",
"number": 42418,
"title": "Custom nn.Parameter initialization in PreTrainedModel subclasses is overwritten by post_init()/from_pretrained() causing NaNs/Zeros",
"body": "### System Info\n\n- `transformers` version: 4.57.1\n- Platform: Linux-4.18.0-147.mt20200626.413.el8_1.x86_64-x86_64-with-glibc2.35\n- Python version: 3.10.14\n- Huggingface_hub version: 0.35.3\n- Safetensors version: 0.6.2\n- Accelerate version: 1.11.0\n- Accelerate config: not found\n- DeepSpeed version: 0.18.2\n- PyTorch version (accelerator?): 2.7.1+cu126 (CUDA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: No\n- Using GPU in script?: No\n- GPU type: NVIDIA A100-SXM4-80GB\n\n\n### Who can help?\n\n@Cyrilvallez @zucchini-nlp \n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n```python\nimport numpy as np\nimport os\nimport random\nimport torch\nimport torch.nn as nn\n\nfrom transformers import Qwen3VLForConditionalGeneration\n\n\ndef seed_everything(TORCH_SEED):\n random.seed(TORCH_SEED)\n os.environ[\"PYTHONHASHSEED\"] = str(TORCH_SEED)\n np.random.seed(TORCH_SEED)\n torch.manual_seed(TORCH_SEED)\n torch.cuda.manual_seed(TORCH_SEED)\n torch.cuda.manual_seed_all(TORCH_SEED)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = False\n\n\nseed_everything(66)\n\n\nclass TestModel1(Qwen3VLForConditionalGeneration):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.action_head = nn.Linear(1024, 7)\n self.positional_embedding = nn.Parameter(torch.randn(16, 1152))\n self.post_init()\n\n\nclass TestModel2(nn.Module):\n def __init__(self, *args, model_path, **kwargs):\n super().__init__(*args, **kwargs)\n self.model = Qwen3VLForConditionalGeneration.from_pretrained(model_path)\n self.action_head = nn.Linear(1024, 7)\n self.positional_embedding = nn.Parameter(torch.randn(16, 1152))\n\n\ntest_model1 = TestModel1.from_pretrained(\"Qwen/Qwen3-VL-4B-Instruct\")\ntest_model2 = TestModel2(model_path=\"Qwen/Qwen3-VL-4B-Instruct\")\nprint(test_model1.positional_embedding)\nprint(test_model1.positional_embedding.mean(), test_model1.positional_embedding.std())\nprint(test_model2.positional_embedding)\nprint(test_model2.positional_embedding.mean(), test_model2.positional_embedding.std())\n````\n\n### Expected behavior\n\nWhen subclassing a model (inheriting from PreTrainedModel, e.g., Qwen3VLForConditionalGeneration, LlamaForCausalLM) to add custom learnable parameters, user-defined initialization in __init__ is often silently overwritten.\n\nThis occurs because from_pretrained (or the end of __init__) triggers self.post_init(), which recursively calls _init_weights. This mechanism re-initializes all parameters, ignoring the explicit initialization code provided by the user in __init__.\n\nIn the specific case of Qwen3-VL (and potentially others), this re-initialization results in NaNs or Zeros, rendering the model unusable without manual intervention.\n\nSteps to reproduce The following script demonstrates the issue. Note: I used torch.randn for the custom parameter initialization. While I understand that torch.randn samples from a standard normal distribution and does not guarantee an exact sample mean of 0 and std of 1, it should result in valid float values. The observed NaNs/Zeros confirm that this initialization is being discarded and replaced by a faulty internal initialization logic.",
"url": "https://github.com/huggingface/transformers/issues/42418",
"state": "open",
"labels": [
"Usage",
"Feature request",
"bug"
],
"created_at": "2025-11-26T10:29:57Z",
"updated_at": "2025-12-01T15:10:32Z",
"comments": 10,
"user": "Noietch"
},
{
"repo": "huggingface/diffusers",
"number": 12720,
"title": "how to quantization wan 2.2 vace after loading lora?",
"body": "```python3\ndiffusers.WanVACEPipeline.from_pretrained('linoyts/Wan2.2-VACE-Fun-14B-diffusers', vae=diffusers.AutoencoderKLWan.from_pretrained('linoyts/Wan2.2-VACE-Fun-14B-diffusers', subfolder='vae', torch_dtype=torch.float32), torch_dtype=torch.bfloat16, quantization_config=diffusers.PipelineQuantizationConfig(quant_backend='bitsandbytes_8bit', quant_kwargs={'load_in_8bit':True}, components_to_quantize=['transformer', 'transformer_2'])).save_pretrained('wan')\n```\nnormally I can save the quantization model in this way\nBut now I want to merge lora and the quantization and then save the model with lora. How?\n\n```python3\nwan = diffusers.WanVACEPipeline.from_pretrained('linoyts/Wan2.2-VACE-Fun-14B-diffusers', vae=diffusers.AutoencoderKLWan.from_pretrained('linoyts/Wan2.2-VACE-Fun-14B-diffusers', subfolder='vae', torch_dtype=torch.float32), torch_dtype=torch.bfloat16)\nwan.load_lora_weights('lightx2v/Wan2.2-Lightning', weight_name='Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1/high_noise_model.safetensors', adapter_name='lightning')\nwan.load_lora_weights('lightx2v/Wan2.2-Lightning', weight_name='Wan2.2-I2V-A14B-4steps-lora-rank64-Seko-V1/low_noise_model.safetensors', adapter_name='lightning_2', load_into_transformer_2=True)\nwan.set_adapters(['lightning', 'lightning_2'], adapter_weights=[1] * 2)\n\nhow to quantization and save_pretrained?\n```\n@yiyixuxu @DN6",
"url": "https://github.com/huggingface/diffusers/issues/12720",
"state": "open",
"labels": [],
"created_at": "2025-11-26T10:11:38Z",
"updated_at": "2025-12-11T17:29:30Z",
"user": "chaowenguo"
},
{
"repo": "vllm-project/vllm",
"number": 29489,
"title": "[Usage]: Removing last generated token from output and kv cache",
"body": "### Your current environment\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.28.3\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.13.5 | packaged by conda-forge | (main, Jun 16 2025, 08:27:50) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.8.0-87-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA B200\nGPU 1: NVIDIA B200\nGPU 2: NVIDIA B200\nGPU 3: NVIDIA B200\nGPU 4: NVIDIA B200\nGPU 5: NVIDIA B200\nGPU 6: NVIDIA B200\nGPU 7: NVIDIA B200\n\nNvidia driver version : 570.195.03\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 224\nOn-line CPU(s) list: 0-223\nVendor ID: GenuineIntel\nModel name: INTEL(R) XEON(R) PLATINUM 8570\nCPU family: 6\nModel: 207\nThread(s) per core: 2\nCore(s) per socket: 56\nSocket(s): 2\nStepping: 2\nCPU(s) scaling MHz: 33%\nCPU max MHz: 4000.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities ibpb_exit_to_user\nVirtualization: VT-x\nL1d cache: 5.3 MiB (112 instances)\nL1i cache: 3.5 MiB (112 instances)\nL2 cache: 224 MiB (112 instances)\nL3 cache: 600 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-55,112-167\nNUMA node1 CPU(s): 56-111,168-223\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI",
"url": "https://github.com/vllm-project/vllm/issues/29489",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-26T09:35:37Z",
"updated_at": "2025-11-26T09:36:37Z",
"comments": 0,
"user": "josefdra"
},
{
"repo": "huggingface/diffusers",
"number": 12719,
"title": "how to use quantization and device_map=balance to run qwen-image on kaggle T4 * 2",
"body": "```python3\n!python3 -m pip install -U diffusers peft bitsandbytes protobuf\nimport diffusers, torch, math\nqwen = diffusers.QwenImagePipeline.from_pretrained('Qwen/Qwen-Image', quantization_config=diffusers.PipelineQuantizationConfig(quant_backend='bitsandbytes_4bit', quant_kwargs={'load_in_4bit':True, 'bnb_4bit_quant_type':'nf4', 'bnb_4bit_compute_dtype':torch.float16}, components_to_quantize=['transformer', 'text_encoder']), torch_dtype=torch.float16, device_map='balanced')\nprint(qwen.hf_device_map)\nqwen.scheduler = diffusers.FlowMatchEulerDiscreteScheduler.from_config({'base_image_seq_len':256, 'base_shift':math.log(3), 'invert_sigmas':False, 'max_image_seq_len':8192, 'max_shift':math.log(3), 'num_train_timesteps':1000, 'shift':1, 'shift_terminal':None, 'stochastic_sampling':False, 'time_shift_type':'exponential', 'use_beta_sigmas':False, 'use_dynamic_shifting':True, 'use_exponential_sigmas':False, 'use_karras_sigmas':False})\nqwen.load_lora_weights('lightx2v/Qwen-Image-Lightning', weight_name='Qwen-Image-Lightning-4steps-V2.0.safetensors', adapter_name='lightning')\nqwen.set_adapters('lightning', adapter_weights=1)\nqwen(prompt='a beautiful girl', height=1280, width=720, num_inference_steps=4, true_cfg_scale=1).images[0].save('a.png')\n```\n\nWARNING:accelerate.big_modeling:Some parameters are on the meta device because they were offloaded to the cpu.\n{'text_encoder': 'cpu', 'vae': 0} where is the transformer ?\n\nNotImplementedError: Cannot copy out of meta tensor; no data!\n\nI want to ask how to make the above code work in kaggle. why 16G * 2 vram still not enough to run q4 quantization qwen-image? I want to take full advantage of 2 gpu. Do I need max_memory?\n\nfull error logs:\n/usr/local/lib/python3.11/dist-packages/torch/utils/_contextlib.py in decorate_context(*args, **kwargs)\n 114 def decorate_context(*args, **kwargs):\n 115 with ctx_factory():\n--> 116 return func(*args, **kwargs)\n 117 \n 118 return decorate_context\n\n/usr/local/lib/python3.11/dist-packages/diffusers/pipelines/qwenimage/pipeline_qwenimage.py in __call__(self, prompt, negative_prompt, true_cfg_scale, height, width, num_inference_steps, sigmas, guidance_scale, num_images_per_prompt, generator, latents, prompt_embeds, prompt_embeds_mask, negative_prompt_embeds, negative_prompt_embeds_mask, output_type, return_dict, attention_kwargs, callback_on_step_end, callback_on_step_end_tensor_inputs, max_sequence_length)\n 566 )\n 567 do_true_cfg = true_cfg_scale > 1 and has_neg_prompt\n--> 568 prompt_embeds, prompt_embeds_mask = self.encode_prompt(\n 569 prompt=prompt,\n 570 prompt_embeds=prompt_embeds,\n\n/usr/local/lib/python3.11/dist-packages/diffusers/pipelines/qwenimage/pipeline_qwenimage.py in encode_prompt(self, prompt, device, num_images_per_prompt, prompt_embeds, prompt_embeds_mask, max_sequence_length)\n 252 \n 253 if prompt_embeds is None:\n--> 254 prompt_embeds, prompt_embeds_mask = self._get_qwen_prompt_embeds(prompt, device)\n 255 \n 256 prompt_embeds = prompt_embeds[:, :max_sequence_length]\n\n/usr/local/lib/python3.11/dist-packages/diffusers/pipelines/qwenimage/pipeline_qwenimage.py in _get_qwen_prompt_embeds(self, prompt, device, dtype)\n 203 txt, max_length=self.tokenizer_max_length + drop_idx, padding=True, truncation=True, return_tensors=\"pt\"\n 204 ).to(device)\n--> 205 encoder_hidden_states = self.text_encoder(\n 206 input_ids=txt_tokens.input_ids,\n 207 attention_mask=txt_tokens.attention_mask,\n\n/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py in _wrapped_call_impl(self, *args, **kwargs)\n 1737 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]\n 1738 else:\n-> 1739 return self._call_impl(*args, **kwargs)\n 1740 \n 1741 # torchrec tests the code consistency with the following code\n\n/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py in _call_impl(self, *args, **kwargs)\n 1748 or _global_backward_pre_hooks or _global_backward_hooks\n 1749 or _global_forward_hooks or _global_forward_pre_hooks):\n-> 1750 return forward_call(*args, **kwargs)\n 1751 \n 1752 result = None\n\n/usr/local/lib/python3.11/dist-packages/accelerate/hooks.py in new_forward(module, *args, **kwargs)\n 173 output = module._old_forward(*args, **kwargs)\n 174 else:\n--> 175 output = module._old_forward(*args, **kwargs)\n 176 return module._hf_hook.post_forward(module, output)\n 177 \n\n/usr/local/lib/python3.11/dist-packages/transformers/utils/generic.py in wrapper(self, *args, **kwargs)\n 941 \n 942 try:\n--> 943 output = func(self, *args, **kwargs)\n 944 if is_requested_to_return_tuple or (is_configured_to_return_tuple and is_top_level_module):\n 945 ",
"url": "https://github.com/huggingface/diffusers/issues/12719",
"state": "open",
"labels": [],
"created_at": "2025-11-26T08:35:46Z",
"updated_at": "2025-11-26T09:15:54Z",
"user": "chaowenguo"
},
{
"repo": "vllm-project/vllm",
"number": 29474,
"title": "[P/D][Metrics] Consider combined/summed metrics (e.g. ttft and e2e_request_latency) for prefill and decode instances",
"body": "### Your current environment\n\n
The output of python collect_env.py
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (GCC) 14.2.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.4 (main, Aug 29 2025, 09:21:27) [GCC 14.2.0] (64-bit runtime)\nPython platform : Linux-5.15.0-118-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.61\nCUDA_MODULE_LOADING set to :\nGPU models and configuration :\nGPU 0: NVIDIA H100 80GB HBM3\nGPU 1: NVIDIA H100 80GB HBM3\nGPU 2: NVIDIA H100 80GB HBM3\nGPU 3: NVIDIA H100 80GB HBM3\nGPU 4: NVIDIA H100 80GB HBM3\nGPU 5: NVIDIA H100 80GB HBM3\nGPU 6: NVIDIA H100 80GB HBM3\nGPU 7: NVIDIA H100 80GB HBM3\n\nNvidia driver version : 570.158.01\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 9654 96-Core Processor\nCPU family: 25\nModel: 17\nThread(s) per core: 1\nCore(s) per socket: 96\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU max MHz: 3707.8120\nCPU min MHz: 1500.0000\nBogoMIPS: 4793.01\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 invpcid_single hw_pstate ssbd mba ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d\nVirtualization: AMD-V\nL1d cache: 6 MiB (192 instances)\nL1i cache: 6 MiB (192 instances)\nL2 cache: 192 MiB (192 instances)\nL3 cache: 768 MiB (24 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-95\nNUMA node1 CPU(s): 96-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIB",
"url": "https://github.com/vllm-project/vllm/issues/29871",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-02T11:25:25Z",
"updated_at": "2025-12-02T15:30:53Z",
"comments": 2,
"user": "muelphil"
},
{
"repo": "vllm-project/vllm",
"number": 29866,
"title": "[Doc]:",
"body": "### \ud83d\udcda The doc issue\n\n# Installation des biblioth\u00e8ques XAI\n!pip install shap\n!pip install lime\n!pip install alibi\n!pip install interpret\n!pip install dalex\n!pip install eli5\n\n\n### Suggest a potential alternative/fix\n\n# Installation des biblioth\u00e8ques XAI\n!pip install shap\n!pip install lime\n!pip install alibi\n!pip install interpret\n!pip install dalex\n!pip install eli5\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29866",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-12-02T10:43:04Z",
"updated_at": "2025-12-02T10:50:10Z",
"comments": 0,
"user": "hassaballahmahamatahmat5-cpu"
},
{
"repo": "vllm-project/vllm",
"number": 29865,
"title": "[Doc]:",
"body": "### \ud83d\udcda The doc issue\n\n# Installation des biblioth\u00e8ques XAI\n!pip install shap\n!pip install lime\n!pip install alibi\n!pip install interpret\n!pip install dalex\n!pip install eli5\n\n\n### Suggest a potential alternative/fix\n\n# Installation des biblioth\u00e8ques XAI\n!pip install shap\n!pip install lime\n!pip install alibi\n!pip install interpret\n!pip install dalex\n!pip install eli5\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29865",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-12-02T10:43:01Z",
"updated_at": "2025-12-02T10:50:00Z",
"comments": 0,
"user": "hassaballahmahamatahmat5-cpu"
},
{
"repo": "vllm-project/vllm",
"number": 29864,
"title": "[Usage]: I am unable to run the GLM-4.5-Air-REAP-82B-A12B-nvfp4 model on an RTX 5090.",
"body": "### Your current environment\n\n\n I am unable to run the GLM-4.5-Air-REAP-82B-A12B-nvfp4 model on an RTX 5090.\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 12.3.0-1ubuntu1~22.04.2) 12.3.0\nClang version : Could not collect\nCMake version : version 4.2.0\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.10.0.dev20251124+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.12 (main, Nov 4 2025, 08:48:33) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-6.8.0-87-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA GeForce RTX 5090\nGPU 1: NVIDIA GeForce RTX 5090\nGPU 2: NVIDIA GeForce RTX 5090\nGPU 3: NVIDIA GeForce RTX 5090\n\nNvidia driver version : 570.172.08\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 112\nOn-line CPU(s) list: 0-111\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Gold 6330 CPU @ 2.00GHz\nCPU family: 6\nModel: 106\nThread(s) per core: 2\nCore(s) per socket: 28\nSocket(s): 2\nStepping: 6\nCPU max MHz: 3100.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4000.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 2.6 MiB (56 instances)\nL1i cache: 1.8 MiB (56 instances)\nL2 cache: 70 MiB (56 instances)\nL3 cache: 84 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-27,56-83\nNUMA node1 CPU(s): 28-55,84-111\nVulnerability Gather data sampling: Mitigation; Microcode\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI SW loop, KVM SW loop\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nVulnerability Vmscape: Not affected\n",
"url": "https://github.com/vllm-project/vllm/issues/29864",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-12-02T10:13:31Z",
"updated_at": "2025-12-05T17:06:30Z",
"comments": 2,
"user": "east612-ai"
},
{
"repo": "huggingface/diffusers",
"number": 12772,
"title": "How to convert diffusers model to wan2.2 format",
"body": "I see convert_wan_to_diffusers.py in diffusers repo, but no convert_diffusers_to_wan.py. Do you have plan to upload a convert scripts?\n",
"url": "https://github.com/huggingface/diffusers/issues/12772",
"state": "open",
"labels": [],
"created_at": "2025-12-02T09:19:29Z",
"updated_at": "2025-12-02T09:19:29Z",
"user": "wikiwen"
},
{
"repo": "huggingface/diffusers",
"number": 12764,
"title": "When will the img2img pipeline of FLUX.2-dev be released?",
"body": "I see that the current version(0.36.0-dev) only updated the text-to-image pipeline for Flux2. We are looking forward to the update of the image-to-image pipeline!\n",
"url": "https://github.com/huggingface/diffusers/issues/12764",
"state": "open",
"labels": [],
"created_at": "2025-12-01T11:25:35Z",
"updated_at": "2025-12-01T11:41:56Z",
"comments": 1,
"user": "guanxyu"
},
{
"repo": "huggingface/smolagents",
"number": 1890,
"title": "Question: how to use sever-side tools provided by Google Gemini or OpenAI GPT?",
"body": "Gemini has some server-side tools like google_search (https://ai.google.dev/gemini-api/docs/google-search) or google_map. OpenAI also has server-side tools like web_search. Does Smolagents support using such server-side tools from agents? If so, how?",
"url": "https://github.com/huggingface/smolagents/issues/1890",
"state": "open",
"labels": [],
"created_at": "2025-12-01T05:16:01Z",
"updated_at": "2025-12-23T10:49:45Z",
"user": "victorx-deckard"
},
{
"repo": "huggingface/agents-course",
"number": 623,
"title": "Message: Submission received, but no valid/matching task IDs were found in the 1 answers provided. Score did not improve previous record, leaderboard not updated.",
"body": "I am correctly downloading the GAIA 2023 Level 1 validation dataset using snapshot_download and load_dataset. This submission is for Unit 4 Agent Course. \n\n data_dir = snapshot_download(\n repo_id=\"gaia-benchmark/GAIA\",\n repo_type=\"dataset\"\n )\n \n dataset = load_dataset(data_dir, \"2023_level1\", split=\"validation\")\n subset = dataset.select(range(20))\n for item in subset:\n task_id = item.get(\"task_id\")\n question_text = item.get(\"Question\")\n file_name = item.get(\"file_name\")\n\nI experience failures when trying to run the first 20 questions i received only 5 task ids are valid.. When I specifically tried to isolate and run the task ID '935e2cff-ae78-4218-b3f5-115589b19dae' using the filtering method, the evaluation system reported. \n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nWhen running with priority scheduling, e.g.:\n```bash\nvllm serve Qwen/Qwen3-0.6B --scheduling-policy=priority\n```\n\nand using `n` > 1 in the request, like:\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"dummy\")\n\nres = client.chat.completions.create(\n model=client.models.list().data[0].id,\n messages=[{\"role\": \"user\", \"content\": \"What is the meaning of life?\"}],\n n=2\n)\n\nprint(res)\n```\n\nvllm crashes with:\n```python\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] EngineCore encountered a fatal error.\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] Traceback (most recent call last):\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/core.py\", line 835, in run_engine_core\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] engine_core.run_busy_loop()\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/core.py\", line 860, in run_busy_loop\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] self._process_input_queue()\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/core.py\", line 885, in _process_input_queue\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] self._handle_client_request(*req)\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/core.py\", line 907, in _handle_client_request\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] self.add_request(req, request_wave)\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/core.py\", line 291, in add_request\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] self.scheduler.add_request(request)\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/core/sched/scheduler.py\", line 1242, in add_request\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] self.waiting.add_request(request)\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/core/sched/request_queue.py\", line 150, in add_request\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] heapq.heappush(self._heap, (request.priority, request.arrival_time, request))\n(EngineCore_DP0 pid=207394) ERROR 11-30 15:14:29 [core.py:844] TypeError: '<' not supported between instances of 'Request' and 'Request'\n(EngineCore_DP0 pid=207394) Process EngineCore_DP0:\n(APIServer pid=207278) ERROR 11-30 15:14:29 [async_llm.py:525] AsyncLLM output_handler failed.\n(APIServer pid=207278) ERROR 11-30 15:14:29 [async_llm.py:525] Traceback (most recent call last):\n(APIServer pid=207278) ERROR 11-30 15:14:29 [async_llm.py:525] File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/async_llm.py\", line 477, in output_handler\n(APIServer pid=207278) ERROR 11-30 15:14:29 [async_llm.py:525] outputs = await engine_core.get_output_async()\n(APIServer pid=207278) ERROR 11-30 15:14:29 [async_llm.py:525] File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/core_client.py\", line 883, in get_output_async\n(APIServer pid=207278) ERROR 11-30 15:14:29 [async_llm.py:525] raise self._format_exception(outputs) from None\n(APIServer pid=207278) ERROR 11-30 15:14:29 [async_llm.py:525] vllm.v1.engine.exceptions.EngineDeadError: EngineCore encountered an issue. See stack trace (above) for the root cause.\n(EngineCore_DP0 pid=207394) Traceback (most recent call last):\n(EngineCore_DP0 pid=207394) File \"/usr/lib/python3.10/multiprocessing/process.py\", line 314, in _bootstrap\n(EngineCore_DP0 pid=207394) self.run()\n(EngineCore_DP0 pid=207394) File \"/usr/lib/python3.10/multiprocessing/process.py\", line 108, in run\n(EngineCore_DP0 pid=207394) self._target(*self._args, **self._kwargs)\n(EngineCore_DP0 pid=207394) File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/core.py\", line 846, in run_engine_core\n(EngineCore_DP0 pid=207394) raise e\n(EngineCore_DP0 pid=207394) File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/core.py\", line 835, in run_engine_core\n(EngineCore_DP0 pid=207394) engine_core.run_busy_loop()\n(EngineCore_DP0 pid=207394) File \"/home/user/code/debug/.venv/lib/python3.10/site-packages/vllm/v1/engine/core.py\", line 860, in",
"url": "https://github.com/vllm-project/vllm/issues/29747",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-11-30T13:20:23Z",
"updated_at": "2025-12-02T22:42:30Z",
"comments": 3,
"user": "hibukipanim"
},
{
"repo": "vllm-project/vllm",
"number": 29735,
"title": "[Usage]:Accessing free_blocks count from LLMEngine or LLM ?",
"body": "### Your current environment\n\n```text\nNone\n```\n\n### How would you like to use vllm\n\nI'm doing research on key-value caching optimization. I want to know how to determine the number of free blocks during runtime. I tried manually creating the engine, but I couldn't find the method after searching through the code.\nAI keeps providing methods that have already been abandoned.\nI would be very grateful for any help, as this has been puzzling me for hours.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29735",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-29T19:21:50Z",
"updated_at": "2025-12-05T14:01:42Z",
"comments": 4,
"user": "H-T-H"
},
{
"repo": "vllm-project/vllm",
"number": 29722,
"title": "[RFC]: Add Balance Scheduling",
"body": "### Motivation.\n\n**Limitations of the current vLLM v1 scheduling strategy**\nvLLM v1 scheduling currently enables chunkedprefill by default, which processes prefill and decode requests simultaneously in a single scheduling session. This can impact the overall system throughput and performance in some scenarios.\n\nBalance scheduling addresses this issue by synchronizing the number of running queues across all schedulers to delay the scheduling of new requests, thereby improving the overall system's steady-state decoding time. This achieves:\n\u2705Adding `balance_gather` to the scheduler synchronizes the number of requests in the running queues between DPs.\n\u2705Balance scheduling improves the decode steady-state time, thereby increasing the overall output throughput of the inference system.\n\n\n### Proposed Change.\n\n **1.Feature Overview**\n\nIn the vLLM scheduler, running requests (i.e., requests that are already undergoing pre-filled computation) have the highest priority, followed by waiting requests (i.e., requests that have not yet been computed).\n\n\nAs shown in the diagram above, when the entire inference system exits from a steady state, the scheduler will schedule a batch of new requests for prefill operations and then synchronize them among the dynamic programming (DP) models. This can cause some DP models that are entirely decoded to synchronize with the number of prefilled tokens. Frequent prefill scheduling by certain DP models can lead to a deterioration in the overall system output throughput.\n\nBalance scheduling synchronizes the number of running queue requests across different DPs, and only schedules new requests for prefilling when at least every scheduler has fewer than max_nun_requst.\n\n **2.Implementation Design**\n\n **3.Experiment Results**\n- Fixed-length input scenario: In the performance test scenario with 3.5K fixed-length input and 1.5K fixed-length output, the throughput performance was improved by approximately **18%** after adding balance scheduling.\n\n| Method | Model | Input Len | Request Count | Output Len | BatchSize | Average TTFT | Average TPOT | e2e duration | Input Token Throughput | Output Token Throughput | Request Throughput\n| ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |\n| Baseline | DeepSeekV3.1 | 3500 | 512 | 1500 | 128 | 6600 | 86.85 | 591.9s | 3030.5 | 1297.3 | 0.86 |\n| Balance scheduling | DeepSeekV3.1 | 3500 | 512 | 1500 | 128 | 7012 | 70.63 | 501.7s | 3575.7 | 1530.7 | 1.02 |\n\n**4.Demo PR**\n\n[#29721 ](https://github.com/vllm-project/vllm/pull/29721)\n\n\n### Feedback Period.\n\nNo response\n\n### CC List.\n\nNo response\n\n### Any Other Things.\n\nNo response\n\n### Before submitting a new issue...\n\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29722",
"state": "open",
"labels": [
"RFC"
],
"created_at": "2025-11-29T09:28:43Z",
"updated_at": "2025-12-02T08:23:33Z",
"comments": 0,
"user": "GDzhu01"
},
{
"repo": "vllm-project/vllm",
"number": 29707,
"title": "[Usage]: Workaround to run model on GPUs with Compute Capability < 8.0?",
"body": "### Your current environment\n\nProblem:\nI am unable to run the Qwen3-VL-32B-Instruct-AWQ-4bit model due to a CUDA compute capability requirement. My hardware consists of two NVIDIA QUADRO RTX 5000 cards (16GB each, 32GB total) with a compute capability of 7.5. The software framework (likely a recent version of PyTorch or a specific library) raises an error:\n\n\"GPUs with compute capability < 8.0 are not supported.\"\n\nQuestion:\nAre there any workarounds to run this model on my older QUADRO RTX 5000 GPUs? Thanks in advance.\n\n\n```\n vllm collect-env\nINFO 11-29 20:49:15 [__init__.py:216] Automatically detected platform cuda.\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.30.3\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 | packaged by Anaconda, Inc. | (main, Jun 5 2025, 13:09:17) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-6.14.0-27-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.0.140\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration :\nGPU 0: Quadro RTX 5000\nGPU 1: Quadro RTX 5000\n\nNvidia driver version : 580.65.06\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 20\nOn-line CPU(s) list: 0-19\nVendor ID: GenuineIntel\nModel name: Intel(R) Core(TM) i9-10900X CPU @ 3.70GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 10\nSocket(s): 1\nStepping: 7\nCPU(s) scaling MHz: 28%\nCPU max MHz: 4700.0000\nCPU min MHz: 1200.0000\nBogoMIPS: 7399.70\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cdp_l3 ssbd mba ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm mpx rdt_a avx512f avx512dq rdseed adx smap clflushopt clwb intel_pt avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512_vnni md_clear flush_l1d arch_capabilities\nL1d cache: 320 KiB (10 instances)\nL1i cache: 320 KiB (10 instances)\nL2 cache: 10 MiB (10 instances)\nL3 cache: 19.3 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-19\nVulnerability Gather data sampling: Vulnerable\nVulnerability Ghostwrite: Not affected\nVulnerability Itlb multihit: KVM: Mitigation: VMX unsupported\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT vulnerable\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Mitigation; Enhanced IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Aut",
"url": "https://github.com/vllm-project/vllm/issues/29707",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-29T00:47:39Z",
"updated_at": "2025-11-30T06:04:29Z",
"comments": 5,
"user": "seasoncool"
},
{
"repo": "vllm-project/vllm",
"number": 29679,
"title": "[Usage]: Get request total time",
"body": "### Your current environment\n\n```text\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version : Could not collect\nCMake version : version 3.28.0\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.19 | packaged by conda-forge | (main, Oct 22 2025, 22:29:10) [GCC 14.3.0] (64-bit runtime)\nPython platform : Linux-6.8.0-1030-azure-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : GPU 0: NVIDIA H100 NVL\nNvidia driver version : 535.247.01\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.10.2\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 40\nOn-line CPU(s) list: 0-39\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 9V84 96-Core Processor\nCPU family: 25\nModel: 17\nThread(s) per core: 1\nCore(s) per socket: 40\nSocket(s): 1\nStepping: 1\nBogoMIPS: 4800.05\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl tsc_reliable nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves user_shstk avx512_bf16 clzero xsaveerptr rdpru arat avx512vbmi umip avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq rdpid fsrm\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 1.3 MiB (40 instances)\nL1i cache: 1.3 MiB (40 instances)\nL2 cache: 40 MiB (40 instances)\nL3 cache: 160 MiB (5 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-39\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.5.2\n[pip3] numpy==1.26.4\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n",
"url": "https://github.com/vllm-project/vllm/issues/29679",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-28T14:03:16Z",
"updated_at": "2025-12-01T09:34:12Z",
"comments": 5,
"user": "chwundermsft"
},
{
"repo": "huggingface/lerobot",
"number": 2543,
"title": "Different finetune loss given policy.type=pi0 / policy.path=lerobot/pi0_base. What is the difference?",
"body": "Hi, I have two different configurations:\n1. `\t--dataset.repo_id=BBBBBBob/libero_goal_lerobot \\\n\t--dataset.root=/home/j84403411/data/libero/libero_goal_lerobot \\\n\t--policy.path=lerobot/pi0_base \\\n\t--policy.push_to_hub=false \\\n\t--policy.use_proprio=true \\\n\t--output_dir=/home/j84403411/checkpoint/libero/pi0/libero_goal_proprio \\\n --policy.dtype=bfloat16 \\\n --steps=40_000 \\\n --batch_size=16 \\\n --rename_map='{\"observation.images.image\":\"observation.images.base_0_rgb\", \"observation.images.wrist_image\":\"observation.images.left_wrist_0_rgb\"}' \\ `\nand\n2.\n `\t--dataset.repo_id=BBBBBBob/libero_goal_lerobot \\\n\t--dataset.root=/home/j84403411/data/libero/libero_goal_lerobot \\\n\t--policy.type=pi0 \\\n --policy.pretrained_path=lerobot/pi0_base \\\n\t--policy.push_to_hub=false \\\n\t--policy.use_proprio=true \\\n\t--output_dir=/home/j84403411/checkpoint/libero/pi0/libero_goal_proprio \\\n --policy.dtype=bfloat16 \\\n --steps=40_000 \\\n --batch_size=16 \\\n --policy.input_features='{\"observation.state\": {\"type\": \"STATE\", \"shape\": [8]},\n\t\t\"observation.images.wrist_image\": {\"type\": \"VISUAL\", \"shape\": [3, 256, 256]},\n\t \t\"observation.images.image\": {\"type\": \"VISUAL\", \"shape\": [3, 256, 256]},\n\t\t}' \\\n --policy.output_features='{\"action\": {\"type\": \"ACTION\", \"shape\": [7]}}' \\ `\n\n\nThe loss trained from the second configuration is 10 times higher than the first one. What caused the difference? Do you know if different checkpoints are loaded in this case? I appreciate your help! ",
"url": "https://github.com/huggingface/lerobot/issues/2543",
"state": "closed",
"labels": [],
"created_at": "2025-11-28T12:34:38Z",
"updated_at": "2025-12-01T11:25:17Z",
"user": "BBBBBBob"
},
{
"repo": "huggingface/transformers.js",
"number": 1467,
"title": "Missing the following inputs: input_points, input_labels (or input_boxes)",
"body": "### Question\n\nthanks for your excellent works!\n\nI just write test code for SlimSAM model powered by transformers.js referring to this example(with some improvements): https://github.com/huggingface/transformers.js-examples/blob/main/segment-anything-webgpu/index.js\n\nmy code for `decode` method:\n```js\n// Decode segmentation\nasync function decode() {\n if (!imageEmbeddings || isDecoding || isEncoding) return;\n\n if (isDecoding) {\n decodePending = true;\n return;\n }\n isDecoding = true;\n\n try {\n let input_points = null;\n let input_labels = null;\n let input_boxes = null;\n let outputs = null;\n \n if (promptMode == \"point\" && points.length > 0) {\n const reshaped = imageprocessed.reshaped_input_sizes[0]; // [H, W]\n const scaledPoints = points.map(p => [\n p.x * reshaped[1],\n p.y * reshaped[0]\n ]);\n const labels = points.map(p => BigInt(p.label));\n\n input_points = new Tensor(\"float32\", scaledPoints.flat(), [1, 1, points.length, 2]);\n input_labels = new Tensor(\"int64\", labels, [1, 1, points.length]);\n\n // Fallback: if no prompts, skip\n if (!input_points) return;\n\n // Run model with point mode\n outputs = await model({\n ...imageEmbeddings,\n input_points: input_points,\n input_labels: input_labels,\n input_boxes: null\n });\n }\n\n if (promptMode == \"box\" && box) {\n const reshaped = imageprocessed.reshaped_input_sizes[0];\n const [x1, y1, x2, y2] = [\n box.x1 * reshaped[1],\n box.y1 * reshaped[0],\n box.x2 * reshaped[1],\n box.y2 * reshaped[0]\n ];\n input_boxes = new Tensor(\"float32\", [x1, y1, x2, y2], [1, 1, 4]);\n\n // Fallback: if no prompts, skip\n if (!input_boxes) return;\n\n // Run model with box mode\n outputs = await model({\n ...imageEmbeddings,\n input_points: null,\n input_labels: null,\n input_boxes: input_boxes\n });\n }\n\n // Post-process\n const masks = await processor.post_process_masks(\n outputs.pred_masks,\n imageprocessed.original_sizes,\n imageprocessed.reshaped_input_sizes\n );\n\n const scores = outputs.iou_scores.data;\n updateMask(masks[0], scores); // masks[0] is [3, H, W]\n\n } catch (e) {\n console.error(\"Decode error:\", e);\n statusEl.textContent = \"\u274c Segmentation failed.\";\n } finally {\n isDecoding = false;\n if (decodePending) {\n decodePending = false;\n decode();\n }\n }\n}\n```\nit supports 2 prompt modes: `point` &` box` which selected by users on UI elements (html not provided).\nbut error printed every time when running `decode` method (at the line of calling `outputs = await model(...)`), the error message is:\n\nwith box prompt mode:\n`Error: An error occurred during model execution: \"Missing the following inputs: input_points, input_labels.`\n\nwith point prompt mode:\n`Error: An error occurred during model execution: \"Missing the following inputs: input_boxes.`\n\n\nShould I pass all three parameters(input_points/input_labels/input_boxes) simultaneously, regardless of which prompt mode I\u2019m using? How could I support point & box at the same time, since no demo codes found on internet. thanks!\n\n```\nversion: transformers.js 3.5.0 from https://cdn.jsdelivr.net/npm/@huggingface/transformers@3.5.0\nos: Windows 10\nchorme: 142\nmodel: Xenova/slimsam-77-uniform\n```",
"url": "https://github.com/huggingface/transformers.js/issues/1467",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-11-28T10:01:04Z",
"updated_at": "2025-12-01T04:04:59Z",
"user": "sherlockchou86"
},
{
"repo": "vllm-project/vllm",
"number": 29643,
"title": "[Usage]: Enabling Tool call in the Python SDK",
"body": "### Your current environment\n\nHi Team,\n\nI am currently exploring VLLM to enable tool calling, and I need some support with this. It would be very helpful if you could provide the corresponding Python code.\n\nWhat I\u2019m trying to achieve is to configure the Python package with the same settings that I use when starting the VLLM server. The configuration I\u2019m using is:\n\nvllm serve DeepSeek-R1-0528-Qwen3-8B \\\n --served-model-name deepseek \\\n --gpu_memory_utilization 0.5 \\\n --max_num_seqs 20 \\\n --max_model_len 10000 \\\n --enable-auto-tool-choice \\\n --tool-call-parser deepseek_v3 \\\n --chat-template tool_chat_template_deepseekr1.jinja \\\n --port 5050 \\\n --max_num_batched_tokens 5000\n\nI need to replicate this exact configuration in Python.\n\nYour support would be greatly appreciated. Please respond at your earliest convenience.\n\nIf you want, I can also write the **Python code equivalent** for these VLLM configurations.\n\nBest Regards\nMadan \n\n\n### How would you like to use vllm\n\n\n\nI want to use vLLM to serve a model with tool-calling support enabled. Specifically, I need to run the model with the same configuration parameters that I currently use when launching the vLLM server from the command line. These settings include GPU memory utilization, maximum sequence limits, tool-calling options, a custom tool-call parser, and a custom chat template.\n\nMy goal is to reproduce the following server configuration within a Python environment using the vLLM Python API:\n\nvllm serve DeepSeek-R1-0528-Qwen3-8B \\\n --served-model-name deepseek \\\n --gpu_memory_utilization 0.5 \\\n --max_num_seqs 20 \\\n --max_model_len 10000 \\\n --enable-auto-tool-choice \\\n --tool-call-parser deepseek_v3 \\\n --chat-template tool_chat_template_deepseekr1.jinja \\\n --port 5050 \\\n --max_num_batched_tokens 5000\n`\n\nIn short, I need Python code that sets these exact configurations so I can run vLLM programmatically with tool calling enabled.\n\nIf you want, I can also provide the **Python code equivalent** for this configuration.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29643",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-28T04:39:47Z",
"updated_at": "2025-12-01T14:54:47Z",
"comments": 2,
"user": "Madan1215"
},
{
"repo": "vllm-project/vllm",
"number": 29641,
"title": "[Bug]: Max Tokens not being honoured in Chat Completions for GPTOSS model",
"body": "### Your current environment\n\nIt seems that in the latest version of vllm 0.11+ Chat Completions has stopped honouring `max_tokens` with GPTOSS 120B model, the below request payload has stopped working with `max_tokens` earlier the same payload would provide an output to the limit of the `max_tokens` provided.. \n\nInterestingly if you look at the `usage` tokens, it's showing `completion_tokens` as 500 but the output is BLANK.\n\n```json\n{\n \"messages\": [\n {\n \"role\": \"user\",\n \"content\": \"What is the role of AI in medicine?\"\n }\n ],\n \"model\": \"openai/gpt-oss-120b\",\n \"max_tokens\": 500,\n \"reasoning\": {\"effort\": \"low\"},\n \"stream\": false\n}\n```\n\ngetting BLANK output, even though the `usage` is showing token counts created is matching max_tokens \n\n```json\n{\n \"id\": \"chatcmpl-c71e934ac0b74bd4b8f99fe9b5516ea3\",\n \"object\": \"chat.completion\",\n \"created\": 1764300020,\n \"model\": \"openai/gpt-oss-120b\",\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": null,\n \"refusal\": null,\n \"annotations\": null,\n \"audio\": null,\n \"function_call\": null,\n \"tool_calls\": [],\n \"reasoning\": \"Need to answer.\",\n \"reasoning_content\": \"Need to answer.\"\n },\n \"logprobs\": null,\n \"finish_reason\": \"length\",\n \"stop_reason\": null,\n \"token_ids\": null\n }\n ],\n \"service_tier\": null,\n \"system_fingerprint\": null,\n \"usage\": {\n \"prompt_tokens\": 78,\n \"total_tokens\": 578,\n \"completion_tokens\": 500,\n \"prompt_tokens_details\": null\n },\n \"prompt_logprobs\": null,\n \"prompt_token_ids\": null,\n \"kv_transfer_params\": null\n}\n```\n\nWhen you remove the `max_tokens`, we get the output which shows `usage_token` to have `completion_tokens` to be around 1600 tokens..\nIt seems that starting from vllm 0.11+ version, the auto-truncation using the `max_tokens` has stopped working\n\n```json\n{\n \"id\": \"chatcmpl-61b60144d43147e2b007158712ad4920\",\n \"object\": \"chat.completion\",\n \"created\": 1764300423,\n \"model\": \"openai/gpt-oss-120b\",\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"**The role of AI in medicine is expanding rapidly and touches virtually every aspect of healthcare\u2014from the way doctors diagnose patients to how hospitals run their operations.** Below is a structured overview that covers the major domains, concrete examples, benefits, challenges, and future directions.\\n\\n---\\n\\n## 1. Clinical Care\\n\\n| Sub\u2011area | What AI Does | Real\u2011World Examples | Benefits |\\n|----------|--------------|---------------------|----------|\\n| **Diagnostics** | Image analysis, pattern recognition, risk stratification | \u2022 Radiology: Google\u202fDeepMind\u2019s AI detects lung cancer on CT scans with >95% accuracy.The output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\u2022 Dermatology: FDA\u2011cleared apps (e.g., SkinVision) classify skin lesions from photos.
\u2022 Pathology: Paige.ai assists in detecting prostate cancer in biopsy slides. | Faster, more consistent readings; can catch subtle findings that human eyes miss. |\\n| **Predictive Analytics** | Forecast disease onset, complications, readmission risk | \u2022 Sepsis prediction models (e.g., Epic Sepsis Model) trigger alerts hours before clinical signs.
\u2022 Cardiovascular risk calculators incorporating genomics and wearables. | Enables proactive interventions, reduces morbidity and cost. |\\n| **Treatment Planning** | Decision support, dose optimisation, drug selection | \u2022 IBM Watson for Oncology (clinical trial matching).
\u2022 Radiation oncology: AI\u2011driven dose\u2011painting to spare healthy tissue.
\u2022 Pharmacogenomics: AI predicts drug\u2011gene interactions. | Personalises therapy, improves outcomes, reduces adverse events. |\\n| **Robotics & Minimally Invasive Surgery** | Real\u2011time image guidance, autonomous suturing, task automation | \u2022 Da Vinci Surgical System (augmented with AI for instrument tracking).
\u2022 VERDICT AI for autonomous suturing in animal models. | Increases precision, reduces surgeon fatigue, shortens recovery. |\\n\\n---\\n\\n## 2. Patient\u2011Facing Applications\\n\\n| Application | Description | Example |\\n|-------------|-------------|---------|\\n| **Virtual Assistants & Chatbots** | Symptom triage, medication reminders, mental\u2011health chat | \u2022 Babylon Health (AI\u2011driven triage).
\u2022 Woebot (CBT\u2011based mental\u2011health chatbot). |\\n| **Telemedicine Enhancements** | Real\u2011time vitals extraction from video, automated note\u2011taking | \u2022 KardiaMobile ECG integration with AI\u2011based arrhythmia detection. |\\n| **Wearables & Remote Monitoring** | Continuous data streams analysed for early alerts | \u2022 Apple Watch ECG + AI arrhythmia detection; Fitbit heart\u2011rate trend alerts. |\\n\\n---\\n\\n## 3. Operational & Administrative Efficiency\\n\\n| Domain | AI Functions | Example |", "url": "https://github.com/vllm-project/vllm/issues/29641", "state": "closed", "labels": [ "bug" ], "created_at": "2025-11-28T03:39:34Z", "updated_at": "2025-12-21T02:39:32Z", "comments": 16, "user": "soodrohit" }, { "repo": "huggingface/transformers", "number": 42464, "title": "Add SAM 3D Objects Encoder", "body": "### Model description\n\n## Model Description\n\nSAM 3D Objects is Meta AI's foundation model for 3D object reconstruction from single images. I'm proposing to add the **encoder component** (DINOv2-based Vision Transformer) to Transformers.\n\n**Scope**: Encoder only, not the full 3D generation pipeline (which includes Gaussian Splatting/Mesh decoders better suited for Diffusers).\n\n## Open source status\n\n- [x] The model implementation available\n- [x] The model weights are available\n\n## Provide useful links for the implementation\n\n- **Model Card**: https://huggingface.co/facebook/sam-3d-objects\n- **Paper**: https://arxiv.org/abs/2511.16624\n- **Original Repository**: https://github.com/facebookresearch/sam-3d-objects\n- **Blog Post**: https://ai.meta.com/blog/sam-3d/\n\n## Implementation Progress\n\nI have already implemented this model and it's ready for review:\n\n\u2705 **Implementation Complete:**\n- `Sam3DObjectsEncoderConfig` - Configuration with DINO variant support\n- `Sam3DObjectsEncoder` - Main encoder model\n- `Sam3DObjectsEncoderForMasks` - Variant for mask encoding\n- `Sam3DObjectsImageProcessor` - Image preprocessing\n- Comprehensive test suite: **28/28 tests passing**\n- Full documentation\n\n**Test Results:**\ncollected 29 items\n28 passed, 1 skipped in 4.92s\n\n**Example Usage:**\n```python\nfrom transformers.models.sam3d_objects import (\n Sam3DObjectsEncoder,\n Sam3DObjectsEncoderConfig,\n Sam3DObjectsImageProcessor,\n)\n\nconfig = Sam3DObjectsEncoderConfig.from_dino_config(\"dinov2_vitl14\")\nmodel = Sam3DObjectsEncoder(config)\nprocessor = Sam3DObjectsImageProcessor()\n\ninputs = processor(images=image, return_tensors=\"pt\")\noutputs = model(**inputs)\nembeddings = outputs.last_hidden_state\n```\n\n## Questions\n\n1. Is there interest in adding the SAM 3D Objects Encoder to Transformers?\n2. Should this be limited to the encoder component (my recommendation)?\n3. Should I submit a PR, or are there any requirements I should address first?\n\n## Additional Context\n\n- The encoder is based on DINOv2 and fits naturally in Transformers\n- Full 3D generation pipeline would be better suited for Diffusers\n- Model is gated on Hub (requires license acceptance)\n- Implementation follows Transformers patterns and guidelines\n\nI'm ready to submit a PR and address any feedback.\n\n### Open source status\n\n- [x] The model implementation is available\n- [x] The model weights are available\n\n### Provide useful links for the implementation\n\n## Links\n\n- **Model Card**: https://huggingface.co/facebook/sam-3d-objects\n- **Paper**: https://arxiv.org/abs/2511.16624 (SAM 3D: 3Dfy Anything in Images)\n- **Original Repository**: https://github.com/facebookresearch/sam-3d-objects\n- **Blog Post**: https://ai.meta.com/blog/sam-3d/\n- **Project Page**: https://ai.meta.com/sam3d/\n\n## Authors\n\n**SAM 3D Team** from Meta AI\n\nFor the complete author list and contributions, see:\n- [ArXiv Paper](https://arxiv.org/abs/2511.16624)\n- [Original Repository](https://github.com/facebookresearch/sam-3d-objects)\n\n*Note: This is a large collaborative project with many contributors from Meta Superintelligence Labs.*\n\n## Implementation Details\n\n**Model Type**: Vision Encoder (DINOv2-based) \n**Architecture**: Vision Transformer (ViT) \n**Variants Supported**: \n- ViT-S/14 (384 dim)\n- ViT-B/14 (768 dim)\n- ViT-L/14 (1024 dim)\n- ViT-G/14 (1536 dim)\n\n**Input**: RGB images (224x224 or 518x518) \n**Output**: Visual embeddings for 3D generation tasks\n\n**License**: SAM License (gated model on HuggingFace Hub)", "url": "https://github.com/huggingface/transformers/issues/42464", "state": "open", "labels": [ "New model" ], "created_at": "2025-11-27T19:48:28Z", "updated_at": "2025-12-05T10:32:33Z", "comments": 1, "user": "Aznix07" }, { "repo": "vllm-project/vllm", "number": 29584, "title": "[Usage]: Can KV Cache be disabled in non-autoregressive generation tasks?", "body": "### Your current environment\n\n```text\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.28.3\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.15 (main, Oct 3 2024, 07:27:34) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-6.8.0-87-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to :\nGPU models and configuration :\nGPU 0: NVIDIA GeForce RTX 3090\nGPU 1: NVIDIA GeForce RTX 3090\n\nNvidia driver version : 575.57.08\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n vLLM Info\n==============================\nROCM Version : Could not collect\nvLLM Version : 0.11.2\nvLLM Build Flags:\n CUDA Archs: Not Set; ROCm: Disabled\nGPU Topology:\n GPU0 GPU1 CPU Affinity NUMA Affinity GPU NUMA ID\nGPU0 X SYS 0-23,48-71 0 N/A\nGPU1 SYS X 24-47,72-95 1 N/A\n\nLegend:\n\n X = Self\n SYS = Connection traversing PCIe as well as the SMP interconnect between NUMA nodes (e.g., QPI/UPI)\n NODE = Connection traversing PCIe as well as the interconnect between PCIe Host Bridges within a NUMA node\n PHB = Connection traversing PCIe as well as a PCIe Host Bridge (typically the CPU)\n PXB = Connection traversing multiple PCIe bridges (without traversing the PCIe Host Bridge)\n PIX = Connection traversing at most a single PCIe bridge\n NV# = Connection traversing a bonded set of # NVLinks\n```\n\n### How would you like to use vllm\n\nHello vLLM team,\n\nCurrently, vLLM (v0.11.2) enables KV cache for certain LLM-based pooling and reranking models, such as the Qwen3-Embedding series, even when `--no-enable-chunked-prefill` and `--no-enable-prefix-caching` are set. This leads to unnecessary GPU memory usage.\n\nWould it be possible to disable KV cache for pooling and reranking models under these conditions?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.", "url": "https://github.com/vllm-project/vllm/issues/29584", "state": "open", "labels": [ "usage" ], "created_at": "2025-11-27T05:30:08Z", "updated_at": "2025-12-05T02:40:28Z", "comments": 5, "user": "GitEventhandler" }, { "repo": "vllm-project/vllm", "number": 29574, "title": "[Performance]: Using vLLM to accelerate VLM models, does the vision encoding part currently support parallel processing, or is it still being processed serially?", "body": "### Proposal to improve performance\n\nI found that currently, images of different sizes are processed sequentially, which significantly slows down the processing speed. How can we adapt to parallel processing? Should we resize or pad all images to the same size for batch processing, or can we run multiple encoder models in parallel? Thank you.\n\n### Report of performance regression\n\n_No response_\n\n### Misc discussion on performance\n\n_No response_\n\n### Your current environment (if you think it is necessary)\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.", "url": "https://github.com/vllm-project/vllm/issues/29574", "state": "open", "labels": [ "performance" ], "created_at": "2025-11-27T03:51:36Z", "updated_at": "2025-11-27T10:54:09Z", "comments": 2, "user": "NewZxy" }, { "repo": "vllm-project/vllm", "number": 29564, "title": "[Doc]: Make PyTorch profiler gzip and CUDA time dump configurable", "body": "### \ud83d\udcda The doc issue\n\nWe observed that enabling both use_gzip and dump_self_cuda_time_total in the vLLM torch profiler introduces significant overhead during profiling.\n\nFor example, when profiling 10 randomly generated requests (1000 input tokens, 200 output tokens) on an A100 using the Qwen3-32B model, we found that gzip compression of the profiling trace and dumping the CUDA time table take ~68 seconds, dominating the overall profiling time.\n\nThe main sources of overhead appear to be:\n1. Gzip compression of the profiling trace file\n2. Generation and dumping of the CUDA time summary table\n\nAfter disabling these two features, the total profiling dump time is reduced to ~18 seconds.\n\nIn many profiling scenarios (e.g., quick performance checks or small-scale experiments), users may not need gzip compression or the CUDA time table. Therefore, it would be helpful to make these two behaviors individually configurable via environment variables\u2014enabled by default for completeness, but optionally turnable off when faster profiling turnaround is preferred. Moreover, gzip compression could potentially be performed asynchronously after the trace is dumped, allowing lower-latency profiling in staging or pre-production environments.\n\nThis patch proposes adding such configurability so users can selectively disable gzip compression and/or CUDA time table generation when they want a faster and lighter profiling workflow.\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.", "url": "https://github.com/vllm-project/vllm/issues/29564", "state": "closed", "labels": [ "documentation" ], "created_at": "2025-11-27T02:21:20Z", "updated_at": "2025-12-01T04:30:48Z", "comments": 1, "user": "zhangruoxu" }, { "repo": "vllm-project/vllm", "number": 29562, "title": "[Bug]: \"\\n\\n\" content between reasoning and tool_call content when tool_call and stream mode", "body": "### Your current environment\n\n
\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nhttps://github.com/QwenLM/Qwen3/issues/1755\n\nWhen stream mode true, the response contains content \"\\n\\n\" between reasoning and tool_call; but with stream model false, it didn't generate content \"\\n\\n\".\n\nIs there some thing different, I don't want the content \"\\n\\n\" between reasoning and tool_call.\n\nThe output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\n\n\n from vllm.entrypoints.cli.main import main\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/entrypoints/cli/__init__.py\", line 3, in \n from vllm.entrypoints.cli.benchmark.latency import BenchmarkLatencySubcommand\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/entrypoints/cli/benchmark/latency.py\", line 5, in \n from vllm.benchmarks.latency import add_cli_args, main\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/benchmarks/latency.py\", line 17, in \n from vllm.engine.arg_utils import EngineArgs\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/engine/arg_utils.py\", line 35, in \n from vllm.attention.backends.registry import AttentionBackendEnum\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/attention/__init__.py\", line 4, in \n from vllm.attention.backends.abstract import (\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/attention/backends/abstract.py\", line 9, in \n from vllm.model_executor.layers.linear import ColumnParallelLinear\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/model_executor/__init__.py\", line 4, in \n from vllm.model_executor.parameter import BasevLLMParameter, PackedvLLMParameter\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/model_executor/parameter.py\", line 11, in \n from vllm.distributed import (\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/distributed/__init__.py\", line 4, in \n from .communication_op import *\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/distributed/communication_op.py\", line 9, in \n from .parallel_state import get_tp_group\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/distributed/parallel_state.py\", line 250, in \n direct_register_custom_op(\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/utils/torch_utils.py\", line 640, in direct_register_custom_op\n from vllm.platforms import current_platform\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/platforms/__init__.py\", line 257, in __getattr__\n _current_platform = resolve_obj_by_qualname(platform_cls_qualname)()\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/utils/import_utils.py\", line 89, in resolve_obj_by_qualname\n module = importlib.import_module(module_name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/importlib/__init__.py\", line 90, in import_module\n return _bootstrap._gcd_import(name[level:], package, level)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/dell/miniconda3/envs/py312/lib/python3.12/site-packages/vllm/platforms/cuda.py\", line 16, in \n import vllm._C # noqa\n ^^^^^^^^^^^^^^\nImportError: libtorch_cuda.so: cannot open shared object file: No such file or directory\n```\n\n\n\n\n### How you are installing vllm\n\n```sh\npip install vllm\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29472",
"state": "open",
"labels": [
"installation"
],
"created_at": "2025-11-26T02:41:18Z",
"updated_at": "2026-01-01T12:28:29Z",
"comments": 2,
"user": "goactiongo"
},
{
"repo": "vllm-project/vllm",
"number": 29436,
"title": "[Bug]: vLLM Serve with LMCache enabled produces wrong output for GPT-OSS-20B",
"body": "### Your current environment\n\n
\u2022 Qubits: superposition (both 0 & 1) & entanglement. | Use visual metaphors: a spinning top (superposition) and two dancers always in sync (entanglement). |\\n| **2.2 Basic Operations** | \u2022 Quantum gates (Pauli X, H, CNOT).
\u2022 The role of interference. | A tiny \u201creversible\u201d logic of the quantum \u201cif\u2011then\u201d that flips outcomes. |\\n| **2.3 Measuring As a Collapses** | \u2022 Outcome collapse on measurement.
\u2022 Probabilities & expectation values. | Compare to a gamble: you only learn the re", "url": "https://github.com/vllm-project/vllm/issues/29436", "state": "open", "labels": [ "bug" ], "created_at": "2025-11-25T19:27:24Z", "updated_at": "2025-11-25T19:27:24Z", "comments": 0, "user": "ksuma2109" }, { "repo": "vllm-project/vllm", "number": 29409, "title": "[Usage]: Custom Logits Processors V1 how to get tokenizer into processor", "body": "### Problem with tokenizer\n\nFor the second day now, I've been unable to figure out how to get a tokenizer inside a custom processor. I used the processor from the documentation as an example. I examined each object through debug, but couldn't find where to extract the tokenizer. In v0, this was done simply at the request level, by passing an argument to the object. \nHow to pass a tokenizer to the processor?\n```python import torch\nfrom vllm.config import VllmConfig\nfrom vllm.sampling_params import SamplingParams\nfrom vllm.v1.sample.logits_processor import (BatchUpdate,\n LogitsProcessor,\n MoveDirectionality)\n\n\n\nclass DummyLogitsProcessor(LogitsProcessor):\n \"\"\"Fake logit processor to support unit testing and examples\"\"\"\n\n @classmethod\n def validate_params(cls, params: SamplingParams):\n target_token: int | None = params.extra_args and params.extra_args.get(\n \"target_token\"\n )\n \n \n if target_token is not None and not isinstance(target_token, int):\n raise ValueError(f\"target_token value {target_token} is not int\")\n\n def __init__(self, vllm_config: \"VllmConfig\", device: torch.device,\n is_pin_memory: bool):\n self.req_info: dict[int, int] = {}\n \n def is_argmax_invariant(self) -> bool:\n \"\"\"Never impacts greedy sampling\"\"\"\n return False\n\n def update_state(self, batch_update: BatchUpdate | None):\n \n if not batch_update:\n return\n # Process added requests.\n for index, params, _, _ in batch_update.added:\n assert params is not None\n self.validate_params(params)\n if params.extra_args and (target_token :=\n params.extra_args.get(\"target_token\")):\n self.req_info[index] = target_token\n else: \n self.req_info.pop(index, None)\n\n if self.req_info:\n # Process removed requests.\n for index in batch_update.removed:\n self.req_info.pop(index, None)\n\n # Process moved requests, unidirectional move (a->b) and swap\n # (a<->b)\n for adx, bdx, direct in batch_update.moved:\n a_val = self.req_info.pop(adx, None)\n b_val = self.req_info.pop(bdx, None)\n if a_val is not None:\n self.req_info[bdx] = a_val\n if direct == MoveDirectionality.SWAP and b_val is not None:\n self.req_info[adx] = b_val\n\n def apply(self, logits: torch.Tensor) -> torch.Tensor:\n if not self.req_info:\n return logits\n # Save target values before modification\n cols = torch.tensor(\n list(self.req_info.values()), dtype=torch.long, device=logits.device\n )\n rows = torch.tensor(\n list(self.req_info.keys()), dtype=torch.long, device=logits.device\n )\n values_to_keep = logits[rows, cols].clone()\n\n # Mask all but target tokens\n logits[rows] = float('-inf')\n logits[rows, cols] = values_to_keep\n\n return logits\n\n```\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.", "url": "https://github.com/vllm-project/vllm/issues/29409", "state": "closed", "labels": [ "usage" ], "created_at": "2025-11-25T13:24:17Z", "updated_at": "2025-12-02T10:33:18Z", "comments": 6, "user": "cvadim130" }, { "repo": "vllm-project/vllm", "number": 29389, "title": "[Bug]: race condition in shm_broadcast.py", "body": "### Your current environment\n\n\n from transformers.cli.transformers import main\n File \"/home/master-andreas/panopticon/test_env/lib/python3.12/site-packages/transformers/cli/transformers.py\", line 23, in \n from transformers.cli.serve import Serve\n File \"/home/master-andreas/panopticon/test_env/lib/python3.12/site-packages/transformers/cli/serve.py\", line 351, in \n class Serve:\n File \"/home/master-andreas/panopticon/test_env/lib/python3.12/site-packages/transformers/cli/serve.py\", line 658, in Serve\n ) -> ChatCompletionChunk:\n ^^^^^^^^^^^^^^^^^^^\nNameError: name 'ChatCompletionChunk' is not defined\n\n### Who can help?\n\n@yonigozlan\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```py\n\"\"\"\nTest script for SAM3 text prompting only.\nThis script demonstrates how to use SAM3 for text-based segmentation on images.\n\"\"\"\n\nimport torch\nfrom PIL import Image\nimport requests\nfrom transformers import Sam3Processor, Sam3Model\nimport os\n\n\nINFERENCE_RESOLUTION = (1008, 1008) # If run with anything else other than 1008 it fails\n# INFERENCE_RESOLUTION = (1400, 1400)\n\n\ndef test_sam3_text_prompting():\n \"\"\"Test SAM3 with text prompting on a sample image.\"\"\"\n\n # Set device\n device = \"cpu\"\n print(f\"Using device: {device}\")\n\n # Load model and processor\n print(\"Loading SAM3 model and processor...\")\n model = Sam3Model.from_pretrained(\"facebook/sam3\").to(device)\n processor = Sam3Processor.from_pretrained(\"facebook/sam3\")\n\n # Load a sample image\n print(\"Loading sample image...\")\n image_url = \"http://images.cocodataset.org/val2017/000000077595.jpg\"\n image = Image.open(requests.get(image_url, stream=True).raw).convert(\"RGB\")\n\n # Define text prompts to test\n text_prompts = [\"cat\", \"ear\", \"eye\"]\n\n for text_prompt in text_prompts:\n print(f\"\\nTesting text prompt: '{text_prompt}'\")\n\n # Prepare inputs\n inputs = processor(images=image, text=text_prompt, size=INFERENCE_RESOLUTION, return_tensors=\"pt\").to(device)\n\n # Run inference\n with torch.no_grad():\n outputs = model(**inputs)\n\n # Post-process results\n results = processor.post_process_instance_segmentation(\n outputs,\n threshold=0.5,\n mask_threshold=0.5,\n target_sizes=inputs.get(\"original_sizes\").tolist()\n )[0]\n\n # Display results\n num_objects = len(results['masks'])\n print(f\"Found {num_objects} objects matching '{text_prompt}'\")\n\n if num_objects > 0:\n # Show scores for first few objects\n scores = results['scores']\n print(f\"Confidence scores: {scores[:min(3, len(scores))].tolist()}\")\n\n # Show bounding boxes for first object\n if 'boxes' in results and len(results['boxes']) > 0:\n box = results['boxes'][0]\n print(f\"First object bounding box (xyxy): {box.tolist()}\")\n\n\nif __name__ == \"__main__\":\n print(\"SAM3 Text Prompting Test Script\")\n print(\"=\" * 40)\n\n try:\n test_sam3_text_prompting()\n print(\"\\n\u2713 All tests completed successfully!\")\n\n except Exception as e:\n print(f\"\\n\u2717 Test failed with error: {e}\")\n raise\n\n```\n\nOutput when INFERENCE_RESOLUTION=[1400, 1400]:\n```sh\n$ py test_sam3_text.py\nSAM3 Text Prompting Test Script\n========================================\nUsing device: cpu\nLoading SAM3 model and processor...\nLoading weights: 100%|\u2588| 1468/1468 [00:00<00:00, 2709.52it/s, Materializing param=vision_encoder.neck.fpn\nLoading sample image...\n\nTesting text prompt: 'cat'\n\n\u2717 Test failed with error: The size of tensor a (10000) must match the size of tensor b (5184) at non-singleton dimension 2\nTraceback (most recent call last):\n File \"/home/master-andreas/panopticon/test_sam3_text.py\", line 124, in \n test_sam3_text_prompting()\n File \"/home/master-andreas/panopticon/test_sam3_text.py\", line 48, in test_sam3_text_prompting\n outputs = model(**inputs)\n ^^^^^^^^^^^^^^^\n File \"/home/master-andreas/panopticon/test_env/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1775, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/master-andreas/panopticon/test_env/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1786, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/master-andreas/panopticon/test_env/lib/python3.12/site-packages/transformers/utils/generic.py\", line 938, in wrapper\n ",
"url": "https://github.com/huggingface/transformers/issues/42331",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-11-21T22:17:08Z",
"updated_at": "2025-12-10T22:46:39Z",
"comments": 3,
"user": "Kallinteris-Andreas"
},
{
"repo": "huggingface/lerobot",
"number": 2500,
"title": "question about the gr00t policy",
"body": "hi,\n\nI see here https://huggingface.co/docs/lerobot/en/groot that gr00t is intergrated into lerobot.\n\nis it in sync with the original repo: https://github.com/NVIDIA/Isaac-GR00T ?\n\nI see in original repo that the dataset used to fine-tune, is a bit different from the original lerobot format, like libero dataset (https://huggingface.co/datasets/physical-intelligence/libero) used in pi model ,\ntherefore i wonder what dataset format should be used here in lerbot policy training ?\n\nany example dataset that is passed to `--dataset.repo_id=$DATASET_ID` ?\n\nis it a post-processed dataset ?",
"url": "https://github.com/huggingface/lerobot/issues/2500",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-11-21T21:45:19Z",
"updated_at": "2025-12-03T14:03:34Z",
"user": "yanan1116"
},
{
"repo": "vllm-project/vllm",
"number": 29192,
"title": "Tool Calling Parsers Fail to Populate tool_calls Array for Qwen2.5-Coder Models",
"body": "# Tool Calling Parsers Fail to Populate `tool_calls` Array for Qwen2.5-Coder Models\n\n## Environment\n- **vLLM Version**: v0.11.2.dev115+g56669c1f2 (Blackwell build)\n- **Model**: Qwen/Qwen2.5-Coder-14B-Instruct-AWQ\n- **Quantization**: AWQ\n- **Python Version**: 3.x (Docker container)\n- **GPU**: NVIDIA GeForce RTX 5080 (16GB, Blackwell/sm_120)\n- **Platform**: WSL2, Linux 6.6.87.2-microsoft-standard-WSL2\n\n## Description\nWhen using tool calling with Qwen2.5-Coder models, the model correctly generates tool calls in `` XML format, but both `qwen3_xml` and `qwen3_coder` parsers fail to extract these tool calls into the `tool_calls` array in the API response. The tool call information remains in the `content` field but the `tool_calls` array stays empty.\n\n## Steps to Reproduce\n\n1. Start vLLM with Qwen2.5-Coder and tool calling parser:\n```bash\npython -m vllm.entrypoints.openai.api_server \\\n --model Qwen/Qwen2.5-Coder-14B-Instruct-AWQ \\\n --quantization awq \\\n --enable-auto-tool-choice \\\n --tool-call-parser qwen3_xml # or qwen3_coder\n```\n\n2. Send a tool calling request:\n```bash\ncurl -s http://localhost:8002/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"qwen2.5-coder-14b-awq\",\n \"messages\": [{\"role\": \"user\", \"content\": \"What is the weather in San Francisco?\"}],\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"description\": \"Get the current weather for a location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"The city and state, e.g. San Francisco, CA\"\n }\n },\n \"required\": [\"location\"]\n }\n }\n }\n ],\n \"tool_choice\": \"auto\"\n }'\n```\n\n## Actual Output\n```json\n{\n \"id\": \"chatcmpl-xxx\",\n \"object\": \"chat.completion\",\n \"model\": \"qwen2.5-coder-14b-awq\",\n \"choices\": [\n {\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"\\n{\\n \\\"name\\\": \\\"get_weather\\\",\\n \\\"arguments\\\": {\\n \\\"location\\\": \\\"San Francisco, CA\\\"\\n }\\n}\\n \",\n \"tool_calls\": []\n }\n }\n ]\n}\n```\n\n## Expected Output\n```json\n{\n \"id\": \"chatcmpl-xxx\",\n \"object\": \"chat.completion\",\n \"model\": \"qwen2.5-coder-14b-awq\",\n \"choices\": [\n {\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"\",\n \"tool_calls\": [\n {\n \"type\": \"function\",\n \"id\": \"call_0\",\n \"function\": {\n \"name\": \"get_weather\",\n \"arguments\": \"{\\\"location\\\": \\\"San Francisco, CA\\\"}\"\n }\n }\n ]\n }\n }\n ]\n}\n```\n\n## Analysis\n\n### Model Output (Correct)\nThe model correctly generates tool calls in the expected `` XML format:\n```xml\n\n{\n \"name\": \"get_weather\",\n \"arguments\": {\n \"location\": \"San Francisco, CA\"\n }\n}\n \n```\n\n### Parser Behavior (Incorrect)\nBoth recommended parsers fail to extract tool calls:\n- **hermes parser**: Expects `` tags, doesn't match `` tags\n- **qwen3_xml parser**: Designed for `` tags but doesn't populate `tool_calls` array\n- **qwen3_coder parser**: Also designed for Qwen but fails to populate array\n\n### Root Cause\nThe parsers appear to load correctly (visible in logs as `'tool_call_parser': 'qwen3_xml'`) but the extraction logic fails to populate the OpenAI-compatible `tool_calls` array structure.\n\n## Workaround\nManual extraction from the `content` field:\n\n```python\nimport re\nimport json\n\ndef extract_tool_calls(response):\n \"\"\"Extract tool calls from Qwen2.5-Coder tags\"\"\"\n content = response['choices'][0]['message']['content']\n pattern = r'\\s*({.*?})\\s* '\n match = re.search(pattern, content, re.DOTALL)\n\n if match:\n tool_data = json.loads(match.group(1))\n return [{\n \"type\": \"function\",\n \"function\": {\n \"name\": tool_data[\"name\"],\n \"arguments\": json.dumps(tool_data[\"arguments\"])\n }\n }]\n return []\n```\n\n## Additional Context\n\n### Multi-AI Consultation Results\nConsulted with multiple AI models for parser recommendation:\n- **Qwen3 Coder (480B)**: Recommended `qwen3_xml` parser\n- **DeepSeek V3.1**: Ranked `qwen3_xml` (90% confidence), `qwen3_coder` (80% confidence)\n- **Claude Sonnet 4.5**: Confirmed tag mismatch between Hermes and Qwen formats\n\nAll models agreed that the parser selection is correct, suggesting the issue is in the parser implementation rather than configuration.\n\n### vLLM Configuration\n```python\n{\n 'tool_call_parser': 'qwen3_xml', # Confirmed in logs\n 'enable_auto_tool_choice': True,\n 'model': 'Qwen/Qwen2.5-Coder-14B-Instruct-AWQ',\n 'quantization': 'awq',\n 'max_model_len': 8192\n}\n```\n\n## Impact\n- **Severity**: High - Breaks OpenAI API compatibility for tool calling\n- **Affected Models**: Likely all Qwen2.5-Coder variants\n-",
"url": "https://github.com/vllm-project/vllm/issues/29192",
"state": "open",
"labels": [],
"created_at": "2025-11-21T18:31:19Z",
"updated_at": "2025-11-21T18:31:19Z",
"comments": 0,
"user": "Platano78"
},
{
"repo": "vllm-project/vllm",
"number": 29180,
"title": "[Bug]: Recorded `EngineCoreEventType.QUEUED` time is off",
"body": "### Your current environment\n\n\\nYou are an image classifier. Output only one word: safe or nsfw.\"\nsampling_params = SamplingParams(temperature=0.0, max_tokens=8)\n\nbatch_inputs = []\nfor i in range(64):\n img = Image.open(f\"/path/to/images/{i}.jpg\").convert(\"RGB\")\n batch_inputs.append({\n \"prompt\": prompt,\n \"multi_modal_data\": {\"image\": img},\n })\n\noutputs = llm.generate(batch_inputs, sampling_params=sampling_params, use_tqdm=True)\n```\n\n### Expected behavior\nFor pure-text batches, Adding requests is >2000 it/s such as qwen3vl.\nAttempted solutions (all ineffective)\n\n### my attempt to speed up\nIncrease CPU cores / set OMP_NUM_THREADS=16 \u2192 no speedup\nmm_processor_kwargs={\"max_dynamic_patch\": 1, ...} \u2192 seems no speedup\nPre-resize images to 384\u00d7384 \u2192 helps a little (~55 it/s) but still far from ideal\n\n\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.\n\n\n\nThank you for the great work on vLLM! Looking forward to a simple way to slove it",
"url": "https://github.com/vllm-project/vllm/issues/29177",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-21T10:56:29Z",
"updated_at": "2025-12-01T14:08:22Z",
"comments": 3,
"user": "Passenger12138"
},
{
"repo": "huggingface/trl",
"number": 4554,
"title": "Better packing of data with best-fit decrease strategy",
"body": "Hello,\n\nWhen using packing with the bfd strategy, it looks like too much truncation is done when the seq_length is smaller than the average length of the sequences we want to pack.\n\nFor example : \n```python\nfrom datasets import Dataset\nfrom trl import pack_dataset\n\nexamples = {\n \"input_ids\": [[1, 2, 3, 4], [5, 6], [7, 8, 9], [10]],\n \"attention_mask\": [[1, 1, 1, 1], [1, 0], [1, 0, 0], [1]],\n}\ndataset = Dataset.from_dict(examples)\n\npacked_dataset = pack_dataset(dataset, seq_length=3, strategy=\"bfd\")\nprint(packed_dataset )\n```\n\nresults in:\n```python\n{'input_ids': [[1, 2, 3], [7, 8, 9], [5, 6, 10]],\n 'attention_mask': [[1, 1, 1], [1, 0, 0], [1, 0, 1]],\n 'seq_lengths': [[3], [3], [2, 1]]}\n```\nSo the token '4' is missing from the training tokens.\n\n\nIn a extreme case:\n```python\nexamples_2 = {\n \"input_ids\": [[0, 0], [1, 2, 3, 4], [5, 6, 7, 8, 9], [10]],\n \"attention_mask\": [[1, 1], [1, 1, 1, 1], [1, 1, 1, 1, 1], [1]],\n}\ndataset_2 = Dataset.from_dict(examples_2)\nprint(pack_dataset(dataset_2, seq_length=1, strategy=\"bfd\")[:])\n```\nresults in:\n```python\n{'input_ids': [[0], [1], [5], [10]],\n 'attention_mask': [[1], [1], [1], [1]],\n 'seq_lengths': [[1], [1], [1], [1]]}\n```\n\nSo here we are basically applying truncation to every sequence instead of having twelve sequences of one token.\n\n\nIf we put ourself in a more usefull setting, when I was finetunning on some very long sequences with a seq_lenfth of 4096, the majority of the tokens was discarded y the bfd packing. On my dataset, the bfd method kept only 0.2% of the total training tokens.\n\nIs the behavior normal ?\nI would find it useful to add an option to still have tokens that are deleted in other sequences, even if this is less than ideal. It would be a good compromise between the current versions of bfd and wrapped.",
"url": "https://github.com/huggingface/trl/issues/4554",
"state": "closed",
"labels": [
"\u2728 enhancement",
"\u2753 question"
],
"created_at": "2025-11-21T07:53:55Z",
"updated_at": "2025-12-16T20:37:02Z",
"comments": 3,
"user": "ntnq4"
},
{
"repo": "vllm-project/vllm",
"number": 29148,
"title": "[Usage]: Deployment of the embedding models",
"body": "### Your current environment\n\n```text\n============================== \n System Info \n============================== \nOS : Ubuntu 22.04.5 LTS (x86_64) \nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 \nClang version : Could not collect \nCMake version : version 3.22.1\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.16 (main, Dec 11 2024, 16:24:50) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-5.15.0-161-generic-x86_64-with-glibc2.35\n\n============================== \n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.61\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA GeForce RTX 5090\nGPU 1: NVIDIA GeForce RTX 5090\nGPU 2: NVIDIA GeForce RTX 5090\nGPU 3: NVIDIA GeForce RTX 5090\nGPU 4: NVIDIA GeForce RTX 5090\nGPU 5: NVIDIA GeForce RTX 5090\nGPU 6: NVIDIA GeForce RTX 5090\nGPU 7: NVIDIA GeForce RTX 5090\n\nNvidia driver version : 570.172.08\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.7.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n```\n\n\n### How would you like to use vllm\n\nWhen deploying the embedding model, I found that the actual GPU memory usage included not only the model itself but also kv_cache. Is this a reasonable phenomenon? In version v0.9.0, the GPU memory usage was only for the model itself.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29148",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-21T03:57:59Z",
"updated_at": "2025-11-21T06:17:18Z",
"comments": 3,
"user": "Root970103"
},
{
"repo": "vllm-project/vllm",
"number": 29139,
"title": "[Feature]: Optimize collectives in TP MoE case using torch.compile pass",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nTo avoid redundant work in MoE models in the TP case, sequence parallelism was added to the Deepseek model definition in #24134 and expanded to other models in #24982. However, to avoid performing surgery on the linear layer, the current approach performs more communication than necessary. With a torch.compile custom pass, we can rewrite the graph to remove the redundant computation.\n\n### More details\n\nBefore the SP optimization, the ops in the model were:\n```\n- o_proj:[num_tokens, ...] -> [num_tokens, ...] (incomplete results)\n- all_reduce:[num_tokens, ...] -> [num_tokens, ...]\n- router:[num_tokens, ...] -> [num_tokens, ...]\n- experts:[num_tokens, ...] -> [num_tokens, ...]\n- ...\n```\n\nWith sequence parallel enabled, this becomes:\n```\n- o_proj: [num_tokens, ...] -> [num_tokens, ...] (incomplete results)\n- all_reduce: [num_tokens, ...] -> [num_tokens, ...]\n- chunk: [num_tokens, ...] -> [num_tokens/tp, ...]\n- router: [num_tokens/tp, ...] -> [num_tokens/tp, ...]\n- experts: [num_tokens/tp, ...] -> [num_tokens/tp, ...]\n- all_gather: [num_tokens/tp, ...] -> [num_tokens, ...]\n```\n\nAdditionally, experts now properly do the dp+tp<->ep dispatch instead of just the original replicated dp<->ep dispatch.\n\nNotice that the `all_reduce` does redundant communication as each TP rank only requires partial results. With a compile pass, we can convert the `all_reduce` -> `chunk` sequence into a `reduce_scatter`:\n\n```\n- o_proj: [num_tokens, ...] -> [num_tokens, ...] (incomplete results)\n- reduce_scatter: [num_tokens, ...] -> [num_tokens/tp, ...]\n- router: [num_tokens/tp, ...] -> [num_tokens/tp, ...]\n- experts: [num_tokens/tp, ...] -> [num_tokens/tp, ...]\n- all_gather: [num_tokens/tp, ...] -> [num_tokens, ...]\n```\n\nWe should create a new `SequenceParallelismMoEPass`, controlled by a new `PassConfig.enable_sp_moe` flag (following the new naming convention in #27995) so that it can be turned on independently of regular SP. We will likely need to pad the number of tokens to a multiple of TP size, although like described in #29136, there are alternatives.\n\n### Alternatives\n\nAlternatively, the original optimization could be done as a compile pass as well, which would significantly clean up the MoE model definitions. However, that would mean that `VLLM_COMPILE` compilation mode would be required for this optimization and if compilation is disabled, the optimization would be disabled as well. Generally we accept lower performance in eager mode as compilation is on by default, but I know there was a reason this was done this way (don't remember why).\n\n### Additional context\n\nOriginal proposal comment: https://github.com/vllm-project/vllm/pull/24982#pullrequestreview-3259494618\n\ncc @tlrmchlsmth @bnellnm @robertgshaw2-redhat @alexm-redhat @zou3519 @nvpohanh @youkaichao \n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29139",
"state": "open",
"labels": [
"help wanted",
"good first issue",
"performance",
"feature request",
"torch.compile"
],
"created_at": "2025-11-21T01:36:06Z",
"updated_at": "2025-12-07T15:39:48Z",
"comments": 19,
"user": "ProExpertProg"
},
{
"repo": "vllm-project/vllm",
"number": 29097,
"title": "[Docs] Feedback for `/en/latest/`",
"body": "### \ud83d\udcda The doc issue\n\nno\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29097",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-11-20T14:53:44Z",
"updated_at": "2025-11-21T07:51:57Z",
"comments": 2,
"user": "ch950684-svg"
},
{
"repo": "vllm-project/vllm",
"number": 29089,
"title": "[Performance]: Can we use CUDA graph to accelerate the Qwen2_5omniAudioEncoder in Qwen2.5-Omni-3B?",
"body": "### Proposal to improve performance\n\n![\"Image\"](\"https://github.com/user-attachments/assets/535d7854-b9db-4e40-8f85-1abe08b4d35e\")
\nThe trace graph shows that Qwen2_5omniAudioEncoder has a large number of small kernel startups, indicating significant room for optimization.\nCan we use CUDA graph to accelerate the Qwen2_5omniAudioEncoder in Qwen2.5-Omni-3B?\n\n### Report of performance regression\n\n_No response_\n\n### Misc discussion on performance\n\n_No response_\n\n### Your current environment (if you think it is necessary)\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29089",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-11-20T12:13:58Z",
"updated_at": "2025-11-20T12:13:58Z",
"comments": 0,
"user": "xq25478"
},
{
"repo": "vllm-project/vllm",
"number": 29078,
"title": "[Performance]: \u591a\u5b9e\u4f8b\u5bfc\u81f4\u7684cpu\u5360\u7528\u8fc7\u9ad8",
"body": "### Your current environment\nGPU: RTX4090\ncuda version: cuda12.8\nvllm version: 0.11.0\n\n\u4e2d\u6587\uff1a\u6211\u4f7f\u7528triton server\u7684 vllm backend \u542f\u52a8\u4e864\u4e2a minerU2.5 \u6a21\u578b\u7684\u5b9e\u4f8b\uff0c\u6211\u7684\u670d\u52a1\u5668\u4e0a\u67092\u5f20\u5361\uff0c\u6211\u6bcf\u5f20\u5361\u542f\u52a8\u4e861\u4e2a\u5b9e\u4f8b\uff0c\u6211\u53d1\u73b0cpu\u8d1f\u8f7d\u6709\u65f6\u5019\u6781\u9ad8\uff0c\u51e0\u4e4e\u5360\u6ee1\u4e86\u6211\u7684\u670d\u52a1\u5668\uff0c\u6211\u7684\u670d\u52a1\u5668\u670996\u6838\uff0cvllm backend\u4f7f\u7528\u7684\u662fAsyncLLMEngine\uff0c\u6211\u89c2\u5bdf\u5230\u5728\u5355\u5361\u4e0a\u542f\u52a8\u4e00\u4e2a\u5b9e\u4f8b\u65f6\uff0c\u6211\u53d1\u9001200\u5f20\u5c0f\u5c3a\u5bf8\u7684\u6587\u5b57\u56fe\u505aOCR\u65f6\uff0cfps\u53ef\u4ee5\u8fbe\u5230\u6700\u9ad8\uff0c\u4e5f\u5c31\u662f\u6bcf\u79d2\u53ef\u4ee5\u5904\u7406200\u5f20\u7684\u56fe\u7247\uff0ccpu\u8d1f\u8f7d\u572840-50%\u5de6\u53f3\uff0c\u4e3a\u4e86\u8fdb\u4e00\u6b65\u589e\u52a0\u6027\u80fd\uff0c\u6211\u5728\u4e24\u5f20\u5361\u4e0a\u5404\u542f\u52a8\u4e86\u4e00\u4e2a\u5b9e\u4f8b\uff0c\u4f46\u662f\u6211\u89c2\u5bdf\u5230\u6b64\u65f6cpu\u8d1f\u8f7d\u51e0\u4e4e\u8fbe\u523099%\uff0c\u5360\u7528\u4e86\u6781\u9ad8\u7684cpu\uff0c\u6bcf\u4e2a\u5b9e\u4f8b\u7684fps\u53ea\u6709120\u5de6\u53f3\uff0c\u6027\u80fd\u51e0\u4e4e\u6ca1\u6709\u63d0\u5347\u3002\n\n\u6211\u505a\u4e86\u5927\u91cf\u7684\u6d4b\u8bd5\uff0c\u6211\u5f00\u59cb\u4ee5\u4e3a\u662ftriton server\u7684\u95ee\u9898\uff0c\u4f46\u7ecf\u8fc7\u6392\u67e5\uff0c\u6211\u8ba4\u4e3a\u95ee\u9898\u53ef\u80fd\u51fa\u73b0\u5728vllm\u63a8\u7406\u65f6\u5360\u7528\u4e86\u5f88\u9ad8\u7684cpu\uff0c\u56e0\u4e3a\u6211\u4e0d\u4f7f\u7528triton server\uff0c\u4f7f\u7528 `vllm serve`\u6765\u6a21\u62df\u540c\u6837\u7684\u60c5\u51b5\uff0c\u6bcf\u4e2avllm\u5b9e\u4f8b\u63a8\u7406\u65f6\u4e5f\u5360\u7528\u6389\u4e8620-30%\u7684cpu\uff0c\u5982\u679c\u8fd9\u6837\uff0c\u6211\u7684\u670d\u52a1\u5668\u5373\u4f7f\u6709\u518d\u591a\u7684GPU\uff0c\u4e5f\u4e0d\u80fd\u591f\u63d0\u5347\u6a21\u578b\u7684\u6027\u80fd\uff0c\u6211\u8be5\u5982\u4f55\u8c03\u8bd5\uff1f\nenglish\uff1a\nI launched 4 instances of the minerU2.5 model using the vllm backend of Triton Server. My server is equipped with 2 GPUs, with 1 instance running on each GPU. However, I noticed that the CPU load sometimes spikes to extremely high levels, nearly maxing out the server\u2014which has 192 CPU cores. The vllm backend uses AsyncLLMEngine.\n\nWhen running a single instance on one GPU and sending 200 small-sized text images for OCR, I achieved the highest FPS\u2014processing up to 200 images per second\u2014with the CPU load hovering around 40-50%. To further improve performance, I launched one instance on each of the two GPUs. But in this scenario, the CPU load reached nearly 99% (extremely high usage), and each instance only achieved around 120 FPS, with almost no performance gain.\n\nI conducted numerous tests. Initially, I suspected the issue was with Triton Server, but after troubleshooting, I believe the problem lies in the high CPU usage during vllm inference. Even when not using Triton Server\u2014simulating the same scenario with `vllm serve`\u2014each vllm instance consumes 20-30% of the CPU. If this persists, adding more GPUs to the server will not improve model performance. How should I debug this?\n\n\n### How would you like to use vllm\n\nI want to run inference of a [[MinerU2.5-2509-1.2B]().](https://huggingface.co/opendatalab/MinerU2.5-2509-1.2B) I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29078",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-20T08:26:35Z",
"updated_at": "2025-11-21T02:17:51Z",
"comments": 4,
"user": "zjq1996518"
},
{
"repo": "huggingface/transformers",
"number": 42291,
"title": "Can we disable IPython progress bar and use normal tqdm bar?",
"body": "I like the normal tqdm bar much better, it is lighter, cleaner, simpler, and less stress on my eyes (no green color). I would love to have an option to use tqdm bar and not IPython bar. ",
"url": "https://github.com/huggingface/transformers/issues/42291",
"state": "closed",
"labels": [],
"created_at": "2025-11-20T01:26:11Z",
"updated_at": "2025-12-28T08:02:45Z",
"comments": 1,
"user": "weathon"
},
{
"repo": "vllm-project/vllm",
"number": 29023,
"title": "[Feature]: Disable logging `/metrics`",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\n- IGW hits `/metrics` continuously to understand the current load on the system\n- This leads to an overload of logs\n- We can disable this with `--disable-uvicorn-access-log`, but lose access to all access logs\n\nWe should have `--disable-uvicorn-metrics-access-log` to avoid logging * just * metrics. Per Gemini, we can do this with something like:\n\n```python\n# Define the routes for which access logs should be disabled\nEXCLUDE_PATHS = [\"/health\", \"/metrics\"]\n\nclass EndpointFilter(logging.Filter):\n def filter(self, record: logging.LogRecord) -> bool:\n # Check if the log record contains arguments and if the path matches an excluded path\n if record.args and len(record.args) >= 3:\n path = record.args[2] # The path is typically the third argument in uvicorn access logs\n if path in EXCLUDE_PATHS:\n return False # Exclude this log record\n return True # Include all other log records\n```\n\nCreate a command line arg like `--disable-uvicorn-metrics-access-log`which selectively disables logging hits to `/metrics`\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29023",
"state": "open",
"labels": [
"help wanted",
"good first issue",
"feature request"
],
"created_at": "2025-11-19T18:25:48Z",
"updated_at": "2025-11-19T21:57:34Z",
"comments": 5,
"user": "robertgshaw2-redhat"
},
{
"repo": "huggingface/sentence-transformers",
"number": 3575,
"title": "How to override model's `max_seq_length`?",
"body": "It seems that impossible to override model's max length from `sentence_bert_config.json`. \n\n```python\nfrom sentence_transformers import SentenceTransformer\n\nm = SentenceTransformer(\"intfloat/e5-small\", tokenizer_kwargs={\"model_max_length\":3})\nprint(m.tokenize([\"hi hi hi hi hi hi hi hi hi hi hi hi hi\"]))\n# {'input_ids': tensor([[ 101, 7632, 7632, 7632, 7632, 7632, 7632, 7632, 7632, 7632, 7632, 7632,\n# 7632, 7632, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\nprint(m.tokenize([\"hi hi hi hi hi hi hi hi hi hi hi hi hi\"], truncation=True))\n# {'input_ids': tensor([[ 101, 7632, 7632, 7632, 7632, 7632, 7632, 7632, 7632, 7632, 7632, 7632,\n# 7632, 7632, 102]]), 'token_type_ids': tensor([[0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]])}\nprint(m[0].tokenizer([\"hi hi hi hi hi hi hi hi hi hi hi hi hi\"], truncation=True))\n# {'input_ids': [[101, 7632, 102]], 'token_type_ids': [[0, 0, 0]], 'attention_mask': [[1, 1, 1]]}\n\nm.max_seq_length = 3\nprint(m.tokenize([\"hi hi hi hi hi hi hi hi hi hi hi hi hi\"]))\n# {'input_ids': tensor([[ 101, 7632, 102]]), 'token_type_ids': tensor([[0, 0, 0]]), 'attention_mask': tensor([[1, 1, 1]])}\n```\n\nThis is happening because during load it load `max_seq_length` from `sentence_bert_config` and then in `Transformers` it will override `max_seq_length` only it wasn't set in `sentence_bert_config` https://github.com/huggingface/sentence-transformers/blob/ad28c0a982acc39c73abdf0019faca10f227ef28/sentence_transformers/models/Transformer.py#L101-L118 even if `model_max_length` is passed in `tokenizer_kwargs` and then `max_seq_length` will be used as `max_length` instead of passed in kwargs https://github.com/huggingface/sentence-transformers/blob/ad28c0a982acc39c73abdf0019faca10f227ef28/sentence_transformers/models/Transformer.py#L319-L327\n\nProbably this can be fixed by\n\n```diff\nmax_seq_length = min(max_seq_length, self.tokenizer.model_max_length)\n```\n\n\nSource https://github.com/embeddings-benchmark/mteb/pull/3587#discussion_r2542434603\nI think this is cause of https://github.com/huggingface/sentence-transformers/issues/3187",
"url": "https://github.com/huggingface/sentence-transformers/issues/3575",
"state": "open",
"labels": [],
"created_at": "2025-11-19T16:42:27Z",
"updated_at": "2025-11-20T13:47:13Z",
"user": "Samoed"
},
{
"repo": "huggingface/trl",
"number": 4546,
"title": "Does TRL support PipelineRL for compute efficiency?",
"body": "Hi \ud83d\udc4b,\n\nI'm trying to understand whether TRL currently supports (or plans to support) the PipelineRL approach described here:\n\n- Paper: [https://arxiv.org/pdf/2509.19128v2](https://arxiv.org/pdf/2509.19128v2?utm_source=chatgpt.com)\n- Overview: [https://arxiv.org/html/2509.19128](https://arxiv.org/html/2509.19128?utm_source=chatgpt.com)\n\nPipelineRL introduces an actor\u2013learner pipeline with in-flight weight updates, where actors keep generating while the learner updates weights concurrently. This reduces policy lag and improves GPU utilization for long-context RL runs.\n\n\nDoes TRL currently support this kind of pipelineRL workflow, or is there a recommended way to approximate it using the existing TRL trainers (GRPO + vLLM)?\n\nIf not, I'd love suggestions or best practices for building something similar on top of TRL.\n\nThanks! \ud83d\ude4f",
"url": "https://github.com/huggingface/trl/issues/4546",
"state": "open",
"labels": [
"\u2728 enhancement",
"\u2753 question"
],
"created_at": "2025-11-19T12:39:29Z",
"updated_at": "2025-11-22T12:43:54Z",
"comments": 3,
"user": "harisarang"
},
{
"repo": "vllm-project/vllm",
"number": 28996,
"title": "[Usage]: How to run a single data parallel deployment across multiple nodes without ray",
"body": "### Your current environment\n\n2 Nodes, each node has 8 H20 GPUs.\n\n### How would you like to use vllm\n\nAccording to https://docs.vllm.ai/en/latest/serving/data_parallel_deployment/#internal-load-balancing\n\n```shell\n# node0\nvllm serve Qwen3-Coder-480B-A35B-Instruct --trust-remote-code --max-num-seqs 64 --max-model-len 131072 --port $PORT0 --host :: --data-parallel-size 2 --data-parallel-size-local 1 --data-parallel-address $NODE0_IPV6 --data-parallel-rpc-port $PORT1\n\n# node1\nvllm serve Qwen3-Coder-480B-A35B-Instruct --trust-remote-code --max-num-seqs 64 --max-model-len 131072 --headless --data-parallel-size 2 --data-parallel-size-local 1 --data-parallel-start-rank 1 --data-parallel-address $NODE0_IPV6 --data-parallel-rpc-port $NODE0_PORT1\n```\nbut all of them are hanging on waiting for init message from front-end.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28996",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-19T06:47:22Z",
"updated_at": "2025-11-27T06:17:22Z",
"comments": 3,
"user": "crystalww"
},
{
"repo": "vllm-project/vllm",
"number": 28986,
"title": "[Feature]: Fused Kernel for GPT-OSS Router",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\n![\"Image\"](\"https://github.com/user-attachments/assets/31eba061-522c-4521-b0a9-9f25bb36c3df\")
\n\n- Right now, we spend ~3.5% of the layer in the expert selection\n- The operation is unfused\n\nWrite a fused kernel like we have for deepseek grouped_topk\n\n### Alternatives\n\n- torch compile\n- triton\n- cuda\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28986",
"state": "open",
"labels": [
"help wanted",
"good first issue",
"feature request"
],
"created_at": "2025-11-19T03:18:25Z",
"updated_at": "2025-12-12T16:16:37Z",
"comments": 7,
"user": "robertgshaw2-redhat"
},
{
"repo": "huggingface/transformers.js",
"number": 1458,
"title": "ONNX Backend Env variable",
"body": "### Question\n\nHi, \n\nFor some context, I'm building an application that uses some of the models on huggingface as an annotation tool that helps create annotations for training a specialised model.\n\nAs for the specialised model, I am able to export them to onnx, and I was able to run this model in the same application, but I have to manually install the same onnxruntime-web version to be able to do so. I looked into the docs [here](https://huggingface.co/docs/transformers.js/api/backends/onnx#module_backends/onnx.createInferenceSession), but I cannot access these functions through `env.backends.onnx`. I've tried `console.log(env.backends.onnx.isONNXProxy())` and got \n```\nUncaught (in promise) TypeError: env.backends.onnx.isONNXProxy is not a function\n```\n\nIs there a way I can access the same inference session through this package? \n\n---------------------------------\n\nMy `package.json`\n\n```\n{\n \"dependencies\": {\n \"@huggingface/transformers\": \"3.7.5\",\n \"onnxruntime-web\": \"1.22.0-dev.20250409-89f8206ba4\"\n },\n}\n\n```",
"url": "https://github.com/huggingface/transformers.js/issues/1458",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-11-19T01:26:02Z",
"updated_at": "2025-11-25T15:36:13Z",
"user": "Heinrik-20"
},
{
"repo": "vllm-project/vllm",
"number": 28956,
"title": "[Bug]: OOM when profiling multimodal model with multiple images",
"body": "### Your current environment\n\nvLLM 0.11.0\n\n### \ud83d\udc1b Describe the bug\n\nAs per title. \n\nThe error log is as follows:\n```\n[multiproc_executor.py:671] Traceback (most recent call last):\n[multiproc_executor.py:671] File \"/root/miniconda3/lib/python3.11/site-packages/vllm/v1/executor/multiproc_executor.py\", line 666, in worker_busy_loop\n[multiproc_executor.py:671] output = func(*args, **kwargs)\n[multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^^^^^\n[multiproc_executor.py:671] File \"/root/miniconda3/lib/python3.11/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n[multiproc_executor.py:671] return func(*args, **kwargs)\n[multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^^^^^\n[multiproc_executor.py:671] File \"/root/miniconda3/lib/python3.11/site-packages/vllm/v1/worker/gpu_worker.py\", line 263, in determine_available_memory\n[multiproc_executor.py:671] self.model_runner.profile_run()\n[multiproc_executor.py:671] File \"/root/miniconda3/lib/python3.11/site-packages/vllm/v1/worker/gpu_model_runner.py\", line 3379, in profile_run\n[multiproc_executor.py:671] expanded = output.new_zeros(\n[multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^\n[multiproc_executor.py:671] torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 3.00 GiB. GPU 6 has a total capacity of 139.81 GiB of which 2.58 GiB is free. Including non-PyTorch memory, this process has 137.21 GiB memory in use. Of the allocated memory 134.77 GiB is allocated by PyTorch, and 255.64 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)\n```\nLooks like we only need **ONE** encoder cache with shape `(encoder_budget, encoder_output_shape[-1])` rather than `len(dummy_encoder_outputs)` ones.\nhttps://github.com/vllm-project/vllm/blob/da8dadf68b5a2af849e7c5fd35ce9b8525d8d398/vllm/v1/worker/gpu_model_runner.py#L4128-L4144\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28956",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-11-18T17:36:55Z",
"updated_at": "2025-11-25T12:38:37Z",
"comments": 7,
"user": "imShZh"
},
{
"repo": "huggingface/lerobot",
"number": 2475,
"title": "Why there is difference between async inference and local inference in image resize?",
"body": "I read code between `src/lerobot/async_inference/policy_server.py` and `src/lerobot/scripts/lerobot_record.py`. I found difference in these 2 code about inference which causes different image shape\n1. `src/lerobot/scripts/lerobot_record.py` use this to deal with observation\nAnd `prepare_observation_for_inference` is like this:\n```python\ndef prepare_observation_for_inference(\n observation: dict[str, np.ndarray],\n device: torch.device,\n task: str | None = None,\n robot_type: str | None = None,\n) -> RobotObservation:\n for name in observation:\n observation[name] = torch.from_numpy(observation[name])\n if \"image\" in name:\n observation[name] = observation[name].type(torch.float32) / 255\n observation[name] = observation[name].permute(2, 0, 1).contiguous()\n observation[name] = observation[name].unsqueeze(0)\n observation[name] = observation[name].to(device)\n observation[\"task\"] = task if task else \"\"\n observation[\"robot_type\"] = robot_type if robot_type else \"\"\n return observation\n```\n\nHere no **resize** operation in images i think. \n\n2. in async_inference policy_server.py,it uses\nBut here function`prepare_raw_observation` makes sense on image shape\n```python\ndef prepare_raw_observation(\n robot_obs: RawObservation,\n lerobot_features: dict[str, dict],\n policy_image_features: dict[str, PolicyFeature],\n) -> Observation:\n \"\"\"Matches keys from the raw robot_obs dict to the keys expected by a given policy (passed as\n policy_image_features).\"\"\"\n # 1. {motor.pos1:value1, motor.pos2:value2, ..., laptop:np.ndarray} ->\n # -> {observation.state:[value1,value2,...], observation.images.laptop:np.ndarray}\n lerobot_obs = make_lerobot_observation(robot_obs, lerobot_features)\n # 2. Greps all observation.images.<> keys\n image_keys = list(filter(is_image_key, lerobot_obs))\n # state's shape is expected as (B, state_dim)\n state_dict = {OBS_STATE: extract_state_from_raw_observation(lerobot_obs)}\n image_dict = {\n image_k: extract_images_from_raw_observation(lerobot_obs, image_k) for image_k in image_keys\n }\n # Turns the image features to (C, H, W) with H, W matching the policy image features.\n # This reduces the resolution of the images\n image_dict = {\n key: resize_robot_observation_image(torch.tensor(lerobot_obs[key]), policy_image_features[key].shape)\n for key in image_keys\n }\n if \"task\" in robot_obs:\n state_dict[\"task\"] = robot_obs[\"task\"]\n return {**state_dict, **image_dict}\n```\nHere the shape of observation images is modified to policy config\n```python\ndef resize_robot_observation_image(image: torch.tensor, resize_dims: tuple[int, int, int]) -> torch.tensor:\n assert image.ndim == 3, f\"Image must be (C, H, W)! Received {image.shape}\"\n # (H, W, C) -> (C, H, W) for resizing from robot obsevation resolution to policy image resolution\n image = image.permute(2, 0, 1)\n dims = (resize_dims[1], resize_dims[2])\n # Add batch dimension for interpolate: (C, H, W) -> (1, C, H, W)\n image_batched = image.unsqueeze(0)\n # Interpolate and remove batch dimension: (1, C, H, W) -> (C, H, W)\n resized = torch.nn.functional.interpolate(image_batched, size=dims, mode=\"bilinear\", align_corners=False)\n\n return resized.squeeze(0)\n``` \n\nI found this when I can inference correctly locally by made weird action outputs from async inference. it must be caused by my didn't resize input image when training. -.-\n\n\nversion:deb9596bd3796c03ae3a5a6b81b63c1dba296256\n",
"url": "https://github.com/huggingface/lerobot/issues/2475",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-11-18T14:32:17Z",
"updated_at": "2025-11-24T02:23:13Z",
"user": "milong26"
},
{
"repo": "vllm-project/vllm",
"number": 28943,
"title": "[Usage]: what's the right way to run embedding model in vllm 0.11.0",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\nin vllm 0.8.7\uff0cI use following code to run local vllm\uff0call is right\uff1a\n```\n self.engine_args = EngineArgs(\n model=self.model_path,\n dtype='half',\n task=\"embed\",\n trust_remote_code=True,\n limit_mm_per_prompt={\"image\": 1},\n )\n e = asdict(self.engine_args)\n self.max_len = 100\n self.llm = LLM(**e)\n out = self.llm.embed(datas)\n```\nBut in vllm 0.11.0 according to the document https://www.aidoczh.com/vllm/models/pooling_models.html\uff0cit use runner=='pooling' to run embedding task. What's the diffenence? Could the 'task' arg 'embed' still take effect?\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28943",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-18T13:47:57Z",
"updated_at": "2025-11-20T10:49:12Z",
"comments": 3,
"user": "neverneverendup"
},
{
"repo": "huggingface/trl",
"number": 4541,
"title": "Is attn_implementation=sdpa not supported when using SFTTrainer with mllama?",
"body": "When trying to use `sdpa` with mllama I get an error using the default collator. Upon writing my own collator it works.\nWhen using `eager` implementation it gives cuda oom error. Is `sdpa` not supported?",
"url": "https://github.com/huggingface/trl/issues/4541",
"state": "open",
"labels": [],
"created_at": "2025-11-18T11:57:01Z",
"updated_at": "2025-11-18T11:57:01Z",
"comments": 0,
"user": "osaidr"
},
{
"repo": "vllm-project/vllm",
"number": 28930,
"title": "[Usage]: How to build a qwen3vl embedding model with a custom mlp layer on the top use vllm?",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n\n```\n\nHi friends! I train a sft model built upon qwen3vl 2b model, we put a mlp layer on it to compress the embedding size of the backbone model. Now I want to use vllm 0.11.0 to serve it but I meet some confuse. Here is my custom class code\n\n```\nfrom argparse import Namespace\nfrom dataclasses import asdict\nfrom typing import Literal, NamedTuple, Optional, TypedDict, Union, get_args\nimport torch\nimport torch.nn as nn\nfrom vllm.model_executor.models.qwen3_vl import Qwen3VLForConditionalGeneration\n\nfrom vllm.v1.pool.metadata import PoolingMetadata\nfrom vllm.v1.sample.metadata import SamplingMetadata\n\nfrom vllm.config import VllmConfig\nfrom vllm.multimodal import MULTIMODAL_REGISTRY\n\n\nclass CustomQwenVL3BPool(nn.Module):\n def __init__(\n self\n ):\n super().__init__()\n self.out = torch.nn.Sequential(\n torch.nn.Linear(2048, 512),\n torch.nn.SiLU(),\n torch.nn.Linear(512, 128)\n )\n\n def get_prompt_lens(self,\n hidden_states: Union[torch.Tensor, list[torch.Tensor]],\n pooling_metadata: PoolingMetadata,\n ) -> torch.Tensor:\n return pooling_metadata.prompt_lens\n\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n pooling_metadata: PoolingMetadata,\n ) -> Union[list[torch.Tensor], torch.Tensor]:\n # 1 \u63d0\u53d6lasttoken\n prompt_lens = self.get_prompt_lens(hidden_states, pooling_metadata)\n last_token_flat_indices = torch.cumsum(prompt_lens, dim=0) - 1\n hidden_states = hidden_states[last_token_flat_indices]\n # 2 mlp\u538b\u7f29\u7ef4\u5ea6\n mlp_output = self.out(hidden_states)\n # 3 \u6b63\u5219\u5316\u8f93\u51fa\uff0c\u9700\u8981check\u4e0bvllm\u662f\u5426\u4f1a\u518d\u6b21norm\n normalized_output = F.normalize(mlp_output, p=2, dim=-1)\n return normalized_output\n \n\nclass CustomQwen3VLForConditionalGeneration(Qwen3VLForConditionalGeneration):\n def __init__(self, *, vllm_config: VllmConfig, prefix: str = \"\"):\n super().__init__(vllm_config=vllm_config, prefix=prefix)\n self._pooler = CustomQwenVL3BPool()\n```\nWhen I run above code using local mode of vllm , error log says **\"[adapters.py:79] ST projector loading failed\".Does anybody know why?** BTW\uff0cwhat's the best practice to make a custom embedding model with mlp in vllm 0.11.0\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28930",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-18T10:32:07Z",
"updated_at": "2025-12-23T04:49:30Z",
"comments": 10,
"user": "neverneverendup"
},
{
"repo": "vllm-project/vllm",
"number": 28929,
"title": "[Usage]: How",
"body": "=",
"url": "https://github.com/vllm-project/vllm/issues/28929",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-18T10:26:17Z",
"updated_at": "2025-11-18T10:30:53Z",
"comments": 0,
"user": "neverneverendup"
},
{
"repo": "huggingface/datasets",
"number": 7869,
"title": "Why does dataset merge fail when tools have different parameters?",
"body": "Hi, I have a question about SFT (Supervised Fine-tuning) for an agent model.\n\nSuppose I want to fine-tune an agent model that may receive two different tools: tool1 and tool2. These tools have different parameters and types in their schema definitions.\n\nWhen I try to merge datasets containing different tool definitions, I get the following error:\n\nTypeError: Couldn't cast array of type\nstruct, ... , servicerId: struct>\nto\n{\n 'refundFee': {'description': Value(dtype='string'), 'type': Value(dtype='string')},\n ...\n 'templateId': {'description': Value(dtype='string'), 'type': Value(dtype='string')}\n}\nFrom my understanding, the merge fails because the tools column's nested structure is different across datasets \u2014 e.g., one struct contains an extra field servicerId while the other does not. This causes HuggingFace Datasets (and its underlying Apache Arrow schema) to reject the merge.\n\nMy question is: why is it designed this way?\n\nIs this strict schema matching a hard requirement of the library?\nIs there a recommended way to merge datasets with different tool schemas (different parameters and types)?\nFor an agent model supporting multiple tools, what's the best practice for preparing/merging training data without losing flexibility?\nAny guidance or design rationale would be greatly appreciated. Thanks!",
"url": "https://github.com/huggingface/datasets/issues/7869",
"state": "open",
"labels": [],
"created_at": "2025-11-18T08:33:04Z",
"updated_at": "2025-11-30T03:52:07Z",
"comments": 1,
"user": "hitszxs"
},
{
"repo": "vllm-project/vllm",
"number": 28903,
"title": "[Bug]: vllm inference on qwen3-vl when use_upstream_fa is False",
"body": "### Your current environment\n\npip show torch vllm flash-attn\n\nName: torch\nVersion: 2.8.0\n\n---\nName: vllm\nVersion: 0.11.0\n\n\nName: flash_attn\nVersion: 2.8.3\n\n\n\n### \ud83d\udc1b Describe the bug\n\nunit-test code as the follows,\nwhen simple qwen3-0.6B can run; but qwen3-vl-4b not run\n```python\n#coding=utf-8\n\"\"\"\n\u5199\u5355\u5143\u6d4b\u8bd5\u6765\u9a8c\u8bc1FA\u548cVLLM\u7684\u53ef\u7528\u6027\u548c\u517c\u5bb9\u6027\n\"\"\"\n\nimport torch\nfrom flash_attn import flash_attn_func\nimport unittest\nimport vllm\n# from vllm.attention.backends import get_attn_backend\n\nclass TestFA_VLLM(unittest.TestCase):\n def testFA(self,):\n # \u68c0\u67e5CUDA\u662f\u5426\u53ef\u7528\u53ca\u8bbe\u5907\n print(f\"CUDA available: {torch.cuda.is_available()}\")\n print(f\"Current device: {torch.cuda.current_device()}\")\n print(f\"Device name: {torch.cuda.get_device_name()}\")\n\n # \u5c1d\u8bd5\u521b\u5efa\u4e00\u4e2a\u7b80\u5355\u7684\u5f20\u91cf\u5e76\u79fb\u52a8\u5230GPU\n try:\n q = torch.randn(1, 1, 16, 64, dtype=torch.float16, device='cuda')\n k = torch.randn(1, 1, 16, 64, dtype=torch.float16, device='cuda')\n v = torch.randn(1, 1, 16, 64, dtype=torch.float16, device='cuda')\n output = flash_attn_func(q, k, v)\n print(\"FlashAttention test passed!\")\n except Exception as e:\n print(f\"FlashAttention test failed: {e}\")\n \n def oriTestVLLM(self,):\n # \u6253\u5370\u5f53\u524d\u4f7f\u7528\u7684attention\u540e\u7aef\n print(\"Available CUDA devices:\", torch.cuda.device_count())\n print(\"Current device:\", torch.cuda.current_device())\n print(\"Device name:\", torch.cuda.get_device_name())\n\n # \u68c0\u67e5vLLM\u914d\u7f6e\n print(\"vLLM version:\", vllm.__version__)\n\n # \u5c1d\u8bd5\u521b\u5efa\u4e00\u4e2a\u5c0f\u6a21\u578b\u6765\u89e6\u53d1\u540e\u7aef\u521d\u59cb\u5316\n try:\n from vllm import LLM\n llm = LLM(model=\"Qwen/Qwen3-0.6B\", max_model_len=256)\n print(\"vLLM\u521d\u59cb\u5316\u6210\u529f!\")\n prompt = \"\u8fd9\u662f\u4e00\u4e2a\u6d4b\u8bd5\u63d0\u793a\u3002\"\n response = llm.generate(prompt)\n print(\"rollout\u6d4b\u8bd5\u6210\u529f! \u751f\u6210\u7684\u6587\u672c:\", response)\n except Exception as e:\n print(f\"vLLM\u521d\u59cb\u5316\u5931\u8d25: {e}\")\n \n def testVLLM(self,):\n # \u6253\u5370\u5f53\u524d\u4f7f\u7528\u7684attention\u540e\u7aef\n print(\"Available CUDA devices:\", torch.cuda.device_count())\n print(\"Current device:\", torch.cuda.current_device())\n print(\"Device name:\", torch.cuda.get_device_name())\n\n # \u5c1d\u8bd5\u521b\u5efa\u4e00\u4e2a\u5c0f\u6a21\u578b\u6765\u89e6\u53d1\u540e\u7aef\u521d\u59cb\u5316\n try:\n MODEL_PATH = \"Qwen/Qwen3-VL-4B-Instruct\"\n from vllm import LLM\n from vllm import LLM, SamplingParams\n from vllm.assets.image import ImageAsset # vLLM \u5185\u7f6e\u5de5\u5177\uff0c\u5e2e\u4f60\u628a\u8def\u5f84 \u2192 PIL\n from vllm.assets.video import VideoAsset # \u5982\u679c\u4ee5\u540e\u60f3\u52a0\u89c6\u9891\u540c\u7406\n\n # \u968f\u4fbf\u7528\u4e00\u5f20\u56fe\u5c31\u884c\n image_path = \"\"\n from PIL import Image\n image = Image.open(image_path)\n # \u65b9\u5f0f B\uff1aURL\n # image = ImageAsset(\"image\", \"https://xxx.jpg\").pil_image\n\n # Qwen3-VL \u8981\u6c42\u7684\u5bf9\u8bdd\u6a21\u677f\n messages = [\n {\n \"role\": \"user\",\n \"content\": [\n {\"type\": \"image\", \"image\": image}, # \u56fe\u50cf\u5b57\u6bb5\n {\"type\": \"text\", \"text\": \"\u8bf7\u63cf\u8ff0\u8fd9\u5f20\u56fe\u7247\u3002\"}\n ]\n }\n ]\n # \u7528 transformers \u7684 apply_chat_template \u628a messages \u2192 \u6a21\u578b\u8f93\u5165\n from transformers import AutoTokenizer\n tok = AutoTokenizer.from_pretrained(MODEL_PATH)\n prompt = tok.apply_chat_template(\n messages,\n tokenize=False,\n add_generation_prompt=True\n )\n # ---------- \u2463 \u751f\u6210 ----------\n sampling_params = SamplingParams(\n temperature=0.7,\n max_tokens=512,\n stop_token_ids=[tok.eos_token_id, tok.convert_tokens_to_ids(\"<|im_end|>\")]\n )\n\n llm = LLM(model=MODEL_PATH, max_model_len=4096, \n limit_mm_per_prompt={\"image\": 1, \"video\": 0}, # \u6bcf\u5f20 prompt \u6700\u591a 1 \u5f20\u56fe\n dtype=\"bfloat16\", # A100/H100 \u53ef\u5f00\uff1b\u6d88\u8d39\u5361\u7528 \"float16\"\n gpu_memory_utilization=0.9,)\n print(\"vLLM\u521d\u59cb\u5316\u6210\u529f!\")\n \n\n outputs = llm.generate(\n {\"prompt\": prompt, \"multi_modal_data\": {\"image\": image}}, # \u5173\u952e\uff1a\u628a\u56fe\u4e5f\u4f20\u8fdb\u53bb\n sampling_params=sampling_params\n )\n\n response = outputs[0].outputs[0].text\n print(\"rollout\u6d4b\u8bd5\u6210\u529f! \u751f\u6210\u7684\u6587\u672c:\", response)\n except Exception as e:\n print(f\"vLLM\u521d\u59cb\u5316\u5931\u8d25: {e}\")\n\nif __name__ == \"__main__\":\n unittest.main()\n```\n\nerror is :vllm/vllm_flash_attn/flash_attn_interface.py\", line 233, in flash_attn_varlen_func [rank0]: out, softmax_lse = torch.ops._vllm_fa2_C.varlen_fwd( [rank0]: File \"/usr/local/lib/python3.10/dist-packages/torch/_ops.py\", line 1243, in __call__ [rank0]: return self._op(*args, **kwargs) [rank0]: torch.AcceleratorError: CUDA error: the provided PTX was compiled with an unsupported toolchain.\n\n\nThen, I review the code in https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/models/qwen3_vl.py#L375\n\nit default set use_upstream_fa = False, when I change it to True, it works? the vllm version is 0.11.0\n\n### Before submitting a new issue...\n\n-",
"url": "https://github.com/vllm-project/vllm/issues/28903",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-11-18T03:54:11Z",
"updated_at": "2025-11-18T08:18:09Z",
"comments": 1,
"user": "hedes1992"
},
{
"repo": "huggingface/lerobot",
"number": 2465,
"title": "loss:nan grdn:nan How to solve the gradient explosion problem in PI05 training?",
"body": "When training Pi05 using Lerobot, has anyone encountered a situation where gradients explode immediately after training? Errors occur when the batch_size is set to 64 or 32. How can this be resolved? \n\nBelow are my training commands and error logs.\n\npython src/lerobot/scripts/lerobot_train.py --dataset.repo_id=aa_merged280 --policy.type=pi05 \\\n--output_dir=./outputs/pi05_training2 --job_name=pi05_training2 \\\n--policy.pretrained_path=lerobot/pi05_base --policy.compile_model=true \\\n--policy.gradient_checkpointing=true --wandb.enable=true --policy.dtype=bfloat16 \\\n--steps=100000 --policy.device=cuda --batch_size=32 --policy.push_to_hub=false\n\nINFO 2025-11-17 22:07:40 ot_train.py:351 step:200 smpl:6K ep:9 epch:0.03 loss:nan grdn:nan lr:2.5e-06 updt_s:4.478 data_s:0.038\nWARNING 2025-11-17 22:07:40 db_utils.py:141 WandB logging of key \"loss_per_dim\" was ignored as its type \"\" is not handled by this wrapper.\nINFO 2025-11-17 22:22:38 ot_train.py:351 step:400 smpl:13K ep:18 epch:0.06 loss:nan grdn:nan lr:7.5e-06 updt_s:4.458 data_s:0.022\nWARNING 2025-11-17 22:22:38 db_utils.py:141 WandB logging of key \"loss_per_dim\" was ignored as its type \"\" is not handled by this wrapper.\nINFO 2025-11-17 22:37:34 ot_train.py:351 step:600 smpl:19K ep:27 epch:0.10 loss:nan grdn:nan lr:1.3e-05 updt_s:4.456 data_s:0.022\nWARNING 2025-11-17 22:37:34 db_utils.py:141 WandB logging of key \"loss_per_dim\" was ignored as its type \"\" is not handled by this wrapper.\nINFO 2025-11-17 22:52:31 ot_train.py:351 step:800 smpl:26K ep:36 epch:0.13 loss:nan grdn:nan lr:1.8e-05 updt_s:4.456 data_s:0.022\nWARNING 2025-11-17 22:52:31 db_utils.py:141 WandB logging of key \"loss_per_dim\" was ignored as its type \"\" is not handled by this wrapper.\nINFO 2025-11-17 23:07:29 ot_train.py:351 step:1K smpl:32K ep:45 epch:0.16 loss:nan grdn:nan lr:2.3e-05 updt_s:4.459 data_s:0.022\n",
"url": "https://github.com/huggingface/lerobot/issues/2465",
"state": "open",
"labels": [
"bug",
"policies",
"training"
],
"created_at": "2025-11-18T03:46:28Z",
"updated_at": "2025-12-03T16:13:56Z",
"user": "Lilgeneric"
},
{
"repo": "huggingface/lerobot",
"number": 2464,
"title": "Questions about Pi0.5 Model Training Details and High Level Planning Implementation",
"body": "Hello, while studying the Pi0.5 model, I have two questions regarding the model implementation that I would like to ask you:\n1\u3001The paper mentions that the model adopts two-stage pre-training and designs a comprehensive loss function. However, when checking the compute_loss part in the open-source code, it is found that currently only the action loss is calculated, and the loss related to the VLM (Vision-Language Model) in the pre-training stage is not reflected. I would like to confirm whether this part is implemented elsewhere in the code or if there are other design considerations?\n2\u3001The ablation experiments in the paper show that the jointly trained Pi0.5 performs excellently in explicit and implicit High Level planning, even better than GPT4 and manual upper-level planning. However, from the open-source model code, the implementation part related to the High Level planning step has not been found for the time being. I would like to know how this part of the function is reflected in the code?\nLooking forward to your reply, thank you!",
"url": "https://github.com/huggingface/lerobot/issues/2464",
"state": "open",
"labels": [
"question",
"training"
],
"created_at": "2025-11-18T01:27:59Z",
"updated_at": "2025-11-20T10:45:34Z",
"user": "Ginldaj"
},
{
"repo": "vllm-project/vllm",
"number": 28876,
"title": "[CI Failure]: should test_cumem.py use spawn or fork in cuda?",
"body": "### Name of failing test\n\ntests/basic_correctness/test_cumem.py\n\n### Basic information\n\n- [ ] Flaky test\n- [x] Can reproduce locally\n- [ ] Caused by external libraries (e.g. bug in `transformers`)\n\n### \ud83e\uddea Describe the failing test\n\nThe test only fails locally for me when I use vllm main branch and on the CI of my PR, error is caused by cuda tests using `fork` instead of `spawn` I think, in the CI, there is a line that's trying for force spawn: https://github.com/vllm-project/vllm/blob/f2b8e1c5510cf3621dc4b910f0eba5289d9fee88/.buildkite/test-pipeline.yaml#L99-L100, but looks like it's not effective. I looked at the function that decides to use fork or spawn: https://github.com/vllm-project/vllm/blob/f8b19c0ffd65f7f6f01a0da4a39b6890f5db40cb/tests/utils.py#L1027 and I don't think it looks like the flag `VLLM_WORKER_MULTIPROC_METHOD`. Although the issue doesn't repro in the main vllm CI. Wondering how do we fix this?\n\n```\nFAILED basic_correctness/test_cumem.py::test_python_error - RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method\nFAILED basic_correctness/test_cumem.py::test_basic_cumem - RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method\nFAILED basic_correctness/test_cumem.py::test_cumem_with_cudagraph - RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method\nFAILED basic_correctness/test_cumem.py::test_end_to_end[hmellor/tiny-random-LlamaForCausalLM] - RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method\nFAILED basic_correctness/test_cumem.py::test_end_to_end[facebook/opt-125m] - RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method\nFAILED basic_correctness/test_cumem.py::test_deep_sleep - RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method\nFAILED basic_correctness/test_cumem.py::test_deep_sleep_async - RuntimeError: Cannot re-initialize CUDA in forked subprocess. To use CUDA with multiprocessing, you must use the 'spawn' start method\n\n```\n\n### \ud83d\udcdd History of failing test\n\nhttps://buildkite.com/vllm/ci/builds/39127/steps/canvas?jid=019a84f5-0fbf-46f3-859f-42c02a2d3de1\n\n### CC List.\n\n_No response_",
"url": "https://github.com/vllm-project/vllm/issues/28876",
"state": "open",
"labels": [
"ci-failure"
],
"created_at": "2025-11-17T18:58:08Z",
"updated_at": "2025-11-17T20:59:14Z",
"comments": 1,
"user": "jerryzh168"
},
{
"repo": "vllm-project/vllm",
"number": 28868,
"title": "[Bug]: When compiling with ranges, we should pass the range information to Inductor",
"body": "### Your current environment\n\nmain\n\n### \ud83d\udc1b Describe the bug\n\nMight be more of a feature request. Context is that https://github.com/vllm-project/vllm/pull/24248 adds a new compile ranges API, where a user can specify which ranges to compile on.\n\nWe should tell Inductor how to constrain the compilation on the symints of the compile ranges\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28868",
"state": "open",
"labels": [
"bug",
"torch.compile"
],
"created_at": "2025-11-17T15:41:50Z",
"updated_at": "2026-01-05T23:37:12Z",
"comments": 1,
"user": "zou3519"
},
{
"repo": "vllm-project/vllm",
"number": 28866,
"title": "[Usage]: When is going to be the next release?",
"body": "Hi everyone,\n\nThank you for developing such a great tool!\n\nI was wondering when the next release is scheduled. I\u2019m interested in running Gemma3-text type architecture GGUF quantized models with VLLM. Are there any alternatives to do this with the latest release (v0.11.0)?\n\nI also noticed that you merged this PR with the working solution on October 9:\n\nhttps://github.com/vllm-project/vllm/pull/26189",
"url": "https://github.com/vllm-project/vllm/issues/28866",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-17T15:24:47Z",
"updated_at": "2025-11-19T10:51:47Z",
"comments": 1,
"user": "Invalid-coder"
},
{
"repo": "huggingface/transformers",
"number": 42241,
"title": "How to use padding with Mistral?",
"body": "I'm trying to understand how to use Mistral with `batch_size` > 1. One aspect of this is setting `padding=\"longest\"` in, e.g., `MistralCommonTokenizer.encode()`. But I'm getting `TypeError: 'set' object is not callable` when I try this. Example:\n```python\nimport torch\nfrom transformers import MistralForCausalLM, MistralCommonTokenizer\n\ntokenizer = MistralCommonTokenizer.from_pretrained(\"mistralai/Mistral-7B-Instruct-v0.3\")\nmodel = MistralForCausalLM.from_pretrained(\n \"mistralai/Mistral-7B-Instruct-v0.3\",\n dtype=torch.bfloat16,\n attn_implementation=\"sdpa\",\n device_map=\"auto\",\n)\n\nmessages = [\n \"You are a pirate chatbot who always responds in pirate speak!\",\n \"Who are you?\",\n]\n\nmodel_inputs = tokenizer.encode(messages, return_tensors=\"pt\", padding=\"longest\").to(\n model.device\n)\n```\n\nOutput:\n```\n---------------------------------------------------------------------------\nTypeError Traceback (most recent call last)\nCell In[1], line 17\n 5 model = MistralForCausalLM.from_pretrained(\n 6 \"mistralai/Mistral-7B-Instruct-v0.3\",\n 7 dtype=torch.bfloat16,\n 8 attn_implementation=\"sdpa\",\n 9 device_map=\"auto\",\n 10 )\n 12 messages = [\n 13 \"You are a pirate chatbot who always responds in pirate speak!\",\n 14 \"Who are you?\",\n 15 ]\n---> 17 model_inputs = tokenizer.encode(messages, return_tensors=\"pt\", padding=\"longest\").to(\n 18 model.device\n 19 )\n 21 generated_ids = model.generate(model_inputs, max_new_tokens=100, do_sample=True)\n 22 tokenizer.batch_decode(generated_ids)[0]\n\nFile ~/ad_hoc_analysis/src/asr_and_summarization/.venv/lib/python3.13/site-packages/transformers/tokenization_mistral_common.py:407, in MistralCommonTokenizer.encode(self, text, text_pair, add_special_tokens, padding, truncation, max_length, stride, pad_to_multiple_of, padding_side, return_tensors, verbose, **kwargs)\n 404 if text_pair:\n 405 raise ValueError(\"`MistralCommonTokenizer.encode` does not support `text_pair`.\")\n--> 407 padding_strategy, truncation_strategy, max_length, _ = self._get_padding_truncation_strategies(\n 408 padding=padding,\n 409 truncation=truncation,\n 410 max_length=max_length,\n 411 pad_to_multiple_of=pad_to_multiple_of,\n 412 verbose=verbose,\n 413 )\n 415 encoded_inputs = self._encode_plus(\n 416 text,\n 417 add_special_tokens=add_special_tokens,\n (...) 429 verbose=verbose,\n 430 )\n 432 return encoded_inputs[\"input_ids\"]\n\nFile ~/ad_hoc_analysis/src/asr_and_summarization/.venv/lib/python3.13/site-packages/transformers/tokenization_mistral_common.py:1034, in MistralCommonTokenizer._get_padding_truncation_strategies(self, padding, truncation, max_length, pad_to_multiple_of, verbose, **kwargs)\n 1031 max_length = self.model_max_length\n 1033 # Test if we have a padding token\n-> 1034 if padding_strategy != PaddingStrategy.DO_NOT_PAD and (self.pad_token is None or self.pad_token_id < 0):\n 1035 raise ValueError(\n 1036 \"Asking to pad but the tokenizer does not have a padding token. \"\n 1037 \"Please select a token to use as `pad_token` `(tokenizer.pad_token = tokenizer.eos_token e.g.)` \"\n 1038 \"or add a new pad token via `tokenizer.add_special_tokens({'pad_token': '[PAD]'})`.\"\n 1039 )\n 1041 # Check that we will truncate to a multiple of pad_to_multiple_of if both are provided\n\nFile ~/ad_hoc_analysis/src/asr_and_summarization/.venv/lib/python3.13/site-packages/transformers/tokenization_mistral_common.py:334, in MistralCommonTokenizer.pad_token(self)\n 329 @property\n 330 def pad_token(self) -> str:\n 331 \"\"\"\n 332 String associated to the padding token in the vocabulary.\n 333 \"\"\"\n--> 334 return self.convert_ids_to_tokens(self.pad_token_id)\n\nFile ~/ad_hoc_analysis/src/asr_and_summarization/.venv/lib/python3.13/site-packages/transformers/tokenization_mistral_common.py:548, in MistralCommonTokenizer.convert_ids_to_tokens(self, ids, skip_special_tokens)\n 546 tokens: list[str] = []\n 547 for token_id in ids:\n--> 548 if self._is_control_token(token_id) and skip_special_tokens:\n 549 continue\n 550 tokens.append(self.tokenizer.instruct_tokenizer.tokenizer.id_to_piece(token_id))\n\nFile ~/ad_hoc_analysis/src/asr_and_summarization/.venv/lib/python3.13/site-packages/transformers/tokenization_mistral_common.py:513, in MistralCommonTokenizer._is_control_token(self, token_id)\n 511 def _is_control_token(self, token_id: int) -> bool:\n 512 if self._tokenizer_type == MistralTokenizerType.spm:\n--> 513 return token_id in self.tokenizer.instruct_tokenizer.tokenizer._control_tokens()\n 514 elif self._tokenizer_type == MistralTokenizerType.tekken:\n 515 return token_id < self.tokenizer.instruct_tokenizer.tokenizer.num_special_tokens\n\nTypeError: 'set' object is not callable\n```\n\nEnv:\n```\n- `transformers` version: 4.57.1",
"url": "https://github.com/huggingface/transformers/issues/42241",
"state": "closed",
"labels": [],
"created_at": "2025-11-17T12:54:21Z",
"updated_at": "2025-11-19T06:11:44Z",
"user": "TopCoder2K"
},
{
"repo": "huggingface/chat-ui",
"number": 1986,
"title": "HI i would like to use default_headers={ \"X-HF-Bill-To\": \"org-name\" } in my chatui local deployment how i can??",
"body": "Hi, \n\nSo i want to bill my Inference usage to my organization and like to pass default_headers={\n \"X-HF-Bill-To\": \"org-name\"\n } parameter how i can do that?? ",
"url": "https://github.com/huggingface/chat-ui/issues/1986",
"state": "open",
"labels": [
"support"
],
"created_at": "2025-11-17T08:33:41Z",
"updated_at": "2025-11-17T08:33:41Z",
"user": "aditya-oss-prog"
},
{
"repo": "huggingface/diffusers",
"number": 12672,
"title": "How to set pipe \"requires_grad=true\"\uff1f",
"body": "I have set the variable and the model \"requires_grad=true\" with the following:\n` pipe.transformer.requires_grad = True\n pipe.vae.requires_grad = True`\n`prev_sample = prev_sample.detach().requires_grad_(True)`\nbut the \"requires_grad\" of result by the pipe is still not true:\n `image_tar = pipe.vae.decode(prev_sample, return_dict=False)[0]`\n\"image_tar\" still can not requires_grad, so how to set pipe \"requires_grad=true\"\uff1f(all the operation is during inference stage.)",
"url": "https://github.com/huggingface/diffusers/issues/12672",
"state": "closed",
"labels": [],
"created_at": "2025-11-17T03:36:43Z",
"updated_at": "2025-11-20T12:19:20Z",
"user": "micklexqg"
},
{
"repo": "huggingface/diffusers",
"number": 12669,
"title": "Flux1-Dev inference with single file ComfyUI/SD-Forge Safetensors",
"body": "Is it possible to run inference with diffusers using a single-file safetensors created for ComfyUI/SD-Forge?\n\nIt looks like FluxPipeline.from_single_file() might be intended for this purpose, but I'm getting the following errors:\n\n```\nimport torch\nfrom diffusers import FluxPipeline\n\npipe = FluxPipeline.from_single_file(\"./flux1-dev-fp8.safetensors\", torch_dtype=torch.float8_e4m3fn, use_safetensors=True)\n```\n\n```\nTraceback (most recent call last):\n File \"/home/user/flux/imgen.py\", line 9, in \n pipe = FluxPipeline.from_single_file(\"./flux1-dev-fp8.safetensors\", torch_dtype=torch.float8_e4m3fn, use_safetensors=True)\n File \"/home/user/.local/lib/python3.13/site-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n return fn(*args, **kwargs)\n File \"/home/user/.local/lib/python3.13/site-packages/diffusers/loaders/single_file.py\", line 509, in from_single_file\n loaded_sub_model = load_single_file_sub_model(\n library_name=library_name,\n ...<11 lines>...\n **kwargs,\n )\n File \"/home/user/.local/lib/python3.13/site-packages/diffusers/loaders/single_file.py\", line 127, in load_single_file_sub_model\n loaded_sub_model = create_diffusers_t5_model_from_checkpoint(\n class_obj,\n ...<4 lines>...\n local_files_only=local_files_only,\n )\n File \"/home/user/.local/lib/python3.13/site-packages/diffusers/loaders/single_file_utils.py\", line 2156, in create_diffusers_t5_model_from_checkpoint\n model.load_state_dict(diffusers_format_checkpoint)\n ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/.local/lib/python3.13/site-packages/torch/nn/modules/module.py\", line 2641, in load_state_dict\n raise RuntimeError(\n ...<3 lines>...\n )\nRuntimeError: Error(s) in loading state_dict for T5EncoderModel:\n\tMissing key(s) in state_dict: \"encoder.embed_tokens.weight\". \n```\n\nI checked the safetensors file and the T5 encoder is present. However, it is named differently, which confuses diffusers.",
"url": "https://github.com/huggingface/diffusers/issues/12669",
"state": "open",
"labels": [],
"created_at": "2025-11-16T11:57:48Z",
"updated_at": "2025-12-03T16:53:58Z",
"comments": 12,
"user": "ddpasa"
},
{
"repo": "huggingface/ai-deadlines",
"number": 41,
"title": "How to indicate ARR deadlines",
"body": "Right now the yaml format assumes conferences with locations and dates, but ACL ARR has rolling deadlines not tied to a physical conference. We are largely operating around these deadlines. How can we incorporate these into this system?",
"url": "https://github.com/huggingface/ai-deadlines/issues/41",
"state": "open",
"labels": [],
"created_at": "2025-11-15T00:26:33Z",
"updated_at": "2025-11-15T00:26:33Z",
"user": "morrisalp"
},
{
"repo": "huggingface/diffusers",
"number": 12662,
"title": "question on stable_audio_transformer.py",
"body": "Execuse me, I am leaning the code of `class StableAudioDiTModel` , I do not know what is the argument ` global_states_input_dim` used to? It seems that it is a must component that should be packed before the hidden_states sequence. and its default dim seems larger then the transformer inner_dim. What is that componenet means? If it is used to take in additional conditions, that seems can be done in the encoder outside. and compared with the concatenate, I think it may be better to repeat condition embedding to the sequence length and concat on hidden_dim. \n\nAnd what is the ` sample_size: int = 1024,` parameter used in the model creation? it seems not used during `forward` call \n\nThe func doc of `class StableAudioDiTModel:forward`, it said ``` encoder_attention_mask (`torch.Tensor` of shape `(batch_size, sequence_len)`, *optional*):```. why the shape of encoder_attention_mask is batch_size X sequence_len instead of batch_size X encoder_sequence_len to be identical with the shape of the input `encoder_hidden_states` \n\nand why thee return value of this `forward` is the direct `(hidden_states,)` but not `(hidden_states * attention_mask, )`? \n\nabout the `class StableAudioDiTModel forward`, what is the shape of parameters `rotary_embedding` and `timestep`? \n\nwhy the global_embedding is concated before the hidden_states? I think hidden_states is what we want to generated during DiT pipeline. while encoder_hidden_states is the condition signal, so global_embedding should be used to en-rich the encoder_hidden_states. and the action of concate the global_embedding before the input hidden_states sequence will change the input seq_length, according to[ [1]](https://github.com/Stability-AI/stable-audio-tools/blob/main/docs/conditioning.md#input-concatenation), the concatenation should be done in the feature_dim direction, is it? \nIt seems using normal LayerNorm layer instead of adaLN layer?",
"url": "https://github.com/huggingface/diffusers/issues/12662",
"state": "open",
"labels": [],
"created_at": "2025-11-14T09:26:01Z",
"updated_at": "2025-11-25T08:53:39Z",
"comments": 1,
"user": "JohnHerry"
},
{
"repo": "vllm-project/vllm",
"number": 28717,
"title": "[Usage]: Errors running vLLM docker in a closed environment with gpt-oss-120b on RTX 6000 Pro",
"body": "### Your current environment\n\nCan't get vLLM to start with the below configuration. Seems to have issues loading in the model .safetensors. Any ideas on what could be causing it?\n\nvllm version: 0.11.1\n\nCPU: Intel Xeon w7-2595X\nGPU: NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition\nModel: https://huggingface.co/openai/gpt-oss-120b/tree/main\n\nCommand:\ndocker run --rm --name vllm --gpus=all --runtime=nvidia -p 8000:8000 -e HF_HUB_OFFLINE=1 --ipc=host -v opt/models/cache/:/root/.cache/huggingface/hub vllm/vllm-openai:latest --model openai/gpt-oss-120b\n\nAlso tried: \ndocker run --rm --name vllm --gpus=all --runtime=nvidia -p 8000:8000 -e HF_HUB_OFFLINE=1 --ipc=host -v opt/models/cache/:/root/.cache/huggingface/hub vllm/vllm-openai:latest --model openai/gpt-oss-120b\n\nwith the same output.\n\n\nOutput:\nINFO 11-12 06:23:18 [__init__.py:216] Automatically detected platform cuda.\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m INFO 11-12 06:23:21 [api_server.py:1839] vLLM API server version 0.11.0\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m INFO 11-12 06:23:21 [utils.py:233] non-default args: {'model': 'openai/gpt-oss-120b'}\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m INFO 11-12 06:23:21 [arg_utils.py:504] HF_HUB_OFFLINE is True, replace model_id [openai/gpt-oss-120b] to model_path [/root/.cache/huggingface/hub/models--openai--gpt-oss-120b/snapshots/b5c939de8f754692c1647ca79fbf85e8c1e70f8a]\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m `torch_dtype` is deprecated! Use `dtype` instead!\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m INFO 11-12 06:23:26 [model.py:547] Resolved architecture: GptOssForCausalLM\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m ERROR 11-12 06:23:26 [config.py:278] Error retrieving safetensors: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/root/.cache/huggingface/hub/models--openai--gpt-oss-120b/snapshots/b5c939de8f754692c1647ca79fbf85e8c1e70f8a'. Use `repo_type` argument if needed., retrying 1 of 2\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m ERROR 11-12 06:23:28 [config.py:276] Error retrieving safetensors: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/root/.cache/huggingface/hub/models--openai--gpt-oss-120b/snapshots/b5c939de8f754692c1647ca79fbf85e8c1e70f8a'. Use `repo_type` argument if needed.\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m INFO 11-12 06:23:28 [model.py:1730] Downcasting torch.float32 to torch.bfloat16.\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m INFO 11-12 06:23:28 [model.py:1510] Using max model len 131072\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m INFO 11-12 06:23:29 [scheduler.py:205] Chunked prefill is enabled with max_num_batched_tokens=8192.\n\u001b[1;36m(APIServer pid=1)\u001b[0;0m INFO 11-12 06:23:29 [config.py:271] Overriding max cuda graph capture size to 992 for performance.\nINFO 11-12 06:23:31 [__init__.py:216] Automatically detected platform cuda.\n\u001b[1;36m(EngineCore_DP0 pid=308)\u001b[0;0m INFO 11-12 06:23:33 [core.py:644] Waiting for init message from front-end.\n\u001b[1;36m(EngineCore_DP0 pid=308)\u001b[0;0m INFO 11-12 06:23:33 [core.py:77] Initializing a V1 LLM engine (v0.11.0) with config: model='/root/.cache/huggingface/hub/models--openai--gpt-oss-120b/snapshots/b5c939de8f754692c1647ca79fbf85e8c1e70f8a', speculative_config=None, tokenizer='/root/.cache/huggingface/hub/models--openai--gpt-oss-120b/snapshots/b5c939de8f754692c1647ca79fbf85e8c1e70f8a', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=auto, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=mxfp4, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser='openai_gptoss'), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=/root/.cache/huggingface/hub/models--openai--gpt-oss-120b/snapshots/b5c939de8f754692c1647ca79fbf85e8c1e70f8a, enable_prefix_caching=True, chunked_prefill_enabled=True, pooler_config=None, compilation_config={\"level\":3,\"debug_dump_path\":\"\",\"cache_dir\":\"\",\"backend\":\"\",\"custom_ops\":[],\"splitting_ops\":[\"vllm.unified_attention\",\"vllm.unified_attention_with_output\",\"vllm.mamba_mixer2\",\"vllm.mamba_mixer\",\"vllm.short_conv\",\"vllm.linear_attention\",\"vllm.plamo2_mamba_mixer\",\"vllm.gdn_attention\",\"vllm.sparse_attn_indexer\"],\"use_inductor\":true,\"compile_sizes\":[],\"inductor_compile_config\":{\"enable_auto_functionalized_v2\":false},\"inductor_passes\":{},\"cudagraph_mode\":[2,1],\"use_cudagraph\":true,\"cudagraph_num_of_warmups\":1,\"cudagraph_capture_sizes\":[992,976,960,944,928,912,896,880,864,848,832,816,800,784,768,752,736,720,704,688,672,656,640,624,608,592,576,560,544,528,512,496,480,464,448,432,416,400,384,368,352,336,320,304,288,272,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48",
"url": "https://github.com/vllm-project/vllm/issues/28717",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-14T08:49:48Z",
"updated_at": "2025-11-20T15:45:21Z",
"comments": 3,
"user": "antonkarlsson1"
},
{
"repo": "huggingface/trl",
"number": 4525,
"title": "How to modify the advantage computation in GRPOTrainer",
"body": "I\u2019m looking to customize the advantage computation used in the DAPO algorithm. Do I need to subclass the full GRPOTrainer to do this, or is it sufficient to overwrite the logic in _generate_and_score_completions, since that method appears to handle the advantage calculation?",
"url": "https://github.com/huggingface/trl/issues/4525",
"state": "open",
"labels": [
"\u2753 question",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-11-14T03:48:17Z",
"updated_at": "2025-11-14T11:37:18Z",
"user": "Tuziking"
},
{
"repo": "huggingface/transformers",
"number": 42200,
"title": "Request of rewriting implementation of prediction_step in trainer.py",
"body": "### System Info\n\nAny system. Because it's a problem coming from source code.\n\n### Who can help?\n\n@SunMarc \n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nHi, i am talking about an issue that was reported 5 years ago but still exists in 2025, specifically, 13th Nov, 2025.\n\nI quote one of the issues that was discussed before, ignored by sgugger. Please find the link below\nhttps://discuss.huggingface.co/t/cuda-out-of-memory-when-using-trainer-with-compute-metrics/2941\n\nWhen i was about to fine tune a LLM today, i ran into the same issue but i got saved by one folk's solution provided in this discussion.\n\nHow to reproduce (you should have a GPU, no quantization, just full fine tuning):\n\n1. Find a random decoder-only text2text LLM, let's say Qwen3 0.6B.\n\n2. Prepare a train dataset (>0 rows) and eval dataset (>850 rows).\n\n3. Set eval_on_start = True, either TrainingArguments or SFTConfig could work.\n\n4. Implement your own compute_metrics BUT DON'T implement preprocess_logits_for_metrics.\n5. start training (don't need deepspeed or accelerate, just trainer.train())\n\nWhat would happen?\nFirst it would go through the evaluation dataset because i set eval_on_start=True, the model would go really fast originally but then it would go extremely slow. Finally, you would get an error that says numpy is trying to allocate a ridiculously big array to memory.\n\n![\"Image\"](\"https://github.com/user-attachments/assets/e1885324-fb09-48b6-8bfd-d36306c2a156\")
\n\nOne of the folk who seems to be inspired by example code provided the implementation of preprocess_logits_for_metrics, which solved problem i encountered perfectly. The evaluation run is done within 2 mins.\n\nWhy it would happen?\n\nI briefly go over the source code of evaluation_loop and i located prediction_step.\n\nprediction_step says it would return a tuple of three optional torch.Tensor (loss, logits, label).\n\n![\"Image\"](\"https://github.com/user-attachments/assets/537032b1-9371-4852-bed8-8f31cd6a0437\")
\n\nBut most of the time, the returned logits is a tuple.\n\nWhy?\n\nif you look at the the function that processes logits before logits is returned:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/d6f7f3b1-6c2a-4298-b4c2-f0ab85fa88cf\")
\n\nThis function would receive all kinds of \"tensors\". The type of \"tensors\" could be list, tuple, Mapping or torch.Tensor.\n\nDoes it change the variable, called \"tensors\", from other data types to torch.Tensor?\n\nNo.\n\ntype(tensors)(........) would preserve the original type of tensors. It means if the variable \"tensors\" (i hate this variable name because it is misleading and confusing) is a tuple, after this function, it's still a tuple!!!!!\n\nIt's a recursive function btw. I would love doing recursion in programming competition, but not in huggingface codebase!!! It also implies a fact that the input of nested_detach could be complexly nested, like ([],())\n\nSo this function doesn't guarantee the logits is a torch.Tensor.\n\nNor does the implementation of prediction_step before nested_detach was called in prediction_step\n\n![\"Image\"](\"https://github.com/user-attachments/assets/451982b4-648b-4876-a2b7-c9d748899fd1\")
\n\nSo, the logits is not always a torch.Tensor, which is contradictory to what the type hint says, what did developers do?\n\nThey developed preprocess_logits_for_metrics.\nSo that user could fix it ON THEIR OWN IMPLEMENTATION.\n\n(preprocess_logits_for_metrics is called within evaluation_loop to clean the mess, specifically, logits, returned by prediction_step())\n![\"Image\"](\"https://github.com/user-attachments/assets/c7494018-e282-4577-b824-3db9c9e57609\")
\n\nIt's such a lazy fix. Why a regular user is expected to implement their own preprocess_logits_f\nor_metrics, to deal with a poorly-designed prediction_step?\n\nIt has been 5 years since the person who reported it.........\n\nIf a user-defined compute_metrics is not provided to Trainer or SFTTrainer, the prediction_step would return (loss, none, none), which skips the whole problem and this is why users said the issue of \"slow evaluation\" is gone when they don't provide compute_metrics.\n\nI would like to make a Pull Request to fix it but i don't have enough time and energy to do this massive amount of work.\n\nA temporary fix is to let users know when they need to make their own compute_metrics, they also have to implement preprocess_logits_for_metrics. Different models would have different styles of implementations but for text2text decoder only LLM.\n\n![\"Image\"](\"https://github.com/user-attachments/assets/125fffe3-d8cc-44c7-9a96-35a11500d975\")
\n\n(Another thing is that the variable called ",
"url": "https://github.com/huggingface/transformers/issues/42200",
"state": "open",
"labels": [
"Good Second Issue",
"bug"
],
"created_at": "2025-11-14T00:13:40Z",
"updated_at": "2025-12-18T14:29:32Z",
"comments": 3,
"user": "Yacklin"
},
{
"repo": "huggingface/transformers",
"number": 42197,
"title": "Attempt to access socket despite HF_HUB_OFFLINE = 1 if cache warmed outside current process",
"body": "### System Info\n\n- `transformers` version: 4.57.1\n- Platform: Linux-6.6.84.1-microsoft-standard-WSL2-x86_64-with-glibc2.35\n- Python version: 3.13.0\n- Huggingface_hub version: 0.36.0\n- Safetensors version: 0.6.2\n- Accelerate version: not installed\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.9.1+cpu (NA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: No\n\n### Who can help?\n\n@ydshieh I have created a reproducible example of the issue I mentioned in https://github.com/huggingface/transformers/issues/41311#issuecomment-3508674325.\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nReproducible example: https://github.com/fr1ll/HF_HUB_OFFLINE\n\n\nWarming the cache in a subprocess, then disabling sockets, then loading the same model should work.\nHowever, it fails with an attempt to access a socket and then \"Can't load\" errors.\n\nThe script named `subprocess-warm_then_offline-load.py` reproduces this error.\n\nInterestingly, warming the cache in process, then disabling sockets, then loading the same model works.\nThis is reproduced in `inprocess-warm_then_offline-load.py` in the repo above.\n\n### Expected behavior\n\nWhen a model has already been loaded into the cache (\"warm cache\"), if `HF_OFFLINE_MODE` = `\"1\"`, a Transformers pipeline should be able to load the model without accessing any network sockets.",
"url": "https://github.com/huggingface/transformers/issues/42197",
"state": "closed",
"labels": [
"Good Second Issue",
"bug"
],
"created_at": "2025-11-13T21:38:29Z",
"updated_at": "2025-11-24T09:33:54Z",
"comments": 6,
"user": "fr1ll"
},
{
"repo": "vllm-project/vllm",
"number": 28646,
"title": "[Feature][P2]: Implement CI Build Time and Size Guards",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\n### Description\nOnce we optimize the Docker build, we need to prevent regressions. Create CI checks that fail if build time exceeds thresholds or if image size grows beyond acceptable limits. Also set up monitoring dashboards.\n\n### What You'll Do\n1. Create Python scripts to check image metrics:\n - `check-image-size.py` (extend existing wheel size checker)\n - `check-build-time.py`\n - `check-image-layers.py`\n2. Add these checks to CI pipeline after image build\n3. Set appropriate thresholds (configurable)\n4. Create Buildkite annotations for warnings\n5. Set up CloudWatch dashboard for metrics (optional)\n\n### Deliverables\n- [ ] Python scripts for checking metrics\n- [ ] Integration into test-template-ci.j2\n- [ ] Configurable thresholds via environment variables\n- [ ] Documentation on how to adjust thresholds\n- [ ] CloudWatch dashboard (optional)\n\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28646",
"state": "open",
"labels": [
"feature request",
"ci/build"
],
"created_at": "2025-11-13T12:50:34Z",
"updated_at": "2025-11-13T18:55:29Z",
"comments": 0,
"user": "rzabarazesh"
},
{
"repo": "huggingface/diffusers",
"number": 12650,
"title": "Question about the `# Copied from` system",
"body": "Hi team! \ud83d\udc4b\n\nWhile working on improving docstrings and type hints across scheduler files (issue #9567), I've noticed the `# Copied from` pattern used extensively throughout the codebase.\n\nExamples:\n- Functions like `betas_for_alpha_bar` are duplicated across multiple schedulers\n- Output classes like `DDPMSchedulerOutput` are copied with name replacements (e.g., DDPM->EulerDiscrete)\n\nMy question: What's the rationale behind this duplication system instead of:\n1. Using a shared utils.py or common.py file for common functions\n2. Using class inheritance for similar Output classes\n\nI understand there might be good architectural reasons (module independence, API stability, avoiding circular dependencies, etc.), but this isn't documented anywhere that I could find.\n\nSuggested action: Regardless of the answer, I think we should either:\n- Option A: Refactor to use inheritance/shared utilities (if the current system is legacy)\n- Option B: Document this design decision in:\n - A CONTRIBUTING.md or architecture doc\n - Comments in the utils/check_copies.py script itself\n - Another README in the diffusers directory\n\nThis would help future contributors (like me! \ud83d\ude05) understand why this pattern exists and how to work with it properly when improving documentation. What do you think?\n\nThanks for maintaining such a great library! \ud83d\ude80",
"url": "https://github.com/huggingface/diffusers/issues/12650",
"state": "open",
"labels": [],
"created_at": "2025-11-13T11:53:22Z",
"updated_at": "2025-12-21T22:44:03Z",
"comments": 3,
"user": "delmalih"
},
{
"repo": "huggingface/transformers",
"number": 42179,
"title": "Add TileLang Kernel Support",
"body": "### Feature request\n\nI would like to propose adding support for TileLang kernel in the transformers library. TileLang is a modular approach for writing attention kernels that could provide flexibility and performance benefits.\ngithub link: https://github.com/tile-ai/tilelang\n- Add TileLang as an optional attention backend\n- Provide configuration options similar to existing attention mechanisms\n- Ensure compatibility with existing model architectures\n- Add proper multi-GPU support and synchronization\n\n### Motivation\n\n- Enhanced Modularity\nTileLang offers a more modular approach to writing attention kernels, making it easier for researchers and developers to modify and optimize the implementation for specific use cases.\n\n- Performance Comparison\nIntegrating TileLang would allow users to benchmark its performance directly against existing attention implementations, such as Flex Attention and Flash Attention. This would foster a better understanding of how different kernels can impact model performance and efficiency.\n\n- Community Engagement\nSupporting TileLang in the Transformers library would attract a broader community of developers interested in optimizing transformer models, thus enhancing collaboration and innovation.\n\n- Flexibility\nTileLang's architecture is designed for ease of modification, allowing users to experiment with and refine attention mechanisms more effectively.\n\n### Your contribution\n\nI've experimented with TileLang kernel on transformers models and found it works well in single-GPU scenarios. However, when enabling multi-GPU inference using `device_map='auto'`, I encounter NaN tensors. This may be related to tensor synchronization issues in distributed settings.\n\nI'm willing to help with testing and potentially contributing to the implementation once the multi-GPU synchronization issue is understood and resolved.\n\nI also have 3 questions:\n1. Is there any existing plan or roadmap for TileLang integration?\n2. Are there specific guidelines for adding new attention backends?\n3. What would be the recommended approach for handling multi-GPU synchronization in custom kernels?\n",
"url": "https://github.com/huggingface/transformers/issues/42179",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-11-13T11:38:33Z",
"updated_at": "2025-11-13T11:38:33Z",
"comments": 0,
"user": "crownz248"
},
{
"repo": "huggingface/tokenizers",
"number": 1885,
"title": "Feature request: Characters delimiter argument",
"body": "I wish to develop a k-mer-character-based BPE tokenizer using your beautiful Rust package, for genomic applications. Unfortunately, it doesn't seem to support defining a characters delimiter. As I see it, it is a pretty straightforward change, instead of iterating a word by character, first split it by the delimiter and then iterate. Also, when merges are computed, in the string representation the character delimiter should also be considered. In that way, a multi-character word splitting could have been made feasible. Right now I am using a modified Python version of the BPE tokenizer made by the genius [Yikai-Liao](https://github.com/Yikai-Liao/efficient_bpe/blob/main/ebpe_v2.py), however it would be nice to see that happening in Rust as well, and natively supported by huggingface. Unfortunately, I am still novice in working with Rust, otherwise I would make a pull request with the suggested changes. Is it something that can be worked out in the future? Or is there a way to do this with the current implementation? Thank you!",
"url": "https://github.com/huggingface/tokenizers/issues/1885",
"state": "open",
"labels": [],
"created_at": "2025-11-13T10:40:29Z",
"updated_at": "2025-11-28T07:51:07Z",
"comments": 1,
"user": "VasLem"
},
{
"repo": "vllm-project/vllm",
"number": 28629,
"title": "[Usage]: TPOT per request information was not collected by vllm bench serve",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.2 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 4.1.0\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+xpu\nIs debug build : False\nCUDA used to build PyTorch : None\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.14.0-1006-intel-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : False\nCUDA runtime version : No CUDA\nCUDA_MODULE_LOADING set to : N/A\nGPU models and configuration : No CUDA\nNvidia driver version : No CUDA\ncuDNN version : No CUDA\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 32\nOn-line CPU(s) list: 0-31\nVendor ID: GenuineIntel\nBIOS Vendor ID: Intel(R) Corporation\nModel name: Intel(R) Xeon(R) w5-3435X\nBIOS Model name: Intel(R) Xeon(R) w5-3435X CPU @ 3.1GHz\nBIOS CPU family: 179\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 16\nSocket(s): 1\nStepping: 8\nCPU(s) scaling MHz: 45%\nCPU max MHz: 4700.0000\nCPU min MHz: 800.0000\nBogoMIPS: 6192.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 768 KiB (16 instances)\nL1i cache: 512 KiB (16 instances)\nL2 cache: 32 MiB (16 instances)\nL3 cache: 45 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-31\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and ",
"url": "https://github.com/vllm-project/vllm/issues/28629",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-13T09:20:19Z",
"updated_at": "2025-11-13T09:20:19Z",
"comments": 0,
"user": "jlwang1996"
},
{
"repo": "vllm-project/vllm",
"number": 28626,
"title": "[Bug]:Qwen3-VL-32B-AWQ model memory usage: 8k context limit with 40GB VRAM?",
"body": "### Your current environment\n\n
Env info snipped
\n\n```\nCollecting environment information...\nuv is set\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.1 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.28.3\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-5.15.0-152-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA H20\nGPU 1: NVIDIA H20\nGPU 2: NVIDIA H20\nGPU 3: NVIDIA H20\nGPU 4: NVIDIA H20\nGPU 5: NVIDIA H20\nGPU 6: NVIDIA H20\nGPU 7: NVIDIA H20\n\nNvidia driver version : 570.172.08\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 128\nOn-line CPU(s) list: 0-127\nVendor ID: GenuineIntel\nBIOS Vendor ID: Intel(R) Corporation\nModel name: INTEL(R) XEON(R) PLATINUM 8562Y+\nBIOS Model name: INTEL(R) XEON(R) PLATINUM 8562Y+ CPU @ 2.8GHz\nBIOS CPU family: 179\nCPU family: 6\nModel: 207\nThread(s) per core: 2\nCore(s) per socket: 32\nSocket(s): 2\nStepping: 2\nCPU(s) scaling MHz: 73%\nCPU max MHz: 4100.0000\nCPU min MHz: 800.0000\nBogoMIPS: 5600.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 3 MiB (64 instances)\nL1i cache: 2 MiB (64 instances)\nL2 cache: 128 MiB (64 instances)\nL3 cache: 120 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-31,64-95\nNUMA node1 CPU(s): 32-63,96-127\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target s", "url": "https://github.com/vllm-project/vllm/issues/29474", "state": "open", "labels": [ "usage", "kv-connector" ], "created_at": "2025-11-26T02:50:17Z", "updated_at": "2025-11-26T08:31:18Z", "comments": 1, "user": "mgw2168-1" }, { "repo": "vllm-project/vllm", "number": 29472, "title": "[Installation]: how to Install vllm on dell promax gb10", "body": "### Your current environment\n\nI failed to install vllm on dell promax gb10 , mesages as followed\n\nnvcc --version\nnvcc: NVIDIA (R) Cuda compiler driver\nCopyright (c) 2005-2025 NVIDIA Corporation\nBuilt on Wed_Aug_20_01:57:39_PM_PDT_2025\nCuda compilation tools, release 13.0, V13.0.88\nBuild cuda_13.0.r13.0/compiler.36424714_0\n\n\npip install vllm\nSuccessfully installed torch-2.9.0 torchaudio-2.9.0 torchvision-0.24.0 vllm-0.11.2\n\n```\n(py312) dell@promaxgb10-0843:~/test/vllm/Qwen$ vllm -V\nTraceback (most recent call last):\n File \"/home/dell/miniconda3/envs/py312/bin/vllm\", line 3, in\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nvLLM serve command with LMCache enabled produces wrong output with GPT OSS 20B for subsequent invocations with the same prompt\n\nSteps to reproduce:\nCommand to start the server:\n```\nLMCACHE_CONFIG_FILE=lmcache_cpu.yaml\nvllm serve openai/gpt-oss-20b --port 8000 --kv-transfer-config '{\"kv_connector\":\"LMCacheConnectorV1\", \"kv_role\":\"kv_both\"}'\n```\n\nInvocation:\n```\ncurl 127.0.0.1:8000/v1/chat/completions -H \"Content-Type: application/json\" -d '{\"model\": \"openai/gpt-oss-20b\", \"messages\": [ {\"role\": \"user\", \"content\": \"What is Amazon SageMaker?\"}]}'\n```\n\nFirst invocation:\n```\n{\n\"id\":\"chatcmpl-951ca7178b1e4226b0343cb070033487\",\n\"object\":\"chat.completion\",\n\"created\":1764098087,\n\"model\":\"openai/gpt-oss-20b\",\n\"choices\":[\n{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"**Amazon SageMaker** is Amazon Web Services\u2019 fully\u2011managed platform that lets you build, train, tune, and deploy machine\u2011learning models fast\u2014without managing the underlying infrastructure.\\n\\nKey capabilities\\n\\n| Feature | What it does |\\n|--------|--------------|\\n| **SageMaker Studio** | A web\u2011based IDE that bundles notebooks, visual debugging, model monitoring, and collaboration tools. |\\n| **Built\u2011in algorithms & frameworks** | Pre\u2011packaged models (XGBoost, Linear Learner, etc.) and support for your own TensorFlow, PyTorch, MXNet, Scikit\u2011learn, R, etc. |\\n| **Auto\u2011ML & automated model tuning** | SageMaker Autopilot automatically searches model architectures and hyper\u2011parameters. |\\n| **Managed training** | Spot, distributed, and GPU training jobs that scale to the required compute. |\\n| **Model deployment** | One\u2011click production endpoints, batch transform, edge inference (SageMaker Edge), and real\u2011time or asynchronous inference. |\\n| **Inference pipelines** | Compose multiple models or processing steps into a single pipeline. |\\n| **Model monitoring & A/B testing** | Continuous evaluation of drift, predictions, and performance metrics. |\\n| **Security & compliance** | VPC, IAM, KMS encryption, private cataloging, and audit trails. |\\n\\nIn short, SageMaker removes the operational burden of ML\u2014so teams can focus on data science and business value rather than servers, networking, and scaling.\",\"refusal\":null,\"annotations\":null,\"audio\":null,\"function_call\":null,\"tool_calls\":[],\"reasoning\":\"User asks \\\"What is Amazon SageMaker?\\\" Short answer. Provide description: fully managed ML service, environment to build, train, deploy models, etc. Should be succinct.\",\"reasoning_content\":\"User asks \\\"What is Amazon SageMaker?\\\" Short answer. Provide description: fully managed ML service, environment to build, train, deploy models, etc. Should be succinct.\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"stop_reason\":null,\"token_ids\":null}],\"service_tier\":null,\"system_fingerprint\":null,\"usage\":{\"prompt_tokens\":75,\"total_tokens\":426,\"completion_tokens\":351,\"prompt_tokens_details\":null},\"prompt_logprobs\":null,\"prompt_token_ids\":null,\"kv_transfer_params\":null}\n```\n\nSecond invocation:\n```\n{\n \"id\": \"chatcmpl-4ebc19fc5c2a41a7bebc01ea8d1c98b1\",\n \"object\": \"chat.completion\",\n \"created\": 1764098160,\n \"model\": \"openai/gpt-oss-20b\",\n \"choices\": [\n {\n \"index\": 0,\n \"message\": {\n \"role\": \"assistant\",\n \"content\": \"Sure! Here\u2019s a basic guide to get you started with writing a cool, informative yet accessible article on **\\\"The Fascinating World of Quantum Computing\\\"** for a general audience. Feel free to adapt the structure, tone, or content to match your style and publication\u2019s guidelines.\\n\\n---\\n\\n## 1. Hook & Context (\u2248150\u2013200 words)\\n\\n- **Start with a vivid anecdote, surprising fact, or a relatable analogy** that introduces the \u201cwow\u201d moment in quantum computing.\\n - *Example:* \u201cImagine a coin that, instead of being heads or tails, can be both at the same time\u2026 until you look at it.\u201d \\n- **Briefly state why this topic matters** to everyday life: faster drug discovery, better encryption, breakthrough materials, etc.\\n\\n> **Tell readers what they\u2019ll learn**: a quick glimpse of quantum fundamentals, why it\u2019s different from classic bits, and how it could reshape technologies.\\n\\n---\\n\\n## 2. What\u2019s a Quantum Computer? (\u2248300 words)\\n\\n| Section | Content | Quick Tips |\\n|---------|---------|------------|\\n| **2.1 \u201cBits\u201d vs. \u201cQubits\u201d** | \u2022 Classical bits (\u201c0\u201d or \u201c1\u201d).The output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\u2022 Qubits: superposition (both 0 & 1) & entanglement. | Use visual metaphors: a spinning top (superposition) and two dancers always in sync (entanglement). |\\n| **2.2 Basic Operations** | \u2022 Quantum gates (Pauli X, H, CNOT).
\u2022 The role of interference. | A tiny \u201creversible\u201d logic of the quantum \u201cif\u2011then\u201d that flips outcomes. |\\n| **2.3 Measuring As a Collapses** | \u2022 Outcome collapse on measurement.
\u2022 Probabilities & expectation values. | Compare to a gamble: you only learn the re", "url": "https://github.com/vllm-project/vllm/issues/29436", "state": "open", "labels": [ "bug" ], "created_at": "2025-11-25T19:27:24Z", "updated_at": "2025-11-25T19:27:24Z", "comments": 0, "user": "ksuma2109" }, { "repo": "vllm-project/vllm", "number": 29409, "title": "[Usage]: Custom Logits Processors V1 how to get tokenizer into processor", "body": "### Problem with tokenizer\n\nFor the second day now, I've been unable to figure out how to get a tokenizer inside a custom processor. I used the processor from the documentation as an example. I examined each object through debug, but couldn't find where to extract the tokenizer. In v0, this was done simply at the request level, by passing an argument to the object. \nHow to pass a tokenizer to the processor?\n```python import torch\nfrom vllm.config import VllmConfig\nfrom vllm.sampling_params import SamplingParams\nfrom vllm.v1.sample.logits_processor import (BatchUpdate,\n LogitsProcessor,\n MoveDirectionality)\n\n\n\nclass DummyLogitsProcessor(LogitsProcessor):\n \"\"\"Fake logit processor to support unit testing and examples\"\"\"\n\n @classmethod\n def validate_params(cls, params: SamplingParams):\n target_token: int | None = params.extra_args and params.extra_args.get(\n \"target_token\"\n )\n \n \n if target_token is not None and not isinstance(target_token, int):\n raise ValueError(f\"target_token value {target_token} is not int\")\n\n def __init__(self, vllm_config: \"VllmConfig\", device: torch.device,\n is_pin_memory: bool):\n self.req_info: dict[int, int] = {}\n \n def is_argmax_invariant(self) -> bool:\n \"\"\"Never impacts greedy sampling\"\"\"\n return False\n\n def update_state(self, batch_update: BatchUpdate | None):\n \n if not batch_update:\n return\n # Process added requests.\n for index, params, _, _ in batch_update.added:\n assert params is not None\n self.validate_params(params)\n if params.extra_args and (target_token :=\n params.extra_args.get(\"target_token\")):\n self.req_info[index] = target_token\n else: \n self.req_info.pop(index, None)\n\n if self.req_info:\n # Process removed requests.\n for index in batch_update.removed:\n self.req_info.pop(index, None)\n\n # Process moved requests, unidirectional move (a->b) and swap\n # (a<->b)\n for adx, bdx, direct in batch_update.moved:\n a_val = self.req_info.pop(adx, None)\n b_val = self.req_info.pop(bdx, None)\n if a_val is not None:\n self.req_info[bdx] = a_val\n if direct == MoveDirectionality.SWAP and b_val is not None:\n self.req_info[adx] = b_val\n\n def apply(self, logits: torch.Tensor) -> torch.Tensor:\n if not self.req_info:\n return logits\n # Save target values before modification\n cols = torch.tensor(\n list(self.req_info.values()), dtype=torch.long, device=logits.device\n )\n rows = torch.tensor(\n list(self.req_info.keys()), dtype=torch.long, device=logits.device\n )\n values_to_keep = logits[rows, cols].clone()\n\n # Mask all but target tokens\n logits[rows] = float('-inf')\n logits[rows, cols] = values_to_keep\n\n return logits\n\n```\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.", "url": "https://github.com/vllm-project/vllm/issues/29409", "state": "closed", "labels": [ "usage" ], "created_at": "2025-11-25T13:24:17Z", "updated_at": "2025-12-02T10:33:18Z", "comments": 6, "user": "cvadim130" }, { "repo": "vllm-project/vllm", "number": 29389, "title": "[Bug]: race condition in shm_broadcast.py", "body": "### Your current environment\n\n
\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\n# Problem\n`ShmRingBuffer` is a lock-free queue, the implementation of which https://github.com/vllm-project/vllm/blob/12c007e288bf5c0ae3bd438036fbafbad88e706b/vllm/distributed/device_communicators/shm_broadcast.py#L98-L153\n\nrelies on the fact that when a flag is written to, signalling a valid state, the associated data is also in a valid state. To illustrate the point, consider the program\n```python\nshm = shared_memory.SharedMemory(..., size=128)\n# set shm to 0\n\n# process 1\nshm[0] = 1\nshm[64] = 1\n\n# process 2\nwhile shm[64] != 1:\n pass\nprint(shm[0])\n```\n`ShmRingBuffer` requires that `print(shm[0])` always prints `1`. **There is no guarantee this is true**. For this to be true,\n1. The Python language/implementation must provide a memory model, which it doesn't. Loosely speaking, a memory model is a set of guarantees on how source code maps to hardware instructions.\n2. Even if we assume the source code maps \"as intended\" to hardware instructions, the hardware must ensure that process 2 must observe the writes to `shm[0]` and `shm[64]` in the same order as process 1.\n\nAn example of 2 breaking down is given in [`race_condition.cpp`](https://gist.github.com/nvjullin/cc52386e291fe41218b54406ece962a0). On an ARM CPU,\n```bash\n$ g++ -std=c++17 race_condition.cpp\n$ ./a.out\nnumber of violations: 5\n# ...\n```\nUnfortunately, I don't know how to demonstrate the same race condition in Python.\n\n\n# What it means\n`ShmRingBuffer` can have corrupted memory and crashes vLLM sporadically. Such a crash would be near impossible to reproduce and debug.\n\n\n# Solutions\nIn order of recommendation:\n1. Remove `ShmRingBuffer` and always use the fallback `self.local_socket.send(serialized_obj)`. This is the simplest.\n2. Use a well-tested lock-free queue implementation and don't write our own. Lock-free programming is notoriously difficult to write correctly, requires expertise to understand and is overall a maintenence nightmare.\n3. Write it in C++ with proper atomics that guarantees the ordering of writes. The implementation should document extensively the proof of its correctness across different architectures. Python provides no tools for lock-free programming, making it impossible to write.\n\nCC @youkaichao @nvpohanh \n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29389",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-25T09:25:52Z",
"updated_at": "2025-11-25T09:25:52Z",
"comments": 0,
"user": "nvjullin"
},
{
"repo": "vllm-project/vllm",
"number": 29382,
"title": "[Doc]: Expert Parallel Deployment says \"Tensor parallel size (always 1 for now)\" is confusing",
"body": "### \ud83d\udcda The doc issue\n\nOn page https://docs.vllm.ai/en/latest/serving/expert_parallel_deployment/#single-node-deployment it says Tensor parallel size can only be 1 but didn't mention the behavior of Attention Layers\n\nOn page https://docs.vllm.ai/en/latest/serving/data_parallel_deployment/ it says The expert layers will by default form a (DP x TP) sized tensor parallel group. To enable expert parallelism, include the --enable-expert-parallel CLI arg (on all nodes in the multi-node case).\n\nwhich is rather confusing.\n\n### Suggest a potential alternative/fix\n\nPoint out the correct behavior of MoE models when TP, EP are both set.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29382",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-11-25T07:54:42Z",
"updated_at": "2025-12-13T17:38:01Z",
"comments": 0,
"user": "xeonliu"
},
{
"repo": "huggingface/transformers",
"number": 42375,
"title": "SAM3 single image inference with multiple text prompt",
"body": "Hi\nI'm trying to run inference on a single image, aiming to get the bbox of objects from several different categories (e.g. \"a person\" and \"a car\").\nthe only example i found for prompting with multiple categories is in the \"Batched Inference with Text Prompts\" example, but then i need to unnecessarily duplicate my image as the # of categories.\n\nis there a different more efficient way of achieving this? \n\np.s\nwhen i try prompting with a list of several categories and a single image i get an error.\n",
"url": "https://github.com/huggingface/transformers/issues/42375",
"state": "open",
"labels": [],
"created_at": "2025-11-25T06:20:09Z",
"updated_at": "2026-01-05T16:16:01Z",
"comments": 9,
"user": "iariav"
},
{
"repo": "huggingface/trl",
"number": 4569,
"title": "[doc issue] doc on \"GRPO with replay buffer\" buggy",
"body": "### Reproduction\n\nThe code example in [doc for \"GRPO with replay buffer\"](https://huggingface.co/docs/trl/main/en/experimental#grpo-with-replay-buffer) is kind of buggy. \n\n- It imports `GRPOWithReplayBufferTrainer` but never used. \n- It uses `GRPOWithReplayBufferConfig` but never imported\n- The code is apparently not executable.\n\n\nBelow is the code example given in the doc: \n\n```python\nfrom trl.experimental.grpo_with_replay_buffer import GRPOWithReplayBufferTrainer\nfrom datasets import load_dataset\n\ndataset = load_dataset(\"trl-internal-testing/zen\", \"standard_prompt_only\", split=\"train\")\n\n# Guarantee that some rewards have 0 std\ndef custom_reward_func(completions, **kwargs):\n if torch.rand(1).item() < 0.25:\n return [0] * len(completions) # simulate some None rewards\n else:\n return torch.rand(len(completions)).tolist()\n\ntraining_args = GRPOWithReplayBufferConfig(\n output_dir=self.tmp_dir,\n learning_rate=1e-4,\n per_device_train_batch_size=4,\n num_generations=4,\n max_completion_length=8,\n replay_buffer_size=8,\n report_to=\"none\",\n)\ntrainer = GRPOTrainer(\n model=\"trl-internal-testing/tiny-Qwen2ForCausalLM-2.5\",\n reward_funcs=[custom_reward_func],\n args=training_args,\n train_dataset=dataset,\n)\n\nprevious_trainable_params = {n: param.clone() for n, param in trainer.model.named_parameters()}\n\ntrainer.train()\n```\n\n\n### System Info\n\nNA\n\n### Checklist\n\n- [x] I have checked that my issue isn't already filed (see [open issues](https://github.com/huggingface/trl/issues?q=is%3Aissue))\n- [x] I have included my system information\n- [x] Any code provided is minimal, complete, and reproducible ([more on MREs](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks))\n- [x] Any code provided is properly formatted in code blocks, (no screenshot, [more on code blocks](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks))\n- [x] Any traceback provided is complete",
"url": "https://github.com/huggingface/trl/issues/4569",
"state": "closed",
"labels": [
"\ud83d\udc1b bug",
"\ud83d\udcda documentation",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-11-25T01:30:28Z",
"updated_at": "2025-11-25T21:28:00Z",
"comments": 2,
"user": "DNXie"
},
{
"repo": "vllm-project/vllm",
"number": 29306,
"title": "[Usage]: dots.llm.inst is not running due to a type error",
"body": "### Your current environment\n\nI'm trying to run dots llm on 4xH100\n\n```\nvllm serve \\\n --uvicorn-log-level=info \\\n rednote-hilab/dots.llm1.inst \\\n --dtype auto \\\n --api-key xxx \\\n --host 0.0.0.0 \\\n --port 8000 \\\n --tensor-parallel-size 4\n --ipc=host \\\n --trust-remote-code\n```\n\nIt failed to run, I got the following crash:\n\n```text\n(EngineCore_DP0 pid=10684) ERROR 11-24 09:41:25 [v1/executor/multiproc_executor.py:230] Worker proc VllmWorker-1 died unexpectedly, shutting down executor.\n(EngineCore_DP0 pid=10684) Process EngineCore_DP0:\n(EngineCore_DP0 pid=10684) Traceback (most recent call last):\n(EngineCore_DP0 pid=10684) File \"/usr/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n(EngineCore_DP0 pid=10684) self.run()\n(EngineCore_DP0 pid=10684) File \"/usr/lib/python3.12/multiprocessing/process.py\", line 108, in run\n(EngineCore_DP0 pid=10684) self._target(*self._args, **self._kwargs)\n(EngineCore_DP0 pid=10684) File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 846, in run_engine_core\n(EngineCore_DP0 pid=10684) raise e\n(EngineCore_DP0 pid=10684) File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 833, in run_engine_core\n(EngineCore_DP0 pid=10684) engine_core = EngineCoreProc(*args, **kwargs)\n(EngineCore_DP0 pid=10684) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=10684) File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 606, in __init__\n(EngineCore_DP0 pid=10684) super().__init__(\n(EngineCore_DP0 pid=10684) File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 109, in __init__\n(EngineCore_DP0 pid=10684) num_gpu_blocks, num_cpu_blocks, kv_cache_config = self._initialize_kv_caches(\n(EngineCore_DP0 pid=10684) ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=10684) File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 231, in _initialize_kv_caches\n(EngineCore_DP0 pid=10684) available_gpu_memory = self.model_executor.determine_available_memory()\n(EngineCore_DP0 pid=10684) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=10684) File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/executor/abstract.py\", line 126, in determine_available_memory\n(EngineCore_DP0 pid=10684) return self.collective_rpc(\"determine_available_memory\")\n(EngineCore_DP0 pid=10684) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=10684) File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/executor/multiproc_executor.py\", line 358, in collective_rpc\n(EngineCore_DP0 pid=10684) return aggregate(get_response())\n(EngineCore_DP0 pid=10684) ^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=10684) File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/executor/multiproc_executor.py\", line 341, in get_response\n(EngineCore_DP0 pid=10684) raise RuntimeError(\n(EngineCore_DP0 pid=10684) RuntimeError: Worker failed with error 'TypeError: can't multiply sequence by non-int of type 'float'\n\n\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] EngineCore failed to start.\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] Traceback (most recent call last):\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 833, in run_engine_core\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] engine_core = EngineCoreProc(*args, **kwargs)\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 606, in __init__\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] super().__init__(\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 109, in __init__\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] num_gpu_blocks, num_cpu_blocks, kv_cache_config = self._initialize_kv_caches(\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] File \"/home/ubuntu/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 231, in _initialize_kv_caches\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] available_gpu_memory = self.model_executor.determine_available_memory()\n(EngineCore_DP0 pid=11385) ERROR 11-24 09:45:27 [v1/engine/core.py:842] ",
"url": "https://github.com/vllm-project/vllm/issues/29306",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-24T09:48:08Z",
"updated_at": "2025-11-28T23:25:27Z",
"comments": 1,
"user": "rain-1"
},
{
"repo": "huggingface/transformers",
"number": 42353,
"title": "SAM3 point mode is not supported yet?",
"body": "In [SAM3 official example](https://github.com/facebookresearch/sam3/blob/main/examples/sam3_for_sam1_task_example.ipynb\n), they also support point mode. But it seems that transforms has not supported yet?\n",
"url": "https://github.com/huggingface/transformers/issues/42353",
"state": "closed",
"labels": [],
"created_at": "2025-11-24T07:16:52Z",
"updated_at": "2025-11-26T15:16:25Z",
"comments": 1,
"user": "haofanwang"
},
{
"repo": "vllm-project/vllm",
"number": 29297,
"title": "[Bug]: What should the image embedding input be like? I have tested with multiple cases but it all fails",
"body": "### Your current environment\n\n```text\n==============================\n System Info\n==============================\nOS : Red Hat Enterprise Linux release 8.10 (Ootpa) (x86_64)\nGCC version : (GCC) 8.5.0 20210514 (Red Hat 8.5.0-26)\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.28\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.19 (main, Oct 21 2025, 16:43:05) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-4.18.0-553.50.1.el8_10.x86_64-x86_64-with-glibc2.28\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA A100-SXM4-40GB\nGPU 1: NVIDIA A100-SXM4-40GB\n\nNvidia driver version : 575.51.03\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nByte Order: Little Endian\nCPU(s): 128\nOn-line CPU(s) list: 0-127\nThread(s) per core: 1\nCore(s) per socket: 64\nSocket(s): 2\nNUMA node(s): 8\nVendor ID: AuthenticAMD\nCPU family: 23\nModel: 49\nModel name: AMD EPYC 7742 64-Core Processor\nStepping: 0\nCPU MHz: 2250.000\nCPU max MHz: 2250.0000\nCPU min MHz: 1500.0000\nBogoMIPS: 4491.72\nVirtualization: AMD-V\nL1d cache: 32K\nL1i cache: 32K\nL2 cache: 512K\nL3 cache: 16384K\nNUMA node0 CPU(s): 0-15\nNUMA node1 CPU(s): 16-31\nNUMA node2 CPU(s): 32-47\nNUMA node3 CPU(s): 48-63\nNUMA node4 CPU(s): 64-79\nNUMA node5 CPU(s): 80-95\nNUMA node6 CPU(s): 96-111\nNUMA node7 CPU(s): 112-127\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sme sev sev_es\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.5.2\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.16.0\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-cufile-cu12==1.13.1.3\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-cutlass-dsl==4.3.0\n[pip3] nvidia-ml-py==13.580.82\n[pip3] nvidia-nccl-cu12==2.27.5\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvshmem-cu12==3.3.20\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] pyzmq==27.1.0\n[pip3] torch==2.9.0\n[pip3] torchaudio==2.9.0\n[pip3] torchvision==0.24.0\n[pip3] transformers==4.57.1\n[pip3] triton==3.5.0\n[conda] flashinfer-python 0.5.2 pypi_0 pypi\n[conda] numpy 2.2.6 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.8.4.1 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.8.93 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.8.90 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.10.2.21 pypi_0 pypi\n[conda] nvidia-cudn",
"url": "https://github.com/vllm-project/vllm/issues/29297",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-24T06:02:09Z",
"updated_at": "2025-11-26T13:00:17Z",
"comments": 2,
"user": "DamonZhao-sfu"
},
{
"repo": "vllm-project/vllm",
"number": 29294,
"title": "[CPU Backend] [Doc]: Update Installation Docs for Arm CPUs",
"body": "### \ud83d\udcda The doc issue\n\nThis page https://docs.vllm.ai/en/stable/getting_started/installation/cpu/#arm-aarch64 is very out-dated.\nWe now release Arm CPU wheels and images thanks to #26931 and #27331\n\nWe need to update that page to reflect that :)\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29294",
"state": "closed",
"labels": [
"documentation",
"cpu"
],
"created_at": "2025-11-24T05:33:46Z",
"updated_at": "2025-12-15T19:46:26Z",
"comments": 5,
"user": "fadara01"
},
{
"repo": "vllm-project/vllm",
"number": 29286,
"title": "[Performance]: cache system prompt token ids",
"body": "### Proposal to improve performance\n\nAs system prompt can be very long now, tokenize the system prompt can be slow. \n\nUsing H20, tokenize 5000 tokens cost about 10ms as below:\n\n\n\nSystem prompts are usually fixed and reusable, so cache the system prompt can be profitable.\n\nSpecificly:\n1. In **apply_hf_chat_template** method we can separate the system prompt from other prompts, we can use condition **cache_system_prompt = truncate_prompt_tokens is None and not tokenize and len(conversation) > 1 and conversation[0].get(\"role\") == \"system\"** to judge when we should separate the system prompt.\n2. In **_normalize_prompt_text_to_input** method we judge that whether system prompt is in the dict ({system prompt: token ids}) that we can reuse, then concat system prompt token ids and prompt token ids as the final input_ids.\n\nI am willing to contribute to this opt and looking forward to your suggestions!\n\n### Report of performance regression\n\nThe above cost can be profitable.\n\n### Misc discussion on performance\n\n_No response_\n\n### Your current environment (if you think it is necessary)\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29286",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-11-24T01:55:32Z",
"updated_at": "2025-11-28T08:57:06Z",
"comments": 2,
"user": "Eviannn"
},
{
"repo": "vllm-project/vllm",
"number": 29281,
"title": "[Usage]: Removing last generated token from output and kv cache",
"body": "### Your current environment\n\n```text\nvLLM 0.11.2\n```\n\n\n### How would you like to use vllm\n\nHey guys,\n\ni am currently working on a research project where i load a moe-like model and i want to do routing based on the sequence state.\nThe goal is to let expert 0 generate until it reaches the eos token, then remove the eos token and finish generation with expert 1 until the eos token is hit a second time.\nI want to do this to use different strengths of both models.\nMy current approach is to modify GPUModelRunner and Scheduler to remove the eos token from output, reduce num_computed_tokens by 1 and compute a static routing tensor based on the sequence state which i pass as additional model input, to route to expert 0 or 1.\n\nNow i am having some issues with unexpected output, especially with tensor_parallelism>1 on multiple gpus.\n\nI was wondering if there already is a reliable solution to remove the last generated token from output and kv cache, so that the computation leading to eos does not interfere with the second expert.\n\nOr maybe there is even a better way to do this?\n\nThank you!\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29281",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-23T22:39:16Z",
"updated_at": "2025-11-26T09:33:53Z",
"comments": 0,
"user": "josefdra"
},
{
"repo": "vllm-project/vllm",
"number": 29277,
"title": "[Usage]: Creating and accessing per request arguments inside vLLM model",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nI want to implement token compression techniques on the output embeddings of Qwen-2.5VL which would occur dynamically as the number of requests change. Is there anyway to implement this in vLLM? I see that SamplingParams seem to be the only way to use per request custom arguments but I don\u2019t believe it can be accessed within the model code directly?\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29277",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-23T21:59:31Z",
"updated_at": "2025-11-23T21:59:31Z",
"comments": 0,
"user": "minlu21"
},
{
"repo": "huggingface/transformers",
"number": 42344,
"title": "How to fine-tune SAM 3D models?",
"body": "### Model description\n\nThe recently released SAM 3D work is truly remarkable. Do you plan to integrate it into Transformers and enable fine-tuning?\nhttps://huggingface.co/facebook/sam-3d-objects\n\n### Open source status\n\n- [x] The model implementation is available\n- [x] The model weights are available\n\n### Provide useful links for the implementation\n\n_No response_",
"url": "https://github.com/huggingface/transformers/issues/42344",
"state": "open",
"labels": [
"New model"
],
"created_at": "2025-11-23T17:40:57Z",
"updated_at": "2025-11-23T17:40:57Z",
"user": "bruno686"
},
{
"repo": "vllm-project/vllm",
"number": 29264,
"title": "[Usage]: Monkey Patching SamplingParams",
"body": "### Your current environment\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.28.3\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.13.5 | packaged by conda-forge | (main, Jun 16 2025, 08:27:50) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.8.0-87-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA B200\nGPU 1: NVIDIA B200\nGPU 2: NVIDIA B200\nGPU 3: NVIDIA B200\nGPU 4: NVIDIA B200\nGPU 5: NVIDIA B200\nGPU 6: NVIDIA B200\nGPU 7: NVIDIA B200\n\nNvidia driver version : 570.195.03\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 224\nOn-line CPU(s) list: 0-223\nVendor ID: GenuineIntel\nModel name: INTEL(R) XEON(R) PLATINUM 8570\nCPU family: 6\nModel: 207\nThread(s) per core: 2\nCore(s) per socket: 56\nSocket(s): 2\nStepping: 2\nCPU(s) scaling MHz: 31%\nCPU max MHz: 4000.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities ibpb_exit_to_user\nVirtualization: VT-x\nL1d cache: 5.3 MiB (112 instances)\nL1i cache: 3.5 MiB (112 instances)\nL2 cache: 224 MiB (112 instances)\nL3 cache: 600 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-55,112-167\nNUMA node1 CPU(s): 56-111,168-223\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI",
"url": "https://github.com/vllm-project/vllm/issues/29264",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-23T11:45:54Z",
"updated_at": "2025-11-24T13:03:50Z",
"comments": 2,
"user": "josefdra"
},
{
"repo": "vllm-project/vllm",
"number": 29263,
"title": "[Feature]: Enable flash attention (and/or FlashMLA) for AMD GPUs",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nIn [this page from flash-attention](https://github.com/Dao-AILab/flash-attention?tab=readme-ov-file#amd-rocm-support), I checked that the upstream `flash-attention` currently has composable_kernel (for newer AMD GPUs) and WIP triton (for older RNDA GPUs, etc.) implementations. As well as [flash MLA](https://github.com/deepseek-ai/FlashMLA?tab=readme-ov-file#amd-instinct).\n\nIs it possible to enable `vllm.vllm_flash_attn._vllm_fa2_C` and more modules for AMD GPUs?\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29263",
"state": "closed",
"labels": [
"feature request",
"rocm"
],
"created_at": "2025-11-23T11:28:47Z",
"updated_at": "2025-12-05T01:54:08Z",
"comments": 4,
"user": "Inokinoki"
},
{
"repo": "vllm-project/vllm",
"number": 29245,
"title": "[Usage]: \u542f\u52a8 qwen3 vl \u8d85\u7ea7\u8d85\u7ea7\u8d85\u7ea7\u6162\uff0csglang \u542f\u52a8\u5f88\u5feb\uff0c\u53ef\u80fd\u7684\u539f\u56e0\u662f\u4ec0\u4e48\uff1f",
"body": "### Your current environment\n\n\u8fde\u6267\u884c python collect_env.py \u90fd\u5f88\u6162\uff0c\u73af\u5883\u662f\u76f4\u63a5 uv \u5b89\u88c5\u7684\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.2 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 4.1.2\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.9.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.3 (main, Jun 18 2025, 17:59:45) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-5.10.134-19.100.al8.x86_64-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.9.86\nCUDA_MODULE_LOADING set to : \nGPU models and configuration : \nGPU 0: NVIDIA L20Y\nGPU 1: NVIDIA L20Y\nGPU 2: NVIDIA L20Y\nGPU 3: NVIDIA L20Y\nGPU 4: NVIDIA L20Y\nGPU 5: NVIDIA L20Y\nGPU 6: NVIDIA L20Y\nGPU 7: NVIDIA L20Y\n\nNvidia driver version : 570.148.08\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.10.2\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.10.2\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Platinum 8468V\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 8\nCPU(s) scaling MHz: 70%\nCPU max MHz: 3800.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4800.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req hfi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm uintr md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 195 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; ",
"url": "https://github.com/vllm-project/vllm/issues/29245",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-22T20:41:27Z",
"updated_at": "2025-12-11T11:23:54Z",
"comments": 3,
"user": "hucorz"
},
{
"repo": "huggingface/candle",
"number": 3208,
"title": "`cudarc` dynamic loading support",
"body": "Currently, `candle` uses `cudarc` with the `dynamic-linking` feature, which requires the executable to find the DLLs or SOs at startup. However, it would be more convenient if `candle` also supported the `dynamic-loading` feature from `cudarc` to load DLLs or SOs at runtime.\nIs it possible for `candle` to support it?",
"url": "https://github.com/huggingface/candle/issues/3208",
"state": "open",
"labels": [],
"created_at": "2025-11-22T18:18:25Z",
"updated_at": "2025-11-25T09:00:27Z",
"comments": 7,
"user": "mayocream"
},
{
"repo": "huggingface/transformers",
"number": 42331,
"title": "SAM3 does not support custom inference resolutions",
"body": "### System Info\n\nNote: I am running the latest git version, sys Info should not be relevant to the issue\n$ transformers env \nTraceback (most recent call last):\n File \"/home/master-andreas/panopticon/test_env/bin/transformers\", line 3, in The output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\n
\n\n### \ud83d\udc1b Describe the bug\n\nWhen running benchmarking with the CLI:\n\n- on one side the serving point `vllm serve ...`\n- on the other side the benchmarking client : `vllm bench serve...`\n(note that the two are running on the same machine, there is no networking delay)\n\nI noticed that the `EngineCoreEventType.QUEUED` event recorder on the server side didn't match the time of posting the request. In my understanding these two should events should be approximately equivalent. These values aren't off by a few milliseconds, but here the mismatch can be pretty big, up to a few seconds. \n\nI think the reason might be because adding [request to the scheduler](https://github.com/vllm-project/vllm/blob/fcb1d570bb8f95f5b7ded716a52fec902c535f0e/vllm/v1/core/sched/scheduler.py#L1166) cannot be done when the engine is running a decoding or a prefill, see the [`_process_input_queue` function](https://github.com/vllm-project/vllm/blob/fcb1d570bb8f95f5b7ded716a52fec902c535f0e/vllm/v1/engine/core.py#L801), where `add_request()` ultimately gets called. This can introduce delays before the queued event gets recorded, having \"floating\" requests that are not tracked in the logs.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/29180",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-11-21T12:58:36Z",
"updated_at": "2025-11-30T20:56:44Z",
"comments": 4,
"user": "sducouedic"
},
{
"repo": "vllm-project/vllm",
"number": 29177,
"title": "[Usage]: Vllm + Intervl model local infra Image preprocessing / request adding becomes bottleneck even with more CPU cores \u2014 how to accelerate?",
"body": "### Your current environment\n\nvllm 0.11.0\n\n\n### How would you like to use vllm\n\n### current phenomenon\nWhen doing **batched image classification** (64 images per batch) with InternVL3_5-1B, the bottleneck is clearly in the **\"Adding requests\"** phase (image preprocessing). \nEven after increasing CPU cores and setting `OMP_NUM_THREADS=16`, the preprocessing speed stays around **50 it/s**, while the actual generation phase is extremely fast (>1500 prompts/s).\n\n```text\nAdding requests: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 64/64 [00:01<00:00, 52.67it/s] \u2190 bottleneck\nProcessed prompts: 100%|\u2588| 64/64 [00:00<00:00, 1515.23it/s, est. speed input: 812805.23 tok/s]\n```\nThis means ~95% of the total latency is spent on CPU-side image preprocessing, \uff08I have disabled dynamic resolution\uff09\n\n\n### Minimal Reproducible Example\n```python\nimport os\nfrom PIL import Image\nfrom vllm import LLM, SamplingParams\nfrom transformers import AutoTokenizer\n\nmodel_path = \"/data/code/haobang.geng/models/InternVL3_5-1B\"\ntokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)\n\nllm = LLM(\n model=model_path,\n dtype=\"bfloat16\",\n max_model_len=4096,\n gpu_memory_utilization=0.95,\n limit_mm_per_prompt={\"image\": 1},\n trust_remote_code=True,\n enforce_eager=False,\n)\n\nprompt = \"\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nRunning models on the latest stable vLLM release: https://huggingface.co/QuantTrio/Qwen3-VL-32B-Instruct-AWQ\nThe model size is 20GB, and my GPU has 40GB VRAM total.\nUsing parameter: --gpu-memory-utilization 0.9\nWhy am I only getting around 8k max context length? Do VL models really hog that much VRAM?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28626",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-13T08:00:20Z",
"updated_at": "2025-11-17T07:08:47Z",
"comments": 3,
"user": "maxin9966"
},
{
"repo": "vllm-project/vllm",
"number": 28622,
"title": "[Bug]: Can we able to benchmark Quantized MOE models Either W8A8 or W8A16 ?",
"body": "### Your current environment\n\nThe output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThe output of
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.2 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.22.1\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.18 (main, Jun 4 2025, 08:56:00) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.14.0-33-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.0.140\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : GPU 0: NVIDIA RTX 6000 Ada Generation\nNvidia driver version : 575.57.08\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.11.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 52\nOn-line CPU(s) list: 0-51\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) w7-2595X\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 26\nSocket(s): 1\nStepping: 8\nCPU(s) scaling MHz: 21%\nCPU max MHz: 4800.0000\nCPU min MHz: 800.0000\nBogoMIPS: 5616.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 1.2 MiB (26 instances)\nL1i cache: 832 KiB (26 instances)\nL2 cache: 52 MiB (26 instances)\nL3 cache: 48.8 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-51\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not af",
"url": "https://github.com/vllm-project/vllm/issues/28622",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-13T07:26:56Z",
"updated_at": "2025-11-13T07:27:06Z",
"comments": 0,
"user": "logesh13"
},
{
"repo": "vllm-project/vllm",
"number": 28610,
"title": "[Usage]: Does 0.11.0 suport tree attenton with eagle?",
"body": "### Your current environment\n\nDoes 0.11.0 suport tree attenton with eagle? Do I need to enable it manually?\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28610",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-13T03:35:02Z",
"updated_at": "2025-12-03T17:08:16Z",
"comments": 1,
"user": "wincle"
},
{
"repo": "huggingface/datasets",
"number": 7864,
"title": "add_column and add_item erroneously(?) require new_fingerprint parameter",
"body": "### Describe the bug\n\nContradicting their documentation (which doesn't mention the parameter at all), both Dataset.add_column and Dataset.add_item require a new_fingerprint string. This parameter is passed directly to the dataset constructor, which has the fingerprint parameter listed as optional; is there any reason it shouldn't be optional in these methods as well? \n\n### Steps to reproduce the bug\n\nReproduction steps:\n\n1. Look at the function signature for add_column: https://github.com/huggingface/datasets/blob/17f40a318a1f8c7d33c2a4dd17934f81d14a7f57/src/datasets/arrow_dataset.py#L6078\n2. Repeat for add_item: https://github.com/huggingface/datasets/blob/17f40a318a1f8c7d33c2a4dd17934f81d14a7f57/src/datasets/arrow_dataset.py#L6336\n\n### Expected behavior\n\nadd_column and add_item should either set the fingerprint parameter to optional or include it in their docstrings\n\n### Environment info\n\nNot environment-dependent",
"url": "https://github.com/huggingface/datasets/issues/7864",
"state": "open",
"labels": [],
"created_at": "2025-11-13T02:56:49Z",
"updated_at": "2025-12-07T14:41:40Z",
"comments": 2,
"user": "echthesia"
},
{
"repo": "vllm-project/vllm",
"number": 28566,
"title": "[Usage]: pd disagg scenario , I discover in the decoder , also has the prefill operation, is it normal ?",
"body": "### Your current environment\n\nwhen num_computed_tokens is less than num_prompt_tokens, it will enter prefill operation\n\n![\"Image\"](\"https://github.com/user-attachments/assets/bab96187-37c8-4ea2-ba68-9f52dda07f6b\")
\n\n\nand i found, num_computed_tokens is possible less than num_prompt_tokens, because num_prompt_tokens is len(block_ids) * self.block_size, event num_prompt_tokens is just equal to num_prompt_tokens, it do num_computed_tokens -= 1, why ? this cause num_computed_tokens is never equal to num_prompt_tokens\n\n![\"Image\"](\"https://github.com/user-attachments/assets/81eb6f4f-f0db-45f8-8934-64bd8ea21988\")
\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28566",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-12T16:18:53Z",
"updated_at": "2025-11-12T16:18:53Z",
"comments": 0,
"user": "yangshanjun"
},
{
"repo": "vllm-project/vllm",
"number": 28564,
"title": "[Usage]: Can't get ModernBert models to run in vllm serve",
"body": "### Your current environment\n\nI am trying to download and use ModernBertModel with the vllm serve feature. \nAt first I thought it was an issue with the model so I switched from trying to use BertEmbed with Alibaba-NLP/gte-modernbert-base since it appears in the docs as a model that supports embedding. \nSource: https://docs.vllm.ai/en/latest/models/supported_models/#pooling-models \n\nI download and run it like this. \nDownload:\n`huggingface-cli download Alibaba-NLP/gte-modernbert-base --local-dir models/bert --local-dir-use-symlinks False`\nServe (example, I have used many iterations):\n`vllm serve models/bert2 --host 0.0.0.0 --port 8003 --task embed --trust-remote-code --gpu-memory-utilization 0.3`\n\nNo matter what I get this: Assertion failed, The model should be a generative or pooling model when task is set to 'embedding'. [type=assertion_error, input_value=ArgsKwargs((), {'model': ...rocessor_plugin': None}), input_type=ArgsKwargs]\n\nI tried setting runner but that didn't do a thing. I really have no clue why it says this model is supported in the docs. I have searched through other issues and documentation to try out a bunch of solutions but obviously none have worked so far. Been trying to figure this out for hours now and I am losing my mind (not relevant ig, need to vent).\n\n### How would you like to use vllm\n\nI want to run inference of a Alibaba-NLP/gte-modernbert-base or any ModernBertModel. I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28564",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-12T15:51:18Z",
"updated_at": "2025-11-12T15:51:18Z",
"comments": 0,
"user": "Logikschleifen"
},
{
"repo": "vllm-project/vllm",
"number": 28527,
"title": "\ud83d\udca1 Bounty Platform for vLLM",
"body": "Hi vLLM team! \ud83d\udc4b\n\nI wanted to share **Roxonn** - a decentralized bounty platform for accelerating AI/ML development.\n\n**What is Roxonn?**\n\u2705 Fund GitHub issues with crypto bounties (XDC, USDC, ROXN)\n\u2705 Notify 300+ AI/ML developers\n\u2705 Auto-pay when PRs merge via blockchain\n\u2705 Zero crypto setup needed\n\n**Quick flow:**\n1. Register repo (GitHub App)\n2. Fund pool with USDC (stable pricing)\n3. Assign bounties to features\n4. PR merged \u2192 automatic payment\n\n**Perfect for AI/ML:**\n- Access to research community\n- **Only 1% total platform fee**\n- Transparent payments\n\nLearn more: **https://roxonn.com**\n\n*No pressure - sharing a resource!*",
"url": "https://github.com/vllm-project/vllm/issues/28527",
"state": "closed",
"labels": [],
"created_at": "2025-11-12T07:50:33Z",
"updated_at": "2025-11-13T12:36:15Z",
"comments": 0,
"user": "dineshroxonn"
},
{
"repo": "huggingface/transformers",
"number": 42154,
"title": "\ud83d\udca1 Bounty Platform for Hugging Face Transformers",
"body": "Hi Hugging Face Transformers team! \ud83d\udc4b\n\nI wanted to share **Roxonn** - a decentralized bounty platform for accelerating AI/ML development.\n\n**What is Roxonn?**\n\u2705 Fund GitHub issues with crypto bounties (XDC, USDC, ROXN)\n\u2705 Notify 300+ AI/ML developers\n\u2705 Auto-pay when PRs merge via blockchain\n\u2705 Zero crypto setup needed\n\n**Quick flow:**\n1. Register repo (GitHub App)\n2. Fund pool with USDC (stable pricing)\n3. Assign bounties to features\n4. PR merged \u2192 automatic payment\n\n**Perfect for AI/ML:**\n- Access to research community\n- **Only 1% total platform fee**\n- Transparent payments\n\nLearn more: **https://roxonn.com**\n\n*No pressure - sharing a resource!*",
"url": "https://github.com/huggingface/transformers/issues/42154",
"state": "closed",
"labels": [],
"created_at": "2025-11-12T07:49:59Z",
"updated_at": "2025-11-17T11:40:10Z",
"comments": 2,
"user": "dineshroxonn"
},
{
"repo": "vllm-project/vllm",
"number": 28508,
"title": "[Usage]: KVCacheManager Parameter question",
"body": "\n\nI noticed that the parameter \u201cself.req_to_block_hashes\u201d has been removed from KVCacheManager since version v0.10.0. But this parameter is still preserved in the official documentation. Could you please provide an explanation of this change? \n\n- [Document Description](https://docs.vllm.ai/en/v0.9.2/api/vllm/v1/core/kv_cache_manager.html)\n\n- [Version v0.10.0 code](https://github.com/vllm-project/vllm/blob/v0.10.0/vllm/v1/core/kv_cache_manager.py)\n\n\n\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28508",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-12T03:10:18Z",
"updated_at": "2025-11-16T08:33:45Z",
"comments": 1,
"user": "Liziqi-77"
},
{
"repo": "huggingface/diffusers",
"number": 12638,
"title": "How to design network with DiT blocks that are friendly to Tensorrt fp16 conversion?",
"body": "We had a network that structed as `a convnet pre-encoder -> DiT blocks -> final block for last sampling`, it worked well with torch format and onnx format, but when we tried to convert it into tensorrt fp16 format, the inference will get value overflow. we had seen the data differene [between onnx and trt fp16, with polygraphy.] get larger and larger following those DiT blocks. My question is, how to make the whole model design more friendly to mix-precision inference? to let the DiT blocks less sensitive to value precision. Should I make the convnet pre-encoder and final blocks more complex, or more simple? Thanks",
"url": "https://github.com/huggingface/diffusers/issues/12638",
"state": "open",
"labels": [],
"created_at": "2025-11-12T02:23:37Z",
"updated_at": "2025-11-12T02:23:37Z",
"user": "JohnHerry"
},
{
"repo": "huggingface/lerobot",
"number": 2428,
"title": "how to eval the real world recorded dataset?",
"body": "can lerobot eval the real world dataset with metric such as mse? I check the eval script and found that now it can only eval the sim env dataset",
"url": "https://github.com/huggingface/lerobot/issues/2428",
"state": "open",
"labels": [
"question",
"evaluation"
],
"created_at": "2025-11-12T02:08:44Z",
"updated_at": "2025-11-19T16:55:42Z",
"user": "shs822"
},
{
"repo": "vllm-project/vllm",
"number": 28505,
"title": "[Feature]: Is there a plan to introduce the new feature nano-pearl, a new engineering effort in speculative reasoning.",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nNano-pearl can support speculative inference with higher concurrency (larger batch sizes) and is seamlessly compatible with algorithms like Eagle. Is there a plan to introduce it?\ngithub\uff1ahttps://github.com/smart-lty/nano-PEARL\n\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28505",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-11-12T01:34:22Z",
"updated_at": "2025-11-17T06:14:09Z",
"comments": 1,
"user": "Lexlum"
},
{
"repo": "vllm-project/vllm",
"number": 28498,
"title": "[Bug][RL]: Port Conflict",
"body": "### Your current environment\n\n- bug report:\n\n```\nHello vLLM team, I'm running into a suspicious ZMQ socket bug with my 2P 4D configuration for DeepSeek-V3 (see below). I thought it is caused by reusing same nodes for many vLLM launches, but now it happened also at a clean node. Seems like a DP bug of sorts. Please find logs attached. vllm==0.11.0.\n```\n\n```bash\n[1;36m(APIServer pid=670293)[0;0m File \"XXX/.venv/lib/python3.12/site-packages/vllm/v1/engine/async_llm.py\", line 134, in __init__\n[1;36m(APIServer pid=670293)[0;0m self.engine_core = EngineCoreClient.make_async_mp_client(\n[1;36m(APIServer pid=670293)[0;0m ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[1;36m(APIServer pid=670293)[0;0m File \"XXX/.venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py\", line 101, in make_async_mp_client\n[1;36m(APIServer pid=670293)[0;0m return DPLBAsyncMPClient(*client_args)\n[1;36m(APIServer pid=670293)[0;0m ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[1;36m(APIServer pid=670293)[0;0m File \"XXX/.venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py\", line 1125, in __init__\n[1;36m(APIServer pid=670293)[0;0m super().__init__(vllm_config, executor_class, log_stats,\n[1;36m(APIServer pid=670293)[0;0m File \"XXX/.venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py\", line 975, in __init__\n[1;36m(APIServer pid=670293)[0;0m super().__init__(vllm_config, executor_class, log_stats,\n[1;36m(APIServer pid=670293)[0;0m File \"XXX/.venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py\", line 769, in __init__\n[1;36m(APIServer pid=670293)[0;0m super().__init__(\n[1;36m(APIServer pid=670293)[0;0m File \"XXX/.venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py\", line 466, in __init__\n[1;36m(APIServer pid=670293)[0;0m self.resources.output_socket = make_zmq_socket(\n[1;36m(APIServer pid=670293)[0;0m ^^^^^^^^^^^^^^^^\n[1;36m(APIServer pid=670293)[0;0m File \"XXX/.venv/lib/python3.12/site-packages/vllm/utils/__init__.py\", line 2983, in make_zmq_socket\n[1;36m(APIServer pid=670293)[0;0m socket.bind(path)\n[1;36m(APIServer pid=670293)[0;0m File \"XXX/.venv/lib/python3.12/site-packages/zmq/sugar/socket.py\", line 320, in bind\n[1;36m(APIServer pid=670293)[0;0m super().bind(addr)\n[1;36m(APIServer pid=670293)[0;0m File \"zmq/backend/cython/_zmq.py\", line 1009, in zmq.backend.cython._zmq.Socket.bind\n[1;36m(APIServer pid=670293)[0;0m File \"zmq/backend/cython/_zmq.py\", line 190, in zmq.backend.cython._zmq._check_rc\n[1;36m(APIServer pid=670293)[0;0m zmq.error.ZMQError: Address already in use (addr='tcp://slurm-h200-206-017:59251')\n```\n\n### \ud83d\udc1b Describe the bug\n\nFrom Nick:\n```\nI think the problem is that each DP worker finds/assigns free ports dynamically/independently.. so there is a race condtion. I'm not sure of an immediate workaround apart from just re-attempt to start things when this happens. We'll have to look at how to catch and re-find a port if possible (though I have a memory this might be nontrivial).\n```\n\nFrom Reporter:\n```\nReceived init message: EngineHandshakeMetadata(addresses=EngineZmqAddresses(inputs=['tcp://slurm-h200-207-083:60613'], outputs=['tcp://slurm-h200-207-083:36865'], coordinator_input='tcp://slurm-h200-207-083:34575', coordinator_output='tcp://slurm-h200-207-083:48025', frontend_stats_publish_address='ipc:///tmp/88ec875f-3de9-46ec-9947-6d1d6573b910'), parallel_config={'data_parallel_master_ip': 'slurm-h200-207-083', 'data_parallel_master_port': 41917, '_data_parallel_master_port_list': [60545, 36835, 47971, 37001], 'data_parallel_size': 32})\n```\n\nI'm looking at the code and I see that all code paths for getting ports eventually to go to _get_open_port, and that in _get_open_port there is basically no defence against choosing the same port twice. Can you please confirm my understanding?\n\n_get_open_port in main is here: https://github.com/vllm-project/vllm/blob/main/vllm/utils/network_utils.py#L177\n\nUPD: I imagine the assumption here is that once a code path gets a port, that code path will use it immediately, and thus the port will be come busy. It doesn't seem to hold though.\n\n\nEven where all sockets that vLLM chose for itself are unique, I get the stack trace below.\nI have the following explanation in mind:\n\n- vLLM chooses zmq ports before launching the engines\n- launching the engines takes ~5 mins\n- by the time the engines are launched, something can listen on this port, like for example Ray\n- **It looks the right solution is to hold on to then chosen ports immediately are they are chosen.**\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28498",
"state": "open",
"labels": [
"bug",
"help wanted",
"good first issue"
],
"created_at": "2025-11-11T22:51:35Z",
"updated_at": "2025-12-04T07:35:31Z",
"comments": 13,
"user": "robertgshaw2-redhat"
},
{
"repo": "vllm-project/vllm",
"number": 28489,
"title": "[Usage]: Online continuous batching",
"body": "### Current environment\n\n```\n==============================\n System Info\n==============================\nOS : macOS 26.1 (arm64)\nGCC version : Could not collect\nClang version : 17.0.0 (clang-1700.4.4.1)\nCMake version : Could not collect\nLibc version : N/A\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0\nIs debug build : False\nCUDA used to build PyTorch : None\nROCM used to build PyTorch : N/A\n==============================\n Python Environment\n==============================\nPython version : 3.12.6 (v3.12.6:a4a2d2b0d85, Sep 6 2024, 16:08:03) [Clang 13.0.0 (clang-1300.0.29.30)] (64-bit runtime)\nPython platform : macOS-26.1-arm64-arm-64bit\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : False\nCUDA runtime version : No CUDA\nCUDA_MODULE_LOADING set to : N/A\nGPU models and configuration : No CUDA\nNvidia driver version : No CUDA\ncuDNN version : No CUDA\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n==============================\n CPU Info\n==============================\nApple M2\n==============================\nVersions of relevant libraries\n==============================\n[pip3] numpy==2.2.6\n[pip3] nvidia-ml-py==13.580.82\n[pip3] pyzmq==27.0.0\n[pip3] sentence-transformers==5.1.2\n[pip3] spacy-transformers==1.3.9\n[pip3] torch==2.8.0\n[pip3] torchaudio==2.8.0\n[pip3] torchvision==0.23.0\n[pip3] transformers==4.57.1\n[conda] Could not collect\n==============================\n vLLM Info\n==============================\nROCM Version : Could not collect\nvLLM Version : 0.11.0\nvLLM Build Flags:\n CUDA Archs: Not Set; ROCm: Disabled\nGPU Topology:\n Could not collect\n==============================\n Environment Variables\n==============================\nPYTORCH_NVML_BASED_CUDA_CHECK=1\nTORCHINDUCTOR_COMPILE_THREADS=1\n\n```\n\nHello,\n\nI am looking to run an LLM (using vLLM) within a FastAPI application. My goal is to achieve online, continuous batching.\n\nI want the application to continuously receive requests from external clients, and have vLLM automatically batch them up for parallel inference.\n\nIn the past, I used the LLM() engine wrapped in RayServe. While this worked, it seemed to create a new internal deployment each time, which I want to avoid.\n\nI am now trying to achieve this without RayServe, using the AsyncLLMEngine directly (don't know If I need the async, read online).\n\nHere is an example of my current code. I'm running for test purposes on a cpu, but I have another issue on GPU (very long inference time, like minutes, with Ray, only 2-3 seconds).\n\n```\n# Model:\nengine_args = AsyncEngineArgs(\n model=path,\n tensor_parallel_size=1,\n gpu_memory_utilization=0.7,\n enforce_eager=False,\n disable_custom_all_reduce=False,\n max_model_len=2048,\n trust_remote_code=True,\n enable_log_requests=False,\n max_num_seqs=10\n )\n\nmodel_ = AsyncLLMEngine.from_engine_args(engine_args)\n\n# Params\nsampling_params = SamplingParams(\n n=1,\n best_of=None,\n presence_penalty=0.0,\n frequency_penalty=0.0,\n temperature=0,\n top_p=1.0,\n top_k=1,\n stop=my_stop_token,\n stop_token_ids=[my_eos_token_id],\n ignore_eos=False,\n max_tokens=2048,\n logprobs=None,\n skip_special_tokens=True\n )\n\noutputs_generator = model_.generate(prompt, sampling_params, request_id)\n\nfinal_output = None\nasync for request_output in outputs_generator:\n if request_output.finished:\n final_output = request_output\n break\n\nif final_output and final_output.outputs:\n result = final_output.outputs[0].text\n```\n\nIn my local test, I got the error when I try as example 3 inferences, calling 3 times self.model.generate() with 1 inputs and not 1 time self.model.generate() with 3 inputs.\nError: `Assertion failed: !_current_out (src/router.cpp:166)`\n\nIs it possible to achieve what I'm asking by always calling a generate() for internal batching, or the solution it's only by \"collecting\" the prompts with a management and then calling a centralized generate()?\nThanks\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28489",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-11T20:51:58Z",
"updated_at": "2025-11-11T20:53:47Z",
"comments": 0,
"user": "GenVr"
},
{
"repo": "huggingface/trl",
"number": 4507,
"title": "Can a multimodal model like Gemma be trained in the same way as a text-only model like Qwen, but with the goal of improving only its text capabilities?",
"body": "As stated in the title, I hope to improve only the text capabilities of Gemma 3, but it doesn\u2019t seem to have worked as expected. The model I used is gemma-3-4b-it, and I conducted the following simple tests:\n```python\n dataset = Dataset.from_list(\n [\n {\"prompt\": \"What is 2+2?\", \"task\": \"math\"},\n {\"prompt\": \"Write a function that returns the sum of two numbers.\", \"task\": \"code\"},\n {\"prompt\": \"What is 3*4?\", \"task\": \"math\"},\n {\"prompt\": \"Write a function that returns the product of two numbers.\", \"task\": \"code\"},\n ]\n )\n```\nThese data shouldn\u2019t cause Gemma to generate excessively long responses, but according to the logs, its output length is quite large: ```'completions/mean_length': 4096.0, 'completions/min_length': 4096.0, 'completions/max_length': 4096```\nThis doesn\u2019t seem normal.\n",
"url": "https://github.com/huggingface/trl/issues/4507",
"state": "open",
"labels": [
"\ud83d\udc1b bug",
"\u23f3 needs more info"
],
"created_at": "2025-11-11T15:59:51Z",
"updated_at": "2025-11-21T05:58:50Z",
"comments": 0,
"user": "Tuziking"
},
{
"repo": "vllm-project/vllm",
"number": 28472,
"title": "[Usage]: Will the reasoning_content in the chat template still be applied correctly after switching reasoning_content to reasoning",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nWill the message.reasoning_content for (which exists in default chat_template for qwen3-next-thinking qwen3-vl-thinking or other qwen3-thinking series or glm4.5 or kimi-k2-thinking or other models) in the chat template still be applied correctly after changing reasoning_content to reasoning (apply reasoning on ai message to reasoning_content on chat template)\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28472",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-11T15:04:11Z",
"updated_at": "2025-11-13T06:25:29Z",
"comments": 4,
"user": "zhcn000000"
},
{
"repo": "vllm-project/vllm",
"number": 28456,
"title": "[Usage]: benchmark_moe Usage",
"body": "### Your current environment\n\n```text\n(EngineCore_DP0 pid=7498) INFO 11-10 11:42:48 [shm_broadcast.py:466] No available shared memory broadcast block found in 60 seconds. This typically happens when some processes are hanging or doing some time-consuming work (e.g. compilation).\n(APIServer pid=7416) INFO 11-10 11:42:50 [loggers.py:127] Engine 000: Avg prompt throughput: 104162.6 tokens/s, Avg generation throughput: 10.0tokens/s, Running: 100 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.1%, Prefix cache hit rate: 98.6%\n(APIServer pid=7416) INFO 11-10 11:43:00 [loggers.py:127] Engine 000: Avg prompt throughput: 0.0 tokens/s, Avg generation throughput: 0.0 tokens/s, Running: 100 reqs, Waiting: 0 reqs, GPU KV cache usage: 10.1%, Prefix cache hit rate: 98.6%\n(APIServer pid=7416) INFO 11-10 11:43:20 [loggers.py:127] Engine 000: Avg prompt throughput: 5.1 tokens/s, Avg generation throughput: 0.1 tokens/s, Running: 1 reqs, Waiting: 0 reqs, GPU KV cache usage: 0.1%, Prefix cache hit rate: 98.6%\n\n\n\n\nCollecting environment information...==============================\n System Info==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)GCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collectCMake version : version 3.28.3\nLibc version : glibc-2.39\n============================== PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128Is debug build : False\nCUDA used to build PyTorch : 12.8ROCM used to build PyTorch : N/A\n==============================\n Python Environment==============================\nPython version : 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] (64-bit runtime)Python platform : Linux-6.8.0-85-generic-x86_64-with-glibc2.39\n\n============================== CUDA / GPU Info\n==============================Is CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration :\nGPU 0: Tesla V100-PCIE-16GB\nGPU 1: Tesla V100-PCIE-16GB\n\nNvidia driver version : 570.195.03\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 43 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 24\nOn-line CPU(s) list: 0-23\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7402P 24-Core Processor\nCPU family: 23\nModel: 49\nThread(s) per core: 1\nCore(s) per socket: 24\nSocket(s): 1\nStepping: 0\nFrequency boost: disabled\nCPU(s) scaling MHz: 74%\nCPU max MHz: 2800.0000\nCPU min MHz: 1500.0000\nBogoMIPS: 5599.64\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 cqm rdt_a rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local clzero irperf xsaveerptr rdpru wbnoinvd arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif v_spec_ctrl umip rdpid overflow_recov succor smca sev sev_es\nVirtualization: AMD-V\nL1d cache: 768 KiB (24 instances)\nL1i cache: 768 KiB (24 instances)\nL2 cache: 12 MiB (24 instances)\nL3 cache: 1",
"url": "https://github.com/vllm-project/vllm/issues/28456",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-11T09:22:33Z",
"updated_at": "2025-11-21T01:43:41Z",
"comments": 6,
"user": "ekmekovski"
},
{
"repo": "huggingface/lerobot",
"number": 2422,
"title": "Running inference on Libero with pi0",
"body": "Hello, I am trying to run inference with pi0 but the commands referenced in this issue #683 are outdated I believe. What would the commands be to run inference in Lerobot, and also running inference with pi0 in Libero? Additionally, if there is any documentation for these commands in general for fine-tuning and eval, that would be great!",
"url": "https://github.com/huggingface/lerobot/issues/2422",
"state": "open",
"labels": [
"question",
"policies",
"evaluation"
],
"created_at": "2025-11-11T09:22:25Z",
"updated_at": "2025-11-19T16:53:27Z",
"user": "thomasdeng2027"
},
{
"repo": "huggingface/lerobot",
"number": 2421,
"title": "Seeking assistance with tactile data acquisition",
"body": "I want to simultaneously collect tactile and visual data, with tactile data sampled at 150 fps and visual data at 30 fps. Each time an image frame is saved, I also want to store all tactile data collected during that time interval as additional features associated with the image.\n\nWhat would be the best approach to implement this? Which parts of the source code should I modify?",
"url": "https://github.com/huggingface/lerobot/issues/2421",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-11-11T02:49:57Z",
"updated_at": "2025-11-19T16:53:05Z",
"user": "zhoushaoxiang"
},
{
"repo": "vllm-project/vllm",
"number": 28438,
"title": "[Usage]: How do I install vLLM nightly?",
"body": "### Your current environment\n\nThe output of collect_env.py\n```text\n==============================\n System Info\n==============================\nOS : Ubuntu 20.04.5 LTS (x86_64)\nGCC version : (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nClang version : Could not collect\nCMake version : version 3.16.3\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.5.1+cu121\nIs debug build : False\nCUDA used to build PyTorch : 12.1\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.18 (main, Jun 5 2025, 13:14:17) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-5.4.250-2-velinux1u1-amd64-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.4.131\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version : 535.129.03\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nByte Order: Little Endian\nAddress sizes: 46 bits physical, 57 bits virtual\nCPU(s): 112\nOn-line CPU(s) list: 0-108\nOff-line CPU(s) list: 109-111\nThread(s) per core: 1\nCore(s) per socket: 28\nSocket(s): 2\nNUMA node(s): 2\nVendor ID: GenuineIntel\nCPU family: 6\nModel: 106\nModel name: Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz\nStepping: 6\nCPU MHz: 2294.608\nBogoMIPS: 4589.21\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 1.3 MiB\nL1i cache: 896 KiB\nL2 cache: 35 MiB\nL3 cache: 54 MiB\nNUMA node0 CPU(s): 0-55\nNUMA node1 CPU(s): 56-111\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\nVulnerability Retbleed: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq av",
"url": "https://github.com/vllm-project/vllm/issues/28438",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-11T02:24:47Z",
"updated_at": "2025-11-12T01:54:42Z",
"comments": 2,
"user": "LittleLucifer1"
},
{
"repo": "vllm-project/vllm",
"number": 28425,
"title": "[Feature][RL]: Fix Fp8 Weight Loading for RL",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nFeedback from RL community that vLLM weight loading in fp8 is bad for RL\n- https://vllm-dev.slack.com/archives/C07UUL8E61Z/p1762811441757529\n\nThe cause is clear: in [fp8.py](https://github.com/vllm-project/vllm/blob/bf6a3d0ff5a69e0a30567f2ad417530c002eaa4e/vllm/model_executor/layers/quantization/fp8.py#L490) in process_weights_after_loading there is a lot of parameter wrapping that drops .weight_loader attribute. \n\nThere's a patch from the Moonshot team that fixes this issue and there's a [PR](https://github.com/vllm-project/vllm/pull/24488) with this patch that never got any comments. The [patch](https://github.com/MoonshotAI/checkpoint-engine/blob/main/patches/vllm_fp8.patch) only works on top of v0.10.2rc1. Shortly after that tag, this [PR](https://github.com/vllm-project/vllm/pull/23280) made fp8 weight updates even trickier by transposing weight_inv_scale parameter for CUTLASS. \n\nI don't know how to patch any vLLM version after this PR to be able to call model.load_weights after the engine has started. It is a bummer, because DeepSeek wide EP inference is quite a bit faster in v0.11.0.\n\nWe need to fix this ASAP\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28425",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-11-10T21:59:02Z",
"updated_at": "2025-11-10T23:25:37Z",
"comments": 1,
"user": "robertgshaw2-redhat"
},
{
"repo": "huggingface/transformers.js",
"number": 1450,
"title": "SmolVLM2 500M Video Instruct - Video inference",
"body": "### Question\n\nHey, is it possible to setup **video** inference through **transformers.js** (may be somehow else?) for the model SmolVLM2 500M Video Instruct? I can't make it work, but I saw, that it is possible in py transformers.\n\nI want to create something similar to https://huggingface.co/spaces/HuggingFaceTB/SmolVLM2-HighlightGenerator/tree/main but with full local WebGPU inference.\n\nThanks in advance. cc: @xenova ",
"url": "https://github.com/huggingface/transformers.js/issues/1450",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-11-10T19:51:07Z",
"updated_at": "2025-11-12T07:46:32Z",
"user": "youchi1"
},
{
"repo": "vllm-project/vllm",
"number": 28409,
"title": "[Usage]: There is any performance benchmark between running vLLM server via docker image and python?",
"body": "### Your current environment\n\n```text\n\nI mean, if I run a service with the vLLM docker image, it has any performance upgrade if comparing with running it as a python service (e.g., importing vllm package, setting up vllm inference, handling payload/responses, etc)?\n\n```\n\n\n### How would you like to use vllm\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28409",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-10T17:56:14Z",
"updated_at": "2025-11-10T17:56:14Z",
"comments": 0,
"user": "rafaelsandroni"
},
{
"repo": "vllm-project/vllm",
"number": 28393,
"title": "[Feature]: Does vllm-jax plan to support GPU acceleration?",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nDoes vllm-jax plan to support GPU acceleration?\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28393",
"state": "closed",
"labels": [
"feature request"
],
"created_at": "2025-11-10T12:28:20Z",
"updated_at": "2025-11-10T21:44:57Z",
"comments": 2,
"user": "south-ocean"
},
{
"repo": "vllm-project/vllm",
"number": 28388,
"title": "[Bug]: \u65b0\u7248\u7684vllm\u5df2\u7ecf\u5e9f\u5f03\u4e86v0\u4ee3\u7801\uff0c\u800c\u5bf9qwen-omni\u7cfb\u5217\u7684\u6a21\u578b\u652f\u6301\u4ec5\u9650\u4e8ev0\uff0c\u4f3c\u4e4e\u662f\u56e0\u4e3a\u8fd9\u4e2a\u539f\u56e0\uff0c\u6211\u4eec\u65e0\u6cd5\u4f7f\u7528\u6700\u65b0\u7248\u7684vllm\u63a8\u7406qwen-omni\u6a21\u578b",
"body": "### Your current environment\n\nName: vllm\nVersion: 0.10.2\n\n### \ud83d\udc1b Describe the bug\n\n\u4e0b\u9762\u7684\u5b98\u65b9\u6837\u4f8b\u4ee3\u7801\u4f3c\u4e4e\u662f\u65e0\u6cd5\u8fd0\u884c\u7684\uff0c\u4f1a\u5bf9\u5176\u4e2d\u7684\u97f3\u9891\u4f7f\u7528\u53c2\u6570\n\"mm_processor_kwargs\": {\n \"use_audio_in_video\": True,\n },\n\u8fdb\u884c\u62a5\u9519\uff1a\n```python\n# SPDX-License-Identifier: Apache-2.0\n# SPDX-FileCopyrightText: Copyright contributors to the vLLM project\n\"\"\"\nThis example shows how to use vLLM for running offline inference\nwith the correct prompt format on Qwen2.5-Omni (thinker only).\n\"\"\"\n\nfrom typing import NamedTuple\n\nimport vllm.envs as envs\nfrom vllm import LLM, SamplingParams\nfrom vllm.assets.audio import AudioAsset\nfrom vllm.assets.image import ImageAsset\nfrom vllm.assets.video import VideoAsset\nfrom vllm.multimodal.image import convert_image_mode\nfrom vllm.utils import FlexibleArgumentParser\n\n\nclass QueryResult(NamedTuple):\n inputs: dict\n limit_mm_per_prompt: dict[str, int]\n\n\n# NOTE: The default `max_num_seqs` and `max_model_len` may result in OOM on\n# lower-end GPUs.\n# Unless specified, these settings have been tested to work on a single L4.\n\ndefault_system = (\n \"You are Qwen, a virtual human developed by the Qwen Team, Alibaba \"\n \"Group, capable of perceiving auditory and visual inputs, as well as \"\n \"generating text and speech.\"\n)\n\n\ndef get_mixed_modalities_query() -> QueryResult:\n question = (\n \"What is recited in the audio? \"\n \"What is the content of this image? Why is this video funny?\"\n )\n prompt = (\n f\"<|im_start|>system\\n{default_system}<|im_end|>\\n\"\n \"<|im_start|>user\\n<|audio_bos|><|AUDIO|><|audio_eos|>\"\n \"<|vision_bos|><|IMAGE|><|vision_eos|>\"\n \"<|vision_bos|><|VIDEO|><|vision_eos|>\"\n f\"{question}<|im_end|>\\n\"\n f\"<|im_start|>assistant\\n\"\n )\n return QueryResult(\n inputs={\n \"prompt\": prompt,\n \"multi_modal_data\": {\n \"audio\": AudioAsset(\"mary_had_lamb\").audio_and_sample_rate,\n \"image\": convert_image_mode(\n ImageAsset(\"cherry_blossom\").pil_image, \"RGB\"\n ),\n \"video\": VideoAsset(name=\"baby_reading\", num_frames=16).np_ndarrays,\n },\n },\n limit_mm_per_prompt={\"audio\": 1, \"image\": 1, \"video\": 1},\n )\n\n\ndef get_use_audio_in_video_query() -> QueryResult:\n question = (\n \"Describe the content of the video, then convert what the baby say into text.\"\n )\n prompt = (\n f\"<|im_start|>system\\n{default_system}<|im_end|>\\n\"\n \"<|im_start|>user\\n<|vision_bos|><|VIDEO|><|vision_eos|>\"\n f\"{question}<|im_end|>\\n\"\n f\"<|im_start|>assistant\\n\"\n )\n asset = VideoAsset(name=\"baby_reading\", num_frames=16)\n audio = asset.get_audio(sampling_rate=16000)\n assert not envs.VLLM_USE_V1, (\n \"V1 does not support use_audio_in_video. \"\n \"Please launch this example with \"\n \"`VLLM_USE_V1=0`.\"\n )\n return QueryResult(\n inputs={\n \"prompt\": prompt,\n \"multi_modal_data\": {\n \"video\": asset.np_ndarrays,\n \"audio\": audio,\n },\n \"mm_processor_kwargs\": {\n \"use_audio_in_video\": True,\n },\n },\n limit_mm_per_prompt={\"audio\": 1, \"video\": 1},\n )\n\n\ndef get_multi_audios_query() -> QueryResult:\n question = \"Are these two audio clips the same?\"\n prompt = (\n f\"<|im_start|>system\\n{default_system}<|im_end|>\\n\"\n \"<|im_start|>user\\n<|audio_bos|><|AUDIO|><|audio_eos|>\"\n \"<|audio_bos|><|AUDIO|><|audio_eos|>\"\n f\"{question}<|im_end|>\\n\"\n f\"<|im_start|>assistant\\n\"\n )\n return QueryResult(\n inputs={\n \"prompt\": prompt,\n \"multi_modal_data\": {\n \"audio\": [\n AudioAsset(\"winning_call\").audio_and_sample_rate,\n AudioAsset(\"mary_had_lamb\").audio_and_sample_rate,\n ],\n },\n },\n limit_mm_per_prompt={\n \"audio\": 2,\n },\n )\n\n\nquery_map = {\n \"mixed_modalities\": get_mixed_modalities_query,\n \"use_audio_in_video\": get_use_audio_in_video_query,\n \"multi_audios\": get_multi_audios_query,\n}\n\n\ndef main(args):\n model_name = \"Qwen/Qwen2.5-Omni-7B\"\n query_result = query_map[args.query_type]()\n\n llm = LLM(\n model=model_name,\n max_model_len=5632,\n max_num_seqs=5,\n limit_mm_per_prompt=query_result.limit_mm_per_prompt,\n seed=args.seed,\n )\n\n # We set temperature to 0.2 so that outputs can be different\n # even when all prompts are identical when running batch inference.\n sampling_params = SamplingParams(temperature=0.2, max_tokens=64)\n\n outputs = llm.generate(query_result.inputs, sampling_params=sampling_params)\n\n for o in outputs:\n generated_text = o.outputs[0].text\n print(generated_text)\n\n\ndef parse_args():\n parser = FlexibleArgumentParser(\n description=\"Demo on using vLLM for offline inference with \"\n \"audio language models\"\n )\n ",
"url": "https://github.com/vllm-project/vllm/issues/28388",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-10T09:23:33Z",
"updated_at": "2025-11-16T05:51:42Z",
"comments": 1,
"user": "Lee-xeo"
},
{
"repo": "huggingface/accelerate",
"number": 3836,
"title": "When using gradient accumulation, does the order of optimizer.zero_grad() affect training?",
"body": "if I use accelerate+deepspeed to train a model, and I set \n`deepspeed_config:\n gradient_accumulation_steps: 8\n offload_optimizer_device: cpu\n offload_param_device: cpu\n zero3_init_flag: false\n zero_stage: 2`\n\ndoes the order of the order of backward(), step(), zero_grad() affect training?\nFor example:\n`for batch in training_dataloader:\n with accelerator.accumulate(model):\n inputs, targets = batch\n outputs = model(inputs)\n loss = loss_function(outputs, targets)\n accelerator.backward(loss)\n optimizer.step()\n scheduler.step()\n optimizer.zero_grad()`\n\nand\n`for batch in training_dataloader:\n with accelerator.accumulate(model):\n optimizer.zero_grad()\n inputs, targets = batch\n outputs = model(inputs)\n loss = loss_function(outputs, targets)\n accelerator.backward(loss)\n optimizer.step()\n scheduler.step()\n `\n\nI want to know whether the two situations will yield the same result. During gradient accumulation training, when the model needs to update the parameters and `accelerate.sync_gradients=True`, will using the second method clear the gradients, causing the gradient accumulation to be incorrect, so that at this point there is only one sample?",
"url": "https://github.com/huggingface/accelerate/issues/3836",
"state": "closed",
"labels": [],
"created_at": "2025-11-10T03:11:21Z",
"updated_at": "2025-12-20T15:24:00Z",
"comments": 3,
"user": "polestarss"
},
{
"repo": "huggingface/transformers",
"number": 42113,
"title": "Add AutoMergeAdapters: Official Utility to Combine Multiple LoRA Adapters into One Unified Model",
"body": "### Feature request\n\nIntroduce a new built-in class AutoMergeAdapters to the Transformers/PEFT ecosystem that enables users to merge multiple LoRA adapters trained on different domains or datasets into a single model.\n\nThis feature simplifies the process of creating multi-domain fine-tuned models for inference and deployment, without manual merging scripts\n\n### Motivation\n\nToday, users can fine-tune models with LoRA adapters easily using PEFT, but they face a major bottleneck when trying to combine more than one adapter.\n\nCurrent limitations:\n\nOnly one LoRA adapter can be merged using merge_and_unload()\n\nManual merges are error-prone and undocumented\n\nModel config alignment must be handled manually\n\nNo built-in CLI or user-friendly API for adapter composition\n\nA high-level API for multi-adapter merging would:\n\nPromote adapter reusability across domains\n\nSimplify deployment of multi-domain, multi-skill models\n\nReduce code duplication across community projects\n\n### Your contribution\n\nI would like to implement this feature and contribute the following:\n\nDevelop the AutoMergeAdapters class under src/transformers/adapters/auto_merge_adapters.py to support merging multiple LoRA adapters with optional weighted combination and compatibility validation.\n\nExtend transformers-cli by adding a new merge-adapters command for CLI-based merging and model export.\n\nAdd unit and integration tests in tests/adapters/test_auto_merge_adapters.py to ensure correctness for weighted merges, config mismatches, and adapter integrity.\n\nProvide documentation including a usage guide and a sample notebook under examples/adapters/merge_multiple_adapters.ipynb.\n\nPublish a demo merged model to the Hugging Face Hub for reproducibility and reference.\n\nOpen a clean, well-tested PR and iterate based on maintainer feedback.\n\nHappy to start implementation once the approach is approved. Looking forward to guidance if any adjustments are required.",
"url": "https://github.com/huggingface/transformers/issues/42113",
"state": "closed",
"labels": [
"Feature request"
],
"created_at": "2025-11-09T18:43:20Z",
"updated_at": "2025-11-10T16:58:34Z",
"comments": 1,
"user": "3015pavan"
},
{
"repo": "huggingface/transformers",
"number": 42111,
"title": "Add thinking-budget support (max_thinking_tokens) for reasoning-capable chat models",
"body": "### Feature request\n\nA built-in way to cap how many tokens a reasoning model spends inside its `` \u2026 `` block. Today, we can only control the total response length via ``max_new_tokens``. No parameter limits the internal reasoning segment when ``enable_thinking=True``.\n\n### Motivation\n\n- Reasoning models (e.g., Qwen3 series) often produce very long thought blocks, which can blow past latency budgets before the final answer starts.\n- Users need a simple, model-agnostic control to bound that \u201cthinking\u201d cost without disabling reasoning entirely.\n- The Qwen docs (https://qwen.readthedocs.io/en/latest/getting_started/quickstart.html#thinking-budget) already describe a brute-force approach (two-step generation) to implement \u201cthinking budgets\u201d.\n\n### Your contribution\n\nI want to submit a PR that:\n\n- Extends ``GenerationConfig`` with:\n``max_thinking_tokens``: integer budget for reasoning tokens.\n``begin_thinking_token_id / end_thinking_token_id``: marker IDs so generation knows where the thinking span begins/ends.\n- Add a ``MaxThinkingTokensLogitsProcessor`` that watches the active ```` block. Once the budget is reached, it forces end_thinking_token_id, ensuring the model exits reasoning and continues with the final response.\n- Document the new parameter in reasoning-model guides (EXAONE, CWM, etc.) and show how to wire the thinking-token IDs until configs do it automatically.\n- Provide unit coverage so ``_get_logits_processor`` injects the new processor whenever the config is fully specified.",
"url": "https://github.com/huggingface/transformers/issues/42111",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-11-09T10:09:11Z",
"updated_at": "2025-11-09T10:09:11Z",
"comments": 0,
"user": "AndresAlgaba"
},
{
"repo": "vllm-project/vllm",
"number": 28362,
"title": "[Usage]: Can't get vLLM to run on an Intel 125H with XPU and Arc graphics",
"body": "### Your current environment\n\n```text\n\nCollecting environment information... \n============================== \n System Info \n============================== \nOS : Ubuntu 24.04.3 LTS (x86_64) \nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 \nClang version : Could not collect \nCMake version : version 4.1.2 \nLibc version : glibc-2.39 \n \n============================== \n PyTorch Info \n============================== \nPyTorch version : 2.8.0+xpu \nIs debug build : False ",
"url": "https://github.com/vllm-project/vllm/issues/28362",
"state": "open",
"labels": [
"usage",
"intel-gpu"
],
"created_at": "2025-11-09T09:45:05Z",
"updated_at": "2025-11-12T00:19:39Z",
"comments": 2,
"user": "phlibi"
},
{
"repo": "vllm-project/vllm",
"number": 28350,
"title": "[Doc]: Running VLLM via Docker Swarm With Support for Tensor Parallelism",
"body": "### \ud83d\udcda Running VLLM via Docker Swarm With Support for Tensor Parallelism\n\nThere's no documentation that I have found outlining how to run VLLM in a docker swarm when utilizing tensor parallelism. The issue is that ```ipc=host``` is not an available option within docker swarm. Consulting the AI feature on the VLLM website suggests to use the ```shm``` option which is available to swarm, but this produces continued failures on startup.\n\nPlease advise how to run VLLM via docker swarm utilizing tensor parallelism. thx\n\n",
"url": "https://github.com/vllm-project/vllm/issues/28350",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-11-08T21:11:15Z",
"updated_at": "2025-11-19T16:37:31Z",
"comments": 2,
"user": "ep5000"
},
{
"repo": "vllm-project/vllm",
"number": 28348,
"title": "[Usage]: Does vllm support max_pixels in prompt on Qwen3-VL reasoning?",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nI want to run inference of Qwen3-VL-A3B-Instruct, I tried to set max_pixels but it doesn't work.\n\nimport json\nimport base64\nimport requests\nimg_path = r\".\\images\\MMMU\\735_1.jpg\"\nbase64_str = base64.b64encode(open(img_path, 'rb').read()).decode()\nurl = \"http://71.10.29.136:8000/v1/chat/completions\"\npayload = json.dumps(\n {\n \"model\": \"qwen3-vl-30b\",\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"\"\n },\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"text\",\n \"text\": \"Question: \"\n },\n {\n \"type\": \"image_url\",\n \"image_url\": {\n \"url\": f\"data:image/jpg;base64,{base64_str}\"\n },\n \"max_pixels\": 192 * 96 ## this is not work.... ##\n },\n {\n \"type\": \"text\",\n \"text\": \" How does the green and photosynthesising mistletoe impact the tree it is hosting? Options:\\\\nA. It will grow down into the roots and kill the tree.\\\\nB. Mistletoe is beneficial and increases the growth of the plant.\\\\nC. It just uses the tree for support and does not damage it.\\\\nD. I don't know and don't want to guess.\\\\nE. It has a very damaging impact on the health of the plant but localised to the place of infection.\\\\n Please select the correct answer from the options above. \\\\n Only answer with the option letter, e.g. A, B, C, D, E, F, G, H, I. *DO NOT output any other information*. \\\\n\"\n }\n ]\n }\n ],\n \"n\": 1,\n \"top_p\": 0.001,\n \"top_k\": 1,\n \"temperature\": 0.01,\n \"max_tokens\": 8192\n }\n)\n\nheaders = {\n 'Content-Type': 'application/json',\n 'Authorization': 'Bearer EMPTY'\n}\n\nresponse = requests.request(\"POST\", url, headers=headers, data=payload)\nprint(response.text)\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28348",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-08T16:06:07Z",
"updated_at": "2025-11-08T16:56:17Z",
"comments": 1,
"user": "leijie-ww"
},
{
"repo": "vllm-project/vllm",
"number": 28344,
"title": "[Usage]: Function calling Request's sampling_params.structured_outputs is None?",
"body": "\n\nHi, I used openai server API to build a LLM backend when I tried to deploy a MCP server. I discovered that the prompt of vllm engine combined system prompt, tool lists and user prompt. but i saw sampling_params.structured_outputs is None. Although the result seemed correct\uff0c I think it's important to use structured output when generating function calling.But why not use structured output when generate JSON? Please explain\uff0cthanks a lot.\n\nBelow start a vllm backend.\n```\npython -m vllm.entrypoints.openai.api_server \\\n --model /workspace/models/qwen-2.5B/models--Qwen--Qwen2.5-1.5B-Instruct/snapshots/989aa7980e4cf806f80c7fef2b1adb7bc71aa306/ \\\n --served-model-name \"qwen-2.5b\" \\\n --port 8000 \\\n --trust-remote-code \\\n --enable-auto-tool-choice \\\n --tool-call-parser hermes\n```\n\nBelow is input of vllm engine.\n```\n(APIServer pid=703600) > /workspace/vllm/vllm/entrypoints/openai/serving_chat.py(326)create_chat_completion()\n(APIServer pid=703600) -> generator = self.engine_client.generate(\n(APIServer pid=703600) ['<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within \\n{\"type\": \"function\", \"function\": {\"name\": \"weather\", \"description\": \"\u57ce\u5e02\u5929\u6c14\u67e5\u8be2\", \"parameters\": {\"type\": \"object\", \"properties\": {\"city\": {\"type\": \"string\"}}, \"required\": [\"city\"]}}}\\n{\"type\": \"function\", \"function\": {\"name\": \"stock\", \"description\": \"\u80a1\u7968\u4ef7\u683c\u67e5\u8be2\", \"parameters\": {\"type\": \"object\", \"properties\": {\"code\": {\"type\": \"string\"}}, \"required\": [\"code\"]}}}\\n \\n\\nFor each function call, return a json object with function name and arguments within \\n{\"name\": , \"arguments\": }\\n <|im_end|>\\n<|im_start|>user\\n\u67e5\u8be2\u5317\u4eac\u5929\u6c14\u548c\u8d35\u5dde\u8305\u53f0\u80a1\u4ef7<|im_end|>\\n<|im_start|>assistant\\n']\n(Pdb) sampling_params.structured_outputs\n(Pdb) sampling_params\n(APIServer pid=703600) SamplingParams(n=1, presence_penalty=0.0, frequency_penalty=0.0, repetition_penalty=1.1, temperature=0.7, top_p=0.8, top_k=20, min_p=0.0, seed=None, stop=[], stop_token_ids=[], bad_words=[], include_stop_str_in_output=False, ignore_eos=False, max_tokens=32549, min_tokens=0, logprobs=None, prompt_logprobs=None, skip_special_tokens=False, spaces_between_special_tokens=True, truncate_prompt_tokens=None, **structured_outputs=None,** extra_args=None)\n```\nBelow is output of vllm engine.\n```\n(APIServer pid=703600) > /workspace/vllm/vllm/entrypoints/openai/serving_chat.py(1290)chat_completion_full_generator()\n(APIServer pid=703600) -> async for res in result_generator:\n(Pdb) final_res\n(APIServer pid=703600) RequestOutput(request_id=chatcmpl-573ea011c8894432bf8aa9d1468cae60, prompt='<|im_start|>system\\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.\\n\\n# Tools\\n\\nYou may call one or more functions to assist with the user query.\\n\\nYou are provided with function signatures within \\n{\"type\": \"function\", \"function\": {\"name\": \"weather\", \"description\": \"\u57ce\u5e02\u5929\u6c14\u67e5\u8be2\", \"parameters\": {\"type\": \"object\", \"properties\": {\"city\": {\"type\": \"string\"}}, \"required\": [\"city\"]}}}\\n{\"type\": \"function\", \"function\": {\"name\": \"stock\", \"description\": \"\u80a1\u7968\u4ef7\u683c\u67e5\u8be2\", \"parameters\": {\"type\": \"object\", \"properties\": {\"code\": {\"type\": \"string\"}}, \"required\": [\"code\"]}}}\\n \\n\\nFor each function call, return a json object with function name and arguments within \\n{\"name\": , \"arguments\": }\\n <|im_end|>\\n<|im_start|>user\\n\u67e5\u8be2\u5317\u4eac\u5929\u6c14\u548c\u8d35\u5dde\u8305\u53f0\u80a1\u4ef7<|im_end|>\\n<|im_start|>assistant\\n', prompt_token_ids=[151644, 8948, 198, 2610, 525, 1207, 16948, 11, 3465, 553, 54364, 14817, 13, 1446, 525, 264, 10950, 17847, 382, 2, 13852, 271, 2610, 1231, 1618, 825, 476, 803, 5746, 311, 7789, 448, 279, 1196, 3239, 382, 2610, 525, 3897, 448, 729, 32628, 2878, 366, 15918, 1472, 15918, 29, 11874, 9492, 510, 27, 15918, 397, 4913, 1313, 788, 330, 1688, 497, 330, 1688, 788, 5212, 606, 788, 330, 15206, 497, 330, 4684, 788, 330, 99490, 104307, 51154, 497, 330, 13786, 788, 5212, 1313, 788, 330, 1700, 497, 330, 13193, 788, 5212, 8926, 788, 5212, 1313, 788, 330, 917, 9207, 2137, 330, 6279, 788, 4383, 8926, 1341, 3417, 532, 4913, 1313, 788, 330, 1688, 497, 330, 1688, 788, 5212, 606, 788, 330, 13479, 497, 330, 4684, 788, 330, 104023, 97480, 51154, 497, 330, 13786, 788, 5212, 1313, 788, 330, 1700, 497, 330, 13193, 788, 5212, 1851, 788, 5212, 1313, 788, 330, 917, 9207, 2137, 330, 6279, 788, 4383, 1851, 1341, 3417, 532, 522, 15918, 1339, 2461, 1817, 729, 1618, 11, 470, 264, 2951, 1633, 448, 729, 829, 323, 5977, 2878, 220, 151657, 151658, 11874, 9492, 510, 151657, 198, 4913, 606, 788, 366, 1688, 11494, 8066, 330, 16370, 788, 366, 2116, 56080, 40432, 31296, 151658, 151645, 198, 151644, 872, 198, 51154, 68990, 104307, 33108, 102345, 109625, 105281, 151645, 198, 151644, 77091",
"url": "https://github.com/vllm-project/vllm/issues/28344",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-08T08:57:17Z",
"updated_at": "2025-11-10T07:51:51Z",
"comments": 5,
"user": "wtr0504"
},
{
"repo": "vllm-project/vllm",
"number": 28340,
"title": "[Installation]: Need offline wheel for vLLM 0.11.0rc2 (pip download fails) to deploy qwen3_vl_235b_a22b_instruct_i18n",
"body": "### Your current environment\n\nI need to install vLLM 0.11.0rc2 in an offline environment.\nIs there an official wheel (.whl) available for vLLM==0.11.0rc2 that I can download directly?\n\nRunning:\n```\npip download vllm==0.11.0rc2 --pre --extra-index-url https://wheels.vllm.ai/nightly -d wheels\n```\nfails with an error: \n\nLooking in indexes: https://bytedpypi.byted.org/simple/, https://wheels.vllm.ai/nightly\nERROR: Ignored the following yanked versions: 0.2.1\nERROR: Could not find a version that satisfies the requirement vllm==0.11.0rc2 (from versions: 0.0.1, 0.1.0, 0.1.1, 0.1.2, 0.1.3, 0.1.4, 0.1.5, 0.1.6, 0.1.7, 0.2.0, 0.2.1.post1, 0.2.2, 0.2.3, 0.2.4, 0.2.5, 0.2.6, 0.2.7, 0.3.0, 0.3.1, 0.3.2, 0.3.3, 0.4.0, 0.4.0.post1, 0.4.1, 0.4.2, 0.4.3, 0.5.0, 0.5.0.post1, 0.5.1, 0.5.2, 0.5.3, 0.5.3.post1, 0.5.4, 0.5.5, 0.6.0, 0.6.1, 0.6.1.post1, 0.6.1.post2, 0.6.2, 0.6.3, 0.6.3.post1, 0.6.4, 0.6.4.post1, 0.6.5, 0.6.6, 0.6.6.post1, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.8.0, 0.8.1, 0.8.2, 0.8.3, 0.8.4, 0.8.5, 0.8.5.post1, 0.9.0, 0.9.0.1, 0.9.1, 0.9.2, 0.10.0, 0.10.1, 0.10.1.1, 0.10.2, 0.11.0, 0.11.1rc6.dev210+g70af44fd1.cu129)\nERROR: No matching distribution found for vllm==0.11.0rc2.\n\n### How you are installing vllm\n\n```sh\npip download vllm==0.11.0rc2 --pre --extra-index-url https://wheels.vllm.ai/nightly -d wheels\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28340",
"state": "closed",
"labels": [
"installation"
],
"created_at": "2025-11-08T06:05:31Z",
"updated_at": "2025-11-08T06:08:37Z",
"comments": 0,
"user": "FateForever0222"
},
{
"repo": "vllm-project/vllm",
"number": 28310,
"title": "[Doc]: Update GPU requirements to include AMD gfx1150/gfx1151",
"body": "### \ud83d\udcda The doc issue\n\nSummary: The documentation for GPU requirements does not list AMD gfx1150 and gfx1151 architectures, which are now supported.\n\nBackground: Support for AMD gfx1150 and gfx1151 GPUs was added in https://github.com/vllm-project/vllm/pull/25908. The GPU requirements page should be updated to reflect this.\n\nAffected page: https://docs.vllm.ai/en/latest/getting_started/installation/gpu/index.html#requirements\n\nExpected behavior: The GPU requirements page lists AMD gfx1150 and gfx1151 as supported architectures.\n\n\n\n### Suggest a potential alternative/fix\n\nProposed fix: https://github.com/vllm-project/vllm/pull/28308\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28310",
"state": "closed",
"labels": [
"documentation",
"rocm"
],
"created_at": "2025-11-07T17:26:47Z",
"updated_at": "2025-11-08T03:01:08Z",
"comments": 1,
"user": "hammmmy"
},
{
"repo": "huggingface/transformers",
"number": 42093,
"title": "Mbart decoder ignoring index 0 from labels | index 1 from dec in",
"body": "### System Info\n\nI am creating a ocr model using VisionEncoderDecoderModel class by connecting plm vision tower and donut base decoder (mbart model). \n\nI am using teacher forcing method to train the model ( default training and i found out that the model is ignoring index 0 from the target ( index 1 from the decoder_input_ids ). \n\nI read the documentation for mbart and it says lang_code should be the bos for the target labels. but unlike the traditional methods where mbart used for translation task im using it for image - text task. \n\nand when i use the Seq2SeqTrainer to train the model i notice that the model is skipping is index 0 no matter what token is present there. \n\nI made my trainer to print the labels, dec in ( my own shift right just to display ) and pred. this is how it looks: \n\n```python\nlabel: [985, 735, 8, 690, 28264, 1448, 15320, 8, 4467, 18823, 258, 30606, 5965, 2164, 451, 8, 4467, 18823, 35, 2, -100, -100, -100, -100, -100, -100, -100, -100, -100]\ndecin: [2, 985, 735, 8, 690, 28264, 1448, 15320, 8, 4467, 18823, 258, 30606, 5965, 2164, 451, 8, 4467, 18823, 35, 2, 1, 1, 1, 1, 1, 1, 1, 1]\npreds: [735, 8, 690, 28264, 1448, 15320, 8, 4467, 18823, 258, 30606, 5965, 2164, 451, 8, 4467, 18823, 35, 2, 4467, 2, 2, 2, 185, 2, 2, 2, 2]\n\n\nlabel: [15418, 417, 893, 7271, 12, 8, 6583, 13, 46, 6549, 5538, 3632, 388, 8, 3633, 11, 34, 5221, 8, 188, 28, 2234, 8, 22, 11, 8, 26, 8340, 2]\ndecin: [2, 15418, 417, 893, 7271, 12, 8, 6583, 13, 46, 6549, 5538, 3632, 388, 8, 3633, 11, 34, 5221, 8, 188, 28, 2234, 8, 22, 11, 8, 26, 8340]\npreds: [417, 893, 7271, 12, 8, 6583, 13, 46, 6549, 5538, 3632, 388, 8, 3633, 11, 34, 5221, 8, 188, 28, 2234, 8, 22, 11, 8, 26, 8340, 2]\n\n\nlabel: [877, 8, 13, 397, 8, 3038, 10180, 7049, 88, 8, 13, 5348, 9, 36, 208, 123, 11, 12311, 148, 2696, 2, -100, -100, -100, -100, -100, -100, -100, -100]\ndecin: [2, 877, 8, 13, 397, 8, 3038, 10180, 7049, 88, 8, 13, 5348, 9, 36, 208, 123, 11, 12311, 148, 2696, 2, 1, 1, 1, 1, 1, 1, 1]\npreds: [8, 13, 397, 8, 3038, 10180, 7049, 88, 8, 13, 5348, 9, 36, 208, 123, 11, 12311, 148, 2696, 2, 2, 2, 2, 2, 2, 2696, 2, 2]\n```\n\nlets assume that the language code is 0, and thats in the beginning, that will be ignored too. how do i make the model to not ignore the index 0 from the labels? \n\n\n\n\n\n### Who can help?\n\n@ArthurZucker \n@Cyrilvallez \n@yonigozlan \n@molbap \n@zucchini-nlp \n@itazap \n\n### Information\n\n- [x] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nhttps://colab.research.google.com/drive/1nLCDlFyKhqCGu7dhlxJ0JiCRYjG24vbO?usp=sharing\n\n### Expected behavior\n\nI would like the decoder model to not ignore the index 0 from the labels. so that it will be \n\n![\"Image\"](\"https://github.com/user-attachments/assets/18a8e465-c235-4ac5-a9e2-f13d41bec964\")
\n\n ",
"url": "https://github.com/huggingface/transformers/issues/42093",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-11-07T15:46:08Z",
"updated_at": "2025-11-07T16:27:10Z",
"comments": 1,
"user": "jaaabir"
},
{
"repo": "vllm-project/vllm",
"number": 28292,
"title": "[Usage]: Failure to Deploy Llama-3.2-11B-Vision-Instruct Locally via vllm Due to OOM",
"body": "### Your current environment\n\nThe output of \n[\n -- is_valid: all not null\n -- child 0 type: binary\n [\n null,\n null,\n null,\n null,\n null,\n null\n ]\n -- child 1 type: string\n [\n \"/home/tobias/programming/github/datasets/nifti_extracted/T1.nii\",\n \"/home/tobias/programming/github/datasets/nifti_extracted/T2-interleaved.nii\",\n \"/home/tobias/programming/github/datasets/nifti_extracted/T2.nii\",\n \"/home/tobias/programming/github/datasets/nifti_extracted/T2_-interleaved.nii\",\n \"/home/tobias/programming/github/datasets/nifti_extracted/T2_.nii\",\n \"/home/tobias/programming/github/datasets/nifti_extracted/fieldmap.nii\"\n ]\n]\n```\ninstead of containing bytes. The code is copy pasted from PDF, so I wonder what is going wrong here.\n\n### Steps to reproduce the bug\n\nsee the linked comment\n\n### Expected behavior\n\ndownloading should work as smoothly as for pdf\n\n### Environment info\n\n- `datasets` version: 4.4.2.dev0\n- Platform: Linux-6.14.0-33-generic-x86_64-with-glibc2.39\n- Python version: 3.12.3\n- `huggingface_hub` version: 0.35.3\n- PyArrow version: 21.0.0\n- Pandas version: 2.3.3\n- `fsspec` version: 2025.9.0\n",
"url": "https://github.com/huggingface/datasets/issues/7852",
"state": "closed",
"labels": [],
"created_at": "2025-11-06T11:46:33Z",
"updated_at": "2025-11-06T16:20:38Z",
"comments": 2,
"user": "CloseChoice"
},
{
"repo": "huggingface/peft",
"number": 2901,
"title": "AttributeError: 'float' object has no attribute 'meta'",
"body": "### System Info\n\npeft== 0.17.1\ntorch== 2.5.1+cu118\ntransformers==4.57.0\npython==3.12.7\n\n### Who can help?\n\nI am trying to use LoRA with DINOv3 (so a slightly modified vit-b). However, I am hitting after a random number of iterations this error. It is sadly difficult to reproduce. Maybe someone can hint at what is going on?\n\n```\nTraceback (most recent call last):\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_dynamo/output_graph.py\", line 1446, in _call_user_compiler\n compiled_fn = compiler_fn(gm, self.example_inputs())\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_dynamo/repro/after_dynamo.py\", line 129, in __call__\n compiled_gm = compiler_fn(gm, example_inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/__init__.py\", line 2234, in __call__\n return compile_fx(model_, inputs_, config_patches=self.config)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_inductor/compile_fx.py\", line 1521, in compile_fx\n return aot_autograd(\n ^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_dynamo/backends/common.py\", line 72, in __call__\n cg = aot_module_simplified(gm, example_inputs, **self.kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py\", line 1071, in aot_module_simplified\n compiled_fn = dispatch_and_compile()\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py\", line 1056, in dispatch_and_compile\n compiled_fn, _ = create_aot_dispatcher_function(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py\", line 522, in create_aot_dispatcher_function\n return _create_aot_dispatcher_function(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_functorch/aot_autograd.py\", line 759, in _create_aot_dispatcher_function\n compiled_fn, fw_metadata = compiler_fn(\n ^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_functorch/_aot_autograd/jit_compile_runtime_wrappers.py\", line 179, in aot_dispatch_base\n compiled_fw = compiler(fw_module, updated_flat_args)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_inductor/compile_fx.py\", line 1350, in fw_compiler_base\n return _fw_compiler_base(model, example_inputs, is_inference)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_inductor/compile_fx.py\", line 1421, in _fw_compiler_base\n return inner_compile(\n ^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_inductor/compile_fx.py\", line 475, in compile_fx_inner\n return wrap_compiler_debug(_compile_fx_inner, compiler_name=\"inductor\")(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_dynamo/repro/after_aot.py\", line 85, in debug_wrapper\n inner_compiled_fn = compiler_fn(gm, example_inputs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_inductor/compile_fx.py\", line 661, in _compile_fx_inner\n compiled_graph = FxGraphCache.load(\n ^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_inductor/codecache.py\", line 1334, in load\n compiled_graph = compile_fx_fn(\n ^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/torch/_inductor/compile_fx.py\", line 570, in codegen_and_compile\n compiled_graph = fx_codegen_and_compile(gm, example_inputs, **fx_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/dkfz/cluster/gpu/data/OE0441/k539i/miniforge3/envs/nnunetv2/lib/python3.12/site-packages/t",
"url": "https://github.com/huggingface/peft/issues/2901",
"state": "closed",
"labels": [],
"created_at": "2025-11-06T11:24:18Z",
"updated_at": "2025-11-17T15:34:08Z",
"comments": 6,
"user": "Karol-G"
},
{
"repo": "vllm-project/vllm",
"number": 28192,
"title": "[RFC]: Support separate NICs for KV cache traffic and MoE traffic",
"body": "### Motivation.\n\nIn MoE models with large KV caches, KV cache all-to-all and MoE expert communication share the same RNIC, causing congestion and degrading performance. Using dedicated NICs for each traffic type can improve bandwidth utilization and reduce interference.\n\n### Proposed Change.\n\nDoes vLLM currently support routing KV cache traffic and MoE traffic through different NICs?\n\n### Feedback Period.\n\n_No response_\n\n### CC List.\n\n_No response_\n\n### Any Other Things.\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28192",
"state": "open",
"labels": [
"RFC"
],
"created_at": "2025-11-06T07:31:17Z",
"updated_at": "2025-11-06T08:19:56Z",
"comments": 1,
"user": "JayFzh"
},
{
"repo": "vllm-project/vllm",
"number": 28186,
"title": "[Bug] Cannot load qwen3-vl series with lora adapter",
"body": "I fine-tuned the `Qwen3-VL-8B-Instruct` model using Unsloth.\nI moved the saved QLoRA adapter and the `Qwen3-VL-2B-Instruct` model to my vLLM server.\nThen I ran a command to start model serving with vLLM as shown below. (For reference, the vLLM server has no issues\u2014it was already serving official Qwen3-VL models.)\n\n```\ncommand = [\n sys.executable, \n \"-m\", \"vllm.entrypoints.openai.api_server\",\n \"--model\", \"./Qwen3-VL-2B-Instruct\",\n \"--max_model_len\", \"3500\",\n \"--gpu_memory_utilization\", \"0.85\",\n \"--trust-remote-code\",\n \"--host\", \"0.0.0.0\",\n \"--port\", \"8888\",\n\n # for lora adapter\n \"--enable-lora\",\n \"--max-lora-rank\", \"16\", # LoRA rank\n \"--max-loras\", \"1\", \n \"--max-cpu-loras\", \"1\",\n \"--lora-modules\", \"adapter0=./my_lora_adapter\"\n]\n```\n\nI waited for vLLM to properly load the QLoRA adapter, but the following problem occurred : \nhttps://github.com/vllm-project/vllm/issues/26991\n\nWhen I was feeling hopeless, I tried merging the model instead of saving the LoRA adapter separately by using the `save_pretrained_merged()` function as shown below, and then vLLM was able to load and perform inference normally:\n\n```\n save_pretrained_merged( f\"my_16bit_model\", tokenizer, save_method=\"merged_16bit\")\n```\n\nHowever, I don't want to merge the models\u2014I want to load VL model with **LoRA** adapter.\nI\u2019ve seen many posts from others experiencing the same error.\n\nAs of now, what can I do to resolve this issue?",
"url": "https://github.com/vllm-project/vllm/issues/28186",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-06T06:02:33Z",
"updated_at": "2025-11-09T11:16:27Z",
"comments": 4,
"user": "deepNoah"
},
{
"repo": "huggingface/trl",
"number": 4481,
"title": "DPOTrainer._prepare_dataset() adds an extra eos_token to conversationally formatted inputs",
"body": "## Overview\nThe DPOTrainer unconditionally appends the eos_token to both the \"chosen\" and \"rejected\" sequences. Because conversationally formatted inputs will already have the chat template applied, this causes them to have duplicate eos_tokens (Ex. `...<|im_end|><|im_end|>`). \n\nA related problem was reported for the [SFTTrainer](https://github.com/huggingface/trl/issues/3318), where Qwen2.5\u2019s chat template confused the trainer\u2019s logic for detecting whether a sequence already ended with an eos_token_id. The DPO case is slightly different: [DPOTrainer.tokenize_row](https://github.com/huggingface/trl/blob/main/trl/trainer/dpo_trainer.py#L738-L739) explicitly appends tokenizer.eos_token_id to both chosen_input_ids and rejected_input_ids, regardless of whether the text is standard or conversational. Even if the chat template already added the token, it will be added again.\n\n\n## Repro\n```python\nimport trl\nfrom trl import DPOTrainer, DPOConfig\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nfrom datasets import Dataset\nimport torch\n\nMODEL_ID = \"Qwen/Qwen2.5-0.5B-Instruct\"\n\n# Conversational format\nsample_data = {\n \"prompt\": [[{\"role\": \"user\", \"content\": \"What is 2+2?\"}]],\n \"chosen\": [[{\"role\": \"assistant\", \"content\": \"2+2 equals 4.\"}]],\n \"rejected\": [[{\"role\": \"assistant\", \"content\": \"I don't know math.\"}]]\n}\n\n# Convert to dataset\ntrain_dataset = Dataset.from_dict(sample_data)\n\n# Load model and tokenizer\ntokenizer = AutoTokenizer.from_pretrained(MODEL_ID)\nmodel = AutoModelForCausalLM.from_pretrained(\n MODEL_ID,\n dtype=torch.bfloat16,\n device_map=\"auto\"\n)\n\n# Setup DPO config\ndpo_config = DPOConfig(\n output_dir=\"./dpo_output\",\n per_device_train_batch_size=2,\n num_train_epochs=1,\n logging_steps=1,\n remove_unused_columns=False,\n)\n\n# Initialize DPOTrainer\ntrainer = DPOTrainer(\n model=model,\n args=dpo_config,\n train_dataset=train_dataset,\n processing_class=tokenizer,\n)\n\n# Get the processed batch\ntrain_dataloader = trainer.get_train_dataloader()\nbatch = next(iter(train_dataloader))\n\n# Decode and display the preprocessed sequences\nfor idx in range(len(batch[\"chosen_input_ids\"])):\n \n # Show prompt if available\n if \"prompt_input_ids\" in batch:\n prompt_tokens = batch[\"prompt_input_ids\"][idx]\n print(\"-\"*80)\n print(f\"PROMPT:\")\n print(\"-\"*80)\n print(tokenizer.decode(prompt_tokens, skip_special_tokens=False))\n print(\"-\"*80)\n \n # Show full chosen sequence\n chosen_tokens = batch[\"chosen_input_ids\"][idx]\n print(f\"CHOSEN SEQUENCE:\")\n print(\"-\"*80)\n print(tokenizer.decode(chosen_tokens, skip_special_tokens=False))\n print(\"-\"*80 + \"\\n\")\n \n # Show full rejected sequence\n rejected_tokens = batch[\"rejected_input_ids\"][idx]\n print(f\"REJECTED SEQUENCE:\")\n print(\"-\"*80)\n print(tokenizer.decode(rejected_tokens, skip_special_tokens=False))\n print(\"-\"*80)\n```\n\n## Outputs:\nNotice the double `<|im_end|>` tokens for the 'chosen' and 'rejected' columns.\n```\n--------------------------------------------------------------------------------\nPROMPT:\n--------------------------------------------------------------------------------\n<|im_start|>system\nYou are Qwen, created by Alibaba Cloud. You are a helpful assistant.<|im_end|>\n<|im_start|>user\nWhat is 2+2?<|im_end|>\n<|im_start|>assistant\n\n--------------------------------------------------------------------------------\nCHOSEN SEQUENCE:\n--------------------------------------------------------------------------------\n2+2 equals 4.<|im_end|>\n<|im_end|>\n--------------------------------------------------------------------------------\n\nREJECTED SEQUENCE:\n--------------------------------------------------------------------------------\nI don't know math.<|im_end|>\n<|im_end|>\n--------------------------------------------------------------------------------\n```\n\n\n### System Info\n\n- Platform: Linux-6.11.0-1016-nvidia-x86_64-with-glibc2.39\n- Python version: 3.12.11\n- TRL version: 0.24.0\n- PyTorch version: 2.7.1+cu128\n- accelerator(s): NVIDIA H200\n- Transformers version: 4.57.1\n- Accelerate version: 1.11.0\n- Accelerate config: not found\n- Datasets version: 4.4.1\n- HF Hub version: 0.36.0\n- bitsandbytes version: not installed\n- DeepSpeed version: not installed\n- Liger-Kernel version: not installed\n- LLM-Blender version: not installed\n- OpenAI version: not installed\n- PEFT version: not installed\n- vLLM version: not installed\n\n### Checklist\n\n- [x] I have checked that my issue isn't already filed (see [open issues](https://github.com/huggingface/trl/issues?q=is%3Aissue))\n- [x] I have included my system information\n- [x] Any code provided is minimal, complete, and reproducible ([more on MREs](https://docs.github.com/en/get-started/writing-on-github/working-with-advanced-formatting/creating-and-highlighting-code-blocks))\n- [x] Any code provided is properly formatted in code blocks, (no screenshot, [more on code blocks](https://docs.github.com/en/get-started/writing-on-github/wo",
"url": "https://github.com/huggingface/trl/issues/4481",
"state": "open",
"labels": [
"\ud83d\udc1b bug",
"\ud83c\udfcb DPO"
],
"created_at": "2025-11-06T01:17:05Z",
"updated_at": "2025-11-06T18:40:39Z",
"comments": 0,
"user": "DevonPeroutky"
},
{
"repo": "huggingface/trl",
"number": 4468,
"title": "Move RLOOTrainer to trl.experimental",
"body": "## Context\n\nPart of #4223 and #4374 - Moving trainers to experimental submodule for V1.\n\n## Task\n\nMove RLOOTrainer from main trl module to trl.experimental:\n\n- [ ] Move trainer file to trl/experimental/\n- [ ] Update imports in __init__.py files\n- [ ] Update documentation\n- [ ] Add deprecation warning in old location\n- [ ] Update tests\n- [ ] Verify examples still work\n\n## Post-V1 Plan\nMay stay in trl.experimental as maintenance cost is low.\n\n## Related\n- Parent tracking issue: #4374\n- RFC: #4223\n- BCO migration (completed): #4312",
"url": "https://github.com/huggingface/trl/issues/4468",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u2728 enhancement"
],
"created_at": "2025-11-05T21:30:15Z",
"updated_at": "2025-12-05T18:21:41Z",
"comments": 2,
"user": "behroozazarkhalili"
},
{
"repo": "huggingface/trl",
"number": 4466,
"title": "Move PPOTrainer to trl.experimental",
"body": "## Context\n\nPart of #4223 and #4374 - Moving trainers to experimental submodule for V1.\n\n## Task\n\nMove PPOTrainer from main trl module to trl.experimental:\n\n- [ ] Move trainer file to trl/experimental/\n- [ ] Update imports in __init__.py files\n- [ ] Update documentation\n- [ ] Add deprecation warning in old location\n- [ ] Update tests\n- [ ] Verify examples still work\n\n## Post-V1 Plan\nMay stay in trl.experimental as it's an important baseline but requires heavy refactoring.\n\n## Related\n- Parent tracking issue: #4374\n- RFC: #4223\n- BCO migration (completed): #4312",
"url": "https://github.com/huggingface/trl/issues/4466",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u2728 enhancement",
"\ud83c\udfcb PPO"
],
"created_at": "2025-11-05T21:29:54Z",
"updated_at": "2025-11-13T19:01:20Z",
"comments": 0,
"user": "behroozazarkhalili"
},
{
"repo": "huggingface/trl",
"number": 4465,
"title": "Move ORPOTrainer to trl.experimental",
"body": "## Context\n\nPart of #4223 and #4374 - Moving trainers to experimental submodule for V1.\n\n## Task\n\nMove ORPOTrainer from main trl module to trl.experimental:\n\n- [ ] Move trainer file to trl/experimental/\n- [ ] Update imports in __init__.py files\n- [ ] Update documentation\n- [ ] Add deprecation warning in old location\n- [ ] Update tests\n- [ ] Verify examples still work\n\n## Post-V1 Plan\nMay stay in trl.experimental.\n\n## Related\n- Parent tracking issue: #4374\n- RFC: #4223\n- BCO migration (completed): #4312",
"url": "https://github.com/huggingface/trl/issues/4465",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u2728 enhancement",
"\ud83c\udfcb ORPO"
],
"created_at": "2025-11-05T21:29:44Z",
"updated_at": "2025-11-21T06:36:32Z",
"comments": 0,
"user": "behroozazarkhalili"
},
{
"repo": "huggingface/trl",
"number": 4463,
"title": "Move KTOTrainer to trl.experimental",
"body": "## Context\n\nPart of #4223 and #4374 - Moving trainers to experimental submodule for V1.\n\n## Task\n\nMove KTOTrainer from main trl module to trl.experimental:\n\n- [ ] Move trainer file to trl/experimental/\n- [ ] Update imports in __init__.py files\n- [ ] Update documentation\n- [ ] Add deprecation warning in old location\n- [ ] Update tests\n- [ ] Verify examples still work\n\n## Post-V1 Plan\nMay be promoted to main codebase after refactoring.\n\n## Related\n- Parent tracking issue: #4374\n- RFC: #4223\n- BCO migration (completed): #4312",
"url": "https://github.com/huggingface/trl/issues/4463",
"state": "open",
"labels": [
"\ud83d\udcda documentation",
"\u2728 enhancement",
"\ud83c\udfcb KTO"
],
"created_at": "2025-11-05T21:29:25Z",
"updated_at": "2025-11-05T21:29:50Z",
"comments": 0,
"user": "behroozazarkhalili"
},
{
"repo": "huggingface/trl",
"number": 4461,
"title": "Move OnlineDPOTrainer to trl.experimental",
"body": "## Context\n\nPart of #4223 and #4374 - Moving trainers to experimental submodule for V1.\n\n## Task\n\nMove OnlineDPOTrainer from main trl module to trl.experimental:\n\n- [ ] Move trainer file to trl/experimental/\n- [ ] Update imports in __init__.py files\n- [ ] Update documentation\n- [ ] Add deprecation warning in old location\n- [ ] Update tests\n- [ ] Verify examples still work\n\n## Post-V1 Plan\nMay be removed based on usage and maintenance requirements.\n\n## Related\n- Parent tracking issue: #4374\n- RFC: #4223\n- BCO migration (completed): #4312",
"url": "https://github.com/huggingface/trl/issues/4461",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u2728 enhancement",
"\ud83c\udfcb Online DPO"
],
"created_at": "2025-11-05T21:28:08Z",
"updated_at": "2025-11-24T01:13:07Z",
"comments": 1,
"user": "behroozazarkhalili"
},
{
"repo": "vllm-project/vllm",
"number": 28152,
"title": "[Feature]: Factor out `zero_expert_num` from `FusedMoE`",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nWe have many special cases in `FusedMoE` for `zero_expert_num`\n\nThis parameter is used exclusively for `LongCatFlash`. We should factor this out of `FusedMoe` and put the complexity into the model file.\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28152",
"state": "open",
"labels": [
"help wanted",
"feature request"
],
"created_at": "2025-11-05T19:05:54Z",
"updated_at": "2025-11-06T20:08:23Z",
"comments": 0,
"user": "robertgshaw2-redhat"
},
{
"repo": "vllm-project/vllm",
"number": 28150,
"title": "[Bug]: -O.mode=NONE (or -cc.mode=NONE) should work",
"body": "### Your current environment\n\nmain\n\n### \ud83d\udc1b Describe the bug\n\nRight now -O.mode only accepts integer levels. Ideally it would accept ints and the string.\n\n`vllm serve -O.mode=NONE` # doesn't work\n`vllm serve -O.mode=0` # does work\n\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28150",
"state": "closed",
"labels": [
"bug",
"help wanted",
"good first issue",
"torch.compile"
],
"created_at": "2025-11-05T18:28:23Z",
"updated_at": "2025-11-12T00:46:20Z",
"comments": 1,
"user": "zou3519"
},
{
"repo": "vllm-project/vllm",
"number": 28137,
"title": "[Feature]: Refactor `aiter_shared_expert_fusion`",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nWe have a special case in `FusedMoE` layer for `aiter_shared_expert_fusion` which creates various if branches spattered across the layer\n\nWe should factor this out\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28137",
"state": "open",
"labels": [
"help wanted"
],
"created_at": "2025-11-05T15:54:09Z",
"updated_at": "2025-12-20T22:00:55Z",
"comments": 3,
"user": "robertgshaw2-redhat"
},
{
"repo": "vllm-project/vllm",
"number": 28132,
"title": "[Usage]: How do I assign a specific GPU to a vLLM docker container?",
"body": "### Your current environment\n\nstock vllm-openai:v0.11.0 docker image\nrootless Docker v.27.5.1 on Ubuntu 22.04.5 LTS on physical hardware\nNvidia Driver Version: 570.133.20\nCUDA Version: 12.8\nGPUs: 4x H100 (NVLink), numbered 0,1,2,3\n\n### How would you like to use vllm\n\nI want to run inference of [SmolLM3-3B](https://huggingface.co/HuggingFaceTB/SmolLM3-3B). The exact model doesn't matter, this happens with other models as well.\n\ni want to run this model using Docker. This basically works. However, it alway picks a different GPU than what i specify in CUDA_VISIBLE_DEVICES. Out of my four GPUs, 0 and 1 are idle. I would like the container to use GPU 0. But no matter what I try, it always decides to run on GPU 1. I can verify this using `nvtop`.\n\nThis is my compose file:\n```yaml\nservices:\n vllm-smol:\n container_name: smollm-3b\n image: vllm/vllm-openai:v0.11.0\n volumes:\n - ./smollm-3b/models:/models\n gpus: \"all\"\n environment:\n HF_HOME: \"/models\"\n CUDA_VISIBLE_DEVICES: \"0\"\n command: >\n --model HuggingFaceTB/SmolLM3-3B\n --enable-auto-tool-choice\n --tool-call-parser=hermes\n --gpu-memory-utilization 0.1875\n labels:\n```\nThis way, the vLLM container starts and inferencing runs fine. But it decides to use GPU 1 instead of GPU 0\n\ni have also tried this, as docker compose will only accept `gpus: \"all\"`:\n```yaml\ndocker run -d \\\n --name smollm-3b \\\n -v \"$(pwd)/smollm-3b/models:/models\" \\\n --gpus \"device=0\" \\\n -e HF_HOME=\"/models\" \\\n -e CUDA_VISIBLE_DEVICES=\"0\" \\\n vllm/vllm-openai:v0.11.0 \\\n --model HuggingFaceTB/SmolLM3-3B \\\n --enable-auto-tool-choice \\\n --tool-call-parser=hermes \\\n --gpu-memory-utilization 0.1875\n```\nThis gives me an error during container startup: `RuntimeError: No CUDA GPUs are available`\nOmitting `CUDA_VISIBLE_DEVICES` gives the same error.\n\nAnd finally, there is also this attempt:\n```yaml\nservices:\n vllm-smol:\n container_name: smollm-3b\n image: vllm/vllm-openai:v0.11.0\n volumes:\n - ./smollm-3b/models:/models\n deploy:\n resources:\n reservations:\n devices:\n - driver: nvidia\n device_ids: ['0']\n capabilities: [gpu]\n environment:\n HF_HOME: \"/models\"\n # CUDA_VISIBLE_DEVICES: \"0\"\n command: >\n --model HuggingFaceTB/SmolLM3-3B\n --enable-auto-tool-choice\n --tool-call-parser=hermes\n --gpu-memory-utilization 0.1875\n```\nErrors are, once again, identical with and without `CUDA_VISIBLE_DEVICES`: `RuntimeError: No CUDA GPUs are available`\n\nAm I doing something fundamentally wrong here? All i want is to use a specific GPU (GPU 0 in my case)\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28132",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-05T14:42:17Z",
"updated_at": "2025-11-06T14:54:41Z",
"comments": 1,
"user": "lindner-tj"
},
{
"repo": "huggingface/lerobot",
"number": 2389,
"title": "How to resolve the issue that GROOT cannot train properly? Below is my training configuration and error log.",
"body": "How to resolve the issue that GROOT cannot train properly? Below is my training configuration and error log.\n\naccelerate launch \\\n --multi_gpu \\\n --num_processes=2 \\\n $(which lerobot-train) \\\n --output_dir=./outputs/groot_training \\\n --save_checkpoint=true \\\n --batch_size=8 \\\n --steps=200000 \\\n --save_freq=20000 \\\n --log_freq=200 \\\n --policy.type=groot \\\n --policy.push_to_hub=false \\\n --policy.repo_id=your_repo_id \\\n --dataset.root=/home/ruijia/wxl/data/train_segdata_wrist_20251028_200/ \\\n --dataset.repo_id=ur_wrist_data \\\n --wandb.enable=false \\\n --wandb.disable_artifact=false \\\n --job_name=grapdata\n\n\n\n[rank1]:[W1105 18:09:16.255729052 CUDAGuardImpl.h:119] Warning: CUDA warning: an illegal memory access was encountered (function destroyEvent)\nterminate called after throwing an instance of 'c10::Error'\n[rank1]:[E1105 18:09:16.257152106 ProcessGroupNCCL.cpp:1899] [PG ID 0 PG GUID 0(default_pg) Rank 1] Process group watchdog thread terminated with exception: CUDA error: an illegal memory access was encountered\nCUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect.\nFor debugging consider passing CUDA_LAUNCH_BLOCKING=1\nCompile with `TORCH_USE_CUDA_DSA` to enable device-side assertions.\n\nException raised from c10_cuda_check_implementation at /pytorch/c10/cuda/CUDAException.cpp:43 (most recent call first):\nframe #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::basic_string, std::allocator >) + 0x98 (0x7c3dcab785e8 in /home/ruijia/miniconda3/envs/lerobot_pi05/lib/python3.10/site-packages/torch/lib/libc10.so)",
"url": "https://github.com/huggingface/lerobot/issues/2389",
"state": "open",
"labels": [
"training"
],
"created_at": "2025-11-05T10:17:59Z",
"updated_at": "2025-11-07T17:47:50Z",
"user": "wuxiaolianggit"
},
{
"repo": "huggingface/lerobot",
"number": 2388,
"title": "how to improve the generalization of the vla model like gr00t",
"body": "After fine-tuning the gr00t, i found that it only work for the prompt within the dataset, it is difficult for it to understand new words and new item that need to grab. \nso whether there is a method can protect the generalization, if i can create a new layer to map the output of the model to new dimensionality?",
"url": "https://github.com/huggingface/lerobot/issues/2388",
"state": "open",
"labels": [],
"created_at": "2025-11-05T10:06:11Z",
"updated_at": "2025-11-05T10:44:38Z",
"user": "Temmp1e"
},
{
"repo": "vllm-project/vllm",
"number": 28119,
"title": "[Feature]: Will we support async scheduler for pipeline parallel?",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nSGLang already have https://github.com/sgl-project/sglang/pull/11852\n\nAnd I see huge perf gap on SM120 PP because of this.\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28119",
"state": "closed",
"labels": [
"feature request"
],
"created_at": "2025-11-05T09:55:57Z",
"updated_at": "2025-11-07T06:14:19Z",
"comments": 4,
"user": "weireweire"
},
{
"repo": "huggingface/gsplat.js",
"number": 122,
"title": "I want to add an object (such as a robot) to move around in the model. How can this be achieved?",
"body": "I want to add an object (such as a robot) to move around in the model. How can this be achieved?",
"url": "https://github.com/huggingface/gsplat.js/issues/122",
"state": "open",
"labels": [],
"created_at": "2025-11-05T09:16:39Z",
"updated_at": "2025-11-05T09:16:39Z",
"user": "ThinkingInGIS"
},
{
"repo": "vllm-project/vllm",
"number": 28104,
"title": "[Usage]: vllm bench serve\u4e0d\u80fd\u7528sharegpt\u6570\u636e\u96c6",
"body": "### Your current environment\n\n```text\n\u6211\u8fd0\u884c\u4ee5\u4e0bbencmmarks\u547d\u4ee4\uff1avllm bench serve --model Qwen3 --tokenizer /mnt/workspace/models --host 127.0.0.1 --port 80 --num-prompts 400 --percentile-metrics ttft,tpot,itl,e2el --metric-percentiles 90,95,99 --dataset-name sharegpt --data\nset-path /mnt/workspace/benchmarks/sharegpt/ShareGPT_V3_unfiltered_cleaned_split.json --sharegpt-output-len 512\n\u4f1a\u62a5\u4e00\u4e0b\u9519\u8bef\uff1a/usr/local/lib/python3.12/dist-packages/torch/cuda/init.py:61: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.\nimport pynvml # type: ignore[import]\nINFO 11-04 22:14:30 [init.py:243] Automatically detected platform cuda.\nINFO 11-04 22:14:32 [init.py:31] Available plugins for group vllm.general_plugins:\nINFO 11-04 22:14:32 [init.py:33] - lora_filesystem_resolver -> vllm.plugins.lora_resolvers.filesystem_resolver:register_filesystem_resolver\nINFO 11-04 22:14:32 [init.py:36] All plugins in this group will be loaded. Set to control which plugins to load.\nusage: vllm bench serve [options]\nvllm bench [options] serve: error: argument --dataset-name: invalid choice: 'sharegpt' (choose from random). \u8bf7\u95ee\u4e3a\u4ec0\u4e48\u6211\u8fd9\u4e2a\u4f1a\u62a5\u9519\uff1f\uff1f\uff1fVLLM_PLUGINS\n```\n\n\n### How would you like to use vllm\n\nhow to solve it\uff1f\uff1f\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28104",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-05T06:18:02Z",
"updated_at": "2025-11-06T14:24:46Z",
"comments": 1,
"user": "uOnePiece"
},
{
"repo": "vllm-project/vllm",
"number": 28070,
"title": "[Usage]: Is there a way to control default thinking behaviour of a model?",
"body": "### Your current environment\n\nIs there a way to control default thinking behaviour for models deployed through vllm.\nAs per https://docs.vllm.ai/en/stable/features/reasoning_outputs.html,\nIBM Grantie 3.2 reasoning is disabled by default.\nQwen3, GLM 4.6, Deepseek V3.1 all have reasoning enabled by default.\nIt would be great if there is a way to control this from vllm.\n--override-generation-config allows user to override temperature and other params at deployment.\nBut this does not work for reasoning.\nI have tried\n`docker run -d --runtime nvidia -e TRANSFORMERS_OFFLINE=1 -e DEBUG=\"true\" -p 8000:8000 --ipc=host vllm/vllm-openai:v0.11.0 --reasoning-parser qwen3 --model Qwen/Qwen3-4B --override-generation-config '{\"chat_template_kwargs\": {\"enable_thinking\": false}}'`\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28070",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-04T22:03:32Z",
"updated_at": "2025-12-30T03:38:48Z",
"comments": 0,
"user": "yz342"
},
{
"repo": "vllm-project/vllm",
"number": 28056,
"title": "[Bug]: Missing libarm_compute.so in Arm CPU pip installed wheels",
"body": "### Your current environment\n\n\\nFree OCR.\u201c}\n ]\n }],\n \"vllm_xargs\": {\n \"ngram_size\": 30,\n \"window_size\": 100,\n \"whitelist_token_ids\": \"[128821, 128822]\"\n },\n \"temperature\": 0.0,\n \"max_tokens\": 4096\n }\n\n\n\"finish_reason\":\"stop\" but \"completion_tokens\":200+ \uff0ccannot output the complete image content.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28035",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-04T09:50:53Z",
"updated_at": "2025-11-04T09:50:53Z",
"comments": 0,
"user": "sixgod-666"
},
{
"repo": "vllm-project/vllm",
"number": 28031,
"title": "[Usage]: Error: Failed to initialize the TMA descriptor 700",
"body": "### Your current environment\n\nvllm0.11.0 to train Qwen3-vl-8B \n\nThe following error message appears intermittently during training.\n```\n[36m(WorkerDict pid=82555)\u001b[0m TMA Desc Addr: 0x7f4e2736b080\n\u001b[36m(WorkerDict pid=82555)\u001b[0m format 9\n\u001b[36m(WorkerDict pid=82555)\u001b[0m dim 4\n\u001b[36m(WorkerDict pid=82555)\u001b[0m gmem_address 0xa9bdcd0000\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalDim (128,415,2,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalStrides (2,2048,1024,0,0)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m boxDim (64,128,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m elementStrides (1,1,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m interleave 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m swizzle 3\n\u001b[36m(WorkerDict pid=82555)\u001b[0m l2Promotion 2\n\u001b[36m(WorkerDict pid=82555)\u001b[0m oobFill 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m Error: Failed to initialize the TMA descriptor 700\n\u001b[36m(WorkerDict pid=82555)\u001b[0m TMA Desc Addr: 0x7f4e2736b080\n\u001b[36m(WorkerDict pid=82555)\u001b[0m format 9\n\u001b[36m(WorkerDict pid=82555)\u001b[0m dim 4\n\u001b[36m(WorkerDict pid=82555)\u001b[0m gmem_address 0xa46a000000\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalDim (128,16,2,61647,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalStrides (2,512,256,8192,0)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m boxDim (64,128,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m elementStrides (1,1,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m interleave 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m swizzle 3\n\u001b[36m(WorkerDict pid=82555)\u001b[0m l2Promotion 2\n\u001b[36m(WorkerDict pid=82555)\u001b[0m oobFill 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m Error: Failed to initialize the TMA descriptor 700\n\u001b[36m(WorkerDict pid=82555)\u001b[0m TMA Desc Addr: 0x7f4e2736b080\n\u001b[36m(WorkerDict pid=82555)\u001b[0m format 9\n\u001b[36m(WorkerDict pid=82555)\u001b[0m dim 4\n\u001b[36m(WorkerDict pid=82555)\u001b[0m gmem_address 0xa48819e000\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalDim (128,16,2,61647,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalStrides (2,512,256,8192,0)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m boxDim (64,128,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m elementStrides (1,1,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m interleave 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m swizzle 3\n\u001b[36m(WorkerDict pid=82555)\u001b[0m l2Promotion 2\n\u001b[36m(WorkerDict pid=82555)\u001b[0m oobFill 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m Error: Failed to initialize the TMA descriptor 700\n\u001b[36m(WorkerDict pid=82555)\u001b[0m TMA Desc Addr: 0x7f4e2736b080\n\u001b[36m(WorkerDict pid=82555)\u001b[0m format 9\n\u001b[36m(WorkerDict pid=82555)\u001b[0m dim 4\n\u001b[36m(WorkerDict pid=82555)\u001b[0m gmem_address 0xa46a000000\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalDim (128,16,2,61647,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalStrides (2,512,256,8192,0)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m boxDim (64,128,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m elementStrides (1,1,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m interleave 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m swizzle 3\n\u001b[36m(WorkerDict pid=82555)\u001b[0m l2Promotion 2\n\u001b[36m(WorkerDict pid=82555)\u001b[0m oobFill 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m Error: Failed to initialize the TMA descriptor 700\n\u001b[36m(WorkerDict pid=82555)\u001b[0m TMA Desc Addr: 0x7f4e2736b080\n\u001b[36m(WorkerDict pid=82555)\u001b[0m format 9\n\u001b[36m(WorkerDict pid=82555)\u001b[0m dim 4\n\u001b[36m(WorkerDict pid=82555)\u001b[0m gmem_address 0xa48819e000\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalDim (128,16,2,61647,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m globalStrides (2,512,256,8192,0)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m boxDim (64,128,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m elementStrides (1,1,1,1,1)\n\u001b[36m(WorkerDict pid=82555)\u001b[0m interleave 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m swizzle 3\n\u001b[36m(WorkerDict pid=82555)\u001b[0m l2Promotion 2\n\u001b[36m(WorkerDict pid=82555)\u001b[0m oobFill 0\n\u001b[36m(WorkerDict pid=82555)\u001b[0m Error: Failed to initialize the TMA descriptor 700\n\u001b[36m(WorkerDict pid=82555)\u001b[0m CUDA error (/workspace/.deps/vllm-flash-attn-src/hopper/flash_fwd_launch_template.h:191): an illegal memory access was encountered\n\u001b[36m(WorkerDict pid=82558)\u001b[0m l2Promotion 2\n\u001b[36m(WorkerDict pid=82558)\u001b[0m l2Promotion 2\n\u001b[36m(WorkerDict pid=82558)\u001b[0m l2Promotion 2\n\u001b[36m(WorkerDict pid=82558)\u001b[0m l2Promotion 2\n\u001b[36m(WorkerDict pid=82558)\u001b[0m l2Promotion 2\n```\n\n\nthen the error message below is being repeated, but training has not stopped.\n\n```\n[36m(WorkerDict pid=134586)\u001b[0m [rank7]:[W1104 07:52:01.751088784 TCPStore.cpp:125] [c10d] recvValue failed on SocketImpl(fd=90, addr=[train-kubeflow-72-46805-20251104102107-master-0]:49384, remote=[train-kubeflow-72-46805-20251104102107-master-0]:32991): Connection reset by peer\u001b[32m [repeated 6x across cluster]\u001b[0m\n\u001b[36m(WorkerDict pid=134586)\u001b[0m Exception raised from recvBytes at /pytorch/torch/csrc/distributed/c10d/Utils.hpp:679 (most recent call first):\u001b[32m [repeated 6x across cluster]\u001b[0m\n\u001b[36m(WorkerDict pid=134580)\u001b[0m frame #0: c10::Error::Error(c10::SourceLocation, std::__cxx11::ba",
"url": "https://github.com/vllm-project/vllm/issues/28031",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-04T08:13:45Z",
"updated_at": "2025-12-11T08:18:15Z",
"comments": 4,
"user": "DBMing"
},
{
"repo": "vllm-project/vllm",
"number": 28016,
"title": "[Usage]: How to recognize PDFs in DeepSeek-OCR with openai",
"body": "### Your current environment\n```\nvllm serve deepseek-ai/DeepSeek-OCR --logits_processors vllm.model_executor.models.deepseek_ocr.NGramPerReqLogitsProcessor --no-enable-prefix-caching --mm-processor-cache-gb 0\n```\n\n\n\n### How would you like to use vllm\n\nHow to recognize PDFs and convert PDFs to Markdown with DeepSeek-OCR via an OpenAI-compatible API?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28016",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-04T03:35:38Z",
"updated_at": "2025-11-04T07:33:07Z",
"comments": 2,
"user": "shoted"
},
{
"repo": "vllm-project/vllm",
"number": 28003,
"title": "[Usage]:",
"body": "### Your current environment\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version : Could not collect\nCMake version : version 4.1.0\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-6.8.0-54-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : GPU 0: NVIDIA H100 NVL\nNvidia driver version : 570.86.10\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 48\nOn-line CPU(s) list: 0-47\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 9654 96-Core Processor\nCPU family: 25\nModel: 17\nThread(s) per core: 1\nCore(s) per socket: 1\nSocket(s): 48\nStepping: 1\nBogoMIPS: 4799.59\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy svm cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw perfctr_core ssbd ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves avx512_bf16 clzero xsaveerptr wbnoinvd arat npt lbrv nrip_save tsc_scale vmcb_clean flushbyasid pausefilter pfthreshold v_vmsave_vmload vgif vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid fsrm flush_l1d arch_capabilities\nVirtualization: AMD-V\nL1d cache: 3 MiB (48 instances)\nL1i cache: 3 MiB (48 instances)\nL2 cache: 24 MiB (48 instances)\nL3 cache: 768 MiB (48 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-47\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.3.1\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.14.1\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-cufile-cu12==1.13.1.3\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cuspa",
"url": "https://github.com/vllm-project/vllm/issues/28003",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-03T21:19:15Z",
"updated_at": "2025-11-26T15:32:40Z",
"comments": 1,
"user": "amitmvyas"
},
{
"repo": "vllm-project/vllm",
"number": 27995,
"title": "[RFC]: Make PassConfig flags less verbose",
"body": "### Motivation.\n\nAlmost all `PassConfig` field names have `enable_` in the name, which is unnecessarily verbose. They are also pretty long, and sometimes not descriptive enough. Finally, `enable_fusion` should be split into rmsnorm+quant and activation+quant flags as we want to control these flags separately.\n\n### Proposed Change.\n\nWe should rename the flags:\n- `enable_async_tp` -> `fuse_gemm_comms`\n- `enable_attn_fusion` -> `fuse_attn_quant` \n- `enable_fi_allreduce_fusion` -> `fuse_allreduce_rms` \n- `enable_fusion` -> `fuse_norm_quant`, `fuse_act_quant`\n- `enable_noop` -> `eliminate_noops`\n- `enable_sequence_parallelism` -> `enable_sp`\n\nFor future RoPE-based fusion passes, the flags will look like:\n- `enable_qknorm_rope_fusion` -> `fuse_qknorm_rope`\n- `enable_rope_cache_fusion` -> `fuse_rope_cache`\n- ...\n\nWe can deprecate the original flags in the next release and map them to the new ones, and remove them 1 or even 2 releases later (shouldn't be hard to support). These flags will be used less commonly after `-O` optimization levels land anyway.\n\n### Feedback Period.\n\n1 week, 11/3 - 11/7\n\n### CC List.\n\n@zou3519 @youkaichao @mgoin @ilmarkov @nvpohanh @pavanimajety \n\n### Any Other Things.\n\nWith passes following a common construction convention, we can also add a `full_pass_pipeline` arg where users can control the exact order of the passes if necessary, but that is less likely to be needed urgently and can be added later.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27995",
"state": "closed",
"labels": [
"help wanted",
"good first issue",
"RFC",
"torch.compile"
],
"created_at": "2025-11-03T17:49:29Z",
"updated_at": "2025-12-03T19:53:01Z",
"comments": 7,
"user": "ProExpertProg"
},
{
"repo": "huggingface/peft",
"number": 2888,
"title": "Potential remote code execution via untrusted tokenizer_kwargs in PromptEmbedding",
"body": "### Description\n\nA remote code execution vector exists in the PEFT prompt-tuning flow. A remote `adapter_config.json` can inject loader kwargs that are forwarded to `AutoTokenizer.from_pretrained` calls. If an attacker sets `\"tokenizer_kwargs\": {\"trust_remote_code\": true}` and points `tokenizer_name_or_path` at an attacker-controlled repo, constructing the prompt embedding will cause `AutoTokenizer.from_pretrained(...)` to import and run code from that repo. This happens during normal initialization and requires no further user interaction.\n\n### Root Cause \n\n`PromptEmbedding` trusts and forwards fields from config into `AutoTokenizer.from_pretrained` without validating or sanitizing them:\n\nhttps://github.com/huggingface/peft/blob/30a19a08f9ef85ce1095b9ac69e78269121525e2/src/peft/tuners/prompt_tuning/model.py#L78-L84 \n\n### Impact\n\nThis issue turns remote configuration files into attack vectors. Any user who loads a malicious adapter config can have arbitrary code executed on their machine. The compromise is silent, requires no extra user action beyond `from_pretrained`, and is easy to weaponize by publishing a seemingly legitimate config that explicitly set `trust_remote_code=True` and points to attacker code. Consequences include command execution, credential and data theft, file tampering, and worm infection if environment tokens or write permissions are present. This should be fixed urgently by treating config-supplied kwargs as untrusted: filter or reject sensitive parameters such as `trust_remote_code`. \n\n### Who can help?\n\n@benjaminbossan @githubnemo\n\n### Reproduction\n\nA malicious remote config can look like:\n\n```json\n{\n \"base_model_name_or_path\": \"XManFromXlab/peft-prompt-embedding-rce\",\n \"tokenizer_name_or_path\": \"XManFromXlab/peft-prompt-embedding-rce\"\n \"tokenizer_kwargs\": { \"trust_remote_code\": true }\n}\n```\n\nWhen users are attracted to the repo and use peft to load the config from remote repo\n\n```python\nfrom peft import PromptEmbedding, PromptTuningConfig\nfrom transformers import AutoModelForSeq2SeqLM\n\nt5_model = AutoModelForSeq2SeqLM.from_pretrained(\"t5-base\")\n\nexample_model = \"XManFromXlab/peft-prompt-embedding-rce\"\nconfig = PromptTuningConfig.from_pretrained(example_model, trust_remote_code=False)\nprompt_embedding = PromptEmbedding(config, t5_model.shared)\n```\n\nDuring `PromptEmbedding` initialization the code reads `tokenizer_kwargs` from the remote config and calls `AutoTokenizer.from_pretrained(config.tokenizer_name_or_path, **tokenizer_kwargs)`. Because `trust_remote_code` was injected via the config, the loader imports and executes the attacker\u2019s backend code, demonstrating RCE.\n\n\n### Expected behavior\n\n\nIn my example, the above code will print the message 'Execute Malicious Payload!!!!!!', which indicates the execution of malicious scripts.\n\n```bash\n$ python3 main.py \nExecute Malicious Payload!!!!!! \nExecute Malicious Payload!!!!!!\nExecute Malicious Payload!!!!!! \n```",
"url": "https://github.com/huggingface/peft/issues/2888",
"state": "closed",
"labels": [],
"created_at": "2025-11-03T16:04:52Z",
"updated_at": "2025-11-04T17:50:28Z",
"comments": 3,
"user": "Vancir"
},
{
"repo": "huggingface/lerobot",
"number": 2371,
"title": "memory increase continuously during training Groot",
"body": "### System Info\n\n```Shell\n- lerobot version: 0.4.1\n- Platform: Linux-5.4.250-2-velinux1u3-amd64-x86_64-with-glibc2.31\n- Python version: 3.10.15\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 2.1.3\n- PyTorch version: 2.7.1+cu126\n- Is PyTorch built with CUDA support?: True\n- Cuda version: 12.6\n- GPU model: NVIDIA GeForce RTX 4090\n- Using GPU in script?: \n```\n\n### Information\n\n- [ ] One of the scripts in the examples/ folder of LeRobot\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nrun\n` lerobot-train \\\n --output_dir=$OUTPUT_DIR \\\n --save_checkpoint=true \\\n --batch_size=64 \\\n --steps=10000 \\\n --save_freq=1000 \\\n --log_freq=100 \\\n --policy.push_to_hub=false \\\n --policy.type=groot \\\n --dataset.repo_id=$DATASET_ID \\\n --dataset.root=$DATASET_ROOT_DIR \\\n --dataset.streaming=false \\\n --dataset.image_transforms.enable=true \\\n --wandb.enable=true \\\n --wandb.mode=offline \\\n --wandb.project=groot_test \\\n --job_name=$JOB_NAME \\`\n\n### Expected behavior\n\nmemory increase until out of memory",
"url": "https://github.com/huggingface/lerobot/issues/2371",
"state": "open",
"labels": [
"question",
"policies",
"performance"
],
"created_at": "2025-11-03T14:38:52Z",
"updated_at": "2025-12-31T13:17:11Z",
"user": "caoran2025"
},
{
"repo": "vllm-project/vllm",
"number": 27982,
"title": "[Usage]: How can I access or return hidden states (representations) after generation?",
"body": "### Your current environment\n\nIn my training pipeline (GRPO), I need to access hidden-state representations of all layers and store prompt representations alongside generated sequences.\nIs there any supported way to extract or return hidden states from the vLLM inference engine?\n\nEnvironment\nvllm==0.11.0\nPython 3.12\n\n### How would you like to use vllm\n\n\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27982",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-03T13:01:51Z",
"updated_at": "2025-11-04T03:07:40Z",
"comments": 1,
"user": "hakbari14"
},
{
"repo": "huggingface/lerobot",
"number": 2368,
"title": "Release 0.5.0",
"body": "A Github Issue created for the upcoming release to discuss the planned features & changes:\n\n* Audio PR #967 \n* Bump transformers dependency to +v5",
"url": "https://github.com/huggingface/lerobot/issues/2368",
"state": "open",
"labels": [
"bug",
"question",
"dependencies"
],
"created_at": "2025-11-03T12:46:51Z",
"updated_at": "2025-12-24T00:08:16Z",
"user": "imstevenpmwork"
},
{
"repo": "vllm-project/vllm",
"number": 27981,
"title": "[Usage]: qwenvl2.5\u5982\u4f55\u6307\u5b9amax_pixels",
"body": "### Your current environment\n\n\u5982\u9898\uff0c\u6211\u5c1d\u8bd5\u4e86``--mm-processor-kwargs {\"max_pixels\": $MAX_PIXELS}``\u65e0\u6548\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27981",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-03T12:38:34Z",
"updated_at": "2025-11-04T08:19:54Z",
"comments": 3,
"user": "aJupyter"
},
{
"repo": "huggingface/accelerate",
"number": 3829,
"title": "Does Accelerate automatically set the DataLoader\u2019s sampler to a DistributedSampler?",
"body": "```python\nfrom accelerate import Accelerator\naccelerator = Accelerator()\n\ndevice = accelerator.device\nmodel, optimizer, training_dataloader, scheduler = accelerator.prepare(\n model, optimizer, training_dataloader, scheduler\n)\n\nfor batch in training_dataloader:\n optimizer.zero_grad()\n inputs, targets = batch\n outputs = model(inputs)\n loss = loss_function(outputs, targets)\n accelerator.backward(loss)\n optimizer.step()\n scheduler.step()\n```\n\nWe know that in PyTorch DDP training the DataLoader must use torch.utils.data.DistributedSampler. In this code, when using Accelerate, do we need to manually set DistributedSampler when constructing the `training_dataloader`, or will Accelerate automatically modify the dataloader\u2019s sampler to support DDP later? (In other words, when we build the dataloader for Accelerate, can we completely ignore DistributedSampler and just leave it as we would for single\u2011GPU training?)",
"url": "https://github.com/huggingface/accelerate/issues/3829",
"state": "closed",
"labels": [],
"created_at": "2025-11-03T07:17:29Z",
"updated_at": "2025-12-16T15:09:43Z",
"comments": 2,
"user": "caixxiong"
},
{
"repo": "vllm-project/vllm",
"number": 27957,
"title": "[Usage]: What is the difference between embedding task and pooler task?",
"body": "### Your current environment\n\nAny document about this? \n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27957",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-03T03:38:39Z",
"updated_at": "2025-11-03T10:20:18Z",
"comments": 1,
"user": "sleepwalker2017"
},
{
"repo": "vllm-project/vllm",
"number": 27949,
"title": "[Usage]: How do I deploy GGUF models with vLLM via Docker correct?",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\nHere is the output from `sudo python3 collect_env.py`\n\n```\nTraceback (most recent call last):\n File \"/export/nvme/vllm/collect_env.py\", line 18, in \n import regex as re\nModuleNotFoundError: No module named 'regex'\n```\n\n### How would you like to use vllm\n\nI am using an Ubuntu 22.04 LTS LXC in Proxmox.\n\nI have Docker installed.\n\nI downloaded `https://huggingface.co/unsloth/DeepSeek-R1-Distill-Llama-70B-GGUF/resolve/main/DeepSeek-R1-Distill-Llama-70B-Q4_K_M.gguf?download=true` to `/export/nvme/huggingface/DeepSeek-R1-Distill-Llama-70B-GGUF/DeepSeek-R1-Distill-Llama-70B-Q4_K_M.gguf` via `wget`.\n\nThe command that I am trying to use to start said Docker container is:\n\n```\nsudo docker run --runtime nvidia --gpus all \\\n --name vllm \\\n -v /export/nvme/huggingface/DeepSeek-R1-Distill-Llama-70B-Q4_K_M-GGUF:/root/.cache/huggingface/DeepSeek-R1-Distill-Llama-70B-Q4_K_M-GGUF \\\n -v /export/nvme/vllm:/export/nvme/vllm \\\n -e TRANSFORMERS_OFFLINE=1 \\\n --shm-size=16G \\\n -v /dev/shm:/dev/shm \\\n -p 0.0.0.0:8000:8000 \\\n --security-opt apparmor:unconfined \\\n vllm/vllm-openai:v0.8.5 \\\n --model /root/.cache/huggingface/DeepSeek-R1-Distill-Llama-70B-Q4_K_M-GGUF/DeepSeek-R1-Distill-Llama-70B-Q4_K_M.gguf \\\n --tokenizer /root/.cache/huggingface/DeepSeek-R1-Distill-Llama-70B \\\n --tensor-parallel-size 2 \\\n --max-model-len=32K \\\n --chat-template=/export/nvme/vllm/examples/tool_chat_template_deepseekr1.jinja\n```\n\nBut this is the error message that I get:\n```\nINFO 11-02 15:21:55 [__init__.py:239] Automatically detected platform cuda.\nINFO 11-02 15:21:59 [api_server.py:1043] vLLM API server version 0.8.5\nINFO 11-02 15:21:59 [api_server.py:1044] args: Namespace(host=None, port=8000, uvicorn_log_level='info', disable_uvicorn_access_log=False, allow_credentials=False, allowed_origins=['*'], allowed_methods=['*'], allowed_headers=['*'], api_key=None, lora_modules=None, prompt_adapters=None, chat_template='/export/nvme/vllm/examples/tool_chat_template_deepseekr1.jinja', chat_template_content_format='auto', response_role='assistant', ssl_keyfile=None, ssl_certfile=None, ssl_ca_certs=None, enable_ssl_refresh=False, ssl_cert_reqs=0, root_path=None, middleware=[], return_tokens_as_token_ids=False, disable_frontend_multiprocessing=False, enable_request_id_headers=False, enable_auto_tool_choice=False, tool_call_parser=None, tool_parser_plugin='', model='/root/.cache/huggingface/DeepSeek-R1-Distill-Llama-70B-Q4_K_M-GGUF/DeepSeek-R1-Distill-Llama-70B-Q4_K_M.gguf', task='auto', tokenizer='/root/.cache/huggingface/DeepSeek-R1-Distill-Llama-70B', hf_config_path=None, skip_tokenizer_init=False, revision=None, code_revision=None, tokenizer_revision=None, tokenizer_mode='auto', trust_remote_code=False, allowed_local_media_path=None, load_format='auto', download_dir=None, model_loader_extra_config={}, use_tqdm_on_load=True, config_format=, dtype='auto', max_model_len=32768, guided_decoding_backend='auto', reasoning_parser=None, logits_processor_pattern=None, model_impl='auto', distributed_executor_backend=None, pipeline_parallel_size=1, tensor_parallel_size=2, data_parallel_size=1, enable_expert_parallel=False, max_parallel_loading_workers=None, ray_workers_use_nsight=False, disable_custom_all_reduce=False, block_size=None, gpu_memory_utilization=0.9, swap_space=4, kv_cache_dtype='auto', num_gpu_blocks_override=None, enable_prefix_caching=None, prefix_caching_hash_algo='builtin', cpu_offload_gb=0, calculate_kv_scales=False, disable_sliding_window=False, use_v2_block_manager=True, seed=None, max_logprobs=20, disable_log_stats=False, quantization=None, rope_scaling=None, rope_theta=None, hf_token=None, hf_overrides=None, enforce_eager=False, max_seq_len_to_capture=8192, tokenizer_pool_size=0, tokenizer_pool_type='ray', tokenizer_pool_extra_config={}, limit_mm_per_prompt={}, mm_processor_kwargs=None, disable_mm_preprocessor_cache=False, enable_lora=None, enable_lora_bias=False, max_loras=1, max_lora_rank=16, lora_extra_vocab_size=256, lora_dtype='auto', long_lora_scaling_factors=None, max_cpu_loras=None, fully_sharded_loras=False, enable_prompt_adapter=None, max_prompt_adapters=1, max_prompt_adapter_token=0, device='auto', speculative_config=None, ignore_patterns=[], served_model_name=None, qlora_adapter_name_or_path=None, show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None, disable_async_output_proc=False, max_num_batched_tokens=None, max_num_seqs=None, max_num_partial_prefills=1, max_long_partial_prefills=1, long_prefill_token_threshold=0, num_lookahead_slots=0, scheduler_delay_factor=0.0, preemption_mode=None, num_scheduler_steps=1, multi_step_stream_outputs=True, scheduling_policy='fcfs', enable_chunked_prefill=None, disable_chunked_mm_input=False, scheduler_cls='vllm.core.scheduler.Scheduler', override_neuron_config=None, override_pooler_config=None, compilati",
"url": "https://github.com/vllm-project/vllm/issues/27949",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-02T23:33:49Z",
"updated_at": "2025-11-02T23:36:44Z",
"comments": 1,
"user": "alpha754293"
},
{
"repo": "huggingface/xet-core",
"number": 549,
"title": "How to get the \"Xet backed hash\"?",
"body": "Hi,\n\nOn HuggingFace, every page has a \"Xet backed hash\" (I've attached an example below) and I am trying to figure out how to compute that locally.\n\nI've read the documentation and it says there are 4 types of different hashes but it's not really clear how a \"Xet backed hash\" is calculated.\n\nSo I was just wondering if you can you tell me how I can get the \"Xet backed hash\" on a local file?\n\nThank you for your time.\n\n![\"Image\"](\"https://github.com/user-attachments/assets/9fad42a3-e15b-4734-b57a-a769b5b77577\")
",
"url": "https://github.com/huggingface/xet-core/issues/549",
"state": "closed",
"labels": [],
"created_at": "2025-11-02T09:40:39Z",
"updated_at": "2025-11-06T16:20:25Z",
"user": "arch-btw"
},
{
"repo": "huggingface/lerobot",
"number": 2360,
"title": "diffusion transformer",
"body": "\u8bf7\u95ee\u6709\u5927\u4f6c\u5728lerobot\u4e2d\u5c06diffusion unet\u6539\u4e3aDiT\u8fc7\u5417",
"url": "https://github.com/huggingface/lerobot/issues/2360",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-11-02T09:05:30Z",
"updated_at": "2025-11-12T09:01:59Z",
"user": "Benxiaogu"
},
{
"repo": "vllm-project/vllm",
"number": 27928,
"title": "[Bug]: What happened to /get_world_size ?",
"body": "### Your current environment\n\nvllm 0.11.0\ntrl 0.24.0\npython 3.12\nlinux amd64\n\n### \ud83d\udc1b Describe the bug\n\nTRL is expecting a `/get_world_size` route https://github.com/huggingface/trl/blob/main/trl/extras/vllm_client.py#L279 for its GRPO trainer. That gives a 404 on the latest version of vLLM. \n\nWas this changed to another route? I can't seem to find it\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27928",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-01T22:56:45Z",
"updated_at": "2025-11-03T02:42:14Z",
"comments": 1,
"user": "pbarker-synth"
},
{
"repo": "huggingface/lerobot",
"number": 2356,
"title": "AsyncInference only running one action chunk",
"body": "I have my SO101 arms connected to my computer, and I'm running an asynchronous server on a cloud GPU with a RTX 4090.\n\nWhen I start running Pi0.5, the model is loaded and the SO101 makes its first move by setting the robot to be at its middle position, but then no further actions are made although the server logs new observations and action sequences being generated.\n\nThe robot moves to this position and doesn't move further:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/0499680b-4072-4c90-acda-e4fc1af18e64\")
\n\nI have one wrist camera and one top-down view camera. Here is my client command:\n```\npython3 -m lerobot.async_inference.robot_client \\\n --server_address=ip:port \\\n --robot.type=so101_follower \\\n --robot.port=/dev/ttyACM0 \\\n --robot.id=arm \\\n --robot.cameras=\"{ base_0_rgb: {type: opencv, index_or_path: \\\"/dev/video2\\\", width: 640, height: 480, fps: 30}, left_wrist_0_rgb: {type: opencv, index_or_path: \\\"/dev/video0\\\", width: 640, height: 480, fps: 30}}\" \\\n --policy_device=cuda \\\n --aggregate_fn_name=weighted_average \\\n --debug_visualize_queue_size=True \\\n --task=\"Pick up the orange and place it on the plate\" \\\n --policy_type=pi05 \\\n --pretrained_name_or_path=lerobot/pi05_base \\\n --actions_per_chunk=50 \\\n --chunk_size_threshold=0.0 \\\n --debug_visualize_queue_size=True\n```\n\nHere are my server logs:\n```\n(lerobot) root@eff66f201198:/workspace/arm-x64# ./robot.sh runpod async-server\nINFO 2025-11-01 20:17:34 y_server.py:421 {'fps': 30,\n 'host': '0.0.0.0',\n 'inference_latency': 0.03333333333333333,\n 'obs_queue_timeout': 2,\n 'port': 8080}\nINFO 2025-11-01 20:17:34 y_server.py:431 PolicyServer started on 0.0.0.0:8080\nINFO 2025-11-01 20:18:03 y_server.py:112 Client ipv4:129.97.131.28:23025 connected and ready\nINFO 2025-11-01 20:18:03 y_server.py:138 Receiving policy instructions from ipv4:129.97.131.28:23025 | Policy type: pi05 | Pretrained name or path: lerobot/pi05_base | Actions per chunk: 50 | Device: cuda\nThe PI05 model is a direct port of the OpenPI implementation. \nThis implementation follows the original OpenPI structure for compatibility. \nOriginal implementation: https://github.com/Physical-Intelligence/openpi\nINFO 2025-11-01 20:18:03 ils/utils.py:43 Cuda backend detected, using cuda.\nWARNING 2025-11-01 20:18:03 /policies.py:82 Device 'mps' is not available. Switching to 'cuda'.\nINFO 2025-11-01 20:18:03 ils/utils.py:43 Cuda backend detected, using cuda.\nWARNING 2025-11-01 20:18:03 /policies.py:82 Device 'mps' is not available. Switching to 'cuda'.\nLoading model from: lerobot/pi05_base\n\u2713 Loaded state dict from model.safetensors\nWARNING 2025-11-01 20:19:08 ng_pi05.py:1023 Vision embedding key might need handling: paligemma_with_expert.paligemma.model.vision_tower.vision_model.embeddings.patch_embedding.bias\nWARNING 2025-11-01 20:19:08 ng_pi05.py:1023 Vision embedding key might need handling: paligemma_with_expert.paligemma.model.vision_tower.vision_model.embeddings.patch_embedding.weight\nRemapped: action_in_proj.bias -> model.action_in_proj.bias\nRemapped: action_in_proj.weight -> model.action_in_proj.weight\nRemapped: action_out_proj.bias -> model.action_out_proj.bias\nRemapped: action_out_proj.weight -> model.action_out_proj.weight\nRemapped: paligemma_with_expert.gemma_expert.lm_head.weight -> model.paligemma_with_expert.gemma_expert.lm_head.weight\nRemapped: paligemma_with_expert.gemma_expert.model.layers.0.input_layernorm.dense.bias -> model.paligemma_with_expert.gemma_expert.model.layers.0.input_layernorm.dense.bias\nRemapped: paligemma_with_expert.gemma_expert.model.layers.0.input_layernorm.dense.weight -> model.paligemma_with_expert.gemma_expert.model.layers.0.input_layernorm.dense.weight\nRemapped: paligemma_with_expert.gemma_expert.model.layers.0.mlp.down_proj.weight -> model.paligemma_with_expert.gemma_expert.model.layers.0.mlp.down_proj.weight\nRemapped: paligemma_with_expert.gemma_expert.model.layers.0.mlp.gate_proj.weight -> model.paligemma_with_expert.gemma_expert.model.layers.0.mlp.gate_proj.weight\nRemapped: paligemma_with_expert.gemma_expert.model.layers.0.mlp.up_proj.weight -> model.paligemma_with_expert.gemma_expert.model.layers.0.mlp.up_proj.weight\nRemapped 812 state dict keys\nWarning: Could not remap state dict keys: Error(s) in loading state_dict for PI05Policy:\n\tMissing key(s) in state_dict: \"model.paligemma_with_expert.paligemma.model.language_model.embed_tokens.weight\". \nINFO 2025-11-01 20:19:43 y_server.py:171 Time taken to put policy on cuda: 99.9787 seconds\nINFO 2025-11-01 20:19:43 ort/utils.py:74 Starting receiver\nINFO 2025-11-01 20:20:02 y_server.py:226 Running inference for observation #0 (must_go: True)\nINFO 2025-11-01 20:20:03 ort/utils.py:74 Starting receiver\nINFO 2025-11-01 20:20:04 y_server.py:362 Preprocessing and inference took 1.3530s, action shape: torch.Size([1, 50, 32])\nINFO 2025-11-01 20:20:04 y_server.py:392 Observation ",
"url": "https://github.com/huggingface/lerobot/issues/2356",
"state": "open",
"labels": [
"question",
"robots"
],
"created_at": "2025-11-01T20:31:10Z",
"updated_at": "2025-12-23T01:10:35Z",
"user": "kevinjosethomas"
},
{
"repo": "vllm-project/vllm",
"number": 27916,
"title": "[Feature]: Does the latest version support LoRa for visual models?",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nWhen I loaded the QWEN2.5-VL model fine-tuned by LoRa using vllm version 0.8.4, I encountered the following prompt:\n\n> Regarding multimodal models, vLLM currently only supports adding LoRA to language model, visual.blocks.31.mlp.up_proj will be ignored.\n\nI found an issue https://github.com/vllm-project/vllm/issues/26422 with a similar problem, but it seems the PR hasn't been merged into master. How can I enable loading visual-side LORA parameters and using VLLM to accelerate inference?\n\nLooking forward to your reply\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27916",
"state": "closed",
"labels": [
"feature request"
],
"created_at": "2025-11-01T12:23:36Z",
"updated_at": "2025-12-26T12:48:22Z",
"comments": 1,
"user": "SmartNight-cc"
},
{
"repo": "huggingface/lerobot",
"number": 2354,
"title": "Cannot reproduce SmolVLA results on LIBERO benchmark",
"body": "Hello,\n\nI am trying to reproduce LIBERO benchmark results of [SmolVLA](https://huggingface.co/HuggingFaceVLA/smolvla_libero).\nHowever, I can't reproduce results on neither [leaderboard](https://huggingface.co/spaces/HuggingFaceVLA/libero-vla-leaderboard) and [paper](https://arxiv.org/abs/2506.01844)\n\nI am working on NVIDIA Jetson AGX Orin Developer Kit (Jetpack 6.2.1, Jetson Linux 36.4.4)\nand below is my pip list\n\nHello,\n\nI am trying to reproduce the LIBERO benchmark results of [SmolVLA](https://huggingface.co/HuggingFaceVLA/smolvla_libero). \nHowever, I can't reproduce the results on either the [leaderboard](https://huggingface.co/spaces/HuggingFaceVLA/libero-vla-leaderboard) or the [paper](https://arxiv.org/abs/2506.01844).\n\nI am working on an NVIDIA Jetson AGX Orin Developer Kit (JetPack 6.2.1, Jetson Linux 36.4.4), \nand below is my pip list.\n\n
The output of python collect_env.py
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.2 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.22.1\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.18 (main, Jun 4 2025, 08:56:00) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.14.0-33-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.0.140\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : GPU 0: NVIDIA RTX 6000 Ada Generation\nNvidia driver version : 575.57.08\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.11.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.11.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 52\nOn-line CPU(s) list: 0-51\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) w7-2595X\nCPU family: 6\nModel: 143\nThread(s) per core: 2\nCore(s) per socket: 26\nSocket(s): 1\nStepping: 8\nCPU(s) scaling MHz: 21%\nCPU max MHz: 4800.0000\nCPU min MHz: 800.0000\nBogoMIPS: 5616.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 intel_ppin cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect user_shstk avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req vnmi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr ibt amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 1.2 MiB (26 instances)\nL1i cache: 832 KiB (26 instances)\nL2 cache: 52 MiB (26 instances)\nL3 cache: 48.8 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-51\nVulnerability Gather data sampling: Not affected\nVulnerability Ghostwrite: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not af",
"url": "https://github.com/vllm-project/vllm/issues/28622",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-13T07:26:56Z",
"updated_at": "2025-11-13T07:27:06Z",
"comments": 0,
"user": "logesh13"
},
{
"repo": "vllm-project/vllm",
"number": 28610,
"title": "[Usage]: Does 0.11.0 suport tree attenton with eagle?",
"body": "### Your current environment\n\nDoes 0.11.0 suport tree attenton with eagle? Do I need to enable it manually?\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28610",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-13T03:35:02Z",
"updated_at": "2025-12-03T17:08:16Z",
"comments": 1,
"user": "wincle"
},
{
"repo": "huggingface/datasets",
"number": 7864,
"title": "add_column and add_item erroneously(?) require new_fingerprint parameter",
"body": "### Describe the bug\n\nContradicting their documentation (which doesn't mention the parameter at all), both Dataset.add_column and Dataset.add_item require a new_fingerprint string. This parameter is passed directly to the dataset constructor, which has the fingerprint parameter listed as optional; is there any reason it shouldn't be optional in these methods as well? \n\n### Steps to reproduce the bug\n\nReproduction steps:\n\n1. Look at the function signature for add_column: https://github.com/huggingface/datasets/blob/17f40a318a1f8c7d33c2a4dd17934f81d14a7f57/src/datasets/arrow_dataset.py#L6078\n2. Repeat for add_item: https://github.com/huggingface/datasets/blob/17f40a318a1f8c7d33c2a4dd17934f81d14a7f57/src/datasets/arrow_dataset.py#L6336\n\n### Expected behavior\n\nadd_column and add_item should either set the fingerprint parameter to optional or include it in their docstrings\n\n### Environment info\n\nNot environment-dependent",
"url": "https://github.com/huggingface/datasets/issues/7864",
"state": "open",
"labels": [],
"created_at": "2025-11-13T02:56:49Z",
"updated_at": "2025-12-07T14:41:40Z",
"comments": 2,
"user": "echthesia"
},
{
"repo": "vllm-project/vllm",
"number": 28566,
"title": "[Usage]: pd disagg scenario , I discover in the decoder , also has the prefill operation, is it normal ?",
"body": "### Your current environment\n\nwhen num_computed_tokens is less than num_prompt_tokens, it will enter prefill operation\n\npython collect_env.py\n\n```text\n==============================\n System Info\n==============================\nOS : Ubuntu 20.04.5 LTS (x86_64)\nGCC version : (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nClang version : Could not collect\nCMake version : version 3.16.3\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.5.1+cu121\nIs debug build : False\nCUDA used to build PyTorch : 12.1\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.18 (main, Jun 5 2025, 13:14:17) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-5.4.250-2-velinux1u1-amd64-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.4.131\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA A100-SXM4-80GB\nGPU 1: NVIDIA A100-SXM4-80GB\nGPU 2: NVIDIA A100-SXM4-80GB\nGPU 3: NVIDIA A100-SXM4-80GB\nGPU 4: NVIDIA A100-SXM4-80GB\nGPU 5: NVIDIA A100-SXM4-80GB\nGPU 6: NVIDIA A100-SXM4-80GB\nGPU 7: NVIDIA A100-SXM4-80GB\n\nNvidia driver version : 535.129.03\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.7.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.7.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nByte Order: Little Endian\nAddress sizes: 46 bits physical, 57 bits virtual\nCPU(s): 112\nOn-line CPU(s) list: 0-108\nOff-line CPU(s) list: 109-111\nThread(s) per core: 1\nCore(s) per socket: 28\nSocket(s): 2\nNUMA node(s): 2\nVendor ID: GenuineIntel\nCPU family: 6\nModel: 106\nModel name: Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz\nStepping: 6\nCPU MHz: 2294.608\nBogoMIPS: 4589.21\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 1.3 MiB\nL1i cache: 896 KiB\nL2 cache: 35 MiB\nL3 cache: 54 MiB\nNUMA node0 CPU(s): 0-55\nNUMA node1 CPU(s): 56-111\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Vulnerable: Clear CPU buffers attempted, no microcode; SMT Host state unknown\nVulnerability Retbleed: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch cpuid_fault invpcid_single ssbd ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves wbnoinvd arat avx512vbmi umip pku ospke avx512_vbmi2 gf",
"url": "https://github.com/vllm-project/vllm/issues/28292",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-07T12:01:04Z",
"updated_at": "2026-01-06T00:06:43Z",
"comments": 5,
"user": "LittleLucifer1"
},
{
"repo": "huggingface/transformers",
"number": 42086,
"title": "Does Trainer uses grad scaler for training?",
"body": "I am not able to see the grad scaler usage in Trainer code. If not using it then I need to understand how are we using mixed precision training with fp16 precision without grad scaler.",
"url": "https://github.com/huggingface/transformers/issues/42086",
"state": "closed",
"labels": [],
"created_at": "2025-11-07T10:10:16Z",
"updated_at": "2025-11-13T07:58:33Z",
"comments": 2,
"user": "quic-meetkuma"
},
{
"repo": "vllm-project/vllm",
"number": 28283,
"title": "[Bug]: nccl stuck issue",
"body": "### Your current environment\n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nI am using a docker container for vLLM. I noticed that when I use `nvidia/cuda:13.0.X-cudnn-devel-ubuntu24.04` with `tp > 1`, it gets stuck here: `INFO 11-07 09:24:25 [pynccl.py:111] vLLM is using nccl==2.27.5`. But it works fine with `nvidia/cuda:12.9.X-cudnn-devel-ubuntu24.04` because I assume `12.9` is the current default now.\n\nMy question is: why does the CUDA image version really matter with vLLM? Just asking since I'm not experiencing this with SGLang, where `tp > 1` still works well even if I use either `12.8`, `12.9`, or even `13.0` `nvidia/cuda` image.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28283",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-07T09:36:01Z",
"updated_at": "2025-11-07T09:40:17Z",
"comments": 1,
"user": "seindum"
},
{
"repo": "vllm-project/vllm",
"number": 28262,
"title": "[Bug]: [gpt-oss] Responses API incorrect input/output handling",
"body": "### Your current environment\n\nAny env\n\n### \ud83d\udc1b Describe the bug\n\nThere is currently an implementation issue with gpt-oss on the Responses API in vLLM. This can be seen clearly in the [test which continues a conversation between API requests here](https://github.com/vllm-project/vllm/blob/4bf56c79cc252d285d0cb4f5edf323f02af735ca/tests/entrypoints/openai/test_response_api_with_harmony.py#L715).\n\nFrom the first request, the model outputs the following tokens (whitespace added for clarity):\n```\n<|channel|>analysis<|message|>\n\tUser asks for weather in Paris today. We have no direct API call yet, but we can use get_weather function. Coordinates for Paris: latitude 48.8566, longitude 2.3522. We'll call get_weather.\n<|end|>\n<|start|>assistant<|channel|>commentary to=functions.get_weather <|constrain|>json<|message|>\n\t{\"latitude\":48.8566,\"longitude\":2.3522}\n<|call|>\n```\nWhen the output items from the first request are passed in as input to the second request, the tokens look like this (whitespace added for clarity):\n```\n<|start|>user<|message|>\n\tWhat's the weather like in Paris today?\n<|end|>\n<|start|>assistant<|message|>\n\tUser asks for weather in Paris today. We have no direct API call yet, but we can use get_weather function. Coordinates for Paris: latitude 48.8566, longitude 2.3522. We'll call get_weather.\n<|end|>\n<|start|>assistant to=functions.get_weather<|channel|>commentary json<|message|>\n\t{\"latitude\":48.8566,\"longitude\":2.3522}\n<|call|>\n<|start|>functions.get_weather<|message|>\n\t20\n<|end|>\n```\n\nWe lose `<|channel|>analysis` on the reasoning message, and we do not set `<|channel|>commentary` on the tool call output ([documentation reference](https://cookbook.openai.com/articles/openai-harmony#handling-tool-calls)).\n\nThere are a lot of edge cases and challenges to properly represent Harmony Message metadata when the Responses API input/output types do not include that metadata, but we can improve on the current implementation. \n\nThe changes we can make are:\n- A reasoning message should use the channel of the message that follows it. For example:\n - The reasoning message prior to a function tool call should be on the commentary channel\n - If the commentary channel is not enabled (no function tools enabled), all reasoning messages are on the analysis channel\n - All other reasoning messages are on the analysis channel\n- Set the content_type for function tools to be `<|constrain|>json` always\n- Input items which are FunctionCallOutput should be set to be on the commentary channel\n- Other types of tool related input types should be on the analysis channel\n\nThese changes should would be made to [serving_responses.py](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/openai/serving_responses.py) and [harmony_utils.py](https://github.com/vllm-project/vllm/blob/main/vllm/entrypoints/harmony_utils.py). Similar changes can be done for the chat completions path as well, but that should be out of scope for this issue.\n\nWith the changes described above, gpt-oss should have a significantly reduced error rate when outputting header tokens on longer conversations involving tools. \n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28262",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-07T02:51:56Z",
"updated_at": "2025-11-08T19:39:06Z",
"comments": 1,
"user": "alecsolder"
},
{
"repo": "huggingface/lerobot",
"number": 2399,
"title": "Are there plans to support LoRa fine-tuning?",
"body": "",
"url": "https://github.com/huggingface/lerobot/issues/2399",
"state": "open",
"labels": [
"question",
"performance",
"training"
],
"created_at": "2025-11-07T02:37:45Z",
"updated_at": "2025-11-10T10:23:33Z",
"user": "Hukongtao"
},
{
"repo": "huggingface/candle",
"number": 3167,
"title": "Qwen 3-1.7b looks like something is wrong and doesn't stop properly.",
"body": "Candle version: main\nPlatform: Mac Studio Max M1\nMode: Qwen 3-1.7b, (download by huggingface-cli)\nExecute cmd:\n\ngit clone https://github.com/huggingface/candle.git\ncd candle-examples\ncargo run --release --example qwen -- \\\n--prompt \"What is the speed of light?\" \\\n--model 3-1.7b \\\n--tokenizer-file ../../models/qwen3-1.7b/tokenizer.json \\\n--weight-files \"../../models/qwen3-1.7b/model-00001-of-00002.safetensors,../../models/qwen3-1.7b/model-00002-of-00002.safetensors\" \\\n--temperature 0.3 \\\n--top-p 0.5 \\\n--repeat-penalty 1.5 \\\n--repeat-last-n 16\n\nGot:\n\n```\nQwen 3-1.7B \n\n\nRunning `target/release/examples/qwen --prompt 'What is the speed of light?' --model 3-1.7b --tokenizer-file ../../models/qwen3-1.7b/tokenizer.json --weight-files ../../models/qwen3-1.7b/model-00001-of-00002.safetensors,../../models/qwen3-1.7b/model-00002-of-00002.safetensors --temperature 0.3 --top-p 0.5 --repeat-penalty 1.5 --repeat-last-n 16`\navx: false, neon: true, simd128: false, f16c: false\ntemp: 0.30 repeat-penalty: 1.50 repeat-last-n: 16\nretrieved the files in 300.917\u00b5s\nRunning on CPU, to run on GPU(metal), build this example with `--features metal`\nloaded the model in 7.719477208s\nWhat is the speed of light? What are its properties?\n\nThe Speed Of Light\n\nWhat is the speed of light? What are its properties?\n\nThe Speed Of Light\n\nWhat is the speed of light? What are its properties?\n\nThe Speed Of Light\n\nWhat is the speed of light? What are its properties?\n\nThe Speed Of Light\n\nWhat is the speed of light? What are its properties?\n\nThe Speed Of Light\n\nWhat is the speed of light? What are its properties?\n\nThe Speed...\n\n^C\n```",
"url": "https://github.com/huggingface/candle/issues/3167",
"state": "open",
"labels": [],
"created_at": "2025-11-07T02:23:05Z",
"updated_at": "2025-11-08T07:52:18Z",
"comments": 6,
"user": "xiuno"
},
{
"repo": "huggingface/lerobot",
"number": 2398,
"title": "how to accelerate the iteration in dataset",
"body": "hi, i want to get the frames of specific episode index\n\nwhen `episode_index_target` is large, like 100, it takes a lot of time to run.\n\nany solution to improve the iteration speed ?\n\nthanks.\n\n`lerobot.__version__ == '0.1.0'`\n\n```python\ndataset = LeRobotDataset('yananchen/robomimic_lift')\nframes = []\nfor sample in dataset:\n if sample[\"episode_index\"] == episode_index_target:\n frames.append(sample)\n```",
"url": "https://github.com/huggingface/lerobot/issues/2398",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-11-06T21:37:33Z",
"updated_at": "2025-11-10T20:52:57Z",
"user": "yanan1116"
},
{
"repo": "vllm-project/vllm",
"number": 28246,
"title": "[Bug]: Return Token Ids not returning Gen Token Ids for GPT-OSS-120b",
"body": "### Your current environment\n\nThe output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nUsing docker image vllm/vllm-openai:latest\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nWhen passing in return_token_ids flag to v1/chat/completions endpoint for GPTOSS-120b, only prompt_token_ids are returned and not token_ids. We have not seen this happen with any other model except GPTOSS-120b\n\n```\ncurl --location 'http://localhost:8015/v1/chat/completions' \\\n --header 'Content-Type: application/json' \\\n --data '{\n \"model\": \"gpt-oss-120b\",\n \"messages\": [{\"content\": \"Hello!\", \"role\": \"user\"}],\n \"temperature\": 0,\n \"return_token_ids\": true\n }'\n```\n\n`{\"id\":\"chatcmpl-a19161b8131141e2a79495025adb40eb\",\"object\":\"chat.completion\",\"created\":1762462711,\"model\":\"gpt-oss-120b\",\"choices\":[{\"index\":0,\"message\":{\"role\":\"assistant\",\"content\":\"Hello! How can I help you today?\",\"refusal\":null,\"annotations\":null,\"audio\":null,\"function_call\":null,\"tool_calls\":[],\"reasoning_content\":\"The user says \\\"Hello!\\\" We should respond politely. No special instructions. Just greet back.\"},\"logprobs\":null,\"finish_reason\":\"stop\",\"stop_reason\":null,\"token_ids\":null}],\"service_tier\":null,\"system_fingerprint\":null,\"usage\":{\"prompt_tokens\":71,\"total_tokens\":109,\"completion_tokens\":38,\"prompt_tokens_details\":null},\"prompt_logprobs\":null,\"prompt_token_ids\":[200006,17360,200008,3575,553,17554,162016,11,261,4410,6439,2359,22203,656,7788,17527,558,87447,100594,25,220,1323,19,12,3218,198,6576,3521,25,220,1323,20,12,994,12,3218,279,30377,289,25,14093,279,2,13888,18403,25,8450,11,1721,13,21030,2804,413,7360,395,1753,3176,13,200007,200006,77944,200008,200007,200006,1428,200008,13225,0,200007,200006,173781],\"kv_transfer_params\":null}`\n\nI've also included in the docker container setup \n\n```\ndocker run --rm -d --name vllm-gpt-oss-120b \\\n --gpus '\"device=4,5\"' \\\n --shm-size=16g \\\n -e TORCH_CUDA_ARCH_LIST=\"9.0\" \\\n -v /mlf1-shared/user/gpt-oss-120b:/opt/model \\\n -p ${PORT}:${PORT} \\\n vllm/vllm-openai:latest\\\n --model /opt/model \\\n --served-model-name \"${SERVED_MODEL_NAME}\" \\\n --tensor-parallel-size \"${TP_SIZE}\" \\\n --gpu-memory-utilization \"${GPU_UTIL}\" \\\n --max-num-seqs 64 \\\n --port ${PORT}\n```\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28246",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-06T21:08:16Z",
"updated_at": "2025-11-07T00:18:25Z",
"comments": 1,
"user": "sophies-cerebras"
},
{
"repo": "vllm-project/vllm",
"number": 28236,
"title": "[Feature]: Implement naive prepare/finalize class to replace naive dispatching in fused_moe/layer.py",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nThe `FusedMoE` layer has a special case dispatch/combine for EP+DP when there is no specific all2all backend specified. This makes the code in `layer.py` a bit confusing and hard to follow. One way to simplify this is to implement a proper `FusedMoEPrepareAndFinalize` subclass for naive dispatch/combine.\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28236",
"state": "open",
"labels": [
"help wanted",
"good first issue",
"feature request"
],
"created_at": "2025-11-06T18:38:38Z",
"updated_at": "2025-11-12T06:36:29Z",
"comments": 4,
"user": "bnellnm"
},
{
"repo": "vllm-project/vllm",
"number": 28233,
"title": "[Usage]: LogitProcessor vLLM 0.9.1 run the same prompt 50 times with batching, apply logitprocessor independently on each",
"body": "### Your current environment\n\nGoal\nRun the same prompt 50 times through vLLM 0.9.1, generating independent outputs with a custom LogitsProcessor that forces a comma token after some pattern \"xyz\" appears in each generation.\nWhat You Want\n\nBatched execution: Process all 50 generations efficiently in parallel\nIndependent state: Each of the 50 generations should have its own state in the logits processor\nPattern detection: When text ends with \"xyz\", mask all tokens except comma },\nOne-time application: Each generation should only apply the comma mask once\n\nCurrent Hurdles\n1. Processor Signature Confusion\nvLLM V0 (0.9.1) uses signature: __call__(prompt_token_ids, generated_token_ids, logits)\n\nprompt_token_ids: The input prompt tokens (same for all 50)\ngenerated_token_ids: Tokens generated so far (different per generation)\nProblem: No built-in request ID to distinguish between the 50 generations\n\n2. State Management\nWhen using the same prompt 50 times:\n\nAll generations share identical prompt_token_ids\nCan't use prompt as unique identifier\nUsing generated_token_ids as key works initially, but becomes complex as sequences diverge\nState dictionary grows indefinitely without cleanup\n\n3. Batching vs Sequential\n\nBatching (llm.generate([prompt]*50)): Processor is called for all 50 in interleaved order, making state tracking difficult\nSequential (50 separate calls): Works reliably but loses parallel efficiency\n\nWorking Solution (Sequential)\nfor i in range(50):\n processor = LookAheadProcessor(tokenizer) # Fresh processor each time\n sampling_params = SamplingParams(..., logits_processors=[processor])\n output = llm.generate([prompt], sampling_params)\nThis works because each generation gets its own processor instance.\nThe Core Problem\nvLLM V0's logits processor API doesn't provide per-request identifiers in batched scenarios, making it impossible to maintain independent state for identical prompts without workarounds like using (prompt_tokens, generated_tokens) tuples as keys - which still fails when generations produce identical token sequences early on. Anyone knows a solution to this problem ?\n\n### How would you like to use vllm\n\n\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28233",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-06T18:11:32Z",
"updated_at": "2025-11-06T18:11:32Z",
"comments": 0,
"user": "jindalankush28"
},
{
"repo": "vllm-project/vllm",
"number": 28230,
"title": "[Bug]: GPU VRAM continuously increase during Qwen3-VL usage over days until OOM",
"body": "### Your current environment\n\nSetup:\ndocker run -d \\\n --runtime nvidia \\\n --gpus '\"device=3,4,5,6\"' \\\n -e TRANSFORMERS_OFFLINE=1 \\\n -e DEBUG=\"true\" \\\n -p 8000:8000 \\\n --ipc=host \\\n vllm/vllm-openai:v0.11.0 \\\n --gpu-memory-utilization 0.95 \\\n --model Qwen/Qwen3-VL-235B-A22B-Instruct-FP8 \\\n --tensor-parallel-size 4 \\\n --mm-encoder-tp-mode data \\\n --enable-auto-tool-choice \\\n --tool-call-parser hermes \\\n --limit-mm-per-prompt.video 0\nServer: 8*H200 with CUDA=12.6.\n\n### \ud83d\udc1b Describe the bug\n\nThis is the same issue described in \nhttps://github.com/vllm-project/vllm/issues/27466\nhttps://github.com/vllm-project/vllm/issues/27452\nVRAM continuously increase over days after usage with vision. When available VRAM drops below 500MB, OOM occurs during new requests.\nAs described in other posts, removing mm_encoder_tp_mode=\"data\" or --enforce-eager does not work either.\nThere is currently no acceptable solution.\nIs there a memory leakage? It is understood that VRAM usage may go up during vision task, but that should be cleared. VRAM cannot continuously increase and eventually hit OOM.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28230",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-11-06T17:19:18Z",
"updated_at": "2025-12-02T16:50:26Z",
"comments": 15,
"user": "yz342"
},
{
"repo": "huggingface/datasets",
"number": 7852,
"title": "Problems with NifTI",
"body": "### Describe the bug\n\nThere are currently 2 problems with the new NifTI feature:\n1. dealing with zipped files, this is mentioned and explained [here](https://github.com/huggingface/datasets/pull/7815#issuecomment-3496199503)\n2. when uploading via the `niftifolder` feature, the resulting parquet only contains relative paths to the nifti files:\n\n```bash\ntable['nifti']\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nWe now have vllm wheels for Arm CPUs in pypi thanks to https://github.com/vllm-project/vllm/pull/26931 and https://github.com/vllm-project/vllm/pull/27331\n\nYou can install Arm CPU wheels with:\n```\npip install --pre vllm==0.11.1rc3+cpu --extra-index-url https://wheels.vllm.ai/0.11.1rc3%2Bcpu/\n```\n\nHowever it will currently fail, unless you ldpreload ACL: \n```\nWARNING 10-29 12:33:18 [interface.py:171] Failed to import from vllm._C: ImportError('libarm_compute.so: cannot open shared object file: No such file or directory')\nWe need to figure out how to package libarm_compute.so in the wheel\n ```\n\nBest way to reproduce this locally is: \n- build vllm from main locally with `VLLM_TARGET_DEVICE=cpu python3 setup.py bdist_wheel`\n- remove `vllm/deps` which contains the libarm_compute.so\n- pip install the wheel you built\n\nthen you will run into the issue (because it will try to load libarm_compute.so under vllm/.deps/arm_compute-src/build/)\n\nNote: ACL/oneDNN are built in vllm here: \n\nWe need to figure out how to bundle `libarm_compute.so` in the wheel to avoid this.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28056",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-11-04T17:22:55Z",
"updated_at": "2025-11-13T05:43:10Z",
"comments": 2,
"user": "fadara01"
},
{
"repo": "vllm-project/vllm",
"number": 28046,
"title": "Qwen3-Omni model inference : ValueError: Either SamplingParams or PoolingParams must be provided.",
"body": "### Your current environment\n\n```text\nThe output of `python web_demo.py`\n```\n\nThe above mentioned method provides the error below \n```\n\nqwen/Qwen3-Omni/collect_env.py\", line 287, in get_vllm_version\n from vllm import __version__, __version_tuple__\nImportError: cannot import name '__version__' from 'vllm' (unknown location)\n```\nwhile the envs installed are below:\n\n```\n\n pip list\nPackage Version Editable project location\n--------------------------------- --------------------------------- ----------------------------------------------------------\naccelerate 1.11.0\naiofiles 24.1.0\naiohappyeyeballs 2.6.1\naiohttp 3.13.2\naiosignal 1.4.0\nairportsdata 20250909\nannotated-doc 0.0.3\nannotated-types 0.7.0\nanyio 4.11.0\nastor 0.8.1\nasync-timeout 5.0.1\nattrs 25.4.0\naudioread 3.1.0\nav 16.0.1\nblake3 1.0.8\nBrotli 1.1.0\ncachetools 6.2.1\ncertifi 2025.10.5\ncffi 2.0.0\ncharset-normalizer 3.4.4\nclick 8.2.1\ncloudpickle 3.1.2\ncmake 4.1.2\ncompressed-tensors 0.10.2\ncupy-cuda12x 13.6.0\ndecorator 5.2.1\ndepyf 0.18.0\ndill 0.4.0\ndiskcache 5.6.3\ndistro 1.9.0\ndnspython 2.8.0\neinops 0.8.1\nemail-validator 2.3.0\nexceptiongroup 1.3.0\nfastapi 0.121.0\nfastapi-cli 0.0.14\nfastapi-cloud-cli 0.3.1\nfastrlock 0.8.3\nffmpy 0.6.4\nfilelock 3.20.0\nflash_attn 2.8.3\nfrozenlist 1.8.0\nfsspec 2025.10.0\ngguf 0.17.1\ngradio 5.44.1\ngradio_client 1.12.1\ngroovy 0.1.2\nh11 0.16.0\nhf-xet 1.2.0\nhttpcore 1.0.9\nhttptools 0.7.1\nhttpx 0.28.1\nhuggingface-hub 0.36.0\nidna 3.11\ninteregular 0.3.3\nJinja2 3.1.6\njiter 0.11.1\njoblib 1.5.2\njsonschema 4.25.1\njsonschema-specifications 2025.9.1\nlark 1.2.2\nlazy_loader 0.4\nlibrosa 0.11.0\nllguidance 0.7.30\nllvmlite 0.44.0\nlm-format-enforcer 0.10.12\nmarkdown-it-py 4.0.0\nMarkupSafe 3.0.3\nmdurl 0.1.2\nmistral_common 1.8.5\nmpmath 1.3.0\nmsgpack 1.1.2\nmsgspec 0.19.0\nmultidict 6.7.0\nnest-asyncio 1.6.0\nnetworkx 3.4.2\nninja 1.13.0\nnumba 0.61.2\nnumpy 2.2.6\nnvidia-cublas-cu12 12.6.4.1\nnvidia-cuda-cupti-cu12 12.6.80\nnvidia-cuda-nvrtc-cu12 12.6.77\nnvidia-cuda-runtime-cu12 12.6.77\nnvidia-cudnn-cu12 9.5.1.17\nnvidia-cufft-cu12 11.3.0.4\nnvidia-cufile-cu12 1.11.1.6\nnvidia-curand-cu12 10.3.7.77\nnvidia-cusolver-cu12 11.7.1.2\nnvidia-cusparse-cu12 12.5.4.2\nnvidia-cusparselt-cu12 0.6.3\nnvidia-nccl-cu12 2.26.2\nnvidia-nvjitlink-cu12 12.6.85\nnvidia-nvtx-cu12 12.6.77\nopenai 1.90.0\nopencv-python-headless 4.12.0.88\norjson 3.11.4\noutlines 0.1.11\noutlines_core 0.1.26\npackaging 25.0\npandas 2.3.3\npartial-json-parser 0.2.1.1.post6\npillow 11.3.0\npip 25.2\nplatformdirs 4.5.0\npooch 1.8.2\nprometheus_client 0.23.1\nprometheus-fastapi-instrumentator 7.1.0\npropcache ",
"url": "https://github.com/vllm-project/vllm/issues/28046",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-11-04T13:59:57Z",
"updated_at": "2025-11-24T19:24:39Z",
"comments": 22,
"user": "Tortoise17"
},
{
"repo": "vllm-project/vllm",
"number": 28045,
"title": "[Doc]: Any detailed documentation about how to load_weights in customized vllm model?",
"body": "### \ud83d\udcda The doc issue\n\nI don't know how to modify the attention and how the load_model works.\n\nThe documentation says too few, I find it's hard to understand.\n\nAnyone has some more detailed experience? Thank you!\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/28045",
"state": "open",
"labels": [
"documentation"
],
"created_at": "2025-11-04T13:23:25Z",
"updated_at": "2025-11-05T02:07:55Z",
"comments": 0,
"user": "sleepwalker2017"
},
{
"repo": "vllm-project/vllm",
"number": 28035,
"title": "[Usage]: deepseek-ocr The output token count is too low and unstable.",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\npython3 -m vllm.entrypoints.openai.api_server --served-model-name deepseek-ocr --model deepseekocr --tensor-parallel-size 1 --gpu-memory-utilization 0.95 --disable-log-requests --logits_processors vllm.model_executor.models.deepseek_ocr:NGramPerReqLogitsProcessor\n\n {\n \"model\": \"DeepSeek-OCR\",\n \"messages\": [{\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"image_url\",\n \"image_url\": {\"url\": f\"data:image/jpeg;base64,{self.image_to_base64(image_path)}\"}\n },\n {\"type\": \"text\", \"text\": \u201dThe output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\n\n(APIServer pid=1033476) sys.exit(load_entry_point('vllm==0.11.1rc6.dev33+g3a5de7d2d.cpu', 'console_scripts', 'vllm')())\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm/entrypoints/cli/main.py\", line 73, in main\n(APIServer pid=1033476) args.dispatch_function(args)\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm/entrypoints/cli/serve.py\", line 59, in cmd\n(APIServer pid=1033476) uvloop.run(run_server(args))\n(APIServer pid=1033476) File \"/home/maxgameone/.local/lib/python3.12/site-packages/uvloop/__init__.py\", line 109, in run\n(APIServer pid=1033476) return __asyncio.run(\n(APIServer pid=1033476) ^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/asyncio/runners.py\", line 194, in run\n(APIServer pid=1033476) return runner.run(main)\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/asyncio/runners.py\", line 118, in run\n(APIServer pid=1033476) return self._loop.run_until_complete(task)\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"uvloop/loop.pyx\", line 1518, in uvloop.loop.Loop.run_until_complete\n(APIServer pid=1033476) File \"/home/maxgameone/.local/lib/python3.12/site-packages/uvloop/__init__.py\", line 61, in wrapper\n(APIServer pid=1033476) return await main\n(APIServer pid=1033476) ^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm/entrypoints/openai/api_server.py\", line 1910, in run_server\n(APIServer pid=1033476) await run_server_worker(listen_address, sock, args, **uvicorn_kwargs)\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm/entrypoints/openai/api_server.py\", line 1926, in run_server_worker\n(APIServer pid=1033476) async with build_async_engine_client(\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/contextlib.py\", line 210, in __aenter__\n(APIServer pid=1033476) return await anext(self.gen)\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm/entrypoints/openai/api_server.py\", line 185, in build_async_engine_client\n(APIServer pid=1033476) async with build_async_engine_client_from_engine_args(\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/contextlib.py\", line 210, in __aenter__\n(APIServer pid=1033476) return await anext(self.gen)\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm/entrypoints/openai/api_server.py\", line 232, in build_async_engine_client_from_engine_args\n(APIServer pid=1033476) async_llm = AsyncLLM.from_vllm_config(\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm/utils/func_utils.py\", line 116, in inner\n(APIServer pid=1033476) return fn(*args, **kwargs)\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm/v1/engine/async_llm.py\", line 218, in from_vllm_config\n(APIServer pid=1033476) return cls(\n(APIServer pid=1033476) ^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm/v1/engine/async_llm.py\", line 140, in __init__\n(APIServer pid=1033476) self.engine_core = EngineCoreClient.make_async_mp_client(\n(APIServer pid=1033476) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/lib/python3.12/site-packages/vllm-0.11.1rc6.dev33+g3a5de7d2d.cpu-py3.12-linux-x86_64.egg/vllm",
"url": "https://github.com/vllm-project/vllm/issues/27912",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-11-01T07:40:04Z",
"updated_at": "2025-11-01T07:40:04Z",
"comments": 0,
"user": "maxgameone"
},
{
"repo": "vllm-project/vllm",
"number": 27899,
"title": "[Bug]: Inductor specialize after 2.9 rebase",
"body": "### Your current environment\n\nNA\n\n### \ud83d\udc1b Describe the bug\n\nCould you or someone have a look at compile ranges [PR](https://github.com/vllm-project/vllm/pull/24252) again? It seems to stop working with the update to pytorch 2.9. We started getting failed assertions in generated code like it was compiled for a single shape. Could you explain how to let the inductor know that we compile for a range not for a single shape?\nExample of the assertion. Compilation was done for a range (512, 8192)\nassert_size_stride(arg0_1, (8192, s4, s94), (s4*s94, s94, 1))\n\nCan you add quick repro instructions?\n\nSure, on the PR branch:\nvllm serve meta-llama/Meta-Llama-3.1-70B-Instruct --disable-log-requests --no-enable-prefix-caching -tp 4 -dp 1 --max-num-seqs 256 --load-format dummy --port 8001 --compilation-config '{\"pass_config\":{\"enable_fusion\":false,\"enable_attn_fusion\":false,\"enable_noop\":true,\"enable_sequence_parallelism\":false,\"enable_async_tp\":false,\"enable_fi_allreduce_fusion\":true}}'\n\ncc @ilmarkov ",
"url": "https://github.com/vllm-project/vllm/issues/27899",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-31T22:16:27Z",
"updated_at": "2025-11-07T00:03:25Z",
"comments": 7,
"user": "laithsakka"
},
{
"repo": "vllm-project/vllm",
"number": 27898,
"title": "[Doc]: Multi-node EP on EFA (i.e. no IBGDA/DeepEP)",
"body": "### \ud83d\udcda The doc issue\n\nUsecase: On AWS we have EFA for high bandwidth interconnect, not Infiniband, so no IBGDA.\n\nThe [documentation](https://docs.vllm.ai/en/latest/serving/expert_parallel_deployment.html#backend-selection-guide) indicates that the DeepEP kernels should be used for multi/inter-node EP, and pplx for single node. However, [DeepEP indicates that they only support IBGDA for inter-node comms](https://github.com/deepseek-ai/DeepEP/issues/369).\n\npplx has good support for EFA. Is pplx for single node, DeepEP for multi-node a suggestion based on testing, or a hard requirement?\n\nIn addition, it appears that the EP size cannot be configured and is always TP x DP. Is there any way to set EP size to equal TP size (for example), so we can have each node be a DP group and limit EP alltoall's to intra-node (NVLink) only?\n\nThank you!\n\nEDIT: per https://github.com/vllm-project/vllm/issues/27633 it appears this may be problematic, although since pplx supports EFA as a transportation layer, this seems bizarre. Specific docs around usage on EFA would be helpful.",
"url": "https://github.com/vllm-project/vllm/issues/27898",
"state": "open",
"labels": [
"documentation"
],
"created_at": "2025-10-31T21:22:28Z",
"updated_at": "2025-11-06T19:50:07Z",
"comments": 1,
"user": "nathan-az"
},
{
"repo": "huggingface/peft",
"number": 2884,
"title": "[Question/Bug] How to safely continue LoRA fine-tuning under DeepSpeed ZeRO-3 (multi-stage training with modules_to_save)",
"body": "Hi,\nI\u2019m trying to perform multi-stage LoRA fine-tuning under DeepSpeed ZeRO-3 using PEFT.\nHowever, continuing training on an existing LoRA checkpoint without merging causes a series of errors and conflicts.\n\n\nProblem\n\nWhen I load the LoRA from Stage 1 and attempt to continue training:\n\t\u2022\tload_state_dict() throws shape mismatch (e.g. [0, hidden_size])\n\t\u2022\tresize_token_embeddings() fails (empty tensor)\n\t\u2022\tGPU memory usage explodes (batch size drops from 4 \u2192 1)\n\nQuestion\n\nWhat\u2019s the recommended practice for continuing LoRA fine-tuning under ZeRO-3?\n\t\u2022\tShould we always merge the previous adapter (merge_and_unload()) before starting Stage 2?\n\t\u2022\tOr is there a way to safely keep the existing adapter and continue training?\n\n\n\n\n### Who can help?\n\n_No response_\n\n### Reproduction\n\nSetup\n\t\u2022\tStage 1: LoRA fine-tuning with modules_to_save=['wte','ff_out']\n\t\u2022\tStage 2: Continue training on a new dataset (without merging)\n\t\u2022\tUsing DeepSpeed ZeRO-3 (zero3_init_flag=False)\n\n\n### Expected behavior\n\nExpected Behavior\n\nPEFT should provide a consistent way to:\n\t\u2022\tContinue fine-tuning LoRA adapters across multiple stages with ZeRO-3 enabled.\n\t\u2022\tAvoid re-initialization or memory explosion when modules_to_save is used.",
"url": "https://github.com/huggingface/peft/issues/2884",
"state": "closed",
"labels": [],
"created_at": "2025-10-31T20:13:12Z",
"updated_at": "2025-12-09T15:05:26Z",
"user": "XiangZhang-zx"
},
{
"repo": "huggingface/lerobot",
"number": 2351,
"title": "Details of adapting SmolVLA to other robotic arms with different configurations",
"body": "I want to deploy the untuned `smolvla_base` model directly onto my AgileX PIPER robotic arm.I ran into the following two issues along the way:\n1. Missing normalization parameters in the metadata.\n```\n File \"/home/zwt/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n return func(*args, **kwargs)\n File \"/home/zwt/Projects/lerobot/lerobot/common/policies/smolvla/modeling_smolvla.py\", line 434, in select_action\n batch = self._prepare_batch(batch)\n File \"/home/zwt/Projects/lerobot/lerobot/common/policies/smolvla/modeling_smolvla.py\", line 412, in _prepare_batch\n batch = self.normalize_inputs(batch)\n File \"/home/zwt/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n File \"/home/zwt/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/zwt/miniconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n return func(*args, **kwargs)\n File \"/home/zwt/Projects/lerobot/lerobot/common/policies/normalize.py\", line 170, in forward\n assert not torch.isinf(mean).any(), _no_stats_error_str(\"mean\")\nAssertionError: `mean` is infinity. You should either initialize with `stats` as an argument, or use a pretrained model.\n```\nThe error was resolved when I copied the normalization parameters from other training results, but I'm not sure if this is the correct way to run `smolvla_base` directly.\n2. I've noticed that different robotic arms may have different degrees of freedom, or even if they have the same degrees of freedom, the range of rotation of the same joint can vary. I'm unsure whether this range of rotation mapping is necessary when transferring the model to other robotic arms.It seems there is similar operation for the aloha in the code.\n```\n def _pi_aloha_decode_state(self, state):\n # Flip the joints.\n for motor_idx in [1, 2, 8, 9]:\n state[:, motor_idx] *= -1\n # Reverse the gripper transformation that is being applied by the Aloha runtime.\n for motor_idx in [6, 13]:\n state[:, motor_idx] = aloha_gripper_to_angular(state[:, motor_idx])\n return state\n\n def _pi_aloha_encode_actions(self, actions):\n # Flip the joints.\n for motor_idx in [1, 2, 8, 9]:\n actions[:, :, motor_idx] *= -1\n # Reverse the gripper transformation that is being applied by the Aloha runtime.\n for motor_idx in [6, 13]:\n actions[:, :, motor_idx] = aloha_gripper_from_angular(actions[:, :, motor_idx])\n return actions\n\n def _pi_aloha_encode_actions_inv(self, actions):\n # Flip the joints again.\n for motor_idx in [1, 2, 8, 9]:\n actions[:, :, motor_idx] *= -1\n # Reverse the gripper transformation that is being applied by the Aloha runtime.\n for motor_idx in [6, 13]:\n actions[:, :, motor_idx] = aloha_gripper_from_angular_inv(actions[:, :, motor_idx])\n return actions\n```\nbtw, is it a meaningful operation to directly run smolvla_base? This is just one of my sudden thoughts. ",
"url": "https://github.com/huggingface/lerobot/issues/2351",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-31T14:55:35Z",
"updated_at": "2025-12-14T14:47:04Z",
"user": "yquanli"
},
{
"repo": "vllm-project/vllm",
"number": 27880,
"title": "[Installation]: [HELP]How to install the latest main version of vllm",
"body": "### Your current environment\n\nI clone the vllm code, and run install commands, but it fails, Help!!\n\n### How you are installing vllm\n\n```sh\nVLLM_USE_PRECOMPILED=1 uv pip install --editable .\nUsing Python 3.10.12 environment at: /home/alice/.venv\n \u00d7 No solution found when resolving dependencies:\n \u2570\u2500\u25b6 Because there is no version of xformers{platform_machine == 'x86_64' and sys_platform == 'linux'}==0.0.33+5d4b92a5.d20251029 and vllm==0.11.1rc6.dev16+g933cdea44.precompiled depends\n on xformers{platform_machine == 'x86_64' and sys_platform == 'linux'}==0.0.33+5d4b92a5.d20251029, we can conclude that vllm==0.11.1rc6.dev16+g933cdea44.precompiled cannot be used.\n And because only vllm==0.11.1rc6.dev16+g933cdea44.precompiled is available and you require vllm, we can conclude that your requirements are unsatisfiable.\n(alice) alice@dc53-p31-t0-n067:~/vllm_bak$ uv pip install -e .\nUsing Python 3.10.12 environment at: /home/alice/.venv\n \u00d7 No solution found when resolving dependencies:\n \u2570\u2500\u25b6 Because there is no version of xformers{platform_machine == 'x86_64' and sys_platform == 'linux'}==0.0.33+5d4b92a5.d20251029 and vllm==0.11.1rc6.dev16+g933cdea44.cu126 depends on\n xformers{platform_machine == 'x86_64' and sys_platform == 'linux'}==0.0.33+5d4b92a5.d20251029, we can conclude that vllm==0.11.1rc6.dev16+g933cdea44.cu126 cannot be used.\n And because only vllm==0.11.1rc6.dev16+g933cdea44.cu126 is available and you require vllm, we can conclude that your requirements are unsatisfiable.```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27880",
"state": "closed",
"labels": [
"installation"
],
"created_at": "2025-10-31T13:57:20Z",
"updated_at": "2025-11-13T07:25:13Z",
"comments": 7,
"user": "sleepwalker2017"
},
{
"repo": "vllm-project/vllm",
"number": 27877,
"title": "[Usage]: How to install nightly version??? Why this command doesn't work?",
"body": "### Your current environment\n\nI run this to install vllm with the latest code. But, the installed vllm doesn't include the code I need. \n\nI check the `siglip.py` file, it's modified 4 days ago. \n\nBut in the vllm installed, it doesn't contain this commit! https://github.com/vllm-project/vllm/pull/27566/files#diff-ca771e5a262cbf32fb481c518bea41d0e341414e021d6542e421abb98cceec61\n\n\nwhy is this?\n\nI use this command.\n```text\npip install -U vllm \\\n --pre \\\n --extra-index-url https://wheels.vllm.ai/nightly```\n\n`pip install -U vllm \\\n --pre \\\n --extra-index-url https://wheels.vllm.ai/nightly\nDefaulting to user installation because normal site-packages is not writeable\nLooking in indexes: https://bytedpypi.byted.org/simple, https://bytedpypi.byted.org/simple, https://wheels.vllm.ai/nightly\nRequirement already satisfied: vllm in /home/alice/.local/lib/python3.10/site-packages (0.11.0)\nCollecting vllm\n Downloading https://wheels.vllm.ai/nightly/vllm-0.11.1rc6.dev16%2Bg933cdea44.cu129-cp38-abi3-manylinux1_x86_64.whl (479.0 MB)\n \u2501\u257a\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501\u2501 17.8/479.0 MB 575.3 kB/s eta 0:13:22`\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27877",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-31T12:29:51Z",
"updated_at": "2025-10-31T12:38:19Z",
"comments": 0,
"user": "sleepwalker2017"
},
{
"repo": "vllm-project/vllm",
"number": 27875,
"title": "[Usage]: how to get profiler on OpenAI server",
"body": "### Your current environment\n\n```text\nINFO 10-31 10:27:06 [importing.py:17] Triton not installed or not compatible; certain GPU-related functions will not be available.\nWARNING 10-31 10:27:06 [importing.py:29] Triton is not installed. Using dummy decorators. Install it via `pip install triton` to enable kernel compilation.\nINFO 10-31 10:27:08 [__init__.py:39] Available plugins for group vllm.platform_plugins:\nINFO 10-31 10:27:08 [__init__.py:41] - ascend -> vllm_ascend:register\nINFO 10-31 10:27:08 [__init__.py:44] All plugins in this group will be loaded. Set `VLLM_PLUGINS` to control which plugins to load.\nINFO 10-31 10:27:08 [__init__.py:235] Platform plugin ascend is activated\nWARNING 10-31 10:27:12 [_custom_ops.py:22] Failed to import from vllm._C with ModuleNotFoundError(\"No module named 'vllm._C'\")\nCollecting environment information...\nPyTorch version: 2.5.1\nIs debug build: False\n\nOS: Ubuntu 22.04.5 LTS (aarch64)\nGCC version: (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version: Could not collect\nCMake version: version 4.1.0\nLibc version: glibc-2.35\n\nPython version: 3.11.13 (main, Jul 26 2025, 07:27:32) [GCC 11.4.0] (64-bit runtime)\nPython platform: Linux-5.10.0-60.18.0.50.r865_35.hce2.aarch64-aarch64-with-glibc2.35\n\nCPU:\nArchitecture: aarch64\nCPU op-mode(s): 64-bit\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: HiSilicon\nBIOS Vendor ID: HiSilicon\nModel name: Kunpeng-920\nBIOS Model name: HUAWEI Kunpeng 920 5250\nModel: 0\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 4\nStepping: 0x1\nBogoMIPS: 200.00\nFlags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm jscvt fcma dcpop asimddp asimdfhm ssbs\nL1d cache: 12 MiB (192 instances)\nL1i cache: 12 MiB (192 instances)\nL2 cache: 96 MiB (192 instances)\nL3 cache: 192 MiB (8 instances)\nNUMA node(s): 8\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\nNUMA node4 CPU(s): 96-119\nNUMA node5 CPU(s): 120-143\nNUMA node6 CPU(s): 144-167\nNUMA node7 CPU(s): 168-191\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; __user pointer sanitization\nVulnerability Spectre v2: Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\nVersions of relevant libraries:\n[pip3] numpy==1.26.4\n[pip3] pyzmq==27.0.2\n[pip3] torch==2.5.1\n[pip3] torch-npu==2.5.1.post1\n[pip3] torchvision==0.20.1\n[pip3] transformers==4.52.4\n[conda] Could not collect\nvLLM Version: 0.9.1\nvLLM Ascend Version: 0.9.2.dev0+g0740d1021.d20251029 (git sha: 0740d1021, date: 20251029)\n\nENV Variables:\nATB_OPSRUNNER_KERNEL_CACHE_LOCAL_COUNT=1\nATB_STREAM_SYNC_EVERY_RUNNER_ENABLE=0\nATB_OPSRUNNER_SETUP_CACHE_ENABLE=1\nATB_WORKSPACE_MEM_ALLOC_GLOBAL=0\nATB_DEVICE_TILING_BUFFER_BLOCK_NUM=32\nATB_STREAM_SYNC_EVERY_KERNEL_ENABLE=0\nVLLM_TORCH_PROFILER_DIR=/workspace/prof\nATB_OPSRUNNER_KERNEL_CACHE_GLOABL_COUNT=5\nATB_HOME_PATH=/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_0\nASCEND_TOOLKIT_HOME=/usr/local/Ascend/ascend-toolkit/latest\nATB_COMPARE_TILING_EVERY_KERNEL=0\nASCEND_OPP_PATH=/usr/local/Ascend/ascend-toolkit/latest/opp\nLD_LIBRARY_PATH=/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_0/lib:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_0/examples:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_0/tests/atbopstest:/usr/local/Ascend/ascend-toolkit/latest/tools/aml/lib64:/usr/local/Ascend/ascend-toolkit/latest/tools/aml/lib64/plugin:/usr/local/Ascend/ascend-toolkit/latest/lib64:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/opskernel:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/nnengine:/usr/local/Ascend/ascend-toolkit/latest/opp/built-in/op_impl/ai_core/tbe/op_tiling/lib/linux/aarch64:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_0/lib:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_0/examples:/usr/local/Ascend/nnal/atb/latest/atb/cxx_abi_0/tests/atbopstest:/usr/local/Ascend/ascend-toolkit/latest/tools/aml/lib64:/usr/local/Ascend/ascend-toolkit/latest/tools/aml/lib64/plugin:/usr/local/Ascend/ascend-toolkit/latest/lib64:/usr/local/Ascend/ascend-toolkit/latest/lib64/plugin/opskernel:/usr",
"url": "https://github.com/vllm-project/vllm/issues/27875",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-31T10:33:49Z",
"updated_at": "2025-10-31T14:38:04Z",
"comments": 1,
"user": "zhaohaixu"
},
{
"repo": "vllm-project/vllm",
"number": 27872,
"title": "[Feature]: AFD support load customer connect model from local path.",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nAdd `afd_connector_module_path` field in AFDConfig, user can implement customer afd connect, but don't need change vllm code.\n\nhttps://github.com/vllm-project/vllm/pull/25162 merge after.\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27872",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-10-31T09:08:50Z",
"updated_at": "2025-12-08T03:32:33Z",
"comments": 1,
"user": "lengrongfu"
},
{
"repo": "huggingface/trl",
"number": 4413,
"title": "What is the default value of num_processes?",
"body": "Based on the documentation on page docs/source/grpo_trainer.md, num_processes is used but nowhere does the documentation define what num_processes is or what is its default value.",
"url": "https://github.com/huggingface/trl/issues/4413",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u2753 question",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-10-31T05:01:23Z",
"updated_at": "2025-10-31T17:31:33Z",
"user": "thisisraghavkumar"
},
{
"repo": "huggingface/diffusers",
"number": 12564,
"title": "[Proposals Welcome] Fal Flashpack integration for faster model loading",
"body": "Hey! \ud83d\udc4b\n\nWe've had a request to explore integrating Fal's Flashpack for faster DiT and Text Encoder loading (https://github.com/huggingface/diffusers/issues/12550). Before we jump into implementation, we wanted to open this up to the community to gather ideas and hear from anyone who's experimented with this.\n\nWe'd love your input on:\n1. Performance: Has anyone tried it? What kind of speedups did you see? Are there any performance trade-offs?\n2. Integration Design: How would you approach it if you were to integrating this into Diffusers? Describe your design at a high level - how would we support this in our existing framework and what would the API look like?\n\nWe're looking for proposals and ideas rather than PRs at this stage. We're genuinely interested in hearing different approaches and perspectives from the community on this.\n\nFeel free to share your thoughts!\n",
"url": "https://github.com/huggingface/diffusers/issues/12564",
"state": "open",
"labels": [
"help wanted",
"contributions-welcome"
],
"created_at": "2025-10-31T02:25:55Z",
"updated_at": "2025-10-31T12:26:13Z",
"comments": 2,
"user": "yiyixuxu"
},
{
"repo": "vllm-project/vllm",
"number": 27832,
"title": "[RFC]: Remap `CompilationConfig` from `-O` to `-cc` in CLI",
"body": "### Motivation.\n\nWith #20283 (and #26847), we're repurposing `-O0`/`-O1`/`-O2`/`-O3` to map to `optimization_level` instead of `CompilationConfig.level`/`CompilationConfig.mode`. This leaves us in a slightly confusing state where `-O` can refer to optimization level or compilation config depending on what follows it:\n- `-O0` -> `optimization_level=0`\n- `-O 3` -> `optimization_level=3`\n- `-O {\"cudagraph_mode\": \"NONE\"}` -> `CompilationConfig(cudagraph_mode=\"NONE\")`\n- `-O.use_inductor=False` -> `CompilationConfig(use_inductor=False)`\n- `--compilation-config.backend=eager` -> `CompilationConfig(backend=\"eager\")`\n\nThis is bad UX, and we should fix it. However, a CLI shorthand for `CompilationConfig` is still needed so users can easily compose different properties.\n\n### Proposed Change.\n\nWe should create a new shorthand for `CompilationConfig` should be `-cc`. Other options are `-c` and `-C`, but as discussed [here](https://github.com/vllm-project/vllm/pull/26847#discussion_r2439248068), single letters are not \"pythonic\" and capital letters are worse (extra `Shift` keystroke + less pythonic). However, the exact shorthand is up for discussion. React below to cast your vote.\n\nExample changes:\n- `-O0` -> `-O0` (unchanged)\n- `-O 3` -> `-O 3` (unchanged)\n- `-O {\"cudagraph_mode\": \"NONE\"}` -> `-cc {\"cudagraph_mode\": \"NONE\"}`\n- `-O.use_inductor=False` -> `-cc.use_inductor=False`\n- `--compilation-config.backend=eager` -> `--compilation-config.backend=eager` (unchanged)\n\n### Feedback Period.\n\nOne week, 10/30 - 11/5\n\n### CC List.\n\n@hmellor @morrison-turnansky @zou3519\n\n### Any Other Things.\n\nVote for your preferred shorthand:\n- \ud83d\udc4d for `-cc`\n- \ud83d\udc4e for `-O` (keep it the same)\n- \ud83c\udf89 for `-C`\n- \ud83d\ude80 for `-c`\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27832",
"state": "closed",
"labels": [
"help wanted",
"good first issue",
"RFC",
"torch.compile"
],
"created_at": "2025-10-30T20:29:31Z",
"updated_at": "2025-11-28T21:51:13Z",
"comments": 3,
"user": "ProExpertProg"
},
{
"repo": "huggingface/trl",
"number": 4407,
"title": "Complete paper index",
"body": "These are the papers mentioned at least one in the codebase.\n\n- [ ] https://huggingface.co/papers/1707.06347\n- [x] https://huggingface.co/papers/1909.08593 (only mentioned in notebook, no need to have in paper index)\n- [x] https://huggingface.co/papers/1910.02054 #4551\n- [ ] https://huggingface.co/papers/1910.10683\n- [x] https://huggingface.co/papers/2106.09685 #4441\n- [ ] https://huggingface.co/papers/2211.14275\n- [x] https://huggingface.co/papers/2305.10425 #3990\n- [x] https://huggingface.co/papers/2305.18290 #3937\n- [ ] https://huggingface.co/papers/2306.13649\n- [x] https://huggingface.co/papers/2307.09288 #4094\n- [x] https://huggingface.co/papers/2309.06657 #4441\n- [ ] https://huggingface.co/papers/2309.16240 #3906\n- [x] https://huggingface.co/papers/2310.12036 #3990\n- [ ] https://huggingface.co/papers/2312.00886\n- [x] https://huggingface.co/papers/2312.09244 #4094\n- [ ] https://huggingface.co/papers/2401.08417\n- [x] https://huggingface.co/papers/2402.00856 #3990\n- [x] https://huggingface.co/papers/2402.01306 #4440 \n- [x] https://huggingface.co/papers/2402.03300 #4441\n- [ ] https://huggingface.co/papers/2402.04792\n- [x] https://huggingface.co/papers/2402.05369 #3990\n- [ ] https://huggingface.co/papers/2402.09353\n- [x] https://huggingface.co/papers/2402.14740 #3801\n- [x] https://huggingface.co/papers/2403.00409 #3990\n- [ ] https://huggingface.co/papers/2403.07691\n- [x] https://huggingface.co/papers/2403.17031 (these are implementations details, no need to have in paper index)\n- [x] https://huggingface.co/papers/2404.04656 #3990\n- [ ] https://huggingface.co/papers/2404.09656\n- [ ] https://huggingface.co/papers/2404.19733\n- [x] https://huggingface.co/papers/2405.00675 #3900\n- [ ] https://huggingface.co/papers/2405.14734\n- [ ] https://huggingface.co/papers/2405.16436\n- [ ] https://huggingface.co/papers/2405.21046\n- [x] https://huggingface.co/papers/2406.05882 #3990\n- [x] https://huggingface.co/papers/2406.08414 #3990\n- [ ] https://huggingface.co/papers/2406.11827 #3906\n- [x] https://huggingface.co/papers/2407.21783 (LLaMA 3 paper, no need to have in paper index)\n- [x] https://huggingface.co/papers/2408.06266 #3990\n- [ ] https://huggingface.co/papers/2409.06411 #3906\n- [ ] https://huggingface.co/papers/2409.20370\n- [ ] https://huggingface.co/papers/2411.10442\n- [ ] https://huggingface.co/papers/2501.03262\n- [x] https://huggingface.co/papers/2501.03884 #3824\n- [ ] https://huggingface.co/papers/2501.12599 (Kimi 1.5 paper mentioned in an example, no need to have in paper index)\n- [ ] https://huggingface.co/papers/2501.12948\n- [x] https://huggingface.co/papers/2503.14476 #3937\n- [x] https://huggingface.co/papers/2503.20783 #3937\n- [x] https://huggingface.co/papers/2503.24290 (link to justify beta=0 in the doc, no need to have in paper index)\n- [ ] https://huggingface.co/papers/2505.07291\n- [x] https://huggingface.co/papers/2506.01939 #4580 \n- [x] https://huggingface.co/papers/2507.18071 #3775\n- [x] https://huggingface.co/papers/2508.00180 #3855\n- [x] https://huggingface.co/papers/2508.05629 #4042\n- [x] https://huggingface.co/papers/2508.08221 #3935\n- [x] https://huggingface.co/papers/2508.09726 #3989\n\n\n",
"url": "https://github.com/huggingface/trl/issues/4407",
"state": "open",
"labels": [
"\ud83d\udcda documentation"
],
"created_at": "2025-10-30T20:23:26Z",
"updated_at": "2025-12-24T05:50:21Z",
"comments": 4,
"user": "qgallouedec"
},
{
"repo": "vllm-project/vllm",
"number": 27830,
"title": "[Usage]: GPS OSS 120b on L40S (Ada)",
"body": "### Your current environment\n\n(Just a general question)\n\n\n### How would you like to use vllm\n\nI want to run inference of a GPT OSS 120b with multiple L40S. I read the [docs](https://docs.vllm.ai/projects/recipes/en/latest/OpenAI/GPT-OSS.html) as it clearly says it is not natively supported yet. After I had no success with vLLM it worked plug-and-play with Ollama. My question is, if there is any road map where I can see the progress? Or is it even possible to contribute on that problem? Unfortunately I am not familiar with GPUs. However I need to get it running. Any suggestion is highly appreciated. Even a clear description of the problem and what would be required to solve, is a real advantage. Thank you.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27830",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-30T20:07:42Z",
"updated_at": "2025-11-17T12:46:43Z",
"comments": 6,
"user": "Hansehart"
},
{
"repo": "vllm-project/vllm",
"number": 27823,
"title": "[Doc]: Multi-node distributed guide issues",
"body": "### \ud83d\udcda The doc issue\n\nFor context, see a recent issue (https://github.com/ROCm/ROCm/issues/5567) where a user was trying to set up distributed inference with `ray` by following guidance at https://docs.vllm.ai/en/v0.8.0/serving/distributed_serving.html#running-vllm-on-multiple-nodes. I ran into several issues setting this up on AMD GPUs that I believe might be deficiencies in the vLLM docs:\n\n- The `run_cluster.sh` script passes `--gpus all` which I believe is NVIDIA-only, needed to remove this from the script\n- I had to add `--distributed_executor_backend=\"ray\"` to the `vllm serve` command to get vLLM to use the `ray` cluster that the script sets up\n- I had to set NCCL_SOCKET_IFNAME and GLOO_SOCKET_IFNAME to the appropriate network interfaces, otherwise ran into a NCCL connection error\n- Relevant environment variables (NCCL_SOCKET_IFNAME, GLOO_SOCKET_IFNAME, NCCL_DEBUG) are not propagated to the Docker containers that the script creates; I worked around this by adding them to the `ray` invocation in `run_cluster.sh`, but I don't see a reason why the script shouldn't pass these to the container automatically\n\nI also needed to set `--enforce-eager` but I believe that is an issue specific to our current rocm/vllm Docker images.\n\nFor the above issues I'm not sure which are general gaps in the documentation, which are AMD-specific, and which might have arisen from our Docker images. The image I used and got working was `rocm/vllm:latest` which at the time had vLLM 0.11.\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27823",
"state": "open",
"labels": [
"documentation"
],
"created_at": "2025-10-30T18:33:04Z",
"updated_at": "2025-10-30T18:33:04Z",
"comments": 0,
"user": "schung-amd"
},
{
"repo": "huggingface/trl",
"number": 4399,
"title": "Update or remove some of the notebooks",
"body": "I suspect these notebooks to be outdated, if so they should be either updated or removed.\n- gpt2-sentiment-control.ipynb\n- best_of_n.ipynb\n- gpt2-sentiment.ipynb",
"url": "https://github.com/huggingface/trl/issues/4399",
"state": "closed",
"labels": [
"\ud83d\udcda documentation"
],
"created_at": "2025-10-30T15:34:36Z",
"updated_at": "2025-11-04T23:52:50Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "huggingface/trl",
"number": 4397,
"title": "Remove or move Multi Adapter RL",
"body": "I don't think this make sense to have this as a whole section in the doc. Either remove it or update and move it to PEFT integration",
"url": "https://github.com/huggingface/trl/issues/4397",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u26a1 PEFT"
],
"created_at": "2025-10-30T15:12:58Z",
"updated_at": "2025-11-04T23:57:56Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "huggingface/transformers",
"number": 41948,
"title": "Does Qwen2VLImageProcessor treat two consecutive images as one group/feature?",
"body": "When looking at Qwen3-VL model's image processor (which uses Qwen2-VL's one), I found the following lines of code hard to understand.\n\n`L296-300` checks the number of input images (`patches.shape[0]`), and repeat the last one to make it divisible by `temporal_patch_size`.\nThis make the model processes two consecutive images as a single feature due to the use of 3DConv with temporal_patch_size=2 by default.\n\nhttps://github.com/huggingface/transformers/blob/76fc50a1527a7db593a6057903b749598f7000a9/src/transformers/models/qwen2_vl/image_processing_qwen2_vl.py#L293-L300\n\nBut as I understand, Qwen2-VL paper mentions that it repeats each input image `temporal_patch_size` times.\nDid I misunderstand the code?.?\n\n![\"Image\"](\"https://github.com/user-attachments/assets/fc697460-e0a2-49fa-99b8-ea3e733bb097\")
\n\n",
"url": "https://github.com/huggingface/transformers/issues/41948",
"state": "closed",
"labels": [],
"created_at": "2025-10-30T09:23:50Z",
"updated_at": "2025-10-31T01:01:09Z",
"comments": 3,
"user": "priancho"
},
{
"repo": "huggingface/transformers",
"number": 41947,
"title": "why Smolvlm-256M-Instruct slower then Internvl-v2-1B ?",
"body": "As title, Smolvlm have smaller model size (1/4 less matrix multiplication), smaller input embedding. But, both torch.CudaEvent, timer.perf_counter with torch.sync report the slower inference time ?\n\nI wonder that does this related with the wrong implementation of Smolvlm in transformers ?\ninference performance comparison : \ninternvl-1B >\ninp_embed : (1, 547, 896)\ntrainable params: 17,596,416 || all params: 647,260,288 || trainable%: 2.7186\n\nsmolvlm-256M >\ninp_embed : (1, 171, 576)\ntrainable params: 9,768,960 || all params: 172,742,976 || trainable%: 5.6552\n\n---\n\nmodel init (all flags turns on, especially flash attention!) :\n```python\n if 'internvl' in self.variant.lower():\n if '3_5' in self.variant:\n self.model = AutoModelForImageTextToText.from_pretrained(self.variant, torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True)\n # internvl3.5, lm_head is not part of language_model !?\n lm_head = self.model.lm_head\n self.model = self.model.language_model\n self.model.lm_head = lm_head\n else:\n self.model = AutoModel.from_pretrained(\"OpenGVLab/InternVL2-1B\", torch_dtype=torch.bfloat16, low_cpu_mem_usage=True, use_flash_attn=True, trust_remote_code=True)\n self.model = self.model.language_model\n \n try:\n self.model.embed_tokens = self.model.base_model.embed_tokens\n except:\n self.model.embed_tokens = self.model.model.tok_embeddings\n elif 'smolvlm' in self.variant.lower():\n self.model = AutoModelForImageTextToText.from_pretrained(\"HuggingFaceTB/SmolVLM-256M-Instruct\", torch_dtype=torch.bfloat16, _attn_implementation=\"flash_attention_2\", trust_remote_code=True)\n lm_head = self.model.lm_head\n self.model = self.model.model.text_model\n self.model.lm_head = lm_head\n # self.model.embed_tokens already built-in!\n else:\n raise ValueError(f\"Carefull: Variant {self.variant} not tested.\")\n```\n\ncode snippet to measure fps :\n```python\nfor _ in range(30):\n _, _, _ = self.model(model_input)\n print('warm up done!')\n\nprof = Tch_prof(device=self.device)\n#prof = CudaEvent_Tch_prof(device=self.device)\nwith torch.no_grad():\n with prof: \n pred_speed_wps, pred_route, language = self.model(model_input, device=self.device) \n # timer + sync :\n # internvl v2-1b, lang mode : 0.3302s > 330ms ; no-lang mode : 0.0972s > 97ms (10 FPS) ?\n # smolvlm 256m, 0.3974s > 390ms ; no-lang : 0.1 s > 100ms ?\n\n # CudaEvent + sync : \n # internvl v2-1b, no-lang : 82.55ms ?\n # smolvlm 256m > no-lang : 90.68ms ? \n \n print(prof.get_profile())\n\n```\n\ncode snippet for timer classes :\n```python\nclass Tch_prof(object):\n def __init__(self, device):\n self.device = device\n self.hw_type = 'gpu'\n self.tlt_time = {\n 'cpu' : 0,\n 'gpu' : 0\n }\n\n def __enter__(self):\n torch.cuda.current_stream(self.device).synchronize()\n self.s = time.perf_counter() \n \n def __exit__(self, *exc):\n torch.cuda.current_stream(self.device).synchronize()\n self.tlt_time[self.hw_type] += time.perf_counter() - self.s\n \n def get_profile(self, hw_type='all'):\n if hw_type == 'all':\n return self.tlt_time\n elif hw_type in self.tlt_time.keys():\n return self.tlt_time[hw_type]\n else:\n raise RuntimeError(f\"No such hardware type {hw_type}\") \n\n\nclass CudaEvent_Tch_prof(object):\n def __init__(self, device):\n self.device = device\n self.start = torch.cuda.Event(enable_timing=True)\n self.end = torch.cuda.Event(enable_timing=True)\n\n def __enter__(self):\n self.start.record()\n \n def __exit__(self, *exc):\n self.end.record()\n torch.cuda.current_stream(self.device).synchronize()\n self.tlt_time = self.start.elapsed_time(self.end) \n \n def get_profile(self):\n return self.tlt_time \n```\n\nAny suggestion will be helpful !!",
"url": "https://github.com/huggingface/transformers/issues/41947",
"state": "closed",
"labels": [],
"created_at": "2025-10-30T08:10:28Z",
"updated_at": "2025-10-31T11:47:44Z",
"comments": 4,
"user": "HuangChiEn"
},
{
"repo": "huggingface/trl",
"number": 4386,
"title": "Reference supported trainers in Liger Kernel integration guide",
"body": "Currently, we only have an example with SFT, and it's hard to know which trainer supports liger. We should list the trainer which support liger.",
"url": "https://github.com/huggingface/trl/issues/4386",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\ud83c\udfcb SFT"
],
"created_at": "2025-10-30T04:08:04Z",
"updated_at": "2025-11-03T18:16:04Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "huggingface/trl",
"number": 4385,
"title": "Use a common 'trl-lib` namespace for the models/datasets/spaces",
"body": "In the doc, we have examples using different namespaces, like `kashif/stack-llama-2`, `edbeeching/gpt-neo-125M-imdb` etc. we should unify all these examples to use a common `trl-lib` namespace.",
"url": "https://github.com/huggingface/trl/issues/4385",
"state": "open",
"labels": [
"\ud83d\udcda documentation",
"\u2728 enhancement"
],
"created_at": "2025-10-30T04:04:10Z",
"updated_at": "2025-10-30T04:04:38Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "huggingface/trl",
"number": 4384,
"title": "Write the subsection \"Multi-Node Training\"",
"body": "This section must be written, with a simple code example, and a link to the `accelerate` documentation",
"url": "https://github.com/huggingface/trl/issues/4384",
"state": "open",
"labels": [
"\ud83d\udcda documentation",
"\u26a1accelerate"
],
"created_at": "2025-10-30T03:57:53Z",
"updated_at": "2025-12-08T16:23:23Z",
"comments": 2,
"user": "qgallouedec"
},
{
"repo": "huggingface/trl",
"number": 4383,
"title": "Add PEFT subsection to \"Reducing Memory Usage\"",
"body": "PEFT is a major technique to reduce memory usage of the training. We should have a small section pointing to the PEFT integration guide",
"url": "https://github.com/huggingface/trl/issues/4383",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u2728 enhancement",
"\u26a1 PEFT"
],
"created_at": "2025-10-30T03:55:55Z",
"updated_at": "2025-11-07T00:03:01Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "huggingface/trl",
"number": 4382,
"title": "Populate \"Speeding Up Training\"",
"body": "Currently, this section only mentions vLLM. We should have a small guide for other methods, like flash attention.\nIdeally, to avoid repetition, we should have a very light example, and a link to the place in the doc where it's more extensively discussed, example vLLM pointing to vLLM integration guide",
"url": "https://github.com/huggingface/trl/issues/4382",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u26a1accelerate"
],
"created_at": "2025-10-30T03:54:34Z",
"updated_at": "2025-12-01T09:47:23Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "huggingface/trl",
"number": 4380,
"title": "Fully transition from `flash-attn` to `kernels`",
"body": "The new recommended way to use flash attention is to use kernels. We should update our tests, and documentation to use `kernels` instead of \"flash_attention2\". Eg\n\nhttps://github.com/huggingface/trl/blob/1eb561c3e9133892a2e907d84123b46e40cbc5a0/docs/source/reducing_memory_usage.md#L149\n\n```diff\n- training_args = DPOConfig(..., padding_free=True, model_init_kwargs={\"attn_implementation\": \"flash_attention_2\"}) \n+ training_args = DPOConfig(..., padding_free=True, model_init_kwargs={\"attn_implementation\": \"kernels-community/flash-attn2\"}) \n```",
"url": "https://github.com/huggingface/trl/issues/4380",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u2728 enhancement"
],
"created_at": "2025-10-30T03:46:07Z",
"updated_at": "2025-11-13T04:07:35Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "huggingface/trl",
"number": 4379,
"title": "Remove or populate \"Training customization\"",
"body": "Currently, this part of the documentation shows some possible customizations that applies to all trainers https://huggingface.co/docs/trl/main/en/customization\n\nHowever, it only features a few examples. This sections would make sense if it gets populated with other customizations, or removed. This thread can be used to discussed additional customizations",
"url": "https://github.com/huggingface/trl/issues/4379",
"state": "closed",
"labels": [
"\ud83d\udcda documentation"
],
"created_at": "2025-10-30T03:41:02Z",
"updated_at": "2025-12-01T09:39:09Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "huggingface/trl",
"number": 4378,
"title": "Extend basic usage example to all supported CLIs",
"body": "currently https://huggingface.co/docs/trl/main/en/clis?command_line=Reward#basic-usage shows only basic example usage for SFT, DPO and Reward. We should have it for all supported CLIs (ie, GRPO, RLOO, KTO)",
"url": "https://github.com/huggingface/trl/issues/4378",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\ud83c\udfcb KTO",
"\ud83c\udfcb RLOO",
"\ud83d\udcf1 cli",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-10-30T03:35:36Z",
"updated_at": "2025-11-14T01:13:17Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "vllm-project/vllm",
"number": 27783,
"title": "[Usage]: Model performance different from api",
"body": "### Your current environment\n\n```text\nvllm==0.10.0\n```\n\n\n### How would you like to use vllm\n\nI'm running model Qwen3-8B with vllm. I also run the same experiment using Qwen3-8B api. But I find the result is quite different, the accuracy of api-model on my task is much higher than the vllm-model. I use the same temperature and top_k. \n\nIs there anyone else meeting the same question (the api-model is stronger than the vllm-model)?\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27783",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-30T03:30:02Z",
"updated_at": "2025-10-30T03:30:02Z",
"comments": 0,
"user": "fny21"
},
{
"repo": "vllm-project/vllm",
"number": 27782,
"title": "[Usage]: The same configuration v0.11.0 will report insufficient video memory compared to v0.8.5",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nThe server is a 4090 with 4 cards\nDocker runs vllm openai: v0.8.5 deployment command: \"command: --model /models/Qwen3/Qwen3-30B-A3B --enable-reasoning --reasoning-parser deepseek_r1 --tensor_parallel_size 4\" Can be deployed and started normally, switch the image version to v0.11.0, and run the command \"command: --model /models/Qwen3/Qwen3-30B-A3B --reasoning-parser deepseek_r1 --tensor_parallel_size 4\" It will report that the graphics card memory is insufficient, and the error log is:\nCapturing CUDA graphs (mixed prefill-decode, PIECEWISE): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 67/67 [00:19<00:00, 3.43it/s]\nCapturing CUDA graphs (decode, FULL): 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 35/35 [00:07<00:00, 4.78it/s]\nvllm | (Worker_TP3 pid=263) INFO 10-29 19:57:20 [gpu_model_runner.py:3480] Graph capturing finished in 28 secs, took 1.88 GiB\nvllm | (Worker_TP1 pid=261) INFO 10-29 19:57:20 [gpu_model_runner.py:3480] Graph capturing finished in 28 secs, took 1.88 GiB\nvllm | (Worker_TP0 pid=260) INFO 10-29 19:57:20 [gpu_model_runner.py:3480] Graph capturing finished in 28 secs, took 1.88 GiB\nvllm | (Worker_TP2 pid=262) INFO 10-29 19:57:20 [gpu_model_runner.py:3480] Graph capturing finished in 28 secs, took 1.88 GiB\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] WorkerProc hit an exception.\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] Traceback (most recent call last):\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/worker/gpu_model_runner.py\", line 3217, in _dummy_sampler_run\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] sampler_output = self.sampler(logits=logits,\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^^^^^^^^^^^\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1773, in _wrapped_call_impl\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] return self._call_impl(*args, **kwargs)\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1784, in _call_impl\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] return forward_call(*args, **kwargs)\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/sample/sampler.py\", line 100, in forward\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] sampled, processed_logprobs = self.sample(logits, sampling_metadata)\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/sample/sampler.py\", line 180, in sample\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] random_sampled, processed_logprobs = self.topk_topp_sampler(\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^^^^^^^\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1773, in _wrapped_call_impl\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] return self._call_impl(*args, **kwargs)\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1784, in _call_impl\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] return forward_call(*args, **kwargs)\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nvllm | (Worker_TP2 pid=262) ERROR 10-29 19:57:21 [multiproc_executor.py:671] File \"/usr/local/lib/python3.12/dist-packages/vllm/v1/sample/ops/topk_topp_sampler.py\", line 122, in forward_cuda\nvllm | (Worker",
"url": "https://github.com/vllm-project/vllm/issues/27782",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-30T03:24:54Z",
"updated_at": "2025-11-06T06:53:15Z",
"comments": 2,
"user": "lan-qh"
},
{
"repo": "huggingface/trl",
"number": 4376,
"title": "Rewrite `peft_integration.md`",
"body": "This section of the documentation is widely outdated and rely only on PPO.\n\nIdeally, we should have a clear documentation that shows how to use peft with SFT, DPO and GRPO at least, via the `peft_config` argument. We could have additional subsection about QLoRA and prompt-tuning.",
"url": "https://github.com/huggingface/trl/issues/4376",
"state": "closed",
"labels": [],
"created_at": "2025-10-30T03:23:24Z",
"updated_at": "2025-11-24T10:39:27Z",
"comments": 0,
"user": "qgallouedec"
},
{
"repo": "vllm-project/vllm",
"number": 27778,
"title": "[Usage]: Is DP + PP a possible way to use vLLM?",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nHi there, I wonder if we can adopt DP + PP in vLLM to form a heterogeneous inference pipeline. For example, If i have two V100 32G GPUs and one A100 80G GPU, can I utilize them in pipeline parallelism with vLLM? I might use V100 as the first stage, and A100 as the second. \nConsider that V100's compute ability is lower than A100, this would result in unbalance, and the V100 stage becomes a bottleneck. Thus I would like to use two V100s in DP at the first PP stage.\nIs this possible with the current released vLLM version?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27778",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-30T02:05:06Z",
"updated_at": "2025-10-30T02:05:06Z",
"comments": 0,
"user": "oldcpple"
},
{
"repo": "vllm-project/vllm",
"number": 27746,
"title": "[Bug]: `strict` value in function definitions causes request error when using Mistral tokenizer",
"body": "### Your current environment\n\nTested with latest vllm source build from main\n\n### \ud83d\udc1b Describe the bug\n\nStart vLLM with a model that uses the mistral tokenizer:\n\n```\nvllm serve mistralai/Mistral-Small-24B-Instruct-2501 \\\n --enable-auto-tool-choice \\\n --tool-call-parser mistral \\\n --tokenizer-mode mistral\n```\n\nSend a simple tool call request with the `strict` parameter set to a value of `False`:\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"fake\")\n\ntools = [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_current_time\",\n \"description\": \"Get the current time in UTC\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {},\n \"required\": []\n },\n \"strict\": False,\n }\n },\n]\nmodel = client.models.list().data[0].id\nresponse = client.chat.completions.create(\n model=model,\n messages=[{\"role\": \"user\", \"content\": \"What is the current time?\"}],\n tools=tools,\n)\nprint(\"Success!\")\n```\n\nThe request fails with a 400 error like:\n\n`openai.BadRequestError: Error code: 400 - {'error': {'message': '1 validation error for Tool\\nfunction.strict\\n Extra inputs are not permitted [type=extra_forbidden, input_value=False, input_type=bool]\\n For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden 1 validation error for Tool\\nfunction.strict\\n Extra inputs are not permitted [type=extra_forbidden, input_value=False, input_type=bool]\\n For further information visit https://errors.pydantic.dev/2.12/v/extra_forbidden', 'type': 'BadRequestError', 'param': None, 'code': 400}}`\n\nStart vLLM without the mistral tokenizer and the request succeeds.\n\nNote that this is explicitly NOT about making `strict=True` actually enforce structured outputs. The scope of this is simply to not return a validation error when this parameter is passed with any valid value when the `mistral` tokenizer is in use. The current behavior breaks some client frameworks that always pass this value, even when it has a value of `False`.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27746",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-29T14:33:13Z",
"updated_at": "2025-10-30T19:14:50Z",
"comments": 4,
"user": "bbrowning"
},
{
"repo": "huggingface/trl",
"number": 4368,
"title": "GKD: multimodal inputs?",
"body": "Does the Generalized Knowledge Distillation trainer (GKDTrainer) support multimodal inputs (VLMs)?\nIf yes, what's the expected dataset format? There is no example of this in the documentation.\n\nThanks!",
"url": "https://github.com/huggingface/trl/issues/4368",
"state": "closed",
"labels": [
"\ud83d\udcda documentation",
"\u2753 question",
"\ud83c\udfcb GKD"
],
"created_at": "2025-10-29T14:08:44Z",
"updated_at": "2025-11-07T19:26:23Z",
"comments": 2,
"user": "e-zorzi"
},
{
"repo": "huggingface/lerobot",
"number": 2338,
"title": "policy gr00t not found when do async inference with gr00t",
"body": "### System Info\n\n```Shell\nlerobot version: \n3f8c5d98 (HEAD -> main, origin/main, origin/HEAD) fix(video_key typo): fixing video_key typo in update_video_info (#2323)\n```\n\n### Information\n\n- [ ] One of the scripts in the examples/ folder of LeRobot\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nI have installed the following packages:\n\npip install \"torch>=2.2.1,<2.8.0\" \"torchvision>=0.21.0,<0.23.0\" # --index-url https://download.pytorch.org/whl/cu1XX\npip install ninja \"packaging>=24.2,<26.0\" # flash attention dependencies\npip install \"flash-attn>=2.5.9,<3.0.0\" --no-build-isolation\npython -c \"import flash_attn; print(f'Flash Attention {flash_attn.__version__} imported successfully')\"\n\npip install lerobot[groot]\n\nThen I ran asyn inference server:\npython -m lerobot.async_inference.policy_server \\\n --host=127.0.0.1 \\\n --port=8080\n\nwhen the async inference client send policy gr00t, the server complains no groot as below:\n\nERROR 2025-10-29 05:30:24 /_server.py:636 Exception calling application: Policy type groot not supported. Supported policies: ['act', 'smolvla', 'diffusion', 'tdmpc', 'vqbet', 'pi0', 'pi05']\n\n\nFinetuning a pi05 model is OK on the same code\nAny idea why this happens?\n\n\n\n\n\n### Expected behavior\n\nShould not complain about no groot policy\n\n",
"url": "https://github.com/huggingface/lerobot/issues/2338",
"state": "closed",
"labels": [
"bug",
"question",
"policies"
],
"created_at": "2025-10-29T05:36:20Z",
"updated_at": "2025-11-21T15:34:21Z",
"user": "jcl2023"
},
{
"repo": "huggingface/lerobot",
"number": 2337,
"title": "Can I continue reinforcement learning in HIL-SERL using a pi0",
"body": "Can I continue reinforcement learning in HIL-SERL using a pi0 model from LERobot that has been fine-tuned via imitation learning?",
"url": "https://github.com/huggingface/lerobot/issues/2337",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-29T04:30:26Z",
"updated_at": "2025-11-11T03:13:23Z",
"user": "pparkgyuhyeon"
},
{
"repo": "huggingface/peft",
"number": 2878,
"title": "peft \" target_modules='all-linear' \" have different behavior between x86 and aarch ?",
"body": "### System Info\n\ni have tested on 2 arch (x86, arm) then find this bug.\nboth arch have peft==0.17.1\n\n### Who can help?\n\n@benjaminbossan @githubnemo\n\n### Reproduction\n\nReproduction script : bug_reprod.py\n```python\nfrom transformers import AutoModelForImageTextToText\n\nmodel = AutoModelForImageTextToText.from_pretrained(\"OpenGVLab/InternVL3_5-1B-HF\", trust_remote_code=True)\nlm_head = model.lm_head\nmodel = model.language_model\nmodel.lm_head = lm_head\n\nfrom peft import get_peft_model\nfrom peft import LoraConfig\n\npeft_config = LoraConfig(\n inference_mode=False, \n r=12,\n target_modules=\"all-linear\",\n)\nbug_model = get_peft_model(model, peft_config)\nbug_model.print_trainable_parameters()\nbreakpoint() # p bug_model, you will find lm_head have different results\n```\nput bug_reprod.py to x86 and aarch, run it you will find it have different results on lm_head!\nfollowing figure show the error :\n\n#### x86\n![\"Image\"](\"https://github.com/user-attachments/assets/b33df3f2-15bc-4855-b6cb-c1b84e7ba9d9\")
\n \n#### aarch\n![\"Image\"](\"https://github.com/user-attachments/assets/1bfcd649-9bc9-44ff-a74e-5a26a7070c49\")
\n\n### Expected behavior\n\n`target_module='all-linear'` should exclude lm_head in lora tuning. At least, x86, arm arch should have identical behavior.\n\n",
"url": "https://github.com/huggingface/peft/issues/2878",
"state": "closed",
"labels": [],
"created_at": "2025-10-29T03:43:02Z",
"updated_at": "2025-12-07T15:03:33Z",
"comments": 4,
"user": "HuangChiEn"
},
{
"repo": "huggingface/peft",
"number": 2877,
"title": "peft config 'all-linear' include lm_head, is there anyway to remove it ?",
"body": "I'm not sure is it a bug or my modification affect the peft ?\n> since some issue reveal that 'all-linear' will not include the lm_head\n```python\nif 'internvl' in self.variant.lower():\n if '3_5' in self.variant:\n self.model = AutoModelForImageTextToText.from_pretrained(self.variant, trust_remote_code=True)\n # internvl3.5, lm_head is not part of language_model !?\n lm_head = self.model.lm_head\n self.model = self.model.language_model\n self.model.lm_head = lm_head\n\n# then \nfrom peft import get_peft_model\nfrom peft import LoraConfig\n \nprint('Using PEFT model')\npeft_config = LoraConfig(\n inference_mode=False, \n r=self.lora_r,\n lora_alpha=self.lora_alpha,\n lora_dropout=self.lora_dropout,\n target_modules=\"all-linear\",\n)\nself.model = get_peft_model(self.model, peft_config)\n```\nif the modification do affect the peft config, is there any way to exclude the lm_head by setting LoraConfig ?\n\npeft version : 0.17.0\nCan anyone kindly give me some suggestion ? \nmany TKS ~\n\n---\n\nUpdate : \npeft have different behavior between x86 and aarch ?\n\nerror message while loading the pretrain weight : \n![\"Image\"](\"https://github.com/user-attachments/assets/e13b167f-4215-446a-9f7b-42ba7d690029\")
\n\n#### x86 arch, normal\n![\"Image\"](\"https://github.com/user-attachments/assets/ee758c1e-46b7-4edd-9b2a-21d8f6fbfa5b\")
\n\n#### aarch, bug occurs\n![\"Image\"](\"https://github.com/user-attachments/assets/94755fc8-67da-4200-a408-7929cec0f6f4\")
",
"url": "https://github.com/huggingface/peft/issues/2877",
"state": "closed",
"labels": [],
"created_at": "2025-10-29T02:19:21Z",
"updated_at": "2025-10-29T03:43:20Z",
"comments": 1,
"user": "HuangChiEn"
},
{
"repo": "huggingface/lerobot",
"number": 2335,
"title": "How to Visualize All Episodes of a LeRobot Dataset Locally?",
"body": "Hi everyone, I have a question about LeRobot datasets. I'd like to inspect my data locally, but using the command\n_lerobot-dataset-viz --repo-id=${HF_USER}/record-test --episode-index=0_\nonly allows me to view one episode at a time, which is quite cumbersome.\n\nIs there a way to visualize all episodes of a dataset locally\u2014similar to [visualize dataset online](https://huggingface.co/spaces/lerobot/visualize_dataset), \nwhere I can easily browse through all episodes?\n\nThanks!",
"url": "https://github.com/huggingface/lerobot/issues/2335",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-10-29T02:01:01Z",
"updated_at": "2025-12-29T12:18:57Z",
"user": "Vacuame"
},
{
"repo": "vllm-project/vllm",
"number": 27692,
"title": "it run on rtx 5060 ti 16 gb",
"body": "### Your current environment\n\n\nhttps://github.com/bokkob556644-coder/suc-vllm-rtx-5060-ti-16-gb/blob/main/suc_vllm.txt\n\n### How would you like to use vllm\n\n[I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n](https://github.com/bokkob556644-coder/suc-vllm-rtx-5060-ti-16-gb/blob/main/suc_vllm.txt)\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27692",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-28T21:43:00Z",
"updated_at": "2025-10-28T21:43:16Z",
"comments": 1,
"user": "bokkob556644-coder"
},
{
"repo": "huggingface/transformers",
"number": 41919,
"title": "LFM2 image_processing_lfm2_vl_fast.py Mean Std swapped?",
"body": "### System Info\n\nIn LFM2-VL image_processing_lfm2_vl_fast.py line 212 following the MEAN and STD from imagenet is used for preprocessing.\nHowever it seems like they are swapped:\n image_mean = IMAGENET_STANDARD_STD\n image_std = IMAGENET_STANDARD_MEAN\n\nor is this correct ?\n\n### Who can help?\n\n@Cyrilvallez \n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nHave a look at https://github.com/huggingface/transformers/blob/main/src/transformers/models/lfm2_vl/image_processing_lfm2_vl_fast.py\n\n### Expected behavior\n\nNot optimized VLM Behaviour",
"url": "https://github.com/huggingface/transformers/issues/41919",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-28T16:17:44Z",
"updated_at": "2025-10-31T15:02:40Z",
"comments": 4,
"user": "florianvoss-commit"
},
{
"repo": "vllm-project/vllm",
"number": 27667,
"title": "[Usage]: DeepseekOCR on CPU missing implementation for fused_topk",
"body": "### Your current environment\n\nTry to test if it is possible to run DeepseekOCR on CPU using current git main branch.\n\nFails because there is no implementation of `fused_topk` for CPU.\n\n```\nINFO 10-28 15:41:18 [v1/worker/cpu_model_runner.py:77] Warming up model for the compilation...\nERROR: Traceback (most recent call last):\n File \"/opt/venv/lib/python3.12/site-packages/starlette/routing.py\", line 677, in lifespan\n async with self.lifespan_context(app) as maybe_state:\n ^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/starlette/routing.py\", line 566, in __aenter__\n await self._router.startup()\n File \"/opt/venv/lib/python3.12/site-packages/starlette/routing.py\", line 654, in startup\n await handler()\n File \"/app/start_server.py\", line 161, in startup_event\n initialize_model()\n File \"/app/start_server.py\", line 84, in initialize_model\n llm = LLM(\n ^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/entrypoints/llm.py\", line 336, in __init__\n self.llm_engine = LLMEngine.from_engine_args(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/engine/llm_engine.py\", line 188, in from_engine_args\n return cls(\n ^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/engine/llm_engine.py\", line 122, in __init__\n self.engine_core = EngineCoreClient.make_client(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py\", line 95, in make_client\n return InprocClient(vllm_config, executor_class, log_stats)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/engine/core_client.py\", line 264, in __init__\n self.engine_core = EngineCore(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 109, in __init__\n num_gpu_blocks, num_cpu_blocks, kv_cache_config = self._initialize_kv_caches(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 234, in _initialize_kv_caches\n self.model_executor.initialize_from_config(kv_cache_configs)\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/executor/abstract.py\", line 113, in initialize_from_config\n self.collective_rpc(\"compile_or_warm_up_model\")\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/executor/uniproc_executor.py\", line 73, in collective_rpc\n return [run_method(self.driver_worker, method, args, kwargs)]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/serial_utils.py\", line 459, in run_method\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/worker/cpu_worker.py\", line 105, in compile_or_warm_up_model\n self.model_runner.warming_up_model()\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/worker/cpu_model_runner.py\", line 80, in warming_up_model\n self._dummy_run(\n File \"/opt/venv/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 120, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/v1/worker/gpu_model_runner.py\", line 3464, in _dummy_run\n outputs = self.model(\n ^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1773, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1784, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/model_executor/models/deepseek_ocr.py\", line 582, in forward\n hidden_states = self.language_model(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1773, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1784, in _call_impl\n return forward_call(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/vllm/model_executor/models/deepseek.py\", line 495, in forward\n hidden_states = self.model(\n ^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1773, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/opt/venv/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1784, in _call_impl\n return forward_call(*args, **kwargs)\n ",
"url": "https://github.com/vllm-project/vllm/issues/27667",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-28T16:14:40Z",
"updated_at": "2025-10-28T16:14:40Z",
"comments": 0,
"user": "brainlag"
},
{
"repo": "vllm-project/vllm",
"number": 27661,
"title": "[RFC]: Consolidated tool call parser implementations by type (JSON, Python, XML, Harmony)",
"body": "### Motivation.\n\nWhen someone wants to add a new tool call parser today, they typically choose an existing tool call parser that looks close to what is needed, copy it into a new file, and adjust things here and there as needed for their specific model. Sometimes tests get added, and sometimes not. Sometimes the changes to the copied parser make meaningful fixes, and sometimes the changes to the copied parser add bugs.\n\nGenerally, we have a few buckets of tool call parsers based on the format the models are trained to output - JSON, Python, XML, or Hamony style tool calls. But, we have N different implementations of streaming partial JSON parsing, N different python parsing, and so on. Instead of multiple copies of each of those, ideally we'd maintain one high quality implementation for streaming partial JSON parsing that's extensible enough to handle the needs of individual model differences.\n\n### Proposed Change.\n\nThe overall change I propose is a refactoring of the existing tool call parsers, lowering the burden to add a new tool call parser, reducing the maintenance and bug permutations possible, and providing us higher test coverage of all tool call parsers so we can systematically track and fix bugs as reported in one place.\n\nGeneral steps proposed:\n\n**Test coverage**\n\nBefore starting any refactor, the focus will be on building confidence in the existing state of all our tool call parsers by focusing on adding and extending their test suites.\n\n- [ ] Add a new common tool call parser unit test suite for all tool call parsers lacking any tests\n - #27599\n- [ ] Reorganize existing tool call parser tests to cleanly separate unit tests that just need a tokenizer from integration tests that need actual running inference servers.\n - Today we have `tests/tool_use` that is mostly integration tests, and `tests/entrypoints/openai/tool_parsers` that is mostly unit tests, but there's a mix of each in both. The plan is to move integration tests to `tests/tool_use` since that's where most of those live, and unit tests in `tests/entrypoints/openai/tool_parsers` that can all be run without an accelerator and execute quickly.\n- [ ] Review the history of each tool call parser, bugs filed against that tool call parser, and special statements in the code of each tool parser to identify special case handling. Create a test for each of these special cases.\n- [ ] Refactor existing tool call parser tests to use the common test suite for all tool call parsers while retaining any model-specific tests required by the previous review of parsers.\n- [ ] File issues of type bug for every test in the common suite that is marked as \"expected fail\" for various tool call parsers. There will be a number of these, with tool call parsers that do not meet the standards of the common suite today. These represent low-hanging fruit for us to find and fix for each parser.\n - Some fixes may be trivial, and can happen before consolidating implementations just to incrementally raise the quality of our parsers. Some fixes may not be trivial, and may only happen after consolidating implementations.\n\n**Refactoring and consolidation**\n\nAfter we have the expanded test suite, we'll have the confidence to undertake this refactor without introducing a lot of new bugs as each parser has some bespoke logic today that needs to be accounted for.\n\n- [ ] Consolidate all the partial and streaming JSON parsing logic into a central place that every JSON-style tool call parser consumes. Ensure no test regressions\n- [ ] Consolidate all the partial and streaming Python parsing logic into a central place that every Python-style tool call parser consumes.\n\n**Post-consolidation bug squashing and docs**\n\n- [ ] Remove any remaining `xfail` markers in the test suite across all tool parser test suites.\n- [ ] Update contributor docs that discuss how to add a new tool call parser, how to reuse the common logic for JSON, Python, XML, etc parsing instead of writing new, and how to use the new common test suite to simplify testing of the new parser. \n\n### Feedback Period.\n\nThis is ongoing work and feedback is accepted at any time while this issue is open. Initial stages of expanding our test coverage have already started, but there's at least a couple of weeks to provide feedback before work gets to the point of actual refactoring and consolidating of the tool call parsers.\n\n### CC List.\n\n_No response_\n\n### Any Other Things.\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27661",
"state": "open",
"labels": [
"RFC"
],
"created_at": "2025-10-28T14:54:10Z",
"updated_at": "2025-10-30T16:14:09Z",
"comments": 2,
"user": "bbrowning"
},
{
"repo": "huggingface/lerobot",
"number": 2329,
"title": "smolvla base model ( the Vlm part) to other model",
"body": "Can I change smolvla base model ( the Vlm part) to other model?\nWhat should I do?\nThanks",
"url": "https://github.com/huggingface/lerobot/issues/2329",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-28T12:28:44Z",
"updated_at": "2025-10-31T15:09:12Z",
"user": "smartparrot"
},
{
"repo": "vllm-project/vllm",
"number": 27649,
"title": "[Usage]: Qwen3-32B on RTX PRO 6000 (55s First Token Delay and 15t/s)",
"body": "Why does the Qwen3-32B model take 55 seconds before producing the first token, and why is the generation speed only 15t/s?\n\nMy vLLM configuration:\n\nDevice: GB202GL [RTX PRO 6000 Blackwell Server Edition]\n\nNvidia Driver Version\uff1a580.95.05\nCUDA Version\uff1a13.0\n\nDocker configuration: \n\n```sh\nPORT=8085\nMODEL_PATH=Qwen/Qwen3-32B\nSERVED_MODEL_NAME=vLLM-Qwen3-32B\n\ndocker run -d \\\n --runtime nvidia \\\n --gpus all \\\n -v /data/projects/docker/vllm/.cache/huggingface:/root/.cache/huggingface \\\n -p $PORT:8000 \\\n --env \"HUGGING_FACE_HUB_TOKEN=$HUGGING_FACE_HUB_TOKEN\" \\\n --name $SERVED_MODEL_NAME \\\n --restart unless-stopped \\\n --ipc=host \\\n vllm/vllm-openai:v0.11.0 \\\n --model /root/.cache/huggingface/$MODEL_PATH \\\n --served-model-name $SERVED_MODEL_NAME \\\n --dtype bfloat16 \\\n --gpu-memory-utilization 0.92 \\\n --max-model-len 32768 \\\n --max-num-seqs 64 \\\n --tensor-parallel-size 1 \\\n --api-key sk-vx023nmlrtTmlC\n```",
"url": "https://github.com/vllm-project/vllm/issues/27649",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-28T10:49:43Z",
"updated_at": "2025-11-07T02:30:26Z",
"comments": 4,
"user": "yizhitangtongxue"
},
{
"repo": "vllm-project/vllm",
"number": 27646,
"title": "[Usage]: How to use vllm bench serve to bench remote deployed vllm models (can't bench when ep enabled!!!)",
"body": "### Your current environment\n\nI deployed dpskv3 in a remote server using:\n```\nexport VLLM_USE_V1=1\nexport VLLM_ALL2ALL_BACKEND=deepep_low_latency\nvllm serve /models/hf/models--deepseek-ai--DeepSeek-V3 --tensor-parallel-size 1 --data-parallel-size 8 --enable-expert-parallel --no-enforce-eager --load-format dummy\n```\n\nAnd on another server:\n```\nVLLM_USE_V1=1 vllm bench serve --model /models/hf/models--deepseek-ai--DeepSeek-V3/ --endpoint /v1/completions --dataset-name sharegpt --dataset-path /datasets/ShareGPT/ShareGPT_V3_unfiltered_cleaned_split.json --num-prompts 10 --ready-check-timeout-sec 0 --ip 10.102.212.22 --port 8000 \n```\nwhere 10.102.212.22 is the server ip\uff0c 8000 is the default port\n\nAnd I got this below error on server:\n```\n\"POST /v1/completions HTTP/1.1\" 404 Not Found\n```\n\n\n### How would you like to use vllm\n\nI want to run inference of a deepseekv3.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27646",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-28T09:56:37Z",
"updated_at": "2025-10-28T15:23:06Z",
"comments": 3,
"user": "Valerianding"
},
{
"repo": "huggingface/transformers",
"number": 41910,
"title": "Breaking change about AWQ Fused modules due to Attention Refactor",
"body": "### System Info\n\ntransformers==5.0.0dev\nautoawq==0.2.9\nautoawq_kernels==0.0.9\ntorch==2.6.0+cu124\n\n### Who can help?\n\nDue to PR #35235, the `past_key_values` is no longer a returned value of attention modules.\n\nHowever, when using AWQ models with Fused modules [AWQ Fused modules docs](https://huggingface.co/docs/transformers/main/en/quantization/awq#fused-modules), there will be an error like issue #38554\n\n```bash\n hidden_states, _ = self.self_attn(\nValueError: too many values to unpack (expected 2)\n```\n\nSo we can hack the `awq.modules.fused.attn.QuantAttentionFused` to avoid returning `past_key_values`. Therefore, I create a primary PR #41909 to fix it.\n\nHowever, for special `rope_type` such as LLaMA3, the RoPE implementation in AutoAWQ will cause error, since `awq.modules.fused.attn.RoPE` supports default RoPE only.\n\nMaybe we can implement and maintain `AwqRoPE` and `AwqQuantAttentionFused` in `transformers.integrations.awq`? Or we can maintain `huggingface/AutoAWQ` as `casper-hansen/AutoAWQ` is archived.\n\nI'd like to refine my PR to help transformers fix this bug!\n\n@SunMarc @MekkCyber\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n```python\n\nfrom transformers import AwqConfig, AutoModelForCausalLM, AutoTokenizer\n\n\n# model_path = \"./llama-3.1-8b-instruct-awq\"\nmodel_path = \"./qwen2.5-7b-instruct-awq\"\n# model_path = \"./qwen3-8b-awq\"\n\nawq_config = AwqConfig(\n bits=4,\n do_fuse=True,\n fuse_max_seq_len=8192\n)\n\nmodel = AutoModelForCausalLM.from_pretrained(model_path, quantization_config=awq_config).to(\"cuda:0\")\nprint(model)\ntokenizer = AutoTokenizer.from_pretrained(model_path)\n\nmax_new_tokens = 1024 if \"qwen3\" in model_path else 32\n\n\nmessages = []\n\nprompt1 = \"What is the result of 3+5?\"\nmessages.append({\"role\": \"user\", \"content\": prompt1})\ntext1 = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\ninputs1 = tokenizer(text1, return_tensors=\"pt\").to(\"cuda:0\")\n\ngenerated_ids1 = model.generate(**inputs1, max_new_tokens=max_new_tokens)\noutput_ids1 = generated_ids1[0, len(inputs1.input_ids[0]) :].tolist()\noutput1 = tokenizer.decode(output_ids1, skip_special_tokens=True)\nmessages.append({\"role\": \"assistant\", \"content\": output1})\nprint(\"Output 1:\", output1)\n\nprompt2 = \"What about adding 10 to that result?\"\nmessages.append({\"role\": \"user\", \"content\": prompt2})\ntext2 = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\ninputs2 = tokenizer(text2, return_tensors=\"pt\").to(\"cuda:0\")\n\ngenerated_ids2 = model.generate(**inputs2, max_new_tokens=max_new_tokens)\noutput_ids2 = generated_ids2[0, len(inputs2.input_ids[0]) :].tolist()\noutput2 = tokenizer.decode(output_ids2, skip_special_tokens=True)\nmessages.append({\"role\": \"assistant\", \"content\": output2})\nprint(\"Output 2:\", output2)\n\n```\n\n### Expected behavior\n\nThere is no error.",
"url": "https://github.com/huggingface/transformers/issues/41910",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-28T08:29:03Z",
"updated_at": "2025-11-20T13:41:34Z",
"comments": 3,
"user": "fanqiNO1"
},
{
"repo": "vllm-project/vllm",
"number": 27636,
"title": "[Usage]: vllm\u5982\u4f55\u4fdd\u7559qwen3-vl\u4e2d\u7684special token",
"body": "### Your current environment\n\n\u6211\u5fae\u8c03\u8fc7\u7684qwen3-vl\u6a21\u578b\u7684grounding\u683c\u5f0f\u4e3a\uff1a<|object_ref_start|>\u56fe\u7247<|object_ref_end|><|box_start|>(x1,y1),(x2,y2)<|box_end|>\n\u4f7f\u7528vllm serve\u63a8\u7406\u7684\u683c\u5f0f\u662f\uff1a\u56fe\u7247(460,66),(683,252)\uff0c\u8fd9\u4e2a\u662f\u76f4\u63a5\u5ffd\u7565\u4e86special token\u4e48\uff0c\u662f\u5426\u6709\u65b9\u6cd5\u53ef\u4ee5\u4fdd\u7559\u3002\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27636",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-28T06:52:16Z",
"updated_at": "2025-10-28T06:52:16Z",
"comments": 0,
"user": "qfs666"
},
{
"repo": "huggingface/diffusers",
"number": 12553,
"title": "Reason to move from OpenCV to ffmpeg",
"body": "I see that `diffusers.utils.export_to_video()` encourages ffmpeg usage instead of OpenCV. Can you share the reason? I'm looking for a way to add video decoding to my project so I'm collecting arguments.",
"url": "https://github.com/huggingface/diffusers/issues/12553",
"state": "open",
"labels": [],
"created_at": "2025-10-28T06:49:48Z",
"updated_at": "2025-11-07T13:27:03Z",
"comments": 10,
"user": "Wovchena"
},
{
"repo": "vllm-project/vllm",
"number": 27634,
"title": "[Usage]: how to use --quantization option of `vllm serve`\uff1f",
"body": "### Your current environment\n\n```text\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu129\nIs debug build : False\nCUDA used to build PyTorch : 12.9\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.12 (main, Aug 15 2025, 14:32:43) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-5.15.0-160-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 11.5.119\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : GPU 0: NVIDIA GeForce RTX 4090 D\nNvidia driver version : 570.195.03\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual \nByte Order: Little Endian\nCPU(s): 32\nOn-line CPU(s) list: 0-31\nVendor ID: AuthenticAMD\nModel name: AMD Ryzen 9 9950X3D 16-Core Processor\nCPU family: 26\nModel: 68\nThread(s) per core: 2\nCore(s) per socket: 16\nSocket(s): 1\nStepping: 0\nFrequency boost: enabled\nCPU max MHz: 8839.3555\nCPU min MHz: 3000.0000\nBogoMIPS: 8583.32\nFlags: fpu vme de pse tsc msr pae mce cx8\n apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall \nnx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc\n cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 sse\n4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm ex\ntapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tc\ne topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l\n3 hw_pstate ssbd mba ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase tsc_adj\nust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx \nsmap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt \nxsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx\n_vnni avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd cppc arat npt lbr\nv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefi\nlter pfthreshold v_vmsave_vmload vgif v_spec_ctrl avx512vbmi umip pku ospke\n avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcnt\ndq rdpid bus_lock_detect movdiri movdir64b overflow_recov succor smca fsrm avx512_vp2intersect flush_l1d\nVirtualization: AMD-V\nL1d cache: 768 KiB (16 instances)\nL1i cache: 512 KiB (16 instances)\nL2 cache: 16 MiB (16 instances)\nL3 cache: 128 MiB (2 instances)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-31\nVulnerability Gather data sampling: Not affected\nVulnerability Indirect target selection: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic I\nBRS; IBPB conditional; STIBP always-on; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsa: Not",
"url": "https://github.com/vllm-project/vllm/issues/27634",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-28T06:24:38Z",
"updated_at": "2025-10-28T15:57:47Z",
"comments": 3,
"user": "Septemberlemon"
},
{
"repo": "huggingface/candle",
"number": 3151,
"title": "Tensor conversion to_vec1() failing on 0.9.2-alpha.1 - Metal",
"body": "Dependencies\n\n```toml\ncandle-core = { git = \"https://github.com/huggingface/candle\", rev = \"df618f8\", features = [\"metal\"] }\ncandle-nn = { git = \"https://github.com/huggingface/candle\", rev = \"df618f8\", features = [\"metal\"] }\ncandle-transformers = { git = \"https://github.com/huggingface/candle\", rev = \"df618f8\", features = [\"metal\"] }\n```\n\nRunning on Macbook M2 Pro - Metal - Tahoe 26.0.1\n\nSince upgrading to 0.9.2-alpha.1, BERT operations on Metal have started hanging when converting rank-1 tensor to Vec<32>. This seems to be affecting any ops that attempt to synchronize or move data from GPU to CPU. Not sure if this is directly related to the update but rolling back to 0.9.1 or using CPU as device fixes the issue. \n\nSome example of ops that are failing...\n\n```rust\ntensor.device().synchronize()\ntensor.to_device()\ntensor.to_vec1()\n```\n\nActual code being run...\n\n```rust\nlet (token_ids, token_type_ids, attention_mask) = self.encode_text(text)?;\n\nlet hidden_states = self\n .forward_model(&token_ids, &token_type_ids, &attention_mask)\n .await\n .map_err(|e| {\n log::error!(\"Failed to forward to model: {}\", e);\n e\n })?;\n\nlet embeddings = self\n .apply_mean_pooling(&hidden_states, &attention_mask)\n .map_err(|e| {\n log::error!(\"Failed to apply mean pooling: {}\", e);\n e\n })?;\n\n...\n\nfn apply_mean_pooling(\n &self,\n hidden_states: &Tensor,\n attention_mask: &Tensor,\n ) -> Result> {\n log::info!(\"Applying mean pooling to hidden states...\");\n\n let attention_mask_for_pooling = attention_mask\n .to_dtype(hidden_states.dtype())?\n .unsqueeze(2)?;\n let sum_mask = attention_mask_for_pooling.sum(1)?;\n\n let pooled = (hidden_states.broadcast_mul(&attention_mask_for_pooling)?).sum(1)?;\n let sum_mask_safe = sum_mask.clamp(NUMERICAL_STABILITY_EPSILON, f32::MAX)?;\n let pooled = pooled.broadcast_div(&sum_mask_safe)?;\n\n let denom = pooled\n .sqr()?\n .sum_keepdim(1)?\n .sqrt()?\n .clamp(NUMERICAL_STABILITY_EPSILON, f32::MAX)?;\n\n let pooled = pooled.broadcast_div(&denom)?;\n let pooled = pooled.squeeze(0)?;\n \n // HANGING HERE ... no errors\n // Tensor shape - Tensor[dims 1024; f32, metal:4294968337]\n let embeddings = pooled.to_vec1::().map_err(|e| Error::TensorOp {\n operation: format!(\"Failed to convert tensor to f32 vector: {}\", e),\n })?;\n\n Ok(embeddings)\n }\n```",
"url": "https://github.com/huggingface/candle/issues/3151",
"state": "closed",
"labels": [],
"created_at": "2025-10-27T21:36:17Z",
"updated_at": "2025-11-06T22:44:14Z",
"comments": 2,
"user": "si-harps"
},
{
"repo": "vllm-project/vllm",
"number": 27604,
"title": "[Bug]: Is Flashinfer Attn backend supposed to work with FP8 KV cache on Hopper?",
"body": "### Your current environment\n\n
pip list
\n\n```\nabsl-py==2.3.1\naccelerate==1.10.1\naiohappyeyeballs==2.6.1\naiohttp==3.13.0\naiosignal==1.4.0\nannotated-types==0.7.0\nantlr4-python3-runtime==4.9.3\nanyio==4.9.0\nargon2-cffi==23.1.0\nargon2-cffi-bindings==21.2.0\narrow==1.3.0\nasttokens==3.0.0\nasync-lru==2.0.5\nattrs==23.2.0\nav==15.1.0\nbabel==2.17.0\nbddl==1.0.1\nbeautifulsoup4==4.13.4\nbleach==6.2.0\nblinker==1.7.0\ncertifi==2025.1.31\ncffi==1.17.1\ncharset-normalizer==3.4.1\nclick==8.3.0\ncloudpickle==3.1.1\ncmake==3.31.6\ncomm==0.2.2\ncontourpy==1.3.2\ncryptography==41.0.7\ncuda-bindings==12.8.0\ncuda-python==12.8.0\ncycler==0.12.1\nCython==3.0.12\ndataclasses==0.6\ndatasets==4.1.1\ndbus-python==1.3.2\ndebugpy==1.8.14\ndecorator==5.2.1\ndeepdiff==8.6.1\ndefusedxml==0.7.1\ndiffusers @ file:///opt/diffusers-0.34.0.dev0-py3-none-any.whl#sha256=cf07a8004c994f02e0d41e9bface90486f53a98cd3abdda39972c5ffe7009d87\ndill==0.4.0\ndistro==1.9.0\ndocopt==0.6.2\ndocutils==0.21.2\ndraccus==0.10.0\neasydict==1.13\negl_probe @ git+https://github.com/huggingface/egl_probe.git@eb5e5f882236a5668e43a0e78121aaa10cdf2243\neinops==0.8.1\netils==1.13.0\nevdev==1.9.2\nexecuting==2.2.0\nFarama-Notifications==0.0.4\nfastjsonschema==2.21.1\nfilelock==3.18.0\nfonttools==4.57.0\nfqdn==1.5.1\nfrozenlist==1.8.0\nfsspec==2025.3.2\nfuture==1.0.0\ngitdb==4.0.12\nGitPython==3.1.45\nglfw==2.10.0\ngrpcio==1.75.1\ngym==0.26.2\ngym-notices==0.1.0\ngymnasium==0.29.1\nh11==0.14.0\nh5py==3.13.0\nhf-xet==1.1.10\nhf_transfer==0.1.9\nhttpcore==1.0.8\nhttplib2==0.20.4\nhttpx==0.28.1\nhuggingface-hub==0.35.3\nhydra-core==1.3.2\nid==1.5.0\nidna==3.10\nimageio==2.37.0\nimageio-ffmpeg==0.6.0\nimportlib_metadata==8.6.1\nimportlib_resources==6.5.2\niniconfig==2.1.0\ninquirerpy==0.3.4\nipykernel==6.29.5\nipython==9.1.0\nipython_pygments_lexers==1.1.1\nipywidgets==8.1.6\nisoduration==20.11.0\njaraco.classes==3.4.0\njaraco.context==6.0.1\njaraco.functools==4.1.0\njedi==0.19.2\njeepney==0.9.0\nJinja2==3.1.6\njson5==0.12.0\njsonlines==4.0.0\njsonpointer==3.0.0\njsonschema==4.23.0\njsonschema-specifications==2025.4.1\njupyter==1.1.1\njupyter-console==6.6.3\njupyter-events==0.12.0\njupyter-lsp==2.2.5\njupyter_client==8.6.3\njupyter_core==5.7.2\njupyter_server==2.15.0\njupyter_server_terminals==0.5.3\njupyterlab==4.4.1\njupyterlab_myst==2.4.2\njupyterlab_pygments==0.3.0\njupyterlab_server==2.27.3\njupyterlab_widgets==3.0.14\njupytext==1.17.3\nkeyring==25.6.0\nkiwisolver==1.4.8\nlaunchpadlib==1.11.0\nlazr.restfulclient==0.14.6\nlazr.uri==1.0.6\n-e git+https://github.com/huggingface/lerobot@6f5bb4d4a49fbdb47acfeaa2c190b5fa125f645a#egg=lerobot\nlibero @ git+https://github.com/huggingface/lerobot-libero.git@b053a4b0de70a3f2d736abe0f9a9ee64477365df\nllvmlite==0.45.1\nMako==1.3.10\nMarkdown==3.9\nmarkdown-it-py==3.0.0\nMarkupSafe==3.0.2\nmatplotlib==3.10.1\nmatplotlib-inline==0.1.7\nmdit-py-plugins==0.5.0\nmdurl==0.1.2\nmergedeep==1.3.4\nmistune==3.1.3\nmore-itertools==10.7.0\nmpmath==1.3.0\nmujoco==3.3.2\nmultidict==6.7.0\nmultiprocess==0.70.16\nmypy_extensions==1.1.0\nnbclient==0.10.2\nnbconvert==7.16.6\nnbformat==5.10.4\nnest-asyncio==1.6.0\nnetworkx==3.4.2\nnh3==0.2.21\nninja==1.11.1.4\nnotebook==7.4.1\nnotebook_shim==0.2.4\nnum2words==0.5.14\nnumba==0.62.1\nnumpy==2.2.5\noauthlib==3.2.2\nomegaconf==2.3.0\nonnx==1.17.0\nopencv-contrib-python==4.11.0.86\nopencv-python==4.11.0\nopencv-python-headless==4.12.0.88\noptimum==1.24.0\norderly-set==5.5.0\noverrides==7.7.0\npackaging==25.0\npandas==2.3.3\npandocfilters==1.5.1\nparso==0.8.4\npexpect==4.9.0\npfzy==0.3.4\npillow==11.2.1\npkginfo==1.12.1.2\nplatformdirs==4.3.7\npluggy==1.6.0\nprometheus_client==0.21.1\nprompt_toolkit==3.0.51\npropcache==0.4.1\nprotobuf==6.30.2\npsutil==7.0.0\nptyprocess==0.7.0\npure_eval==0.2.3\npyarrow==21.0.0\npyav==14.2.1\npycparser==2.22\npycuda==2025.1\npydantic==2.12.1\npydantic_core==2.41.3\nPygments==2.19.1\nPyGObject==3.48.2\nPyJWT==2.7.0\npynput==1.8.1\nPyOpenGL==3.1.10\nPyOpenGL-accelerate==3.1.10\npyparsing==3.1.1\npyrsistent==0.20.0\npyserial==3.5\npytest==8.4.2\npython-apt==2.7.7+ubuntu4\npython-dateutil==2.9.0.post0\npython-json-logger==3.3.0\npython-xlib==0.33\npytools==2025.1.2\npytz==2025.2\nPyYAML==6.0.2\npyyaml-include==1.4.1\npyzmq==26.4.0\nreadme_renderer==44.0\nreferencing==0.36.2\nregex==2024.11.6\nrequests==2.32.3\nrequests-toolbelt=", "url": "https://github.com/huggingface/lerobot/issues/2354", "state": "open", "labels": [ "question", "policies", "simulation" ], "created_at": "2025-11-01T11:20:05Z", "updated_at": "2026-01-05T08:38:48Z", "user": "Hesh0629" }, { "repo": "huggingface/trl", "number": 4419, "title": "GRPO with reward model. CUDA out of memory. How to fix? Thank you very much.", "body": "train_grpo.py:\n```python\nimport argparse\nimport os\nfrom typing import Callable, Dict, List, Optional\n\nimport torch\nfrom datasets import Dataset, load_dataset\nfrom transformers import (\n AutoModelForCausalLM,\n AutoTokenizer,\n AutoModelForSequenceClassification,\n pipeline,\n set_seed,\n)\nfrom trl import GRPOConfig, GRPOTrainer\n\n\nclass CombinedReward:\n \"\"\"Combine multiple reward sources with weights.\n\n Each reward function follows signature:\n reward_fn(completions: List[str], prompts: List[str], **kwargs) -> List[float]\n \"\"\"\n\n def __init__(\n self,\n reward_fns: List[Callable[[List[str], List[str]], List[float]]],\n weights: Optional[List[float]] = None,\n ) -> None:\n if not reward_fns:\n raise ValueError(\"reward_fns must not be empty\")\n self.reward_fns = reward_fns\n self.weights = weights or [1.0] * len(reward_fns)\n if len(self.weights) != len(self.reward_fns):\n raise ValueError(\"weights length must match reward_fns length\")\n\n def __call__(self, completions: List[str], prompts: List[str], **kwargs) -> List[float]:\n if not completions:\n return []\n all_scores: List[List[float]] = []\n for reward_fn in self.reward_fns:\n scores = reward_fn(completions, prompts, **kwargs)\n if len(scores) != len(completions):\n raise ValueError(\"All reward functions must return scores for each completion\")\n all_scores.append(scores)\n # weighted sum\n totals: List[float] = [0.0] * len(completions)\n for w, scores in zip(self.weights, all_scores):\n for i, s in enumerate(scores):\n totals[i] += w * float(s)\n return totals\n\n\ndef build_reward_model_fn(\n reward_model_name: str,\n device: Optional[str] = None,\n normalize: bool = True,\n) -> Callable[[List[str], List[str]], List[float]]:\n \"\"\"Create a reward function using a sequence classification model.\n\n Returns a function that outputs a scalar reward per completion.\n \"\"\"\n rm_tokenizer = AutoTokenizer.from_pretrained(reward_model_name, use_fast=True)\n \n # ensure padding token exists for batched inference\n if rm_tokenizer.pad_token is None:\n candidate = rm_tokenizer.eos_token or rm_tokenizer.sep_token or rm_tokenizer.cls_token or rm_tokenizer.unk_token\n if candidate is not None:\n rm_tokenizer.pad_token = candidate\n else:\n rm_tokenizer.add_special_tokens({\"pad_token\": \"[PAD]\"})\n rm_model = AutoModelForSequenceClassification.from_pretrained(reward_model_name, torch_dtype=torch.float16, \n device_map=\"auto\")\n if getattr(rm_model.config, \"pad_token_id\", None) is None and rm_tokenizer.pad_token_id is not None:\n rm_model.config.pad_token_id = rm_tokenizer.pad_token_id\n\n\n # use a pipeline for batching and device placement\n pipe_device = 0 if (device == \"cuda\" or (device is None and torch.cuda.is_available())) else -1\n rm_pipe = pipeline(\n task=\"text-classification\",\n model=rm_model,\n tokenizer=rm_tokenizer,\n # device=pipe_device,\n truncation=True,\n top_k=None,\n function_to_apply=\"none\", # use raw logits so we can map scores directly\n return_all_scores=True,\n )\n\n def reward_fn(completions: List[str], prompts: List[str], **kwargs) -> List[float]:\n del prompts # unused here\n outputs = rm_pipe(completions, batch_size=kwargs.get(\"batch_size\", 2))\n scores: List[float] = []\n for out in outputs:\n # If binary classifier, use logit of positive class; otherwise sum weighted by label index\n if len(out) == 1:\n scores.append(float(out[0][\"score\"]))\n else:\n # prefer last class as \"more positive\"\n scores.append(float(out[-1][\"score\"]))\n if not normalize:\n return scores\n # z-norm for stability (per-batch)\n t = torch.tensor(scores, dtype=torch.float32)\n std = float(t.std().clamp(min=1e-6))\n mean = float(t.mean())\n normed = ((t - mean) / std).tolist()\n return [float(x) for x in normed]\n\n return reward_fn\n\n\ndef build_keyword_reward_fn(keywords: List[str], case_sensitive: bool = False, bonus: float = 1.0) -> Callable[[List[str], List[str]], List[float]]:\n ks = keywords if case_sensitive else [k.lower() for k in keywords]\n\n def reward_fn(completions: List[str], prompts: List[str], **kwargs) -> List[float]:\n del prompts\n scores: List[float] = []\n for text in completions:\n t = text if case_sensitive else text.lower()\n count = sum(1 for k in ks if k in t)\n scores.append(bonus * float(count))\n return scores\n\n return reward_fn\n\n\ndef build_length_reward_fn(target_min: int, target_max: int, scale: float = 1.0) -> Callable[[List[str], List[str]], Li", "url": "https://github.com/huggingface/trl/issues/4419", "state": "open", "labels": [ "\ud83c\udfcb Reward", "\ud83c\udfcb GRPO" ], "created_at": "2025-11-01T10:29:28Z", "updated_at": "2025-11-20T12:26:50Z", "user": "guotong1988" }, { "repo": "vllm-project/vllm", "number": 27912, "title": "[Usage]: How should I use the CPU to deploy QWEN3 VL 30B-A3B?", "body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n(APIServer pid=1033476) Traceback (most recent call last):\n(APIServer pid=1033476) File \"/home/maxgameone/anaconda3/bin/vllm\", line 33, in\nThe output of
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Amazon Linux 2023.7.20250428 (x86_64)\nGCC version : (GCC) 11.5.0 20240719 (Red Hat 11.5.0-5)\nClang version : Could not collect\nCMake version : version 3.26.4\nLibc version : glibc-2.34\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.6 (main, May 6 2025, 20:22:13) [GCC 11.5.0 20240719 (Red Hat 11.5.0-5)] (64-bit runtime)\nPython platform : Linux-6.1.134-150.224.amzn2023.x86_64-x86_64-with-glibc2.34\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA H100 80GB HBM3\nGPU 1: NVIDIA H100 80GB HBM3\nGPU 2: NVIDIA H100 80GB HBM3\nGPU 3: NVIDIA H100 80GB HBM3\nGPU 4: NVIDIA H100 80GB HBM3\nGPU 5: NVIDIA H100 80GB HBM3\nGPU 6: NVIDIA H100 80GB HBM3\nGPU 7: NVIDIA H100 80GB HBM3\n\nNvidia driver version : 570.133.20\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 1\nBogoMIPS: 5299.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (12 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.3.1\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pi",
"url": "https://github.com/vllm-project/vllm/issues/27604",
"state": "open",
"labels": [
"bug",
"nvidia"
],
"created_at": "2025-10-27T20:22:37Z",
"updated_at": "2025-11-06T02:37:17Z",
"comments": 10,
"user": "jmkuebler"
},
{
"repo": "huggingface/smolagents",
"number": 1834,
"title": "Discussion: how to edit the messages sent to the underlying LLM",
"body": "Hi! I'm working on a feature to allow a user to add callbacks to modify the content before it is sent to the LLM, inside the agent loop. \n\nI noticed this strange behavior where the first user message must start with \"New Task:\", otherwise I get this cryptic and misleading error message.\n\n\n\"\"Error:\\nError while parsing tool call from model output: The model output does not contain any JSON blob.\\nNow let's retry: take care not to repeat previous errors! If you have retried several times, try a completely different approach.\\n\"\"\n\nSo I think I have two (or maybe one question):\n\n1. Is my approach to control the messages flow by wrapping the `generate` member function of a Smolagent correct? Or do you recommend a better way to modify messages before sending them to the underlying LLM?\n2. Is it expected that the first user message needs to start with New Task:, or have I found a bug or missing assertion somewhere in the code? Thanks!\n\nhttps://github.com/mozilla-ai/any-agent/blob/f2475d7507c5a78e241ff5f0883b546d796d29fc/src/any_agent/callbacks/wrappers/smolagents.py#L75\n\nI'm on smolagents==1.22.0, python 3.13.\n\nUPDATE: I'm no longer sure that adding \"New Task:\" is the fix, I am still seeing intermittent errors even when I have that text added. It seems like there some sort of race condition, I'm confused about where the \"messages\" content should be edited, since it seems like maybe it's being stored or referenced in multiple conditions? Any help appreciated!\n",
"url": "https://github.com/huggingface/smolagents/issues/1834",
"state": "closed",
"labels": [],
"created_at": "2025-10-27T17:28:38Z",
"updated_at": "2025-10-27T19:02:39Z",
"user": "njbrake"
},
{
"repo": "huggingface/peft",
"number": 2873,
"title": "Can I use Lora fine-tuning twice?",
"body": "I\u2019m planning to work with a two-stage LoRA fine-tuning pipeline (Stage 1: SFT with code completion outputs; Stage 2: SFT with full-code outputs; RL follows). My question is:\nWhen I continue training the same LoRA adapter in Stage 2, will I risk overwriting or degrading the knowledge learned during Stage 1 ? In other words, does continuing on the same adapter effectively preserve the Stage 1 capabilities, or should I be using a separate adapter (or merging strategy) to ensure both sets of skills remain intact?\nThank you for any guidance or best\u2010practice pointers!",
"url": "https://github.com/huggingface/peft/issues/2873",
"state": "closed",
"labels": [],
"created_at": "2025-10-27T12:51:45Z",
"updated_at": "2025-12-05T15:05:00Z",
"comments": 8,
"user": "tohokulgq"
},
{
"repo": "vllm-project/vllm",
"number": 27572,
"title": "[Bug]: chat/completions stream intermittently returns null as finish_reason",
"body": "### Your current environment\n\n```\nMy env:\nvllm 0.10.0\n```\n\n\n### \ud83d\udc1b Describe the bug\n\n\n```\n\n+ curl -kLsS https://127.0.0.1:7888/v1/chat/completions -H 'Content-Type: application/json' --data '{\n \"model\": \"ibm/granite-3-8b-instruct\",\n \"stream\": true,\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a helpful assistant.\"\n },\n {\n \"role\": \"user\",\n \"content\": \"What is the weather like in Warsaw?\"\n }\n ],\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_current_weather\",\n \"description\": \"Get the current weather in a given location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"The city and state, e.g. San Francisco, CA\"\n },\n \"unit\": {\n \"type\": \"string\",\n \"enum\": [\"celsius\", \"fahrenheit\"]\n }\n }\n },\n \"required\": [\"location\"]\n }\n }\n ],\n \"tool_choice\": \"auto\"\n }'\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"role\":\"assistant\",\"content\":\"\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"<\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"tool\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"_\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"call\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\">\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: [DONE]\n```\nThis happens after running several requests sequentially.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27572",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-27T12:14:03Z",
"updated_at": "2025-11-24T20:27:24Z",
"comments": 13,
"user": "shuynh2017"
},
{
"repo": "huggingface/chat-ui",
"number": 1957,
"title": "Fail to use proxy",
"body": "How to make this web app go through local proxy? \n\nI tried a few methods, all of which don't work. \n",
"url": "https://github.com/huggingface/chat-ui/issues/1957",
"state": "open",
"labels": [
"support"
],
"created_at": "2025-10-27T06:31:51Z",
"updated_at": "2025-10-30T03:31:24Z",
"comments": 2,
"user": "geek0011"
},
{
"repo": "huggingface/diffusers",
"number": 12547,
"title": "Fine tuning Dreambooth Flux Kontext I2I Error: the following arguments are required: --instance_prompt",
"body": "### Describe the bug\n\nHello HF team, @sayakpaul @bghira\n\nI'm encountering a persistent issue when trying to fine-tune the black-forest-labs/FLUX.1-Kontext-dev model using the train_dreambooth_lora_flux_kontext.py script.\n\nI am following the [official README instructions](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md#training-kontext) for Image-to-Image (I2I) finetuning. My goal is to train a transformation on my own dataset, which is structured for I2I (condition image, target image, and text instruction).\n\n### The Problem\nEvery time I run the script with the correct arguments for I2I finetuning, I get a : `the following arguments are required: --instance_prompt`\n\nWhen I run this [Reproduction], I receive the error: `the following arguments are required: --instance_prompt.`\n\nTo isolate the issue from my personal dataset, I also tested the exact example command provided in the documentation (the one using `kontext-community/relighting`). I found that this command also fails with `the identical the following arguments are required: --instance_prompt` error.\n\nGiven that both my custom command and the official example command are failing in the same way, I am trying to understand the origin of this error. It seems the `--instance_prompt` argument is being required even when all I2I-specific arguments are provided.\n\n### Environment\n**Script**: `examples/dreambooth/train_dreambooth_lora_flux_kontext.py`\n\n**Diffusers Version**: I am using the specific commit `05e7a854d0a5661f5b433f6dd5954c224b104f0b` (installed via `pip install -e .` from a clone), as recommended in the README.\n\nCould you please help me understand why this might be happening? Is this expected behavior, or am I perhaps missing a configuration step?\n\nThank you for your time!\n\n### Reproduction\n\n### How to Reproduce\nI am running the following command, which provides all the necessary arguments for I2I finetuning using my (`dataset_name`, `image_column`, `cond_image_column`, and `caption_column`) using my public dataset:\n\n```\naccelerate launch /local-git-path/train_dreambooth_lora_flux_kontext.py \\\n --pretrained_model_name_or_path=\"black-forest-labs/FLUX.1-Kontext-dev\" \\\n --output_dir=\"/local-path/kontext-finetuning-v1\" \\\n --dataset_name=\"MichaelMelgarejoTotto/mi-dataset-kontext\" \\\n --image_column=\"output\" \\\n --cond_image_column=\"file_name\" \\\n --caption_column=\"instruccion\" \\\n --mixed_precision=\"bf16\" \\\n --resolution=1024 \\\n --train_batch_size=1 \\\n --guidance_scale=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --optimizer=\"adamw\" \\\n --use_8bit_adam \\\n --cache_latents \\\n --learning_rate=1e-4 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=200 \\\n --max_train_steps=1000 \\\n --rank=16 \\\n --seed=\"0\" \n```\n\n\n### Logs\n\n```shell\ntrain_dreambooth_lora_flux_kontext.py: error: the following arguments are required: --instance_prompt\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.35.0.dev0\n- Platform: Linux-4.18.0-305.3.1.el8.x86_64-x86_64-with-glibc2.28\n- Running on Google Colab?: No\n- Python version: 3.10.19\n- PyTorch version (GPU?): 2.7.1+cu118 (False)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.36.0\n- Transformers version: 4.57.1\n- Accelerate version: 1.11.0\n- PEFT version: 0.17.1\n- Bitsandbytes version: 0.48.1\n- Safetensors version: 0.6.2\n- xFormers version: not installed\n- Accelerator: NA\n- Using GPU in script?: \n- Using distributed or parallel set-up in script?: \n\n![\"Image\"](\"https://github.com/user-attachments/assets/52a5168d-0089-4aab-834e-fa39cab0034d\")
\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/12547",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-27T00:21:34Z",
"updated_at": "2025-10-28T02:31:42Z",
"comments": 7,
"user": "MichaelMelgarejoFlorez"
},
{
"repo": "huggingface/transformers",
"number": 41876,
"title": "LlamaAttention num_heads",
"body": "### System Info\n\nIn older version of transformers, LlamaAttention init attribute num_heads. \n\nclass LlamaAttention(nn.Module):\n def __init__(self, config):\n self.num_heads = config.num_attention_heads\n self.head_dim = config.hidden_size // config.num_attention_heads\n\nHowever, in the recent versions, this attribute has been removed and thus causing mismatched when using previous codes. It ssems num_key_value_heads is also deprecated. This issue could be addressed by adding:\n self.num_heads = config.num_attention_heads # shanhx\n self.num_key_value_heads = config.num_key_value_heads\n\nIs there any reasons why these attributes are removed? Is it intended or a bug?\n\n\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nAt least the num_heads stil remained at 4.44. But missed in 4.54. \n\n### Expected behavior\n\nMissing many attributes in LlamaAttention. ",
"url": "https://github.com/huggingface/transformers/issues/41876",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-27T00:07:31Z",
"updated_at": "2025-10-31T00:13:31Z",
"comments": 2,
"user": "shanhx2000"
},
{
"repo": "huggingface/transformers",
"number": 41874,
"title": "Distributed training of SigCLIP",
"body": "https://github.com/huggingface/transformers/blob/v4.57.1/src/transformers/models/siglip/modeling_siglip.py#L983, here define how to compute sigclip loss. In sigclip, different tpu will exchange data with each other. I want to know how to train a model in this way.",
"url": "https://github.com/huggingface/transformers/issues/41874",
"state": "closed",
"labels": [],
"created_at": "2025-10-26T14:43:51Z",
"updated_at": "2025-12-04T08:02:55Z",
"comments": 1,
"user": "zyk1559676097-dot"
},
{
"repo": "huggingface/transformers",
"number": 41861,
"title": "transformers.Adafactor is almost 2x slower on Windows than Linux - even WSL is slow what can be reason?",
"body": "I am training Qwen Image model with Kohya Musubi tuner : https://github.com/kohya-ss/musubi-tuner\n \nExactly same setup and same machine on Linux is almost 2x faster\n\n9.5 second / it vs 5.8 second / it\n\nOn Windows it can't utilize GPU power it utilizes like 250 watt out of 575 watt\n\nWhat can be culprit?\n\ntransformers==4.54.1\ntorch 2.8\nCUDA 12.9\n\ntested on RTX 5090\n\nthis is what codex tells but i don't know if it is true doesnt make sense to me\n\n![\"Image\"](\"https://github.com/user-attachments/assets/81b687c7-801e-4265-a2fd-6d1eae065637\")
\n\n### Who can help?\n\ntrainer: @SunMarc \nkernels: @MekkCyber @drbh \n\n\n",
"url": "https://github.com/huggingface/transformers/issues/41861",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-25T15:49:47Z",
"updated_at": "2025-12-03T08:02:55Z",
"user": "FurkanGozukara"
},
{
"repo": "huggingface/transformers",
"number": 41859,
"title": "Human Verification not working?",
"body": "### System Info\n\nHello! I need your help because I can't verify my identity via email: I receive a link, open it, but get a blank page and nothing else(((\nI've tried several times.\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n1. Navigate to the Hugging Face website.\n2. Register or log in to your account.\n3. Go to the identity verification section.\n4. Submit a request for the identity verification link.\n5. Get the confirmation email to arrive.\n6. Follow confirmation link in email\n7. Get blank page in site example https://huggingface.co/email_confirmation/zKFZszGtcabRsYOURYmCQkXdfzIY\n\n### Expected behavior\n\nThe identity verification link should work",
"url": "https://github.com/huggingface/transformers/issues/41859",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-25T10:48:52Z",
"updated_at": "2025-10-26T12:29:10Z",
"comments": 4,
"user": "thefued"
},
{
"repo": "huggingface/lerobot",
"number": 2311,
"title": "Question: How I can train only online without dataset?",
"body": "How I can train only online? without need of dataset. Can I do it without hugging face repo id? only local?\nI try like that without success:\n\n```\n cat > \"train_cfg.json\" <<'JSON'\n {\n \"job_name\": \"hilserl_fetch_pick_v4_cpu\",\n \"seed\": 0,\n \"env\": {\n \"type\": \"gymnasium-robotics\",\n \"task\": \"FetchPickAndPlace-v4\",\n \"episode_length\": 200,\n \"features_map\": {\n \"action\": \"action\",\n \"agent_pos\": \"observation.state\",\n \"top\": \"observation.image\",\n \"pixels/top\": \"observation.image\"\n },\n \"features\": {\n \"action\": {\n \"type\": \"ACTION\",\n \"shape\": [\n 4\n ]\n },\n \"agent_pos\": {\n \"type\": \"STATE\",\n \"shape\": [\n 4\n ]\n },\n \"pixels/top\": {\n \"type\": \"VISUAL\",\n \"shape\": [\n 480,\n 480,\n 3\n ]\n }\n }\n },\n \"policy\": {\n \"type\": \"sac\",\n \"device\": \"cpu\",\n \"concurrency\": {\n \"actor\": \"threads\",\n \"learner\": \"threads\"\n },\n \"repo_id\": \"None\",\n \"push_to_hub\": false\n },\n \"dataset\": { \n \"repo_id\": \"online-buffer\",\n \"root\": \"${{ github.workspace }}/dataset\",\n \"use_imagenet_stats\": true\n }\n }\n JSON\n\n mkdir -p dataset/online-buffer\n\n export HF_HUB_OFFLINE=1\n export HF_HUB_DISABLE_TELEMETRY=1\n export HF_DATASETS_OFFLINE=1\n export WANDB_MODE=disabled\n\n # Launch learner and actor (one shell)\n python -m lerobot.rl.learner --config_path \"train_cfg.json\"\n python -m lerobot.rl.actor --config_path \"train_cfg.json\"\n```",
"url": "https://github.com/huggingface/lerobot/issues/2311",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-10-25T05:07:48Z",
"updated_at": "2025-10-27T08:50:11Z",
"user": "talregev"
},
{
"repo": "vllm-project/vllm",
"number": 27505,
"title": "[Bug]: Value error, Found conflicts between 'rope_type=default' (modern field) and 'type=mrope'",
"body": "### Your current environment\n\n![\"Image\"](\"https://github.com/user-attachments/assets/c6b13dff-1d6a-4872-a959-f8076fff43e6\")
\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27505",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-25T04:39:53Z",
"updated_at": "2025-10-26T07:33:27Z",
"comments": 1,
"user": "asirgogogo"
},
{
"repo": "vllm-project/vllm",
"number": 27504,
"title": "[Usage]: `add_vision_id` ignored for Qwen 2.5-VL-32B-Instruct",
"body": "### Your current environment\n\n```text\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.3 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.8.0-85-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA RTX A6000\nGPU 1: NVIDIA RTX A6000\nGPU 2: NVIDIA RTX A6000\nGPU 3: NVIDIA RTX A6000\n\nNvidia driver version : 570.124.06\ncuDNN version : Probably one of the following:\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn.so.9.8.0\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_adv.so.9.8.0\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_cnn.so.9.8.0\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_engines_precompiled.so.9.8.0\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_engines_runtime_compiled.so.9.8.0\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_graph.so.9.8.0\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_heuristic.so.9.8.0\n/usr/local/cuda-12.0/targets/x86_64-linux/lib/libcudnn_ops.so.9.8.0\n/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn.so.9.8.0\n/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_adv.so.9.8.0\n/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_cnn.so.9.8.0\n/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_engines_precompiled.so.9.8.0\n/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_engines_runtime_compiled.so.9.8.0\n/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_graph.so.9.8.0\n/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_heuristic.so.9.8.0\n/usr/local/cuda-12.8/targets/x86_64-linux/lib/libcudnn_ops.so.9.8.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 32\nOn-line CPU(s) list: 0-31\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Gold 6346 CPU @ 3.10GHz\nCPU family: 6\nModel: 106\nThread(s) per core: 1\nCore(s) per socket: 16\nSocket(s): 2\nStepping: 6\nCPU(s) scaling MHz: 23%\nCPU max MHz: 3600.0000\nCPU min MHz: 800.0000\nBogoMIPS: 6200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 intel_ppin ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect wbnoinvd dtherm ida arat pln pts vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid fsrm md_clear pconfig flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 1.5 MiB (32 instances)\nL1i cache: 1 MiB (32 instances)\nL2 cache: 40 MiB (32 instances)\nL3 cache: 72 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-15\nNUMA node1 CPU(s): 16-3",
"url": "https://github.com/vllm-project/vllm/issues/27504",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-25T03:42:44Z",
"updated_at": "2025-10-26T07:32:49Z",
"comments": 1,
"user": "justachetan"
},
{
"repo": "huggingface/lighteval",
"number": 1028,
"title": "How to evaluate MMLU-Pro",
"body": "Hi,\n\nThank you for the wonderful work!\n\nI just want to ask how to perform the evaluation on MMLU-Pro, as I don't see any related code besides the README.",
"url": "https://github.com/huggingface/lighteval/issues/1028",
"state": "open",
"labels": [],
"created_at": "2025-10-24T20:03:10Z",
"updated_at": "2025-11-04T10:40:46Z",
"user": "qhz991029"
},
{
"repo": "huggingface/tokenizers",
"number": 1879,
"title": "rust tokenizer",
"body": "Hello.\n\nIs there a rust tokenizer please? Chat gpt told me there used to be.\n\nBest regards!",
"url": "https://github.com/huggingface/tokenizers/issues/1879",
"state": "open",
"labels": [],
"created_at": "2025-10-24T17:03:04Z",
"updated_at": "2025-10-24T22:03:31Z",
"comments": 2,
"user": "gogo2464"
},
{
"repo": "vllm-project/vllm",
"number": 27482,
"title": "[Bug]: `return_token_ids` missing tokens when using tool calls",
"body": "### Your current environment\n\nTesting with latest vLLM builds from main, as of Fri Oct 24th 2025 (when this bug was opened).\n\n\n### \ud83d\udc1b Describe the bug\n\nThe `return_token_ids` parameter that is supposed to return all generated token ids back to the client is missing quite a few tokens for Chat Completion streaming requests that result in tool calls being generated. Exactly how many and where they are missing in the request will depend on the tool call parser in use as well as the exact request format.\n\nHere's a minimal reproducer.\n\nFirst, run vLLM with a tool call parser and model. I use a Granite model for testing here, but it should be roughly the same for any model with a tool call parser.\n\n```\nvllm serve ibm-granite/granite-3.3-8b-instruct \\\n --enable-auto-tool-choice \\\n --tool-call-parser granite\n```\n\nThen, send a streaming tool call request to the server and check the response for missing tokens:\n\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(base_url=\"http://localhost:8000/v1\", api_key=\"fake\")\n\ntools = [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_current_weather\",\n \"description\": \"Get the current weather in a given location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": { \"type\": \"string\", \"description\": \"The city, e.g. San Francisco, CA\" },\n \"unit\": { \"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"] }\n },\n \"required\": [\"location\"]\n }\n }\n },\n]\nresponse = client.chat.completions.create(\n model=\"ibm-granite/granite-3.3-8b-instruct\",\n messages=[{\"role\": \"user\", \"content\": \"What is the weather in Sydney in celsius?\"}],\n tools=tools,\n tool_choice=\"auto\",\n stream=True,\n stream_options={\n \"include_usage\": True,\n \"continuous_usage_stats\": True,\n },\n extra_body={\"return_token_ids\": True},\n)\n\nreturned_token_ids = []\nlast_completion_tokens = 0\nfor event in response:\n if not getattr(event, \"choices\", None):\n continue\n choice = event.choices[0]\n usage = event.usage\n if hasattr(choice, \"token_ids\"):\n returned_token_ids.extend(choice.token_ids)\n num_token_ids = len(choice.token_ids)\n else:\n num_token_ids = 0\n elapsed_completion_tokens = usage.completion_tokens - last_completion_tokens\n if elapsed_completion_tokens != num_token_ids:\n raise ValueError(\n \"Model generated more tokens than returned by return_token_ids!\\n\"\n f\"All tokens returned so far: {returned_token_ids}\"\n )\n last_completion_tokens = usage.completion_tokens\n```\n\nRunning that, I get the following output:\n\n```\npython return_token_ids_test.py\nTraceback (most recent call last):\n File \"/Volumes/SourceCode/vllm/return_token_ids_test.py\", line 49, in \n raise ValueError(\nValueError: Model generated more tokens than returned by return_token_ids!\nAll tokens returned so far: [49154, 48685]\n```\n\nIf I add a bit of debug logging into vLLM server side and run it again, I can see the list of tokens that should have been returned:\n\n`current_token_ids: [49154, 7739, 8299, 563, 3447, 2645, 563, 313, 16716, 6161, 910, 392, 313, 2243, 563, 313, 3308, 101, 3263, 3918, 313, 426, 563, 313, 371, 81, 1700, 81, 15859, 48685]`\n\nAll of the tokens between the first and last in that list were missed by `return_token_ids`.\n\nThis code is not executed for every generated token when tool call parser (or reasoning parsers, most likely) are in use: https://github.com/vllm-project/vllm/blob/61089465a6101790635ed96c26df3e9a57d8d2c9/vllm/entrypoints/openai/serving_chat.py#L1090\n\nThe reason is because we return early at: https://github.com/vllm-project/vllm/blob/61089465a6101790635ed96c26df3e9a57d8d2c9/vllm/entrypoints/openai/serving_chat.py#L1063\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27482",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-24T16:10:31Z",
"updated_at": "2025-12-04T19:09:41Z",
"comments": 2,
"user": "bbrowning"
},
{
"repo": "vllm-project/vllm",
"number": 27479,
"title": "[Bug]: Low GPU utilization with Embedding Model",
"body": "### Your current environment\n\n![\"Image\"](\"https://github.com/user-attachments/assets/6606bef5-1153-4f2d-bf08-82da249d6e8d\")
\n\nChart 2: IterableDatasetDict I also tried to use IterableDatasetDict and IterableDataset. The memory curve is \"smoother,\" but the result is the same: it grows indefinitely and the training become stale. ![\"Image\"](\"https://github.com/user-attachments/assets/ee90c1a1-6c3b-4135-9edc-90955cb1695a\")
\n\nAny feedback or guidance on how to manage this memory would be greatly appreciated!\n\n### Steps to reproduce the bug\n\nWIP, I'll add some code that manage to reproduce this error, but not straightforward. \n\n### Expected behavior\n\nThe memory usage should remain relatively constant or plateau after a few steps. Memory used for sampling one dataset should be released before or during the sampling of the next dataset.\n\n### Environment info\n\nPython: 3.12\nDatasets: 4.3.0\nSentenceTransformers: 5.1.1",
"url": "https://github.com/huggingface/datasets/issues/7829",
"state": "open",
"labels": [],
"created_at": "2025-10-24T09:51:38Z",
"updated_at": "2025-11-06T13:31:26Z",
"comments": 4,
"user": "raphaelsty"
},
{
"repo": "huggingface/transformers",
"number": 41842,
"title": "Incorrect usage of `num_items_in_batch`?",
"body": "It seems that `num_items_in_batch` is computed for all items in the batch [here](https://github.com/huggingface/transformers/blob/9c20660138830ca362533551ca978c27b48283a1/src/transformers/trainer.py#L2430).\n\nHowever, when loss is computed in the `training_step`, it is computed for each input in the batch one by one. Does it make sense to pass `num_items_in_batch` (for the whole batch) or should that number be for that particular input only?\n\nRight now, the entire batch's `num_items_in_batch` is used [here](https://github.com/huggingface/transformers/blob/9c20660138830ca362533551ca978c27b48283a1/src/transformers/trainer.py#L2486).",
"url": "https://github.com/huggingface/transformers/issues/41842",
"state": "closed",
"labels": [],
"created_at": "2025-10-24T07:36:00Z",
"updated_at": "2025-12-01T08:02:48Z",
"comments": 2,
"user": "gohar94"
},
{
"repo": "vllm-project/vllm",
"number": 27463,
"title": "[Usage]: How to request DeepSeek-OCR with http request",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\ni want to request DeepSeek-OCR with http, is any example for it?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27463",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-24T07:07:29Z",
"updated_at": "2025-10-29T17:26:49Z",
"comments": 8,
"user": "YosanHo"
},
{
"repo": "huggingface/lerobot",
"number": 2306,
"title": "how to use groot without flash attention",
"body": "my system is ubuntu 20.04 with glibc 2.3.1 which is not supported flash attention, If I can modify the config of groot to use it with normal attention?",
"url": "https://github.com/huggingface/lerobot/issues/2306",
"state": "open",
"labels": [
"question",
"policies",
"dependencies"
],
"created_at": "2025-10-24T06:35:18Z",
"updated_at": "2025-11-04T01:28:38Z",
"user": "shs822"
},
{
"repo": "huggingface/lerobot",
"number": 2305,
"title": "Error dependence about the `Transformer` library",
"body": "### System Info\n\n```Shell\n- lerobot version: 0.4.0\n- Platform: Linux-6.14.0-29-generic-x86_64-with-glibc2.39\n- Python version: 3.12.12\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 2.2.6\n- PyTorch version: 2.7.0+cu128\n- Is PyTorch built with CUDA support?: True\n- Cuda version: 12.8\n- GPU model: NVIDIA RTX PRO 6000 Blackwell Workstation Edition\n- Using GPU in script?: \n```\n\n### Information\n\n- [ ] One of the scripts in the examples/ folder of LeRobot\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n# Environment\n\nI used the `uv` tools to auto-solve the environment. The `pyproject.toml` is shown as following.\n```\n[project]\nname = \"openpi-pytorch-env2\"\nversion = \"0.1.0\"\ndescription = \"Add your description here\"\nrequires-python = \"==3.12.12\"\ndependencies = [\n # Pytorch \u4f9d\u8d56\u9879\n \"torch==2.7.0\",\n \"torchvision==0.22.0\",\n \"torchaudio==2.7.0\",\n \"pytorch_lightning\",\n\n # lerobot-libero\n \"libero @ git+https://github.com/huggingface/lerobot-libero.git#egg=libero\",\n\n # lerobot\n \"lerobot[all] @ git+https://github.com/huggingface/lerobot.git@v0.4.0\",\n\n]\n\n[tool.uv.sources]\ntorch = { index = \"pytorch-cu128\" }\ntorchvision = { index = \"pytorch-cu128\" }\ntorchaudio = { index = \"pytorch-cu128\" }\n\n[[tool.uv.index]]\nname = \"pytorch-cu128\"\nurl = \"https://download.pytorch.org/whl/cu128\"\nexplicit = true\n```\n\n# BUG Report\n\nWhen I was running the `pi0` code\n\n```\nimport os\nimport torch\nfrom lerobot.policies.pi0.modeling_pi0 import PI0Policy\nfrom transformers import AutoTokenizer\nMODEL_PATH = os.path.expanduser(\"~/Models/pi0_base\")\npolicy = PI0Policy.from_pretrained(MODEL_PATH)\n```\n\nThere are errors like:\n\n\n```\nAn incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues\nImportError: cannot import name 'check' from 'transformers.models.siglip' (/opt/miniforge3/envs/pi0_torch2/lib/python3.12/site-packages/transformers/models/siglip/__init__.py)\n\nDuring handling of the above exception, another exception occurred:\n\n File \"/home/robot/pi0/openpi_pytorch2/test_simple.py\", line 22, in \n policy = PI0Policy.from_pretrained(MODEL_PATH)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nValueError: An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues\n```\n\nThe transformer lib is auto-solved by the `pyproject.toml` in `lerobot` lib. Can you solve the error? Thanks\n\n### Expected behavior\n\nLoading the weights successfully.",
"url": "https://github.com/huggingface/lerobot/issues/2305",
"state": "open",
"labels": [
"question",
"policies",
"dependencies"
],
"created_at": "2025-10-24T05:59:32Z",
"updated_at": "2025-11-14T16:01:49Z",
"user": "sunshineharry"
},
{
"repo": "vllm-project/vllm",
"number": 27454,
"title": "[Usage]: How to set the expert id on each EP by myself after setting EP in Deepseek (how to reorder experts?)",
"body": "### Your current environment\n\n```text\nvllm 0.8.5\n```\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27454",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-24T03:15:16Z",
"updated_at": "2025-10-24T07:27:50Z",
"comments": 2,
"user": "HameWu"
},
{
"repo": "vllm-project/vllm",
"number": 27448,
"title": "[Usage]: how to pass multi turn multimode messages to Vllm?",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27448",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-24T02:41:45Z",
"updated_at": "2025-10-24T03:33:13Z",
"comments": 1,
"user": "cqray1990"
},
{
"repo": "huggingface/lerobot",
"number": 2304,
"title": "How to load local model?",
"body": "For example, i'm trying to fine-tune pi0, so I downloaded pi0_base locallly and save it in [position A,like lerobot/models/pi0_base] ,which has 5 files in total,including model.safetensors.\n\nThen how to load it in code? I used to just set model.path=[position A] But followed tuorial, it uses pretrained_path_or_name as key words.\n\nHowover, my code raised error here:\n```python\n print(f\"Loading model from: {pretrained_name_or_path}\")\n try:\n from transformers.utils import cached_file\n\n # Try safetensors first\n resolved_file = cached_file(\n pretrained_name_or_path,\n \"model.safetensors\",\n cache_dir=kwargs.get(\"cache_dir\"),\n force_download=kwargs.get(\"force_download\", False),\n resume_download=kwargs.get(\"resume_download\"),\n proxies=kwargs.get(\"proxies\"),\n use_auth_token=kwargs.get(\"use_auth_token\"),\n revision=kwargs.get(\"revision\"),\n # local_files_only=kwargs.get(\"local_files_only\", False),\n local_files_only=True # I set this for experiment but failed too\n )\n from safetensors.torch import load_file\n\n original_state_dict = load_file(resolved_file)\n print(\"\u2713 Loaded state dict from model.safetensors\")\n except Exception as e:\n print(f\"Could not load state dict from remote files: {e}\")\n print(\"Returning model without loading pretrained weights\")\n return model\n```\nIts outputs:\nLoading model from: /home/user/working_folder/lerobot/local/model/pi0_base (I use this absolute path) \nCould not load state dict from remote files: /home/user/working_folder/lerobot/local/model/pi0_base does not appear to have a file named model.safetensors. Checkout 'https://huggingface.co//home/user/working_folder/lerobot/local/model/pi0_base/tree/main' for available files.\n\nIt seems that the program see my pretrain_path_or_name as a repo_id :/\n\nHow can I introduce local pretrained path?\n\n* Ok I know that my file is incorrect. It's my bad not code's\n",
"url": "https://github.com/huggingface/lerobot/issues/2304",
"state": "closed",
"labels": [],
"created_at": "2025-10-24T01:59:26Z",
"updated_at": "2025-10-24T02:33:25Z",
"user": "milong26"
},
{
"repo": "vllm-project/vllm",
"number": 27441,
"title": "[Bug]: vllm/v1/core/sched/scheduler.py: Unintended reordering of requests during scheduling",
"body": "### Your current environment\n\n![\"Image\"](\"https://github.com/user-attachments/assets/4e81a662-c527-4b15-a5d1-8e78150961e8\")
\n\nThe figure compares the request-serving timelines of the original (left) and fixed (right) versions.\n* X-axis: Time\n* Y-axis: Request ID (submission order)\n* Green: Duration while the request is in `self.waiting`\n* Black: Time between GPU memory allocation and completion of the request\u2019s prefill computation\n* Red: Time between the end of prefill computation and GPU memory release (while waiting for the remote decoder to read KV blocks)\n\nThe scheduling policy used is FCFS.\nIn the original version, requests are shuffled under resource pressure. After applying the fix, the request serving order remains consistent, as expected.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27441",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-23T22:35:50Z",
"updated_at": "2025-11-22T04:20:35Z",
"comments": 1,
"user": "dongha-yoon"
},
{
"repo": "huggingface/lerobot",
"number": 2303,
"title": "Question: Does the follower arm have an api for scripting movement?",
"body": "Hi, apologies if this has been answered before or if it's not the right place to ask. I've been using the SO-101 arms for imitation learning, but recently I've wanted to try and test out the follower arm for embodied reasoning models such as Gemini ER 1.5. To do this, I figure I would need to have some way to map outputs from the ER model (coordinates or general, high-level movements) to movements for the SO-101. Does the SO-101 has an API for this type of low-level movement control, e.g. if I just wanted to move it pre-scripted in space using coordinates or motor motion? What would the code for this type of low-level movement look? \n\nThank you so much for any and all help!",
"url": "https://github.com/huggingface/lerobot/issues/2303",
"state": "open",
"labels": [
"question",
"robots",
"python"
],
"created_at": "2025-10-23T20:40:56Z",
"updated_at": "2025-10-23T22:29:28Z",
"user": "Buttmunky1"
},
{
"repo": "huggingface/lerobot",
"number": 2294,
"title": "Question about the HuggingFaceVLA/smolvla_libero Model Configuration",
"body": "Hello,\n\nLerobot has officially ported [LIBERO](https://github.com/huggingface/lerobot/issues/1369#issuecomment-3323183721), and we can use the checkpoint at [HuggingFaceVLA/smolvla_libero](https://huggingface.co/HuggingFaceVLA/smolvla_libero) to evaluate the LIBERO benchmark.\n\nHowever, the model configuration of [HuggingFaceVLA/smolvla_libero](https://huggingface.co/HuggingFaceVLA/smolvla_libero) appears to differ from the [original model](https://huggingface.co/lerobot/smolvla_base). For example:\n\n[lerobot/smolvla_base](https://huggingface.co/lerobot/smolvla_base/blob/main/config.json)\n```json\n{\n \"vlm_model_name\": \"HuggingFaceTB/SmolVLM2-500M-Video-Instruct\",\n \"load_vlm_weights\": true,\n \"add_image_special_tokens\": false,\n \"attention_mode\": \"cross_attn\",\n \"prefix_length\": 0,\n \"pad_language_to\": \"max_length\",\n \"num_expert_layers\": 0,\n \"num_vlm_layers\": 16,\n \"self_attn_every_n_layers\": 2,\n \"expert_width_multiplier\": 0.75\n}\n```\n\n[HuggingFaceVLA/smolvla_libero](https://huggingface.co/HuggingFaceVLA/smolvla_libero/blob/main/config.json)\n```json\n{\n \"vlm_model_name\": \"HuggingFaceTB/SmolVLM2-500M-Instruct\",\n \"load_vlm_weights\": true,\n \"add_image_special_tokens\": false,\n \"attention_mode\": \"cross_attn\",\n \"prefix_length\": 0,\n \"pad_language_to\": \"longest\",\n \"num_expert_layers\": -1,\n \"num_vlm_layers\": 0, <- it becomes 32 when model is initialized\n \"self_attn_every_n_layers\": 2,\n \"expert_width_multiplier\": 0.5,\n}\n```\n\nIn particular, `num_vlm_layers` is set to 32 across all layers, which is not consistent with the [paper](https://arxiv.org/pdf/2506.01844) where they use half of them (16 layers).\nCould you provide the original model checkpoint and the training recipe so we can reproduce the LIBERO benchmark performance?",
"url": "https://github.com/huggingface/lerobot/issues/2294",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-23T13:37:48Z",
"updated_at": "2025-10-30T07:49:17Z",
"user": "Hesh0629"
},
{
"repo": "vllm-project/vllm",
"number": 27413,
"title": "[Usage]: how to request a qwen2.5-VL-7B classify model served by vllm using openai SDK?",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nI launch a server with the following command to serving a Qwen2.5-VL-7B model finetued for seqence classification. (this model replaced the lm_head with a 2 classes score_head)\n\nThe launch command is :\n```\nvllm serve --model=//video_classification/qwenvl_7b_video_cls/v5-20251011-121851/2340_vllm_format --served_model_name Qwen2.5-7B-shenhe --task=classify --port=8080 --tensor-parallel-size=2\n```\n\nI don't know how to request the server with the openAI sdk.\nI use the code snnipet showed below which works well with pure text, but it got 400 bad request when I put the video url into the prompt\n\nthis works well:\n```\n# SPDX-License-Identifier: Apache-2.0\n# SPDX-FileCopyrightText: Copyright contributors to the vLLM project\n\"\"\"Example Python client for classification API using vLLM API server\nNOTE:\n start a supported classification model server with `vllm serve`, e.g.\n vllm serve jason9693/Qwen2.5-1.5B-apeach\n\"\"\"\n\nimport argparse\nimport pprint\n\nimport requests\n\n\ndef post_http_request(payload: dict, api_url: str) -> requests.Response:\n headers = {\"User-Agent\": \"Test Client\"}\n response = requests.post(api_url, headers=headers, json=payload)\n return response\n\n\ndef parse_args():\n parse = argparse.ArgumentParser()\n parse.add_argument(\"--host\", type=str, default=\"localhost\")\n parse.add_argument(\"--port\", type=int, default=8000)\n parse.add_argument(\"--model\", type=str, default=\"jason9693/Qwen2.5-1.5B-apeach\")\n return parse.parse_args()\n\n\ndef main(args):\n host = args.host\n port = args.port\n model_name = args.model\n\n api_url = f\"http://{host}:{port}/classify\"\n prompts = [\n \"Hello, my name is\",\n \"The president of the United States is\",\n \"The capital of France is\",\n \"The future of AI is\",\n ]\n\n payload = {\n \"model\": model_name,\n \"input\": prompts,\n }\n\n classify_response = post_http_request(payload=payload, api_url=api_url)\n pprint.pprint(classify_response.json())\n\n\nif __name__ == \"__main__\":\n args = parse_args()\n main(args)\n```\n\nbut if I replace the prompts with multimodal data, the server doesn't work.\n```\nvideo_url = \"https://js-ad.a.yximgs.com/bs2/ad_nieuwland-material/t2i2v/videos/3525031242883943515-140276939618048_24597237897733_v0_1759927515165406_3.mp4\"\n\n prompts = [\n {\"role\": \"user\", \"content\": [\n {\"type\": \"text\", \"text\": \"\u4f60\u662f\u4e00\u4e2a\u4e13\u4e1a\u7684\u89c6\u9891\u8d28\u91cf\u5206\u6790\u5e08\uff0c\u8bf7\u4f60\u4ed4\u7ec6\u5224\u65ad\u4e0b\u65b9\u63d0\u4f9b\u7684\u89c6\u9891\u662f\u5426\u5b58\u5728\u8d28\u91cf\u95ee\u9898\\n\u8d28\u91cf\u95ee\u9898\u5305\u62ec\u4f46\u4e0d\u9650\u4e8e\uff1a\\n1.\u753b\u9762\u8d28\u91cf\u5dee,\u753b\u9762\u6a21\u7cca\uff0c\u4eae\u5ea6\u95ea\u70c1\\n2.\u753b\u9762\u4e2d\u6587\u5b57\u5b58\u5728\u6a21\u7cca\u95ee\u9898\\n3.\u89c6\u9891\u753b\u9762\u4e0d\u7b26\u5408\u771f\u5b9e\u7269\u7406\u903b\u8f91\uff0c\u4f8b\u5982\u51ed\u7a7a\u4ea7\u751f\u7684\u4eba\u7269\u80a2\u4f53\u3001\u5934\u50cf\u3001\u624b\u6307\u624b\u81c2\u6570\u91cf\u4e0d\u5bf9\uff0c\u817f\u90e8\u4e0d\u81ea\u7136\u7b49\u95ee\u9898\\n4.\u753b\u9762\u8fd0\u52a8\u4e0d\u7b26\u5408\u7269\u7406\u89c4\u5f8b\uff0c\u4f8b\u5982\u51ed\u7a7a\u4ea7\u751f\u7684\u7269\u4f53\uff0c\u753b\u9762\u5361\u987f\u3001\u6643\u52a8\u3001\u6296\u52a8\u3001\u8df3\u52a8\u7b49\\n\\n\u5982\u679c\u89c6\u9891\u5b58\u5728\u95ee\u9898\u8bf7\u8fd4\u56de0\uff0c\u5982\u679c\u89c6\u9891\u4e0d\u5b58\u5728\u95ee\u9898\u8bf7\u8fd4\u56de1\u3002\\n## \u89c6\u9891\u5185\u5bb9\u5982\u4e0b\\n\"},\n {\"type\": \"video\", \"video\": f\"{video_url}\"},\n ]\n }\n ]\n\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27413",
"state": "open",
"labels": [
"good first issue",
"usage"
],
"created_at": "2025-10-23T12:32:25Z",
"updated_at": "2025-10-25T00:18:54Z",
"comments": 12,
"user": "muziyongshixin"
},
{
"repo": "huggingface/transformers.js",
"number": 1447,
"title": "How to use half precision ONNX models?",
"body": "### Question\n\nHi,\n\nI just exported a detection model with fp16 using optimum.\n`--dtype fp16 `\n\nThis is my pipeline:\n\n```javascript\nconst model = await AutoModel.from_pretrained(\n \"./onnx_llama\",\n { dtype: \"fp16\", device: \"cpu\" } \nconst processor = await AutoProcessor.from_pretrained(\"./onnx_llama\");\nconst { pixel_values, reshaped_input_sizes } = await processor(image);\nconst buffer = await fs.readFile(\"image3.jpg\");\nconst blob = new Blob([buffer]);\n\nconst image = await RawImage.fromBlob(blob);\nconst { pixel_values, reshaped_input_sizes } = await processor(image);\nconst { output0 } = await model({ pixel_values: tensor });\n```\nUsing this results in:\nAn error occurred during model execution: \"Error: Unexpected input data type. Actual: (tensor(float)) , expected: (tensor(float16))\".\n\nWhich makes sense, however when i try to convert to fp16 \"manually\"\n\n```javascript\nconst fp16data = Float16Array.from(pixel_values.data); //float32ArrayToUint16Array(pixel_values.data);\nconst tensor = new Tensor(\"float16\", fp16data, pixel_values.dims);\nconst { output0 } = await model({ pixel_values:tensor });\n```\n\nI get:\n`Tensor.data must be a typed array (4) for float16 tensors, but got typed array (0).`\n\nWhat's going on here? I tried to converting the `pixel_data.data` to a UInt16Array manually but that has no effect as it gets converted to a Float16Array in the tensor constructor anyway.\n\nHelp is much appreciated!\n\nThanks",
"url": "https://github.com/huggingface/transformers.js/issues/1447",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-10-23T09:18:26Z",
"updated_at": "2025-10-23T09:18:26Z",
"user": "richarddd"
},
{
"repo": "huggingface/transformers",
"number": 41810,
"title": "How do you use t5gemma decoder with a different encoder?",
"body": "I am trying to combine the t5gemma decoder with a pretrained deberta encoder that I have trained from scratch using `EncoderDecoderModel`. \n\nHere is the code:\n\n```\nmodel_1 = \"WikiQuality/pre_filtered.am\"\nmodel_2 = \"google/t5gemma-2b-2b-ul2\"\n\nencoder = AutoModel.from_pretrained(model_1)\ndecoder = AutoModel.from_pretrained(model_2, dtype=torch.bfloat16)\n\nmodel = EncoderDecoderModel(encoder=encoder, decoder=decoder)\n```\n\nThe above code raises the error:\n```\nAttributeError: 'T5GemmaConfig' object has no attribute 'hidden_size'\n```\n\nFrom this I understand that `hidden_size` is accesible from `decoder.config.decoder.hidden_size` and not `decoder.config.hidden_size`, which is where EncoderDecoderModel is looking. So I change my code to load the encoder-decoder model to this:\n\n```\nmodel = EncoderDecoderModel(encoder=encoder, decoder=decoder.decoder)\n```\nThis gives me the following error:\n\n```\nValueError: Unrecognized model identifier: t5_gemma_module. Should contain one of aimv2, aimv2_vision_model, albert, align, altclip, apertus, arcee, aria, aria_text, audio-spectrogram-transformer, autoformer, aya_vision, bamba, bark, bart, beit, bert, bert-generation, big_bird, bigbird_pegasus, biogpt, bit, bitnet, blenderbot, blenderbot-small, blip, blip-2, blip_2_qformer, bloom, blt, bridgetower, bros, camembert, canine, chameleon, chinese_clip, chinese_clip_vision_model, clap, clip, clip_text_model, clip_vision_model, clipseg, clvp, code_llama, codegen, cohere, cohere2, cohere2_vision, colpali, colqwen2, conditional_detr, convbert, convnext, convnextv2, cpmant, csm, ctrl, cvt, d_fine, dab-detr, dac, data2vec-audio, data2vec-text, data2vec-vision, dbrx, deberta, deberta-v2, decision_transformer, deepseek_v2, deepseek_v3, deepseek_vl, deepseek_vl_hybrid, deformable_detr, deit, depth_anything, depth_pro, deta, detr, dia, diffllama, dinat, dinov2, dinov2_with_registers, dinov3_convnext, dinov3_vit, distilbert, doge, donut-swin, dots1, dpr, dpt, edgetam, edgetam_video, edgetam_vision_model, efficientformer, efficientloftr, efficientnet, electra, emu3, encodec, encoder-decoder, eomt, ernie, ernie4_5, ernie4_5_moe, ernie_m, esm, evolla, exaone4, falcon, falcon_h1, falcon_mamba, fastspeech2_conformer, fastspeech2_conformer_with_hifigan, flaubert, flava, flex_olmo, florence2, fnet, focalnet, fsmt, funnel, fuyu, gemma, gemma2, gemma3, gemma3_text, gemma3n, gemma3n_audio, gemma3n_text, gemma3n_vision, git, glm, glm4, glm4_moe, glm4v, glm4v_moe, glm4v_moe_text, glm4v_text, glpn, got_ocr2, gpt-sw3, gpt2, gpt_bigcode, gpt_neo, gpt_neox, gpt_neox_japanese, gpt_oss, gptj, gptsan-japanese, granite, granite_speech, granitemoe, granitemoehybrid, granitemoeshared, granitevision, graphormer, grounding-dino, groupvit, helium, hgnet_v2, hiera, hubert, hunyuan_v1_dense, hunyuan_v1_moe, ibert, idefics, idefics2, idefics3, idefics3_vision, ijepa, imagegpt, informer, instructblip, instructblipvideo, internvl, internvl_vision, jamba, janus, jetmoe, jukebox, kosmos-2, kosmos-2.5, kyutai_speech_to_text, layoutlm, layoutlmv2, layoutlmv3, led, levit, lfm2, lfm2_vl, lightglue, lilt, llama, llama4, llama4_text, llava, llava_next, llava_next_video, llava_onevision, longcat_flash, longformer, longt5, luke, lxmert, m2m_100, mamba, mamba2, marian, markuplm, mask2former, maskformer, maskformer-swin, mbart, mctct, mega, megatron-bert, metaclip_2, mgp-str, mimi, minimax, ministral, mistral, mistral3, mixtral, mlcd, mllama, mm-grounding-dino, mobilebert, mobilenet_v1, mobilenet_v2, mobilevit, mobilevitv2, modernbert, modernbert-decoder, moonshine, moshi, mpnet, mpt, mra, mt5, musicgen, musicgen_melody, mvp, nat, nemotron, nezha, nllb-moe, nougat, nystromformer, olmo, olmo2, olmo3, olmoe, omdet-turbo, oneformer, open-llama, openai-gpt, opt, ovis2, owlv2, owlvit, paligemma, parakeet, parakeet_ctc, parakeet_encoder, patchtsmixer, patchtst, pegasus, pegasus_x, perceiver, perception_encoder, perception_lm, persimmon, phi, phi3, phi4_multimodal, phimoe, pix2struct, pixtral, plbart, poolformer, pop2piano, prompt_depth_anything, prophetnet, pvt, pvt_v2, qdqbert, qwen2, qwen2_5_omni, qwen2_5_vl, qwen2_5_vl_text, qwen2_audio, qwen2_audio_encoder, qwen2_moe, qwen2_vl, qwen2_vl_text, qwen3, qwen3_moe, qwen3_next, qwen3_omni_moe, qwen3_vl, qwen3_vl_moe, qwen3_vl_moe_text, qwen3_vl_text, rag, realm, recurrent_gemma, reformer, regnet, rembert, resnet, retribert, roberta, roberta-prelayernorm, roc_bert, roformer, rt_detr, rt_detr_resnet, rt_detr_v2, rwkv, sam, sam2, sam2_hiera_det_model, sam2_video, sam2_vision_model, sam_hq, sam_hq_vision_model, sam_vision_model, seamless_m4t, seamless_m4t_v2, seed_oss, segformer, seggpt, sew, sew-d, shieldgemma2, siglip, siglip2, siglip2_vision_model, siglip_vision_model, smollm3, smolvlm, smolvlm_vision, speech-encoder-decoder, speech_to_text, speech_to_text_2, speecht5, splinter, squeezebert, stablelm, starcoder2, superglue, superpoint, swiftformer, swin, swin2sr, swinv2, switch_transformers, t5, t5gemma, table-transformer, tapas, textnet, tim",
"url": "https://github.com/huggingface/transformers/issues/41810",
"state": "closed",
"labels": [],
"created_at": "2025-10-23T08:48:19Z",
"updated_at": "2025-12-01T08:02:53Z",
"comments": 1,
"user": "kushaltatariya"
},
{
"repo": "huggingface/accelerate",
"number": 3818,
"title": "Duplicate W&B initialization in offline mode",
"body": "### System Info\n\n```Shell\n- `Accelerate` version: 1.10.1\n```\n\n### Information\n\n- [x] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [x] One of the scripts in the examples/ folder of Accelerate or an officially supported `no_trainer` script in the `examples` folder of the `transformers` repo (such as `run_no_trainer_glue.py`)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nWhen using Accelerate with `wandb` in **offline mode**, two separate W&B runs are created for a single training process.\nThis happens because both the `start` and the `store_init_configuration` method of `WandBTracker` call `wandb.init()`, which leads to redundant initialization. \n\nhttps://github.com/huggingface/accelerate/blob/a12beee389f6bd37cfae0aba233db03f375f7f80/src/accelerate/tracking.py#L318-L325\n\nhttps://github.com/huggingface/accelerate/blob/a12beee389f6bd37cfae0aba233db03f375f7f80/src/accelerate/tracking.py#L343-L350\n\nIs there any plan to refine the duplication?\n\n### Expected behavior\n\ninitialize wandb run only 1 time",
"url": "https://github.com/huggingface/accelerate/issues/3818",
"state": "closed",
"labels": [
"good first issue"
],
"created_at": "2025-10-23T02:19:38Z",
"updated_at": "2025-12-16T13:10:48Z",
"comments": 3,
"user": "ShuyUSTC"
},
{
"repo": "vllm-project/vllm",
"number": 27347,
"title": "[Usage]: vllm: error: unrecognized arguments: --all2all-backend deepep_low_latency",
"body": "### Your current environment\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.2 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : Could not collect\nCMake version : version 3.31.6\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.10.0 (default, Mar 3 2022, 09:58:08) [GCC 7.5.0] (64-bit runtime)\nPython platform : Linux-5.15.0-89-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA H200\nGPU 1: NVIDIA H200\nGPU 2: NVIDIA H200\nGPU 3: NVIDIA H200\nGPU 4: NVIDIA H200\nGPU 5: NVIDIA H200\nGPU 6: NVIDIA H200\nGPU 7: NVIDIA H200\n\nNvidia driver version : 570.133.20\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.8.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.8.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0\nOff-line CPU(s) list: 1-191\nVendor ID: GenuineIntel\nModel name: INTEL(R) XEON(R) PLATINUM 8558\nCPU family: 6\nModel: 207\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 2\nCPU(s) scaling MHz: 76%\nCPU max MHz: 4000.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 520 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mit",
"url": "https://github.com/vllm-project/vllm/issues/27347",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-22T14:36:18Z",
"updated_at": "2025-10-22T15:07:13Z",
"comments": 1,
"user": "Valerianding"
},
{
"repo": "vllm-project/vllm",
"number": 27343,
"title": "[Usage]: Can't get result from /pooling api when using Qwen2.5-Math-PRM-7B online",
"body": "### Your current environment\n\n```\n\nThe output of `python collect_env.py`\n\nCollecting environment information... [140/1781]\n============================== \n System Info \n============================== \nOS : Ubuntu 22.04.5 LTS (x86_64) \nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0 \nClang version : Could not collect \nCMake version : version 3.22.1 \nLibc version : glibc-2.35 \n \n============================== \n PyTorch Info \n============================== \nPyTorch version : 2.8.0+cu128 \nIs debug build : False \nCUDA used to build PyTorch : 12.8 \nROCM used to build PyTorch : N/A \n \n============================== \n Python Environment \n============================== \nPython version : 3.12.12 | packaged by Anaconda, Inc. | (main, Oct 21 2025, 20:16:04) [GCC 11.2.0] (64-bit runti\nme) \nPython platform : Linux-5.15.0-153-generic-x86_64-with-glibc2.35 \n \n============================== \n CUDA / GPU Info \n============================== \nIs CUDA available : True \nCUDA runtime version : 12.4.99\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\nGPU 2: NVIDIA A800 80GB PCIe\nGPU 3: NVIDIA A800 80GB PCIe\nGPU 4: NVIDIA A100 80GB PCIe\nGPU 5: NVIDIA A100 80GB PCIe\nGPU 6: NVIDIA A800 80GB PCIe\nGPU 7: NVIDIA A800 80GB PCIe\n\nNvidia driver version : 550.54.15\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n============================== \n CPU Info \n============================== \nArchitecture: x86_64 \nCPU op-mode(s): 32-bit, 64-bit \nAddress sizes: 46 bits physical, 48 bits virt",
"url": "https://github.com/vllm-project/vllm/issues/27343",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-22T13:36:51Z",
"updated_at": "2025-10-23T03:39:13Z",
"comments": 3,
"user": "zgc6668"
},
{
"repo": "huggingface/transformers.js",
"number": 1446,
"title": "Zhare-AI/sd-1-5-webgpu on HuggingFace.co lists itself as Transformer.js supported?",
"body": "### Question\n\n[Zhare-AI/sd-1-5-webgpu](https://huggingface.co/Zhare-AI/sd-1-5-webgpu) is a `text-to-image` model and is marked as Transformers.js compatible, and even shows demo code using Transformers.js on its `huggingface.co` page. Their example code fails with an error saying `text-to-image` is not supported in Transformers.js.\n\nThe problem is `text-to-image` is not supported in 3.7.6 and does not appear to even be supported in the v4 branch. I asked them on their `huggingface.co` discussions what version of Transformers.js their model is compatible with but no reply yet. Apparently someone else asked them the same thing 18 days ago and never got a reply.\n\nI am very interested adding a Transformers.js demo for `text-to-image` to my Blazor WASM library [SpawnDev.BlazorJS.TransformersJS](https://github.com/LostBeard/SpawnDev.BlazorJS.TransformersJS), but not sure what I am missing.",
"url": "https://github.com/huggingface/transformers.js/issues/1446",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-10-22T12:20:16Z",
"updated_at": "2025-10-24T14:33:17Z",
"user": "LostBeard"
},
{
"repo": "vllm-project/vllm",
"number": 27336,
"title": "[Feature]: Make promt_token_ids optional in streaming response (disable by default)",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nStarting with v0.10.2, the first server-sent event (SSE) in streaming responses now includes the full list of `prompt_token_ids`.\nWhile this can be useful for debugging or detailed inspection, it introduces several practical issues in production environments: \n\n1. Large payload size: \nFor long prompts, this significantly increases the size of the first streaming event. This can increase latency, cause network throttling, and reduce streaming responsiveness.\n\n2. Parser and infrastructure limitations: \nSome clients and intermediate parsers have message size limits. The larger first event may cause them to fail or disconnect, requiring changes across multiple components in existing systems that previously handled smaller initial events.\n\n3. Breaking change in behavior:\nPreviously, streaming responses did not include prompt token IDs, so this change affects compatibility with existing clients expecting smaller events.\n\n\n### Suggested Fix\nMake the inclusion of prompt_token_ids optional per request and disabled by default (same as `return_token_ids`), restoring the previous behavior.\n\n\n### Alternatives\n\nAlternatively, provide an API flag or configuration option to exclude `prompt_token_ids` globally for the entire server, so that no streaming response include this field.\n\n### Additional context\n\nFor example, the first streaming response for a prompt of ~130k tokens can now exceed 600KB, while some parsers and scanners have default buffer sizes of 64KB (which was previously sufficient).\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27336",
"state": "closed",
"labels": [
"feature request"
],
"created_at": "2025-10-22T11:42:41Z",
"updated_at": "2025-10-27T11:06:45Z",
"comments": 1,
"user": "Gruner-atero"
},
{
"repo": "huggingface/transformers",
"number": 41775,
"title": "Hugging Face website and models not reachable",
"body": "### System Info\n\n```\n$ pip show transformers\nName: transformers\nVersion: 4.57.1\nSummary: State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow\nHome-page: https://github.com/huggingface/transformers\nAuthor: The Hugging Face team (past and future) with the help of all our contributors (https://github.com/huggingface/transformers/graphs/contributors)\nAuthor-email: transformers@huggingface.co\n```\n\n```\n$ python --version\nPython 3.12.3\n```\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n1. `python -c 'from transformers import pipeline; pipeline = pipeline(task=\"text-generation\", model=\"Qwen/Qwen2.5-1.5B\")'`\n\nI am getting connection issues:\n```\nOSError: We couldn't connect to 'https://huggingface.co' to load the files, and couldn't find them in the cached files.\nCheck your internet connection or see how to run the library in offline mode at 'https://huggingface.co/docs/transformers/installation#offline-mode'.\n```\n\nIt rather funny that it recommends checking https://huggingface.co/docs/transformers/installation#offline-mode when https://huggingface.co is not reachable :-) Maybe this information, e.g. about mirrors, could be hosted somewhere else?\n\n### Expected behavior\n\nThe examples should work as documented.",
"url": "https://github.com/huggingface/transformers/issues/41775",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-22T07:40:32Z",
"updated_at": "2025-11-21T08:10:00Z",
"comments": 8,
"user": "christian-rauch"
},
{
"repo": "vllm-project/vllm",
"number": 27319,
"title": "[Usage]: Quantized FusedMoE crashed in graph compiled stage",
"body": "### Your current environment\n\n```text\n==============================\n System Info\n==============================\nOS : Ubuntu 24.04.2 LTS (x86_64)\nGCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0\nClang version : 19.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.4.3 25224 d366fa84f3fdcbd4b10847ebd5db572ae12a34fb)\nCMake version : version 3.31.6\nLibc version : glibc-2.39\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+rocm6.4\nIs debug build : False\nCUDA used to build PyTorch : N/A\nROCM used to build PyTorch : 6.4.43482-0f2d60242\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 | packaged by conda-forge | (main, Jun 4 2025, 14:45:31) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-6.8.0-79-generic-x86_64-with-glibc2.39\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : AMD Radeon PRO W7900 Dual Slot (gfx1100)\nNvidia driver version : Could not collect\ncuDNN version : Could not collect\nHIP runtime version : 6.4.43482\nMIOpen runtime version : 3.4.0\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 256\nOn-line CPU(s) list: 0-255\nVendor ID: AuthenticAMD\nBIOS Vendor ID: Advanced Micro Devices, Inc.\nModel name: AMD EPYC 9554 64-Core Processor\nBIOS Model name: AMD EPYC 9554 64-Core Processor Unknown CPU @ 3.1GHz\nBIOS CPU family: 107\nCPU family: 25\nModel: 17\nThread(s) per core: 2\nCore(s) per socket: 64\nSocket(s): 2\nStepping: 1\nFrequency boost: enabled\nCPU(s) scaling MHz: 51%\nCPU max MHz: 3100.0000\nCPU min MHz: 1500.0000\nBogoMIPS: 6199.71\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good amd_lbr_v2 nopl nonstop_tsc cpuid extd_apicid aperfmperf rapl pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw ibs skinit wdt tce topoext perfctr_core perfctr_nb bpext perfctr_llc mwaitx cpb cat_l3 cdp_l3 hw_pstate ssbd mba perfmon_v2 ibrs ibpb stibp ibrs_enhanced vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local user_shstk avx512_bf16 clzero irperf xsaveerptr rdpru wbnoinvd amd_ppin cppc amd_ibpb_ret arat npt lbrv svm_lock nrip_save tsc_scale vmcb_clean flushbyasid decodeassists pausefilter pfthreshold avic v_vmsave_vmload vgif x2avic v_spec_ctrl vnmi avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg avx512_vpopcntdq la57 rdpid overflow_recov succor smca fsrm flush_l1d debug_swap\nVirtualization: AMD-V\nL1d cache: 4 MiB (128 instances)\nL1i cache: 4 MiB (128 instances)\nL2 cache: 128 MiB (128 instances)\nL3 cache: 512 MiB (16 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-63,128-191\nNUMA node1 CPU(s): 64-127,192-255\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; Safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: ",
"url": "https://github.com/vllm-project/vllm/issues/27319",
"state": "closed",
"labels": [
"rocm",
"usage"
],
"created_at": "2025-10-22T06:29:32Z",
"updated_at": "2025-10-24T02:19:55Z",
"comments": 1,
"user": "Rus-P"
},
{
"repo": "vllm-project/vllm",
"number": 27298,
"title": "[Doc]: Update metrics documentation to remove V0 references and add v1 changes.",
"body": "## Problem\n\nThe metrics documentation in `docs/design/metrics.md` still contains references to V0 metrics implementation, but V0 metrics have been removed after @njhill 's PR https://github.com/vllm-project/vllm/pull/27215 was merged. To avoid confusion, I think we should remove this and update it with the new set of v1 metrics.\n\nWas curious if we want to keep this v0 reference and add the v1 details on top of this. \n\n### Suggest a potential alternative/fix\n\n1. Remove all V0 references from the metrics documentation.\n2. Update the introduction to focus on V1 metrics only.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27298",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-10-21T22:08:48Z",
"updated_at": "2025-10-22T13:29:17Z",
"comments": 1,
"user": "atalhens"
},
{
"repo": "vllm-project/vllm",
"number": 27268,
"title": "[Usage]: failed to infer device type on GCP COS despite nvidia container toolkit installed",
"body": "### Your current environment\n\nI failed to run this script on GCP COS. \n\n### How would you like to use vllm\n\nI was trying to use VLLM on a Google Cloud (GCP) Container-Optimized OS (COS) instance via Docker. \n\nI followed GCP's [documentation](https://cloud.google.com/container-optimized-os/docs/how-to/run-gpus) to install the nvidia driver, including mapping nvidia driver-related dirs to the Docker container. All tests worked fine. \n\nHowever, when trying to start a VLLM server via Docker, I got the error that `libcuda.so.1` cannot be found and VLLM failed to infer device info. I tried to change the target dirs in the mapping to like `/usr/local/lib`, `/usr/local/cuda/lib`, etc. But no luck. \n\nI also tried adding the flags `--runtime nvidia --gpus all` per [this instruction](https://docs.vllm.ai/en/v0.8.4/deployment/docker.html) but got the error that `Error response from daemon: unknown or invalid runtime name: nvidia.`\n\nIf someone can shed the light of where vllm official Docker image looks for CUDA stuff, it will be greatly appreciated. Thanks in advance. \n\nThe complete command and error: \n```\n$ docker run -v ~/.cache/huggingface:/root/.cache/huggingface --env \"HUGGING_FACE_HUB_TOKEN=\" -p 8010:8000 --ipc=host vllm/vllm-openai:latest --model mistralai/Mistral-7B-v0.1\nINFO 10-21 08:13:18 [__init__.py:220] No platform detected, vLLM is running on UnspecifiedPlatform\nWARNING 10-21 08:13:23 [_custom_ops.py:20] Failed to import from vllm._C with ImportError('libcuda.so.1: cannot open shared object file: No such file or directory')\nTraceback (most recent call last):\n File \"\", line 198, in _run_module_as_main\n File \"\", line 88, in _run_code\n File \"/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/api_server.py\", line 1949, in \n parser = make_arg_parser(parser)\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/openai/cli_args.py\", line 263, in make_arg_parser\n parser = AsyncEngineArgs.add_cli_args(parser)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/engine/arg_utils.py\", line 1714, in add_cli_args\n parser = EngineArgs.add_cli_args(parser)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/engine/arg_utils.py\", line 919, in add_cli_args\n vllm_kwargs = get_kwargs(VllmConfig)\n ^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/engine/arg_utils.py\", line 281, in get_kwargs\n return copy.deepcopy(_compute_kwargs(cls))\n ^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/vllm/engine/arg_utils.py\", line 182, in _compute_kwargs\n default = field.default_factory()\n ^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.12/dist-packages/pydantic/_internal/_dataclasses.py\", line 123, in __init__\n s.__pydantic_validator__.validate_python(ArgsKwargs(args, kwargs), self_instance=s)\n File \"/usr/local/lib/python3.12/dist-packages/vllm/config/device.py\", line 58, in __post_init__\n raise RuntimeError(\nRuntimeError: Failed to infer device type, please set the environment variable `VLLM_LOGGING_LEVEL=DEBUG` to turn on verbose logging to help debug the issue.\n```\n\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27268",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-21T15:24:21Z",
"updated_at": "2025-10-21T15:24:21Z",
"comments": 0,
"user": "forrestbao"
},
{
"repo": "vllm-project/vllm",
"number": 27265,
"title": "[Usage]: Cannot register custom model (Out-of-Tree Model Integration)",
"body": "```\n### Your current environment\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flake8==7.1.1\n[pip3] flashinfer==0.1.6+cu124torch2.4\n[pip3] flashinfer-python==0.2.5\n[pip3] mypy-extensions==1.0.0\n[pip3] numpy==1.26.4\n[pip3] nvidia-cublas-cu12==12.4.5.8\n[pip3] nvidia-cuda-cupti-cu12==12.4.127\n[pip3] nvidia-cuda-nvrtc-cu12==12.4.127\n[pip3] nvidia-cuda-runtime-cu12==12.4.127\n[pip3] nvidia-cudnn-cu12==9.1.0.70\n[pip3] nvidia-cufft-cu12==11.2.1.3\n[pip3] nvidia-curand-cu12==10.3.5.147\n[pip3] nvidia-cusolver-cu12==11.6.1.9\n[pip3] nvidia-cusparse-cu12==12.3.1.170\n[pip3] nvidia-cusparselt-cu12==0.6.2\n[pip3] nvidia-ml-py==12.560.30\n[pip3] nvidia-modelopt==0.31.0\n[pip3] nvidia-modelopt-core==0.31.0\n[pip3] nvidia-nccl-cu12==2.21.5\n[pip3] nvidia-nvjitlink-cu12==12.4.127\n[pip3] nvidia-nvtx-cu12==12.4.127\n[pip3] pynvml==12.0.0\n[pip3] pyzmq==26.2.0\n[pip3] sentence-transformers==3.3.1\n[pip3] torch==2.6.0\n[pip3] torch_memory_saver==0.0.6\n[pip3] torchao==0.9.0\n[pip3] torchaudio==2.6.0\n[pip3] torchdata==0.11.0\n[pip3] torchprofile==0.0.4\n[pip3] torchtext==0.18.0\n[pip3] torchvision==0.21.0\n[pip3] transformer_engine_torch==2.3.0\n[pip3] transformers==4.51.1\n[pip3] triton==3.2.0\n[conda] flashinfer 0.1.6+cu124torch2.4 pypi_0 pypi\n[conda] flashinfer-python 0.2.5 pypi_0 pypi\n[conda] numpy 1.26.4 pypi_0 pypi\n[conda] nvidia-cublas-cu12 12.4.5.8 pypi_0 pypi\n[conda] nvidia-cuda-cupti-cu12 12.4.127 pypi_0 pypi\n[conda] nvidia-cuda-nvrtc-cu12 12.4.127 pypi_0 pypi\n[conda] nvidia-cuda-runtime-cu12 12.4.127 pypi_0 pypi\n[conda] nvidia-cudnn-cu12 9.1.0.70 pypi_0 pypi\n[conda] nvidia-cufft-cu12 11.2.1.3 pypi_0 pypi\n[conda] nvidia-curand-cu12 10.3.5.147 pypi_0 pypi\n[conda] nvidia-cusolver-cu12 11.6.1.9 pypi_0 pypi\n[conda] nvidia-cusparse-cu12 12.3.1.170 pypi_0 pypi\n[conda] nvidia-cusparselt-cu12 0.6.2 pypi_0 pypi\n[conda] nvidia-ml-py 12.560.30 pypi_0 pypi\n[conda] nvidia-modelopt 0.31.0 pypi_0 pypi\n[conda] nvidia-modelopt-core 0.31.0 pypi_0 pypi\n[conda] nvidia-nccl-cu12 2.21.5 pypi_0 pypi\n[conda] nvidia-nvjitlink-cu12 12.4.127 pypi_0 pypi\n[conda] nvidia-nvtx-cu12 12.4.127 pypi_0 pypi\n[conda] pynvml 12.0.0 pypi_0 pypi\n[conda] pyzmq 26.2.0 pypi_0 pypi\n[conda] sentence-transformers 3.3.1 pypi_0 pypi\n[conda] torch 2.6.0 pypi_0 pypi\n[conda] torch-memory-saver 0.0.6 pypi_0 pypi\n[conda] torchao 0.9.0 pypi_0 pypi\n[conda] torchaudio 2.6.0 pypi_0 pypi\n[conda] torchdata 0.11.0 pypi_0 pypi\n[conda] torchprofile 0.0.4 pypi_0 pypi\n[conda] torchtext 0.18.0 pypi_0 pypi\n[conda] torchvision 0.21.0 pypi_0 pypi\n[conda] transformer-engine-torch 2.3.0 pypi_0 pypi\n[conda] transformers 4.51.1 pypi_0 pypi\n[conda] triton 3.2.0 pypi_0 pypi\n\n==============================\n vLLM Info\n==============================\nROCM Version : Could not collect\nvLLM Version : 0.8.5.post1\n```\n\n# How would you like to use vllm\n\nHi, I'm trying to integrate a custom multi-modal model (Qwen2_5_VLForConditionalGeneration_Vilavt) using the out-of-tree plugin system, following the official documentation and the vllm_add_dummy_model example.\n\n### The Issue:\n\nThe model loading behavior is inconsistent between single-GPU and multi-GPU (tensor parallel) modes:\n\n- Single-GPU (CUDA_VISIBLE_DEVICES=0): Everything works perfectly. The engine initializes, and I can run inference.\n- Multi-GPU (CUDA_VISIBLE_DEVICES=0,1,2,3): The engine fails to start. Although the logs from VllmWorker processes show that my custom model is successfully registered, the main EngineCore process throws a ValueError, complaining that the model cannot be found.\n\nI've successfully created a package `vllm_vilavt`, installed it with `pip install -e .` , and my `setup.py` correctly points to a register() function in the entry_points.\n\n\nMy `setup.py`:\n```\nfrom setuptools import setup, find_packages\n\nsetup(\n name=\"vllm_vilavt\",\n version=\"0.1\",\n packages=find_packages(),\n entry_points={\n \"vllm.general_plugins\":\n [\"register_vilavt_model = vllm_",
"url": "https://github.com/vllm-project/vllm/issues/27265",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-21T14:17:17Z",
"updated_at": "2025-10-25T13:19:40Z",
"comments": 1,
"user": "Hyperwjf"
},
{
"repo": "vllm-project/vllm",
"number": 27263,
"title": "[Responses API] Support tool calling and ouput token streaming",
"body": "Splitting off from #14721\n\n> FYI a start has been made here https://github.com/vllm-project/vllm/pull/20504\n> \n> That PR (which was merged to `main` on [7/9/2025](https://github.com/vllm-project/vllm/pull/20504#event-18495144925)) explicitly has an unchecked boxes for\n> \n> * [ ] Tool/functional calling support\n> * [ ] Output token streaming\n> \n> Any plans to implement those features? I think that is what is needed to support agentic coding tools like codex. See:\n> \n> * https://docs.vllm.ai/projects/recipes/en/latest/OpenAI/GPT-OSS.html#harmony-format-support \n\n _Originally posted by @bartlettroscoe in [#14721](https://github.com/vllm-project/vllm/issues/14721#issuecomment-3321963360)_",
"url": "https://github.com/vllm-project/vllm/issues/27263",
"state": "open",
"labels": [],
"created_at": "2025-10-21T12:36:44Z",
"updated_at": "2025-12-07T01:06:46Z",
"comments": 4,
"user": "markmc"
},
{
"repo": "vllm-project/vllm",
"number": 27252,
"title": "[Usage]: \u201d@app.post(\"/generate\")\u201c API is support qwen2_vl or not?",
"body": "### Your current environment\n\ni want tot know \u201d@app.post(\"/generate\")\u201c API support qwen2_vl or not?\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27252",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-21T07:30:11Z",
"updated_at": "2025-10-21T07:30:11Z",
"comments": 0,
"user": "wwkww"
},
{
"repo": "huggingface/lerobot",
"number": 2269,
"title": "how to configure pi0_base to train with single camera dataset",
"body": "\n\nHi,\nI'm trying to train pi0_base with \"lerobot/aloha_sim_transfer_cube_human\" dataset which has only one camera input \"observation.images.top\". However, pi0 seems to expect three camera inputs:\n\"observation.images.base_0_rgb\",\n\"observation.images.left_wrist_0_rgb\",\n\"observation.images.right_wrist_0_rgb\"\n\n\"ValueError: All image features are missing from the batch. At least one expected. (batch: dict_keys(['action', 'next.reward', 'next.done', 'next.truncated', 'info', 'action_is_pad', 'task', 'index', 'task_index', 'observation.images.top', 'observation.state', 'observation.language.tokens', 'observation.language.attention_mask'])) (image_features: {'observation.images.base_0_rgb': PolicyFeature(type=, shape=(3, 224, 224)), 'observation.images.left_wrist_0_rgb': PolicyFeature(type=, shape=(3, 224, 224)), 'observation.images.right_wrist_0_rgb': PolicyFeature(type=, shape=(3, 224, 224))}) Exception in thread Thread-2 (_pin_memory_loop): Traceback (most recent call last): File \"/root/.local/share/mamba/envs/lerobot/lib/python3.10/threading.py\", line 1016, in _bootstrap_inner\"\n\nIs there a command-line argument I can use to set the single camera input to train with the pi0_base model?\n",
"url": "https://github.com/huggingface/lerobot/issues/2269",
"state": "open",
"labels": [
"question",
"policies",
"dataset"
],
"created_at": "2025-10-21T01:32:50Z",
"updated_at": "2025-10-21T17:36:17Z",
"user": "dalishi"
},
{
"repo": "vllm-project/vllm",
"number": 27233,
"title": "gguf run good",
"body": "### Your current environment\n\nfrom vllm import LLM, SamplingParams\n\ngguf_path = \"/home/m/Desktop/vllm/vllm/examples/offline_inference/basic/Qwen3-1.7B-GGUF/Qwen3-1.7B-Q6_K.gguf\"\n\nllm = LLM(\n gguf_path,\n tokenizer=\"Qwen/Qwen3-1.7B\"\n)\n\nparams = SamplingParams(\n temperature=0.8,\n top_p=0.9,\n top_k=40,\n max_tokens=200,\n)\n\noutputs = llm.generate([\"Who is Napoleon Bonaparte?\"], params)\nprint(outputs[0].outputs[0].text)\n\n\n### How would you like to use vllm\n\nI want to run inferevenv) m@m-HP-Z440-Workstation:~/Desktop/vllm/vllm/examples/offline_inference/basic$ \n(venv) m@m-HP-Z440-Workstation:~/Desktop/vllm/vllm/examples/offline_inference/basic$ python3\nPython 3.12.3 (main, Aug 14 2025, 17:47:21) [GCC 13.3.0] on linux\nType \"help\", \"copyright\", \"credits\" or \"license\" for more information.\n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> \n>>> from vllm import LLM, SamplingParams\nINFO 10-21 03:05:39 [__init__.py:216] Automatically detected platform cuda.\n>>> \n>>> gguf_path = \"/home/m/Desktop/vllm/vllm/examples/offline_inference/basic/Qwen3-1.7B-GGUF/Qwen3-1.7B-Q6_K.gguf\"\n>>> \n>>> llm = LLM(\n... gguf_path,\n... tokenizer=\"Qwen/Qwen3-1.7B\"\n... )\nINFO 10-21 03:05:41 [utils.py:233] non-default args: {'tokenizer': 'Qwen/Qwen3-1.7B', 'disable_log_stats': True, 'model': '/home/m/Desktop/vllm/vllm/examples/offline_inference/basic/Qwen3-1.7B-GGUF/Qwen3-1.7B-Q6_K.gguf'}\nINFO 10-21 03:06:14 [model.py:547] Resolved architecture: Qwen3ForCausalLM\n`torch_dtype` is deprecated! Use `dtype` instead!\nERROR 10-21 03:06:14 [config.py:278] Error retrieving safetensors: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/home/m/Desktop/vllm/vllm/examples/offline_inference/basic/Qwen3-1.7B-GGUF/Qwen3-1.7B-Q6_K.gguf'. Use `repo_type` argument if needed., retrying 1 of 2\nERROR 10-21 03:06:16 [config.py:276] Error retrieving safetensors: Repo id must be in the form 'repo_name' or 'namespace/repo_name': '/home/m/Desktop/vllm/vllm/examples/offline_inference/basic/Qwen3-1.7B-GGUF/Qwen3-1.7B-Q6_K.gguf'. Use `repo_type` argument if needed.\nINFO 10-21 03:06:16 [model.py:1730] Downcasting torch.float32 to torch.bfloat16.\nINFO 10-21 03:06:16 [model.py:1510] Using max model len 32768\nINFO 10-21 03:06:16 [scheduler.py:205] Chunked prefill is enabled with max_num_batched_tokens=8192.\n(EngineCore_DP0 pid=67528) INFO 10-21 03:06:41 [core.py:644] Waiting for init message from front-end.\n(EngineCore_DP0 pid=67528) INFO 10-21 03:06:41 [core.py:77] Initializing a V1 LLM engine (v0.11.0) with config: model='/home/m/Desktop/vllm/vllm/examples/offline_inference/basic/Qwen3-1.7B-GGUF/Qwen3-1.7B-Q6_K.gguf', speculative_config=None, tokenizer='Qwen/Qwen3-1.7B', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=False, dtype=torch.bfloat16, max_seq_len=32768, download_dir=None, load_format=gguf, tensor_parallel_size=1, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=gguf, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, structured_outputs_config=StructuredOutputsConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_parser=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=/home/m/Desktop/vllm/vllm/examples/offline_inference/basic/Qwen3-1.7B-GGUF/Qwen3-1.7B-Q6_K.gguf, enable_prefix_caching=True, chunked_prefill_enabled=True, pooler_config=None, compilation_config={\"level\":3,\"debug_dump_path\":\"\",\"cache_dir\":\"\",\"backend\":\"\",\"custom_ops\":[],\"splitting_ops\":[\"vllm.unified_attention\",\"vllm.unified_attention_with_output\",\"vllm.mamba_mixer2\",\"vllm.mamba_mixer\",\"vllm.short_conv\",\"vllm.linear_attention\",\"vllm.plamo2_mamba_mixer\",\"vllm.gdn_attention\",\"vllm.sparse_attn_indexer\"],\"use_inductor\":true,\"compile_sizes\":[],\"inductor_compile_config\":{\"enable_auto_functionalized_v2\":false},\"inductor_passes\":{},\"cudagraph_mode\":[2,1],\"use_cudagraph\":true,\"cudagraph_num_of_warmups\":1,\"cudagraph_capture_sizes\":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],\"cudagraph_copy_inputs\":false,\"full_cuda_graph\":false,\"use_inductor_graph_partition\":false,\"pass_config\":{},\"max_capture_size\":512,\"local_cache_dir\":null}\n[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n[Gloo] Rank 0 is connected to 0 peer ranks. Expected number of connected peer ranks is : 0\n[Gloo] Rank 0 is connected to 0 peer ranks. Ex",
"url": "https://github.com/vllm-project/vllm/issues/27233",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-21T00:11:26Z",
"updated_at": "2025-10-22T00:44:10Z",
"comments": 12,
"user": "kmnnmk212-source"
},
{
"repo": "vllm-project/vllm",
"number": 27228,
"title": "[Installation]: Compatibility with PyTorch 2.9.0?",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How you are installing vllm\n\nIs there a version of vllm that is compatible with the latest PyTorch release 2.9.0?\n\n```\npip install vllm==0.11.0\npip install torch==2.9.0\n```\n\n```\n$ vllm bench latency --input-len 256 --output-len 256 --model Qwen3/Qwen3-8B --batch-size 1\nterminate called after throwing an instance of 'std::bad_alloc'\n what(): std::bad_alloc\nAborted (core dumped)\n```\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27228",
"state": "closed",
"labels": [
"installation"
],
"created_at": "2025-10-20T21:10:24Z",
"updated_at": "2025-10-21T22:40:15Z",
"comments": 3,
"user": "andrewor14"
},
{
"repo": "vllm-project/vllm",
"number": 27208,
"title": "[Feature]: Upgrade CUDA version to 12.9.1 in docker images",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nThe current builds display warning logs like these\n```\nWarning: please use at least NVCC 12.9 for the best DeepGEMM performance\n```\n\nCan we bump this version easily?\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27208",
"state": "closed",
"labels": [
"feature request"
],
"created_at": "2025-10-20T16:08:49Z",
"updated_at": "2025-10-21T21:20:19Z",
"comments": 1,
"user": "jhuntbach-bc"
},
{
"repo": "huggingface/lerobot",
"number": 2259,
"title": "Clarifications on fine-tuning on different envs and embodiments",
"body": "Hi everyone,\nI\u2019m currently working on fine-tuning SmolVLA and \u03c0\u2080 using **[RLBench](https://github.com/stepjam/RLBench)**. The robot setup is a Franka Emika Panda (7DoF + gripper), and I\u2019ve already collected custom LeRobot datasets for a pick-and-place task ([available on my Hugging Face](https://huggingface.co/RonPlusSign)) with 500 demo episodes.\n\nI\u2019ve successfully fine-tuned [OpenVLA](https://github.com/openvla/openvla) using its official repository, where the action space is defined as \u0394EEF pose (Euler rotation) + gripper, and the state as \u0394EEF pose (quaternion rotation) + gripper, using a single observation image (left shoulder), reaching around 22% success rate.\n\nHowever, when trying to fine-tune SmolVLA, despite the training running without issues (loss converges and wandb plots look fine), the evaluation yields 0% success. I suspect I\u2019m misunderstanding how to correctly define the state and action spaces for SmolVLA in this context.\nSince RLBench is not one of the officially supported envs, I created an evaluation script (you can find it [here](https://github.com/RonPlusSign/RLBench/blob/master/test_smolvla.py)), similar to the examples provided in [Robot Learning: A Tutorial](https://github.com/fracapuano/robot-learning-tutorial/blob/main/snippets/ch5/02_using_smolvla.py) (thanks @fracapuano for the amazing work!).\n\n![\"Image\"](\"https://github.com/user-attachments/assets/59175cf4-c458-49f3-96b0-e96bc414e333\")
\n\nFor example, I started the finetuning using:\n```sh\npython src/lerobot/scripts/lerobot_train.py \\\n --policy.path=HuggingFaceVLA/smolvla_libero \\\n --policy.repo_id=RonPlusSign/smolvla_PutRubbishInBin \\\n --dataset.repo_id=RonPlusSign/RLBench-LeRobot-v3-PutRubbishInBin \\\n --batch_size=32 \\\n --output_dir=outputs/train/smolvla_finetuned_rubbish \\\n --policy.device=cuda \\\n --wandb.enable=true \\\n --save_freq=10000 \\\n --steps=60000\n```\n\nI also tested smaller finetunings (e.g. 5k, 10k, 20k steps).\n\nHere are some specific points I\u2019d like to clarify:\n\n1. What are the exact action and state spaces used in SmolVLA and \u03c0\u2080 pretraining? (\u0394EEF pose, absolute EEF pose, joint positions, joint velocities, ... and angle representations e.g. quaternion or Euler).\n\n2. Regarding camera inputs: does the naming or number of cameras affect the model performance? Should I stick to the _exact_ names provided in the `config.json` file, such as `observation.images.image` and `observation.images.image2` (front/wrist), similar to pretraining? Or is it fine to use different camera names and/or add extra views? Is there a way to override the existing input and output features or this means that the pretrain would be wasted?\n\n3. The base model [lerobot/smolvla_base](https://huggingface.co/lerobot/smolvla_base) is pretrained on the SO100/SO101 robot, so I assume it might not transfer well to Franka Panda tasks \u2014 is that correct?\n\n4. Would it make more sense to start from a model trained on Franka, e.g. [HuggingFaceVLA/smolvla_libero](https://huggingface.co/HuggingFaceVLA/smolvla_libero), or it's still a different type of embodiment (it seems with 6DoF+gripper, which is not my case)?\n\n5. Are the datasets [HuggingFaceVLA/libero](https://huggingface.co/datasets/HuggingFaceVLA/libero) and/or [HuggingFaceVLA/smol-libero](https://huggingface.co/datasets/HuggingFaceVLA/smol-libero) the ones used for pretraining [HuggingFaceVLA/smolvla_libero](https://huggingface.co/HuggingFaceVLA/smolvla_libero)?\n\n6. In [HuggingFaceVLA/smol-libero](https://huggingface.co/datasets/HuggingFaceVLA/smol-libero) the actions have dimension 7, which doesn\u2019t clearly map to 7 joint angles + gripper. Are these absolute joint positions, EEF poses, or something else? Does LIBERO use a 6DoF or 7DoF Franka setup? If 6DoF, which joint is excluded?\n\nAny guidance on these points (or pointers to where this information is documented) would be very helpful \u2014 I\u2019ve been trying to align my setup with the pretrained models but haven\u2019t found clear references for these details.\n\nThanks a lot for your time and for maintaining this project!",
"url": "https://github.com/huggingface/lerobot/issues/2259",
"state": "open",
"labels": [
"question",
"policies",
"simulation"
],
"created_at": "2025-10-20T13:24:22Z",
"updated_at": "2025-12-23T10:37:31Z",
"user": "RonPlusSign"
},
{
"repo": "vllm-project/vllm",
"number": 27184,
"title": "[Doc]: Multi-Modal Benchmark is too simple",
"body": "### \ud83d\udcda The doc issue\n\nThe latest doc about Multi-Modal Benchmark shows \uff1a \n- 1\u3001download sharegpt4v_instruct_gpt4-vision_cap100k.json and COCO's 2017 Train images\n- 2\u3001vllm serve and vllm bench serve\nBut there is so much details to concern: \n- 1\u3001delete all json that not is coco`s in sharegpt4v_instruct_gpt4-vision_cap100k.json\n- 2\u3001place COCO's 2017 Train images in /root directory like /train2017/, \n- 3\u3001 vllm serve --allowed-local-media-path /train2017/ , because vllm use the condition:\n```\nif allowed_local_media_path not in filepath.resolve().parents\n```\n the ` filepath.resolve().parents` is [\"/train2017\", \"/\"], so the easiest\u200c way is to place the images in /train2017/ and set `--allowed-local-media-path /train2017/`\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27184",
"state": "open",
"labels": [
"documentation"
],
"created_at": "2025-10-20T06:24:18Z",
"updated_at": "2025-10-20T16:44:17Z",
"comments": 2,
"user": "BigFaceBoy"
},
{
"repo": "vllm-project/vllm",
"number": 27182,
"title": "[Feature]: INT8 Support in Blackwell Arch",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nhello, I want to use w8a8(int8) in blackwell gpus, and when I read the source code, it says, the int8 is not support by sm120. According to the nvidia-ptx-instructions, blackwell series gpus still have a int8 tensor, is there another way we use w8a8 int8 in rtx5090 by vllm now \n![\"Image\"](\"https://github.com/user-attachments/assets/42546583-4124-4d3c-a1ad-ea3fb19d70cf\")
\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27182",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-10-20T06:04:03Z",
"updated_at": "2025-10-20T06:04:03Z",
"comments": 0,
"user": "nhanngoc94245"
},
{
"repo": "huggingface/optimum",
"number": 2376,
"title": "Support qwen2_5_vl for ONNX export",
"body": "### Feature request\n\nI would like to be able to convert [this model](https://huggingface.co/prithivMLmods/DeepCaption-VLA-V2.0-7B) which is based on Qwen 2.5 VL architecture using optimum. Right now, I get the error:\n\n```\nValueError: Trying to export a qwen2_5_vl model, that is a custom or unsupported architecture, but no custom onnx configuration was passed as `custom_onnx_configs`. Please refer to https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#custom-export-of-transformers-models for an example on how to export custom models. Please open an issue at https://github.com/huggingface/optimum/issues if you would like the model type qwen2_5_vl to be supported natively in the ONNX export.\n```\n\nI read the documentation but I have no idea how I'd go about setting the custom onnx config up.\n\n### Motivation\n\nQwen 2.5 VL is a SOTA architecture that is already being used in downstream models (see my example), so it is worth supporting.\n\n### Your contribution\n\nI can do research but I don't have enough experience with this codebase and ML code to contribute a PR.",
"url": "https://github.com/huggingface/optimum/issues/2376",
"state": "open",
"labels": [],
"created_at": "2025-10-19T22:08:28Z",
"updated_at": "2026-01-06T08:03:39Z",
"comments": 8,
"user": "ayan4m1"
},
{
"repo": "huggingface/transformers",
"number": 41731,
"title": "transformers CLI documentation issue ",
"body": "### System Info\n\n- `transformers` version: 5.0.0.dev0\n- Platform: Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.39\n- Python version: 3.12.9\n- Huggingface_hub version: 1.0.0.rc6\n- Safetensors version: 0.6.2\n- Accelerate version: 1.10.1\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)\n- Using distributed or parallel set-up in script?: no\n- Using GPU in script?: yes\n- GPU type: NVIDIA GeForce RTX 3050 Laptop GPU\n\n### Who can help?\n\n@stevhliu \n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] Update the documentation for the transformers-cli\n- [ ] Set the default --fixed flag to \"pipe\" in place of \"infer\" \n\n### Reproduction\n\necho -e \"Plants create [MASK] through a process known as photosynthesis.\" | transformers run --task fill-mask --model google-bert/bert-base-uncased --device 0\n\n(as shown in documentation)\n\n\n**output:-**\n\n![\"Image\"](\"https://github.com/user-attachments/assets/0722d782-748b-4ecf-afa9-e4e6dbe67126\")
\n\n\n\n### Expected behavior\n\n\n**output:**\n\n![\"Image\"](\"https://github.com/user-attachments/assets/1169bfea-8473-48c4-bdd1-f623d16e2f28\")
\n\n\n\n**Fix/updated command:**\n\n echo -e \"Plants create [MASK] through a process known as photosynthesis.\" | transformers run fill-mask --model google-bert/bert-base-uncased --device 0 --format pipe\n\nThis indicates the current working format is:-\n\ntransformers run --model --format [options]\n\n**update**\n\nwe could let the default --format flag be \"pipe\" instead of \"infer\" which is deprecated. so we could also write command as follows for most models :-\n \ntransformers run --model \n\n\n**Action Needed:** (documentation change) \n\nAll documentation for similar models should be updated for the transformer CLI inference \n\nI would like to confirm if my understanding is correct: should I go ahead and raise a PR to update the documentation and set the default as \"pipe\" for --format flag? I am relatively new to open source and would greatly appreciate any guidance or tips you could provide to ensure my contribution is appropriate and follows best practices.\n\n\n\n\n\n\n\n\n",
"url": "https://github.com/huggingface/transformers/issues/41731",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-19T09:31:46Z",
"updated_at": "2025-12-22T08:03:09Z",
"comments": 14,
"user": "ArjunPimpale"
},
{
"repo": "huggingface/chat-ui",
"number": 1947,
"title": "HuggingChat MoM (Mixture-of-Models) Integration Proposal \ud83e\udd17",
"body": "# **HuggingChat MoM (Mixture-of-Models) Integration Proposal \ud83e\udd17**\n\n**Status:** Proposal \n**Date:** 2025-10-19 \n**Version:** 1.0\n**Authors**: vLLM-SR Team\n\n---\n\n## Executive Summary\n\nThis proposal outlines the integration of **vLLM Semantic Router** into HuggingChat as a new **MoM (Mixture-of-Models)** routing option. The integration will enable advanced intelligent routing capabilities including semantic caching, PII detection, and chain-of-thought (CoT) transparency, while maintaining full backward compatibility with the existing Omni (Arch router) implementation.\n\n---\n\n## 1. Motivation\n\n### Current State\n\n- HuggingChat currently supports **Omni** routing via the Arch router (`src/lib/server/router/arch.ts`)\n- Arch router provides basic route selection using LLM-based decision-making\n- Limited visibility into routing decisions and no semantic caching capabilities\n\n### Desired State\n\n- Support **MoM (Mixture-of-Models)** routing via vLLM Semantic Router\n- Enable advanced features: semantic caching, PII detection, intelligent routing\n- Provide transparent chain-of-thought (CoT) information for routing decisions\n- Maintain coexistence of both Omni and MoM routers for gradual rollout\n\n### Business Value\n\n1. **Performance**: Semantic caching reduces latency for repeated queries\n2. **Security**: PII detection protects user privacy\n3. **Transparency**: CoT information builds user trust\n4. **Flexibility**: Users can choose between Omni and MoM routing strategies\n5. **Dashboard Integration**: vLLM-SR dashboard provides monitoring and analytics\n\n### About vLLM Semantic Router\n\n**vLLM Semantic Router** is an intelligent routing system that embodies the **Mixture-of-Models (MoM)** philosophy, with modelName (**MoM**):\n\n```shell\ncurl -X POST http://localhost:8801/v1/chat/completions \\\n -H \"Content-Type: application/json\" \\\n -d '{\n \"model\": \"MoM\",\n \"messages\": [\n {\"role\": \"user\", \"content\": \"What is the derivative of x^2?\"}\n ]\n }'\n```\n\n- **Intelligent Routing**: Routes requests to the optimal model based on semantic understanding of the query, not just keyword matching\n- **Semantic Caching**: Leverages semantic similarity to cache responses, dramatically reducing latency for similar queries (not just exact matches)\n- **Semantic Chain Architecture**: Evolving toward a composable semantic chain where all stages are orchestrated in an extensible pipeline, enabling future enhancements and custom stage integration in work-in-progress \"SemanticChain\".\n- **Three-Stage Pipeline** (Extensible & Composable):\n - **Stage 1 - Prompt Guard**: Security-first approach with jailbreak detection and PII protection\n - **Stage 2 - Router Memory**: Intelligent semantic caching for performance optimization\n - **Stage 3 - Smart Routing**: Multi-level intelligent routing combining three complementary strategies:\n - **Domain Understanding**: Semantic classification of queries into domains (math, coding, general, etc.)\n - **Similarity-Based Routing**: Semantic similarity matching to route similar queries to optimal models\n - **Keyword-Based Routing**: Keyword pattern matching for explicit intent detection\n - These three routing strategies work together to provide comprehensive query understanding and optimal model selection\n - Future stages can be added to the pipeline without disrupting existing functionality\n- **Mixture-of-Models Philosophy**: Recognizes that no single model is optimal for all tasks. By intelligently routing different types of queries to different specialized models, it achieves:\n - Better accuracy through task-specific model selection\n - Cost optimization by using smaller models for simple tasks\n - Performance improvement through semantic understanding\n - Transparency via chain-of-thought visibility\n- **Production-Ready**: Battle-tested with comprehensive error handling, monitoring, and dashboard support\n- **Open Source**: vLLM Community-driven development with active maintenance and feature additions\n\n---\n\n## 2. Goals\n\n### Primary Goals\n\n- \u2705 Integrate vLLM Semantic Router as a new MoM routing option\n- \u2705 Extract and store chain-of-thought (CoT) metadata from vLLM-SR responses\n- \u2705 Support both Omni and MoM routers coexisting in the same system\n- \u2705 Expose CoT information to frontend for visualization\n\n### Secondary Goals\n\n- \u2705 Support A/B testing between Omni and MoM routers\n- \u2705 Integrate with vLLM-SR dashboard for monitoring\n\n---\n\n## 3. Non-Goals\n\n- \u274c Replace Omni router entirely (maintain coexistence)\n- \u274c Modify vLLM Semantic Router codebase\n- \u274c Implement custom semantic caching in HuggingChat (use vLLM-SR's caching)\n- \u274c Create new dashboard (integrate with existing vLLM-SR dashboard)\n- \u274c Support non-OpenAI-compatible endpoints for MoM\n\n---\n\n## 4. Design Principles\n\n### 1. **Backward Compatibility**\n\n- Existing Omni router functionality remains unchanged\n- No breaking changes to current APIs or configurations\n- Both routers can be configured independently\n\n### 2. **Transparency**\n\n- CoT inf",
"url": "https://github.com/huggingface/chat-ui/issues/1947",
"state": "open",
"labels": [
"enhancement"
],
"created_at": "2025-10-19T08:17:14Z",
"updated_at": "2025-10-20T11:12:30Z",
"comments": 3,
"user": "Xunzhuo"
},
{
"repo": "huggingface/tokenizers",
"number": 1877,
"title": "encode bytes directly",
"body": "Is there a way to directly encode bytes with a bpe based HF tokenizer without having to decode the string first? ",
"url": "https://github.com/huggingface/tokenizers/issues/1877",
"state": "open",
"labels": [],
"created_at": "2025-10-19T03:30:39Z",
"updated_at": "2025-11-28T07:43:18Z",
"comments": 2,
"user": "tsengalb99"
},
{
"repo": "vllm-project/vllm",
"number": 27154,
"title": "[Installation]: How to reduce the vllm image",
"body": "### Your current environment\n\nHi,\n\nI looked at docker pull vllm/vllm-openai:latest \u2014 the image is around 12 GB. I\u2019m exploring ways to reduce the vLLM image size specifically for NVIDIA L40s (i use linux amd64). any ideas?\ndoes building vllm from source help to reduce the image?\n\nHere\u2019s what I\u2019ve tried so far (but not sure how to install flashinfer):\n```\nFROM nvidia/cuda:12.1.0-runtime-ubuntu22.04\n\n# Install Python and pip\nRUN apt-get update && apt-get install -y python3 python3-pip && \\\n apt-get clean && rm -rf /var/lib/apt/lists/*\n\n# Install only vLLM and production dependencies\nRUN pip3 install --no-cache-dir vllm\n\n# Set CUDA arch for A100 (8.0)\nENV TORCH_CUDA_ARCH_LIST=\"8.9+PTX\"\n\n# Expose API port\nEXPOSE 8000\n\nENTRYPOINT [\"python3\", \"-m\", \"vllm.entrypoints.openai.api_server\"]\n```\n\nmore infos:\nhttps://discuss.vllm.ai/t/current-vllm-docker-image-size-is-12-64gb-how-to-reduce-it/1204/4\nhttps://docs.vllm.ai/en/latest/deployment/docker.html#building-vllm-s-docker-image-from-source\npr: https://github.com/vllm-project/vllm/pull/22377\n\n### How you are installing vllm\n\n```sh\npip install -vvv vllm\n```\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27154",
"state": "open",
"labels": [
"installation"
],
"created_at": "2025-10-18T17:52:07Z",
"updated_at": "2025-10-20T17:45:39Z",
"comments": 4,
"user": "geraldstanje"
},
{
"repo": "vllm-project/vllm",
"number": 27153,
"title": "[Feature]: Allow vllm bench serve in non-streaming mode with /completions API",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nvLLM\u2019s bench serve currently supports recording benchmark results only in the streaming mode - recording metrics like TTFT, TPOT, ITL etc. For my use case benchmarking [llm-d ](https://github.com/llm-d/llm-d)which uses vLLM, I would like to enable vllm bench serve in non-streaming mode for the openai backend, recording only non-streaming latency metrics like E2E Latency. Overall, the changes required would be as follows:\n\n* Add a new Async Request Function - `async_request_openai_completions_non_streaming()` function in [`vllm/vllm/benchmarks/lib/endpoint_request_func.py`](https://github.com/vllm-project/vllm/blob/main/vllm/benchmarks/lib/endpoint_request_func.py) to support parsing of non-streaming vllm outputs.\n\n* Add a new benchmark argument: `benchmark_streaming`. If `benchmark_streaming` is set to False for the `openai` backend, then the above function `async_request_openai_completions_non_streaming()` is called instead of `async_request_openai_completions`.\n\n* Either modify [`vllm/benchmarks/serve.py`](https://github.com/vllm-project/vllm/blob/main/vllm/benchmarks/serve.py) or design a new benchmark script to calculate and save metrics, excluding streaming-only metrics like TTFT, TPOT and ITL.\n\nHappy to discuss and create PRs for the above implementation. Looking forward to thoughts and feedback.\n\n### Alternatives\n\nAnother option I'm considering is using [benchmark_throughput.py](https://github.com/vllm-project/vllm/blob/main/benchmarks/benchmark_throughput.py). However, it relies on the offline LLM library which does not serve my use-case of benchmarking the vllm server in non-streaming mode.\n\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27153",
"state": "open",
"labels": [
"feature request"
],
"created_at": "2025-10-18T17:47:44Z",
"updated_at": "2025-10-18T20:50:49Z",
"comments": 0,
"user": "susiejojo"
},
{
"repo": "huggingface/candle",
"number": 3137,
"title": "Strategic Discussion: Flicker's Hybrid Architecture for Lightweight Inference + Advanced Training",
"body": "# Strategic Discussion: Flicker's Hybrid Architecture Evolution\n\n## Overview\nThis issue proposes a comprehensive strategic discussion about flicker's positioning and architecture evolution. The detailed proposal is documented in `STRATEGIC_DISCUSSION_PROPOSAL.md`.\n\n## Context\nDuring analysis of flicker's capabilities vs PyTorch, a critical strategic question emerged: Should flicker be primarily a **lightweight inference engine** or evolve into a **comprehensive training framework**?\n\n## Proposed Solution: Hybrid Architecture\nInstead of choosing one direction, we propose a dual-track approach:\n- **flicker-core**: Lightweight inference (current focus) \n- **flicker-train**: Advanced training features\n- **Feature Gates**: Granular control for specific capabilities\n\n## Key Strategic Questions\n\n### 1. Technical Feasibility\n- Is zero-copy gradient system feasible with Rust ownership?\n- How do we implement compile-time training validation?\n- What's the best approach for async-distributed training?\n\n### 2. Market Positioning \n- Does hybrid approach make sense for flicker's goals?\n- How do we balance inference vs training development resources?\n- Will this attract both inference and training users?\n\n### 3. Implementation Priority\n- Which advanced training features should we implement first?\n- How do we ensure seamless transition from inference to training?\n- What performance targets should we set vs PyTorch?\n\n## Revolutionary Differentiators\nThe proposal identifies 4 major areas where Rust could revolutionize ML:\n1. **Zero-Copy Gradient Systems** - Gradients as views, not copies\n2. **Compile-Time Training Validation** - Catch training errors at compile time \n3. **Async-First Training Infrastructure** - True concurrency without GIL\n4. **SIMD-Optimized Research Features** - Hand-optimized kernels impossible in Python\n\n## Benefits\n\u2705 Preserves current lightweight inference advantages\n\u2705 Enables advanced training capabilities unique to Rust \n\u2705 Creates natural upgrade path for users\n\u2705 Positions flicker as both practical tool and research platform\n\n## Next Steps\n1. **Enable GitHub Discussions** to facilitate community input\n2. **Review detailed proposal** in `STRATEGIC_DISCUSSION_PROPOSAL.md`\n3. **Gather feedback** from community on strategic direction\n4. **Validate technical feasibility** of proposed features\n5. **Create implementation roadmap** based on consensus\n\n## Discussion Document\n\ud83d\udccb **Full Proposal**: See `STRATEGIC_DISCUSSION_PROPOSAL.md` for comprehensive analysis including:\n- Current state analysis\n- PyTorch comparison\n- Technical implementation details\n- Code examples of revolutionary features\n- Trade-offs and considerations\n- Community input questions\n\n## Call for Input\nThis represents a potential major evolution for flicker. Community input is essential to validate:\n- Strategic direction alignment with user needs\n- Technical feasibility of proposed features \n- Implementation priority and resource allocation\n- Market positioning effectiveness\n\n**Please review the detailed proposal and share your thoughts on flicker's strategic future.**\n\n---\n*This issue will be converted to a GitHub Discussion once discussions are enabled on the repository.*",
"url": "https://github.com/huggingface/candle/issues/3137",
"state": "closed",
"labels": [],
"created_at": "2025-10-18T17:27:24Z",
"updated_at": "2025-10-21T16:18:51Z",
"comments": 1,
"user": "jagan-nuvai"
},
{
"repo": "huggingface/lerobot",
"number": 2245,
"title": "release 0.4.0 and torch 2.8.0",
"body": "Hello Lerobot Team! :) \nQuick question, do you have a time estimate for:\n\n- lerobot release 0.4.0 (ie next stable release using the new v30 data format)\n- bumping torch to 2.8 \n\nThanks a lot in advance!\n",
"url": "https://github.com/huggingface/lerobot/issues/2245",
"state": "closed",
"labels": [
"question",
"dependencies"
],
"created_at": "2025-10-18T16:57:07Z",
"updated_at": "2025-10-19T18:34:47Z",
"user": "antoinedandi"
},
{
"repo": "huggingface/lerobot",
"number": 2242,
"title": "Is it no longer possible to fine-tune the previously used \u03c00 model?",
"body": "I previously trained a model using the following command for fine-tuning:\n\n`lerobot-train --dataset.repo_id=parkgyuhyeon/slice-clay --policy.path=lerobot/pi0 --output_dir=outputs/train/pi0_slice-clay --job_name=pi0_slice-clay --policy.device=cuda --wandb.enable=false --wandb.project=lerobot --log_freq=10 --steps=50000 --policy.repo_id=parkgyuhyeon/pi0_slice-clay --policy.push_to_hub=false`\n\n\nHowever, after the release of \u03c00.5, I noticed that the new example command includes additional arguments like:\n\n```\n--policy.repo_id=your_repo_id \\\n--policy.compile_model=true \\\n--policy.gradient_checkpointing=true \\\n--policy.dtype=bfloat16 \\\n```\n\n\nIt seems that some new options have been added.\nDoes this mean the model I fine-tuned earlier using \u03c00 can no longer be used?",
"url": "https://github.com/huggingface/lerobot/issues/2242",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-18T08:42:35Z",
"updated_at": "2025-10-20T00:18:03Z",
"user": "pparkgyuhyeon"
},
{
"repo": "huggingface/lerobot",
"number": 2239,
"title": "Models trained using openpi pi0.5 on Lerobot's pi0.5",
"body": "Hi, can I check if models trained using the [pytorch port of openpi's pi0.5](https://github.com/Physical-Intelligence/openpi?tab=readme-ov-file#pytorch-support) are compatible with lerobot's defination of pi0.5?\n\nThanks!",
"url": "https://github.com/huggingface/lerobot/issues/2239",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-18T02:01:45Z",
"updated_at": "2025-10-18T10:54:06Z",
"user": "brycegoh"
},
{
"repo": "huggingface/lerobot",
"number": 2228,
"title": "Trossen WidowX AI model, depth cameras and tests",
"body": "Hi,\n\nWould you be open to receive pull requests to support more recent trossen robotics setups as well as depth cameras? I think for the robot part the pattern is quite well established. For depth cameras we solved it by tweaking a bit the dataset utils.\n\nOur implementation is fairly tested.",
"url": "https://github.com/huggingface/lerobot/issues/2228",
"state": "closed",
"labels": [
"question",
"robots"
],
"created_at": "2025-10-17T09:32:22Z",
"updated_at": "2025-10-31T19:15:25Z",
"user": "lromor"
},
{
"repo": "vllm-project/vllm",
"number": 27090,
"title": "[Usage]: Does vLLM support a data-parallel group spanning multiple nodes when starting an online service?",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nDoes vLLM support a data-parallel group spanning multiple nodes when starting an online service?\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27090",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-17T09:15:04Z",
"updated_at": "2025-10-20T02:37:19Z",
"comments": 2,
"user": "KrisLu999"
},
{
"repo": "vllm-project/vllm",
"number": 27086,
"title": "[Bug]: After enabling P-D Disaggregation, the final output results are not entirely identical.",
"body": "### Your current environment\n\nvllm VERSION\uff1a 0.10.1\n\n### \ud83d\udc1b Describe the bug\n\nWhen I fixed the random seed and ensured all environment variables were consistent, I noticed that launching PD separation with the same configuration produced inconsistent final outputs. This phenomenon may require multiple attempts to fully manifest. I have a question: Is this behavior normal? (under temperature=0 conditions)\n\nvllm startup script \uff08D\uff09\uff0cThe startup process for P nodes is almost identical, except for the use of \u201ckv_producer\u201d.\n```\nVLLM_CFG=(\n --trust-remote-code\n --data-parallel-size 1\n --tensor-parallel-size 8\n --no-enable-prefix-caching\n --no-enable-chunked-prefill\n --kv-transfer-config '{\"kv_connector\":\"NixlConnector\",\"kv_role\":\"kv_consumer\"}'\n)\n```\n\nWhen requested, temperature=0\n```\ncurl -X POST -s http://${HOST_PORT}/v1/completions \\\n-H \"Content-Type: application/json\" \\\n-d '{\n \"model\": \"base_model\",\n \"prompt\": \"xxxx\", # The prompt is identical for every request, and this prompt will also appear.\n \"max_tokens\": 1000,\n \"temperature\": 0,\n \"stream\": true\n}'\nprintf \"\\n\"\n```\n\nMy question is: Does the PD also have a probability of producing non-identical outputs at every step when temperature=0? If this is a normal phenomenon, what causes it? If this is a bug, what might be causing it?\n\nLooking forward to your responses. Thank you.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27086",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-17T07:56:41Z",
"updated_at": "2025-10-20T09:16:21Z",
"comments": 4,
"user": "freedom-cui"
},
{
"repo": "huggingface/lerobot",
"number": 2227,
"title": "How to easily run inference with a trained model",
"body": "Hello, and thank you for sharing such an inspiring project!\n\nI\u2019m currently working with a 7-DoF robotic arm (6 joint axes + 1 gripper) and generating datasets through video recordings for training on smolVLA. Since there\u2019s still some ongoing engineering work related to dataset generation, I\u2019d like to start by understanding how the inference pipeline is implemented.\n\nI have successfully verified the training workflow using the [lerobot/svla_so100_pickplace](https://huggingface.co/datasets/lerobot/svla_so100_pickplace) dataset and produced a trained model. Now, I\u2019m wondering if there is a way to quickly load the trained model and perform inference, similar to how OpenVLA provides a simple demo on Hugging Face \u2014 where the model can be loaded and tested with just a few lines of code.\n\nFor OpenVLA example:\n```\nfrom transformers import AutoModelForVision2Seq, AutoProcessor\nfrom PIL import Image\nimport torch\n\n# Load Processor & VLA\nprocessor = AutoProcessor.from_pretrained(\"openvla/openvla-7b\", trust_remote_code=True)\nvla = AutoModelForVision2Seq.from_pretrained(\n \"openvla/openvla-7b\", \n attn_implementation=\"flash_attention_2\", # [Optional] Requires `flash_attn`\n torch_dtype=torch.bfloat16, \n low_cpu_mem_usage=True, \n trust_remote_code=True\n).to(\"cuda:0\")\n\n# Grab image input & format prompt\nimage: Image.Image = get_from_camera(...)\nprompt = \"In: What action should the robot take to {}?\\nOut:\"\n\n# Predict Action (7-DoF; un-normalize for BridgeData V2)\ninputs = processor(prompt, image).to(\"cuda:0\", dtype=torch.bfloat16)\naction = vla.predict_action(**inputs, unnorm_key=\"bridge_orig\", do_sample=False)\n\n# Execute...\nrobot.act(action, ...)\n```\nI would be very grateful if you could share any related information or references.",
"url": "https://github.com/huggingface/lerobot/issues/2227",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-10-17T05:41:15Z",
"updated_at": "2025-12-16T02:57:00Z",
"user": "Biz-Joe"
},
{
"repo": "huggingface/lerobot",
"number": 2224,
"title": "Can i just modify the json the pretrained policy to adapt it to my own robot?",
"body": "I just want to know if i can just modify the config json(shape of state, size of image .etc) to adapt the model to inference in my modified robot(have different number of feetect and different image resolution)?",
"url": "https://github.com/huggingface/lerobot/issues/2224",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-17T01:33:32Z",
"updated_at": "2025-10-20T16:40:26Z",
"user": "shs822"
},
{
"repo": "huggingface/lerobot",
"number": 2221,
"title": "Question about pre-trained weights usability and performance on Hugging Face models",
"body": "Hello,\n\nI would like to ask whether the weights provided on Hugging Face (for example, under the lerobot author page) can be directly downloaded and used for inference, or if they must be fine-tuned before achieving reasonable performance.\n\nWhen I directly load and evaluate the models (e.g., lerobot/smolvla_base or lerobot/pi05_libero_base), the performance appears extremely poor, almost random. I\u2019m wondering if this is expected behavior or if I might have made a mistake in my setup.\n\nHere\u2019s the list of models I found on Hugging Face:\n\nlerobot/smolvla_base\nlerobot/pi05_base\nlerobot/diffusion_pusht\nlerobot/pi0_base\nlerobot/pi05_libero_base\nlerobot/act_aloha_sim_transfer_cube_human\nlerobot/vqbet_pusht\nlerobot/diffusion_pusht_keypoints\nlerobot/act_aloha_sim_insertion_human\nlerobot/pi0_libero_base\nlerobot/pi05_libero_finetuned\nlerobot/pi05_libero_finetuned_quantiles\nlerobot/pi0_libero_finetuned\n\n\nAre the *_base models supposed to be general pre-trained checkpoints that require downstream fine-tuning (e.g., on LIBERO), while the *_finetuned ones are ready for evaluation?\n\nThank you in advance for your clarification!",
"url": "https://github.com/huggingface/lerobot/issues/2221",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-10-16T14:14:39Z",
"updated_at": "2025-10-31T16:26:45Z",
"user": "MichaelWu99-lab"
},
{
"repo": "vllm-project/vllm",
"number": 27021,
"title": "[Usage]: Need guidance reproducing benchmark results from PR #25337 \u2014 results differ significantly from reported data",
"body": "## Background\nRecently, we have been working on optimizing the position computation for multimodal models in vLLM.\n\nDuring benchmarking, we noticed that our results were not as expected.\n\nTo investigate, we decided to reproduce the benchmark results from [PR #25337](https://github.com/vllm-project/vllm/pull/25337), comparing the performance before and after that PR was merged into the main branch.\n\n- Before PR commit: cf56cf78b47e5f9b6a81ce0d50a94f9291922315\n\n- After PR commit: 30d08911f7cf78287f8da003ddcc99f6ef196f9f\n \n ![\"Image\"](\"https://github.com/user-attachments/assets/afca55db-c443-4c98-ba6b-f656b070af5f\")
\n\nHowever, our reproduced results differ **significantly** from the performance data reported in the PR.\n\nWe\u2019d like to understand whether this discrepancy may be caused by hardware differences, model choice, or benchmark setup.\n\n**Who can help guide me?**\n\n## Model and Environment\n- Model used: Qwen/Qwen3-VL-30B-A3B-Instruct-FP8(The modelQwen3-VL-4B used in the PR could not be found on Hugging Face.)\n\n- GPU: NVIDIA A100 PCIe\n\n- vLLM startup command:\n```bash\nvllm serve \"Qwen/Qwen3-VL-30B-A3B-Instruct-FP8\" \\\n --trust-remote-code \\\n --gpu-memory-utilization 0.9 \\\n --max-model-len 16384\n```\n\n## Benchmark Command\n```bash\nvllm bench serve \\\n --backend openai-chat \\\n --model \"Qwen/Qwen3-VL-30B-A3B-Instruct-FP8\" \\\n --base-url \"http://localhost:8000\" \\\n --endpoint \"/v1/chat/completions\" \\\n --dataset-name \"hf\" \\\n --dataset-path \"lmarena-ai/VisionArena-Chat\" \\\n --num-prompts 100 \\\n --request-rate 10 \\\n --save-result \\\n --result-dir benchmarks_results \\\n --result-filename test.json\n```\n\n## Our Benchmark Results\n### Before PR #25337\n```text\n============ Serving Benchmark Result ============\nSuccessful requests: 100\nRequest rate configured (RPS): 10.00\nBenchmark duration (s): 16.91\nTotal input tokens: 5280\nTotal generated tokens: 11522\nRequest throughput (req/s): 5.91\nOutput token throughput (tok/s): 681.42\nPeak output token throughput (tok/s): 2225.00\nPeak concurrent requests: 97.00\nTotal Token throughput (tok/s): 993.68\n---------------Time to First Token----------------\nMean TTFT (ms): 1176.13\nMedian TTFT (ms): 1185.79\nP99 TTFT (ms): 2178.91\n-----Time per Output Token (excl. 1st token)------\nMean TPOT (ms): 88.39\nMedian TPOT (ms): 78.68\nP99 TPOT (ms): 392.01\n---------------Inter-token Latency----------------\nMean ITL (ms): 77.30\nMedian ITL (ms): 42.31\nP99 ITL (ms): 581.15\n==================================================\n```\n\n### After PR #25337\n```text\n============ Serving Benchmark Result ============\nSuccessful requests: 100\nRequest rate configured (RPS): 10.00\nBenchmark duration (s): 16.89\nTotal input tokens: 5280\nTotal generated tokens: 11640\nRequest throughput (req/s): 5.92\nOutput token throughput (tok/s): 689.02\nPeak output token throughput (tok/s): 2178.00\nPeak concurrent requests: 97.00\nTotal Token throughput (tok/s): 1001.57\n---------------Time to First Token----------------\nMean TTFT (ms): 1193.52\nMedian TTFT (ms): 1285.23\nP99 TTFT (ms): 2111.41\n-----Time per Output Token (excl. 1st token)------\nMean TPOT (ms): 88.84\nMedian TPOT (ms): 78.00\nP99 TPOT (ms): 344.25\n---------------Inter-token Latency----------------\nMean ITL (ms): 76.89\nMedian ITL (ms): 42.30\nP99 ITL (ms): 597.42\n==================================================\n```\n\n## Reference: Benchmark Results from PR #25337\n### Main branch\n```text\n============ Serving Benchmark Result ============\nSuccessful requests: 1000 \nRequest rate configured (RPS): 10.00 \nBenchmark duration (s): 101.85 \nTotal input tokens: 94327 \nTotal generated tokens: 120882 \nRequest throughput (req/s): 9.82 \nOutput token throughput (tok/s): 1186.81 \nPeak output token throughput (tok/s): 2862.00 \nPeak concurrent requests: 133.00 \nTotal Token throughput (tok/s): 2112.91 \n---------------Time to First Token----------------\nMean TTFT (ms): 229.53 \nMedian TTFT (ms): 180.19 \nP99 TTFT (ms): 928.83 \n-----Time per Output Token (excl. 1st token)------\nMean TPOT (ms): ",
"url": "https://github.com/vllm-project/vllm/issues/27021",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-16T12:31:03Z",
"updated_at": "2025-10-17T05:46:32Z",
"comments": 5,
"user": "deitxfge"
},
{
"repo": "vllm-project/vllm",
"number": 27017,
"title": "[Doc]: KV Cache Memory allocations",
"body": "### \ud83d\udcda The doc issue\n\nHello,\nWhen serving a model via vLLM for text(token) generation:\n\n1. Before a new request gets scheduled, does vLLM check if KV cache for a sequence length of `max_model_len` is available for that new request or does it check if KV cache for a sequence length of `input prompt + max_tokens` (if it's less than _max_model_length_) is available for the request? In case the request does not specify a _max_tokens_ does it default to 16?\n2. In case the required KV cache memory is not available, does the server wait until it is available to schedule that new request?\n3. When exactly is the KV cache allocated for a particular request? Do the KV cache blocks get allocated after computing the number of new blocks required for all current requests after each generation step of the model, as mentioned in this [blog post](https://www.aleksagordic.com/blog/vllm)? i.e. the KV cache block is not fully allocated upfront based on the point [1] calculation instead incrementally allocated since the request could finish before it reaches the _max_tokens_ or _max_model_length_ limit?\n\n4. I am trying to understand if the server concurrency can be more than the one specified in the server startup logs (based on the _max_model_len_) and get a clearer understanding of request scheduling.\nexample logs:\n ```\n GPU KV cache size: {X} tokens\n Maximum concurrency for {max_model_len} tokens per request: Y\n ```\n5. The KV cache token and concurrency estimations vLLM gives in the start up logs for the **_Qwen-235B MoE_** model do not match the below formula for `tensor_parallel_size` of 8. It does match for `tensor_parallel_size` of 4 and in general for a different model like **_Llama-70B_**. Is the below formula missing something specifically for the Qwen-235B models at `tensor_parallel_size` of 8?\n```\nnumber of layers * number of KV heads * head dimension * precision/8 * 2 (for K & V) * seq_len bytes\n\nOR \n\n(number of layers * number of KV heads * head dimension * precision/8 * 2 (for K & V) * seq_len)/tensor_parallel_size bytes per GPU\n\ni.e. for Qwen-235B MoE\n(94 * 4 * 128 * 16/8 * 2 * seq_len)/8 bytes per GPU\n```\n\nThanks!\n\n### Suggest a potential alternative/fix\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27017",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-10-16T11:43:43Z",
"updated_at": "2025-11-04T11:08:02Z",
"comments": 7,
"user": "sneha5gsm"
},
{
"repo": "vllm-project/vllm",
"number": 27011,
"title": "[Usage]: Runnig GLM4.5-Air with Speculative Decoding",
"body": "### Your current environment\n```\nThe output of `python collect_env.py`\n```\n### How would you like to use vllm\nI want to run inference of a [GLM-4.5-Air](https://huggingface.co/zai-org/GLM-4.5-Air-FP8) with speculative decoding. From [GLM 4.5](https://huggingface.co/zai-org/GLM-4.5) page, it mentioned `All models use MTP layers and specify --speculative-num-steps 3 --speculative-eagle-topk 1 --speculative-num-draft-tokens 4 to ensure competitive inference speed.`\nThey gave examples of how to use speculative decoding in sglang, but not in vLLM. I was wondering if it is being supported in vLLM\n### Before submitting a new issue...\n- [x]Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27011",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-16T10:17:54Z",
"updated_at": "2025-10-16T10:23:01Z",
"comments": 0,
"user": "aqx95"
},
{
"repo": "vllm-project/vllm",
"number": 27006,
"title": "[Usage]: In vLLM version 0.8.5, when I send an HTTP image URL directly, the model cannot recognize the image content, but it works correctly when I use a base64-encoded image. I\u2019d like to understand why this happens.",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27006",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-16T08:09:29Z",
"updated_at": "2025-10-16T10:33:49Z",
"comments": 4,
"user": "Lislttt"
},
{
"repo": "huggingface/lerobot",
"number": 2218,
"title": "image pad value in pi0/pi05",
"body": "### System Info\n\n```Shell\nthe latest lerobot version\n```\n\n### Information\n\n- [ ] One of the scripts in the examples/ folder of LeRobot\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\ndef resize_with_pad_torch( # see openpi `resize_with_pad_torch` (exact copy)\n images: torch.Tensor,\n height: int,\n width: int,\n mode: str = \"bilinear\",\n) -> torch.Tensor:\n \"\"\"PyTorch version of resize_with_pad. Resizes an image to a target height and width without distortion\n by padding with black. If the image is float32, it must be in the range [-1, 1].\n\n Args:\n images: Tensor of shape [*b, h, w, c] or [*b, c, h, w]\n height: Target height\n width: Target width\n mode: Interpolation mode ('bilinear', 'nearest', etc.)\n\n Returns:\n Resized and padded tensor with same shape format as input\n \"\"\"\n # Check if input is in channels-last format [*b, h, w, c] or channels-first [*b, c, h, w]\n if images.shape[-1] <= 4: # Assume channels-last format\n channels_last = True\n if images.dim() == 3:\n images = images.unsqueeze(0) # Add batch dimension\n images = images.permute(0, 3, 1, 2) # [b, h, w, c] -> [b, c, h, w]\n else:\n channels_last = False\n if images.dim() == 3:\n images = images.unsqueeze(0) # Add batch dimension\n\n batch_size, channels, cur_height, cur_width = images.shape\n\n # Calculate resize ratio\n ratio = max(cur_width / width, cur_height / height)\n resized_height = int(cur_height / ratio)\n resized_width = int(cur_width / ratio)\n\n # Resize\n resized_images = F.interpolate(\n images,\n size=(resized_height, resized_width),\n mode=mode,\n align_corners=False if mode == \"bilinear\" else None,\n )\n\n # Handle dtype-specific clipping\n if images.dtype == torch.uint8:\n resized_images = torch.round(resized_images).clamp(0, 255).to(torch.uint8)\n elif images.dtype == torch.float32:\n resized_images = resized_images.clamp(-1.0, 1.0)\n else:\n raise ValueError(f\"Unsupported image dtype: {images.dtype}\")\n\n # Calculate padding\n pad_h0, remainder_h = divmod(height - resized_height, 2)\n pad_h1 = pad_h0 + remainder_h\n pad_w0, remainder_w = divmod(width - resized_width, 2)\n pad_w1 = pad_w0 + remainder_w\n\n # Pad\n constant_value = 0 if images.dtype == torch.uint8 else -1.0\n padded_images = F.pad(\n resized_images,\n (pad_w0, pad_w1, pad_h0, pad_h1), # left, right, top, bottom\n mode=\"constant\",\n value=constant_value,\n )\n\n # Convert back to original format if needed\n if channels_last:\n padded_images = padded_images.permute(0, 2, 3, 1) # [b, c, h, w] -> [b, h, w, c]\n\n return padded_images\n\n\n\n### Expected behavior\n\n image from lerobot range from 0 to 1 and dtype is float32 , so constant_value in this code is -1 not 0. -1*2-1=-3, so that there are '-3' in the input of siglip embedding ",
"url": "https://github.com/huggingface/lerobot/issues/2218",
"state": "open",
"labels": [
"bug",
"question",
"policies"
],
"created_at": "2025-10-16T06:48:13Z",
"updated_at": "2025-10-17T09:58:49Z",
"user": "Tgzz666"
},
{
"repo": "huggingface/transformers",
"number": 41640,
"title": "AttributeError: BartTokenizerFast has no attribute image_token. Did you mean: 'mask_token'?",
"body": "### System Info\n\nUbuntu\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```python\nimport torch\nimport requests\nfrom PIL import Image\nfrom transformers import AutoProcessor, Florence2ForConditionalGeneration\n\n\nmodel = Florence2ForConditionalGeneration.from_pretrained(\n \"microsoft/Florence-2-large\",\n dtype=torch.bfloat16,\n)\nprocessor = AutoProcessor.from_pretrained(\"microsoft/Florence-2-large\")\n\nurl = \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/car.jpg?download=true\"\nimage = Image.open(requests.get(url, stream=True).raw).convert(\"RGB\")\n\ntask_prompt = \"\"\ninputs = processor(text=task_prompt, images=image, return_tensors=\"pt\").to(model.device, torch.bfloat16)\n\ngenerated_ids = model.generate(\n **inputs,\n max_new_tokens=1024,\n num_beams=3,\n)\ngenerated_text = processor.batch_decode(generated_ids, skip_special_tokens=False)[0]\n\nimage_size = image.size\nparsed_answer = processor.post_process_generation(generated_text, task=task_prompt, image_size=image_size)\n\nprint(parsed_answer)\n```\n\n### Expected behavior\n\n```\n raise AttributeError(f\"{self.__class__.__name__} has no attribute {key}\")\nAttributeError: BartTokenizerFast has no attribute image_token. Did you mean: 'mask_token'?\n```",
"url": "https://github.com/huggingface/transformers/issues/41640",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-16T06:34:02Z",
"updated_at": "2025-10-17T09:00:36Z",
"comments": 5,
"user": "conceptofmind"
},
{
"repo": "huggingface/transformers.js",
"number": 1439,
"title": "Integration to a CLI application created using PKG",
"body": "### Question\n\nI'm trying to bundle a Node.js CLI tool that uses `@xenova/transformers` into a single executable using [pkg](https://github.com/vercel/pkg).\n\nThe build works fine, but when I run the packaged executable, I get this error:\n```\nError: Cannot find module '../bin/napi-v3/linux/x64/onnxruntime_binding.node'\nRequire stack:\n- /snapshot/custom-cli/node_modules/onnxruntime-node/dist/binding.js\n- /snapshot/custom-cli/node_modules/onnxruntime-node/dist/backend.js\n- /snapshot/custom-cli/node_modules/onnxruntime-node/dist/index.js\n- /snapshot/custom-cli/dist/custom-cli.cjs\n```\n\n**Build command:**\n\n`webpack && pkg -t node18-linux -o custom-cli dist/custom-cli.cjs`\n\n**pkg config:**\n\n```\n\"pkg\": {\n \"assets\": [\n \"node_modules/onnxruntime-node/bin/napi-v3/**/onnxruntime_binding.node\"\n ]\n}\n```\n\n\n**Is it possible to give a custom absolute path for ONNX native bindings (something like this):** \n```\nimport { env } from \"@xenova/transformers\";\nenv.backends.onnx.customBindingPath = \"/custom-cli/onnxruntime_binding.node\";\n```\n\nthen the tool could:\n- Extract prebuilt binaries (onnxruntime_binding.node) from a known location (or GitHub ZIP)\n\n- Pass that custom path to @xenova/transformers / onnxruntime-node\n\n- Load correctly even when packaged by pkg\n",
"url": "https://github.com/huggingface/transformers.js/issues/1439",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-10-16T05:30:32Z",
"updated_at": "2025-10-26T23:32:41Z",
"user": "JosephJibi"
},
{
"repo": "huggingface/lerobot",
"number": 2216,
"title": "gpu memory required to finetune pi05",
"body": "I tried to finetune pi05 with rxt a6000 (48GB) and get an insufficient memory error . Does anyone know how much GPU memory is needed to finetune a pi05 policy?\n\nThanks,",
"url": "https://github.com/huggingface/lerobot/issues/2216",
"state": "open",
"labels": [
"question",
"policies",
"performance"
],
"created_at": "2025-10-16T04:46:21Z",
"updated_at": "2025-12-22T07:42:45Z",
"user": "jcl2023"
},
{
"repo": "vllm-project/vllm",
"number": 26981,
"title": "[Usage]: Does vllm support use TokensPrompt for Qwen3VL model",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nMy truncation strategy differs slightly from the standard approach (I wish to preserve the system prompt and the final suffix, only truncating the middle portion). It seems that the current version of vLLM does not support this, so I attempted to pass pre-processed token IDs along with mm_data as input, for example: TokensPrompt(prompt_token_ids=text[:self.max_model_length] + self.suffix_tokens, multi_modal_data=mm_data, mm_processor_kwargs=video_kwargs). \nHowever, I encountered an error. Could you please advise on the correct way to use this?\n\n![\"Image\"](\"https://github.com/user-attachments/assets/935cdcf5-59ff-480b-bbc5-a6426e48a12c\")
\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26981",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-16T03:22:09Z",
"updated_at": "2025-10-27T03:33:53Z",
"comments": 10,
"user": "afalf"
},
{
"repo": "huggingface/lerobot",
"number": 2214,
"title": "Potential Scale Imbalance in smolVLA Embedding Pipeline",
"body": "Hi, I noticed a potential scale inconsistency in the embedding pipeline.\n\nSpecifically, state_emb is not normalized, while both img_emb and lang_emb are explicitly scaled by math.sqrt(emb_dim):\nhttps://github.com/huggingface/lerobot/blob/a6ff3cfebb0304f2c378515dd30ea06fff8f473f/src/lerobot/policies/smolvla/modeling_smolvla.py#L591-L601\n\nIn practice, the numerical magnitude of img_emb tends to be much higher (often in the hundreds), while lang_emb and state_emb remain in the single-digit range. This discrepancy might cause the image features to dominate during multimodal fusion or attention.\n\nRelated code:\nhttps://github.com/huggingface/lerobot/blob/a6ff3cfebb0304f2c378515dd30ea06fff8f473f/src/lerobot/policies/smolvla/modeling_smolvla.py#L561-L566\n\nSuggestion:\nConsider adding a LayerNorm after img_emb (or before the multimodal fusion stage) to align the scale across modalities. This could improve stability during training and quantization.\n\n\u2014\nReported by Tank @ iMotion AI",
"url": "https://github.com/huggingface/lerobot/issues/2214",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-16T02:11:24Z",
"updated_at": "2025-10-17T11:29:36Z",
"user": "kkTkk012"
},
{
"repo": "vllm-project/vllm",
"number": 26964,
"title": "[Bug]: Issue with Deepseek Reasoning parser with Qwen3 2507 chat templates",
"body": "### Your current environment\n\n![\"Image\"](\"https://github.com/user-attachments/assets/eb2f6452-3df0-49f0-a1c5-5b99b56f578a\")
\n\nI think this is the place to raise this issue. Thanks so much!\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26964",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-16T00:39:12Z",
"updated_at": "2025-10-20T17:47:02Z",
"comments": 1,
"user": "MikeNatC"
},
{
"repo": "vllm-project/vllm",
"number": 26949,
"title": "[Bug]: RuntimeError: CUDA driver error: invalid device ordinal when symmetric memory (symm_mem) is enabled in multi-GPU vLLM setup with 4H100 PCIe",
"body": "### My current environment\n\nEnvironment:\nModel: RedHatAI/Llama-4-Scout-17B-16E-Instruct-FP8-dynamic\nvLLM Version: latest main (installed via pip)\nHardware: 4\u00d7 NVIDIA H100 PCIe (80GB)\nDriver: 550.xx\nCUDA: 12.2\nPyTorch: 2.4.0\nOS: Ubuntu 22.04\nLaunch Command:\npython3 -m vllm.entrypoints.api_server \\\n --model /ephemeral/huggingface/models--RedHatAI--Llama-4-Scout-17B-16E-Instruct-FP8-dynamic/snapshots/... \\\n --tensor-parallel-size 4 \\\n --gpu-memory-utilization 0.85 \\\n --kv-cache-dtype fp8_e4m3 \\\n --max-model-len 4000000 \\\n --max-num-seqs 16 \\\n --enable-prefix-caching \\\n --kv-events-config '{\"enable_kv_cache_events\": true, \"publisher\": \"zmq\", \"endpoint\": \"tcp://*:5557\"}'\n\n\n### bug\n\nRuntimeError: CUDA driver error: invalid device ordinal\n(EngineCore_DP0 pid=11546) ERROR [symm_mem.py:88] handle = torch_symm_mem.rendezvous(self.buffer, self.group.group_name)\n(EngineCore_DP0 pid=11546) ERROR WorkerProc failed to start\nRuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {'EngineCore_DP0': 1}\n\nBehavior:\nWhen symm_mem is enabled (default) \u2192 fails with invalid device ordinal\nWhen symm_mem is disabled via --disable-symm-mem \u2192\n\u2705 vLLM engine starts\n\u274c No KV cache event logs (BlockStored, BlockRemoved, etc.)\n\u274c No prefix cache hit metrics\n\nWhat I\u2019ve Tried\n\nVerified all 4 GPUs visible via nvidia-smi\nConfirmed correct CUDA device indexing\nReduced tensor-parallel-size to 2 \u2192 same error\nChecked for NCCL initialization issues \u2014 none\nManually set CUDA_VISIBLE_DEVICES=0,1,2,3\nRebuilt PyTorch + vLLM from source with USE_SYMMETRIC_MEMORY=1 \u2014 same result\nQuestion:\nIs there a known compatibility issue between symmetric memory (torch_symm_mem) and H100 PCIe devices in multi-GPU setups?\nIf so, is there a fallback mechanism to preserve KV event publishing (--kv-events-config) when symmetric memory is disabled?\n\nThanks for looking into it.\n",
"url": "https://github.com/vllm-project/vllm/issues/26949",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-15T22:08:34Z",
"updated_at": "2025-12-25T03:42:49Z",
"comments": 2,
"user": "vadapallij"
},
{
"repo": "vllm-project/vllm",
"number": 26940,
"title": "[Feature]: Support `inf` value for burstiness in benchmarks",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nIn the benchmarks, the burstiness value is used in a gamma distribution to sample the delays between consecutive requests. \n```\ntheta = 1.0 / (current_request_rate * burstiness)\ndelay_ts.append(np.random.gamma(shape=burstiness, scale=theta))\n```\n\n[Theoretically ](https://en.wikipedia.org/wiki/Gamma_distribution)(and this is also what is observed in practice), the generated delays have as mean `1.0 / current_request_rate` and the spread is controlled by the burstiness. When the burstiness is high, we observe lower variance in the delay values, all values being closer to the mean `1.0 / current_request_rate`. When burstiness tends to infinity, we should observe a single generated delay, which is `1.0 / current_request_rate`. In practice, the `np.random.gamma` function generates `nan` as results, so we need to manually condition on `burstiness` value and append `1.0 / current_request_rate` to the list of delays when burstiness becomes infinite.\n\nSee attached image as mathematical proof\n\n![\"Image\"](\"https://github.com/user-attachments/assets/455cfd00-ea8f-44c8-874f-7fdac4faae6d\")
\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26940",
"state": "closed",
"labels": [
"feature request"
],
"created_at": "2025-10-15T19:39:03Z",
"updated_at": "2025-11-03T18:33:19Z",
"comments": 0,
"user": "sducouedic"
},
{
"repo": "vllm-project/vllm",
"number": 26914,
"title": "[Usage]: \u4e3a\u4ec0\u4e48\u5728\u91c7\u96c6\u7684profiling\u4e2d\u770b\u4e0d\u5230\u901a\u4fe1\u7b97\u5b50\uff1f",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\u901a\u8fc7llm.start_profile\u548cstop_profile\uff0c\u6211\u91c7\u96c6\u5230\u4e86profiling\uff0c\u4f46kernel_details\u91cc\u9762\u770b\u4e0d\u5230\u901a\u4fe1\u7b97\u5b50\u3002\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26914",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-15T13:38:14Z",
"updated_at": "2025-10-15T13:38:14Z",
"comments": 0,
"user": "sheep94lion"
},
{
"repo": "vllm-project/vllm",
"number": 26903,
"title": "[Usage]: vLLM for video input",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\nI want to run inference of qwen2.5-vl or qwen2.5-omni. \n\nWhen I convert the video to base64 for api calls (e.g. openai format), I found that vLLM seems to use all the video frames by checking the number of prompt tokens.\n\nIs there any parameter similar to fps to control the sampling rate?\nOr do I need to sample the video externally well in advance, save it as video and then convert to base64?\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26903",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-15T09:29:23Z",
"updated_at": "2025-12-11T03:26:33Z",
"comments": 6,
"user": "King-king424"
},
{
"repo": "huggingface/diffusers",
"number": 12492,
"title": "module transformers has no attribute CLIPFeatureExtractor",
"body": "### System Info\n\nlatest main\n\n### Who can help?\n\n@SunMarc \n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```python\nfrom diffusers import AnimateDiffPipeline\n\npipe = AnimateDiffPipeline.from_pretrained(\"emilianJR/epiCRealism\")\n```\nerror:\n```\nTraceback (most recent call last):\n File \"\", line 1, in \n File \"/opt/venv/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py\", line 89, in _inner_fn\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n File \"/home/jiqing/diffusers/src/diffusers/pipelines/pipeline_utils.py\", line 1024, in from_pretrained\n loaded_sub_model = load_sub_model(\n ^^^^^^^^^^^^^^^\n File \"/home/jiqing/diffusers/src/diffusers/pipelines/pipeline_loading_utils.py\", line 752, in load_sub_model\n class_obj, class_candidates = get_class_obj_and_candidates(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jiqing/diffusers/src/diffusers/pipelines/pipeline_loading_utils.py\", line 419, in get_class_obj_and_candidates\n class_obj = getattr(library, class_name)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/jiqing/transformers/src/transformers/utils/import_utils.py\", line 1920, in __getattr__\n raise AttributeError(f\"module {self.__name__} has no attribute {name}\")\nAttributeError: module transformers has no attribute CLIPFeatureExtractor\n```\n\n\n\n### Expected behavior\n\nAs transformers deprecated FeatureExtractor classes in favor of ImageProcessor classes for image preprocessing. How to handle models that already set FeatureExtractor in model hub like [emilianJR/epiCRealism](https://huggingface.co/emilianJR/epiCRealism/blob/main/feature_extractor/preprocessor_config.json#L11)?",
"url": "https://github.com/huggingface/diffusers/issues/12492",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-15T08:26:05Z",
"updated_at": "2025-11-03T05:02:54Z",
"comments": 3,
"user": "jiqing-feng"
},
{
"repo": "vllm-project/vllm",
"number": 26858,
"title": "[RFC]: Top-level CLI interface for KV cache offloading",
"body": "### Motivation.\n\nCPU (and tier-2 storage) offloading is an important feature in many cases (multi-round QA, document analysis, agent workflow, and reinforcement learning). With the recent advancement in the offloading connector, we already have the vLLM native CPU offloading implemented via the connector API. Also, there are multiple community efforts to provide other offloading implementations (e.g., LMCache, Nixl storage, mooncake) via the same set of APIs.\n\nHowever, there is no clear documentation about how to configure the CPU offloading from the user's perspective. Right now, in order to enable CPU offloading, the user needs to pass a JSON string to `--kv-transfer-config`, which may create a huge mental barrier for new users. Therefore, it would be better to have a simple & clear user interface for users to enable CPU offloading. \n\n### Proposed Change.\n\n\nThis proposal contains two new command-line arguments:\n- `--kv-offloading-size`: a numeric value to control a global offloading buffer size (in GB). When TP > 1, this number should be the total size summed across all the TP ranks. (An alternative is the buffer size for each TP rank.)\n- `--kv-offloading-backend`: a string that specifies which offloading backend to use, such as \"native\", \"lmcache\", \"mooncake\", \"3fs\", or \"nixl\".\n\nThis will give enough clarity to most of the users who want to use the offloading feature, and should be extensible enough to new offloading backends and tier-2 storage.\n\n## Required changes\n\nTo implement this proposal, the following things are needed:\n- Add logic to parse the new CLI argument and store it into vllm config.\n- Add a new module to translate the `--kv-offloading-size` and `--kv-offloading-backend` to the corresponding KV connector config.\n- Add the documentation to the vLLM user guide.\n\n### Feedback Period.\n\n1~2 weeks\n\n### CC List.\n\n@simon-mo @orozery @njhill \n\n### Any Other Things.\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26858",
"state": "closed",
"labels": [
"RFC"
],
"created_at": "2025-10-15T00:11:15Z",
"updated_at": "2025-11-01T07:17:08Z",
"comments": 8,
"user": "ApostaC"
},
{
"repo": "huggingface/diffusers",
"number": 12485,
"title": "How to enable Context Parallelism for training",
"body": "Hi @a-r-r-o-w , I would like to ask you for tips on using Context Parallelism for distributed training.\n\n**Is your feature request related to a problem? Please describe.**\nHere is the minimal code for adapting Context Parallelism into diffusion model training\n\n```python\n# Diffusers Version: 0.36.0.dev0\nfrom diffusers.models._modeling_parallel import ContextParallelConfig\n\n# I have 8 GPUs in total\ncp_config = ContextParallelConfig(ring_degree=1, ulysses_degree=8)\nflux_transformer.enable_parallelism(config=cp_config)\n\nloss = train(flux_transformer)\naccelerator.backward(loss)\ngrad_norm = accelerator.clip_grad_norm_(flux_transformer.parameters(), args.max_grad_norm)\n```\n\nHowever, there is a bug:\n```bash\n[rank5]: Traceback (most recent call last):\n[rank5]: File \"/home/code/diffusers/flux/sft_flux.py\", line 1494, in \n[rank5]: main_with_cleanup(args)\n[rank5]: File \"/home/code/diffusers/flux/sft_flux.py\", line 1460, in main_with_cleanup\n[rank5]: main(args)\n[rank5]: File \"/home/code/diffusers/flux/sft_flux.py\", line 1216, in main\n[rank5]: grad_norm = accelerator.clip_grad_norm_(flux_transformer.parameters(), args.max_grad_norm)\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/accelerate/accelerator.py\", line 2863, in clip_grad_norm_\n[rank5]: return torch.nn.utils.clip_grad_norm_(\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/nn/utils/clip_grad.py\", line 36, in _no_grad_wrapper\n[rank5]: return func(*args, **kwargs)\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/nn/utils/clip_grad.py\", line 222, in clip_grad_norm_\n[rank5]: _clip_grads_with_norm_(parameters, max_norm, total_norm, foreach)\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/nn/utils/clip_grad.py\", line 36, in _no_grad_wrapper\n[rank5]: return func(*args, **kwargs)\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/nn/utils/clip_grad.py\", line 155, in _clip_grads_with_norm_\n[rank5]: clip_coef = max_norm / (total_norm + 1e-6)\n[rank5]: ~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/_tensor.py\", line 39, in wrapped\n[rank5]: return f(*args, **kwargs)\n[rank5]: ^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/_tensor.py\", line 1101, in __rdiv__\n[rank5]: return self.reciprocal() * other\n[rank5]: ^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/_compile.py\", line 53, in inner\n[rank5]: return disable_fn(*args, **kwargs)\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/_dynamo/eval_frame.py\", line 929, in _fn\n[rank5]: return fn(*args, **kwargs)\n[rank5]: ^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/distributed/tensor/_api.py\", line 350, in __torch_dispatch__\n[rank5]: return DTensor._op_dispatcher.dispatch(\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/distributed/tensor/_dispatch.py\", line 166, in dispatch\n[rank5]: self.redistribute_local_args(\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/distributed/tensor/_dispatch.py\", line 303, in redistribute_local_args\n[rank5]: resharded_local_tensor = redistribute_local_tensor(\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/distributed/tensor/_redistribute.py\", line 208, in redistribute_local_tensor\n[rank5]: new_local_tensor = partial_spec._reduce_value(\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/distributed/tensor/_ops/_math_ops.py\", line 126, in _reduce_value\n[rank5]: reduced_tensor = super()._reduce_value(tensor, mesh, mesh_dim)\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/distributed/tensor/placement_types.py\", line 679, in _reduce_value\n[rank5]: return funcol.all_reduce(\n[rank5]: ^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/distributed/_functional_collectives.py\", line 175, in all_reduce\n[rank5]: group_name = _resolve_group_name(group, tag)\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank5]: File \"/home/.local/lib/python3.11/site-packages/torch/distributed/_functional_collectives.py\", line 783, in _resolve_group_name\n[rank5]: return dmesh._dim_group_names[dim]\n[rank5]: ^^^^^^^^^^^^^^^^^^^^^^\n[rank5]: AttributeError: 'DeviceMesh' obj",
"url": "https://github.com/huggingface/diffusers/issues/12485",
"state": "closed",
"labels": [],
"created_at": "2025-10-14T21:48:35Z",
"updated_at": "2025-10-15T20:33:30Z",
"user": "liming-ai"
},
{
"repo": "vllm-project/vllm",
"number": 26840,
"title": "[Doc]: Update AWQ Guide",
"body": "### \ud83d\udcda The doc issue\n\nSituation: AutoAWQ functionality was adopted by llm-compressor but vllm [docs](https://docs.vllm.ai/en/latest/features/quantization/auto_awq.html) point to AutoAWQ which is deprecated\n\n\n### Suggest a potential alternative/fix\n\n1) Update the [AutoAWQ guide](https://github.com/vllm-project/vllm/blob/main/docs/features/quantization/auto_awq.md) to use the [llm-compressor](https://github.com/vllm-project/llm-compressor/tree/2a6a0a34c8a57b6090b5fbac9c0659edf982185c/examples/awq) apis/flow\n2) Make sure to also update links in [quantization doc](https://github.com/vllm-project/vllm/blob/main/docs/features/quantization/README.md)\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26840",
"state": "closed",
"labels": [
"documentation"
],
"created_at": "2025-10-14T20:02:21Z",
"updated_at": "2025-11-03T15:39:12Z",
"comments": 0,
"user": "HDCharles"
},
{
"repo": "vllm-project/vllm",
"number": 26838,
"title": "[Performance]: RTX 6000 PRO - FP8 in sglang is faster",
"body": "### Proposal to improve performance\n\nCan we have a discussion about the sglang FP8 performance vs VLLM performance - \n\nI'm able to get 133 tokens/sec with sglang GLM-4.5-Air-FP8 vs 78 tokens/sec in VLLM \n\n```PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True USE_TRITON_W8A8_FP8_KERNEL=1 SGL_ENABLE_JIT_DEEPGEMM=0 python -m sglang.launch_server --model /mnt/GLM-4.5-FP8/ --tp 4 --host 0.0.0.0 --port 5000 --mem-fraction-static 0.93 --context-length 128000 --enable-metrics --attention-backend flashinfer --tool-call-parser glm45 --reasoning-parser glm45 --served-model-name glm-4.5-air --chunked-prefill-size 8092 --enable-mixed-chunk --cuda-graph-max-bs 32 --kv-cache-dtype fp8_e5m2```\n\nIt is using TRITON \n\nI'm not able to achieve the same speed with VLLM with any methods - neither flashinfer, nor triton etc. - the maximum is always around 78 tokens/sec \n\n1) Any idea how to achieve the same 133tokens/sec in VLLM using triton and same configuration like in sglang? \n2) is it cutlass design that it is not that fast as triton? \n\n\n\n\n\n### Report of performance regression\n\n_No response_\n\n### Misc discussion on performance\n\n_No response_\n\n### Your current environment (if you think it is necessary)\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26838",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-10-14T19:41:14Z",
"updated_at": "2025-12-29T14:52:57Z",
"comments": 10,
"user": "voipmonitor"
},
{
"repo": "vllm-project/vllm",
"number": 26817,
"title": "[Feature]: Add process_weights_after_loading to AttentionImpl",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nCurrently, in the `Attention` layer, we check if `process_weights_after_loading` exists and then call it conditionally, and after that we apply flashinfer-specific logic.\n\nInstead, we should just add a `process_weights_after_loading` method to AttentionImpl (no-op) by default, call it from `Attention.process_weights_after_loading`, and override it in `FlashInferAttentionImpl`.\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\nhttps://github.com/vllm-project/vllm/pull/23016#discussion_r2414787224\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26817",
"state": "closed",
"labels": [
"help wanted",
"good first issue",
"feature request"
],
"created_at": "2025-10-14T15:59:54Z",
"updated_at": "2025-10-16T15:02:31Z",
"comments": 2,
"user": "ProExpertProg"
},
{
"repo": "vllm-project/vllm",
"number": 26806,
"title": "[Usage]: MCP-USE with VLLM gpt-oss:20b via ChatOpenAI",
"body": "### Your current environment\n\n```text\nThe output of `python collect_env.py`\n```\n\n\n### How would you like to use vllm\n\ni am trying to create an agent using gpt-oss:20B with mcp-use \n\nmost times the model returns \"Agent completed the task successfully.\", and sometimes the proper output which is required\n\n### code \n`vllm serve openai/gpt-oss-20b --max-model-len 100000 --gpu-memory-utilization 0.9 --port 8000 --tool-call-parser openai --enable-auto-tool-choice`\n\nclient = MCPClient.from_dict(config)\nllm = ChatOpenAI(\n model=\"openai/gpt-oss-20b\",\n base_url=\"http://127.0.0.1:8000/v1\", \n api_key=\"not-needed\",\n temperature=0.8,\n max_tokens=2048\n)\nagent = MCPAgent(llm=llm, client=client, max_steps=30)\n\n\nalso raising this on mcp-use\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26806",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-14T13:00:38Z",
"updated_at": "2025-11-20T06:33:29Z",
"comments": 2,
"user": "Tahirc1"
},
{
"repo": "vllm-project/vllm",
"number": 26786,
"title": "[Usage]: cuda12.8 docker 0.11.0 Error occurs when launching the model, NCCL error: unhandled cuda error.",
"body": "When I use only a single graphics card, the system can start up normally.\nBelow are Docker configuration files, logs, and environment information.\n\nI encountered this issue when upgrading from version 10.1.1 to 10.2.\n\n[The system generates an error when using dual graphics cards; version 10.1.1 functions correctly, but version 10.2 triggers an error upon execution.](https://github.com/vllm-project/vllm/issues/25813)\n\n### Your current environment\n\n```text\n# vllm collect-env \nINFO 10-14 19:07:58 [__init__.py:216] Automatically detected platform cuda.\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version : Could not collect\nCMake version : version 4.1.0\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA GeForce RTX 4090\nGPU 1: NVIDIA GeForce RTX 4090\n\nNvidia driver version : 571.96\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 46 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 40\nOn-line CPU(s) list: 0-39\nVendor ID: GenuineIntel\nModel name: Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz\nCPU family: 6\nModel: 85\nThread(s) per core: 2\nCore(s) per socket: 20\nSocket(s): 1\nStepping: 4\nBogoMIPS: 4788.75\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc arch_perfmon rep_good nopl xtopology cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pdcm pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch pti ssbd ibrs ibpb stibp fsgsbase bmi1 hle avx2 smep bmi2 erms invpcid rtm avx512f avx512dq rdseed adx smap clflushopt clwb avx512cd avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves md_clear flush_l1d arch_capabilities\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 640 KiB (20 instances)\nL1i cache: 640 KiB (20 instances)\nL2 cache: 20 MiB (20 instances)\nL3 cache: 27.5 MiB (1 instance)\nNUMA node(s): 1\nNUMA node0 CPU(s): 0-39\nVulnerability Gather data sampling: Unknown: Dependent on hypervisor status\nVulnerability Itlb multihit: KVM: Mitigation: VMX unsupported\nVulnerability L1tf: Mitigation; PTE Inversion\nVulnerability Mds: Mitigation; Clear CPU buffers; SMT Host state unknown\nVulnerability Meltdown: Mitigation; PTI\nVulnerability Mmio stale data: Mitigation; Clear CPU buffers; SMT Host state unknown\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Mitigation; IBRS\nVulnerability Spec rstack overflow: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; IBRS; IBPB conditional; STIBP conditional; RSB filling; PBRSB-eIBRS Not affected; BHI SW loop, KVM SW loop\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Mitigation; Clear CPU buffers; SMT Host state unknown\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.3.1\n[pip3] numpy==",
"url": "https://github.com/vllm-project/vllm/issues/26786",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-14T09:01:39Z",
"updated_at": "2025-11-07T17:17:32Z",
"comments": 3,
"user": "ooodwbooo"
},
{
"repo": "vllm-project/vllm",
"number": 26774,
"title": "[Usage]: how to use vllm on CUDA 12.9",
"body": "### Your current environment\n\n```text\nTraceback (most recent call last):\n File \"/vllm-workspace/collect_env.py\", line 825, in \n main()\n File \"/vllm-workspace/collect_env.py\", line 804, in main\n output = get_pretty_env_info()\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/vllm-workspace/collect_env.py\", line 799, in get_pretty_env_info\n return pretty_str(get_env_info())\n ^^^^^^^^^^^^^^\n File \"/vllm-workspace/collect_env.py\", line 619, in get_env_info\n cuda_module_loading=get_cuda_module_loading_config(),\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/vllm-workspace/collect_env.py\", line 540, in get_cuda_module_loading_config\n torch.cuda.init()\n File \"/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py\", line 339, in init\n _lazy_init()\n File \"/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py\", line 372, in _lazy_init\n torch._C._cuda_init()\nRuntimeError: No CUDA GPUs are available\nroot@test2222-7dcd6b94b7-wl6w4:/vllm-workspace# python3 --version\nPython 3.12.1\n```\n\n\n### How would you like to use vllm\n\nMy node CUDA version is 12.9, and the running pod image CUDA variable is 12.8. Will this cause the No CUDA GPUs are available error? Is 12.9 compatible with version 12.8? Should we upgrade the VLLM version or lower the CUDA version of the node to 12.8\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26774",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-14T07:30:56Z",
"updated_at": "2025-10-14T07:40:08Z",
"comments": 1,
"user": "Mrpingdan"
},
{
"repo": "vllm-project/vllm",
"number": 26772,
"title": "[Feature]: Option kv_event default config",
"body": "### \ud83d\ude80 The feature, motivation and pitch\n\nCurrent kv_event config publisher is null, but endpoint is zmq endpoint, so when not set publisher config, vllm cannot start, got a error: `EventPublisher.__init__() got an unexpected keyword argument 'endpoint'`.\n\nCan we change this default publisher to zmq, when start enable_kv_cache_events after use can direct use.\nhttps://github.com/vllm-project/vllm/blob/d32c611f455766c9d67034b5e0f8e66f28f4a3ba/vllm/config/kv_events.py#L20-L24\n\n### Alternatives\n\n_No response_\n\n### Additional context\n\n_No response_\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26772",
"state": "closed",
"labels": [
"feature request"
],
"created_at": "2025-10-14T07:08:58Z",
"updated_at": "2025-10-22T19:19:34Z",
"comments": 5,
"user": "lengrongfu"
},
{
"repo": "vllm-project/vllm",
"number": 26762,
"title": "[Usage]: about curl http://ip:8000/metrics",
"body": "### Your current environment\n\nWhen I run this command, I get the following results: \n# HELP python_gc_objects_collected_total Objects collected during gc\n# TYPE python_gc_objects_collected_total counter\npython_gc_objects_collected_total{generation=\"0\"} 12286.0\npython_gc_objects_collected_total{generation=\"1\"} 1244.0\npython_gc_objects_collected_total{generation=\"2\"} 1326.0\n# HELP python_gc_objects_uncollectable_total Uncollectable objects found during GC\n# TYPE python_gc_objects_uncollectable_total counter\npython_gc_objects_uncollectable_total{generation=\"0\"} 0.0\npython_gc_objects_uncollectable_total{generation=\"1\"} 0.0\npython_gc_objects_uncollectable_total{generation=\"2\"} 0.0\n# HELP python_gc_collections_total Number of times this generation was collected\n# TYPE python_gc_collections_total counter\npython_gc_collections_total{generation=\"0\"} 1378.0\npython_gc_collections_total{generation=\"1\"} 124.0\npython_gc_collections_total{generation=\"2\"} 9.0\n# HELP python_info Python platform information\n# TYPE python_info gauge\npython_info{implementation=\"CPython\",major=\"3\",minor=\"12\",patchlevel=\"11\",version=\"3.12.11\"} 1.0\n# HELP process_virtual_memory_bytes Virtual memory size in bytes.\n# TYPE process_virtual_memory_bytes gauge\nprocess_virtual_memory_bytes 1.1701968896e+010\n# HELP process_resident_memory_bytes Resident memory size in bytes.\n# TYPE process_resident_memory_bytes gauge\nprocess_resident_memory_bytes 1.045848064e+09\n# HELP process_start_time_seconds Start time of the process since unix epoch in seconds.\n# TYPE process_start_time_seconds gauge\nprocess_start_time_seconds 1.76036994809e+09\n# HELP process_cpu_seconds_total Total user and system CPU time spent in seconds.\n# TYPE process_cpu_seconds_total counter\nprocess_cpu_seconds_total 148.44\n# HELP process_open_fds Number of open file descriptors.\n# TYPE process_open_fds gauge\nprocess_open_fds 69.0\n# HELP process_max_fds Maximum number of open file descriptors.\n# TYPE process_max_fds gauge\nprocess_max_fds 1.048576e+06\n# HELP http_requests_total Total number of requests by method, status and handler.\n# TYPE http_requests_total counter\nhttp_requests_total{handler=\"none\",method=\"GET\",status=\"4xx\"} 1.0\n# HELP http_requests_created Total number of requests by method, status and handler.\n# TYPE http_requests_created gauge\nhttp_requests_created{handler=\"none\",method=\"GET\",status=\"4xx\"} 1.7604160309440813e+09\n# HELP http_request_size_bytes Content length of incoming requests by handler. Only value of header is respected. Otherwise ignored. No percentile calculated. \n# TYPE http_request_size_bytes summary\nhttp_request_size_bytes_count{handler=\"none\"} 1.0\nhttp_request_size_bytes_sum{handler=\"none\"} 0.0\n# HELP http_request_size_bytes_created Content length of incoming requests by handler. Only value of header is respected. Otherwise ignored. No percentile calculated. \n# TYPE http_request_size_bytes_created gauge\nhttp_request_size_bytes_created{handler=\"none\"} 1.7604160309442668e+09\n# HELP http_response_size_bytes Content length of outgoing responses by handler. Only value of header is respected. Otherwise ignored. No percentile calculated. \n# TYPE http_response_size_bytes summary\nhttp_response_size_bytes_count{handler=\"none\"} 1.0\nhttp_response_size_bytes_sum{handler=\"none\"} 22.0\n# HELP http_response_size_bytes_created Content length of outgoing responses by handler. Only value of header is respected. Otherwise ignored. No percentile calculated. \n# TYPE http_response_size_bytes_created gauge\nhttp_response_size_bytes_created{handler=\"none\"} 1.7604160309445088e+09\n# HELP http_request_duration_highr_seconds Latency with many buckets but no API specific labels. Made for more accurate percentile calculations. \n# TYPE http_request_duration_highr_seconds histogram\nhttp_request_duration_highr_seconds_bucket{le=\"0.01\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"0.025\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"0.05\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"0.075\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"0.1\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"0.25\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"0.5\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"0.75\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"1.0\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"1.5\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"2.0\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"2.5\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"3.0\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"3.5\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"4.0\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"4.5\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"5.0\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"7.5\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"10.0\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"30.0\"} 1.0\nhttp_request_duration_highr_seconds_bucket{le=\"60.0\"} 1.0\nhttp_request_duration_highr_se",
"url": "https://github.com/vllm-project/vllm/issues/26762",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-14T05:13:30Z",
"updated_at": "2025-10-14T05:13:30Z",
"comments": 0,
"user": "Renoshen"
},
{
"repo": "huggingface/lerobot",
"number": 2194,
"title": "During training with PI0, the loss is very low. Is this normal, and is the training proceeding correctly?",
"body": "I am currently training with PI05.\n\n![\"Image\"](\"https://github.com/user-attachments/assets/5ab3f3e0-82bc-403c-8124-416b330dab14\")
\n\n`INFO 2025-10-14 04:57:11 ot_train.py:299 step:10 smpl:320 ep:0 epch:0.00 loss:0.468 grdn:3.522 lr:1.6e-07 updt_s:4.906 data_s:4.874 INFO 2025-10-14 04:57:59 ot_train.py:299 step:20 smpl:640 ep:0 epch:0.00 loss:0.467 grdn:3.936 lr:4.1e-07 updt_s:4.807 data_s:0.008 INFO 2025-10-14 04:58:48 ot_train.py:299 step:30 smpl:960 ep:0 epch:0.01 loss:0.508 grdn:3.973 lr:6.6e-07 updt_s:4.815 data_s:0.009 INFO 2025-10-14 04:59:36 ot_train.py:299 step:40 smpl:1K ep:1 epch:0.01 loss:0.513 grdn:3.805 lr:9.1e-07 updt_s:4.841 data_s:0.009`\n\nThe loss is very low right from the start of training. Is it training normally?",
"url": "https://github.com/huggingface/lerobot/issues/2194",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-14T05:04:31Z",
"updated_at": "2025-10-14T08:19:29Z",
"user": "pparkgyuhyeon"
},
{
"repo": "huggingface/peft",
"number": 2832,
"title": "Gradient checkpoint with multiple adapters",
"body": "I'm not sure if it can be considered as a bug since I might be using the library differently from how it's supposed to be used.\n\n\n**Context:**\n\n I have a PeftModel that need to be infered with 2 different inputs.\nFor each input I have a pretrained adapter that is frozen and a new adapter for finetuning.\n\nMy forward does:\n```\nfor name, x in inputs:\n mypeft_model.base_model.set_adapter([name+'pretrain',name+'ft'])\n custom_set_pretrain_grad_false_ft_true() #Doing it because set_adapter force gradients to True cf 2759#issue-3363985341\n feature = mypeft_model(x)\n```\n (https://github.com/huggingface/peft/issues/2759#issue-3363985341)\n**Issue:**\n1) if mypeft_model contains cp.checkpoint(mymodule, x), the backpropagation will not update properly the weight of the LoRA layers in my module either because it did not 'see the set_adapter' or it did not 'see the force grad'\n2) A work around I have found is to wrap the whole code inside the loop with a cp.checkpoint but it's super heavy on the memory as I have to store all in GPU until the end of the backbone (ViT-G 40 blocks transformers)\n\n**Question:**\nIs there anyway to 'provide' the context to the backpropagation even using gradient checkpointing when switching adapters in the forward?\nI have not explored huggingface transformers.enable_gradient_checkpointing() since I'm using a custom model and I'm unsure if it fits for my problem.\n",
"url": "https://github.com/huggingface/peft/issues/2832",
"state": "closed",
"labels": [],
"created_at": "2025-10-14T03:53:10Z",
"updated_at": "2025-12-15T08:24:03Z",
"comments": 3,
"user": "NguyenRichard"
},
{
"repo": "huggingface/lerobot",
"number": 2192,
"title": "how to test PI0's output",
"body": "i use this code to test pi0's output:\n\ndef main():\n # Create a directory to store the training checkpoint.\n output_directory = Path(\"outputs/example_aloha_static_coffee\")\n output_directory.mkdir(parents=True, exist_ok=True)\n\n # # Select your device\n device = torch.device(\"cuda\")\n\n # Number of offline training steps (we'll only do offline training for this example.)\n # Adjust as you prefer. 5000 steps are needed to get something worth evaluating.\n training_steps = 500\n log_freq = 1\n\n # When starting from scratch (i.e. not from a pretrained policy), we need to specify 2 things before\n # creating the policy:\n # - input/output shapes: to properly size the policy\n # - dataset stats: for normalization and denormalization of input/outputs\n dataset_metadata = LeRobotDatasetMetadata(\"lerobot/aloha_static_coffee\")\n print(dataset_metadata.features.keys())\n features = dataset_to_policy_features(dataset_metadata.features)\n output_features = {key: ft for key, ft in features.items() if ft.type is FeatureType.ACTION}\n input_features = {key: ft for key, ft in features.items() if key not in output_features}\n\n # Policies are initialized with a configuration class, in this case `PI0Config`. For this example,\n # we'll just use the defaults and so no arguments other than input/output features need to be passed.\n cfg = PI0Config(input_features=input_features, output_features=output_features)\n print(cfg)\n\n # We can now instantiate our policy with this config and the dataset stats.\n policy = PI0Policy(cfg)\n policy.train()\n policy.to(device)\n preprocessor, postprocessor = make_pre_post_processors(cfg, dataset_stats=dataset_metadata.stats)\n\n # We can then instantiate the dataset with these delta_timestamps configuration.\n dataset = LeRobotDataset(\"lerobot/aloha_static_coffee\")\n\n # \u53d6\u4e00\u6761\u6570\u636e\u8fdb\u884c\u8bd5\u9a8c\n state = dataset[20][\"observation.state\"]\n image_cam_high = dataset[20][\"observation.images.cam_high\"]\n image_cam_left_wrist = dataset[20][\"observation.images.cam_left_wrist\"]\n image_cam_low = dataset[20][\"observation.images.cam_low\"]\n image_cam_right_wrist = dataset[20][\"observation.images.cam_right_wrist\"]\n effort = dataset[20][\"observation.effort\"]\n state = state.unsqueeze(0).to(device)\n image_cam_high = image_cam_high.unsqueeze(0).to(device)\n image_cam_left_wrist = image_cam_left_wrist.unsqueeze(0).to(device)\n image_cam_low = image_cam_low.unsqueeze(0).to(device)\n image_cam_right_wrist = image_cam_right_wrist.unsqueeze(0).to(device)\n effort = effort.unsqueeze(0).to(device)\n print(\"State size: \", state.size())\n print(\"Image size: \", image_cam_high.size())\n print(\"Effort size: \", effort.size())\n observation = {\n \"observation.state\": state,\n \"observation.images.cam_high\": image_cam_high,\n \"observation.images.cam_left_wrist\": image_cam_left_wrist,\n \"observation.images.cam_low\": image_cam_low,\n \"observation.images.cam_right_wrist\": image_cam_right_wrist,\n \"observation.effort\": effort,\n }\n\n # \u8f93\u51faaction\n with torch.inference_mode():\n action = policy.select_action(observation)\n numpy_action = action.squeeze(0).to(\"cpu\").numpy()\n print(\"Action: \", numpy_action)\n\n\nbut got an error:\n\nTraceback (most recent call last):\n File \"/home/wjg/trainpi0.py\", line 140, in \n main()\n File \"/home/wjg/trainpi0.py\", line 129, in main\n action = policy.select_action(observation)\n File \"/data/wjg_files/anaconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n return func(*args, **kwargs)\n File \"/data/wjg_files/lerobot/src/lerobot/policies/pi0/modeling_pi0.py\", line 1144, in select_action\n actions = self.predict_action_chunk(batch)[:, : self.config.n_action_steps]\n File \"/data/wjg_files/anaconda3/envs/lerobot/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n return func(*args, **kwargs)\n File \"/data/wjg_files/lerobot/src/lerobot/policies/pi0/modeling_pi0.py\", line 1157, in predict_action_chunk\n lang_tokens, lang_masks = batch[f\"{OBS_LANGUAGE_TOKENS}\"], batch[f\"{OBS_LANGUAGE_ATTENTION_MASK}\"]\nKeyError: 'observation.language.tokens'\n\nhow to solve it?",
"url": "https://github.com/huggingface/lerobot/issues/2192",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-14T03:36:43Z",
"updated_at": "2025-10-17T09:56:46Z",
"user": "Addog666"
},
{
"repo": "vllm-project/vllm",
"number": 26749,
"title": "[Bug]: InternVL: passing image embeddings triggers TypeError: can only concatenate tuple (not \"Tensor\") to tuple in get_multimodal_embeddings, and v1 sanity check then expects a sequence of 2D tensors",
"body": "### Your current environment\n\n\\nWhat is this image?\\nASSISTANT:\"\n\n# 3D embeddings: [B, T, H] just to illustrate the bug (B=1 here)\n# H equals the LM hidden_size for the given weight; using 1024 to reproduce.\nimage_embeds = torch.randn(1, 16, 1024)\n\nout = llm.generate({\n \"prompt\": prompt,\n \"multi_modal_data\": {\"image\": image_embeds}, # or {\"images\": image_embeds}\n})\nprint(out[0].outputs[0].text)\n```\n\n## Actual Behavior / Stack\nOn 0.10.2:\n\n```\nFile \".../vllm/model_executor/models/internvl.py\", line 1328, in get_multimodal_embeddings\n multimodal_embeddings += vision_embeddings\nTypeError: can only concatenate tuple (not \"Tensor\") to tuple\n```\n\nIf we monkey-patch around the above concat, the engine soon asserts:\n\n```\nvllm/v1/worker/utils.py\", line 155, in sanity_check_mm_encoder_outputs\nAssertionError: Expected multimodal embeddings to be a sequence of 2D tensors,\nbut got tensors with shapes [torch.Size([1, 16, 1024])] instead.\nThis is most likely due to incorrect implementation of the model's `get_multimodal_embeddings` method.\n```\n\nSo there are **two inconsistencies**:\n1) `get_multimodal_embeddings` sometimes returns a **Tensor** (3D) but the code path later concatenates assuming a **tuple** of tensors. \n2) v1 expects a **sequence of 2D tensors `[T, H]`**, but the current image-embeddings path can yield a **3D** `[B, T, H]` tensor (batch dimension not flattened), which fails the sanity check.\n\n## Expected Behavior\n- Passing embeddings should **not crash**, whether provided as:\n - a single 2D tensor `[T, H]` (one image), or\n - a 3D tensor `[B, T, H]` (batch of images), or\n - a list/tuple of 2D tensors. \n- `get_multimodal_embeddings` should normalize its outputs to a **sequence of 2D tensors** to satisfy `sanity_check_mm_encoder_outputs`.\n\n## Why this matters\nInternVL supports both pixel inputs and precomputed **embeddings**. The embedding path is useful in production pipelines (pre-encode vision on different hardware, caching, etc.). Currently in 0.10.1/0.10.2 this path is broken due to type/shape inconsistencies, blocking these use-cases.\n\n## Proposed Fix (minimal)\nNormalize to a sequence of 2D tensors before concatenation. For example, in `vllm/model_executor/models/internvl.py` inside `get_multimodal_embeddings(...)`:\n\n```diff\n@@\n- vision_embeddings = self._process_image_input(image_input)\n- if torch.is_tensor(vision_embeddings):\n- vision_embeddings = (vision_embeddings,)\n- multimodal_embeddings += vision_embeddings\n+ vision_embeddings = self._process_image_input(image_input)\n+\n+ # Normalize to tuple[Tensor[T,H], ...]\n+ def _to_2d_seq(x):\n+ import torch\n+ if torch.is_tensor(x):\n+ if x.ndim == 3: # [B, T, H] -> B * [T,H]\n+ return tuple(x.unbind(0))\n+ elif x.ndim == 2: # [T, H]\n+ return (x,)\n+ raise TypeError(f\"vision embeddings must be 2D/3D, got shape {tuple(x.shape)}\")\n+ elif isinstance(x, (list, tuple)):\n+ out = []\n+ for e in x:\n+ out.extend(_to_2d_seq(e))\n+ return tuple(out)\n+ else:\n+ raise TypeError(f\"unexpected type for vision embeddings: {type(x)}\")\n+\n+ vision_embeddings = _to_2d_seq(vision_embeddings)\n+ multimodal_embeddings += vision_embeddings\n```\n\nAdditionally, consider accepting both `\"image\"` and `\"images\"` as modality keys (a few code paths assume `\"images\"`), or clarify in docs which key is canonical.\n\n## Workarounds we tried\n- Wrapping the returned tensor into a tuple (avoids the first `TypeError`), but the v1 sanity check still fails because the output remains 3D.\n- Providing embeddings as a list of 2D tensors `[T, H]` works, but many upstream encoders naturally produce `[B, T, H]`, so normalizing in the model executor is safer.\n- Pixel input path works and can be used as a temporary fallback, but defeats the purpose of passing precomputed embeddings.\n\n## Version Matrix\n- \u2705 Pixel input: OK on 0.10.1 and 0.10.2 \n- \u274c Embedding input: crashe",
"url": "https://github.com/vllm-project/vllm/issues/26749",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-14T03:01:33Z",
"updated_at": "2025-10-14T09:36:22Z",
"comments": 1,
"user": "BlueBlueFF"
},
{
"repo": "huggingface/transformers",
"number": 41554,
"title": "model.from_pretrained( . . . ) not loading needed weights/parameters",
"body": "I am performing quantization of a PatchTSTForPrediction model and attempting to load a saved quantized model for testing. Model is saved using `model.save_pretrained( . . . )`. Testing proceeds perfectly once performed immediately after QAT (Hugging face trainer's handles loading at the end of training); however, when attempting to load a saved quantized (trained) model, the error below occurs. I perform all the pre-quantization preparation so that the model contains all the necessary parameters (untrained) and then try to load the saved checkpoint. How can I force `from_pretrained( . . . )` to load ALL required weights? \n\n`Some weights of the model checkpoint at ./checkpoints/ . . . were not used when initializing PatchTSTForPrediction: ['head.projection.calib_counter', 'head.projection.num_module_called', 'head.projection.obsrv_clipval', 'head.projection.obsrv_clipvaln', 'head.projection.obsrv_w_clipval', 'head.projection.quantize_feature.clip_val', 'head.projection.quantize_feature.clip_valn', 'head.projection.quantize_weight.clip_val', 'model.encoder.layers.0.ff.0.calib_counter', 'model.encoder.layers.0.ff.0.num_module_called', 'model.encoder.layers.0.ff.0.obsrv_clipval', 'model.encoder.layers.0.ff.0.obsrv_clipvaln', 'model.encoder.layers.0.ff.0.obsrv_w_clipval', 'model.encoder.layers.0.ff.0.quantize_feature.clip_val', 'model.encoder.layers.0.ff.0.quantize_feature.clip_valn', 'model.encoder.layers.0.ff.0.quantize_weight.clip_val', 'model.encoder.layers.0.ff.3.calib_counter', 'model.encoder.layers.0.ff.3.num_module_called', 'model.encoder.layers.0.ff.3.obsrv_clipval', 'model.encoder.layers.0.ff.3.obsrv_clipvaln', 'model.encoder.layers.0.ff.3.obsrv_w_clipval', 'model.encoder.layers.0.ff.3.quantize_feature.clip_val', 'model.encoder.layers.0.ff.3.quantize_feature.clip_valn', 'model.encoder.layers.0.ff.3.quantize_weight.clip_val', 'model.encoder.layers.0.self_attn.QBmm52.num_module_called', 'model.encoder.layers.0.self_attn.QBmm52.quantize_m1.clip_val', 'model.encoder.layers.0.self_attn.QBmm52.quantize_m1.clip_valn', 'model.encoder.layers.0.self_attn.QBmm52.quantize_m2.clip_val', 'model.encoder.layers.0.self_attn.QBmm52.quantize_m2.clip_valn', 'model.encoder.layers.0.self_attn.QBmm62.num_module_called', 'model.encoder.layers.0.self_attn.QBmm62.quantize_m1.clip_val', 'model.encoder.layers.0.self_attn.QBmm62.quantize_m1.clip_valn', 'model.encoder.layers.0.self_attn.QBmm62.quantize_m2.clip_val', 'model.encoder.layers.0.self_attn.QBmm62.quantize_m2.clip_valn', 'model.encoder.layers.0.self_attn.k_proj.calib_counter', 'model.encoder.layers.0.self_attn.k_proj.num_module_called', 'model.encoder.layers.0.self_attn.k_proj.obsrv_clipval', 'model.encoder.layers.0.self_attn.k_proj.obsrv_clipvaln', 'model.encoder.layers.0.self_attn.k_proj.obsrv_w_clipval', 'model.encoder.layers.0.self_attn.k_proj.quantize_feature.clip_val', 'model.encoder.layers.0.self_attn.k_proj.quantize_feature.clip_valn', 'model.encoder.layers.0.self_attn.k_proj.quantize_weight.clip_val', 'model.encoder.layers.0.self_attn.out_proj.calib_counter', . . .]\n\nThis IS expected if you are initializing PatchTSTForPrediction from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\nThis IS NOT expected if you are initializing PatchTSTForPrediction from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).` \n\nNB: QAT is simulated. Additional parameters are added to the model after qmodel_prep is called and QAT proceeds as normal. I am using IBM's fms-model-optimizer. ",
"url": "https://github.com/huggingface/transformers/issues/41554",
"state": "closed",
"labels": [],
"created_at": "2025-10-13T23:20:20Z",
"updated_at": "2025-11-24T08:03:05Z",
"comments": 5,
"user": "lorsonblair"
},
{
"repo": "huggingface/lerobot",
"number": 2186,
"title": "how to load pi0?",
"body": "i use this code to load pi0:\n\n```python\nfrom lerobot.policies.pi0.modeling_pi0 import PI0Policy\nimport torch\n\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\npretrained_policy_path = \"lerobot/pi0_libero_base\"\n\npolicy = PI0Policy.from_pretrained(pretrained_policy_path).to(device)\n```\n\nbut throws an error:\n\n```bash\nTraceback (most recent call last):\n File \"/home/wjg/pi0.py\", line 16, in \n policy = PI0Policy.from_pretrained(pretrained_policy_path).to(device)\n File \"/data/wjg_files/lerobot/src/lerobot/policies/pi0/modeling_pi0.py\", line 923, in from_pretrained\n model = cls(config, **kwargs)\n File \"/data/wjg_files/lerobot/src/lerobot/policies/pi0/modeling_pi0.py\", line 872, in __init__\n self.model = PI0Pytorch(config)\n File \"/data/wjg_files/lerobot/src/lerobot/policies/pi0/modeling_pi0.py\", line 513, in __init__\n self.paligemma_with_expert = PaliGemmaWithExpertModel(\n File \"/data/wjg_files/lerobot/src/lerobot/policies/pi0/modeling_pi0.py\", line 337, in __init__\n vlm_config_hf = CONFIG_MAPPING[\"paligemma\"]()\nTypeError: 'NoneType' object is not subscriptable\n```\n\nhow can i load pi0?",
"url": "https://github.com/huggingface/lerobot/issues/2186",
"state": "closed",
"labels": [
"question",
"policies",
"python"
],
"created_at": "2025-10-13T12:24:32Z",
"updated_at": "2025-10-17T09:53:02Z",
"user": "Addog666"
},
{
"repo": "huggingface/accelerate",
"number": 3812,
"title": "RuntimeError during load_state",
"body": "### System Info\n\nThis issue is related to [prior issue 3101](https://github.com/huggingface/accelerate/issues/3101), but it hasn\u2019t been fully resolved yet. The current workaround is to avoid using `safetensors`.\n\n@Narsil suggested using [`load_file/save_file`](https://github.com/huggingface/safetensors/issues/657#issuecomment-3396215002). However, I noticed that accelerate currently uses [save_file](https://github.com/huggingface/accelerate/blob/main/src/accelerate/utils/other.py#L373) for saving and use [load_model](https://github.com/huggingface/accelerate/blob/main/src/accelerate/checkpointing.py#L238) for loading.\n\nIs there any known workaround or recommended fix for this inconsistency?\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] One of the scripts in the examples/ folder of Accelerate or an officially supported `no_trainer` script in the `examples` folder of the `transformers` repo (such as `run_no_trainer_glue.py`)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nPlease see the [prior issue 3101](https://github.com/huggingface/accelerate/issues/3101).\n\n### Expected behavior\n\nPlease see the [prior issue 3101](https://github.com/huggingface/accelerate/issues/3101).",
"url": "https://github.com/huggingface/accelerate/issues/3812",
"state": "closed",
"labels": [],
"created_at": "2025-10-13T11:25:17Z",
"updated_at": "2025-11-21T15:07:49Z",
"comments": 2,
"user": "Silverster98"
},
{
"repo": "huggingface/lerobot",
"number": 2185,
"title": "Has the lerobot data format been modified after June this year?",
"body": "Has the lerobot data format been modified after June this year? The original data can no longer be used.",
"url": "https://github.com/huggingface/lerobot/issues/2185",
"state": "closed",
"labels": [
"question",
"dataset"
],
"created_at": "2025-10-13T10:07:41Z",
"updated_at": "2025-10-14T08:05:04Z",
"user": "Addog666"
},
{
"repo": "huggingface/transformers",
"number": 41539,
"title": "All POETRY operations fail on latest version 4.57.0",
"body": "### System Info\n\nI import transformers (always latest) in my poetry project.\nI use poetry 2.1.2\n\nAfter this transformers release (4.57.0) I regenerated the poetry lock with command: `poetry lock`\n\nThen when retrying to generate the lock again after other updates - it fails with message:\n\n`Could not parse constrains version: `\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nDoing a simple search in the poetry.lock file I found out that transformers latest package needs `optax ()`\nwhich produces this failure because poetry does not know how to parse this type of version.\n\nNote I am sure that this is the problem because commenting out the transformers the lock works fine, and also by using 4.56.2 from September it also works fine and that `optax ()` cannot be found in the lock in this case.\n\n### Expected behavior\n\nA developer should be able to use the latest transformers package version with poetry.",
"url": "https://github.com/huggingface/transformers/issues/41539",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-13T08:40:49Z",
"updated_at": "2025-10-13T14:18:02Z",
"comments": 1,
"user": "bfuia"
},
{
"repo": "vllm-project/vllm",
"number": 26692,
"title": "[Usage]: How to release KVCache?",
"body": "### Your current environment\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 | packaged by conda-forge | (main, Jun 4 2025, 14:45:31) [GCC 13.3.0] (64-bit runtime)\nPython platform : Linux-5.15.0-25-generic-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration :\nGPU 0: NVIDIA L20\nGPU 1: NVIDIA L20\nGPU 2: NVIDIA L20\nGPU 3: NVIDIA L20\n\nNvidia driver version : 550.127.05\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 128\nOn-line CPU(s) list: 0-127\nVendor ID: GenuineIntel\nModel name: INTEL(R) XEON(R) GOLD 6530\nCPU family: 6\nModel: 207\nThread(s) per core: 2\nCore(s) per socket: 32\nSocket(s): 2\nStepping: 2\nCPU max MHz: 4000.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4200.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_act_window hwp_epp hwp_pkg_req avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr avx512_fp16 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 3 MiB (64 instances)\nL1i cache: 2 MiB (64 instances)\nL2 cache: 128 MiB (64 instances)\nL3 cache: 320 MiB (2 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-15,64-79\nNUMA node1 CPU(s): 16-31,80-95\nNUMA node2 CPU(s): 32-47,96-111\nNUMA node3 CPU(s): 48-63,112-127\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-cufile-cu12==1.13.1.3\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3",
"url": "https://github.com/vllm-project/vllm/issues/26692",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-13T08:28:20Z",
"updated_at": "2025-10-13T08:28:20Z",
"comments": 0,
"user": "shenxf1205"
},
{
"repo": "huggingface/lerobot",
"number": 2184,
"title": "How to let an episode realize it has finished the task?",
"body": "I have successfully trained my real-world lerobot to do several simple tasks from human demonstrations. Say, push an object from point A to point B. I noticed that after the robot arm has finished the task, it would return to its initial pose (same as the human demonstration) and stay idle for the remainder of the episode, until time finishes.\n\nOf course, if I manually move the cup back to point A from point B before the time finishes, it would attempt to finish the job again. But I just wanted to know if there's any way the episode can finish itself, or at least yield a signal, after the first successful attempt?\n\nI'm using lerobot_record.py with specified policy file path. The policy is act.\n\nThank you",
"url": "https://github.com/huggingface/lerobot/issues/2184",
"state": "open",
"labels": [],
"created_at": "2025-10-13T06:27:36Z",
"updated_at": "2025-12-22T07:56:00Z",
"user": "genkv"
},
{
"repo": "vllm-project/vllm",
"number": 26660,
"title": "[Usage]: Is there any way to enable beam search in online inference?",
"body": "### Your current environment\n\nIs there any way to enable beam search in the `vllm serve` command? Or beam search is only available in offline inference code?\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\n\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26660",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-12T13:55:07Z",
"updated_at": "2025-10-17T17:12:45Z",
"comments": 1,
"user": "tiesanguaixia"
},
{
"repo": "huggingface/transformers",
"number": 41533,
"title": "Add_Specifical_tokens and resize_toked_embeddings result in an error",
"body": "### System Info\n\nI want to add a few special tokens to my Qwen2.5VL model as separators, and after executing the following code, he received the following error message. I don't know how to solve this problem.\n``` bash\n[rank1]: Traceback (most recent call last):\n[rank1]: RuntimeError: shape '[-1, 151936]' is invalid for input of size 329273399\n[rank0]: Traceback (most recent call last):\n[rank0]: RuntimeError: shape '[-1, 151936]' is invalid for input of size 217038339\n[rank3]: Traceback (most recent call last):\n[rank3]: RuntimeError: shape '[-1, 151936]' is invalid for input of size 116936799\n[rank2]: Traceback (most recent call last):\n[rank2]: RuntimeError: shape '[-1, 151936]' is invalid for input of size 215673318\nTraceback (most recent call last):\nFile \"/home/hk-project-p0022189/tum_yvc3016/miniconda3/envs/qwen2_5-VL/lib/python3.10/site-packages/torch/distributed/elastic/multiprocessing/errors/init.py\", line 355, in wrapper\nraise ChildFailedError(\ntorch.distributed.elastic.multiprocessing.errors.ChildFailedError:\nqwenvl/train/train_livecc.py FAILED\nFailures:\n\nRoot Cause (first observed failure):\nerror_file: \ntraceback : To enable traceback see: https://pytorch.org/docs/stable/elastic/errors.html\n```\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n``` python\nimport os\nimport logging\nimport pathlib\nimport torch\nimport transformers\nimport json\nfrom typing import Dict\nimport shutil\nimport sys\nfrom pathlib import Path\n\nproject_root = Path(__file__).parent.parent.parent\nsys.path.append(str(project_root))\n\nimport qwenvl.train.trainer\nfrom trainer import replace_qwen2_vl_attention_class\n\nfrom transformers import (\n Qwen2VLForConditionalGeneration,\n)\n\nfrom model_code.modeling_qwen2_5_vl import Qwen2_5_VLForConditionalGeneration\n\n\n# from qwenvl.data.data_qwen import make_supervised_data_module\nfrom qwenvl.data.lmm_dataset_for_batch import make_supervised_data_module\nfrom qwenvl.train.argument import (\n ModelArguments,\n DataArguments,\n TrainingArguments,\n)\nfrom transformers import AutoTokenizer, AutoProcessor, Qwen2VLImageProcessor, Trainer\n\nlocal_rank = None\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\n\n\ndef rank0_print(*args):\n if local_rank == 0:\n print(*args)\n\n\ndef add_special_tokens_safely(tokenizer, new_tokens):\n \"\"\"\n \u5b89\u5168\u5730\u5411 tokenizer \u6dfb\u52a0\u65b0\u7684 special tokens\uff0c\u4fdd\u7559\u539f\u6709\u7684 additional_special_tokens\u3002\n\n Args:\n tokenizer: Hugging Face tokenizer\n model: \u5bf9\u5e94\u7684\u8bed\u8a00\u6a21\u578b\n new_tokens: list of str, \u8981\u6dfb\u52a0\u7684\u65b0 token\n\n Returns:\n bool: \u662f\u5426\u6709\u65b0 token \u88ab\u6dfb\u52a0\n \"\"\"\n # \u83b7\u53d6\u5f53\u524d\u8bcd\u8868\u4e2d\u7684\u6240\u6709 token\n current_vocab = set(tokenizer.get_vocab().keys())\n\n # \u8fc7\u6ee4\u51fa\u771f\u6b63\u9700\u8981\u6dfb\u52a0\u7684 token\n tokens_to_add = [t for t in new_tokens if t not in current_vocab]\n if not tokens_to_add:\n rank0_print(\"\ud83d\udfe2 \u6240\u6709\u6307\u5b9a\u7684 token \u5df2\u5b58\u5728\u4e8e\u8bcd\u8868\u4e2d\uff0c\u65e0\u9700\u6dfb\u52a0\u3002\")\n return False\n\n # \u83b7\u53d6\u539f\u6709 additional_special_tokens\uff08\u5982 , \u7b49\uff09\n orig_special_tokens = tokenizer.special_tokens_map.get(\n \"additional_special_tokens\", []\n )\n\n # \u5408\u5e76\uff1a\u4fdd\u7559\u539f\u6709 + \u65b0\u589e\n updated_special_tokens = orig_special_tokens + [\n t for t in tokens_to_add if t not in orig_special_tokens\n ]\n\n rank0_print(f\"\ud83d\udccc \u6b63\u5728\u6dfb\u52a0\u65b0 token: {tokens_to_add}\")\n rank0_print(f\"\ud83d\udd27 \u66f4\u65b0\u540e\u7684 additional_special_tokens \u603b\u6570: {len(updated_special_tokens)}\")\n\n # \u4f7f\u7528 add_special_tokens API\uff08\u4f1a\u81ea\u52a8\u53bb\u91cd\uff09\n num_added = tokenizer.add_special_tokens(\n {\"additional_special_tokens\": updated_special_tokens}\n )\n\n if num_added > 0:\n rank0_print(f\"\u2705 \u6210\u529f\u6dfb\u52a0 {num_added} \u4e2a\u65b0 token \u5230\u8bcd\u8868\")\n\n return num_added > 0\n\n\ndef safe_save_model_for_hf_trainer(trainer: transformers.Trainer, output_dir: str):\n \"\"\"Collects the state dict and dump to disk.\"\"\"\n\n if trainer.deepspeed:\n torch.cuda.synchronize()\n trainer.save_model(output_dir)\n return\n\n state_dict = trainer.model.state_dict()\n if trainer.args.should_save:\n cpu_state_dict = {key: value.cpu() for key, value in state_dict.items()}\n del state_dict\n trainer._save(output_dir, state_dict=cpu_state_dict) # noqa\n\n\ndef set_model(model_args, model):\n if model_args.tune_mm_vision:\n for n, p in model.visual.named_parameters():\n p.requires_grad = True\n else:\n for n, p in model.visual.named_parameters():\n p.requires_grad = False\n\n if model_args.tune_mm_mlp:\n for n, p in model.visual.merger.named_parameters():\n p.requires_grad = True\n else:\n for n, p in model.visual.merger.named_parameters():\n p.requires_grad = False\n\n if model_args.tune_mm_llm:\n for n, p in model.model.named_parameters():\n p.requires_grad = True\n model.lm_head.requires_grad = True\n else:\n for n, p in model.model.named_parameters():\n p.requir",
"url": "https://github.com/huggingface/transformers/issues/41533",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-12T13:50:40Z",
"updated_at": "2025-10-13T14:09:29Z",
"comments": 3,
"user": "jialiangZ"
},
{
"repo": "huggingface/lerobot",
"number": 2181,
"title": "How to chage SmolVLA action_chunk_size?",
"body": "I want to change 'action_chunk_size' from 50 to 10. I ran the command like this : \n'''\npython lerobot/scripts/train.py --policy.path=lerobot/smolvla_base --dataset.repo_id=Datasets/grasp_put --batch_size=16 --steps=40000 --output_dir=outputs/train/vla_chunk10 --job_name=smolvla_training --policy.device=cuda --policy.push_to_hub=false --policy.action_chunk_size=10\n'''\nbut it doesn't work\n'train.py: error: unrecognized arguments: --action_chunk_size=10'\nand I found it can enter this parameter in the terminal :\nusage: train.py [-h] [--policy.action_chunk_size str]\n\nHow should I resolve this problem?",
"url": "https://github.com/huggingface/lerobot/issues/2181",
"state": "closed",
"labels": [
"question",
"policies",
"python"
],
"created_at": "2025-10-12T13:29:35Z",
"updated_at": "2025-10-17T11:25:55Z",
"user": "CCCY-0304"
},
{
"repo": "huggingface/transformers",
"number": 41532,
"title": "where is examples/rag from original paper?",
"body": "### System Info\n\nhttps://arxiv.org/pdf/2005.11401 mentions https://github.com/huggingface/transformers/blob/main/examples/rag but it is not there. Add redirect if possible\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nGo to https://github.com/huggingface/transformers/blob/main/examples/rag\n\n### Expected behavior\n\nsome example instead of 404",
"url": "https://github.com/huggingface/transformers/issues/41532",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-12T13:17:53Z",
"updated_at": "2025-10-17T09:34:15Z",
"user": "IgorKasianenko"
},
{
"repo": "vllm-project/vllm",
"number": 26653,
"title": "[Usage]: Qwen3VL image coordinates issue",
"body": "### Your current environment\n\nHi, i found same image, same prompt, the vLLM serving qwen3vl always have wrong cooridnates back.\n\nthis is vllm return:\n\nResponse: \"{\\\"click_type\\\": \\\"left_click\\\", \\\"coordinate\\\": [815, 961]}\"\n\n![\"Image\"](\"https://github.com/user-attachments/assets/f55cb990-03a1-4ac7-912b-e2796c8b854a\")
\n\nAs you can see, when visualize, the VLLM returned x offset is totally far wrong.\n\nQwen3 official return. Same A3B model.\n\nDoes the input were cropped or something? \n\nMy server side just used: \n\n```\nvllm serve checkpoints/Qwen3-VL-30B-A3B-Instruct \\\n --dtype auto --max-model-len 4096 \\\n --api-key token-abc123 \\\n --gpu_memory_utilization 0.9 \\\n --trust-remote-code \\\n --port 8000 \\\n --served-model-name 'qwen3-vl' \\\n --max-model-len 8k \\\n --limit-mm-per-prompt '{\"video\": 3}' \\\n --enable-auto-tool-choice \\\n --tool-call-parser hermes \n```\n\n**note**: when visualize i have already mapping the cordiantes to image space, here just compare raw output, it still biased much on x-axis.\n\n\n\n### How would you like to use vllm\n\nI want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.\ndfwr\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26653",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-12T07:02:29Z",
"updated_at": "2025-10-13T03:56:53Z",
"comments": 2,
"user": "lucasjinreal"
},
{
"repo": "huggingface/accelerate",
"number": 3811,
"title": "ValueError: Could not find the transformer layer class QwenImageTransformerBlock in the model.",
"body": "Hi, I am trying to fine-tuning qwen-image-edit using accelerate in FSDP mode. I want to warp the ``QwenImageTransformerBlock`` in transformer and ``Qwen2_5_VLVisionBlock,Qwen2_5_VLDecoderLayer`` in text_encoder. I set the environment param\n```\ndef set_fsdp_env():\n os.environ[\"ACCELERATE_USE_FSDP\"] = 'true'\n os.environ[\"FSDP_AUTO_WRAP_POLICY\"] = 'TRANSFORMER_BASED_WRAP'\n os.environ[\"FSDP_BACKWARD_PREFETCH\"] = 'BACKWARD_PRE'\n os.environ[\"FSDP_TRANSFORMER_CLS_TO_WRAP\"] = 'QwenImageTransformerBlock,Qwen2_5_VLVisionBlock,Qwen2_5_VLDecoderLayer'\n os.environ[\"FSDP_CPU_RAM_EFFICIENT_LOADING\"] = 'false'\n```\nand prepare the two models\n```\ntransformer = accelerator.prepare(transformer)\ntext_encoder = accelerator.prepare(text_encoder)\n```\nFinally, I encountered the error raised from ``text_encoder = accelerator.prepare(text_encoder)``\n```\nValueError: Could not find the transformer layer class QwenImageTransformerBlock in the model.\n```\nHow can I resolve this problem? Thanks!\n",
"url": "https://github.com/huggingface/accelerate/issues/3811",
"state": "closed",
"labels": [],
"created_at": "2025-10-11T10:13:14Z",
"updated_at": "2025-11-22T15:06:54Z",
"comments": 2,
"user": "garychan22"
},
{
"repo": "huggingface/lerobot",
"number": 2172,
"title": "Add support for remote GPUs (with async inference!)",
"body": "Hello,\nI'm a student in not the first-world country, and unforturnately, I don't own a PC that would have an NVidia GPU - it costs about $1200 for a decent setup. On the other hand, it costs only $0.12-0.24/hr to rent RTX 4090 instances, so it's pretty cheap to simply rent a computer whenever I need to data collect/train.\n\nBut, to my knowledge LeRobot - unlike e.g. most LLM or vision trainers - runs only locally. I haven't tried, but given Async Inference it should be very feasible to make streaming to a local browser from a remote instance. In particular, for data collection. \n\nThis will make robotics dataset generation (significantly) more accessible.\n\nI may be able to PR this one, it should be straightforward.\n\nCheers.",
"url": "https://github.com/huggingface/lerobot/issues/2172",
"state": "open",
"labels": [
"enhancement",
"question"
],
"created_at": "2025-10-11T08:49:32Z",
"updated_at": "2025-12-19T06:35:21Z",
"user": "MRiabov"
},
{
"repo": "huggingface/transformers",
"number": 41518,
"title": "Add Structured Prompt Templates Registry for LLM / VLM / Diffusion Tasks",
"body": "### Feature request\n\nIntroduce transformers.prompt_templates \u2014 a YAML-based registry and accessor API:\n\n```\nfrom transformers import PromptTemplates\n\nPromptTemplates.get(\"summarization\") # \"Summarize the following text:\"\nPromptTemplates.list_tasks() # [\"summarization\",\"vqa\",\"ocr\",...]\n```\n\n- Templates stored as yaml/json under src/transformers/prompt_templates/templates/.\n- Accessor + validation in registry.py.\n- Optional CLI command transformers-cli list-prompts.\n- Pipelines can import a template by task name instead of hard-coding.\n\n### Motivation\n\nEvery pipeline and model today embeds its own prompt strings (e.g., summarization, OCR, VQA).\nThis duplication makes results inconsistent and hard to benchmark.\nA central registry of task-specific prompt templates would unify defaults and enable easy community additions.\n\n### Your contribution\n\nI\u2019ll implement the registry module, add unit tests and docs, and migrate 1\u20132 pipelines (summarization / captioning) to use it.\nContributor: [@Aki-07](https://github.com/Aki-07)",
"url": "https://github.com/huggingface/transformers/issues/41518",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-10-11T08:10:20Z",
"updated_at": "2025-10-13T15:06:20Z",
"comments": 2,
"user": "Aki-07"
},
{
"repo": "vllm-project/vllm",
"number": 26616,
"title": "[Usage]: How to enable MTP when using Qwen3-Next in local infer ( not vllm serve)",
"body": "### Your current environment\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.2 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0\nClang version : Could not collect\nCMake version : Could not collect\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 | packaged by Anaconda, Inc. | (main, Jun 5 2025, 13:09:17) [GCC 11.2.0] (64-bit runtime)\nPython platform : Linux-4.18.0-2.6.8.kwai.x86_64-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 11.8.89\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration :\nGPU 0: NVIDIA A100 80GB PCIe\nGPU 1: NVIDIA A100 80GB PCIe\nGPU 2: NVIDIA A100 80GB PCIe\nGPU 3: NVIDIA A100 80GB PCIe\n\nNvidia driver version : 550.54.14\ncuDNN version : Probably one of the following:\n/usr/lib/x86_64-linux-gnu/libcudnn.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_infer.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_adv_train.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_infer.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_cnn_train.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_infer.so.8.9.0\n/usr/lib/x86_64-linux-gnu/libcudnn_ops_train.so.8.9.0\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 96\nOn-line CPU(s) list: 0-95\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7V13 64-Core Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 1\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 1\nBogoMIPS: 4890.88\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm rep_good nopl cpuid extd_apicid aperfmperf pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch osvw topoext perfctr_core invpcid_single vmmcall fsgsbase bmi1 avx2 smep bmi2 erms invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 xsaves clzero xsaveerptr arat umip vaes vpclmulqdq rdpid\nHypervisor vendor: Microsoft\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (12 instances)\nNUMA node(s): 4\nNUMA node0 CPU(s): 0-23\nNUMA node1 CPU(s): 24-47\nNUMA node2 CPU(s): 48-71\nNUMA node3 CPU(s): 72-95\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Spec store bypass: Vulnerable\nVulnerability Spectre v1: Vulnerable: __user pointer sanitization and usercopy barriers only; no swapgs barriers\nVulnerability Spectre v2: Vulnerable, STIBP: disabled\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.14.1\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pip3] nvidia-cufile-cu12==1.13.1.3\n[pip3] nvidia-curand-cu12==10.3.9.90\n[pip3] nvidia-cusolver-cu12==11.7.3.90\n[pip3] nvidia-cusparse-cu12==12.5.8.93\n[pip3] nvidia-cusparselt-cu12==0.7.1\n[pip3] nvidia-ml-py==13.580.82\n[pip3] nvidia-nccl-cu12==2.27.3\n[pip3] nvidia-nvjitlink-cu12==12.8.93\n[pip3] nvidia-nvtx-cu12==12.8.90\n[pip3] pyzmq==27.1.0\n[pip3] torch==2.8.0\n[pip3] torchaudio==2.8.0\n[pip3] torchvision==0.23.0\n[pip3] transformers==4.57.0\n[pip3] triton==3.4.0\n[conda] nu",
"url": "https://github.com/vllm-project/vllm/issues/26616",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-11T03:58:14Z",
"updated_at": "2025-10-16T08:45:35Z",
"comments": 1,
"user": "Kimagure7"
},
{
"repo": "vllm-project/vllm",
"number": 26614,
"title": "[Usage]: attn_metadata.seq_lens is not equal to attn_metadata.num_actual_tokens",
"body": "### Your current environment\n\n```\nCollecting environment information...\nuv is set\n==============================\n System Info\n==============================\nOS : Ubuntu 20.04.6 LTS (x86_64)\nGCC version : (Ubuntu 9.4.0-1ubuntu1~20.04.2) 9.4.0\nClang version : Could not collect\nCMake version : version 3.16.3\nLibc version : glibc-2.31\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 (main, Jul 23 2025, 00:34:44) [Clang 20.1.4 ] (64-bit runtime)\nPython platform : Linux-5.4.0-216-generic-x86_64-with-glibc2.31\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA H20\nGPU 1: NVIDIA H20\nGPU 2: NVIDIA H20\nGPU 3: NVIDIA H20\nGPU 4: NVIDIA H20\nGPU 5: NVIDIA H20\nGPU 6: NVIDIA H20\nGPU 7: NVIDIA H20\n\nNvidia driver version : 555.42.06\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nByte Order: Little Endian\nAddress sizes: 52 bits physical, 57 bits virtual\nCPU(s): 224\nOn-line CPU(s) list: 0-223\nThread(s) per core: 2\nCore(s) per socket: 56\nSocket(s): 2\nNUMA node(s): 2\nVendor ID: GenuineIntel\nCPU family: 6\nModel: 143\nModel name: Intel(R) Xeon(R) Platinum 8480+\nStepping: 8\nFrequency boost: enabled\nCPU MHz: 900.000\nCPU max MHz: 2001.0000\nCPU min MHz: 800.0000\nBogoMIPS: 4000.00\nVirtualization: VT-x\nL1d cache: 5.3 MiB\nL1i cache: 3.5 MiB\nL2 cache: 224 MiB\nL3 cache: 210 MiB\nNUMA node0 CPU(s): 0-55,112-167\nNUMA node1 CPU(s): 56-111,168-223\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid cldemote movdiri movdir64b md_clear pconfig flush_l1d arch_capabilities\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] numpy==2.2.0\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-",
"url": "https://github.com/vllm-project/vllm/issues/26614",
"state": "open",
"labels": [
"usage"
],
"created_at": "2025-10-11T03:35:38Z",
"updated_at": "2025-10-11T03:36:31Z",
"comments": 0,
"user": "betacatZ"
},
{
"repo": "vllm-project/vllm",
"number": 26612,
"title": "[Usage]: qwen3vl 30 A3B \u542f\u52a8vllm \u670d\u52a1\u62a5\u9519",
"body": "### \ud83d\udcda The doc issue\n\nA_A800-SXM4-80GB.json']\n(Worker pid=1939690) INFO 10-11 10:42:13 [monitor.py:34] torch.compile takes 85.33 s in total\n(Worker pid=1939690) INFO 10-11 10:42:14 [gpu_worker.py:298] Available KV cache memory: 13.69 GiB\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] EngineCore failed to start.\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] Traceback (most recent call last):\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 699, in run_engine_core\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] engine_core = EngineCoreProc(*args, **kwargs)\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 498, in __init__\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] super().__init__(vllm_config, executor_class, log_stats,\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 92, in __init__\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] self._initialize_kv_caches(vllm_config)\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 199, in _initialize_kv_caches\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] kv_cache_configs = get_kv_cache_configs(vllm_config, kv_cache_specs,\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/core/kv_cache_utils.py\", line 1243, in get_kv_cache_configs\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] check_enough_kv_cache_memory(vllm_config, kv_cache_spec_one_worker,\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/core/kv_cache_utils.py\", line 716, in check_enough_kv_cache_memory\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] raise ValueError(\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:14 [core.py:708] ValueError: To serve at least one request with the models's max seq len (262144), (24.00 GiB KV cache is needed, which is larger than the available KV cache memory (13.69 GiB). Based on the available memory, the estimated maximum model length is 149520. Try increasing `gpu_memory_utilization` or decreasing `max_model_len` when initializing the engine.\n(EngineCore_DP0 pid=1937911) ERROR 10-11 10:42:17 [multiproc_executor.py:154] Worker proc VllmWorker-0 died unexpectedly, shutting down executor.\n(EngineCore_DP0 pid=1937911) Process EngineCore_DP0:\n(EngineCore_DP0 pid=1937911) Traceback (most recent call last):\n(EngineCore_DP0 pid=1937911) File \"/home/ma-user/work/anaconda3_flash_attn/envs/qwen3_vl/lib/python3.12/multiprocessing/process.py\", line 314, in _bootstrap\n(EngineCore_DP0 pid=1937911) self.run()\n(EngineCore_DP0 pid=1937911) File \"/home/ma-user/work/anaconda3_flash_attn/envs/qwen3_vl/lib/python3.12/multiprocessing/process.py\", line 108, in run\n(EngineCore_DP0 pid=1937911) self._target(*self._args, **self._kwargs)\n(EngineCore_DP0 pid=1937911) File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 712, in run_engine_core\n(EngineCore_DP0 pid=1937911) raise e\n(EngineCore_DP0 pid=1937911) File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 699, in run_engine_core\n(EngineCore_DP0 pid=1937911) engine_core = EngineCoreProc(*args, **kwargs)\n(EngineCore_DP0 pid=1937911) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(EngineCore_DP0 pid=1937911) File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 498, in __init__\n(EngineCore_DP0 pid=1937911) super().__init__(vllm_config, executor_class, log_stats,\n(EngineCore_DP0 pid=1937911) File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 92, in __init__\n(EngineCore_DP0 pid=1937911) self._initialize_kv_caches(vllm_config)\n(EngineCore_DP0 pid=1937911) File \"/home/ma-user/work/renkexuan/.venv/lib/python3.12/site-packages/vllm/v1/engine/core.py\", line 199, in _initialize_kv_caches\n(EngineCore_DP0 pid=1937911) kv_cache_configs = get_kv_cache_configs(vllm_config, kv_cache_specs,\n(EngineCore_DP0 pid=1937911) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n(Engin",
"url": "https://github.com/vllm-project/vllm/issues/26612",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-11T02:45:20Z",
"updated_at": "2025-10-16T23:00:39Z",
"comments": 1,
"user": "renkexuan369"
},
{
"repo": "huggingface/lerobot",
"number": 2171,
"title": "Data diffusion and data format conversion",
"body": "1. Can datasets collected in Lerobot format be disseminated?\n2. Can data formats between different Lerobot versions be converted? I noticed that the data format collected in version 0.2.0 is different from the latest data format.\nThank you!",
"url": "https://github.com/huggingface/lerobot/issues/2171",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-10-11T02:16:55Z",
"updated_at": "2025-10-17T02:02:36Z",
"user": "FALCONYU"
},
{
"repo": "vllm-project/vllm",
"number": 26607,
"title": "[Bug]: Since version 0.9.2 comes with nccl built-in, using PCIE causes sys errors. How to disable nccl in vllm for versions after 0.9.2?",
"body": "### Your current environment\n\n![\"Image\"](\"https://github.com/user-attachments/assets/a42c415b-8c5b-4698-aa6f-879edc44d512\")
\n\n\n### \ud83d\udc1b Describe the bug\n\n sh 06_startVllmAPI.sh \nINFO 09-30 10:30:16 [__init__.py:216] Automatically detected platform cuda.\n(APIServer pid=1599676) INFO 09-30 10:30:17 [api_server.py:1896] vLLM API server version 0.10.2\n(APIServer pid=1599676) INFO 09-30 10:30:17 [utils.py:328] non-default args: {'port': 6006, 'model': './autodl-tmp/modelscope/models/GeoGPT/Qwen2.5-72B-GeoGPT', 'tokenizer': './autodl-tmp/modelscope/models/GeoGPT/Qwen2.5-72B-GeoGPT', 'trust_remote_code': True, 'dtype': 'bfloat16', 'served_model_name': ['Qwen2.5-72B-GeoGPT'], 'tensor_parallel_size': 8, 'gpu_memory_utilization': 0.5}\n(APIServer pid=1599676) The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.\n(APIServer pid=1599676) INFO 09-30 10:30:24 [__init__.py:742] Resolved architecture: Qwen2ForCausalLM\n(APIServer pid=1599676) `torch_dtype` is deprecated! Use `dtype` instead!\n(APIServer pid=1599676) INFO 09-30 10:30:24 [__init__.py:1815] Using max model len 131072\n(APIServer pid=1599676) INFO 09-30 10:30:24 [scheduler.py:222] Chunked prefill is enabled with max_num_batched_tokens=2048.\nINFO 09-30 10:30:29 [__init__.py:216] Automatically detected platform cuda.\n(EngineCore_DP0 pid=1600151) INFO 09-30 10:30:31 [core.py:654] Waiting for init message from front-end.\n(EngineCore_DP0 pid=1600151) INFO 09-30 10:30:31 [core.py:76] Initializing a V1 LLM engine (v0.10.2) with config: model='./autodl-tmp/modelscope/models/GeoGPT/Qwen2.5-72B-GeoGPT', speculative_config=None, tokenizer='./autodl-tmp/modelscope/models/GeoGPT/Qwen2.5-72B-GeoGPT', skip_tokenizer_init=False, tokenizer_mode=auto, revision=None, tokenizer_revision=None, trust_remote_code=True, dtype=torch.bfloat16, max_seq_len=131072, download_dir=None, load_format=auto, tensor_parallel_size=8, pipeline_parallel_size=1, data_parallel_size=1, disable_custom_all_reduce=False, quantization=None, enforce_eager=False, kv_cache_dtype=auto, device_config=cuda, decoding_config=DecodingConfig(backend='auto', disable_fallback=False, disable_any_whitespace=False, disable_additional_properties=False, reasoning_backend=''), observability_config=ObservabilityConfig(show_hidden_metrics_for_version=None, otlp_traces_endpoint=None, collect_detailed_traces=None), seed=0, served_model_name=Qwen2.5-72B-GeoGPT, enable_prefix_caching=True, chunked_prefill_enabled=True, use_async_output_proc=True, pooler_config=None, compilation_config={\"level\":3,\"debug_dump_path\":\"\",\"cache_dir\":\"\",\"backend\":\"\",\"custom_ops\":[],\"splitting_ops\":[\"vllm.unified_attention\",\"vllm.unified_attention_with_output\",\"vllm.mamba_mixer2\",\"vllm.mamba_mixer\",\"vllm.short_conv\",\"vllm.linear_attention\",\"vllm.plamo2_mamba_mixer\",\"vllm.gdn_attention\"],\"use_inductor\":true,\"compile_sizes\":[],\"inductor_compile_config\":{\"enable_auto_functionalized_v2\":false},\"inductor_passes\":{},\"cudagraph_mode\":1,\"use_cudagraph\":true,\"cudagraph_num_of_warmups\":1,\"cudagraph_capture_sizes\":[512,504,496,488,480,472,464,456,448,440,432,424,416,408,400,392,384,376,368,360,352,344,336,328,320,312,304,296,288,280,272,264,256,248,240,232,224,216,208,200,192,184,176,168,160,152,144,136,128,120,112,104,96,88,80,72,64,56,48,40,32,24,16,8,4,2,1],\"cudagraph_copy_inputs\":false,\"full_cuda_graph\":false,\"pass_config\":{},\"max_capture_size\":512,\"local_cache_dir\":null}\n(EngineCore_DP0 pid=1600151) WARNING 09-30 10:30:31 [multiproc_worker_utils.py:273] Reducing Torch parallelism from 64 threads to 1 to avoid unnecessary CPU contention. Set OMP_NUM_THREADS in the external environment to tune this value as needed.\n(EngineCore_DP0 pid=1600151) INFO 09-30 10:30:31 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0, 1, 2, 3, 4, 5, 6, 7], buffer_handle=(8, 16777216, 10, 'psm_7e0498ff'), local_subscribe_addr='ipc:///tmp/33a7ec3b-72b3-4984-9ed3-6fc1fb572c4a', remote_subscribe_addr=None, remote_addr_ipv6=False)\nINFO 09-30 10:30:35 [__init__.py:216] Automatically detected platform cuda.\nINFO 09-30 10:30:35 [__init__.py:216] Automatically detected platform cuda.\nINFO 09-30 10:30:35 [__init__.py:216] Automatically detected platform cuda.\nINFO 09-30 10:30:35 [__init__.py:216] Automatically detected platform cuda.\nINFO 09-30 10:30:35 [__init__.py:216] Automatically detected platform cuda.\nINFO 09-30 10:30:35 [__init__.py:216] Automatically detected platform cuda.\nINFO 09-30 10:30:35 [__init__.py:216] Automatically detected platform cuda.\nINFO 09-30 10:30:35 [__init__.py:216] Automatically detected platform cuda.\nINFO 09-30 10:30:40 [shm_broadcast.py:289] vLLM message queue communication handle: Handle(local_reader_ranks=[0], buffer_handle=(1, 10485760, 10, 'psm_1413bf45'), local_subscribe_addr='ipc:///tmp/a",
"url": "https://github.com/vllm-project/vllm/issues/26607",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-11T01:48:50Z",
"updated_at": "2025-10-17T01:09:03Z",
"comments": 0,
"user": "tina0852"
},
{
"repo": "huggingface/hf-hub",
"number": 131,
"title": "InvalidCertificate and how to fix it",
"body": "I am trying to install a DuckDB extension written in Rust (https://github.com/martin-conur/quackformers) that uses the library.\n\nDuring the install, I am getting a\n```\nHfHub(RequestError(Transport(Transport { kind: ConnectionFailed, message: Some(\"tls connection init failed\"), url: Some(Url { scheme: \"https\", cannot_be_a_base: false, username: \"\", password: None, host: Some(Domain(\"huggingface.co\")), port: None, path: \"/sentence-transformers/all-MiniLM-L6-v2/resolve/main/tokenizer.json\", query: None, fragment: None }), source: Some(Custom { kind: InvalidData, error: InvalidCertificate(UnknownIssuer) }) })))\n```\nThe file can be accessed from my environment via curl.\nThe file can be accessed from DuckDB using their `httpfs` extension which is written in C/C++.\n\nI am working in environment with a very strict enterprise proxy and this is most likely what's causing the issue (I have zero issue when running the same commands at home).\n\n1. can the behavior of HfHub with respect to proxy be modified using env variables?\n2. can the behavior of HfHub with respect to TLS certificates be modified using env variables? \n3. where can I find the default value(s) for the proxy settings and the location of certs used by the library\n\nReferences:\n- bug report for quackformer = https://github.com/martin-conur/quackformers/issues/7\n",
"url": "https://github.com/huggingface/hf-hub/issues/131",
"state": "open",
"labels": [],
"created_at": "2025-10-10T14:42:12Z",
"updated_at": "2025-10-10T18:18:28Z",
"user": "sahuguet"
},
{
"repo": "vllm-project/vllm",
"number": 26585,
"title": "[Usage]: use vllm embedding to extract last token hidden states?",
"body": "### Your current environment\n\n```/usr/local/lib/python3.12/dist-packages/torch/cuda/__init__.py:63: FutureWarning: The pynvml package is deprecated. Please install nvidia-ml-py instead. If you did not install pynvml directly, please report this to the maintainers of the package that installed pynvml for you.\n import pynvml # type: ignore[import]\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Ubuntu 22.04.5 LTS (x86_64)\nGCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0\nClang version : 14.0.0-1ubuntu1.1\nCMake version : version 3.21.0\nLibc version : glibc-2.35\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)\nPython platform : Linux-5.10.134-16.3.al8.x86_64-x86_64-with-glibc2.35\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : Could not collect\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : GPU 0: NVIDIA H20-3e\nNvidia driver version : 570.133.20\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 52 bits physical, 57 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: GenuineIntel\nModel name: INTEL(R) XEON(R) PLATINUM 8575C\nCPU family: 6\nModel: 207\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 2\nCPU max MHz: 4000.0000\nCPU min MHz: 800.0000\nBogoMIPS: 5600.00\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts hwp hwp_notify hwp_act_window hwp_epp hwp_pkg_req hfi avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm uintr md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities\nVirtualization: VT-x\nL1d cache: 4.5 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 192 MiB (96 instances)\nL3 cache: 640 MiB (2 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling, PBRSB-eIBRS SW sequence\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-pytho",
"url": "https://github.com/vllm-project/vllm/issues/26585",
"state": "closed",
"labels": [
"usage"
],
"created_at": "2025-10-10T13:01:42Z",
"updated_at": "2025-12-15T06:54:05Z",
"comments": 2,
"user": "rxqy"
},
{
"repo": "vllm-project/vllm",
"number": 26582,
"title": "[Bug]: which triton-kernels version for MXFP4 Triton backend?",
"body": "### Your current environment\n\nvllm v0.11.0 installed via `uv pip install vllm --torch-backend=auto`\n\ntriton + triton-kernels at different commits installed from source\n\n### \ud83d\udc1b Describe the bug\n\n**Which triton + triton-kernels version does one have to install to run GPT-OSS with the MXFP4 Triton backend?**\n\nNo matter which version I try, I always get an error `Failed to import Triton kernels. Please make sure your triton version is compatible.`\n\nClearly, the latest triton-kernels will not work since the code in `vllm.model_executor.layers.fused_moe.gpt_oss_triton_kernels_moe` tries to import from `triton_kernels.routing`, but `triton_kernels.routing` has been deprecated (cf. https://github.com/triton-lang/triton/commit/30ede52aa2aecfd2ab3d6672ed21bbf4eb6438b3).\n\nBut also with older versions I get errors like `ImportError: cannot import name 'triton_key' from 'triton.compiler.compiler` or `Error: No module named 'triton.language.target_info`.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26582",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-10T11:51:59Z",
"updated_at": "2025-12-12T20:30:06Z",
"comments": 8,
"user": "matkle"
},
{
"repo": "huggingface/lerobot",
"number": 2162,
"title": "[Question] How to suppress verbose Svt[info] logs from video encoding during save_episode()?",
"body": "Hi, thank you for this fantastic library!\n\nI am currently using lerobot (Version: 0.3.3) to record and save robotics data. When I use the `dataset.save_episode() method`, I get a large number of verbose log messages prefixed with Svt[info]:\n\n```shell\nSvt[info]: ------------------------------------------- | 0/1 [00:00What is eunoia?\")\nprint(f\"{x1=}\")\n\nt2 = AutoTokenizer.from_pretrained(\"google/gemma-3-4b-it\")\nx2 = t2.tokenize(\"What is eunoia?\")\nprint(f\"{x2=}\")\n``` \n\n### Expected behavior\n\nThe print out of the x1 and x2 should be the same. However,\n\n```\nx1=['', 'Wh', 'at', '\u2581is', '\u2581eu', 'no', 'ia', '?']\nx2=['', 'What', '\u2581is', '\u2581e', 'uno', 'ia', '?']\n```\nLooking more into it, the tokenizer created for HF model (t2) is BPE while the tokenizer created for the GGUF model (t1) is Unigram.",
"url": "https://github.com/huggingface/transformers/issues/41494",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-09T23:27:25Z",
"updated_at": "2025-11-29T08:02:57Z",
"comments": 4,
"user": "amychen85"
},
{
"repo": "vllm-project/vllm",
"number": 26530,
"title": "[Bug]: Fix CVE-2023-48022 in docker image",
"body": "### Your current environment\n\n\n sys.exit(main())\n File \"/cephfs/yuanpuzhen/project/pi_space/lerobot/src/lerobot/scripts/lerobot_eval.py\", line 750, in main\n eval_main()\n File \"/cephfs/yuanpuzhen/project/pi_space/lerobot/src/lerobot/configs/parser.py\", line 225, in wrapper_inner\n response = fn(cfg, *args, **kwargs)\n File \"/cephfs/yuanpuzhen/project/pi_space/lerobot/src/lerobot/scripts/lerobot_eval.py\", line 495, in eval_main\n policy = make_policy(\n File \"/cephfs/yuanpuzhen/project/pi_space/lerobot/src/lerobot/policies/factory.py\", line 386, in make_policy\n policy = policy_cls.from_pretrained(**kwargs)\n File \"/cephfs/yuanpuzhen/project/pi_space/lerobot/src/lerobot/policies/pi0/modeling_pi0.py\", line 923, in from_pretrained\n model = cls(config, **kwargs)\n File \"/cephfs/yuanpuzhen/project/pi_space/lerobot/src/lerobot/policies/pi0/modeling_pi0.py\", line 872, in __init__\n self.model = PI0Pytorch(config)\n File \"/cephfs/yuanpuzhen/project/pi_space/lerobot/src/lerobot/policies/pi0/modeling_pi0.py\", line 545, in __init__\n raise ValueError(msg) from None\nValueError: An incorrect transformer version is used, please create an issue on https://github.com/huggingface/lerobot/issues\nException ignored in: \nTraceback (most recent call last):\n File \"/cephfs/yuanpuzhen/conda_data/envs/libero/lib/python3.10/site-packages/robosuite/utils/binding_utils.py\", line 199, in __del__\n self.gl_ctx.free()\n File \"/cephfs/yuanpuzhen/conda_data/envs/libero/lib/python3.10/site-packages/robosuite/renderers/context/egl_context.py\", line 150, in free\n EGL.eglDestroyContext(EGL_DISPLAY, self._context)\n File \"/cephfs/yuanpuzhen/conda_data/envs/libero/lib/python3.10/site-packages/OpenGL/error.py\", line 230, in glCheckError\n raise self._errorClass(\nOpenGL.raw.EGL._errors.EGLError: EGLError(\n err = EGL_NOT_INITIALIZED,\n baseOperation = eglDestroyContext,\n cArguments = (\n ,\n ,\n ),\n result = 0\n)\n```\n### Expected behavior\n\nExpect to evaluate the given checkpoint, output eval videos and eval_info.json\n\nCan you provide stable transformers and numpy versions for the latest lerobot?\n\nAnd what version of transformers could satisfied the code in PI0Pytorch?\n```\n try:\n from transformers.models.siglip import check\n\n if not check.check_whether_transformers_replace_is_installed_correctly():\n raise ValueError(msg)\n except ImportError:\n raise ValueError(msg) from None\n```",
"url": "https://github.com/huggingface/lerobot/issues/2134",
"state": "closed",
"labels": [],
"created_at": "2025-10-07T12:06:52Z",
"updated_at": "2025-11-14T20:04:50Z",
"user": "PuzhenYuan"
},
{
"repo": "huggingface/diffusers",
"number": 12441,
"title": "Support Wan2.2-Animate",
"body": "[Wan2.2-Animate-14B](https://humanaigc.github.io/wan-animate), it's a unified model for character animation and replacement, with holistic movement and expression replication.\n\nhttps://github.com/user-attachments/assets/351227d0-4edc-4f6c-9bf9-053e53f218e4\n\nWe would like open to the community, if anyone is interested, to integrate this model with Diffusers. Just take into consideration these points:\n\n1. Don't integrate the preprocessing, we can help with that using a modular custom block.\n2. This issue is for more advanced users than know the diffusers library very well.\n\nJust let me know that you're interested and if you have any doubts, feel free to ask, if you open a PR we can help but we are currently busy with other priorities so we ask you to be patient.",
"url": "https://github.com/huggingface/diffusers/issues/12441",
"state": "closed",
"labels": [
"help wanted",
"contributions-welcome"
],
"created_at": "2025-10-06T18:08:21Z",
"updated_at": "2025-11-13T02:52:32Z",
"comments": 0,
"user": "asomoza"
},
{
"repo": "huggingface/lerobot",
"number": 2124,
"title": "Question regarding downsampling and resizing dataset",
"body": "Hi,\n\nThank you for providing this wonderful library! I was curious about how one can take an existing dataset (collected or downloaded) and modify the fps (downsample, resize images, or delete specific episodes (for v3) prior to policy training. I am finding this tricky to do particularly when the dataset is not loaded in code but provided as a parameter to lerobot-train. I've spent time digging around the codebase but didn't see a way that doesn't involve loading the dataset in script first and adjusting this (for resizing, not sure about downsampling fps). Does the codebase provide utility functions for this? Thanks!",
"url": "https://github.com/huggingface/lerobot/issues/2124",
"state": "open",
"labels": [
"question",
"dataset",
"good first issue"
],
"created_at": "2025-10-06T16:07:47Z",
"updated_at": "2025-10-07T20:25:20Z",
"user": "karthikm-0"
},
{
"repo": "huggingface/transformers",
"number": 41363,
"title": "RT-Detr docs should reflect fixed 640x640 input size",
"body": "The authors of RT-Detr mention that the model was trained on 640x640 images and was meant to be used for inference on 640x640 images. Also, the current implementation has certain quirks that make training/inferring on images of different sizes problematic. For example, the pixel masks used for batching images of varying sizes are discarded.\n\nhttps://github.com/huggingface/transformers/blob/0452f28544f3626273d25f07f83c0e5f7da2d47a/src/transformers/models/rt_detr/modeling_rt_detr.py#L1645\n\nThe above are not clear in the current docs. I'll open a PR which adds a few lines in the docs to notify users about these issues.",
"url": "https://github.com/huggingface/transformers/issues/41363",
"state": "closed",
"labels": [
"Documentation"
],
"created_at": "2025-10-06T11:04:37Z",
"updated_at": "2025-11-06T13:24:01Z",
"comments": 4,
"user": "konstantinos-p"
},
{
"repo": "huggingface/tokenizers",
"number": 1873,
"title": "Why is my Python implementation faster than the Rust implementation?",
"body": "I am comparing the tokenizers in the Python and the huggingface implementation as follows\n\n```python\nimport json\nimport time\nfrom transformers import AutoTokenizer\ntokenizer = AutoTokenizer.from_pretrained(\"bert-base-cased\")\n\n[... Define and save the texts as data.json]\nwith open('./data.json', 'w', encoding='utf-8') as f:\n json.dump(texts[:N], f, ensure_ascii=False)\n\nN = 500\nstart = time.time()\nfor text in texts:\n tokenizer(text)\nend = time.time()\nloop_time = end-start\nprint(\"Python in a loop: \",end-start, f\"for {N} examples.\")\n# Python in a loop: 4.231077432632446 for 500 examples.\n\nstart = time.time()\nresults = tokenizer(texts[:N])\nend = time.time()\nbatch_time = end-start\nprint(\"Python as a batch: \",batch_time, f\"for {N} examples.\") \n# Python as a batch: 0.86988 for 500 examples.\n\n```\nand the rust implementation\n\n```rust\nuse tokenizers::tokenizer::{Result as TokenizerResult, Tokenizer,Encoding};\nuse serde_json::Result as SerdeResult;\nuse std::time::Instant;\nuse std::fs::File;\nuse std::io::{BufReader,BufWriter, Write};\nuse std::any::type_name;\nuse rayon::prelude::*;\n\nfn main() -> TokenizerResult<()> {\n // needs http feature enabled\n let tokenizer = Tokenizer::from_pretrained(\"bert-base-cased\", None)?;\n \n let file = File::open(\"./data.json\")?;\n let reader = BufReader::new(file);\n let items: Vec = serde_json::from_reader(reader)?;\n let texts: Vec<&str> = items.iter().map(|s| s.as_str()).collect();\n\n let start = Instant::now();\n for name in texts.iter(){\n let encoding = tokenizer.encode(*name, false)?;\n }\n let duration = start.elapsed();\n println!(\"(1) Execution in loop: {:.6} seconds\", duration.as_secs_f64());\n // (1) Execution in loop: 29.867990 seconds\n\n let start = Instant::now();\n let encoded_items: Vec<_> = texts.par_iter().map(|name| tokenizer.encode(*name, false)).collect();\n let duration = start.elapsed();\n println!(\"(2) Execution with par_iter : time: {:.6} seconds\", duration.as_secs_f64());\n // (2) Execution with par_iter : 3.968467\n \n let start = Instant::now();\n let encoded_items: TokenizerResult> = tokenizer.encode_batch(items2.clone(), false);\n let duration = start.elapsed();\n println!(\"(3) Execution with encode_batch : time: {:.6} seconds\", duration.as_secs_f64());\n // (3) Execution with encode_batch : 3.968467 seconds\n \n\n let start = Instant::now();\n let encoded_items: TokenizerResult> = tokenizer.encode_batch_char_offsets(items2.clone(), false);\n let duration = start.elapsed();\n println!(\"(4) Execution with encode_batch_char_offsets : time: {:.6} seconds\", duration.as_secs_f64());\n // (4) Execution with encode_batch_char_offsets : 6.839765 seconds\n\n let start = Instant::now();\n let encoded_items: TokenizerResult> = tokenizer.encode_batch_fast(items2.clone(), false);\n let duration = start.elapsed();\n println!(\"(5) Execution with encode_batch_fast : time: {:.6} seconds\", duration.as_secs_f64());\n // (5) Execution with encode_batch_fast : 5.758732 seconds\n\n\n Ok(())\n}\n```\n\nYou see that Rust is 10 times slower in a loop and 3 times slower even when parallelization is used.\nWhat is the trick here? How can I make my Rust code as fast (or hopefully faster) than the python code?",
"url": "https://github.com/huggingface/tokenizers/issues/1873",
"state": "closed",
"labels": [],
"created_at": "2025-10-05T08:02:47Z",
"updated_at": "2025-10-08T17:41:28Z",
"comments": 4,
"user": "sambaPython24"
},
{
"repo": "huggingface/transformers",
"number": 41336,
"title": "is there a bug in group_videos_by_shape for qwenvl video preprocessiong?",
"body": "### System Info\n\nin src/transformers/video_utils.py,\ngroup_videos_by_shape\ngrouped_videos = {shape: torch.stack(videos, dim=0) for shape, videos in grouped_videos.items()}, where each video is of shape BTCHW. This will create a new dimension. \nHowever, in qwenvl video preprocess \n batch_size, grid_t, channel = patches.shape[:3]\nIt does not consider the additional dimension created in group_videos_by_shape\nI think we should use torch.cat, not torch.stack?\n@yonigozlan @molbap \n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nrunning video preprocessing with list of video inputs, each with different shape\n\n### Expected behavior\n\nrun without error",
"url": "https://github.com/huggingface/transformers/issues/41336",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-03T22:26:26Z",
"updated_at": "2025-10-03T22:44:43Z",
"comments": 1,
"user": "dichencd"
},
{
"repo": "huggingface/lerobot",
"number": 2111,
"title": "frame deletion",
"body": "Great work on this project! I have a quick question - does LeRobotDataset support frame deletion? For example, in the DROID_lerobot dataset, the first few frames have an action value of 0 and I need to remove them.\nI'd appreciate any insights you can provide. Thank you for your time and help!",
"url": "https://github.com/huggingface/lerobot/issues/2111",
"state": "closed",
"labels": [
"question",
"dataset"
],
"created_at": "2025-10-03T13:05:12Z",
"updated_at": "2025-10-10T12:17:53Z",
"user": "Yysrc"
},
{
"repo": "huggingface/lerobot",
"number": 2108,
"title": "HIL-SERL Transform order for (tanh \u2192 rescale) is reversed",
"body": "In `TanhMultivariateNormalDiag`:\n\n```\ntransforms = [TanhTransform(cache_size=1)]\nif low is not None and high is not None:\n transforms.insert(0, RescaleFromTanh(low, high)) # puts Rescale *before* tanh\n\n```\n\nthis applies RescaleFromTanh then Tanh, which is backwards. should we change it to tanh first, then rescale?\n\nFix\n\n```\ntransforms = [TanhTransform(cache_size=1)]\nif low is not None and high is not None:\n transforms.append(RescaleFromTanh(low, high)) # tanh \u2192 rescale\n```\n\nAlso, when I tried to assign value for low, high. I got error:\n\n```\ntorch/distributions/transforms.py\", line 303, in domain\n domain = self.parts[0].domain\nAttributeError: 'RescaleFromTanh' object has no attribute 'domain'\n```\n\nMight be fixed by adding the following to `class RescaleFromTanh(Transform)`\n\n```\n# Required attributes for PyTorch Transform\nself.domain = constraints.interval(-1.0, 1.0)\nself.codomain = constraints.interval(low, high)\nself.bijective = True\n```",
"url": "https://github.com/huggingface/lerobot/issues/2108",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-10-02T21:44:22Z",
"updated_at": "2025-10-07T20:36:31Z",
"user": "priest-yang"
},
{
"repo": "huggingface/lerobot",
"number": 2107,
"title": "Low Success Rate When Training SmolVLA-0.24B on LIBERO",
"body": "Hi folks, I'm trying to replicate the 0.24B SmolVLA model on the LIBERO dataset. Intuitively, I just changed the base model `vlm_model_name: str = \"HuggingFaceTB/SmolVLM2-256M-Video-Instruct\"`. Here is the command I used to train. \n\n`lerobot-train --policy.type=smolvla --policy.load_vlm_weights=true --dataset.repo_id=HuggingFaceVLA/libero --env.type=libero --env.task=libero_10 --output_dir=./outputs/ --steps=100000 --batch_size=64 --eval.batch_size=1 --eval.n_episodes=1 --eval_freq=1000 --wandb.enable=true`\n\nI trained on a single RTX4090. However, I found that the success rate on the eval set is quite low. The success rate was only 7.5%. Is there anything I did wrong? Attaching the training plots below. \n\n![\"Image\"](\"https://github.com/user-attachments/assets/9bbdcadb-e113-4d9f-b315-4f37b57bde37\")
\n\n![\"Image\"](\"https://github.com/user-attachments/assets/23951a72-a374-4eda-9368-363367e4c746\")
",
"url": "https://github.com/huggingface/lerobot/issues/2107",
"state": "open",
"labels": [
"question",
"policies",
"simulation"
],
"created_at": "2025-10-02T19:11:55Z",
"updated_at": "2025-12-20T09:30:58Z",
"user": "zimgong"
},
{
"repo": "huggingface/optimum-onnx",
"number": 66,
"title": "How to export a stateless whisper model via optimum-cli?",
"body": "I observe that when exporting a Whisper model via Python API, the resulting model is stateless, i.e. the decoder is split into two models.\n```python\nimport os\nfrom optimum.onnxruntime import ORTModelForSpeechSeq2Seq\nORTModelForSpeechSeq2Seq.from_pretrained(\"openai/whisper-tiny\", export=True).save_pretrained(\"./whisper/python\")\nprint(os.listdir(\"./whisper/python\"))\n# ['encoder_model.onnx', 'decoder_with_past_model.onnx', 'decoder_model.onnx', 'config.json', 'generation_config.json']\n```\n\nWhen I export this model via CLI, the decoder model is exported as stateful even if I provide the `--no-post-process` argument.\n```bash\noptimum-cli export onnx --task automatic-speech-recognition -m openai/whisper-tiny --no-post-process ./whisper/cli\nls ./whisper/cli\n\n# added_tokens.json decoder_model.onnx generation_config.json normalizer.json special_tokens_map.json tokenizer.json\n# config.json encoder_model.onnx merges.txt preprocessor_config.json tokenizer_config.json vocab.json\n```\n\nMy environment:\n```\ncertifi==2025.8.3\ncharset-normalizer==3.4.3\ncoloredlogs==15.0.1\nfilelock==3.19.1\nflatbuffers==25.9.23\nfsspec==2025.9.0\nhf-xet==1.1.10\nhuggingface-hub==0.35.3\nhumanfriendly==10.0\nidna==3.10\nJinja2==3.1.6\nMarkupSafe==3.0.3\nml_dtypes==0.5.3\nmpmath==1.3.0\nnetworkx==3.4.2\nnumpy==2.2.6\nnvidia-cublas-cu12==12.8.4.1\nnvidia-cuda-cupti-cu12==12.8.90\nnvidia-cuda-nvrtc-cu12==12.8.93\nnvidia-cuda-runtime-cu12==12.8.90\nnvidia-cudnn-cu12==9.10.2.21\nnvidia-cufft-cu12==11.3.3.83\nnvidia-cufile-cu12==1.13.1.3\nnvidia-curand-cu12==10.3.9.90\nnvidia-cusolver-cu12==11.7.3.90\nnvidia-cusparse-cu12==12.5.8.93\nnvidia-cusparselt-cu12==0.7.1\nnvidia-nccl-cu12==2.27.3\nnvidia-nvjitlink-cu12==12.8.93\nnvidia-nvtx-cu12==12.8.90\nonnx==1.19.0\nonnxruntime==1.23.0\noptimum @ git+https://github.com/huggingface/optimum@a813c95ac088c401547fe15e7a68ac5c6f00f9a7\noptimum-onnx @ git+https://github.com/huggingface/optimum-onnx.git@671b84f78a244594dd21cb1a8a1f7abb8961ea60\npackaging==25.0\nprotobuf==6.32.1\nPyYAML==6.0.3\nregex==2025.9.18\nrequests==2.32.5\nsafetensors==0.6.2\nsympy==1.14.0\ntokenizers==0.21.4\ntorch==2.8.0\ntqdm==4.67.1\ntransformers==4.55.4\ntriton==3.4.0\ntyping_extensions==4.15.0\nurllib3==2.5.0\n\n```\n\nHow to export this model as stateless via optimum-cli? Also, how to export this model as stateful via Python API?\n\nThanks!",
"url": "https://github.com/huggingface/optimum-onnx/issues/66",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-10-02T09:50:03Z",
"updated_at": "2025-10-13T05:33:25Z",
"user": "nikita-savelyevv"
},
{
"repo": "huggingface/lerobot",
"number": 2104,
"title": "Select the VLM backbone for SmolVLA",
"body": "Hi may I ask about the vlm_model_name, is there any model more powerful than HuggingFaceTB/SmolVLM2-500M-Video-Instruct which can be used to train SmolVLA for Lerobot SO101?",
"url": "https://github.com/huggingface/lerobot/issues/2104",
"state": "open",
"labels": [
"question",
"policies",
"good first issue"
],
"created_at": "2025-10-02T07:35:29Z",
"updated_at": "2025-10-11T16:53:59Z",
"user": "Llkhhb"
},
{
"repo": "huggingface/diffusers",
"number": 12415,
"title": "SVG 2 kernels",
"body": "Can we support new sparse kernels in (Neurips 2025)\nhttps://svg-project.github.io/v2/",
"url": "https://github.com/huggingface/diffusers/issues/12415",
"state": "open",
"labels": [],
"created_at": "2025-10-01T10:52:50Z",
"updated_at": "2025-10-01T10:52:50Z",
"comments": 0,
"user": "bhack"
},
{
"repo": "huggingface/lerobot",
"number": 2096,
"title": "How can I change the task name of already recorded episodes?",
"body": "I recorded the dataset using:\n\n--dataset.single_task=\"slice the clay until it becomes 4 pieces\"\n\n\nNow I want to update those recorded episodes to a different task name. How can I do that?",
"url": "https://github.com/huggingface/lerobot/issues/2096",
"state": "open",
"labels": [
"question",
"dataset",
"good first issue"
],
"created_at": "2025-10-01T02:15:49Z",
"updated_at": "2025-10-30T03:48:47Z",
"user": "pparkgyuhyeon"
},
{
"repo": "huggingface/transformers",
"number": 41235,
"title": "i want to request a demo code for StatefulDataLoader , i want to use data checkpoint to recover the train stage`s data state, not only model state , how to use ,StatefulDataLoader or some code to reach it ?",
"body": "i want to request a demo code for StatefulDataLoader , i want to use data checkpoint to recover the train stage`s data state, not only model state , how to use ,StatefulDataLoader or some code to reach it ?\n\nrecover data state ,not only model state , i wish i said my request clearly .\nhow to use accelerate + transformers trainer to train model ,when training is broken ,it can recover from data checkpoint and model checkpoint ? thanks\n\ni wish you understand what i said",
"url": "https://github.com/huggingface/transformers/issues/41235",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-09-30T17:07:07Z",
"updated_at": "2025-11-08T08:04:40Z",
"user": "ldh127"
},
{
"repo": "huggingface/accelerate",
"number": 3802,
"title": "i want to request a demo code for StatefulDataLoader , i want to use data checkpoint to recover the train stage`s data state, not only model state , how to use ,StatefulDataLoader or some code to reach it ?",
"body": "i want to request a demo code for StatefulDataLoader , i want to use data checkpoint to recover the train stage`s data state, not only model state , how to use ,StatefulDataLoader or some code to reach it ? \n\nrecover data state ,not only model state , i wish i said my request clearly .\nhow to use accelerate + transformers trainer to train model ,when training is broken ,it can recover from data checkpoint and model checkpoint ? thanks\n\ni wish you understand what i said \n",
"url": "https://github.com/huggingface/accelerate/issues/3802",
"state": "closed",
"labels": [],
"created_at": "2025-09-30T15:58:32Z",
"updated_at": "2025-11-09T15:06:58Z",
"user": "ldh127"
},
{
"repo": "huggingface/transformers",
"number": 41211,
"title": "Add DEIMv2",
"body": "### Model description\n\nIt would be nice to integrate DEIMv2, a new state-of-the-art model for real-time object detection based on DINOv3. The weights are released under Apache 2.0.\n\nRelated thread: https://github.com/Intellindust-AI-Lab/DEIMv2/issues/20\n\n### Open source status\n\n- [x] The model implementation is available\n- [x] The model weights are available\n\n### Provide useful links for the implementation\n\nCode: https://github.com/Intellindust-AI-Lab/DEIMv2\nWeights (on Google Drive for now): https://github.com/Intellindust-AI-Lab/DEIMv2?tab=readme-ov-file#1-model-zoo\n\nIdeally, the [AutoBackbone API](https://huggingface.co/docs/transformers/main_classes/backbones) can be leveraged to not having to re-implement the entire DINOv3 backbone in `modular_deimv2.py` and `modeling_deimv2.py`. See an example of how this is leveraged for DETR [here](https://github.com/huggingface/transformers/blob/59035fd0e1876f9e526488b61fe43ff8829059f6/src/transformers/models/detr/modeling_detr.py#L280).",
"url": "https://github.com/huggingface/transformers/issues/41211",
"state": "open",
"labels": [
"New model"
],
"created_at": "2025-09-30T09:43:07Z",
"updated_at": "2025-10-04T18:44:06Z",
"comments": 4,
"user": "NielsRogge"
},
{
"repo": "huggingface/transformers",
"number": 41208,
"title": "Integrate mamba SSM kernels from the hub",
"body": "### Feature request\n\nCurrently, mamba kernels are imported via the main source package ex, for [GraniteMoeHybrid](https://github.com/huggingface/transformers/blob/main/src/transformers/models/granitemoehybrid/modeling_granitemoehybrid.py#L44-L46)\n\nCan we migrate this to use the kernels-hub (`kernels-community/mamba-ssm`) variation instead?\n\n### Motivation\n\nRemoves the external dependency. Kernel hub is also integrated at several other places throughout the library.\n\n### Your contribution\n\nI can submit a PR for migrating from the PyPi `mamba_ssm` package to the `kernels` package for mamba ops.",
"url": "https://github.com/huggingface/transformers/issues/41208",
"state": "closed",
"labels": [
"Feature request"
],
"created_at": "2025-09-30T07:50:52Z",
"updated_at": "2025-12-18T10:17:06Z",
"comments": 15,
"user": "romitjain"
},
{
"repo": "huggingface/tokenizers",
"number": 1870,
"title": "How can I convert a trained tokenizer into `transformers` format",
"body": "Hi guys,\n\nI have trained a tokenizer which works pretty well and it is stored in a single `.json` file. Is there any method / API to convert it into a `transformers` toeknizer format?\n\nIf there's no such implementation I am happy to contribute.",
"url": "https://github.com/huggingface/tokenizers/issues/1870",
"state": "closed",
"labels": [],
"created_at": "2025-09-30T06:09:52Z",
"updated_at": "2025-09-30T13:53:53Z",
"comments": 1,
"user": "dibbla"
},
{
"repo": "huggingface/lighteval",
"number": 999,
"title": "How to print all pass@k scores when generating 16 samples?",
"body": "Hi,\n\nI want to print all results of pass@k metrics when generating 16 samples. (e.g., k=1, 2, 4, 8, 16)\n\n```python\n\nmath_500_pass_k_at_16 = LightevalTaskConfig(\n name=\"math_500_pass_k_at_16\",\n suite=[\"custom\"],\n prompt_function=math_500_prompt_fn,\n hf_repo=\"HuggingFaceH4/MATH-500\",\n hf_subset=\"default\",\n hf_avail_splits=[\"test\"],\n evaluation_splits=[\"test\"],\n few_shots_split=None,\n few_shots_select=None,\n generation_size=32768,\n metrics=[\n Metrics.pass_at_k_math(sample_params={\"k\": 1, \"n\": 16}),\n Metrics.pass_at_k_math(sample_params={\"k\": 2, \"n\": 16}),\n Metrics.pass_at_k_math(sample_params={\"k\": 4, \"n\": 16}),\n Metrics.pass_at_k_math(sample_params={\"k\": 8, \"n\": 16}),\n Metrics.pass_at_k_math(sample_params={\"k\": 16, \"n\": 16}),\n ],\n version=2,\n```\n\nBut, I can't see full results that I want. Does anyone know how to resolve it?\n",
"url": "https://github.com/huggingface/lighteval/issues/999",
"state": "open",
"labels": [],
"created_at": "2025-09-29T21:49:44Z",
"updated_at": "2025-10-14T08:04:17Z",
"user": "passing2961"
},
{
"repo": "huggingface/lerobot",
"number": 2083,
"title": "How to train this RL model with my trained data",
"body": "I want this model to load the trained model that I have already generated. So, I modified the output_dir and set resume to true, but then the problem shown in the figure occurred. How can I solve it?\n`{ \"output_dir\": \"outputs/train/2025-09-28/17-28-55_default\", \n\"job_name\": \"default\", \"resume\": true, \n\"seed\": 1000, \"num_workers\": 4, \n\"batch_size\": 256, \n\"steps\": 100000,`\n\nand the origin code is :\n`{ \"output_dir\": null, \n\"job_name\": \"default\", \"resume\": flase, \n\"seed\": 1000, \"num_workers\": 4, \n\"batch_size\": 256, \n\"steps\": 100000,\n`[\n\n![\"Image\"](\"https://github.com/user-attachments/assets/5f46acd3-9a72-41a5-8506-742f5c479c53\")
\n\n](url)",
"url": "https://github.com/huggingface/lerobot/issues/2083",
"state": "open",
"labels": [],
"created_at": "2025-09-29T07:22:08Z",
"updated_at": "2025-10-07T20:32:04Z",
"user": "993984583"
},
{
"repo": "huggingface/lerobot",
"number": 2082,
"title": "How to train this RL model with my model data",
"body": "I want this model to load the trained model that I have already generated. So, I modified the output_dir and set resume to true, but then the problem shown in the figure occurred. How can I solve it?\n`{\n \"output_dir\": \"outputs/train/2025-09-28/17-28-55_default\",\n \"job_name\": \"default\",\n \"resume\": true,\n \"seed\": 1000,\n \"num_workers\": 4,\n \"batch_size\": 256,\n \"steps\": 100000,`[\n\n![\"Image\"](\"https://github.com/user-attachments/assets/df121807-b309-4a5c-bee1-850b0fab2ae0\")
\n\n](url)",
"url": "https://github.com/huggingface/lerobot/issues/2082",
"state": "closed",
"labels": [],
"created_at": "2025-09-29T07:18:52Z",
"updated_at": "2025-10-07T20:33:11Z",
"user": "993984583"
},
{
"repo": "huggingface/sentence-transformers",
"number": 3532,
"title": "What is the proper way to use prompts? Do we have to format/render them ourselves?",
"body": "Hi. First time using the Sentence Transformers library and I had a question regarding using prompts. Specifically, it seems like the [`SentenceTransformer.encode_document`](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode_document) method is a convenient wrapper for the [`SentenceTransformer.encode`](https://sbert.net/docs/package_reference/sentence_transformer/SentenceTransformer.html#sentence_transformers.SentenceTransformer.encode) method in the sense that the prompt `\"document\"` and the task `\"document\"` are selected automatically.\n\nHowever, I'm noticing that the prompt is simply prepended to the provided text rather than having it be formatted. The prompt for `\"document\"` is `title: {title | \"none\"} | text: {content}` and inside the `encode` method simply prepends it: https://github.com/UKPLab/sentence-transformers/blob/7341bf155b4349b88690b78c84beb5aa658c439f/sentence_transformers/SentenceTransformer.py#L1040\n\nMeaning that the resulting input to the embedding model would look like `title: none | text: {OUR_TEXT}`. But what if we wanted to include a `title` value? It seems like we'd have to pre-process the input ourselves. But then what is the point of using `encode_document`?",
"url": "https://github.com/huggingface/sentence-transformers/issues/3532",
"state": "closed",
"labels": [],
"created_at": "2025-09-28T06:32:51Z",
"updated_at": "2025-09-30T10:59:24Z",
"user": "seanswyi"
},
{
"repo": "huggingface/transformers",
"number": 41186,
"title": "Qwen2.5-VL restore tensor multi-image form",
"body": "\nHello, I have recently been experimenting with qwen2.5-vl (https://github.com/huggingface/transformers/blob/v4.52-release/src/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py). I noticed that multiple images are pre-merged here,\n```\nimage_embeds = self.get_image_features(pixel_values, image_grid_thw)\n```\n but I want to process each image individually, such as performing pooling on each image. I found that when I attempt operations like \n```\nimage_embeds.view(n_img, image_embeds.shape[0]//n_img, -1)\n```\n I cannot correctly restore the multi-image format. Could you please advise on how to handle this? \n",
"url": "https://github.com/huggingface/transformers/issues/41186",
"state": "closed",
"labels": [],
"created_at": "2025-09-28T03:36:24Z",
"updated_at": "2025-11-05T08:02:55Z",
"comments": 2,
"user": "NiFangBaAGe"
},
{
"repo": "huggingface/peft",
"number": 2802,
"title": "Guide on training that requires both LoRA and base model forward calls ?",
"body": "Hi, I'm working on some training variants that require hidden states from the base model and the hidden states produced with LoRA. I'm currently initializing two separate model objects:\n```\n from peft import get_peft_model\n m1=AutoModelForCausalLM.from_pretrained(model_path)\n m2=AutoModelForCausalLM.from_pretrained(model_path)\n lora_config = LoraConfig(....)\n m2 = get_peft_model(m2, lora_config)\n```\n\nIs there already an api to call non-lora forward with `m2` object ? I believe it'll be more memory efficient.",
"url": "https://github.com/huggingface/peft/issues/2802",
"state": "closed",
"labels": [],
"created_at": "2025-09-27T23:12:23Z",
"updated_at": "2025-10-15T10:26:15Z",
"comments": 3,
"user": "thangld201"
},
{
"repo": "huggingface/lerobot",
"number": 2072,
"title": "How to run lerobot with RTX 5090? If not possible, please add support",
"body": "### System Info\n\n```Shell\n- lerobot version: 0.3.4\n- Platform: Linux-6.14.0-32-generic-x86_64-with-glibc2.39\n- Python version: 3.12.3\n- Huggingface Hub version: 0.35.1\n- Datasets version: 4.1.1\n- Numpy version: 2.2.6\n- PyTorch version: 2.8.0+cu128\n- Is PyTorch built with CUDA support?: True\n- Cuda version: 12.8\n- GPU model: NVIDIA GeForce RTX 5090\n- Using GPU in script?: Yes\n```\n\n### Information\n\n- [x] One of the scripts in the examples/ folder of LeRobot\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nI am trying to run the train script as shown in the examples\n\n```\npython -m lerobot.scripts.lerobot_train --policy.path=cijerezg/smolvla-test --dataset.repo_id=cijerezg/pick-up-train-v1 --batch_size=48 --steps=20000 --output_dir=outputs/train/my_smolvla_pickup_v9 --job_name=my_smolvla_training --policy.device=cuda --wandb.enable=true --policy.repo_id=pickup_policy_v5 --save_freq=1000\n```\n\n### Expected behavior\n\nI expect it to run, but instead I get the following error: \n\n```\nTraceback (most recent call last):\n File \"\", line 198, in _run_module_as_main\n File \"\", line 88, in _run_code\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/scripts/lerobot_train.py\", line 363, in \n main()\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/scripts/lerobot_train.py\", line 359, in main\n train()\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/configs/parser.py\", line 225, in wrapper_inner\n response = fn(cfg, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/scripts/lerobot_train.py\", line 263, in train\n batch = next(dl_iter)\n ^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/datasets/utils.py\", line 917, in cycle\n yield next(iterator)\n ^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/.venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py\", line 734, in __next__\n data = self._next_data()\n ^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/.venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py\", line 1516, in _next_data\n return self._process_data(data, worker_id)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/.venv/lib/python3.12/site-packages/torch/utils/data/dataloader.py\", line 1551, in _process_data\n data.reraise()\n File \"/home/user/Documents/Research/RL/LeRobot/.venv/lib/python3.12/site-packages/torch/_utils.py\", line 769, in reraise\n raise exception\nNotImplementedError: Caught NotImplementedError in DataLoader worker process 0.\nOriginal Traceback (most recent call last):\n File \"/home/user/Documents/Research/RL/LeRobot/.venv/lib/python3.12/site-packages/torch/utils/data/_utils/worker.py\", line 349, in _worker_loop\n data = fetcher.fetch(index) # type: ignore[possibly-undefined]\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/.venv/lib/python3.12/site-packages/torch/utils/data/_utils/fetch.py\", line 52, in fetch\n data = [self.dataset[idx] for idx in possibly_batched_index]\n ~~~~~~~~~~~~^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/datasets/lerobot_dataset.py\", line 874, in __getitem__\n video_frames = self._query_videos(query_timestamps, ep_idx)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/datasets/lerobot_dataset.py\", line 846, in _query_videos\n frames = decode_video_frames(video_path, shifted_query_ts, self.tolerance_s, self.video_backend)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/datasets/video_utils.py\", line 69, in decode_video_frames\n return decode_video_frames_torchcodec(video_path, timestamps, tolerance_s)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/datasets/video_utils.py\", line 248, in decode_video_frames_torchcodec\n decoder = decoder_cache.get_decoder(str(video_path))\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/lerobot/src/lerobot/datasets/video_utils.py\", line 193, in get_decoder\n decoder = VideoDecoder(file_handle, seek_mode=\"approximate\")\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/RL/LeRobot/.venv/lib/python3.12/site-packages/torchcodec/decoders/_video_decoder.py\", line 89, in __init__\n self._decoder = create_decoder(source=source, seek_mode=seek_mode)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/user/Documents/Research/",
"url": "https://github.com/huggingface/lerobot/issues/2072",
"state": "closed",
"labels": [],
"created_at": "2025-09-27T19:52:42Z",
"updated_at": "2025-11-08T07:53:00Z",
"user": "cijerezg"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3333,
"title": "How to use prefix caching",
"body": "Hi\nI can't find a way to turn on the prefix caching\n\nWhen I run any model, I always get:\nUsing prefix caching = False\n\nThanks a lot",
"url": "https://github.com/huggingface/text-generation-inference/issues/3333",
"state": "open",
"labels": [],
"created_at": "2025-09-27T14:14:37Z",
"updated_at": "2025-09-29T11:52:48Z",
"user": "Noha-Magdy"
},
{
"repo": "huggingface/smol-course",
"number": 259,
"title": "[QUESTION] Is this a bug in smollmv3's chat template?",
"body": "\nHi \n\nI am reading this \nhttps://huggingface.co/learn/smol-course/unit1/2#chat-templates-with-tools\n\nI feel like there is a bug in `HuggingFaceTB/SmolLM3-3B` 's chat template\n\nfrom the example\n\n```\n# Conversation with tool usage\nmessages = [\n {\"role\": \"system\", \"content\": \"You are a helpful assistant with access to tools.\"},\n {\"role\": \"user\", \"content\": \"What's the weather like in Paris?\"},\n {\n \"role\": \"assistant\", \n \"content\": \"I'll check the weather in Paris for you.\",\n \"tool_calls\": [\n {\n \"id\": \"call_1\",\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_weather\",\n \"arguments\": '{\"location\": \"Paris, France\", \"unit\": \"celsius\"}'\n }\n }\n ]\n },\n {\n \"role\": \"tool\",\n \"tool_call_id\": \"call_1\", \n \"content\": '{\"temperature\": 22, \"condition\": \"sunny\", \"humidity\": 60}'\n },\n {\n \"role\": \"assistant\",\n \"content\": \"The weather in Paris is currently sunny with a temperature of 22\u00b0C and 60% humidity. It's a beautiful day!\"\n }\n]\n\n# Apply chat template with tools\nformatted_with_tools = tokenizer.apply_chat_template(\n messages,\n tools=tools,\n tokenize=False,\n add_generation_prompt=False\n)\n\nprint(\"Chat template with tools:\")\nprint(formatted_with_tools)\n```\n\n\nI got this result\n\n```\nChat template with tools:\n<|im_start|>system\n## Metadata\n\nKnowledge Cutoff Date: June 2025\nToday Date: 27 September 2025\nReasoning Mode: /think\n\n## Custom Instructions\n\nYou are a helpful assistant with access to tools.\n\n### Tools\n\nYou may call one or more functions to assist with the user query.\nYou are provided with function signatures within \n{'type': 'function', 'function': {'name': 'get_weather', 'description': 'Get the current weather for a location', 'parameters': {'type': 'object', 'properties': {'location': {'type': 'string', 'description': 'The city and state, e.g. San Francisco, CA'}, 'unit': {'type': 'string', 'enum': ['celsius', 'fahrenheit'], 'description': 'The temperature unit'}}, 'required': ['location']}}}\n{'type': 'function', 'function': {'name': 'calculate', 'description': 'Perform mathematical calculations', 'parameters': {'type': 'object', 'properties': {'expression': {'type': 'string', 'description': 'Mathematical expression to evaluate'}}, 'required': ['expression']}}}\n \n\nFor each function call, return a json object with function name and arguments within \n{\"name\": , \"arguments\": }\n \n\n<|im_end|>\n<|im_start|>user\nWhat's the weather like in Paris?<|im_end|>\n<|im_start|>assistant\nI'll check the weather in Paris for you.<|im_end|>\n<|im_start|>user\n{\"temperature\": 22, \"condition\": \"sunny\", \"humidity\": 60}<|im_end|>\n<|im_start|>assistant\nThe weather in Paris is currently sunny with a temperature of 22\u00b0C and 60% humidity. It's a beautiful day!<|im_end|>\n\n```\n\nWhich is kind of weird.\nThe first thing is there is no tool call in below message\n```\n<|im_start|>assistant\nI'll check the weather in Paris for you.<|im_end|>\n```\n\nI expect it to have ` ... ` in it.\n\nthe second thing is why the `tool` role got replace with `user` role.\nShould not we explicitly specify the role?\n\nCan someone help me with this, please?",
"url": "https://github.com/huggingface/smol-course/issues/259",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-09-27T10:19:37Z",
"updated_at": "2025-11-24T18:40:09Z",
"user": "Nevermetyou65"
},
{
"repo": "huggingface/accelerate",
"number": 3797,
"title": "Question: ReduceLROnPlateau wrapped by AcceleratedScheduler in DDP may multiply LR by num_processes?",
"body": "Hi,\n\nI\u2019m using ReduceLROnPlateau wrapped by AcceleratedScheduler in a multi-GPU / DDP setup (num_processes=8).\n\nMy main process calls:\n```\nlr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n optimizer, mode=\"min\", factor=self.hyper_params['lr_decay_factor'], patience=self.hyper_params['lr_reduce_patient']\n)\nmodel, optimizer, train_loader, val_loader, lr_scheduler, = accelerator.prepare(\n model_bundle.model, optimizer, data_loaders.train_loader, data_loaders.val_loader, lr_scheduler\n)\nfor epoch in range(self.hyper_params['epochs']):\n # train...\n val_loss = self.eval()\n lr_scheduler.step(val_loss)\n\n```\nI noticed that AcceleratedScheduler.step() does:\n```\nnum_processes = AcceleratorState().num_processes\nfor _ in range(num_processes):\n # Special case when using OneCycle and `drop_last` was not used\n if hasattr(self.scheduler, \"total_steps\"):\n if self.scheduler._step_count <= self.scheduler.total_steps:\n self.scheduler.step(*args, **kwargs)\n else:\n self.scheduler.step(*args, **kwargs)\n```\nWill this cause the LR to be reduced num_processes times for a single validation step?\n\nThanks!",
"url": "https://github.com/huggingface/accelerate/issues/3797",
"state": "closed",
"labels": [],
"created_at": "2025-09-26T10:02:20Z",
"updated_at": "2025-11-03T15:08:09Z",
"comments": 1,
"user": "nicelulu"
},
{
"repo": "huggingface/lerobot",
"number": 2050,
"title": "I wonder how to use RL on so101 within sim environment?",
"body": "",
"url": "https://github.com/huggingface/lerobot/issues/2050",
"state": "closed",
"labels": [
"question",
"simulation",
"good first issue"
],
"created_at": "2025-09-26T06:52:38Z",
"updated_at": "2025-10-08T18:04:44Z",
"user": "Temmp1e"
},
{
"repo": "huggingface/lerobot",
"number": 2045,
"title": "I would appreciate it if you could explain how to train the slicing clay model",
"body": "I am planning to conduct a clay-cutting task using pi0. Since this type of task is not typically included among pi0\u2019s foundation model tasks, I would like to inquire how many episodes (and the approximate duration of each) would generally be required for such a custom task.\n\nThe task I have in mind involves cutting clay in this manner, and I am uncertain whether it can be made to work effectively. I would greatly appreciate any realistic advice or guidance you could provide on this matter.\n\n![\"Image\"](\"https://github.com/user-attachments/assets/cd474850-c09a-4ae0-9668-a2ce8c2b3b6e\")
",
"url": "https://github.com/huggingface/lerobot/issues/2045",
"state": "open",
"labels": [],
"created_at": "2025-09-26T00:51:59Z",
"updated_at": "2025-09-26T00:51:59Z",
"user": "pparkgyuhyeon"
},
{
"repo": "huggingface/lerobot",
"number": 2042,
"title": "Question: How to train to get Task Recovery behavior?",
"body": "We would need the robot to be able to detect a failure (like dropping an object) and attempt to correct it to continue with the task.\n\nHow would the training data would look like for this?\n\nThanks",
"url": "https://github.com/huggingface/lerobot/issues/2042",
"state": "open",
"labels": [],
"created_at": "2025-09-25T15:52:55Z",
"updated_at": "2025-09-25T15:52:55Z",
"user": "raul-machine-learning"
},
{
"repo": "huggingface/accelerate",
"number": 3794,
"title": "Error when evaluating with multi-gpu",
"body": "I met a problem when evaluating Llada-8B with multi-gpu ( **Nvidia V100** ) using accelerate+lm_eval. Error occurs when **num_processes>1**.\nbut there is no problem with single GPU, all the other cfgs are the same.\nHow can i solve this problem?\nI use this command to evaluate\n\n accelerate launch --config_file config1.yaml eval_llada.py --tasks ${task} --num_fewshot ${num_fewshot} \\\n --confirm_run_unsafe_code --model llada_dist \\\n --model_args model_path='/raid/data/zhouy/model_data/LLaDA-8B-Instruct', \n gen_length=${length},steps=${length},block_length=${block_length},show_speed=True \n\nThis is my config1.yaml\n\n compute_environment: LOCAL_MACHINE \n debug: false\n distributed_type: MULTI_GPU\n downcast_bf16: 'no'\n enable_cpu_affinity: false\n machine_rank: 0\n main_process_ip: null\n main_process_port: 5678\n main_training_function: main\n mixed_precision: fp16\n num_machines: 1\n num_processes: 2\n rdzv_backend: static\n same_network: true\n tpu_env: []\n tpu_use_cluster: false\n tpu_use_sudo: false\n use_cpu: false\n\nHere is the Error logs:\n\n [rank1]: Traceback (most recent call last):\n [rank1]: File \"/home/zhouy/dllm/Fast-dLLM-main/llada/eval_llada.py\", line 364, in \n [rank1]: cli_evaluate()\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/lm_eval/__main__.py\", line 389, in cli_evaluate\n [rank1]: results = evaluator.simple_evaluate(\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/lm_eval/utils.py\", line 422, in _wrapper\n [rank1]: return fn(*args, **kwargs)\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/lm_eval/evaluator.py\", line 308, in simple_evaluate\n [rank1]: results = evaluate(\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/lm_eval/utils.py\", line 422, in _wrapper\n [rank1]: return fn(*args, **kwargs)\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/lm_eval/evaluator.py\", line 528, in evaluate\n [rank1]: resps = getattr(lm, reqtype)(cloned_reqs)\n [rank1]: File \"/home/zhouy/dllm/Fast-dLLM-main/llada/eval_llada.py\", line 312, in generate_until\n [rank1]: generated_answer, nfe = generate_with_dual_cache(self.model, input_ids, steps=self.steps, gen_length=self.gen_length, block_length=self.block_length, \n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n [rank1]: return func(*args, **kwargs)\n [rank1]: File \"/home/zhouy/dllm/Fast-dLLM-main/llada/generate.py\", line 208, in generate_with_dual_cache\n [rank1]: output = model(x, use_cache=True)\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n [rank1]: return self._call_impl(*args, **kwargs)\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n [rank1]: return forward_call(*args, **kwargs)\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/torch/nn/parallel/distributed.py\", line 1643, in forward\n [rank1]: else self._run_ddp_forward(*inputs, **kwargs)\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/torch/nn/parallel/distributed.py\", line 1459, in _run_ddp_forward\n [rank1]: return self.module(*inputs, **kwargs) # type: ignore[index]\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1736, in _wrapped_call_impl\n [rank1]: return self._call_impl(*args, **kwargs)\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1747, in _call_impl\n [rank1]: return forward_call(*args, **kwargs)\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/accelerate/utils/operations.py\", line 818, in forward\n [rank1]: return model_forward(*args, **kwargs)\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/accelerate/utils/operations.py\", line 806, in __call__\n [rank1]: return convert_to_fp32(self.model_forward(*args, **kwargs))\n [rank1]: File \"/raid/data/zhouy/anaconda3/envs/dllm/lib/python3.9/site-packages/torch/amp/autocast_mode.py\", line 44, in decorate_autocast\n [rank1]: return func(*args, **kwargs)\n [rank1]: File \"/home/zhouy/dllm/Fast-dLLM-main/llada/model/modeling_llada.py\", line 1582, in forward\n [rank1]: outputs = self.model.forward(\n [rank1]: File \"/home/zhouy/dllm/Fast-dLLM-main/llada/model/modeling_llada.py\", line 1479, in forward\n [rank1]: x, cache = block(x, attention_bias=attention_bias, layer_past=layer_past, use_ca",
"url": "https://github.com/huggingface/accelerate/issues/3794",
"state": "closed",
"labels": [],
"created_at": "2025-09-25T14:42:29Z",
"updated_at": "2025-11-03T15:08:12Z",
"comments": 1,
"user": "adfad1"
},
{
"repo": "huggingface/text-embeddings-inference",
"number": 728,
"title": "Compile error in multiple environments for CPU backend",
"body": "### System Info\n\nTEI source code: \n\n- Latest main branch(0c1009bfc49b759fe75eed4fd377b4fbad534ad5); \n- Latest release `v1.8.2`; \n- Release `v1.8.1`\n\nTested platform: \n\n- Win: AMD 7950X+Windows 10 x64 Version 10.0.19045.6332; \n- WSL2: AMD 7950X+Debian 13 on wsl2 (Linux DESKTOP 5.15.167.4-microsoft-standard-WSL2 # 1 SMP Tue Nov 5 00:21:55 UTC 2024 x86_64 GNU/Linux) @ Windows 10 x64 Version 10.0.19045.6332; \n- Linux: Intel 6133*2+Ubuntu 20.04;\n\n(GPUs is not mentioned due to build TEI on CPU)\n\nTested rustup envs: \n\nFreshly installed rustup: default rustup profile: cargo 1.85.1 (d73d2caf9 2024-12-31)\n- Win: Freshly installed rustup & Freshly installed MSVC v143 -VS 2022 C++ build tools+Winodws 11 SDK (10.0.22621.0)+cmake\n- WSL: Freshly installed rustup & gcc (Debian 14.2.0-19) 14.2.0\n- Linux: Freshly installed rustup & gcc (GCC) 10.5.0\n\n### Information\n\n- [ ] Docker\n- [x] The CLI directly\n\n### Tasks\n\n- [x] An officially supported command\n- [ ] My own modifications\n\n### Reproduction\n\nAs docs' recommend, tested on 3 different envs listed above:\n\n1. `curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh`\n2. `cargo install --path router -F mkl --verbose` (added `--verbose` for logging)\n\nShows compile error about **25 undefined references / external symbol** (`'vsTanh', 'vsSub', 'vsSqrt', 'vsSin', 'vsMul', 'vsLn', 'vsFmin', 'vsExp', 'vsDiv', 'vsCos', 'vsAdd', 'vdTanh', 'vdSub', 'vdSqrt', 'vdSin', 'vdMul', 'vdLn', 'vdFmin', 'vdExp', 'vdDiv', 'vdCos', 'vdAdd', 'sgemm_', 'hgemm_', 'dgemm_'`)\n\n### Expected behavior\n\nExpect finishing compile, but:\n\n- Compile v1.8.2/v1.8.1/main (similar error) on Win+MSVC+AMD CPU:\n\n```\n...\nRunning `C:\\Users\\nkh04\\.rustup\\toolchains\\1.85.1-x86_64-pc-windows-msvc\\bin\\rustc.exe --crate-name text_embeddings_router --edition=2021 router\\src\\main.rs --error-format=json --json=diagnostic-rendered-ansi,artifacts,future-incompat --diagnostic-width=115 --crate-type bin --emit=dep-info,link -C opt-level=3 -C panic=abort -C lto=fat -C codegen-units=1 --cfg \"feature=\\\"candle\\\"\" --cfg \"feature=\\\"default\\\"\" --cfg \"feature=\\\"dynamic-linking\\\"\" --cfg \"feature=\\\"http\\\"\" --cfg \"feature=\\\"mkl\\\"\" --check-cfg cfg(docsrs,test) --check-cfg \"cfg(feature, values(\\\"accelerate\\\", \\\"candle\\\", \\\"candle-cuda\\\", \\\"candle-cuda-turing\\\", \\\"candle-cuda-volta\\\", \\\"default\\\", \\\"dynamic-linking\\\", \\\"google\\\", \\\"grpc\\\", \\\"http\\\", \\\"metal\\\", \\\"mkl\\\", \\\"ort\\\", \\\"python\\\", \\\"static-linking\\\"))\" -C metadata=e1406d246b8c925f --out-dir F:\\text-embeddings-inference-1.8.2\\target\\release\\deps -C strip=symbols -L dependency=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps --extern anyhow=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libanyhow-5751be73768123a3.rlib --extern axum=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libaxum-8bc59cf51b8d1ae2.rlib --extern axum_tracing_opentelemetry=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libaxum_tracing_opentelemetry-6919ca207315f42e.rlib --extern base64=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libbase64-20907aaabfa37a5c.rlib --extern clap=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libclap-ded1b8a7f6da29a7.rlib --extern futures=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libfutures-55e1ce906ca8ce43.rlib --extern hf_hub=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libhf_hub-46162d037bf61d01.rlib --extern http=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libhttp-721bb5a8d4ad5af4.rlib --extern init_tracing_opentelemetry=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libinit_tracing_opentelemetry-1130e5d6b02b3c83.rlib --extern intel_mkl_src=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libintel_mkl_src-7de47f7e38d141d5.rlib --extern metrics=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libmetrics-f38f63f59a9e401d.rlib --extern metrics_exporter_prometheus=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libmetrics_exporter_prometheus-3e83484daaaf9a40.rlib --extern mimalloc=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libmimalloc-55786f97dafb497c.rlib --extern num_cpus=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libnum_cpus-26f3f7fb7d16b825.rlib --extern opentelemetry=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libopentelemetry-43ce590757d45ebb.rlib --extern opentelemetry_otlp=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libopentelemetry_otlp-7adf99fb9a924955.rlib --extern opentelemetry_sdk=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libopentelemetry_sdk-48d11cd15d38a406.rlib --extern reqwest=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libreqwest-cdbb64c7917c22c9.rlib --extern serde=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libserde-e13a1b310cb83bc5.rlib --extern serde_json=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libserde_json-c2074a4721fb3f74.rlib --extern simsimd=F:\\text-embeddings-inference-1.8.2\\target\\release\\deps\\libsimsimd-5bf7050b419eab84.rlib --extern text_embeddings_bac",
"url": "https://github.com/huggingface/text-embeddings-inference/issues/728",
"state": "open",
"labels": [
"documentation",
"question"
],
"created_at": "2025-09-25T11:52:16Z",
"updated_at": "2025-11-18T14:49:01Z",
"user": "nkh0472"
},
{
"repo": "huggingface/transformers",
"number": 41141,
"title": "Need a concise example of Tensor Parallelism (TP) training using Trainer/SFTTrainer.",
"body": "### Feature request\n\nI have checked the code and there are few places which talk about TP. I saw from_pretrained method for model contains tp_plan and device_mesh. I also checked that the TrainingArgument can take parallelism_config which defines the TP/CP plan along with FSDP. However, I am not able to successfully stitch things together to make the only TP based training work. Please help.\n\nRef: \n- https://github.com/huggingface/transformers/blob/main/examples/3D_parallel.py\n\n### Motivation\n\nNeed to enable only TP based training, but no tutorial or example is available.\n\n### Your contribution\n\nGiven proper understanding and proper guidance, I can come up with clean example and documentation for the same.",
"url": "https://github.com/huggingface/transformers/issues/41141",
"state": "open",
"labels": [
"Documentation",
"Feature request",
"Tensor Parallel"
],
"created_at": "2025-09-25T03:01:02Z",
"updated_at": "2026-01-04T14:05:36Z",
"comments": 10,
"user": "meet-minimalist"
},
{
"repo": "huggingface/lerobot",
"number": 2034,
"title": "dataset v2.1 and groot n1.5",
"body": "for now, groot dose not support dataset v3.0 to fine_tune ? in this case, should we continue use v2.1 ? and if we already collect data from v3, how we can convert it back to v2.1?",
"url": "https://github.com/huggingface/lerobot/issues/2034",
"state": "open",
"labels": [
"question",
"policies",
"dataset"
],
"created_at": "2025-09-24T21:12:26Z",
"updated_at": "2025-12-24T00:05:45Z",
"user": "zujian-y"
},
{
"repo": "huggingface/tokenizers",
"number": 1868,
"title": "How to set the cache_dir in the Rust implementation?",
"body": "Hey, thank you for your great work with these tokenizers. \n\nWhen I use the tokenizers through the Python API via transformers, I can set a specific cache_dir like this \n```\nfrom transformers import AutoTokenizer\nself.tokenizer = AutoTokenizer.from_pretrained(self.tokenizer_name,cache_dir = self.cache_dir)\n```\n\nHow can I do that in Rust? How can I print the default cache dir (in Rust)?",
"url": "https://github.com/huggingface/tokenizers/issues/1868",
"state": "open",
"labels": [],
"created_at": "2025-09-24T18:50:38Z",
"updated_at": "2025-10-06T04:25:46Z",
"user": "sambaPython24"
},
{
"repo": "huggingface/diffusers",
"number": 12386,
"title": "Implement missing features on ModularPipeline",
"body": "as i'm looking to take advantage of new `ModularPipeline` ask is to implement some currently missing features\n\nmy use case is to convert existing loaded model using standard pipeline into modular pipeline. that functionality was provided via #11915 and is now working.\n\nfirst minor obstacle is that modular pipeline does not have defined params for execution\nin standard pipeline i can inspect `__call__` signature to see which are allowed params\ni currently work around this using\n`possible = [input_param.name for input_param in model.blocks.inputs]`\nplease advise if this is acceptable\n\nsecond one is that modular pipelines don't seem to implement normal callbacks at all (e.g. `callback_on_step_end_tensor_inputs`? at the minimum we need some kind of callback functionality to capture interim latents on each step\n\nthird is more cosmetic - modular pipeline does implement `set_progress_bar_config`, but its not doing anything as its not implement on actual block (tested with `StableDiffusionXLModularPipeline`)\n\ncc @yiyixuxu @DN6 @sayakpaul ",
"url": "https://github.com/huggingface/diffusers/issues/12386",
"state": "open",
"labels": [
"roadmap"
],
"created_at": "2025-09-24T15:49:23Z",
"updated_at": "2025-09-29T05:46:29Z",
"comments": 0,
"user": "vladmandic"
},
{
"repo": "huggingface/candle",
"number": 3096,
"title": "[Question] Minimal documentation/example on including weights in compiled executable",
"body": "Just what the title says: Is there a minimal code example on including weights in the compiled executable using include_bytes. Nervous to implement this without understanding best practices and end up with a suboptimal solution.",
"url": "https://github.com/huggingface/candle/issues/3096",
"state": "closed",
"labels": [],
"created_at": "2025-09-24T02:47:28Z",
"updated_at": "2025-10-07T04:49:26Z",
"comments": 1,
"user": "bitanath"
},
{
"repo": "huggingface/optimum-executorch",
"number": 149,
"title": "Add documentation for how to run each type of exported model on ExecuTorch",
"body": "Blocked on runner / multimodal runner work in ExecuTorch",
"url": "https://github.com/huggingface/optimum-executorch/issues/149",
"state": "open",
"labels": [],
"created_at": "2025-09-23T18:53:55Z",
"updated_at": "2025-09-23T18:54:00Z",
"user": "jackzhxng"
},
{
"repo": "huggingface/safetensors",
"number": 653,
"title": "`get_slice` is slow because it uses `tensors()` method instead of `info()`",
"body": "### Feature request\n\nReplace \n```rust\nself.metadata.tensors().get(name)\n```\nwith\n```rust\nself.metadata.info(name)\n```\nin `get_slice` method\n\n### Motivation\n\nI noticed that the `get_slice` method of `Open` [does](https://github.com/huggingface/safetensors/blob/0816a1ae1d6b731cefd67f061d80d1cadd0dd7bb/bindings/python/src/lib.rs#L851) \n```rust\nself.metadata.tensors().get(name)\n````\ninstead of\n```rust\nself.metadata.info(name)\n```\nlike `get_tensor()` [does](https://github.com/huggingface/safetensors/blob/0816a1ae1d6b731cefd67f061d80d1cadd0dd7bb/bindings/python/src/lib.rs#L638) when retrieving `TensorInfo` by name.\n\nBecause of this, `get_slice` is much slower, since the `tensors()` method [reconstructs](https://github.com/huggingface/safetensors/blob/0816a1ae1d6b731cefd67f061d80d1cadd0dd7bb/safetensors/src/tensor.rs#L633) a new `HashMap` on each call.\n\nIs there any particular reason for this approach? Would it be possible to replace it with `self.metadata.info(name)` to improve performance?\n\n\n### Your contribution\n\nI do not mind doing a PR",
"url": "https://github.com/huggingface/safetensors/issues/653",
"state": "closed",
"labels": [],
"created_at": "2025-09-23T15:09:51Z",
"updated_at": "2025-09-28T16:42:45Z",
"comments": 1,
"user": "PgLoLo"
},
{
"repo": "huggingface/diffusers",
"number": 12375,
"title": "What kernels should we integrate in Diffusers?",
"body": "Now that we have an [integration](https://github.com/huggingface/diffusers/pull/12236) with the `kernels` lib to use Flash Attention 3 (FA3), it'd be nice to gather community interest about which kernels we should try to incorporate in the library through the [`kernels` lib](https://github.com/huggingface/kernels/). FA3 delivers a significant speedup on Hopper GPUs.\n\nI have done some work in the `kernelize` branch to see if replacing `GELU`, `SiLU`, and `RMSNorm` with their optimized kernels would have any speedups on Flux. So far, it hasn't had any. Benchmarking script: https://gist.github.com/sayakpaul/35236dd96e15d9f7d658a7ad11918411. One can compare the changes here: https://github.com/huggingface/diffusers/compare/kernelize?expand=1. \n\n> [!NOTE]\n> The changes in the `kernelize` branch are quite hacky as we're still evaluating things.\n\nPlease use this issue to let us know which kernels we should try to support in Diffusers. Some notes to keep in mind:\n\n* Layers where the `forward()` method is easily replaceable with the `kernelize()` [mechanism](https://github.com/huggingface/kernels/blob/main/docs/source/layers.md#kernelizing-a-model) would be prioritized. A reference is here: https://github.com/huggingface/transformers/pull/38205. \n* Even if a kernel isn't directly compatible with `kernels`, we can try to make it so, like we have for https://huggingface.co/kernels-community/flash-attn3.\n* Not all kernels contribute non-trivial gains in terms of speedup. So, please bear that in mind when proposing a kernel.\n\nCc: @MekkCyber",
"url": "https://github.com/huggingface/diffusers/issues/12375",
"state": "open",
"labels": [
"performance"
],
"created_at": "2025-09-23T09:03:13Z",
"updated_at": "2025-09-30T06:56:39Z",
"comments": 8,
"user": "sayakpaul"
},
{
"repo": "huggingface/peft",
"number": 2798,
"title": "Add stricter type checking in LoraConfig for support with HfArgumentParser",
"body": "### System Info\n\nSystem Info\ntransformers version: 4.57.0.dev0\nPlatform: Linux-5.14.0-284.73.1.el9_2.x86_64-x86_64-with-glibc2.39\nPython version: 3.12.3\nHuggingface_hub version: 0.34.4\nSafetensors version: 0.5.2\nAccelerate version: 1.10.1\nAccelerate config: not found\nDeepSpeed version: not installed\nPyTorch version (accelerator?): 2.8.0+cu128 (CUDA)\nTensorflow version (GPU?): not installed (NA)\nFlax version (CPU?/GPU?/TPU?): not installed (NA)\nJax version: not installed\nJaxLib version: not installed\nUsing distributed or parallel set-up in script?: No\nUsing GPU in script?: No\nGPU type: NVIDIA A100-SXM4-80GB\npeft version: 0.17.1\n\n### Who can help?\n\n@benjaminbossan @githubnemo \n\n### Reproduction\n\n```\nfrom peft import LoraConfig\nfrom transformers import HfArgumentParser\n\np = HfArgumentParser(dataclass_types=LoraConfig) # fails\n```\n\n### Expected behavior\n\nI would expect LoraConfig to be supported by HfArgumentParser.\nAs I understand, this fails because HfArgumentParser does not support fields of type (`Optional[List[str], str]`).\n\nI had raised this in transformers as well, please refer [here](https://github.com/huggingface/transformers/issues/40915).\n\nCan we add stricter type checking for such fields so it can be easily integrated with other libraries and argument parsers?",
"url": "https://github.com/huggingface/peft/issues/2798",
"state": "closed",
"labels": [],
"created_at": "2025-09-23T05:19:34Z",
"updated_at": "2025-09-23T12:37:47Z",
"comments": 3,
"user": "romitjain"
},
{
"repo": "huggingface/lerobot",
"number": 1995,
"title": "Questions about SmolVLA design",
"body": "Hi! I am looking into the details of SmolVLA implementation, and got some questions.\n\nI wonder the following points are necessary, or beneficial for the performance.\n\n\n1.\nhttps://github.com/huggingface/lerobot/blob/f7283193ea9ae932423e3a1e27524a27fa5c0fe5/src/lerobot/policies/smolvla/smolvlm_with_expert.py#L354C63-L354C74\n\nIn the cross-attention layer, the VLM keys and values are linear-projected before the attention interface.\n\nThey have compatible shape without the projection, and ROPE is not applied after the projection (although ROPE is applied in the VLM part, interaction between the ROPEd queries and projected keys might not work as rotation?)\n\n\n2.\nhttps://github.com/huggingface/lerobot/blob/f7283193ea9ae932423e3a1e27524a27fa5c0fe5/src/lerobot/policies/smolvla/modeling_smolvla.py#L566\n\nhttps://github.com/huggingface/lerobot/blob/f7283193ea9ae932423e3a1e27524a27fa5c0fe5/src/lerobot/policies/smolvla/modeling_smolvla.py#L592C1-L593C1\n\nimage and text embeddings are multiplied by `sqrt(dim)` before they are fed to the llm and expert layers.\n\nI could not find the same multiplication in SmolVLM modeling (https://github.com/huggingface/transformers/blob/main/src/transformers/models/smolvlm/modeling_smolvlm.py)\n\nI guess that this multiplication might change the distribution of image-text features.\n\n3.\nSmolVLM and SmolVLA are trained with different ROPE max frequency.\n\nIt seems like SmolVLM is trained with 100_000, and SmolVLA is trained with 10_000.\n\n4.\nIt seems like SmolVLM uses causal mask for all LLM layers. (no bidirectional attention for images)\n\nSmolVLA uses similar mask with PI0 (paligemma).\n",
"url": "https://github.com/huggingface/lerobot/issues/1995",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-09-22T11:53:01Z",
"updated_at": "2025-10-17T01:58:12Z",
"user": "gliese581gg"
},
{
"repo": "huggingface/lerobot",
"number": 1994,
"title": "How to improve success rate and generalization",
"body": "Hi, I have one question regarding the success rate, if I ensure the object appears in the frame of wrist camera at the beginning of dataset collection/inference, will this lead to higher success rate for pick and place task?\n\nMy initial attempt was object appears in the side view camera but does not appear in the wrist camera at the initial point/ beginning of dataset collection/inference.\n\n**Should I ensure object appears in both side view camera and wrist camera at the starting point of program?**",
"url": "https://github.com/huggingface/lerobot/issues/1994",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-09-22T09:55:53Z",
"updated_at": "2025-09-23T09:26:16Z",
"user": "Liu9999ai"
},
{
"repo": "huggingface/smol-course",
"number": 248,
"title": "[QUESTION] About applying chat template for base model via `clone_chat_template` from trl",
"body": "In the course [Supervised Fine-Tuning](https://huggingface.co/learn/smol-course/unit1/3), author uses base model `HuggingFaceTB/SmolLM3-3B-Base` but I choose `HuggingFaceTB/SmolLM2-135M` because it is lighter. However, I found that the base model `SmolLM2-135M` does not have its own chat template but it already had special tokens. However, speical tokens may be incorrect, for example, bos_token and eos_token share the same token `<|endoftext|>`\n\n![\"Image\"](\"https://github.com/user-attachments/assets/87a4cea8-c372-4540-b617-9c41825f5a7e\")
\n\nI also refer to course [LLM Course, Fine-Tuning with SFTTrainer](https://huggingface.co/learn/llm-course/en/chapter11/3?fw=pt#implementation-with-trl) and author uses `setup_chat_format` to create the chat template for base model's tokenizer which does not have its own chat template\n\nHowever, [`setup_chat_format`](https://github.com/huggingface/trl/blob/86f74b486fda475e5530a451d06b835361d959ac/trl/models/utils.py#L87) only supports `chatml` format and will be deprecated in trl version 0.26.0. That is why I use [`clone_chat_template`](https://github.com/huggingface/trl/blob/86f74b486fda475e5530a451d06b835361d959ac/trl/models/utils.py#L165) instead.\n\nBut another issue appears here: while `clone_chat_template` only overwrites eos from source tokenizer to target tokenizer, the `setup_chat_format` overwrites all bos, eos, and pad tokens. After I try to clone `Llama-3.2-Instruct`'s chat template, only eos changes to `<|eot_id|>`\n\n`model, tokenizer, added_tokens = clone_chat_template(model=model, tokenizer=tokenizer, source_tokenizer_path='meta-llama/Llama-3.2-1B-Instruct')`\n\n![\"Image\"](\"https://github.com/user-attachments/assets/4428af4d-b8d8-4974-893f-af4033d516ed\")
\n\nQuestion:\n1. Why in the base model, although the tokenizer does not have a chat template, it already has special tokens?\n2. `clone_chat_template` does not overwrite all special tokens like bos, eos, pad, ... so are there any training SFT impacts, and what is the solution for this?\n\nI am new to SFT and I very appreciate any support. Thank you.\n ",
"url": "https://github.com/huggingface/smol-course/issues/248",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-09-22T03:03:56Z",
"updated_at": "2025-09-22T19:13:17Z",
"user": "binhere"
},
{
"repo": "huggingface/transformers.js",
"number": 1419,
"title": "Why is `token-classification` with T5 not available? (`T5ForTokenClassification`)",
"body": "### Question\n\nIn python `tranformers` i can do:\n```python\nmodel = AutoModelForTokenClassification.from_pretrained(\"google-t5/t5-base\")\n```\nand use it with `Trainer` to train it (quite successfully).\nOr\n```python\nclassifier = pipeline(\"token-classification\", model=\"google-t5/t5-base\")\n```\nand use it for token classification.\n\nInstead, if I try to use it in `transformers.js` (web, 3.7.3):\n```js\nclassifier = await pipeline('token-classification', \"google-t5/t5-base\")\n```\nI receive this error:\n```\nUnsupported model type: t5\n```\n\nHow come? Or there is another way to use T5 for token classification in javascript?\n",
"url": "https://github.com/huggingface/transformers.js/issues/1419",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-09-21T23:30:22Z",
"updated_at": "2025-09-24T21:42:56Z",
"user": "debevv"
},
{
"repo": "huggingface/transformers.js",
"number": 1418,
"title": "EmbeddingGemma usage",
"body": "### Question\n\nI'm new to transformers.js \nI want to use embeddinggemma into my web app and I've looked at the example on its usage at this link:\nhttps://huggingface.co/blog/embeddinggemma#transformersjs\n\nAt the same time I've seen a different code, using pipeline, regarding embeddings:\nhttps://huggingface.co/docs/transformers.js/api/pipelines#pipelinesfeatureextractionpipeline\n\nI'm trying to create a custom pipeline and in typescript I'm building the pipeline like\n\n```ts\nclass EmbeddingPipeline {\n private static instance: Promise | null = null;\n private static model = 'onnx-community/embeddinggemma-300m-ONNX';\n private static readonly task = 'feature-extraction';\n\n // Device rilevato (default wasm)\n private static device: 'webgpu' | 'wasm' = 'wasm';\n private static deviceInitPromise: Promise | null = null;\n\n private static async detectDeviceOnce(): Promise {\n if (this.deviceInitPromise) return this.deviceInitPromise;\n this.deviceInitPromise = (async () => {\n if (typeof navigator !== 'undefined' && 'gpu' in navigator) {\n try {\n const adapter = await (navigator as any).gpu.requestAdapter();\n if (adapter) {\n this.device = 'webgpu';\n return;\n }\n } catch {\n // ignore, fallback to wasm\n }\n }\n this.device = 'wasm';\n })();\n return this.deviceInitPromise;\n }\n\n static getSelectedDevice(): 'webgpu' | 'wasm' {\n return this.device;\n }\n\n static async getInstance(progress_callback?: ProgressCallback): Promise {\n if (this.instance) return this.instance;\n\n // Rileva device una sola volta\n await this.detectDeviceOnce();\n\n const build = async (device: 'webgpu' | 'wasm') =>\n pipeline(\n this.task,\n this.model,\n {\n progress_callback,\n dtype: 'q8',\n device\n }\n ) as Promise;\n\n this.instance = (async (): Promise => {\n try {\n return await build(this.device);\n } catch (e) {\n if (this.device === 'webgpu') {\n // Fallback automatico a wasm\n this.device = 'wasm';\n return await build('wasm');\n }\n throw e;\n }\n })();\n\n return this.instance;\n }\n}\n\n\nconst getEmbeddingDevice = () => EmbeddingPipeline.getSelectedDevice();\nconst embedding_prefixes_per_task: Record = {\n 'query': \"task: search result | query: \",\n 'document': \"title: none | text: \",\n};\n\nexport type EmbeddingTask = 'query' | 'document';\n\nexport const getEmbedding = async (task: EmbeddingTask, text: string): Promise => {\n const extractor = await EmbeddingPipeline.getInstance();\n\n const prefix = embedding_prefixes_per_task[task];\n const result = await extractor(`${prefix}${text}`, { pooling: 'mean', normalize: true });\n\n return result.data as Float32Array;\n};\n```\n\nI'm using the same sentences (with prefixes) used by your example (I'm running both my class and your code to be sure if they matches) and the embedding result is different.\n\nWhat am I doing wrong? Do you have any reference to some proper docs reference that explain properly how this works?\n\nThanks",
"url": "https://github.com/huggingface/transformers.js/issues/1418",
"state": "open",
"labels": [
"question",
"v4"
],
"created_at": "2025-09-21T10:26:22Z",
"updated_at": "2025-11-08T15:33:16Z",
"user": "MithrilMan"
},
{
"repo": "huggingface/diffusers",
"number": 12359,
"title": "Chroma pipeline documentation bug regarding the `guidance_scale` parameter",
"body": "### Describe the bug\n\nFrom my understanding, Chroma is a retrained and dedistilled version of the Flux architecture, so it uses true CFG, unlike Flux. I can indeed confirm that this is true by tracing through the source code. \nHowever, currently the documentation for the `guidance_scale` parameter in the `ChromaPipeline.__call__()` method mentions otherwise, presumably because it was copied over from the `FluxPipeline` documentation. \n\n### Reproduction\n\nThe current documentation for the `guidance_scale` parameter in the `ChromaPipeline.__call__()` method:\n```python\n'''\nguidance_scale (float, optional, defaults to 3.5) \u2014 Embedded guiddance scale is enabled by setting guidance_scale > 1. Higher guidance_scale encourages a model to generate images more aligned with prompt at the expense of lower image quality.\nGuidance-distilled models approximates true classifer-free guidance for guidance_scale > 1. Refer to the [paper](https://huggingface.co/papers/2210.03142) to learn more.\n'''\n```\n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.36.0.dev0\n- Platform: Windows-10-10.0.26100-SP0\n- Running on Google Colab?: No\n- Python version: 3.11.9\n- PyTorch version (GPU?): 2.7.1+cu128 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.34.4\n- Transformers version: 4.55.0\n- Accelerate version: 1.10.0\n- PEFT version: 0.17.0\n- Bitsandbytes version: 0.47.0\n- Safetensors version: 0.6.2\n- xFormers version: 0.0.31.post1\n- Accelerator: NVIDIA GeForce RTX 4090, 24564 MiB\n- Using GPU in script?: No\n- Using distributed or parallel set-up in script?: No\n\n### Who can help?\n\n@stevhliu ",
"url": "https://github.com/huggingface/diffusers/issues/12359",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-09-21T08:34:15Z",
"updated_at": "2025-09-22T20:04:15Z",
"comments": 1,
"user": "mingyi456"
},
{
"repo": "huggingface/trl",
"number": 4110,
"title": "How does `trl` know what part of dataset is prompt and completion in the following situation?",
"body": "### Reproduction\n\n```python\nimport torch\nimport trl as r\nimport peft as p\nimport datasets as d\nimport accelerate as a\nimport transformers as t\n\nallowed_entities = ['AGE', 'EYECOLOR', 'GENDER', 'HEIGHT', 'WEIGHT', 'SEX']\nentity_mapping = {\n \"ACCOUNTNAME\": \"account_name\",\n \"ACCOUNTNUMBER\": \"account_number\",\n \"AGE\": \"age\",\n \"AMOUNT\": \"amount\",\n \"BIC\": \"bic\",\n \"BITCOINADDRESS\": \"bitcoin_address\",\n \"BUILDINGNUMBER\": \"building_number\",\n \"CITY\": \"city\",\n \"COMPANYNAME\": \"company_name\",\n \"COUNTY\": \"county\",\n \"CREDITCARDCVV\": \"credit_card_cvv\",\n \"CREDITCARDISSUER\": \"credit_card_issuer\",\n \"CREDITCARDNUMBER\": \"credit_card_number\",\n \"CURRENCY\": \"currency\",\n \"CURRENCYCODE\": \"currency_code\",\n \"CURRENCYNAME\": \"currency_name\",\n \"CURRENCYSYMBOL\": \"currency_symbol\",\n \"DATE\": \"date\",\n \"DOB\": \"dob\",\n \"EMAIL\": \"email\",\n \"ETHEREUMADDRESS\": \"ethereum_address\",\n \"EYECOLOR\": \"eye_color\",\n \"FIRSTNAME\": \"first_name\",\n \"GENDER\": \"gender\",\n \"HEIGHT\": \"height\",\n \"IBAN\": \"iban\",\n \"IP\": \"ip\",\n \"IPV4\": \"ipv4\",\n \"IPV6\": \"ipv6\",\n \"JOBAREA\": \"job_area\",\n \"JOBTITLE\": \"job_title\",\n \"JOBTYPE\": \"job_type\",\n \"LASTNAME\": \"last_name\",\n \"LITECOINADDRESS\": \"litecoin_address\",\n \"MAC\": \"mac\",\n \"MASKEDNUMBER\": \"masked_number\",\n \"MIDDLENAME\": \"middle_name\",\n \"NEARBYGPSCOORDINATE\": \"nearby_gps_coordinate\",\n \"ORDINALDIRECTION\": \"ordinal_direction\",\n \"PASSWORD\": \"password\",\n \"PHONEIMEI\": \"phone_imei\",\n \"PHONENUMBER\": \"phone_number\",\n \"PIN\": \"pin\",\n \"PREFIX\": \"prefix\",\n \"SECONDARYADDRESS\": \"secondary_address\",\n \"SEX\": \"sex\",\n \"SSN\": \"ssn\",\n \"STATE\": \"state\",\n \"STREET\": \"street\",\n \"TIME\": \"time\",\n \"URL\": \"url\",\n \"USERAGENT\": \"user_agent\",\n \"USERNAME\": \"username\",\n \"VEHICLEVIN\": \"vehicle_vin\",\n \"VEHICLEVRM\": \"vehicle_vrm\",\n \"ZIPCODE\": \"zip_code\"\n}\n\ndef formatting_function(x):\n entities = []\n for entity in x['privacy_mask']:\n if entity['label'] not in allowed_entities:\n entities.append({'value': entity['value'], 'label': entity_mapping[entity['label']]})\n prompt = f\"Extract all the personal information from the following text and classify it: {x['source_text']}\"\n completion = str(entities)\n return {\"text\": f\"### PROMPT\\n{prompt}\\n\\n### COMPLETION\\n{completion}\"}\n\ndef main():\n model_name = \"Qwen/Qwen3-0.6B\"\n dataset_name = \"ai4privacy/pii-masking-200k\"\n\n quantization = False\n quantization_bits = \"8\"\n lora = True\n lora_rank = 8\n lora_alpha = 16\n lora_dropout = 0.05\n use_mixed_precision = True\n # Training parameters\n completion_only_loss = True\n output_dir = f\"/scratch/bminesh-shah/phi-ner/{model_name.replace('/', '-')}_pii_finetuned_prompt_completion\"\n learning_rate = 1e-4\n num_train_epochs = 10\n per_device_train_batch_size = 2\n gradient_accumulation_steps = 8\n\n accelerator = a.Accelerator()\n\n dataset = d.load_dataset(dataset_name)\n dataset = dataset.filter(lambda x: x['language'] == 'en')\n dataset = dataset.remove_columns(['target_text', 'span_labels', 'mbert_text_tokens', 'mbert_bio_labels', 'id', 'language', 'set'])\n dataset = dataset['train']\n dataset = dataset.train_test_split(test_size=0.2, seed=24, shuffle=True)\n print(dataset)\n\n if accelerator.is_main_process:\n dataset = dataset.map(formatting_function, remove_columns=['source_text', 'privacy_mask'])\n print(dataset)\n print(dataset['train'][0])\n\n tokenizer = t.AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n tokenizer.pad_token = tokenizer.eos_token\n tokenizer.padding_side = \"right\"\n\n bnb_config = None\n if quantization and quantization_bits == \"4\":\n bnb_config = t.BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type=\"nf4\", bnb_4bit_compute_dtype=torch.bfloat16, bnb_4bit_use_double_quant=True)\n elif quantization and quantization_bits == \"8\":\n bnb_config = t.BitsAndBytesConfig(load_in_8bit=True)\n\n model = t.AutoModelForCausalLM.from_pretrained(\n model_name,\n quantization_config=bnb_config,\n device_map={\"\": accelerator.process_index},\n dtype=torch.bfloat16 if use_mixed_precision else torch.float32,\n trust_remote_code=True\n )\n\n if quantization:\n model = p.prepare_model_for_kbit_training(model)\n model.config.use_cache = False\n model.config.pretraining_tp = 1\n model.config.pad_token_id = model.config.eos_token_id\n\n if lora:\n lora_config = p.LoraConfig(r=lora_rank, lora_alpha=lora_alpha, lora_dropout=lora_dropout, bias=\"none\", task_type=\"CAUSAL_LM\")\n model = p.get_peft_model(model, lora_config)\n model.train()\n\n sft_config = r.SFTConfig(\n learning_rate=learning_rate,\n num_train_epochs=num_train_epochs,\n per_device_train_batch_size=per_device_train_batch_size,\n gradient_accumulation_steps=gradient_accumulation_steps,\n output_dir=output_dir,\n eval_s",
"url": "https://github.com/huggingface/trl/issues/4110",
"state": "closed",
"labels": [
"\ud83d\udc1b bug",
"\ud83d\udcda documentation"
],
"created_at": "2025-09-19T17:42:26Z",
"updated_at": "2025-09-19T20:02:16Z",
"user": "bminesh-shah"
},
{
"repo": "huggingface/transformers",
"number": 41005,
"title": "Are we have Qwen3VL Official Model Published by Alibaba",
"body": "### Model description\n\nReference - https://huggingface.co/docs/transformers/main/en/model_doc/qwen3_vl#transformers.Qwen3VLForConditionalGeneration\n\nIf not when can we expect any guess?",
"url": "https://github.com/huggingface/transformers/issues/41005",
"state": "closed",
"labels": [
"New model"
],
"created_at": "2025-09-19T13:59:34Z",
"updated_at": "2025-09-20T10:00:04Z",
"comments": 1,
"user": "Dineshkumar-Anandan-ZS0367"
},
{
"repo": "huggingface/transformers",
"number": 40993,
"title": "HfArgumentParser cannot parse TRL Config",
"body": "### System Info\n\ntransformers==4.56.1\ntrl==0.17.0\n\nI used to apply code below\n\n```python\nfrom transformers import HfArgumentParser\nfrom trl import (\n\tScriptArguments, ModelConfig, SFTConfig\n)\nparser = HfArgumentParser((ScriptArguments, SFTConfig, ModelConfig))\nscript_arguments, trainer_config, model_config = parser.parse_args_into_dataclasses()\n```\n\nto parse training args, but after updating transformers to 4.56, it does not work:\n\n```\nTraceback (most recent call last):\n File \"D:\\mytest.py\", line 5, in \n parser = HfArgumentParser((ScriptArguments, SFTConfig, ModelConfig))\n File \"E:\\Anaconda3\\envs\\myopenai\\lib\\site-packages\\transformers\\hf_argparser.py\", line 143, in __init__\n self._add_dataclass_arguments(dtype)\n File \"E:\\Anaconda3\\envs\\myopenai\\lib\\site-packages\\transformers\\hf_argparser.py\", line 260, in _add_dataclass_arguments\n raise RuntimeError(\nRuntimeError: Type resolution failed for . Try declaring the class in global scope or removing line of `from __future__ import annotations` which opts in Postponed Evaluation of Annotations (PEP 563)\n```\n\nHow to fix it?\n\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nRun \n\n```python\nfrom transformers import HfArgumentParser\nfrom trl import (\n\tScriptArguments, ModelConfig, SFTConfig\n)\nparser = HfArgumentParser((ScriptArguments, SFTConfig, ModelConfig))\nscript_arguments, trainer_config, model_config = parser.parse_args_into_dataclasses()\n```\n\n### Expected behavior\n\nIt should be work",
"url": "https://github.com/huggingface/transformers/issues/40993",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-09-19T08:29:48Z",
"updated_at": "2025-09-19T09:06:20Z",
"comments": 5,
"user": "caoyang-sufe"
},
{
"repo": "huggingface/lerobot",
"number": 1978,
"title": "Is there a best fit model to each sim env\uff1f",
"body": "I try to train diffusion\uff0csmolvla\uff0ceven pi0 on the aloha with 200k steps, and found that they all perform much worse (with less than 10% success rate) than act policy, why? Did each env task exist a best-fit policy? or there are problems on my training strategy.",
"url": "https://github.com/huggingface/lerobot/issues/1978",
"state": "closed",
"labels": [
"question",
"policies",
"simulation"
],
"created_at": "2025-09-19T02:45:14Z",
"updated_at": "2025-10-17T11:25:27Z",
"user": "shs822"
},
{
"repo": "huggingface/accelerate",
"number": 3784,
"title": "AttributeError: 'Accelerator' object has no attribute 'deepspeed_config'. Did you mean: 'deepspeed_plugin'?",
"body": "### System Info\n\n```Shell\n- Name: accelerate Version: 1.10.1\n- Name: transformers Version: 4.54.0\n- Name: deepspeed Version: 0.17.5\n- Name: torch Version: 2.8.0\n- Name: wandb Version: 0.21.4\n```\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] One of the scripts in the examples/ folder of Accelerate or an officially supported `no_trainer` script in the `examples` folder of the `transformers` repo (such as `run_no_trainer_glue.py`)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nThis is a deepspeed stage 2 config which is in json:\n\n```\njson = {\n \"fp16\": {\n \"enabled\": false,\n \"auto_cast\": true,\n \"loss_scale\": 0,\n \"loss_scale_window\": 1000,\n \"initial_scale_power\": 16,\n \"hysteresis\": 2,\n \"min_loss_scale\": 1\n },\n \"bf16\": {\n \"enabled\": true\n },\n \"amp\": {\n \"enabled\": false\n },\n \"optimizer\": {\n \"type\": \"AdamW\",\n \"params\": {\n \"lr\": 0.0003,\n \"betas\": [0.9, 0.999],\n \"eps\": 1e-08,\n \"weight_decay\": 0.001\n }\n },\n \"scheduler\": {\n \"type\": \"WarmupLR\",\n \"params\": {\n \"warmup_min_lr\": 0,\n \"warmup_max_lr\": 0.0003,\n \"warmup_num_steps\": 0\n }\n },\n \"zero_optimization\": {\n \"stage\": 2,\n \"allgather_partitions\": true,\n \"allgather_bucket_size\": 5.000000e+08,\n \"overlap_comm\": false,\n \"reduce_scatter\": true,\n \"reduce_bucket_size\": 9.000000e+05,\n \"contiguous_gradients\": true,\n \"use_multi_rank_bucket_allreduce\": false\n },\n \"zero_state\": 2,\n \"gradient_accumulation_steps\": 1,\n \"gradient_clipping\": 1,\n \"train_micro_batch_size_per_gpu\": 4,\n \"mixed_precision\": \"bf16\",\n \"communication_data_type\": \"bf16\",\n \"steps_per_print\": inf\n}\n```\n\nI use `accelerate to spin up 8 workers on an AWS EC2 instance`:\n\n```bash\naccelerate launch --config_file configs/deepspeed.yaml scripts/main.py\n```\n\nThe following error is raised when the `trainer` runs `train`:\n\n```\n File \"/home/ubuntu/llm-classifiier/scripts/main.py\", line 88, in \n train_qwen_any(cli_args, run_args)\n File \"/home/ubuntu/llm-classifiier/scripts/train_qwen.py\", line 138, in train_qwen_any\n trainer.train()\n File \"/home/ubuntu/llm-classifiier/venv/lib/python3.12/site-packages/transformers/trainer.py\", line 2237, in train\n return inner_training_loop(\n ^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/llm-classifiier/venv/lib/python3.12/site-packages/transformers/trainer.py\", line 2758, in _inner_training_loop\n self.control = self.callback_handler.on_train_end(args, self.state, self.control)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/llm-classifiier/venv/lib/python3.12/site-packages/transformers/trainer_callback.py\", line 509, in on_train_end\n return self.call_event(\"on_train_end\", args, state, control)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/llm-classifiier/venv/lib/python3.12/site-packages/transformers/trainer_callback.py\", line 556, in call_event\n result = getattr(callback, event)(\n ^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/llm-classifiier/venv/lib/python3.12/site-packages/transformers/integrations/integration_utils.py\", line 958, in on_train_end\n fake_trainer.save_model(temp_dir)\n File \"/home/ubuntu/llm-classifiier/venv/lib/python3.12/site-packages/transformers/trainer.py\", line 3965, in save_model\n state_dict = self.accelerator.get_state_dict(self.deepspeed)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/home/ubuntu/llm-classifiier/venv/lib/python3.12/site-packages/accelerate/accelerator.py\", line 3903, in get_state_dict\n zero3_sharding = self.deepspeed_config[\"zero_optimization\"][\"stage\"] == 3\n ^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Accelerator' object has no attribute 'deepspeed_config'. Did you mean: 'deepspeed_plugin'?\n```\n\nI am not using zero3 sharding, so I don't know why this is an issue at all!\n\nMy deepspeed.yaml looks like this\n\n```\ncompute_environment: LOCAL_MACHINE\ndebug: true\ndeepspeed_config:\n deepspeed_config_file: configs/deepspeed_stg2.json\ndistributed_type: DEEPSPEED\nenable_cpu_affinity: false\nmachine_rank: 0\nmain_training_function: main\nnum_machines: 1\nnum_processes: 4\nrdzv_backend: static\nsame_network: true\ntpu_env: []\ntpu_use_cluster: false\ntpu_use_sudo: false\nuse_cpu: false\n```\n\nAnd the actual json file is above.\nBecause of this I cannot save my models or state_dicts.\n\n### Expected behavior\n\nUnless I am missing something profound, this really shouldn't be happening.",
"url": "https://github.com/huggingface/accelerate/issues/3784",
"state": "closed",
"labels": [],
"created_at": "2025-09-18T17:07:54Z",
"updated_at": "2025-10-27T15:08:19Z",
"comments": 1,
"user": "alexge233"
},
{
"repo": "huggingface/lerobot",
"number": 1969,
"title": "how to record a multi-task dataset on so101?",
"body": "I found that only can use \"dataset.single_task\" to record , but i need to record a dataset contains more than 3 tasks. how to solve it. ",
"url": "https://github.com/huggingface/lerobot/issues/1969",
"state": "closed",
"labels": [],
"created_at": "2025-09-18T10:18:00Z",
"updated_at": "2025-09-21T02:50:59Z",
"user": "Temmp1e"
},
{
"repo": "huggingface/lerobot",
"number": 1966,
"title": "SO101FollowerEndEffector?",
"body": "I am trying to get inverse kinematics to work on my SO-101, and I found SO100FollowerEndEffector but there is no SO101FollowerEndEffector?\n\nI suspect they are interchangeable, but when I use SO100FollowerEndEffector on my SO-101, it want me to recalibrate it, so I just want to make sure before I break anything.",
"url": "https://github.com/huggingface/lerobot/issues/1966",
"state": "open",
"labels": [
"question",
"robots"
],
"created_at": "2025-09-17T23:56:38Z",
"updated_at": "2025-10-30T08:56:22Z",
"user": "cashlo"
},
{
"repo": "huggingface/lighteval",
"number": 970,
"title": "How to use a configuration file?",
"body": "The documentation makes references to using configuration yaml files like [here](https://huggingface.co/docs/lighteval/main/en/use-litellm-as-backend) but it doesn't give the name of the file or which option to feed the config to lighteval. I tried making a `config.yaml`, `config.yml` in the current directory and trying a `--config` option (doesn't exist).",
"url": "https://github.com/huggingface/lighteval/issues/970",
"state": "closed",
"labels": [],
"created_at": "2025-09-16T20:13:48Z",
"updated_at": "2025-09-24T22:08:32Z",
"user": "oluwandabira"
},
{
"repo": "huggingface/transformers",
"number": 40915,
"title": "HfArgumentParser does not support peft.LoraConfig",
"body": "### System Info\n\n- `transformers` version: 4.57.0.dev0\n- Platform: Linux-5.14.0-284.73.1.el9_2.x86_64-x86_64-with-glibc2.39\n- Python version: 3.12.3\n- Huggingface_hub version: 0.34.4\n- Safetensors version: 0.5.2\n- Accelerate version: 1.10.1\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: No\n- Using GPU in script?: No\n- GPU type: NVIDIA A100-SXM4-80GB\n\n### Who can help?\n\n@ydshieh (I am not really sure who to tag here)\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```python\nfrom peft import LoraConfig # v0.17.1\nfrom transformers import HfArgumentParser # Built from source\n\np = HfArgumentParser(dataclass_types=LoraConfig) # fails\n```\n\n### Expected behavior\n\nI would expect LoraConfig to be supported by HfArgumentParser.\nAs I understand, this fails because HfArgumentParser does not support fields of type (`Optional[List[str], str]`).\n\nIs there a plan to support such fields?",
"url": "https://github.com/huggingface/transformers/issues/40915",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-09-16T16:23:56Z",
"updated_at": "2025-09-23T05:16:14Z",
"comments": 5,
"user": "romitjain"
},
{
"repo": "huggingface/diffusers",
"number": 12338,
"title": "`AutoencoderDC` bug with `pipe.enable_vae_slicing()` and decoding multiple images",
"body": "### Describe the bug\n\nWhen using the Sana_Sprint_1.6B_1024px and the SANA1.5_4.8B_1024px models, I cannot enable VAE slicing when generating multiple images. I guess this issue will affect the rest of the Sana model and pipeline configurations because they all use the same `AutoencoderDC` model.\n\nI traced the issue to the following [line of code](https://github.com/huggingface/diffusers/blob/751e250f70cf446ae342c8a860d92f6a8b78261a/src/diffusers/models/autoencoders/autoencoder_dc.py#L620), and if I remove the `.sample` part the issue seems to be fixed.\n\nI intend to submit a PR for my proposed fix. Can I confirm that this is supposed to be the correct solution?\n\n### Reproduction\n\n```python\nfrom diffusers import SanaSprintPipeline\nimport torch\n\npipe = SanaSprintPipeline.from_pretrained(\"Efficient-Large-Model/Sana_Sprint_1.6B_1024px_diffusers\", text_encoder=text_encoder, torch_dtype=torch.bfloat16)\npipe.to(\"cuda\")\npipe.enable_vae_slicing()\n\nprompt = \"A girl\"\nnum_images_per_prompt = 8\noutput = pipe(\n\tprompt=prompt,\n\theight=1024,\n\twidth=1024,\n\tnum_inference_steps=2,\n\tnum_images_per_prompt=num_images_per_prompt,\n\tintermediate_timesteps=1.3,\n\tmax_timesteps=1.56830,\n\ttimesteps=None\n).images\n```\n\n### Logs\n\n```shell\nTraceback (most recent call last):\n File \"F:\\AI setups\\Diffusers\\scripts\\inference sana-sprint.py\", line 24, in \n output = pipe(\n ^^^^^\n File \"F:\\AI setups\\Diffusers\\diffusers-venv\\Lib\\site-packages\\torch\\utils\\_contextlib.py\", line 116, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"F:\\AI setups\\Diffusers\\diffusers-venv\\Lib\\site-packages\\diffusers\\pipelines\\sana\\pipeline_sana_sprint.py\", line 874, in __call__\n image = self.vae.decode(latents / self.vae.config.scaling_factor, return_dict=False)[0]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"F:\\AI setups\\Diffusers\\diffusers-venv\\Lib\\site-packages\\diffusers\\utils\\accelerate_utils.py\", line 46, in wrapper\n return method(self, *args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"F:\\AI setups\\Diffusers\\diffusers-venv\\Lib\\site-packages\\diffusers\\models\\autoencoders\\autoencoder_dc.py\", line 620, in decode\n decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"F:\\AI setups\\Diffusers\\diffusers-venv\\Lib\\site-packages\\diffusers\\models\\autoencoders\\autoencoder_dc.py\", line 620, in \n decoded_slices = [self._decode(z_slice).sample for z_slice in z.split(1)]\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: 'Tensor' object has no attribute 'sample'\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.36.0.dev0\n- Platform: Windows-10-10.0.26100-SP0\n- Running on Google Colab?: No\n- Python version: 3.11.9\n- PyTorch version (GPU?): 2.7.1+cu128 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.34.4\n- Transformers version: 4.55.0\n- Accelerate version: 1.10.0\n- PEFT version: 0.17.0\n- Bitsandbytes version: 0.47.0\n- Safetensors version: 0.6.2\n- xFormers version: 0.0.31.post1\n- Accelerator: NVIDIA GeForce RTX 4090, 24564 MiB\n- Using GPU in script?: No\n- Using distributed or parallel set-up in script?: No\n\n### Who can help?\n\n@yiyixuxu @DN6 ",
"url": "https://github.com/huggingface/diffusers/issues/12338",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-09-16T12:23:29Z",
"updated_at": "2025-09-22T06:55:35Z",
"comments": 0,
"user": "mingyi456"
},
{
"repo": "huggingface/optimum",
"number": 2355,
"title": "Support exporting text-ranking for BERT models",
"body": "### Feature request\n\nCurrently, `optimum-cli export onnx --model cross-encoder/ms-marco-MiniLM-L-12-v2 cross-encoder--ms-marco-MiniLM-L-12-v2-onnx` says:\n\n```\nValueError: Asked to export a bert model for the task text-ranking (auto-detected), but the Optimum ONNX exporter only supports the tasks feature-extraction, fill-mask, multiple-choice, question-answering, text-classification, token-classification for bert. Please use a supported task. Please open an issue at https://github.com/huggingface/optimum/issues if you would like the task text-ranking to be supported in the ONNX export for bert.\n```\n\n### Motivation\n\nI'm working on a tool that I intend to distribute to others, for example via `brew install`. It's difficult to packaghe and ship Python, and I also want to prioritize speed of many filesystem and related operations, so I'm writing in Rust, using candle.\n\nIt can be a lot of work to implement every single model type by hand in candle. candle-transformers doesn't implement BertForSequenceClassification. Moreover, as model architectures change, I don't want to have to implement each one. It's great to be able to have the entire computation graph stored as data, as in ONNX.\n\n### Your contribution\n\nI'm willing to take a stab at this! If you think it would be helpful, and if you could give a couple pointers how to start!",
"url": "https://github.com/huggingface/optimum/issues/2355",
"state": "closed",
"labels": [
"Stale"
],
"created_at": "2025-09-15T21:23:35Z",
"updated_at": "2025-10-21T02:10:29Z",
"comments": 1,
"user": "kshitijl"
},
{
"repo": "huggingface/lerobot",
"number": 1923,
"title": "Deploying SmolVLA with a simulator",
"body": "Has anyone been able to deploy the SmolVLA model to control say the SO-100 on a simulator like IsaacSim? \nEven if the fine-tuning reliably converges the observed performance on the simulator seems erratic. Do we apply the predicted actions from SmolVLA directly into the Articulation controller as positions? ",
"url": "https://github.com/huggingface/lerobot/issues/1923",
"state": "closed",
"labels": [
"question",
"policies",
"simulation"
],
"created_at": "2025-09-12T21:06:40Z",
"updated_at": "2025-12-11T22:07:02Z",
"user": "aditya1709"
},
{
"repo": "huggingface/swift-transformers",
"number": 237,
"title": "Please help. Seeing issues with Hub when integrating",
"body": "Hello, I'm trying to integrate WhisperKit via https://github.com/argmaxinc/WhisperKit/blob/main/Package.swift but that seems to bring in [swift-transformers](https://github.com/huggingface/swift-transformers) and Hub. I'm seeing issues as below \n\nHub.package.swiftinterface:34:32: warning: 'BinaryDistinctCharacter' is not a member type of struct 'Hub.Hub'\n23:54:09 32 | public init(_ str: Foundation.NSString)\n23:54:09 33 | public init(_ str: Swift.String)\n23:54:09 34 | public init(_ character: Hub.BinaryDistinctCharacter)\n23:54:09 | `- warning: 'BinaryDistinctCharacter' is not a member type of struct 'Hub.Hub'\n23:54:09 35 | public init(_ characters: [Hub.BinaryDistinctCharacter])\n23:54:09 36 | public init(stringLiteral value: Swift.String\n\nI'm on xcode 16.4 and using swift 5.10. Please help!! Thanks in advance! ",
"url": "https://github.com/huggingface/swift-transformers/issues/237",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-09-12T17:06:28Z",
"updated_at": "2025-09-17T15:36:52Z",
"user": "rpatnayakuni22"
},
{
"repo": "huggingface/transformers",
"number": 40815,
"title": "get_decoder feature regression in 4.56.0",
"body": "### System Info\n\nIn the release of transformers v4.56.0, this PR https://github.com/huggingface/transformers/pull/39509 introduced a refactor of the public `get_decoder` method which previously existed on modes by moving it to the PreTrainedModel class.\n\nUnfortunately this introduced a significant behavior change in that `*CausalForLM` models no longer have the same behavior of having `get_decoder()` return the underlying base model.\n\nFor example a `MistralForCausalLM` model named `model` returns `None` when `model.get_decoder()` is called. \n\nThe logic for why is occurring is obvious when looking at the offending PR:\n\n```python\ndef get_decoder(self):\n \"\"\"\n Best-effort lookup of the *decoder* module.\n Order of attempts (covers ~85 % of current usages):\n 1. `self.decoder`\n 2. `self.model` (many wrappers store the decoder here)\n 3. `self.model.get_decoder()` (nested wrappers)\n 4. fallback: raise for the few exotic models that need a bespoke rule\n \"\"\"\n if hasattr(self, \"decoder\"):\n return self.decoder\n\n if hasattr(self, \"model\"):\n inner = self.model\n if hasattr(inner, \"get_decoder\"):\n return inner.get_decoder()\n return inner\n\n return None\n```\n\nIn these cases the `if hasattr(self, \"model\"):` conditional block is entered, and the underlying model has a `get_decoder` method, as it is a `PreTrainedModel`, as all transformers models are. This block will always be entered. At this point we are now in the decoder itself calling its `get_decoder` method. The decoder has no decoder or model attribute, so the function returns `None`, which is the passed to the parent caller.\n\nThere are a couple of ways this could be fixed, but I don't know what their current impact would be on other parts of the code. I may open a PR, but I am quite busy at the moment. @molbap @ArthurZucker since you were the authors and reviewers here, do you mind taking another look at this?\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nUse `get_decoder` on say a `MistralForCausalLM` model.\n\n### Expected behavior\n\nThe underlying `model` attribute should be returned for `*ForCausalLM` models, not None, as these models are decoder only models by transformers convention.",
"url": "https://github.com/huggingface/transformers/issues/40815",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-09-11T09:25:12Z",
"updated_at": "2025-09-16T08:57:14Z",
"comments": 4,
"user": "KyleMylonakisProtopia"
},
{
"repo": "huggingface/transformers",
"number": 40813,
"title": "Incorrect sharding configuration for Starcoder2 model",
"body": "### System Info\n\nTransformers main branch (commit [0f1b128](https://github.com/huggingface/transformers/commit/0f1b128d3359a26bd18be99c26d7f04fb3cba914) )\n- `transformers` version: 4.57.0.dev0\n- Platform: Linux-5.15.0-1030-nvidia-x86_64-with-glibc2.39\n- Python version: 3.12.3\n- Huggingface_hub version: 0.34.4\n- Safetensors version: 0.5.3\n- Accelerate version: 1.10.1\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.8.0a0+5228986c39.nv25.06 (CUDA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: tensor-parallel\n- Using GPU in script?: yes\n- GPU type: NVIDIA H100 80GB HBM3\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nTunning TP inference on `bigcode/starcoder2-7b` throws an error with incorrect tensor shapes due to `base_model_tp_plan` misconfiguration.\n\n`demo.py`:\n```\nimport os\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\nmodel_id = \"bigcode/starcoder2-7b\"\nmodel = AutoModelForCausalLM.from_pretrained(model_id, tp_plan=\"auto\")\n\nmodel._tp_plan['model.layers.*.mlp.c_proj'] = 'rowwise'\nprint(f\"TP plan: {model._tp_plan}, class: {type(model._tp_plan)}\")\n\ntokenizer = AutoTokenizer.from_pretrained(model_id)\nprompt = \"Can I help\"\ninputs = tokenizer(prompt, return_tensors=\"pt\").input_ids.to(model.device)\n\n# distributed run\noutputs = model(inputs)\n\n# print the output\nprint(outputs)\n```\nrun with\n```\ntorchrun --nproc_per_node=2 demo.py\n```\n\nThe correct `base_model_tp_plan` should replace:\n```\n['model.layers.*.mlp.c_proj'] = 'colwise'\n```\nwith \n```\n['model.layers.*.mlp.c_proj'] = 'rowwise'\n```\n\n### Expected behavior\n\nThrows:\n```\n(...)\n[rank0]: File \"/lustre/fs1/portfolios/coreai/users/gkwasniewski/hf-repo/transformers/src/transformers/models/starcoder2/modeling_starcoder2.py\", line 65, in forward\n[rank0]: hidden_states = self.c_proj(hidden_states)\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n[rank0]: return self._call_impl(*args, **kwargs)\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1857, in _call_impl\n[rank0]: return inner()\n[rank0]: ^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/module.py\", line 1805, in inner\n[rank0]: result = forward_call(*args, **kwargs)\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/nn/modules/linear.py\", line 125, in forward\n[rank0]: return F.linear(input, self.weight, self.bias)\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/_compile.py\", line 51, in inner\n[rank0]: return disable_fn(*args, **kwargs)\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/_dynamo/eval_frame.py\", line 850, in _fn\n[rank0]: return fn(*args, **kwargs)\n[rank0]: ^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/distributed/tensor/_api.py\", line 350, in __torch_dispatch__\n[rank0]: return DTensor._op_dispatcher.dispatch(\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/distributed/tensor/_dispatch.py\", line 160, in dispatch\n[rank0]: self.sharding_propagator.propagate(op_info)\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/distributed/tensor/_sharding_prop.py\", line 266, in propagate\n[rank0]: OutputSharding, self.propagate_op_sharding(op_info.schema)\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/distributed/tensor/_sharding_prop.py\", line 45, in __call__\n[rank0]: return self.cache(*args, **kwargs)\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/distributed/tensor/_sharding_prop.py\", line 279, in propagate_op_sharding_non_cached\n[rank0]: out_tensor_meta = self._propagate_tensor_meta_non_cached(op_schema)\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[rank0]: File \"/usr/local/lib/python3.12/dist-packages/torch/distributed/tensor/_sharding_prop.py\", line 126, in _propagate_tensor_meta_non_cached\n[rank0]: fake_out = op_schema.op(*fake_args, **fake_kwargs)\n[rank0]: ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[ra",
"url": "https://github.com/huggingface/transformers/issues/40813",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-09-11T09:02:53Z",
"updated_at": "2025-09-15T08:46:33Z",
"comments": 1,
"user": "greg-kwasniewski1"
},
{
"repo": "huggingface/lerobot",
"number": 1911,
"title": "How to avoid re-write cache data from pyarrow into parquet everytime?",
"body": "Hi Authors,\n\nWhen using lerobot dataset in a pytorch dataloader, lerobot dataset will write a huge cache data which is converted from pyarrow to Apache Parquet. How to avoid that?\n\nI can think of two options:\n\n1. Avoid converting to Parquet data and directly read from parquet data. But this may loose reading performance.\n2. Can we instead store the Parquet data? \n\nThanks.\n\nSonglin",
"url": "https://github.com/huggingface/lerobot/issues/1911",
"state": "open",
"labels": [],
"created_at": "2025-09-10T22:19:25Z",
"updated_at": "2025-09-10T22:19:25Z",
"user": "songlinwei-we"
},
{
"repo": "huggingface/transformers",
"number": 40767,
"title": "3D Object Detection Models",
"body": "### Model description\n\nHi together,\nis there a reason or any other thread where 3D models like those at mmdet3d are discussed to be implemented. I have not found any discussion.\nThanks\n\n### Open source status\n\n- [ ] The model implementation is available\n- [ ] The model weights are available\n\n### Provide useful links for the implementation\n\nBEVFormer: \nhttps://github.com/fundamentalvision/BEVFormer",
"url": "https://github.com/huggingface/transformers/issues/40767",
"state": "open",
"labels": [
"New model"
],
"created_at": "2025-09-09T13:16:33Z",
"updated_at": "2025-11-13T21:18:40Z",
"comments": 3,
"user": "SeucheAchat9115"
},
{
"repo": "huggingface/lerobot",
"number": 1899,
"title": "Has anyone tried to export the smolvla as onnx model for deployment?",
"body": "I have tried to test the trained smolvla model on my PC, it works. I want now to deploy the smolvla on our target board. \n\nI looked into the model structure of smolvla, for the vision-encoder and language embedding parts I can refer to the smolvlm and export them as tow onnx models. I think the robot state embedding also needs to be considered to export as a new onnx model.\n\nThe most important part of smolvla inference, i met several issues and have no good idea how to export it as a onnx model.l.\n\nHas anyone tried and successfully exported the smolvla as onnx models for deployment? Thanks!",
"url": "https://github.com/huggingface/lerobot/issues/1899",
"state": "open",
"labels": [
"question",
"policies",
"performance"
],
"created_at": "2025-09-09T10:41:14Z",
"updated_at": "2025-10-07T20:50:12Z",
"user": "TankerLee"
},
{
"repo": "huggingface/huggingface_hub",
"number": 3339,
"title": "What is the best replacement of HfFileSystem.glob with HfApi",
"body": "In some of our code, we were using something like\n\n```python\nhf_fs = HfFileSystem()\nfiles = hf_fs.glob('my/repo/*/model.onnx')\n```\n\nBut I found that HfFileSystem is much less stable than HfApi, especially in those edge cases (e.g. network unstable)\n\nSo what is the best replacement of HfFileSystem.glob with HfApi? Any suggestions?",
"url": "https://github.com/huggingface/huggingface_hub/issues/3339",
"state": "closed",
"labels": [],
"created_at": "2025-09-09T09:02:07Z",
"updated_at": "2025-09-15T09:12:04Z",
"user": "narugo1992"
},
{
"repo": "huggingface/transformers",
"number": 40754,
"title": "Potentially incorrect value assignment of Llama4TextModel's output in Llama4ForCausalLM's output?",
"body": "### System Info\n\n**System Info** \n- `transformers` version: 4.55.4\n- Platform: Linux-6.15.9-201.fc42.x86_64-x86_64-with-glibc2.41\n- Python version: 3.13.5\n- Huggingface_hub version: 0.34.4\n- Safetensors version: 0.6.2\n- Accelerate version: 1.10.1\n- Accelerate config: \tnot found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: \n- Using GPU in script?: \n- GPU type: NVIDIA RTX A6000\n\n### Who can help?\n\n@ArthurZucker \n@amyeroberts \n@qubvel \n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n**Task Detail**\nObtaining hidden_states from the outputs of Llama4ForCausalLM\n\n**Problem**\nIn the source code [modeling_llama4.py](https://github.com/huggingface/transformers/blob/v4.55.4/src/transformers/models/llama4/modeling_llama4.py), the outputs of Llama4ForCausalLM contains a *hidden_states* (See [line 642](https://github.com/huggingface/transformers/blob/d79b2d981f28b2730d402244ac3c2e9a8c054eee/src/transformers/models/llama4/modeling_llama4.py#L642)), which is assigned with *outputs.hidden_states*. Here, the *outputs* is the output of Llama4TextModel (See [line 619](https://github.com/huggingface/transformers/blob/d79b2d981f28b2730d402244ac3c2e9a8c054eee/src/transformers/models/llama4/modeling_llama4.py#L619C9-L619C16)). However, the output of Llama4TextModel consists of a *last_hidden_state* (assigned the value of *hidden_states*) and a *past_key_values*, but no *hidden_states* (See [line 554-557](https://github.com/huggingface/transformers/blob/d79b2d981f28b2730d402244ac3c2e9a8c054eee/src/transformers/models/llama4/modeling_llama4.py#L554-L557)).\n\nThus, I'm wondering if there is either a typo in [line 642](https://github.com/huggingface/transformers/blob/d79b2d981f28b2730d402244ac3c2e9a8c054eee/src/transformers/models/llama4/modeling_llama4.py#L642) where the *hidden_states=outputs.hidden_states* should be replaced by *hidden_states=outputs.last_hidden_state*, or a typo in [line 555](https://github.com/huggingface/transformers/blob/d79b2d981f28b2730d402244ac3c2e9a8c054eee/src/transformers/models/llama4/modeling_llama4.py#L555C13-L555C45) where the *last_hidden_state=hidden_states* should be replaced by *hidden_states=hidden_states*?\n\nThank you for your patience!\n\n### Expected behavior\n\nAn explanation or a correction of the source code in [modeling_llama4.py](https://github.com/huggingface/transformers/blob/v4.55.4/src/transformers/models/llama4/modeling_llama4.py)",
"url": "https://github.com/huggingface/transformers/issues/40754",
"state": "closed",
"labels": [
"Usage",
"bug"
],
"created_at": "2025-09-08T12:31:39Z",
"updated_at": "2025-09-16T19:25:03Z",
"comments": 3,
"user": "st143575"
},
{
"repo": "huggingface/transformers",
"number": 40752,
"title": "How to extract attention weights for the first generated token?",
"body": "**Title:** Request for clarification: How to extract attention weights for the first generated token?\n\n**Description:**\n\nHi, I'm trying to extract the attention weights **of the first generated token** (i.e., the first new token produced by `generate()`) with respect to the input prompt. However, I'm observing inconsistent behavior in the shape of `attentions` returned by `model.generate(..., output_attentions=True)`.\n\nHere's what I found:\n\n- For `step 0` (the first generation step), `attentions[0][layer].shape` is `(batch, heads, seq_len, seq_len)` \u2014 e.g., `[1, 16, 1178, 1178]`, where `seq_len` equals the input prompt length.\n- This appears to be the **full self-attention matrix of the prompt context**, not the attention of the newly generated token.\n- Starting from `step 1`, the shape becomes `(batch, heads, 1, ctx_len)`, which correctly represents the attention of a single generated token.\n\n**Question:**\n- Is there a way to directly extract the attention weights **from the first generated token** (i.e., the query of the first new token attending to the prompt keys)?\n- Or is the intended behavior to use the last position of the context attention (i.e., `attentions[0][layer][..., -1, :]`) as a proxy for the generation decision?\n\n**Use Case:**\nI want to interpret which parts of the input prompt the model attends to when generating the first output token, for interpretability and analysis purposes.\n\n**Environment:**\n- Transformers version: [4.51.3]\n- Model: [Qwen3]\n- Code snippet:\n ```python\n outputs = model.generate(\n input_ids,\n output_attentions=True,\n return_dict_in_generate=True\n )\n # outputs.attentions[0][layer] has shape (1, 16, 1178, 1178)",
"url": "https://github.com/huggingface/transformers/issues/40752",
"state": "closed",
"labels": [],
"created_at": "2025-09-08T09:53:16Z",
"updated_at": "2025-09-08T11:41:22Z",
"user": "VincentLHH"
},
{
"repo": "huggingface/transformers.js",
"number": 1407,
"title": "Expected time to load a super-resolution model locally",
"body": "### Question\n\nLoading a image super-resolution model locally can take more than 10 seconds on my MacBook Pro (M1 Max). Is this expected behavior?\n```javascript\nenv.allowRemoteModels = false;\nenv.allowLocalModels = true;\nenv.backends.onnx.wasm.wasmPaths = `/wasm/`;\n\nconst upscaler = ref(null);\nonMounted(async () => {\n upscaler.value = await pipeline('image-to-image', 'Xenova/swin2SR-realworld-sr-x4-64-bsrgan-psnr', {\n dtype: 'fp32',\n device: 'webgpu',\n })\n});\n```\nWarnings observed during the model loading:\n```\nort-wasm-simd-threaded.jsep.mjs:100 \n2025-09-08 13:58:52.881399 [W:onnxruntime:, session_state.cc:1280 VerifyEachNodeIsAssignedToAnEp] Some nodes were not assigned to the preferred execution providers which may or may not have an negative impact on performance. e.g. ORT explicitly assigns shape related ops to CPU to improve perf.\n\nort-wasm-simd-threaded.jsep.mjs:100 \n2025-09-08 13:58:52.882499 [W:onnxruntime:, session_state.cc:1282 VerifyEachNodeIsAssignedToAnEp] Rerunning with verbose output on a non-minimal build will show node assignments.\n```\n\n### System Info\nnpm: @huggingface/transformers@3.7.2\nOS: macOS Sequoia 15.6.1\nmodel: Xenova/swin2SR-realworld-sr-x4-64-bsrgan-psnr",
"url": "https://github.com/huggingface/transformers.js/issues/1407",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-09-08T06:26:49Z",
"updated_at": "2025-09-30T19:22:34Z",
"user": "ymtoo"
},
{
"repo": "huggingface/lerobot",
"number": 1891,
"title": "How to checkout a commit id?",
"body": "The underlying datasets supports a \"revision\" flag. Does lerobot?",
"url": "https://github.com/huggingface/lerobot/issues/1891",
"state": "closed",
"labels": [],
"created_at": "2025-09-08T04:39:37Z",
"updated_at": "2025-09-10T22:53:18Z",
"user": "richardrl"
},
{
"repo": "huggingface/transformers",
"number": 40743,
"title": "Support for 4D attention mask for T5",
"body": "### Feature request\n\nCurrently, T5 cannot take 4D attention masks (batch_size, num_heads, seq_len, seq_len) as inputs. Passing a 4D attention_mask and a 4D decoder_attention_mask like so leads to a shape-related exception :\n\n```python\nimport torch\nfrom transformers import AutoTokenizer, T5ForConditionalGeneration\n\ntokenizer = AutoTokenizer.from_pretrained(\"google-t5/t5-small\")\nmodel = T5ForConditionalGeneration.from_pretrained(\"google-t5/t5-small\")\n\ninput_ids = tokenizer(\"Where is\", return_tensors=\"pt\").input_ids\ndecoder_input_ids = tokenizer(\"\", return_tensors=\"pt\").input_ids\n\nbatch_size, seq_len = input_ids.shape\ntgt_len = decoder_input_ids.shape[1]\nnum_heads = model.config.num_heads\n\nattention_mask = torch.ones(batch_size, num_heads, seq_len, seq_len)\ndecoder_attention_mask = torch.ones(batch_size, num_heads, tgt_len, tgt_len).tril(0)\n\nmodel(\n input_ids,\n decoder_input_ids=decoder_input_ids,\n attention_mask=attention_mask,\n decoder_attention_mask=decoder_attention_mask,\n)\n```\n\nOne of the problems in the current code is in the handling of the cross-attention mask. Currently, it is created using the 1D encoder attention mask when supplied. However, in the case of a 4D mask, it seems unclear how to correctly use the encoder mask: therefore, the best solution might be to introduce a new 4D mask argument `cross_attention_mask` of shape (batch_size, num_heads, tgt_len, seq_len)`. This lets the user controls all attention masks if necessary.\n\n### Motivation\n\n4D masks are useful for many purposes, as outlined by #27539 and [this blog post](https://huggingface.co/blog/poedator/4d-masks), but not all models support them.\n\n### Your contribution\n\nI propose to fix the code to handle 4D attention masks, and to add a new `cross_attention_mask` argument to add the possibility to control the cross attention mask manually. I wrote a version of that code in [this fork](https://github.com/Aethor/transformers/tree/t5-4d-attention-mask).\n\nI'm happy to create a PR with my code, but:\n\n1. This is my first transformers contribution, I need help with some things such as handling the \"Copy\" code duplication mechanism of transformers. Should other similar models with copied functions from T5 be changed as well?\n2. Although I wrote a [first test with trivial masks](https://github.com/Aethor/transformers/blob/22dc62edbdbc3f2afeb90a31c75047711c1afc5c/tests/models/t5/test_modeling_t5.py#L1876), I am not entirely sure how to test this\n3. I want to be sure that adding the new `cross_attention` mask parameter is the right way to do this and will be approved",
"url": "https://github.com/huggingface/transformers/issues/40743",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-09-07T07:18:05Z",
"updated_at": "2025-09-09T11:43:33Z",
"comments": 5,
"user": "Aethor"
},
{
"repo": "huggingface/lerobot",
"number": 1882,
"title": "Pretrain - Code for pretraining smolvla",
"body": "## Guidance on Replicating the Pre-training Process with Community Datasets\n\n\nHi team,\n\nFirst off, thank you for the fantastic work on SmolVLA and for open-sourcing the model and code. It's a great contribution to the community.\n\nI am trying to replicate the pre-training process as described in the original paper. I have located the pre-training data on the Hugging Face Hub, specifically:\n\n- `HuggingFaceVLA/community_dataset_v1`\n- `HuggingFaceVLA/community_dataset_v2`\n\nMy plan is to download both datasets and merge them into a single directory, for example `/path/to/my/pretrain_data/`, to serve as the input for the pre-training script.\n\nTo ensure I am on the right track, I would be grateful if you could provide some guidance on the following points:\n\n1: **Data Preparation & Merging**: Regarding the two datasets (community_dataset_v1 and v2), what is the correct procedure for using them together? Should I manually download and merge their contents into a single local directory? I also noticed the data is in a multi-directory (sharded) format, unlike many simpler single-folder datasets. Does the training code handle this structure automatically once the data is prepared locally?\n\n2: **Dataset Configuration**: How should the combined dataset be specified in the configuration file? My main confusion is that the parameter dataset.repo_id appears to be a required field that accepts a single repository ID. How can I configure the training script to use the merged data from both v1 and v2, which I have stored locally?\n\n3: **Training Script & Execution**: Once the data is correctly prepared and configured, could you please point me to the exact script and provide an example command to launch the pre-training? Since the weight of VLM is initialized, so what I need is the script after initializing VLM weight and then train on large-scale community dataset. In particular, I'd like to ask the `dataset.repo_id` if I store v1 and v2 under the same folder? Since I discovered this param cannot be None. \n\nAny help or pointers to the relevant documentation would be greatly appreciated. I believe a short tutorial or a section in the README on pre-training would also be immensely helpful for others in the community looking to build upon your work.\n\nThank you for your time and consideration!",
"url": "https://github.com/huggingface/lerobot/issues/1882",
"state": "closed",
"labels": [
"question",
"dataset"
],
"created_at": "2025-09-07T03:18:04Z",
"updated_at": "2025-09-23T09:06:13Z",
"user": "ruiheng123"
},
{
"repo": "huggingface/transformers",
"number": 40708,
"title": "When using a custom model, it copies the code into Hugging Face\u2019s cache directory.",
"body": "```\n model = AutoModel.from_pretrained(\n model_args.model_name_or_path,\n trust_remote_code=True,\n torch_dtype=compute_dtype,\n device_map=device_map,\n # init_vision=True,\n # init_audio=False,\n # init_tts=False,\n )\n```\n`model_args.model_name_or_path=/mnt/241hdd/wzr/MiniCPM-V-CookBook/MiniCPM-V-4_5`\nThe code actually runs in `/root/.cache/huggingface/modules/transformers_modules/MiniCPM-V-4_5`.\nThis makes my debugging difficult.\nIs there a way to run the code directly?",
"url": "https://github.com/huggingface/transformers/issues/40708",
"state": "closed",
"labels": [],
"created_at": "2025-09-05T07:21:40Z",
"updated_at": "2025-11-15T08:03:16Z",
"comments": 4,
"user": "wzr0108"
},
{
"repo": "huggingface/transformers",
"number": 40690,
"title": "Batches loaded from wrong epoch when resuming from second epoch",
"body": "### System Info\n\n**Required system information**\n```text\n- `transformers` version: 4.57.0.dev0\n- Platform: Linux-5.15.0-133-generic-x86_64-with-glibc2.35\n- Python version: 3.10.12\n- Huggingface_hub version: 0.34.4\n- Safetensors version: 0.6.2\n- Accelerate version: 1.10.1\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)\n- Tensorflow version (GPU?): 2.15.1 (False)\n- Flax version (CPU?/GPU?/TPU?): 0.7.0 (cpu)\n- Jax version: 0.4.13\n- JaxLib version: 0.4.13\n- Using distributed or parallel set-up in script?: no\n- Using GPU in script?: no\n- GPU type: GRID A100D-16C\n```\n\n### Who can help?\n\n@zach-huggingface @SunMarc as it concerns `transfomers`' `Trainer`\n\n### Information\n\n- [x] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n### **1. Bug description**\nLet's take the example of the provided script: \n- number of data points: 10\n- batch size: 2\nSo 1 epoch = 5 steps.\n\nIf we launch a training until the end and monitor the data order:\n- epoch 0: 4, 1, 7, 5, 3, 9, 0, 8, 6, 2\n- epoch 1: 5, 6, **|| 1, 2, 0, 8, 9, 3, 7, 4**\n- epoch 2: 8, 7, 1, 5, 6, 9, 0, 4, 2, 3\n\nBut if we stop the training at step 6 and resume (from character `||`) the training to the end, we get the following data order:\n- epoch 0: 4, 1, _7, 5, 3, 9, 0, 8, 6, 2_\n- epoch 1: 5, 6 **|| 7, 5, 3, 9, 0, 8, 6, 2**\n- epoch 2: 8, 7, 1, 5, 6, 9, 0, 4, 2, 3\n\nWe spotted that the `epoch_dataloader.iteration` is not properly set for the first epoch after resuming. It is initially set to 0, this is why it loads the same order as in epoch 0 (cf data order in italic of the last 4 batches of epoch 0).\n\n### **2. Reproducing the error**\nThe script to run is available at https://github.com/ngazagna-qc/transformers/blob/fix-data-order-resumed-epoch/reproduce_wrong_resumed_epoch.py.\nRun:\n```shell\npython reproduce_wrong_resumed_epoch.py --trainer-class Trainer\n```\n\n### Expected behavior\n\n### **3. Bug fix**\nWe provide the fixed `Trainer` here: https://github.com/ngazagna-qc/transformers/blob/fix-data-order-resumed-epoch/src/transformers/trainer_fixed.py#L56\n\nThe fix only consists to add a line to the `_inner_training_loop` method:\n```python\n if steps_trained_in_current_epoch > 0:\n epoch_dataloader = skip_first_batches(epoch_dataloader, steps_trained_in_current_epoch)\n #### BEGINNING OF THE FIX ####\n epoch_dataloader.iteration = epochs_trained # FIX: set dataloader to correct epoch\n #### END OF THE FIX ####\n steps_skipped = steps_trained_in_current_epoch\n steps_trained_in_current_epoch = 0\n rng_to_sync = True\n```\nIt can be tested that this solves the order by running:\n```shell\npython reproduce_wrong_resumed_epoch.py --trainer-class TrainerFixed\n```",
"url": "https://github.com/huggingface/transformers/issues/40690",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-09-04T11:48:41Z",
"updated_at": "2025-12-03T13:14:04Z",
"comments": 6,
"user": "ngazagna-qc"
},
{
"repo": "huggingface/optimum",
"number": 2347,
"title": "Gemma3n convert to onnx format",
"body": "Hello, \n\nHow do I convert the Gemma3n model to the ONNX format using the OptimumCLI command? \n\nThanks in advance.",
"url": "https://github.com/huggingface/optimum/issues/2347",
"state": "closed",
"labels": [
"Stale"
],
"created_at": "2025-09-04T09:13:19Z",
"updated_at": "2025-10-15T02:09:55Z",
"comments": 2,
"user": "shahizat"
},
{
"repo": "huggingface/transformers",
"number": 40680,
"title": "Idea: Exploring Mathematical Extensions for GPT-style Models (teaser)",
"body": "Hi Transformers team \ud83d\udc4b,\n\nI\u2019ve been experimenting with a conceptual enhancement to GPT-style architectures\u2014introducing mathematical mechanisms for memory and adaptive learning\u2014while keeping the overall transformer backbone intact.\n\nI\u2019ve documented the approach in Markdown (README + comparison notes), but haven\u2019t published it yet. Before I share more, I\u2019d love your input:\n\n- Does this kind of experimental idea fit within the scope of Transformers?\n- Would you be open to viewing or discussing the draft privately?\n\nLooking forward to hearing your thoughts \ud83d\ude4f",
"url": "https://github.com/huggingface/transformers/issues/40680",
"state": "closed",
"labels": [],
"created_at": "2025-09-04T07:23:29Z",
"updated_at": "2025-10-12T08:02:38Z",
"comments": 3,
"user": "muzamil-ashiq"
},
{
"repo": "huggingface/transformers",
"number": 40647,
"title": "how to get response text during training",
"body": "I want to obtain the inferred output text during the evaluation step in the training process, not just the eval loss. \n![\"Image\"](\"https://github.com/user-attachments/assets/9dd432c5-74ea-4290-adff-7865cf3ea481\")
",
"url": "https://github.com/huggingface/transformers/issues/40647",
"state": "closed",
"labels": [],
"created_at": "2025-09-03T10:37:51Z",
"updated_at": "2025-10-12T08:02:43Z",
"user": "zyandtom"
},
{
"repo": "huggingface/diffusers",
"number": 12276,
"title": "The image is blurry.",
"body": "How to solve image blurriness during fine-tuning?",
"url": "https://github.com/huggingface/diffusers/issues/12276",
"state": "open",
"labels": [],
"created_at": "2025-09-03T08:29:38Z",
"updated_at": "2025-09-03T08:29:38Z",
"comments": 0,
"user": "sucessfullys"
},
{
"repo": "huggingface/gym-hil",
"number": 32,
"title": "how to perform hil in sim",
"body": "",
"url": "https://github.com/huggingface/gym-hil/issues/32",
"state": "closed",
"labels": [],
"created_at": "2025-09-02T17:10:05Z",
"updated_at": "2025-09-16T14:02:32Z",
"user": "prathamv0811"
},
{
"repo": "huggingface/transformers",
"number": 40606,
"title": "GPT-OSS attention backends available for SM120 other than Eager?",
"body": "I was wondering any attention backend we can use for long context if using SM120 GPU? Since the \"eager_attention_forward\" uses the naive implementation that computes the full attention in one go, which can lead to OOM for large context, but I couldn't use other implementations since they either do not support sinks or SM120.\n\nMany thanks! ",
"url": "https://github.com/huggingface/transformers/issues/40606",
"state": "closed",
"labels": [],
"created_at": "2025-09-02T03:21:16Z",
"updated_at": "2025-10-12T08:02:48Z",
"comments": 4,
"user": "TheTinyTeddy"
},
{
"repo": "huggingface/peft",
"number": 2764,
"title": "merge_and_unload returns the base (prior to fine-tuning) back!!!!",
"body": "I have fine-tune a model using PEFT and now I want to merge the base model to adapter. This is what I am doing:\n\n\n```\nbase_model = AutoModelForCausalLM(model_id, device_map = 'auto')\n\nmodel_finetuned = PeftModel.from_pretrained(base_model, adapter_path)\n\n```\nNow the size of `model_finetuned `is roughly 42GB but when I do the following to merge the adapter into base:\n\n`merged_model = model_finetuned.()\n`\nthe size of `merged_model `is 36GB and its performance is like the base model, seems the adapter effect is gone.\n\nI remember I used this feature in the past to get merged model, is anything changed? \n\nThis is related post, where the last comment says this is normal, can someone elaborate?\n\nhttps://github.com/huggingface/peft/issues/868\n\nCan I just save the `model_finetuned ` as my merged model, can someone explain what is going on and why the merge_and_unload() is doing opposite of what it is supposed to do.\n",
"url": "https://github.com/huggingface/peft/issues/2764",
"state": "closed",
"labels": [],
"created_at": "2025-09-01T04:07:36Z",
"updated_at": "2025-10-09T15:26:15Z",
"comments": 12,
"user": "manitadayon"
},
{
"repo": "huggingface/lerobot",
"number": 1822,
"title": "As of 08/31/2025, how do you create a v2.1 dataset from raw data?",
"body": "My search is cursory, but I can't find any tutorial or example on creating a v2.1 dataset on the main branch. So, how do you create a Lerobot dataset in the current version? Should I refer to older commits",
"url": "https://github.com/huggingface/lerobot/issues/1822",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-08-31T18:29:34Z",
"updated_at": "2025-10-08T13:02:44Z",
"user": "IrvingF7"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3318,
"title": "Infinite tool call loop: `HuggingFaceModel` and `text-generation-inference`",
"body": "## Description\nHello. Needless to say, amazing library. Please let me know if you'd like me to try something or if you need more info.\n\nI've been going through various local model providers trying to find one that works well, when I cam across a rather shocking bug when running against Huggingface's TGI model host.\n\nThe problem appears whether using the OpenAI \"compatible\" endpoints or the `HuggingfaceModel` with custom `AsyncInferenceClient` and `HuggingFaceProvider`. The latter probably being the official approach, the code included here will be using that.\n\n## System Info\n`curl 127.0.0.1:8080/info | jq`:\n```json\n{\n \"model_id\": \"/models/meta-llama/Meta-Llama-3-8B-Instruct\",\n \"model_sha\": null,\n \"model_pipeline_tag\": null,\n \"max_concurrent_requests\": 128,\n \"max_best_of\": 2,\n \"max_stop_sequences\": 4,\n \"max_input_tokens\": 8191,\n \"max_total_tokens\": 8192,\n \"validation_workers\": 2,\n \"max_client_batch_size\": 4,\n \"router\": \"text-generation-router\",\n \"version\": \"3.3.4-dev0\",\n \"sha\": \"9f38d9305168f4b47c8c46b573f5b2c07881281d\",\n \"docker_label\": \"sha-9f38d93\"\n}\n```\n\n`nvidia-smi`:\n```shell\n+-----------------------------------------------------------------------------------------+\n| NVIDIA-SMI 575.64.05 Driver Version: 575.64.05 CUDA Version: 12.9 |\n|-----------------------------------------+------------------------+----------------------+\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|=========================================+========================+======================|\n| 0 NVIDIA GeForce RTX 4090 Off | 00000000:01:00.0 On | Off |\n| 40% 54C P2 61W / 450W | 21499MiB / 24564MiB | 0% Default |\n| | | N/A |\n+-----------------------------------------+------------------------+----------------------+\n| 1 NVIDIA GeForce RTX 4090 Off | 00000000:48:00.0 Off | Off |\n| 30% 43C P2 52W / 450W | 21394MiB / 24564MiB | 0% Default |\n| | | N/A |\n+-----------------------------------------+------------------------+----------------------+\n```\n\n### Information\n\n- [x] Docker\n- [ ] The CLI directly\n\n### Tasks\n\n- [x] An officially supported command\n- [ ] My own modifications\n\n## Reproduction\n\n### Setup\n\nHere's the `docker-compose.yaml` I'm using to start TGI:\n```yaml\nservices:\n text-generation-inference:\n image: ghcr.io/huggingface/text-generation-inference:latest\n container_name: tgi\n ports:\n - \"8081:80\"\n volumes:\n - ../../../models:/models:ro\n - tgi-data:/data\n environment:\n - RUST_LOG=info\n # I have also tested with 3.1-8B and 3.2-3B with the same end results\n command: >\n --model-id /models/meta-llama/Meta-Llama-3-8B-Instruct\n --hostname 0.0.0.0\n --port 80\n --trust-remote-code\n deploy:\n resources:\n reservations:\n devices:\n - driver: nvidia\n device_ids: [\"0\", \"1\"]\n capabilities: [gpu]\n shm_size: \"64g\"\n healthcheck:\n test: [\"CMD\", \"curl\", \"-f\", \"http://localhost:80/health\"]\n interval: 30s\n timeout: 10s\n retries: 3\n start_period: 60s\n\nvolumes:\n tgi-data:\n driver: local\n```\n\n### Code\n\nAll code is running in a Jupyter notebook.\n\nHere's the common setup cell:\n```python\nfrom huggingface_hub import AsyncInferenceClient\nfrom pydantic_ai.models.huggingface import HuggingFaceModel\nfrom pydantic_ai.providers.huggingface import HuggingFaceProvider\nfrom pydantic_ai.providers.openai import OpenAIProvider\n\nprovider = OpenAIProvider(base_url=\"http://localhost:8081/v1\") # Just used to get the model slug\nmodels = await provider.client.models.list()\n\nclient = AsyncInferenceClient(base_url=\"http://localhost:8081/\")\n\nprint(f\"Connected to TGI. Available models: {len(models.data)}\")\nfor model in models.data:\n print(f\" - {model.id}\")\n\n# Create the model instance\nagent_model = HuggingFaceModel(\n models.data[0].id,\n provider=HuggingFaceProvider(hf_client=client, api_key=\"None\"),\n # Annoyingly, despite this being basically the default profile, Llama 3's tool calls often fall through to the response without this\n profile=ModelProfile(\n supports_tools=True,\n json_schema_transformer=InlineDefsJsonSchemaTransformer\n )\n)\n```\n\n### Working: Basic requests and history\n\n1. Create the basic agent\n```python\nfrom pydantic_ai import Agent\n\nsimple_agent = Agent(model=agent_model)\n```\n\n2. Make a simple request\n```python\nsimple_result = await simple_agent.run(\"Tell me a joke.\")\n\nsimple_result.output # \"Why couldn't the bicycle stand up by itself?\\n\\nBecau",
"url": "https://github.com/huggingface/text-generation-inference/issues/3318",
"state": "open",
"labels": [],
"created_at": "2025-08-31T08:23:46Z",
"updated_at": "2025-08-31T08:58:13Z",
"comments": 1,
"user": "baughmann"
},
{
"repo": "huggingface/diffusers",
"number": 12257,
"title": "[Looking for community contribution] support Wan 2.2 S2V: an audio-driven cinematic video generation model",
"body": "We're super excited about the Wan 2.2 S2V (Speech-to-Video) model and want to get it integrated into Diffusers! This would be an amazing addition, and we're looking for experienced community contributors to help make this happen.\n\n\n- **Project Page**: https://humanaigc.github.io/wan-s2v-webpage/\n- **Source Code**: https://github.com/Wan-Video/Wan2.2#run-speech-to-video-generation\n- **Model Weights**: https://huggingface.co/Wan-AI/Wan2.2-S2V-14B\n\n\nThis is a priority for us, so we will try review fast and actively collabrate with you throughout the process :)\n\n\n",
"url": "https://github.com/huggingface/diffusers/issues/12257",
"state": "open",
"labels": [
"help wanted",
"Good second issue",
"contributions-welcome"
],
"created_at": "2025-08-29T08:04:43Z",
"updated_at": "2025-08-29T10:23:52Z",
"comments": 0,
"user": "yiyixuxu"
},
{
"repo": "huggingface/optimum-onnx",
"number": 44,
"title": "How to use streaming inference for onnx models exported from QWEN3-4B models",
"body": "How to use streaming inference for onnx models exported from QWEN3-4B models",
"url": "https://github.com/huggingface/optimum-onnx/issues/44",
"state": "closed",
"labels": [],
"created_at": "2025-08-29T01:48:07Z",
"updated_at": "2025-10-06T12:29:34Z",
"user": "williamlzw"
},
{
"repo": "huggingface/diffusers",
"number": 12255,
"title": "[BUG] Misleading ValueError when subclassing StableDiffusionImg2ImgPipeline with a mismatched __init__ signature",
"body": "### Describe the bug\n\nWhen subclassing diffusers.StableDiffusionImg2ImgPipeline, if the subclass's __init__ signature does not include the requires_safety_checker: bool = True argument, the default .from_pretrained() loader raises a confusing and indirect ValueError.\n\nThe official documentation for StableDiffusionImg2ImgPipeline confirms that requires_safety_checker is an explicit keyword argument in its __init__ signature.\n\nThe current ValueError (pasted below) reports a component list mismatch between 'kwargs' and 'requires_safety_checker'. This error message hides the true root cause\u2014a TypeError from the signature mismatch\u2014making the problem very difficult to debug.\n\n### Reproduction\n\nThe following minimal script reliably reproduces the error.\n```\n\nfrom diffusers import StableDiffusionImg2ImgPipeline\nfrom diffusers.models import AutoencoderKL, UNet2DConditionModel\nfrom diffusers.schedulers import KarrasDiffusionSchedulers\nfrom transformers import CLIPTextModel, CLIPTokenizer\nfrom typing import Optional, Any\n\n# A custom pipeline inheriting from StableDiffusionImg2ImgPipeline,\n# but with an incorrect __init__ signature. It incorrectly tries\n# to catch `requires_safety_checker` with **kwargs.\nclass MyCustomPipeline(StableDiffusionImg2ImgPipeline):\n def __init__(\n self,\n vae: AutoencoderKL,\n text_encoder: CLIPTextModel,\n tokenizer: CLIPTokenizer,\n unet: UNet2DConditionModel,\n scheduler: KarrasDiffusionSchedulers,\n safety_checker: Optional[Any] = None,\n feature_extractor: Optional[Any] = None,\n image_encoder: Optional[Any] = None,\n **kwargs,\n ):\n super().__init__(\n vae=vae,\n text_encoder=text_encoder,\n tokenizer=tokenizer,\n unet=unet,\n scheduler=scheduler,\n safety_checker=safety_checker,\n feature_extractor=feature_extractor,\n image_encoder=image_encoder,\n **kwargs,\n )\n\n# This line will fail and raise the misleading ValueError.\n# It can be copy-pasted directly to reproduce the bug.\npipe = MyCustomPipeline.from_pretrained(\"runwayml/stable-diffusion-v1-5\")\n```\n### Logs\n\n```shell\nValueError: MyCustomPipeline {\n \"_class_name\": \"MyCustomPipeline\",\n \"_diffusers_version\": \"0.29.0.dev0\", # Replace with your version\n \"feature_extractor\": [\n \"transformers\",\n \"CLIPImageProcessor\"\n ],\n \"image_encoder\": [\n null,\n null\n ],\n \"requires_safety_checker\": true,\n \"safety_checker\": [\n \"stable_diffusion\",\n \"StableDiffusionSafetyChecker\"\n ],\n \"scheduler\": [\n \"diffusers\",\n \"PNDMScheduler\"\n ],\n \"text_encoder\": [\n \"transformers\",\n \"CLIPTextModel\"\n ],\n \"tokenizer\": [\n \"transformers\",\n \"CLIPTokenizer\"\n ],\n \"unet\": [\n \"diffusers\",\n \"UNet2DConditionModel\"\n ],\n \"vae\": [\n \"diffusers\",\n \"AutoencoderKL\"\n ]\n}\n has been incorrectly initialized or is incorrectly implemented. Expected ['feature_extractor', 'image_encoder', 'kwargs', 'safety_checker', 'scheduler', 'text_encoder', 'tokenizer', 'unet', 'vae'] to be defined, but ['feature_extractor', 'image_encoder', 'requires_safety_checker', 'safety_checker', 'scheduler', 'text_encoder', 'tokenizer', 'unet', 'vae'] are defined.\n```\n\n### System Info\n\ndiffusers version: 0.34.0\nPlatform: Linux-5.15.0-78-generic-x86_64-with-glibc2.35\nPython version: 3.12.11 | [GCC 11.2.0]\nPyTorch version: 2.5.1+cu121\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/12255",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-08-28T18:31:14Z",
"updated_at": "2025-08-30T07:41:16Z",
"comments": 2,
"user": "BoostZhu"
},
{
"repo": "huggingface/peft",
"number": 2759,
"title": "PeftModel trainable parameters with multiple adapters",
"body": "### System Info\n\npeft-0.17.1\npython 3.9\n\n### Who can help?\n\n@BenjaminBossan \n\n### Reproduction\n\n**1) modules_to_save gradient true even when is_trainable=False**\n\nThe adapters has both modules_to_save and target_modules\n\n```\npeft_backbone = PeftModel.from_pretrained(\n target_backbone,\n safe_encoder_adapter_path1,\n adapter_name=adapter_name1,\n is_trainable=False\n )\n status = peft_backbone.get_model_status()\n check_trainable_params(target_backbone)\n```\n\n```\ndef check_trainable_params(model, print_layers=True):\n total_params = 0\n trainable_params = 0\n for name, param in model.named_parameters():\n num_params = param.numel()\n total_params += num_params\n if param.requires_grad:\n trainable_params += num_params\n if print_layers:\n print(f\"[TRAINABLE] {name} - shape: {tuple(param.shape)}\")\n elif print_layers:\n print(f\"[FROZEN] {name} - shape: {tuple(param.shape)}\")\n\n print(f\"\\nTotal parameters: {total_params:,}\")\n print(f\"Trainable parameters: {trainable_params:,}\")\n print(f\"Frozen parameters: {total_params - trainable_params:,}\")\n print(f\"Trainable ratio: {100 * trainable_params / total_params:.2f}%\")\n\n return trainable_params, total_params\n```\n\nexample of printed trainable params \n[TRAINABLE] blocks.0.modules_to_save.adapter1.norm1.weight - shape: (1408,)\n[FROZEN] blocks.2.attn.qkv.lora_A.adapter1.weight - shape: (32, 1408)\n\n\n**2) Loading an adapter after using from_pretrained**\n```\npeft_backbone = PeftModel.from_pretrained(\n target_backbone,\n safe_encoder_adapter_path1,\n adapter_name=modality_name,\n is_trainable=False\n)\nstatus = peft_backbone.get_model_status()\ntarget_backbone.load_adapter(safe_encoder_adapter_path2, is_trainable=False, adapter_name=adapter2)\nstatus = peft_backbone.get_model_status()\n```\n\nstatus before load_adapter shows {'adapter1': False} while after the load_adapter {'adapter2': False, 'adapter1': True}\n\nI think the issue comes from BaseTurnerLayer.set_adapter that set True all my adapter1 lora layers' gradient while setting properly the adapter2 lora layers' gradient to False.\nBaseTurnerLayer.set_adapter is called when doing self.add_adapter in PeftModel.load_adapter.\n\n\n\n\n\n\n### Expected behavior\n\n**1) modules_to_save gradient true even when is_trainable=False**\n\nExpecting the gradients for modules_to_save layers to be false. It's working properly for lora layers.\n\n**2) Loading an adapter after using from_pretrained**\n\nExpecting adapter1 to remain gradient false (is_trainable=False during from_pretrained loading) even after loading another adapter.\n\n**Other informations:**\n\nRegarding issue 1), in the code of 2), the modules_to_save for adapter2 were properly set to false when using load_adapter with is_trainable=false.\n\n[TRAINABLE] base_model.model.blocks.39.modules_to_save.adapter1.mlp.fc2.bias - shape: (1408,)\n[FROZEN] base_model.model.blocks.39.modules_to_save.adapter2.norm1.weight - shape: (1408,)\n\nMore generally, is there any reason peftmodel has to change the requires_gradient of adapters when calling set_adapter? (https://github.com/huggingface/peft/issues/2749)\nI assume that it might be related to the fact that there might be a problem to have non activated adapter but with requires_gradient=True?\nWhen using the library I was expecting to be able to set what params needed to be trained on all my adapters upon loading them with from_pretrained and load_adapter (or manually) then simply switch between adapters during the training with set_adapter.\n",
"url": "https://github.com/huggingface/peft/issues/2759",
"state": "closed",
"labels": [],
"created_at": "2025-08-28T16:36:25Z",
"updated_at": "2025-10-06T15:04:09Z",
"comments": 8,
"user": "NguyenRichard"
},
{
"repo": "huggingface/transformers",
"number": 40462,
"title": "Question about RoPE Implementation in modeling_llama: Should torch.cat be repeat_interleave?",
"body": "Hi,\nI was going through the code for `modeling_llama` and the RoPE implementation. I came across the following function:\n\n```\ndef forward(self, x, position_ids):\n inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1).to(x.device)\n position_ids_expanded = position_ids[:, None, :].float()\n\n device_type = x.device.type if isinstance(x.device.type, str) and x.device.type != \"mps\" else \"cpu\"\n with torch.autocast(device_type=device_type, enabled=False): # Force float32\n freqs = (inv_freq_expanded.float() @ position_ids_expanded.float()).transpose(1, 2)\n emb = torch.cat((freqs, freqs), dim=-1)\n cos = emb.cos() * self.attention_scaling\n sin = emb.sin() * self.attention_scaling\n\n return cos.to(dtype=x.dtype), sin.to(dtype=x.dtype)\n\n```\nI believe the line `emb = torch.cat((freqs, freqs), dim=-1)` should be replaced with `repeat_interleave`. This is because the cosine/sine angles for matrix multiplication should be structured like:\n```\n[cos(\u03b8\u2081), cos(\u03b8\u2081), cos(\u03b8\u2082), cos(\u03b8\u2082), cos(\u03b8\u2083), cos(\u03b8\u2083), ...]\n\n```\nThis way, further down the stream when we compute:\n```\nq_embed = (q * cos) + (rotate_half(q) * sin)\n```\n...the values are aligned properly for pairwise rotation. However, the current `torch.cat((freqs, freqs), dim=-1) ` should produce:\n```\n[cos(\u03b8\u2081), cos(\u03b8\u2082), cos(\u03b8\u2083), cos(\u03b8\u2081), cos(\u03b8\u2082), cos(\u03b8\u2083), ...]\n```\nwhich seems incorrect. Am I missing something?\nThanks,\nAbhidip",
"url": "https://github.com/huggingface/transformers/issues/40462",
"state": "closed",
"labels": [],
"created_at": "2025-08-26T16:32:41Z",
"updated_at": "2025-08-27T10:01:11Z",
"comments": 2,
"user": "abhidipbhattacharyya"
},
{
"repo": "huggingface/transformers",
"number": 40459,
"title": "`use_kernels=True` does not invoke custom kernels",
"body": "### System Info\n\n- `transformers` version: 4.56.0.dev0\n- Platform: Linux-5.4.0-216-generic-x86_64-with-glibc2.31\n- Python version: 3.12.7\n- Huggingface_hub version: 0.34.4\n- Safetensors version: 0.6.2\n- Accelerate version: 1.10.0\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.8.0+cu128 (CUDA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: No\n- Using GPU in script?: Yes\n- GPU type: NVIDIA A100-SXM4-80GB\n\n### Who can help?\n\n@ArthurZucker\n\n### Reproduction\n\n```python\nimport logging\nlogging.basicConfig(level=logging.INFO)\n\nimport torch\nfrom transformers import (\n AutoTokenizer, AutoModelForCausalLM,\n)\n\nmodel_id = \"openai/gpt-oss-20b\"\ntokenizer = AutoTokenizer.from_pretrained(model_id)\nmodel = AutoModelForCausalLM.from_pretrained(\n model_id,\n torch_dtype=\"auto\",\n device_map=\"auto,\n use_kernels=True,\n).eval()\n\nmessages = [\n {\"role\": \"system\", \"content\": \"What is Tensor Parallelism?\"},\n]\n\ninputs = tokenizer.apply_chat_template(\n messages,\n add_generation_prompt=True,\n return_tensors=\"pt\",\n return_dict=True,\n reasoning_effort=\"low\",\n).to(model.device)\n\nwith torch.inference_mode():\n generated = model.generate(\n **inputs,\n do_sample=False,\n temperature=None,\n max_new_tokens=64,\n disable_compile=True,\n )\n\ndecoded_generation = tokenizer.batch_decode(generated, skip_special_tokens=True)[0]\nprint(decoded_generation)\n```\n\n### Expected behavior\n\nNoting that I have activated logging, I should be able to see the logs for all the custom kernels being invoked. While the `LigerRMSNorm` is being invoked I do not see the `MegaBlocksMoeMLP` as it should be (as [stated in the modelling file here](https://github.com/huggingface/transformers/blob/263d06fedc17bb28f70dabe2acae562bc617ef9b/src/transformers/models/gpt_oss/modeling_gpt_oss.py#L156)).\n\nI also note that while the `LigerRMSNorm` is invoked but it complains that it cannot be used due to not being compatible with compile:\n```\nINFO:root:Using layer `LigerRMSNorm` from repo `kernels-community/liger_kernels` (revision: main) for layer `LigerRMSNorm`\nINFO:root:Layer does not support torch.compile, using fallback\n```\nI have used `disable_compile=True,` in the `.generate()` method, which should have taken care of the issue.\n\n### Solution\n\nThe way I could invoke the custom kernels was to swap out these lines:\nhttps://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L5241-L5243\n\nWith the following\n```py\n from kernels import Device, Mode, kernelize\n\n kernelize(model, device=Device(type=model.device.type), mode=Mode.INFERENCE)\n```\nWhile this is not the solution, and we should infer what mode the model is in, I thought of listing the current personal solution down for ease of ideation.",
"url": "https://github.com/huggingface/transformers/issues/40459",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-08-26T13:32:35Z",
"updated_at": "2025-09-16T08:50:55Z",
"comments": 1,
"user": "ariG23498"
},
{
"repo": "huggingface/diffusers",
"number": 12241,
"title": "WAN2.1 FLF2V: Incorrect MASK Creation????",
"body": "Hello! I think that it is maybe error. (Or not, please explain it for me!!)\n\nIn **WanImageToVideoPipeline** class in `pipline_wan_i2v.py`, \n![\"Image\"](\"https://github.com/user-attachments/assets/8108a9e9-8632-44a1-93b8-abd9ae6a22cd\")
\n(the code is the part of `prepare_latents` function)\n\n**For I2V**, masking shape like as below:\n```\n[[1, 0, 0, ... , 0]\n[1, 0, 0, ... , 0]\n[1, 0, 0, ... , 0]\n[1, 0, 0, ... , 0]]\n```\nI understood: when the mask is 1, input video frame does not change.\n(*Mask shape: [1, 4, 21, 60, 104] = [B, C, F, H, W])\n \n**But in the FLF2V case,** masking shape like as below:\n```\n[[1, 0, 0, ... , 0]\n[1, 0, 0, ... , 0]\n[1, 0, 0, ... , 0]\n**[1, 0, 0, ... , 1]]**\n```\nHere, **why the last frame mask has 1 only in last channel??**\nIs there anyone who can explain this part? ",
"url": "https://github.com/huggingface/diffusers/issues/12241",
"state": "open",
"labels": [],
"created_at": "2025-08-26T12:23:09Z",
"updated_at": "2025-08-27T02:10:49Z",
"comments": 1,
"user": "KyujinHan"
},
{
"repo": "huggingface/lerobot",
"number": 1792,
"title": "how to train lerobot model offline with offline data?",
"body": "Hi, I'm trying to configure lerobot to train with pre-downloaded models and datasets. I'm stuck, however, with how to organize the model cache and dataset cache, and how to tell the train script I'm using offline everything?\n\nI tried to download the model and dataset:\n```\n$ hf download lerobot/pi0 --cache-dir ~/lerobot_download/hf_models/lerobot/pi0/\n$ hf download lerobot/aloha_sim_transfer_cube_human --repo-type dataset --cache-dir ~/lerobot_download/hf_datasets/lerobot/aloha_sim_transfer_cube_human/\n```\n",
"url": "https://github.com/huggingface/lerobot/issues/1792",
"state": "closed",
"labels": [],
"created_at": "2025-08-26T10:20:56Z",
"updated_at": "2025-09-03T10:48:37Z",
"user": "dalishi"
},
{
"repo": "huggingface/accelerate",
"number": 3748,
"title": "How pass two layer class by use --fsdp_transformer_layer_cls_to_wrap?",
"body": "",
"url": "https://github.com/huggingface/accelerate/issues/3748",
"state": "closed",
"labels": [],
"created_at": "2025-08-26T08:56:32Z",
"updated_at": "2025-08-26T09:14:18Z",
"user": "sunjian2015"
},
{
"repo": "huggingface/diffusers",
"number": 12239,
"title": "Support for InfiniteTalk",
"body": "### Model/Pipeline/Scheduler description\n\nhttps://huggingface.co/MeiGen-AI/InfiniteTalk is a wonderful audio driven video generation model and can also support infinite frame , which is based on wan2.1. The demo and user's workflow is also awesome. some examples: https://www.runninghub.cn/ai-detail/1958438624956203010\n\n### Open source status\n\n- [x] The model implementation is available.\n- [x] The model weights are available (Only relevant if addition is not a scheduler).\n\n### Provide useful links for the implementation\n\nhttps://huggingface.co/MeiGen-AI/InfiniteTalk\nhttps://github.com/MeiGen-AI/InfiniteTalk",
"url": "https://github.com/huggingface/diffusers/issues/12239",
"state": "open",
"labels": [
"help wanted",
"New pipeline/model",
"contributions-welcome"
],
"created_at": "2025-08-26T06:57:43Z",
"updated_at": "2025-09-05T00:18:46Z",
"comments": 1,
"user": "supermeng"
},
{
"repo": "huggingface/transformers",
"number": 40406,
"title": "Cache tokenlizer",
"body": "### Feature request\n\nI am using Grounding DINO, which makes use of the `bert-base-uncanned` tokenlizer. Unfortunately, this model is never downloaded to cache, forcing a remote call to the API. Please allow for tokenlizer to be cached locally.\n\n### Motivation\n\nI want to use my software offline.\n\n### Your contribution\n\nI'm trying to find a way to download it manually as a workaround.",
"url": "https://github.com/huggingface/transformers/issues/40406",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-08-24T08:36:14Z",
"updated_at": "2025-09-10T11:49:06Z",
"comments": 5,
"user": "axymeus"
},
{
"repo": "huggingface/tokenizers",
"number": 1851,
"title": "SentencePieceBPE + Unicode NFD preprocessing leads to noise ?",
"body": "Hi,\nI have had the issue multiple times, so I assume I am doing something wrong.\n\n**Versions:**\n- tokenizers==0.21.4\n- transformers==4.55.4\n\n**Training script**\n\n```py\nfrom transformers import PreTrainedTokenizerFast\nfrom pathlib import Path\nfrom read import get_texts_iter_for_tokenizer\nfrom tokenizers import SentencePieceBPETokenizer, normalizers, pre_tokenizers\n\ndef main():\n output_dir = Path(\"hf_tokenizer\")\n output_dir.mkdir(parents=True, exist_ok=True)\n\n # Dump texts to a file\n texts = get_texts_iter_for_tokenizer()\n\n # Train SentencePiece model\n tokenizer = SentencePieceBPETokenizer()\n\n # Adding normalization and pre_tokenizer\n tokenizer.normalizer = normalizers.Sequence([normalizers.NFD()])\n tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel()\n\n # Adding special tokens and creating trainer instance\n special_tokens = [\"\", \"\", \"\", \"\", \"\"]\n\n # Training from iterator REMEMBER it's training on test set...\n tokenizer.train_from_iterator(texts, special_tokens=special_tokens, show_progress=True)\n\n fast_tokenizer = PreTrainedTokenizerFast(\n tokenizer_object=tokenizer,\n unk_token=\"\",\n pad_token=\"\",\n cls_token=\"\",\n sep_token=\"\",\n mask_token=\"\"\n )\n fast_tokenizer.save_pretrained(str(output_dir))\n```\n\nScript to reproduce bug:\n\n```py\nfrom transformers import PreTrainedTokenizerFast\n\nhf_tokenizer = PreTrainedTokenizerFast.from_pretrained(\"hf_tokenizer\")\n\n# Test\nprint(hf_tokenizer.tokenize(\"\u204ai\u0303 re\u0303 dn\u0303i u\u033esum\"))\n# ['\u00e2\u0123\u012c', 'i', '\u00cc\u0125', '\u0120re', '\u00cc\u0125', '\u0120dn', '\u00cc\u0125', 'i', '\u0120u', '\u00cc\u00be', 'sum']\nprint(hf_tokenizer.decode(hf_tokenizer.encode(\"\u204ai\u0303 re\u0303 dn\u0303i u\u033esum\"))\n# \u00e2\u0123\u012ci\u00cc\u0125\u0120re\u00cc\u0125\u0120dn\u00cc\u0125i\u0120u\u00cc\u00besum\n```\n\nI assume I am doing something wrong around preprocessing / postprocessing ?\n\n\n\n",
"url": "https://github.com/huggingface/tokenizers/issues/1851",
"state": "open",
"labels": [],
"created_at": "2025-08-24T08:28:08Z",
"updated_at": "2025-09-17T09:33:11Z",
"comments": 3,
"user": "PonteIneptique"
},
{
"repo": "huggingface/coreml-examples",
"number": 17,
"title": "how to get absolute depth\uff0cmeters\uff1f",
"body": "how to get absolute depth\uff0cmeters\uff1f",
"url": "https://github.com/huggingface/coreml-examples/issues/17",
"state": "open",
"labels": [],
"created_at": "2025-08-24T03:20:58Z",
"updated_at": "2025-08-24T03:20:58Z",
"user": "jay25208"
},
{
"repo": "huggingface/transformers",
"number": 40398,
"title": "NVIDIA RADIO-L",
"body": "### Model description\n\nWhile exploring, I came across [nvidia/RADIO-L](https://huggingface.co/nvidia/RADIO-L) and was wondering about its current support.\n\n1. May I ask if RADIO-L is already supported in Transformers?\n2. If not, would it be considered suitable to add?\n3. If a model requires trust_remote_code=True, what does that signify regarding its suitability for addition to Transformers?\n\nPlease share the general criteria for models to be added to Transformers.\n\nThank you very much for your guidance\n\ncc: @zucchini-nlp @Rocketknight1 \n\n### Open source status\n\n- [x] The model implementation is available\n- [x] The model weights are available\n\n### Provide useful links for the implementation\n\n_No response_",
"url": "https://github.com/huggingface/transformers/issues/40398",
"state": "open",
"labels": [
"New model"
],
"created_at": "2025-08-23T11:14:42Z",
"updated_at": "2025-08-26T14:44:11Z",
"comments": 4,
"user": "Uvi-12"
},
{
"repo": "huggingface/diffusers",
"number": 12222,
"title": "[Contribution welcome] adding a fast test for Qwen-Image Controlnet Pipeline",
"body": "We are looking for help from community to add a fast time for this PR \nhttps://github.com/huggingface/diffusers/pull/12215\n\nYou can add a file under this folder:\nhttps://github.com/huggingface/diffusers/tree/main/tests/pipelines/qwenimage\n\n\nYou can reference other tests we added for qwee pipelines [example](https://github.com/huggingface/diffusers/blob/main/tests/pipelines/qwenimage/test_qwenimage.py), as well as controlnet fasts tests [example](https://github.com/huggingface/diffusers/tree/main/tests/pipelines/controlnet_flux)",
"url": "https://github.com/huggingface/diffusers/issues/12222",
"state": "closed",
"labels": [
"good first issue",
"help wanted",
"contributions-welcome"
],
"created_at": "2025-08-22T21:04:50Z",
"updated_at": "2025-08-25T01:58:59Z",
"comments": 6,
"user": "yiyixuxu"
},
{
"repo": "huggingface/diffusers",
"number": 12221,
"title": "[Looking for community contribution] support DiffSynth Controlnet in diffusers",
"body": "### Model/Pipeline/Scheduler description\n\nHi!\nWe want to add first party support for DiffSynth controlnet in diffusers, and we are looking for some help from the community! \n\nLet me know if you're interested! \n\n\n### Open source status\n\n- [x] The model implementation is available.\n- [x] The model weights are available (Only relevant if addition is not a scheduler).\n\n### Provide useful links for the implementation\n\nhttps://huggingface.co/SahilCarterr/Qwen-Image-Blockwise-ControlNet-Canny\nhttps://huggingface.co/SahilCarterr/Qwen-Image-Blockwise-ControlNet-Depth",
"url": "https://github.com/huggingface/diffusers/issues/12221",
"state": "open",
"labels": [
"help wanted",
"Good second issue",
"contributions-welcome"
],
"created_at": "2025-08-22T20:49:18Z",
"updated_at": "2025-09-11T10:01:08Z",
"comments": 5,
"user": "yiyixuxu"
},
{
"repo": "huggingface/safetensors",
"number": 649,
"title": "How to determine if a file is a safetensor file",
"body": "Is there a good and fast way to determine if a file is a safetensors file. We would like to avoid reading the whole header. \n\nBackground we are currently trying to add safetensors as a datatype to the Galaxy project: https://github.com/galaxyproject/galaxy/pull/20754",
"url": "https://github.com/huggingface/safetensors/issues/649",
"state": "open",
"labels": [],
"created_at": "2025-08-22T09:17:49Z",
"updated_at": "2025-09-03T11:08:30Z",
"user": "bernt-matthias"
},
{
"repo": "huggingface/lerobot",
"number": 1775,
"title": "What's the finetuning method? Is it all full-finetuning?",
"body": "I could't find any thing about LORA finetuning, is the default method full-finetuning by now?",
"url": "https://github.com/huggingface/lerobot/issues/1775",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-08-22T06:48:25Z",
"updated_at": "2025-10-07T20:55:10Z",
"user": "lin-whale"
},
{
"repo": "huggingface/lerobot",
"number": 1774,
"title": "Finetune smolvla with vision encoder",
"body": "### System Info\n\n```Shell\n- `lerobot` version: 0.1.0\n- Platform: Linux-6.8.0-65-generic-x86_64-with-glibc2.35\n- Python version: 3.10.18\n- Huggingface_hub version: 0.33.4\n- Dataset version: 3.6.0\n- Numpy version: 2.2.6\n- PyTorch version (GPU?): 2.7.1+cu126 (True)\n- Cuda version: 12060\n- Using GPU in script?: \n```\n\n### Information\n\n- [ ] One of the scripts in the examples/ folder of LeRobot\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nnothing\n\n### Expected behavior\n\nI found that when attempting to fine-tune the model to grasp objects of different colors but identical shapes, it consistently grasped the wrong object. I found that the output feature differences from the VLM for the same image, such as \u201cgrasp the green duck into the box\u201d versus \u201cgrasp the yellow duck into the box,\u201d were nearly zero. Is it possible that the VLM has weak color differentiation capabilities? Can the official support fine-tuning the visual encoder together?",
"url": "https://github.com/huggingface/lerobot/issues/1774",
"state": "open",
"labels": [
"question",
"policies",
"good first issue"
],
"created_at": "2025-08-22T05:20:58Z",
"updated_at": "2025-10-08T11:31:02Z",
"user": "THU-yancow"
},
{
"repo": "huggingface/transformers",
"number": 40366,
"title": "[Feature] Support fromjson in jinja2 chat template rendering",
"body": "### Feature request\n\nGLM45 requires `fromjson` in jinja2 to deserialize str typed `tool_calls.function.arguments` to dict within chat template so it can iterate over `arguments`'s k-v within jinja2 chat template. \n\n```\n{% for tc in m.tool_calls %}\n{%- if tc.function %}\n{%- set tc = tc.function %}\n{%- endif %}\n{{ '\\n' + tc.name }}\n{% set _args = tc.arguments | fromjson %}\n{% for k, v in _args.items() %}\n{{ k }} \n{{ v \\| tojson(ensure_ascii=False) if v is not string else v }} \n{% endfor %}\n {% endfor %}\n{% endif %}\n```\n\nhttps://huggingface.co/zai-org/GLM-4.5/blob/main/chat_template.jinja#L75\n\n### Motivation\n\nGLM45 requires `fromjson` in jinja2 to deserialize str typed `tool_calls.function.arguments` to dict within chat template so it can iterate over `arguments`'s k-v within jinja2 chat template. \n\n```\n{% for tc in m.tool_calls %}\n{%- if tc.function %}\n{%- set tc = tc.function %}\n{%- endif %}\n{{ '\\n' + tc.name }}\n{% set _args = tc.arguments | fromjson %}\n{% for k, v in _args.items() %}\n{{ k }} \n{{ v \\| tojson(ensure_ascii=False) if v is not string else v }} \n{% endfor %}\n {% endfor %}\n{% endif %}\n```\n\nhttps://huggingface.co/zai-org/GLM-4.5/blob/main/chat_template.jinja#L75\n\n### Your contribution\n\nI will submit a PR",
"url": "https://github.com/huggingface/transformers/issues/40366",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-08-22T05:11:06Z",
"updated_at": "2025-08-22T05:18:45Z",
"comments": 1,
"user": "byjiang1996"
},
{
"repo": "huggingface/peft",
"number": 2749,
"title": "Set multiple adapters actively when training",
"body": "Hi! In incremental scenarios, I want to train a new adapter while keeping some old adapters actively. Notice that PeftModel can set active adapter by \"model.set_adapter()\". But every time can set only one adapter, where the type of args \"adapter_name\" is \"str\" rather than \"List[str]\". I also notice that class \"PeftMixedModel\" can set multiple adapters actively but only support for inference, and this class uses \"model.base_model.set_adapter()\" to achieve it. So I am not sure can I also set multiple adapters actively when training. My code is as following:\n\n```python\nmodel = AutoModelForCausalLM.from_pretrained()\npeft_config = LoraConfig()\nmodel = get_peft_model(model, peft_config, adapter_name=\"new\")\nmodel.load_adapter(adapter_path, adapter_name=\"old\")\nmodel.base_model.set_adapter([\"new\", \"old\"])\nfor name, param in model.named_parameters():\n if \"lora_A.old\" in name or \"lora_B.old\" in name:\n param.requires_grad = False\ntraining_args = TrainingArguments()\ntrainer = Trainer()\ntrainer.train()\n```\n",
"url": "https://github.com/huggingface/peft/issues/2749",
"state": "closed",
"labels": [],
"created_at": "2025-08-21T09:59:25Z",
"updated_at": "2025-09-29T15:04:15Z",
"comments": 4,
"user": "Yongyi-Liao"
},
{
"repo": "huggingface/lerobot",
"number": 1765,
"title": "Questions about using LIBERO dataset (loss starts extremely high)",
"body": "Hello,\n\nI am training on the \"**IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot**\" dataset, but I encountered an issue(here is the dateset:https://huggingface.co/datasets/IPEC-COMMUNITY/libero_spatial_no_noops_1.0.0_lerobot):\n\nAt the very beginning of training, the loss is extremely high (around 500).\n\nI would like to clarify a few points:\n\nIs the policy output expected to be relative actions or absolute actions?\nDo I need to perform any preprocessing on the dataset? For example:\nNormalizing the gripper action to the range [-1, 1]?\nAny other scaling or transformation?\n\nWhat is the exact relationship between the action and state in the dataset?\nI noticed that trajectories sometimes look different than expected(shown in the figure below).\nDo we need to process either the action or state to align them?\n\nAny guidance on the correct usage of the dataset would be greatly appreciated. Thanks!\n\n![\"Image\"](\"https://github.com/user-attachments/assets/b1102728-4916-405f-9a87-ab190b07f58b\")
\n\n",
"url": "https://github.com/huggingface/lerobot/issues/1765",
"state": "open",
"labels": [
"question",
"dataset",
"simulation"
],
"created_at": "2025-08-21T05:06:51Z",
"updated_at": "2025-09-23T09:46:41Z",
"user": "hamondyan"
},
{
"repo": "huggingface/transformers",
"number": 40330,
"title": "open-qwen2vl-base",
"body": "### Model description\n\nis there any plan to add open-qwen2vl-base model? \n\n### Open source status\n\n- [x] The model implementation is available\n- [x] The model weights are available\n\n### Provide useful links for the implementation\n\n_No response_",
"url": "https://github.com/huggingface/transformers/issues/40330",
"state": "open",
"labels": [
"New model"
],
"created_at": "2025-08-21T02:24:01Z",
"updated_at": "2025-08-23T10:18:28Z",
"comments": 5,
"user": "olccihyeon"
},
{
"repo": "huggingface/tokenizers",
"number": 1850,
"title": "Safe encoding of strings that might contain special token text",
"body": "When feeding untrusted string inputs into an LLM, it's often important not convert any of the input into special tokens, which might indicate message boundaries or other syntax. Among other reasons, this is important for guarding against prompt injection attacks.\n\ntiktoken provides a way to control how the encoding deals with special tokens, using the `allowed_special` and `disallowed_special` arguments. For example.\n\n```python\nenc = tiktoken.get_encoding(\"o200k_base\")\nenc.encode(\"<|endoftext|>\", disallowed_special=[]) # => [27, 91, 419, 1440, 919, 91, 29]\nenc.encode(\"<|endoftext|>\") # => ValueError\nenc.encode(\"<|endoftext|>\", allowed_special=set([\"<|endoftext|>\"]) # => [199999]\n```\n\nHowever, I can't figure out how to avoid tokenizing strings like <|im_start|> into special tokens, when using the tokenizers library. Note that I want to be able to *decode* the special token to its string representation for visualization. However, I want to make sure that when I call `encode`, I don't get a special token -- I tokenize the string representation as if there was no <|im_start|> special token. \n\nMaybe the easiest way to do this is to create two separate tokenizers, by creating new json files, but this is pretty inconvenient.",
"url": "https://github.com/huggingface/tokenizers/issues/1850",
"state": "closed",
"labels": [],
"created_at": "2025-08-21T00:53:17Z",
"updated_at": "2025-09-01T18:03:59Z",
"comments": 5,
"user": "joschu"
},
{
"repo": "huggingface/peft",
"number": 2746,
"title": "Gemma 2/3 Attention: Expected a single attention mask, got 2 instead",
"body": "Hi! I'm getting this error `ValueError: Expected a single attention mask, got 2 instead` at inference (after prompt tuning)--I've only had this happen with the Gemma 2 and 3 models, so it might have something to do with their specific attention mechanism. Is there a workaround (or am I maybe missing something)?\n\nI'm running the following:\n```\nmodel_name = \"google/gemma-2-2b\"\ntokenizer = AutoTokenizer.from_pretrained(model_name)\nmodel = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, device_map=\"auto\")\n\nsoft_model = get_peft_model(model, prompt_config)\n\ninputs = tokenizer(model_instruction, return_tensors=\"pt\")\noutputs = soft_model.generate(\n input_ids=inputs[\"input_ids\"],\n attention_mask=inputs[\"attention_mask\"],\n max_new_tokens=num_gen_tokens,\n eos_token_id=tokenizer.eos_token_id,\n )\n```",
"url": "https://github.com/huggingface/peft/issues/2746",
"state": "closed",
"labels": [],
"created_at": "2025-08-20T18:08:02Z",
"updated_at": "2025-08-27T02:43:22Z",
"comments": 8,
"user": "michelleezhang"
},
{
"repo": "huggingface/transformers",
"number": 40323,
"title": "Is there a plan to add DINOv3 into AutoBackbone?",
"body": "### Feature request\n\nIs there a plan to add DINOv3 to AutoBackbone. At present, DINOv2 is already inside, and I think DINOv3 should be able to inherit it directly. Appreciate a lot.\n\n### Motivation\n\nFor the convenience of use\n\n### Your contribution\n\nDINOv3 should be able to inherit from DINOv2 directly.",
"url": "https://github.com/huggingface/transformers/issues/40323",
"state": "closed",
"labels": [
"Feature request",
"Vision"
],
"created_at": "2025-08-20T16:02:45Z",
"updated_at": "2025-11-11T16:22:08Z",
"comments": 4,
"user": "Farenweh"
},
{
"repo": "huggingface/transformers",
"number": 40263,
"title": "[VLMs] How to process a batch that contains samples with and without images?",
"body": "Is there a **standard** way to process a batch that contains samples with and without images?\n\nFor example:\n\n```python\nfrom transformers import AutoProcessor\nfrom PIL import Image\nimport numpy as np\n\nmodel_id = ... # tested are \"google/gemma-3-4b-it\", \"HuggingFaceM4/idefics2-8b\", \"HuggingFaceM4/Idefics3-8B-Llama3\", \"HuggingFaceTB/SmolVLM2-2.2B-Instruct\", \"llava-hf/llava-1.5-7b-hf\", \"llava-hf/llava-v1.6-mistral-7b-hf\", \"OpenGVLab/InternVL3-8B-hf\", \"Qwen/Qwen2-VL-2B-Instruct\",\"Qwen/Qwen2.5-VL-3B-Instruct\"]\nprocessor = AutoProcessor.from_pretrained(model_id)\n\nmessages = [\n [{\"role\": \"user\", \"content\": [{\"type\": \"text\", \"text\": \"What's the capital of France?\"}]}],\n [{\"role\": \"user\", \"content\": [{\"type\": \"image\"}, {\"type\": \"text\", \"text\": \"What is it?\"}]}],\n]\ntexts = processor.apply_chat_template(messages)\n\nimage = Image.fromarray(\n np.random.uniform(low=0.0, high=255.0, size=(32, 48, 3)).astype(np.uint8)\n)\nimages = [[], [image]]\n\nprocessor(images=images, text=texts)\n```\n\nThis fails for all models I tested.\n\n\n```python\nimages=[image] # The only syntax I found that works for some models: llava-hf/llava-1.5-7b-hf, llava-hf/llava-v1.6-mistral-7b-hf, OpenGVLab/InternVL3-8B-hf, Qwen/Qwen2-VL-2B-Instruct, Qwen/Qwen2.5-VL-3B-Instruct\nimages = [None, [image]] # always fails\nimages = [None, image] # always fails\nimages = [[], [image]] # always fails\n```\n\n### Expected behavior\n\nThere should be a standard / documented way to batch process mixed inputs (some samples with images, some without).\n\n\n",
"url": "https://github.com/huggingface/transformers/issues/40263",
"state": "closed",
"labels": [],
"created_at": "2025-08-19T05:09:36Z",
"updated_at": "2025-09-18T08:08:51Z",
"user": "qgallouedec"
},
{
"repo": "huggingface/diffusers",
"number": 12185,
"title": "What's the difference between DreamBooth LoRa and traditional LoRa?",
"body": "I see a lot of examples using DreamBooth LoRa training code. What's the difference between this and traditional LoRa training? Can this DreamBooth LoRa training code be adapted to standard SFT LoRa code? Does disabling with_prior_preservation return normal LoRa training?",
"url": "https://github.com/huggingface/diffusers/issues/12185",
"state": "open",
"labels": [],
"created_at": "2025-08-19T03:32:30Z",
"updated_at": "2025-08-19T15:04:22Z",
"comments": 3,
"user": "MetaInsight7"
},
{
"repo": "huggingface/trl",
"number": 3918,
"title": "How to use trl-SFTTrainer to train Qwen-30B-A3B?",
"body": "Has anyone tried using TRL to train Qwen-30B-A3B-Instruct-2507?",
"url": "https://github.com/huggingface/trl/issues/3918",
"state": "open",
"labels": [
"\u2753 question"
],
"created_at": "2025-08-19T03:04:36Z",
"updated_at": "2025-08-19T03:11:30Z",
"user": "JeffWb"
},
{
"repo": "huggingface/datasets",
"number": 7739,
"title": "Replacement of \"Sequence\" feature with \"List\" breaks backward compatibility",
"body": "PR #7634 replaced the Sequence feature with List in 4.0.0, so datasets saved with version 4.0.0 with that feature cannot be loaded by earlier versions. There is no clear option in 4.0.0 to use the legacy feature type to preserve backward compatibility.\n\nWhy is this a problem? I have a complex preprocessing and training pipeline dependent on 3.6.0; we manage a very large number of separate datasets that get concatenated during training. If just one of those datasets is saved with 4.0.0, they become unusable, and we have no way of \"fixing\" them. I can load them in 4.0.0 but I can't re-save with the legacy feature type, and I can't load it in 3.6.0 for obvious reasons.\n\nPerhaps I'm missing something here, since the PR says that backward compatibility is preserved; if so, it's not obvious to me how.",
"url": "https://github.com/huggingface/datasets/issues/7739",
"state": "open",
"labels": [],
"created_at": "2025-08-18T17:28:38Z",
"updated_at": "2025-09-10T14:17:50Z",
"comments": 1,
"user": "evmaki"
},
{
"repo": "huggingface/gsplat.js",
"number": 119,
"title": "How to 4DGS (.splatv)",
"body": "How can I generate the .splatv file and get it running on my local server?",
"url": "https://github.com/huggingface/gsplat.js/issues/119",
"state": "open",
"labels": [],
"created_at": "2025-08-18T07:35:04Z",
"updated_at": "2025-08-18T07:35:04Z",
"user": "CetosEdit"
},
{
"repo": "huggingface/diffusers",
"number": 12165,
"title": "Failed to finetune the pre-trained model of 'stable-diffusion-v1-4' on image inpainting task",
"body": "I finetuned the pre-trained model of 'stable-diffusion-inpainting' on image inpainting task, and all work well as the model is trained on image inpainting. But when I finetuned with the pre-trained model of 'stable-diffusion-v1-4' which is trained on text-to-image, the loss is NaN and the result is pure black.\n\nAs the two models have different input channels for unet, I have changed the unet input channels of 'stable-diffusion-v1-4' to be fit for image inpainting task. So far, the code can run but the loss is NaN. I do not know where is the problem, how to finetune the pre-trained model of 'stable-diffusion-inpainting' on image inpainting task ? should I change some hyparameters? Any help will be appreciated, thanks!",
"url": "https://github.com/huggingface/diffusers/issues/12165",
"state": "closed",
"labels": [],
"created_at": "2025-08-17T07:15:36Z",
"updated_at": "2025-09-07T09:35:38Z",
"comments": 7,
"user": "micklexqg"
},
{
"repo": "huggingface/gym-hil",
"number": 27,
"title": "How to close the gripper in gym-hill-sim?",
"body": "Hello all.\n\nI'm using macOS to practice with tutorial gym-hill-sim.\nI figured out how to move robot like x,y,z but, it's impossible to close the gripper....\n\nCould you all please share the correct key?\nChatgpt answered ctrl-key but, it's not working!\n\nThanks in advance.",
"url": "https://github.com/huggingface/gym-hil/issues/27",
"state": "open",
"labels": [],
"created_at": "2025-08-15T13:46:12Z",
"updated_at": "2025-08-15T13:57:26Z",
"user": "cory0619"
},
{
"repo": "huggingface/peft",
"number": 2742,
"title": "RuntimeError: element 0 of tensors does not require grad and does not have a grad_fn",
"body": "Hello, I am fine-tuning the LLaMA-2 7B model on an A100 40 GB GPU. Initially, I was getting a CUDA out-of-memory error. I tried various methods, such as reducing batch size, but none worked. Then I enabled:\n\nmodel.gradient_checkpointing_enable()\n\nAfter doing this, the OOM issue was resolved, but now I get the following error during backpropagation:\n\ntorch.autograd.backward(\n File \".../torch/autograd/__init__.py\", line 354, in backward\n _engine_run_backward(\n File \".../torch/autograd/graph.py\", line 829, in _engine_run_backward\n return Variable._execution_engine.run_backward( \nRuntimeError: element 0 of tensors does not require grad and does not have a grad_fn\n\nI also tried:\n\nmodel.enable_input_require_grads()\n\nbut the error still persists. I suspect the issue is related to enabling gradient checkpointing.\n\n# In model_init()\nreft_model.gradient_checkpointing_enable()\nreft_model.enable_input_require_grads()\n\nIs there something I am missing when using gradient checkpointing in this setup?",
"url": "https://github.com/huggingface/peft/issues/2742",
"state": "closed",
"labels": [],
"created_at": "2025-08-15T06:21:50Z",
"updated_at": "2025-09-23T15:04:07Z",
"comments": 4,
"user": "Mishajain1110"
},
{
"repo": "huggingface/trl",
"number": 3896,
"title": "How to gather completions before computing rewards in GRPOTrainer",
"body": "Hi, \nI found that the `reward_funcs` passed to GRPOTrainer is used per-device.\nThat is, if I set `num_generation=16`, `per_device_train_batch_size=4`, my customized reward function can only receive `4` completions.\nHowever, my customized reward function calculates rewards depending on a global view over all `16` completions for each question.\nHow can I implement this?",
"url": "https://github.com/huggingface/trl/issues/3896",
"state": "closed",
"labels": [
"\u2753 question",
"\ud83c\udfcb Reward",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-08-14T14:41:42Z",
"updated_at": "2025-09-03T14:09:16Z",
"user": "rubickkcibur"
},
{
"repo": "huggingface/peft",
"number": 2738,
"title": "Which base model weights are getting frozen after applying LoRA?",
"body": "I have finetuned LLaVA-v1.5-7B with peft LoRA, and I have found out that after adding the LoRA adapters, all the weights are getting frozen except for the newly added LoRA layers and mm_projector weights (non-LoRA). I will be glad to know the freezing logic implemented by peft since not all the base model weights are getting frozen after applying LoRA.\nAlso, I have not added the mm_projector weights inside the module_to_save.",
"url": "https://github.com/huggingface/peft/issues/2738",
"state": "closed",
"labels": [],
"created_at": "2025-08-13T17:35:10Z",
"updated_at": "2025-08-14T04:20:42Z",
"comments": 1,
"user": "srbh-dl"
},
{
"repo": "huggingface/diffusers",
"number": 12136,
"title": "How to use Diffusers to Convert Safetensors SDXL 1.0 to Onnx?",
"body": "Hello,\n\nI'm trying to convert a safetensors checkpoint for SDXL to onnx format.\n\nI've tried Optimum already but it fails everytime.\n\nPlease help.",
"url": "https://github.com/huggingface/diffusers/issues/12136",
"state": "closed",
"labels": [],
"created_at": "2025-08-13T06:33:22Z",
"updated_at": "2025-10-31T03:13:28Z",
"user": "CypherpunkSamurai"
},
{
"repo": "huggingface/lerobot",
"number": 1712,
"title": "Why hasn't the pi0 model learned the ability to place something in the specified positions? Is it because the number of datasets is insufficient?",
"body": "I am creating a tic-tac-toe board and using yellow and green sandbags as pieces. I have collected a dataset of \"the entire process of a robotic arm picking up yellow sandbags and placing them in nine different positions on the board\". This dataset is used to train the pi0 model to achieve autonomous playing. The collection scope includes: changes in the board scene, motor action status, visual images, and text task instructions. However, when testing the trained pi0 model by giving tasks of placing sandbags in different positions on the board, it turns out that the so101 robotic arm has a poor understanding of position information. It can grab the sandbags just like in the recorded dataset, but most of the time it cannot place them in the specified positions.",
"url": "https://github.com/huggingface/lerobot/issues/1712",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-08-12T10:15:26Z",
"updated_at": "2025-12-22T08:10:47Z",
"user": "Alex-Wlog"
},
{
"repo": "huggingface/transformers",
"number": 40089,
"title": "Could not import module 'AutoTokenizer'. Are this object's requirements defined correctly?",
"body": "### System Info\n\n- torch @ https://download.pytorch.org/whl/cu124/torch-2.6.0%2Bcu124-cp310-cp310-linux_x86_64.whl\n- torchaudio @ https://download.pytorch.org/whl/cu124/torchaudio-2.6.0%2Bcu124-cp310-cp310-linux_x86_64.whl\n- torchvision @ https://download.pytorch.org/whl/cu124/torchvision-0.21.0%2Bcu124-cp310-cp310-linux_x86_64.whl\n- unsloth==2025.6.12\n- unsloth_zoo==2025.6.8\n- accelerate==1.8.1\n- bitsandbytes==0.46.0\n- pydantic==2.11.7\n- pydantic_core==2.33.2\n- tokenizers==0.21.2\n- transformers==4.52.4\n- treelite==4.4.1\n- treescope==0.1.9\n- triton==3.2.0\n- trl==0.19.0\n- xformers==0.0.29.post3\n- sympy==1.13.1\n- cut-cross-entropy==25.1.1\n- Python 3.10.16\n- NVIDIA A10G (CUDA Version: 12.5)\n- Ubuntu 24.04.2 LTS\n\n### Who can help?\n\n@ArthurZucker @itazap\n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nfrom transformers import AutoTokenizer\n\n---------------------------------------------------------------------------\nModuleNotFoundError Traceback (most recent call last)\nFile [~/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py:2045](https://25rhl5xt9dz0f5sq.ml-c7564e33-277.cdpv2-pr.uf1v-9d9i.cloudera.site/lab/tree/project/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py#line=2044), in _LazyModule.__getattr__(self, name)\n 2044 try:\n-> 2045 module = self._get_module(self._class_to_module[name])\n 2046 value = getattr(module, name)\n\nFile [~/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py:2075](https://25rhl5xt9dz0f5sq.ml-c7564e33-277.cdpv2-pr.uf1v-9d9i.cloudera.site/lab/tree/project/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py#line=2074), in _LazyModule._get_module(self, module_name)\n 2074 except Exception as e:\n-> 2075 raise e\n\nFile [~/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py:2073](https://25rhl5xt9dz0f5sq.ml-c7564e33-277.cdpv2-pr.uf1v-9d9i.cloudera.site/lab/tree/project/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py#line=2072), in _LazyModule._get_module(self, module_name)\n 2072 try:\n-> 2073 return importlib.import_module(\".\" + module_name, self.__name__)\n 2074 except Exception as e:\n\nFile /usr/local/lib/python3.10/importlib/__init__.py:126, in import_module(name, package)\n 125 level += 1\n--> 126 return _bootstrap._gcd_import(name[level:], package, level)\n\nFile :1050, in _gcd_import(name, package, level)\n\nFile :1027, in _find_and_load(name, import_)\n\nFile :992, in _find_and_load_unlocked(name, import_)\n\nFile :241, in _call_with_frames_removed(f, *args, **kwds)\n\nFile :1050, in _gcd_import(name, package, level)\n\nFile :1027, in _find_and_load(name, import_)\n\nFile :1004, in _find_and_load_unlocked(name, import_)\n\nModuleNotFoundError: No module named 'transformers.models.ipynb_checkpoints'\n\nThe above exception was the direct cause of the following exception:\n\nModuleNotFoundError Traceback (most recent call last)\nFile [~/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py:2045](https://25rhl5xt9dz0f5sq.ml-c7564e33-277.cdpv2-pr.uf1v-9d9i.cloudera.site/lab/tree/project/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py#line=2044), in _LazyModule.__getattr__(self, name)\n 2044 try:\n-> 2045 module = self._get_module(self._class_to_module[name])\n 2046 value = getattr(module, name)\n\nFile [~/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py:2075](https://25rhl5xt9dz0f5sq.ml-c7564e33-277.cdpv2-pr.uf1v-9d9i.cloudera.site/lab/tree/project/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py#line=2074), in _LazyModule._get_module(self, module_name)\n 2074 except Exception as e:\n-> 2075 raise e\n\nFile [~/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py:2073](https://25rhl5xt9dz0f5sq.ml-c7564e33-277.cdpv2-pr.uf1v-9d9i.cloudera.site/lab/tree/project/.local/lib/python3.10/site-packages/transformers/utils/import_utils.py#line=2072), in _LazyModule._get_module(self, module_name)\n 2072 try:\n-> 2073 return importlib.import_module(\".\" + module_name, self.__name__)\n 2074 except Exception as e:\n\nFile /usr/local/lib/python3.10/importlib/__init__.py:126, in import_module(name, package)\n 125 level += 1\n--> 126 return _bootstrap._gcd_import(name[level:], package, level)\n\nFile :1050, in _gcd_import(name, package, level)\n\nFile :1027, in _find_and_load(name, import_)\n\nFile :1006, in _find_and_load_unlocked(name, i",
"url": "https://github.com/huggingface/transformers/issues/40089",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-08-11T21:44:05Z",
"updated_at": "2025-09-08T03:09:11Z",
"comments": 3,
"user": "octavianBordeanu"
},
{
"repo": "huggingface/candle",
"number": 3052,
"title": "Candle vs. PyTorch performance",
"body": "I'm running https://github.com/huggingface/candle/tree/main/candle-examples/examples/llava vs. https://github.com/fpgaminer/joycaption/blob/main/scripts/batch-caption.py on a Mac m1.\n\nSeeing significant performance difference, Candle seems much slower.\nI enabled accelerate and metal features.\n\nWould love some pointers how to improve it.",
"url": "https://github.com/huggingface/candle/issues/3052",
"state": "open",
"labels": [],
"created_at": "2025-08-11T16:14:17Z",
"updated_at": "2025-11-14T20:05:16Z",
"comments": 8,
"user": "ohaddahan"
},
{
"repo": "huggingface/diffusers",
"number": 12124,
"title": "For qwen-image training file, Maybe \"shuffle\" of dataloader should be \"False\" when custom_instance_prompts is not None and cache_latents is False?",
"body": "### Describe the bug\n\nI think \"shuffle\" of dataloader should be \"False\" when custom_instance_prompts is not None and cache_latents is False. Otherwise, it will lead to errors in the correspondence between prompt embedding and image during training, and prompt will not be followed when performing the task of T2I.\n\n### Reproduction\n\nNone\n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\nNone\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/12124",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-08-11T13:15:21Z",
"updated_at": "2025-08-30T01:57:02Z",
"comments": 2,
"user": "yinguoweiOvO"
},
{
"repo": "huggingface/diffusers",
"number": 12120,
"title": "How to train a lora with distilled flux model, such as flux-schnell???",
"body": "**Is your feature request related to a problem? Please describe.**\nI can use flux as base model to train a lora, but it need 20 steps , it cost a lot of time , and I want to train a lora base on distill model to implement use fewer step make a better image, such as based on flux-schnell model train a lora it only need 4 steps can generate a good image !! and I can train many lora like this, only need 4 steps generated\n\n\n\n**Describe the solution you'd like.**\nI need a script , maybe it locate in examples\\dreambooth\\train_dreambooth_lora_flux_schennl.py \nI want to know to train a lora based on distilled model and get a good result ? \n\n**Describe alternatives you've considered.**\nI want to train many lora for base model( flux or flux-schnell), not only one lora , and I want to generated with fewer steps. So , I want to train loras with distilled model ... how to implment it ? I test scripts : [train_dreambooth_lora_flux.py](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_flux.py) by modify based mode from flux to flux-schnell ,but the result is bad...\n\n**Additional context.**\nany other implement method is OK , ",
"url": "https://github.com/huggingface/diffusers/issues/12120",
"state": "open",
"labels": [],
"created_at": "2025-08-11T03:07:42Z",
"updated_at": "2025-08-11T06:01:45Z",
"user": "Johnson-yue"
},
{
"repo": "huggingface/diffusers",
"number": 12108,
"title": "Qwen Image and Chroma pipeline breaks using schedulers that enable flow matching by parameter.",
"body": "### Describe the bug\n\nSeveral Schedulers support flow matching by using the prediction_type='flow_prediction\" e.g.\n\n```\npipe.scheduler = UniPCMultistepScheduler(prediction_type=\"flow_prediction\", flow_shift=3.16, timestep_spacing='trailing', use_flow_sigmas=True)\n```\n\nHowever Chroma and Qwen Image will not work with these schedulers failing with the error\n```\nValueError: The current scheduler class 's `set_timesteps` does not support custom sigmas schedules. Please check whether you are using the correct scheduler.\n```\n\nCan we have this fixed by either changing the schedulers to have the missing attributes and use them, or by rethinking the way these pipelines handle the timesteps .\n\n### Reproduction\n\n```py\nimport torch\nfrom diffusers import QwenImagePipeline, UniPCMultistepScheduler\n\n\n\npipe = QwenImagePipeline.from_pretrained(\"Qwen/Qwen-Image\",\n torch_dtype=torch.bfloat16)\n#pipe.scheduler = FlowMatchEulerDiscreteScheduler(shift=3.16, use_beta_sigmas=True)\npipe.scheduler = UniPCMultistepScheduler(prediction_type=\"flow_prediction\", flow_shift=3.16, timestep_spacing='trailing', use_flow_sigmas=True)\npipe.to(\"mps\")\npipe(\"a nice picture of an rainbow\")\n```\n\n### Logs\n\n```shell\nFile \"/Volumes/SSD2TB/AI/Diffusers/qwenimagelowmem.py\", line 84, in \n image = pipe(prompt_embeds=prompt_embeds, prompt_embeds_mask=prompt_embeds_mask, \n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/Volumes/SSD2TB/AI/Diffusers/lib/python3.11/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n return func(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^\n File \"/Volumes/SSD2TB/AI/Diffusers/lib/python3.11/site-packages/diffusers/pipelines/qwenimage/pipeline_qwenimage.py\", line 619, in __call__\n timesteps, num_inference_steps = retrieve_timesteps(\n ^^^^^^^^^^^^^^^^^^^\n File \"/Volumes/SSD2TB/AI/Diffusers/lib/python3.11/site-packages/diffusers/pipelines/qwenimage/pipeline_qwenimage.py\", line 119, in retrieve_timesteps\n raise ValueError(\nValueError: The current scheduler class 's `set_timesteps` does not support custom sigmas schedules. Please check whether you are using the correct scheduler.\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.35.0.dev0\n- Platform: macOS-15.5-arm64-arm-64bit\n- Running on Google Colab?: No\n- Python version: 3.11.13\n- PyTorch version (GPU?): 2.6.0 (False)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.34.3\n- Transformers version: 4.52.4\n- Accelerate version: 1.7.0\n- PEFT version: 0.17.0\n- Bitsandbytes version: not installed\n- Safetensors version: 0.5.3\n- xFormers version: not installed\n- Accelerator: Apple M3\n- Using GPU in script?: Yes\n- Using distributed or parallel set-up in script?: No\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/12108",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-08-09T21:34:28Z",
"updated_at": "2025-08-09T21:39:30Z",
"comments": 0,
"user": "Vargol"
},
{
"repo": "huggingface/transformers",
"number": 40056,
"title": "Question: How to write a custome tokenizer form scratch",
"body": "In this guide you introduced how to write a custom model and custom model configuration: [here](https://huggingface.co/docs/transformers/main/en/custom_models), IN addition I want to create a custom tokenizer form scratch why ?\n\nI have a problem of multilevel transcription: the model takes an input utterance and output a 12 multilingual transcript simultaneously . So I want to design a tokenzier such that it take the whole 12 languages as a dict: \n\n```python\n{\n \"lang1\": \"text text\",\n \"lang2\": \"text text\", \n \"lang3\": \"text text\",\n}\n```\n\nand after tokenization\n\n\n```python\n{\n \"input_ids\": \n {\n \"lang1\": \"ids of lang 1\",\n \"lang2\": \"ids of lang 2\",\n \"lang3\": \"ids of lang 2\",\n }\n}\n```\n\n\nHow to do so as I can not find docs of building such custom tkenizer from scratch ?",
"url": "https://github.com/huggingface/transformers/issues/40056",
"state": "closed",
"labels": [],
"created_at": "2025-08-09T16:39:19Z",
"updated_at": "2025-09-24T08:03:02Z",
"user": "obadx"
},
{
"repo": "huggingface/diffusers",
"number": 12107,
"title": "accelerator.init_trackers error when try with a custom object such as list",
"body": "### Describe the bug\n\nI set multiple prompts with nargs for argument \"--validation_prompt \" in \"train_dreambooth.py\":\n\n` parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=[\"A photo of sks dog in a bucket\", \"A sks cat wearing a coat\"],\n nargs=\"*\",\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )`\nbut an error occured at ` if accelerator.is_main_process:\n tracker_name = \"dreambooth-lora\"\n accelerator.init_trackers(tracker_name, config=vars(args))` :\n\"ValueError: value should be one of int, float, str, bool, or torch.Tensor\" \nIs it because tensorboard only support basic Python types and PyTorch tensors but not a custom object such as list?\n\nso how to visualize when has custom object such as list or argument with nargs?\n\n### Reproduction\n\nset the follow argument in \"train_dreambooth.py\" or other similar demos such as \"train_amused.py\":\n\n` parser.add_argument(\n \"--validation_prompt\",\n type=str,\n default=[\"A photo of sks dog in a bucket\", \"A sks cat wearing a coat\"],\n nargs=\"*\",\n help=\"A prompt that is used during validation to verify that the model is learning.\",\n )`\n\nerror occured at ` if accelerator.is_main_process:\n tracker_name = \"dreambooth-lora\"\n accelerator.init_trackers(tracker_name, config=vars(args))` with\n\"ValueError: value should be one of int, float, str, bool, or torch.Tensor\" \n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.33.0.dev0\n- Platform: Linux-6.8.0-55-generic-x86_64-with-glibc2.39\n- Running on Google Colab?: No\n- Python version: 3.10.11\n- PyTorch version (GPU?): 2.7.1+cu126 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.30.1\n- Transformers version: 4.52.4\n- Accelerate version: 1.8.1\n- PEFT version: 0.15.2\n- Bitsandbytes version: 0.45.4\n- Safetensors version: 0.5.3\n- xFormers version: 0.0.27.post2\n- Accelerator: NVIDIA GeForce RTX 3090, 24576 MiB\n- Using GPU in script?: \n- Using distributed or parallel set-up in script?: \n\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/12107",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-08-09T10:04:06Z",
"updated_at": "2025-08-09T10:04:06Z",
"comments": 0,
"user": "micklexqg"
},
{
"repo": "huggingface/diffusers",
"number": 12104,
"title": "IndexError: index 0 is out of bounds for dimension 0 with size 0",
"body": "### Describe the bug\n\n\nWhen I test the mit-han-lab/nunchaku-flux.1-kontext-dev model, it runs normally in a non-concurrent scenario, but throws an error when I try to run it with concurrent requests.\n\nMy GPU is a single RTX 4090D.\n\nHow can I enable multi-concurrency support on a single GPU?\n\nThank you in advance for your help.\n\n\nHere is my error message:\n\n[2025-08-08 17:14:50.242] [info] Initializing QuantizedFluxModel on device 0\n[2025-08-08 17:14:50.382] [info] Loading partial weights from pytorch\n[2025-08-08 17:14:51.445] [info] Done.\nInjecting quantized module\nLoading checkpoint shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 2/2 [00:00<00:00, 99.47it/s]\nLoading pipeline components...: 57%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258c | 4/7 [00:00<00:00, 28.54it/s]You set `add_prefix_space`. The tokenizer needs to be converted from the slow tokenizers\nLoading pipeline components...: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 7/7 [00:00<00:00, 19.02it/s]\n\nGeneration `height` and `width` have been adjusted to 752 and 1360 to fit the model requirements.\nGeneration `height` and `width` have been adjusted to 880 and 1168 to fit the model requirements.\n 43%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258e | 12/28 [00:17<00:23, 1.45s/it]\n 57%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258b | 16/28 [00:18<00:13, 1.17s/it]\n\u5904\u7406\u56fe\u50cf\u65f6\u51fa\u9519: index 29 is out of bounds for dimension 0 with size 29\n\u5904\u7406\u56fe\u50cf\u65f6\u51fa\u9519: index 29 is out of bounds for dimension 0 with size 29\n\n\n\n### Reproduction\n\n```\nimport torch\nfrom diffusers import FluxKontextPipeline\nfrom diffusers.utils import load_image\nfrom concurrent.futures import ThreadPoolExecutor\n\nfrom nunchaku import NunchakuFluxTransformer2dModel\nfrom nunchaku.utils import get_precision\nimport time\n\ndef get_result(image_path,pipeline):\n time_begin = time.time()\n image = load_image(\n image_path\n ).convert(\"RGB\")\n size = image.size\n large_now = 1440\n small_now = round(1440 * (min(size)/max(size)) /32) * 32\n width,height = (large_now,small_now) \\\n if size[0]>size[1] else (small_now,large_now)\n prompt = \"Remove the watermark from the picture\"\n image = pipeline(\n image=image,\n prompt=prompt,\n guidance_scale=2.5,\n num_inference_steps=28,\n height=height,\n width=width,\n ).images[0]\n image.save(image_path[:-4]+\"_result.png\")\n\ndef nunchaku_test(concurrency,pipeline):\n\n test_images = [\"\u623f\u578b\u56fe\u6c34\u5370.jpg\", \"\u5367\u5ba4\u6c34\u5370.png\"] * concurrency\n test_images = test_images[:concurrency] \n\n overall_start = time.time()\n\n with ThreadPoolExecutor(max_workers=concurrency) as executor:\n futures = [executor.submit(get_result, img_path, pipeline) for img_path in test_images]\n\n results = []\n for future in futures:\n try:\n results.append(future.result())\n except Exception as e:\n print(f\"\u5904\u7406\u56fe\u50cf\u65f6\u51fa\u9519: {e}\")\n\n overall_time = time.time() - overall_start\n\n\nif __name__ == '__main__':\n\n\n transformer = NunchakuFluxTransformer2dModel.from_pretrained(\n f\"/root/autodl-tmp/nunchaku-flux.1-kontext-dev/svdq-{get_precision()}_r32-flux.1-kontext-dev.safetensors\"\n )\n\n pipeline = FluxKontextPipeline.from_pretrained(\n \"/root/autodl-tmp/FLUX.1-Kontext-dev\", transformer=transformer, torch_dtype=torch.bfloat16\n ).to(\"cuda\")\n\n nunchaku_test(pipeline,2)\n nunchaku_test(pipeline,4)\n```\n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\n~/FLUX.1-Kontext-Dev-nunchaku# diffusers-cli env\n\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n\n- \ud83e\udd17 Diffusers version: 0.35.0.dev0\n- Platform: Linux-5.15.0-94-generic-x86_64-with-glibc2.35\n- Running on Google Colab?: No\n- Python version: 3.12.3\n- PyTorch version (GPU?): 2.6.0+cu124 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.33.1\n- Transformers version: 4.53.0\n- Accelerate version: 1.8.1\n- PEFT version: not installed\n- Bitsandbytes version: not installed\n- Safetensors version: 0.5.3\n- xFormers version: not installed\n- Accelerator: NVIDIA GeForce RTX 4090 D, 24564 MiB\n- Using GPU in script?: \n- Using distributed or parallel set-up in script?: \n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/12104",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-08-08T09:20:52Z",
"updated_at": "2025-08-17T22:22:37Z",
"comments": 1,
"user": "liushiton"
},
{
"repo": "huggingface/datasets",
"number": 7729,
"title": "OSError: libcudart.so.11.0: cannot open shared object file: No such file or directory",
"body": "> Hi is there any solution for that eror i try to install this one \npip install torch==1.12.1+cpu torchaudio==0.12.1+cpu -f https://download.pytorch.org/whl/torch_stable.html \nthis is working fine but tell me how to install pytorch version that is fit for gpu ",
"url": "https://github.com/huggingface/datasets/issues/7729",
"state": "open",
"labels": [],
"created_at": "2025-08-07T14:07:23Z",
"updated_at": "2025-09-24T02:17:15Z",
"comments": 1,
"user": "SaleemMalikAI"
},
{
"repo": "huggingface/transformers",
"number": 39992,
"title": "[gpt-oss] Transform checkpoint from safetensors to state dict",
"body": "Yesterday I was working on gpt-oss. However, loading the weights give me troubles.\n\u2028For models like Qwen, I did things like this:\n\n1. Create model on meta device\n2. FSDP2 shard it, so it can fit in memory\n3. On each GPU, it read weights from safetensors in a generator style, to save memory.\n4. Chunk the weights and copy to the FSDP\u2019s DTensor.\u2028\nGPT-oss does not apply this routine. Within `from_pretrained`, the mxfp4 quantizer somehow dequantized the weights, yet I cannot find a very clean way to utilize this capability. I have to modify the process, and initialized a CPU version of the model in the CPU memory.\n\nHow can we transform the safetensors to state dict directly?",
"url": "https://github.com/huggingface/transformers/issues/39992",
"state": "closed",
"labels": [],
"created_at": "2025-08-07T13:24:06Z",
"updated_at": "2025-09-15T08:02:55Z",
"comments": 1,
"user": "fingertap"
},
{
"repo": "huggingface/diffusers",
"number": 12094,
"title": "[Wan2.2] pipeline_wan miss the 'shift' parameter which used by Wan2.2-A14B-diffusers.",
"body": "**Firstly, I found that the quality of output using diffusers is poor**\nLater, I found that the pipeline_wan in diffusers[0.34.0] did not support two-stage processing. I noticed that the community had already updated it, so I installed diffusers[0.35.0-dev] by source code and it worked.\n\nThen I found that the scheduler in diffusers does not support the parameter \"shift\", but \"sample_shift\" is an important parameter generated by Wan2.2, which may also lead to differences from the official inference code of Wan2.2. Therefore, the video effect may still be inferior to the original inference code.\n\nhttps://github.com/Wan-Video/Wan2.2/issues/69\n\n**What I need**\nCan the community provide the UniPCMultistepScheduler and DPMSolverMultistepScheduler that support the 'shift' parameter? Or can it be adapted in pipeline_wan so that the shift parameter can be used.\n\nOr is there something wrong with my understanding? How can I correctly use the shift parameter when using diffusers?\n\nThanks!!\ncc @yiyixuxu @a-r-r-o-w \n",
"url": "https://github.com/huggingface/diffusers/issues/12094",
"state": "closed",
"labels": [],
"created_at": "2025-08-07T11:37:36Z",
"updated_at": "2025-08-10T08:43:27Z",
"comments": 7,
"user": "yvmilir"
},
{
"repo": "huggingface/lerobot",
"number": 1687,
"title": "When using AMP to train a model, why are the saved model weights still in fp32?",
"body": "![\"Image\"](\"https://github.com/user-attachments/assets/406a1879-f2f2-43c6-8341-8733873ee911\")
",
"url": "https://github.com/huggingface/lerobot/issues/1687",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-08-06T12:42:40Z",
"updated_at": "2025-08-12T08:52:00Z",
"user": "Hukongtao"
},
{
"repo": "huggingface/diffusers",
"number": 12084,
"title": "Will `cosmos-transfer1` be supported in diffusers in the future?",
"body": "\nHi @a-r-r-o-w and @yiyixuxu :) \n\nFirst of all, thank you for recently enabling cosmos-predict1 models (text2world and video2world) in the diffusers library \u2014 it's super exciting to see them integrated!\n\nI was wondering if there are any plans to also support [cosmos-transfer1](https://github.com/nvidia-cosmos/cosmos-transfer1) in diffusers in the future?\n\nThanks again for your great work! \ud83d\ude4c",
"url": "https://github.com/huggingface/diffusers/issues/12084",
"state": "open",
"labels": [],
"created_at": "2025-08-06T11:22:28Z",
"updated_at": "2025-08-19T12:11:33Z",
"comments": 3,
"user": "rebel-shshin"
},
{
"repo": "huggingface/lerobot",
"number": 1683,
"title": "SmolVLMWithExpertModel",
"body": "Excuse me, I would like to know about each module. In this class, I would like to know how to define inputs.",
"url": "https://github.com/huggingface/lerobot/issues/1683",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-08-06T10:30:21Z",
"updated_at": "2025-08-12T08:52:21Z",
"user": "xjushengjie"
},
{
"repo": "huggingface/lerobot",
"number": 1674,
"title": "How to train smolvla for multi-task",
"body": "I have trained smolvla for aloha_sim_transfer_cube and aloha_sim_insertion, and smolvla performs well in each single task. Now I'd like to train smolvla for multi-task ---- one model can complete the two tasks above. What should I do Now? ",
"url": "https://github.com/huggingface/lerobot/issues/1674",
"state": "closed",
"labels": [],
"created_at": "2025-08-06T02:40:01Z",
"updated_at": "2025-10-15T02:52:29Z",
"user": "w673"
},
{
"repo": "huggingface/diffusers",
"number": 12079,
"title": "API Suggestion: Expose Methods to Convert to Sample Prediction in Schedulers",
"body": "**What API design would you like to have changed or added to the library? Why?**\n\nMy proposal is for schedulers to expose `convert_to_sample_prediction` and `convert_to_prediction_type` methods, which would do the following:\n\n1. `convert_to_sample_prediction`: Converts from a given `prediction_type` to `sample_prediction` (e.g. $x_0$-prediction). This function would accept a `prediction_type` argument which defaults to `self.config.prediction_type`.\n2. `convert_to_prediction_type`: Converts back from `sample_prediction` to the scheduler's `prediction_type`. This is intended to be the inverse function of `convert_to_sample_prediction`.\n\nThe motivating use case I have in mind is to support guidance strategies such as [Adaptive Projected Guidance (APG)](https://arxiv.org/abs/2410.02416) and [Frequency-Decoupled Guidance (FDG)](https://arxiv.org/abs/2506.19713) which prefer to operate with sample / $x_0$-predictions. A code example will be given below.\n\nThe reason I think schedulers should expose these methods explicitly is that performing these operations depend on the scheduler state and definition. For example, the prediction type conversion code in `EulerDiscreteScheduler` depends on the `self.sigmas` schedule:\n\nhttps://github.com/huggingface/diffusers/blob/ba2ba9019f76fd96c532240ed07d3f98343e4041/src/diffusers/schedulers/scheduling_euler_discrete.py#L650-L663\n\nAs a possible alternative, code that uses a scheduler could instead try to infer the prediction type conversion logic from the presence of `alphas_cumprod` (for a DDPM-style conversion) or `sigmas` (for an EDM-style conversion) attributes. However, I think this is unreliable because a scheduler could use `alphas_cumprod` or `sigmas` in a non-standard way. Since schedulers essentially already implement the `convert_to_sample_prediction` logic in their `step` methods, I think it could be relatively easy to implement these methods, and calling code would not have to guess how to do the conversion.\n\nA potential difficulty is ensuring that these methods work well with the `step` method, for example if they are called outside of a denoising loop (so internal state like `self.step_index` may not be properly initialized) or if the conversion can be non-deterministic (for example, when `gamma > 0` in `EulerDiscreteScheduler`).\n\n**What use case would this enable or better enable? Can you give us a code example?**\n\nThe motivating use case is to support guidance strategies which prefer to operate with $x_0$-predictions. For this use case, we want to convert the denoising model prediction to `sample_prediction`, run the guider's `__call__` logic, and then convert back to the scheduler's `prediction_type` (as schedulers currently expect `model_outputs` in that `prediction_type`).\n\nThere may be other potential use cases as well that I haven't thought of.\n\nAs a concrete example, we can imagine modifying `EulerDiscreteScheduler` as follows:\n\n```python\nclass EulerDiscreteScheduler(SchedulerMixin, ConfigMixin):\n ...\n def convert_to_sample_prediction(\n self,\n model_output: torch.Tensor,\n timestep: Union[float, torch.Tensor],\n sample: torch.Tensor,\n prediction_type: Optional[str] = None,\n s_churn: float = 0.0,\n s_tmin: float = 0.0,\n s_tmax: float = float(\"inf\"),\n s_noise: float = 1.0,\n generator: Optional[torch.Generator] = None,\n ) -> torch.Tensor:\n if prediction_type is None:\n prediction_type = self.config.prediction_type\n\n # NOTE: there's a potential catch here if self.step_index isn't properly initialized\n sigma = self.sigmas[self.step_index]\n gamma = min(s_churn / (len(self.sigmas) - 1), 2**0.5 - 1) if s_tmin <= sigma <= s_tmax else 0.0\n sigma_hat = sigma * (gamma + 1)\n\n # NOTE: another potential problem is ensuring consistent computation with `step` if the conversion\n # can be non-deterministic (as below)\n if gamma > 0:\n noise = randn_tensor(\n model_output.shape, dtype=model_output.dtype, device=model_output.device, generator=generator\n )\n eps = noise * s_noise\n sample = sample + eps * (sigma_hat**2 - sigma**2) ** 0.5\n\n # Compute predicted original sample (x_0) from sigma-scaled predicted noise\n # NOTE: \"original_sample\" should not be an expected prediction_type but is left in for\n # backwards compatibility\n if self.config.prediction_type == \"original_sample\" or self.config.prediction_type == \"sample\":\n pred_original_sample = model_output\n elif self.config.prediction_type == \"epsilon\":\n pred_original_sample = sample - sigma_hat * model_output\n elif self.config.prediction_type == \"v_prediction\":\n # denoised = model_output * c_out + input * c_skip\n pred_original_sample = model_output * (-sigma / (sigma**2 + 1) ** 0.5) + (sample / (sigma**2 + 1))\n else:\n raise Valu",
"url": "https://github.com/huggingface/diffusers/issues/12079",
"state": "open",
"labels": [],
"created_at": "2025-08-06T02:24:46Z",
"updated_at": "2025-08-06T02:24:46Z",
"comments": 0,
"user": "dg845"
},
{
"repo": "huggingface/candle",
"number": 3047,
"title": "Can the safetensor files from OpenAI's new gpt-oss-20b work with any existing setup?",
"body": "Is the new gpt-oss-20b a totally different architecture or can I use an existing candle setup, swap out the files and start playing around with gpt-oss-20b?\n",
"url": "https://github.com/huggingface/candle/issues/3047",
"state": "open",
"labels": [],
"created_at": "2025-08-06T01:59:59Z",
"updated_at": "2025-08-06T02:01:52Z",
"comments": 1,
"user": "zcourts"
},
{
"repo": "huggingface/diffusers",
"number": 12078,
"title": "Problem with provided example validation input in the Flux Control finetuning example",
"body": "### Describe the bug\n\nThe help page for the Flux control finetuning example, https://github.com/huggingface/diffusers/blob/main/examples/flux-control/README.md, provides a sample validation input, a pose condition image\n[![](\"https://huggingface.co/api/resolve-cache/models/Adapter/t2iadapter/3c291e0547a1b17bed93428858cdc9b0265c26c7/openpose.png?%2FAdapter%2Ft2iadapter%2Fresolve%2Fmain%2Fopenpose.png=&etag=%2287cc79e12fe5a5bba31ac3098ee7837400b41ffa%22\")
]().\nThe pose conditioned model trained by the script does not process this image properly because it is in BGR format, apparent when comparing it to the openpose spec: \n[![](\"https://github.com/ArtificialShane/OpenPose/raw/master/doc/media/keypoints_pose.png\")
]().\nIt doesn't appear that the validation image is loaded in BGR format properly, in the below line:\nhttps://github.com/huggingface/diffusers/blob/ba2ba9019f76fd96c532240ed07d3f98343e4041/examples/flux-control/train_control_lora_flux.py#L127.\n\nIn my personal experiments, the validation output does not make sense. Below is an example of what my run uploaded to wandb:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/0edc3c88-cfa5-4fae-a6b1-295839136dba\")
\n\n### Reproduction\n\nI ran the below in the command line: \n```\naccelerate launch --config_file=/mnt/localssd/huggingface/accelerate/deepspeed.yaml train_control_lora_flux.py \\\n --pretrained_model_name_or_path=\"black-forest-labs/FLUX.1-dev\" \\\n --dataset_name=\"raulc0399/open_pose_controlnet\" \\\n --output_dir=\"/mnt/localssd/pose-control-lora\" \\\n --mixed_precision=\"bf16\" \\\n --train_batch_size=1 \\\n --rank=64 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n --learning_rate=1e-4 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=5000 \\\n --validation_image=\"openpose.png\" \\\n --validation_prompt=\"A couple, 4k photo, highly detailed\" \\\n --seed=\"0\" \\\n --cache_dir=\"/mnt/localssd/huggingface\" \n```\n\n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\n```\n- \ud83e\udd17 Diffusers version: 0.34.0\n- Platform: Linux-5.10.223-212.873.amzn2.x86_64-x86_64-with-glibc2.35\n- Running on Google Colab?: No\n- Python version: 3.10.8\n- PyTorch version (GPU?): 2.7.1+cu126 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.34.3\n- Transformers version: 4.54.1\n- Accelerate version: 1.9.0\n- PEFT version: 0.17.0\n- Bitsandbytes version: 0.46.1\n- Safetensors version: 0.5.3\n- xFormers version: not installed\n- Accelerator: NVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\n- Using GPU in script?: Yes.\n- Using distributed or parallel set-up in script?: Yes.\n```\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/12078",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-08-05T22:29:35Z",
"updated_at": "2025-08-07T08:47:45Z",
"comments": 1,
"user": "kzhang2"
},
{
"repo": "huggingface/lerobot",
"number": 1672,
"title": "How to resume training?",
"body": "My old setting of training:\n```\n# batch_size: 64\nsteps: 20000\n# output_dir: outputs/train\n```\nin outputs/train/ there are 020000 folder and last folder,eash has pretrained_model and training_state\n\n\nWhen I want to resume training, I read configs/train.py\n\nso I set\n```\nresume: true\noutput_dir: outputs/train/\n# or output_dir: outputs/train/checkpoints/last/pretrained_model/\n# or output_dir: outputs/train/checkpoints/last/pretrained_model/train_config.json\n```\nAll got this:\n\nTraceback (most recent call last):\n File \"/miniconda3/envs/lerobot/lib/python3.10/runpy.py\", line 196, in _run_module_as_main\n return _run_code(code, main_globals, None,\n File \"miniconda3/envs/lerobot/lib/python3.10/runpy.py\", line 86, in _run_code\n exec(code, run_globals)\n File \"//code/lerobot_diy/src/lerobot/scripts/train.py\", line 394, in \n train()\n File \"/code/lerobot_diy/src/lerobot/configs/parser.py\", line 225, in wrapper_inner\n response = fn(cfg, *args, **kwargs)\n File \"/code/lerobot_diy/src/lerobot/scripts/train.py\", line 215, in train\n optimizer, lr_scheduler = make_optimizer_and_scheduler(cfg, policy)\n File \"//code/lerobot_diy/src/lerobot/optim/factory.py\", line 38, in make_optimizer_and_scheduler\n optimizer = cfg.optimizer.build(params)\nAttributeError: 'NoneType' object has no attribute 'build'\n\n\nHow to write command of output dir?\nThanks!",
"url": "https://github.com/huggingface/lerobot/issues/1672",
"state": "closed",
"labels": [],
"created_at": "2025-08-05T14:57:32Z",
"updated_at": "2025-08-06T03:04:28Z",
"user": "milong26"
},
{
"repo": "huggingface/transformers",
"number": 39921,
"title": "[Gemma3N] Not able to add new special tokens to model/tokenizer due to projection error",
"body": "### System Info\n\n```\n- transformers==4.54.1\n- Platform: Linux-5.15.0-1084-aws-x86_64-with-glibc2.31\n- Python version: 3.13\n- TRL version: 0.19.1\n- Huggingface_hub version: 0.33.4\n- Safetensors version: 0.5.3\n- Accelerate version: 1.9.0\n- Accelerate config: \tnot found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.7.1+cu126 (CUDA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: \n- Using GPU in script?: \n- GPU type: NVIDIA H100 80GB HBM3\n```\n\nHi,\n\nThe transformers model class for 'gemma-3n` has issues as below (pasting stacktrace):\n\n```\n trainer.train()\n ~~~~~~~~~~~~~^^\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/transformers/trainer.py\", line 2237, in train\n return inner_training_loop(\n args=args,\n ...<2 lines>...\n ignore_keys_for_eval=ignore_keys_for_eval,\n )\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/transformers/trainer.py\", line 2578, in _inner_training_loop\n tr_loss_step = self.training_step(model, inputs, num_items_in_batch)\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/trl/trainer/sft_trainer.py\", line 914, in training_step\n return super().training_step(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/transformers/trainer.py\", line 3792, in training_step\n loss = self.compute_loss(model, inputs, num_items_in_batch=num_items_in_batch)\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/trl/trainer/sft_trainer.py\", line 868, in compute_loss\n (loss, outputs) = super().compute_loss(\n ~~~~~~~~~~~~~~~~~~~~^\n model, inputs, return_outputs=True, num_items_in_batch=num_items_in_batch\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n )\n ^\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/transformers/trainer.py\", line 3879, in compute_loss\n outputs = model(**inputs)\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/accelerate/utils/operations.py\", line 818, in forward\n return model_forward(*args, **kwargs)\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/accelerate/utils/operations.py\", line 806, in __call__\n return convert_to_fp32(self.model_forward(*args, **kwargs))\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/torch/amp/autocast_mode.py\", line 44, in decorate_autocast\n return func(*args, **kwargs)\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/peft/peft_model.py\", line 1850, in forward\n return self.base_model(\n ~~~~~~~~~~~~~~~^\n input_ids=input_ids,\n ^^^^^^^^^^^^^^^^^^^^\n ...<6 lines>...\n **kwargs,\n ^^^^^^^^^\n )\n ^\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/peft/tuners/tuners_utils.py\", line 222, in forward\n return self.model.forward(*args, **kwargs)\n ~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/transformers/utils/generic.py\", line 961, in wrapper\n output = func(self, *args, **kwargs)\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/transformers/models/gemma3n/modeling_gemma3n.py\", line 2276, in forward\n outputs = self.model(\n input_ids=input_ids,\n ...<14 lines>...\n **lm_kwargs,\n )\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/teamspace/studios/this_studio/.venv/lib/python3.13",
"url": "https://github.com/huggingface/transformers/issues/39921",
"state": "open",
"labels": [
"Usage",
"Good Second Issue",
"bug"
],
"created_at": "2025-08-05T14:43:37Z",
"updated_at": "2025-08-19T19:37:39Z",
"comments": 14,
"user": "debasisdwivedy"
},
{
"repo": "huggingface/transformers",
"number": 39910,
"title": "Question: Llama4 weight reshaping",
"body": "Hi all\n\nI am trying to extract the original Llama4 MoE weights, specifically:\n\n- `experts.w1` (aka `experts.moe_w_in_eD_F`)\n- `experts.w3` (aka `experts.moe_w_swiglu_eD_F`)\n\nI need both of these in the shape `[E, D, N]`, where:\n\n- E is the number of experts (16 for Scout)\n- D is the embedding dimension (5120)\n- N is the intermediate dimension (8192)\n\nI tried just splitting `experts.gate_up_proj` in half along the last dimension to get w1 and w3, but although the dimensions match, the model is outputting nonsense, so I assume the actual order of the weights is wrong.\n\nCould someone help me make sense of this snippet (from `convert_llama4_weights_to_hf`)?\nWhy is this hard coded indexing / reshaping being done and do you have any suggestions for how to get the original weight back?\n\n```python\nelif re.search(r\"(gate|up)_proj\", new_key):\n path = new_key.split(\".\")\n gate_key = re.sub(r\"(gate|up)_proj\", lambda m: \"gate_proj\", new_key)\n up_key = re.sub(r\"(gate|up)_proj\", lambda m: \"up_proj\", new_key)\n if gate_key == new_key:\n state_dict[new_key] = torch.cat(current_parameter, dim=concat_dim)\n elif new_key == up_key:\n if \"experts\" not in new_key:\n state_dict[new_key] = torch.cat(current_parameter, dim=concat_dim)\n else:\n # gate_proj = moe_w_in_eD_F = w1\n gate_proj = state_dict.pop(gate_key)\n gate_proj = [\n gate_proj.reshape(num_experts, -1, 8, 1024)[:, :, k, :].reshape(num_experts, -1, 1024)\n for k in range(8)\n ]\n gate_proj = torch.cat(gate_proj, dim=-1)\n\n # up_proj = moe_w_swiglu_eD_F = w3\n up_proj = [\n k.reshape(num_experts, -1, 8, 1024).reshape(num_experts, -1, 1024)\n for k in current_parameter\n ]\n up_proj = torch.cat(up_proj, dim=-1)\n\n gate_up_proj = torch.cat((gate_proj, up_proj), dim=-1)\n new_key = new_key.replace(\"up_proj\", \"gate_up_proj\")\n state_dict[new_key] = gate_up_proj.contiguous()\n\n tqdm.write(f\"Processing: {key.ljust(50)} ->\\t {new_key}, {state_dict[new_key].shape}\")\n```\n\nThank you!",
"url": "https://github.com/huggingface/transformers/issues/39910",
"state": "closed",
"labels": [],
"created_at": "2025-08-05T10:19:25Z",
"updated_at": "2025-08-13T09:35:52Z",
"comments": 0,
"user": "gskorokhod"
},
{
"repo": "huggingface/datasets",
"number": 7724,
"title": "Can not stepinto load_dataset.py?",
"body": "I set a breakpoint in \"load_dataset.py\" and try to debug my data load codes, but it does not stop at any breakpoints, so \"load_dataset.py\" can not be stepped into ?\n\n",
"url": "https://github.com/huggingface/datasets/issues/7724",
"state": "open",
"labels": [],
"created_at": "2025-08-05T09:28:51Z",
"updated_at": "2025-08-05T09:28:51Z",
"comments": 0,
"user": "micklexqg"
},
{
"repo": "huggingface/lerobot",
"number": 1670,
"title": "How does leroBot address the issue of training heterogeneous datasets?",
"body": "Specifically, suppose I have a dataset A and dataset B. In dataset A, both the state and action are represented as (x, y, z, gripper), where x, y, and z denote the distances moved along the x, y, and z axes, respectively, and gripper represents the on/off state of the gripper. In dataset B, both the state and action are the angles of the corresponding joints of the robotic arm. How can I use these two datasets together for training?",
"url": "https://github.com/huggingface/lerobot/issues/1670",
"state": "open",
"labels": [
"question",
"processor"
],
"created_at": "2025-08-05T08:20:08Z",
"updated_at": "2025-08-12T09:01:57Z",
"user": "mahao18cm"
},
{
"repo": "huggingface/lerobot",
"number": 1667,
"title": "How many episode to have a good result of SmolVLA",
"body": "### System Info\n\n```Shell\nHello, I'm trying to do a simple task like dual hand pick banana to a basket using SmolVLA\uff0cmay I know how many episode to train for having a good result\uff1f\n\nMany thanks\nJulien\n```\n### Reproduction\n\nI've used 100 episode for training, looks like the arm can not pick the banana accurately, sometimes the arms just stay on the head of banana\n\n### Expected behavior\n\nleft hand pick banana and hand it to right hand then right hand put banana into basket",
"url": "https://github.com/huggingface/lerobot/issues/1667",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-08-05T05:12:12Z",
"updated_at": "2025-10-17T11:27:14Z",
"user": "chejulien"
},
{
"repo": "huggingface/lerobot",
"number": 1666,
"title": "Please add multi gpu training support",
"body": "MultiGPU training currently does not work with lerobot as mentioned here https://github.com/huggingface/lerobot/issues/1377\n\nPlease add this support.",
"url": "https://github.com/huggingface/lerobot/issues/1666",
"state": "closed",
"labels": [
"enhancement",
"question",
"policies"
],
"created_at": "2025-08-04T18:06:40Z",
"updated_at": "2025-10-17T09:53:59Z",
"user": "nahidalam"
},
{
"repo": "huggingface/lerobot",
"number": 1663,
"title": "No way to train on subset of features",
"body": "Currently, when loading a policy from a config.json, the input_features seem to be ignored and re-generated from the dataset provided. However, it may not always be desirable to train on all features, perhaps if I have multiple camera views but I only want to train on one.\n\nI would prefer that config.json features are not overwritten, but this would be a breaking change. Do you have suggestions on how we could implement this behavior?",
"url": "https://github.com/huggingface/lerobot/issues/1663",
"state": "open",
"labels": [
"question",
"policies",
"processor"
],
"created_at": "2025-08-04T15:19:35Z",
"updated_at": "2025-08-12T09:03:47Z",
"user": "atyshka"
},
{
"repo": "huggingface/diffusers",
"number": 12060,
"title": "Is there any DiT block defined in the huggingface/diffusers OR huggingface/transformers project?",
"body": "**Is your feature request related to a problem? Please describe.**\nI want to make some experiments about DiT based flow-matching model, I need an implementation of the common DiT block, but did not found it in both huggingface/diffusers and huggingface/transformers. Is there any implementation about it with just some other file names?\n\n**Describe the solution you'd like.**\nA clear DiT implementation\n\n**Describe alternatives you've considered.**\n\n\n**Additional context.**\n\n",
"url": "https://github.com/huggingface/diffusers/issues/12060",
"state": "open",
"labels": [],
"created_at": "2025-08-04T09:40:43Z",
"updated_at": "2025-08-04T10:19:00Z",
"comments": 2,
"user": "JohnHerry"
},
{
"repo": "huggingface/diffusers",
"number": 12052,
"title": "Wan 2.2 with LightX2V offloading tries to multiply tensors from different devices and fails",
"body": "### Describe the bug\n\nAfter @sayakpaul great work in https://github.com/huggingface/diffusers/pull/12040 LightX2V now works. However what doesn't work is adding both a lora and offloading to the transformer_2. I can get away with either (i.e. offload both transformers but add a lora only to transformer and NOT to transformer_2, OR offload just transformer and add a lora to both transformer_2 and transformer). \n\nHowever offloading transformer_2 is quite important, since it causes 2x the VRAM to be used, and even a Q4_K_S model with LightX2V will use >24gb vram (as opposed to <9GB VRAM as in ComfyUI).\n\n### Reproduction\n\nThe script is the same as the one posted by Paul in the #12040 PR with the addition of offloading\n\n```python\nimport torch\nfrom diffusers import WanImageToVideoPipeline\nfrom huggingface_hub import hf_hub_download\nimport requests\nfrom PIL import Image\nfrom diffusers.loaders.lora_conversion_utils import _convert_non_diffusers_wan_lora_to_diffusers\nfrom io import BytesIO\nimport safetensors.torch\n\n# Load a basic transformer model\npipe = WanImageToVideoPipeline.from_pretrained(\n \"Wan-AI/Wan2.2-I2V-A14B-Diffusers\",\n torch_dtype=torch.bfloat16\n)\n\nlora_path = hf_hub_download(\n repo_id=\"Kijai/WanVideo_comfy\",\n filename=\"Lightx2v/lightx2v_I2V_14B_480p_cfg_step_distill_rank128_bf16.safetensors\"\n)\n\n# This is what is different\n\nself.pipe.vae.enable_group_offload(onload_device=onload_device, offload_device=offload_device, offload_type=\"leaf_level\")\nself.pipe.transformer.enable_group_offload(onload_device=onload_device, offload_device=offload_device, offload_type=\"leaf_level\")\n\n# Without this line it works but uses 2x the VRAM\nself.pipe.transformer_2.enable_group_offload(onload_device=onload_device, offload_device=offload_device, offload_type=\"leaf_level\")\n\nself.pipe.text_encoder.enable_group_offload(onload_device=onload_device, offload_device=offload_device, offload_type=\"leaf_level\")\n\npipe.to(\"cuda\")\n\npipe.load_lora_weights(lora_path)\n# print(pipe.transformer.__class__.__name__)\n# print(pipe.transformer.peft_config)\norg_state_dict = safetensors.torch.load_file(lora_path)\nconverted_state_dict = _convert_non_diffusers_wan_lora_to_diffusers(org_state_dict)\npipe.transformer_2.load_lora_adapter(converted_state_dict)\n\nimage_url = \"https://cloud.inference.sh/u/4mg21r6ta37mpaz6ktzwtt8krr/01k1g7k73eebnrmzmc6h0bghq6.png\"\nresponse = requests.get(image_url)\ninput_image = Image.open(BytesIO(response.content)).convert(\"RGB\")\n\nframes = pipe(input_image, \"animate\", num_inference_steps=4, guidance_scale=1.0)\n```\n\n### Logs\n\n```shell\n[t+1m44s256ms] [ERROR] Traceback (most recent call last):\n[t+1m44s256ms] File \"/server/tasks.py\", line 50, in run_task\n[t+1m44s256ms] output = await result\n[t+1m44s256ms] ^^^^^^^^^^^^\n[t+1m44s256ms] File \"/inferencesh/apps/gpu/65b8e0w0x60df8we0x6njqx9kc/src/inference.py\", line 424, in run\n[t+1m44s256ms] output = self.pipe(\n[t+1m44s256ms] ^^^^^^^^^^\n[t+1m44s256ms] File \"/inferencesh/apps/gpu/65b8e0w0x60df8we0x6njqx9kc/venv/3.12/lib/python3.12/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n[t+1m44s256ms] return func(*args, **kwargs)\n[t+1m44s256ms] ^^^^^^^^^^^^^^^^^^^^^\n[t+1m44s256ms] File \"/inferencesh/apps/gpu/65b8e0w0x60df8we0x6njqx9kc/venv/3.12/lib/python3.12/site-packages/diffusers/pipelines/wan/pipeline_wan_i2v.py\", line 754, in __call__\n[t+1m44s256ms] noise_pred = current_model(\n[t+1m44s256ms] ^^^^^^^^^^^^^^\n[t+1m44s256ms] File \"/inferencesh/apps/gpu/65b8e0w0x60df8we0x6njqx9kc/venv/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n[t+1m44s256ms] return self._call_impl(*args, **kwargs)\n[t+1m44s256ms] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[t+1m44s256ms] File \"/inferencesh/apps/gpu/65b8e0w0x60df8we0x6njqx9kc/venv/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n[t+1m44s256ms] return forward_call(*args, **kwargs)\n[t+1m44s256ms] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[t+1m44s256ms] File \"/inferencesh/apps/gpu/65b8e0w0x60df8we0x6njqx9kc/venv/3.12/lib/python3.12/site-packages/diffusers/hooks/hooks.py\", line 189, in new_forward\n[t+1m44s256ms] output = function_reference.forward(*args, **kwargs)\n[t+1m44s256ms] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n[t+1m44s256ms] File \"/inferencesh/apps/gpu/65b8e0w0x60df8we0x6njqx9kc/venv/3.12/lib/python3.12/site-packages/diffusers/models/transformers/transformer_wan.py\", line 639, in forward\n[t+1m44s256ms] temb, timestep_proj, encoder_hidden_states, encoder_hidden_states_image = self.condition_embedder(\n[t+1m44s256ms] ^^^^^^^^^^^^^^^^^^^^^^^^\n[t+1m44s256ms] File \"/inferencesh/apps/gpu/65b8e0w0x60df8we0x6njqx9kc/venv/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n[t+1m44s256ms] return self._",
"url": "https://github.com/huggingface/diffusers/issues/12052",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-08-03T12:43:13Z",
"updated_at": "2025-08-11T15:53:41Z",
"comments": 4,
"user": "luke14free"
},
{
"repo": "huggingface/peft",
"number": 2699,
"title": "UserWarning: Found missing adapter keys while loading the checkpoint",
"body": "I have been fine-tuning different LLM models (mainly Llama family) since last year and use peft with lora config all the time with no issues. \nJust recently I was fine-tuning the llama 70B on multiple GPU using accelerate then saving the adapter once training is done. (This was always my setup since last year)\n\nHowever now I want to load the adapter into the base model as follows:\n\n```\nbase_model = AutoModelForCausalLM.from_pretrained(model_id, dtype= torch.float16, device_map = 'auto', attn_implementation = 'flash_attention_2')\n\nmodel = PeftModel.from_pretrained(base_model, adapter_path)\n```\nNow I am getting this warning:\n```\nUserWarning: Found missing adapter keys while loading the checkpoint: \n```\nThen it lists some Lora weights. I tried changing LoraConfig parameters but still the problem\nPersists.\nCan anyone please tell me what is the issue here and how to fix it.\n\nI am using the latest version of peft, transformers, accelerate,\ntrl\n\nNote: I am also using the same format for model during the training and inference.\n\nI have already looked at this and seems same issue, but I load my model using AutoModelForCasaulLM in both cases:\nhttps://github.com/huggingface/peft/issues/2566\n\n\nNote: This is the warning: `[base_model.model.model.layers.0.self_attn, q_proj.lora_A.default.weight, base_model.model.model.layers.0.self_attn, q_proj.lora_B.default.weight, base_model.model.model.layers.0.self_attn, k_proj.lora_A.default.weight, base_model.model.model.layers.0.self_attn, k_proj.lora_B.default.weight`, ...",
"url": "https://github.com/huggingface/peft/issues/2699",
"state": "closed",
"labels": [],
"created_at": "2025-08-02T20:49:31Z",
"updated_at": "2025-11-09T15:03:46Z",
"comments": 41,
"user": "manitadayon"
},
{
"repo": "huggingface/diffusers",
"number": 12044,
"title": "AttributeError: 'bool' object has no attribute '__module__'. Did you mean: '__mod__'?",
"body": "I am train the Flux.1-dev model and get this error. I found the solution to bring diffuser to version 0.21.0 but then it would beconflict with some other libraries. Is there any solution for this?\n\n```\nTraceback (most recent call last):\n File \"/home/quyetnv/t2i/ai-toolkit/run.py\", line 120, in \n main()\n File \"/home/quyetnv/t2i/ai-toolkit/run.py\", line 108, in main\n raise e\n File \"/home/quyetnv/t2i/ai-toolkit/run.py\", line 96, in main\n job.run()\n File \"/home/quyetnv/t2i/ai-toolkit/jobs/ExtensionJob.py\", line 22, in run\n process.run()\n File \"/home/quyetnv/t2i/ai-toolkit/jobs/process/BaseSDTrainProcess.py\", line 1518, in run\n self.sd.load_model()\n File \"/home/quyetnv/t2i/ai-toolkit/toolkit/stable_diffusion_model.py\", line 788, in load_model\n pipe: Pipe = Pipe(\n File \"/home/quyetnv/.venv/lib/python3.10/site-packages/diffusers/pipelines/flux/pipeline_flux.py\", line 197, in __init__\n self.register_modules(\n File \"/home/quyetnv/.venv/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py\", line 212, in register_modules\n library, class_name = _fetch_class_library_tuple(module)\n File \"/home/quyetnv/.venv/lib/python3.10/site-packages/diffusers/pipelines/pipeline_loading_utils.py\", line 877, in _fetch_class_library_tuple\n library = not_compiled_module.__module__.split(\".\")[0]\nAttributeError: 'bool' object has no attribute '__module__'. Did you mean: '__mod__'?\n``` \nmy version diffusers was installed from requirement of ai-toolkit is 0.35.0 dev3",
"url": "https://github.com/huggingface/diffusers/issues/12044",
"state": "closed",
"labels": [],
"created_at": "2025-08-02T01:37:30Z",
"updated_at": "2025-08-21T01:27:19Z",
"comments": 3,
"user": "qngv"
},
{
"repo": "huggingface/optimum",
"number": 2333,
"title": "Support for exporting t5gemma-2b-2b-prefixlm-it to onnx",
"body": "### Feature request\n\nI\u2019ve tried to export t5gemma-2b-2b-prefixlm-it to onnx using optimum. But it outputs: ValueError: Trying to export a t5gemma model, that is a custom or unsupported architecture, but no custom onnx configuration was passed as `custom_onnx_configs`. Please refer to https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#custom-export-of-transformers-models for an example on how to export custom models. Please open an issue at https://github.com/huggingface/optimum/issues if you would like the model type t5gemma to be supported natively in the ONNX export.\n\nTask: \"text2text-generation\"\n\n### Motivation\n\nI\u2019ve tried, but nothing works...\n\n### Your contribution\n\nconfig.json\n\n{\n \"architectures\": [\n \"T5GemmaForConditionalGeneration\"\n ],\n \"classifier_dropout_rate\": 0.0,\n \"decoder\": {\n \"attention_bias\": false,\n \"attention_dropout\": 0.0,\n \"attn_logit_softcapping\": 50.0,\n \"classifier_dropout_rate\": 0.0,\n \"cross_attention_hidden_size\": 2304,\n \"dropout_rate\": 0.0,\n \"final_logit_softcapping\": 30.0,\n \"head_dim\": 256,\n \"hidden_activation\": \"gelu_pytorch_tanh\",\n \"hidden_size\": 2304,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 9216,\n \"is_decoder\": true,\n \"layer_types\": [\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\"\n ],\n \"max_position_embeddings\": 8192,\n \"model_type\": \"t5_gemma_module\",\n \"num_attention_heads\": 8,\n \"num_hidden_layers\": 26,\n \"num_key_value_heads\": 4,\n \"query_pre_attn_scalar\": 256,\n \"rms_norm_eps\": 1e-06,\n \"rope_theta\": 10000.0,\n \"sliding_window\": 4096,\n \"torch_dtype\": \"bfloat16\",\n \"use_cache\": true,\n \"vocab_size\": 256000\n },\n \"dropout_rate\": 0.0,\n \"encoder\": {\n \"attention_bias\": false,\n \"attention_dropout\": 0.0,\n \"attn_logit_softcapping\": 50.0,\n \"classifier_dropout_rate\": 0.0,\n \"dropout_rate\": 0.0,\n \"final_logit_softcapping\": 30.0,\n \"head_dim\": 256,\n \"hidden_activation\": \"gelu_pytorch_tanh\",\n \"hidden_size\": 2304,\n \"initializer_range\": 0.02,\n \"intermediate_size\": 9216,\n \"layer_types\": [\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\",\n \"sliding_attention\",\n \"full_attention\"\n ],\n \"max_position_embeddings\": 8192,\n \"model_type\": \"t5_gemma_module\",\n \"num_attention_heads\": 8,\n \"num_hidden_layers\": 26,\n \"num_key_value_heads\": 4,\n \"query_pre_attn_scalar\": 256,\n \"rms_norm_eps\": 1e-06,\n \"rope_theta\": 10000.0,\n \"sliding_window\": 4096,\n \"torch_dtype\": \"bfloat16\",\n \"use_cache\": true,\n \"vocab_size\": 256000\n },\n \"eos_token_id\": [\n 1,\n 107\n ],\n \"initializer_range\": 0.02,\n \"is_encoder_decoder\": true,\n \"model_type\": \"t5gemma\",\n \"pad_token_id\": 0,\n \"torch_dtype\": \"bfloat16\",\n \"transformers_version\": \"4.53.0.dev0\",\n \"use_cache\": true\n}",
"url": "https://github.com/huggingface/optimum/issues/2333",
"state": "closed",
"labels": [
"Stale"
],
"created_at": "2025-08-01T16:39:52Z",
"updated_at": "2026-01-03T02:51:13Z",
"comments": 2,
"user": "botan-r"
},
{
"repo": "huggingface/transformers",
"number": 39842,
"title": "Expected behavior of `compute_result` is hard to expect and inconsistent",
"body": "In trainer there exists a parameter `compute_result` given to `compute_metrics` when `batch_eval_metrics` is given to True.\n\nhttps://github.com/huggingface/transformers/blob/1e0665a191f73f6b002209c3dfcda478baac6bac/src/transformers/trainer.py#L370-L375\n\nI think there are several problems for `compute_result`,\n1. User can't expect (1) what happen if `batch_eval_metrics` is given (2) what is given to `compute_result` and when it change from True or False (3) what's HF's intention to implement `compute_metrics` with `compute_result`. since there are very few (only 3 line) instruction for this.\n2. `compute_metrics` sometimes called with `compute_result` and sometimes not, EVEN WHEN `batch_eval_metrics` is present. See below lines. \n\nhttps://github.com/huggingface/transformers/blob/1e0665a191f73f6b002209c3dfcda478baac6bac/src/transformers/trainer.py#L4534-L4547\n\nCreating this issue because I spend long time figuring out this.",
"url": "https://github.com/huggingface/transformers/issues/39842",
"state": "closed",
"labels": [],
"created_at": "2025-08-01T11:43:28Z",
"updated_at": "2025-10-04T08:02:41Z",
"comments": 3,
"user": "MilkClouds"
},
{
"repo": "huggingface/transformers",
"number": 39841,
"title": "MistralCommonTokenizer does not match PreTrainedTokenizer",
"body": "### System Info\n\non docker\nos: ubuntu 24.04\ntransformers: 4.55.0.dev0\nmistral_common: 1.8.3\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nCommand to lauch container:\n\n```bash\ndocker run --gpus all -p 8000:8000 --ipc=host vllm/vllm-openai:latest --model mistralai/Voxtral-Mini-3B-2507\n```\n\n\n### Expected behavior\n\nThe output will finish in:\n\n```bash\nvllm-1 | File \"/usr/local/lib/python3.12/dist-packages/vllm/transformers_utils/tokenizer_group.py\", line 24, in __init__ \nvllm-1 | self.tokenizer = get_tokenizer(self.tokenizer_id, **tokenizer_config)\nvllm-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nvllm-1 | File \"/usr/local/lib/python3.12/dist-packages/vllm/transformers_utils/tokenizer.py\", line 309, in get_tokenizer\nvllm-1 | tokenizer = get_cached_tokenizer(tokenizer)\nvllm-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nvllm-1 | File \"/usr/local/lib/python3.12/dist-packages/vllm/transformers_utils/tokenizer.py\", line 104, in get_cached_tokenizer\nvllm-1 | tokenizer_all_special_tokens = tokenizer.all_special_tokens\nvllm-1 | ^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nvllm-1 | AttributeError: 'MistralCommonTokenizer' object has no attribute 'all_special_tokens'. Did you mean: '_all_special_ids'?\n```\n\nvLLM docker server uses the pretrained tokenizer format:\nhttps://github.com/vllm-project/vllm/blob/49314869887e169be080201ab8bcda14e745c080/vllm/transformers_utils/tokenizer.py#L97-L101\n\nWhich must include: `all_special_ids`, `all_special_tokens`, `all_special_tokens_extended` default properties. However, MistralCommonTokenizer does not have implemented them. Is there a plan to standarize both tokenizers?\n",
"url": "https://github.com/huggingface/transformers/issues/39841",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-08-01T09:16:24Z",
"updated_at": "2025-11-23T08:03:33Z",
"comments": 3,
"user": "Fhrozen"
},
{
"repo": "huggingface/transformers",
"number": 39839,
"title": "pack_image_features RuntimeError when vision_feature_select_strategy=\"full\"",
"body": "### System Info\n\ntransformers 4.54.0\n\n### Who can help?\n\n@zucchini-nlp \n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```\nfrom transformers.models.llava_next import LlavaNextForConditionalGeneration, LlavaNextProcessor\nfrom PIL import Image\nimport requests\nimport torch\n\nmodel = LlavaNextForConditionalGeneration.from_pretrained(\n \"llava-hf/llava-v1.6-vicuna-7b-hf\", \n vision_feature_select_strategy=\"full\",\n torch_dtype=torch.float16,\n device_map=\"auto\",\n )\nprocessor = LlavaNextProcessor.from_pretrained(\"llava-hf/llava-v1.6-vicuna-7b-hf\")\n\nimage = Image.open(\"/data/coco/train2017/000000000009.jpg\")\nprompt = \"USER: \\nWhat is shown in this image? ASSISTANT:\"\ninputs = processor(images=image, text=prompt, truncation=True, return_tensors=\"pt\", vision_feature_select_strategy = \"full\").to(\"cuda\")\n\ninput_embeds = model(inputs.input_ids, pixel_values=inputs.pixel_values, image_sizes=inputs.image_sizes, vision_feature_select_strategy=\"full\")\n```\n\n### Expected behavior\n\nI encountered a bug when running to the line \n`input_embeds = model(inputs.input_ids, pixel_values=inputs.pixel_values, image_sizes=inputs.image_sizes, vision_feature_select_strategy=\"full\")`\nI got:\n```\n in pack_image_features\n image_feature = image_feature.view(num_patch_height, num_patch_width, height, width, -1)\nRuntimeError: shape '[2, 2, 24, 24, -1]' is invalid for input of size 9453568\n```\n\n\nthe shape of image_feature is [4, 577, 4096] currently, I want to know how to fix this?",
"url": "https://github.com/huggingface/transformers/issues/39839",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-08-01T07:55:40Z",
"updated_at": "2025-09-08T08:02:56Z",
"comments": 2,
"user": "llnnnnnn"
},
{
"repo": "huggingface/gsplat.js",
"number": 117,
"title": "How to generate a Mesh mesh?",
"body": "I need a scene where Gaussian Splatting and Mesh are mixed, and I don't know if GSPLAT generates Mesh or not.",
"url": "https://github.com/huggingface/gsplat.js/issues/117",
"state": "open",
"labels": [],
"created_at": "2025-08-01T03:29:22Z",
"updated_at": "2025-08-01T03:29:22Z",
"user": "ZXStudio"
},
{
"repo": "huggingface/diffusers",
"number": 12038,
"title": "Dataset structure for train_text_to_image_lora.py",
"body": "Hello. I am trying to use **train_text_to_image_lora.py** script following the instructions https://github.com/huggingface/diffusers/tree/main/examples/text_to_image\n\nI get errors on data structure and don't know what is the issue on my side.\nI have a folder **data** where I have folder **image** and **csv** file.\n\nC:/Users/XXX//data/\n\n\u251c\u2500\u2500 images/\n\u2502 \u251c\u2500\u2500 image1.jpg\n\u2502 \u251c\u2500\u2500 image2.jpg\n\u2502 \u2514\u2500\u2500 ...\n\u2514\u2500\u2500 captions.csv\n\n**Image** folder contain images and **csv** file contains two columns (image names and captions)\n\nimage, caption\nimage1.jpg, A dragon flying through fire\nimage2.jpg, A knight in shining armor\n\nPlease can you let me know how I should organize my dataset to be able to run the training.\n",
"url": "https://github.com/huggingface/diffusers/issues/12038",
"state": "open",
"labels": [],
"created_at": "2025-07-31T16:10:38Z",
"updated_at": "2025-08-01T16:44:48Z",
"comments": 1,
"user": "HripsimeS"
},
{
"repo": "huggingface/lerobot",
"number": 1632,
"title": "Are there plans to support distributed training?",
"body": "[train.py](https://github.com/huggingface/lerobot/blob/main/src/lerobot/scripts/train.py) currently only supports single-GPU training. Is there a plan to support distributed training in the future?",
"url": "https://github.com/huggingface/lerobot/issues/1632",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-31T03:31:46Z",
"updated_at": "2025-10-17T12:10:40Z",
"user": "Hukongtao"
},
{
"repo": "huggingface/candle",
"number": 3039,
"title": "Request support for Qwen2.5-vl or Fast-VLM",
"body": "I'm trying to call some image-to-text visual models using candle, if anyone knows how to use Qwen2.5-vl or Fast-VLM, can you share it? Appreciate",
"url": "https://github.com/huggingface/candle/issues/3039",
"state": "open",
"labels": [],
"created_at": "2025-07-31T02:41:33Z",
"updated_at": "2025-08-04T12:21:35Z",
"comments": 1,
"user": "826327700"
},
{
"repo": "huggingface/transformers",
"number": 39801,
"title": "ValueError: This model does not support cache_implementation='static'. Please check the following issue: https://github.com/huggingface/transformers/issues/28981",
"body": "### System Info\n\n_prepare_cache_for_generation\n raise ValueError(\nValueError: This model does not support cache_implementation='static'. Please check the following issue: https://github.com/huggingface/transformers/issues/28981\n\nI got this error and i have no clue of how to solve it. I tried different implementations from different people and I always have the same problem.\n\nI used this code: https://mer.vin/2024/11/finetune-llama-3-2-vision-radiology-images/\n\n\nimport os\nfrom unsloth import FastVisionModel\nimport torch\nfrom datasets import load_dataset\nfrom transformers import TextStreamer\nfrom unsloth import is_bf16_supported\nfrom unsloth.trainer import UnslothVisionDataCollator\nfrom trl import SFTTrainer, SFTConfig\n\n# 1. Load the model\n\nmodel, tokenizer = FastVisionModel.from_pretrained(\n \"unsloth/Llama-3.2-11B-Vision-Instruct\",\n load_in_4bit = True,\n use_gradient_checkpointing = \"unsloth\",\n)\n\nmodel = FastVisionModel.get_peft_model(\n model,\n finetune_vision_layers = True,\n finetune_language_layers = True,\n finetune_attention_modules = True,\n finetune_mlp_modules = True,\n r = 16,\n lora_alpha = 16,\n lora_dropout = 0,\n bias = \"none\",\n random_state = 3407,\n use_rslora = False,\n loftq_config = None,\n)\n\n# 2. Load the dataset\n\ndataset = load_dataset(\"unsloth/Radiology_mini\", split = \"train\")\ninstruction = \"You are an expert radiographer. Describe accurately what you see in this image.\"\n\ndef convert_to_conversation(sample):\n conversation = [\n { \"role\": \"user\",\n \"content\" : [\n {\"type\" : \"text\", \"text\" : instruction},\n {\"type\" : \"image\", \"image\" : sample[\"image\"]} ]\n },\n { \"role\" : \"assistant\",\n \"content\" : [\n {\"type\" : \"text\", \"text\" : sample[\"caption\"]} ]\n },\n ]\n return { \"messages\" : conversation }\npass\n\nconverted_dataset = [convert_to_conversation(sample) for sample in dataset]\n\n# 3. Before training\n\nFastVisionModel.for_inference(model)\nimage = dataset[0][\"image\"]\ninstruction = \"You are an expert radiographer. Describe accurately what you see in this image.\"\n\nmessages = [\n {\"role\": \"user\", \"content\": [\n {\"type\": \"image\"},\n {\"type\": \"text\", \"text\": instruction}\n ]}\n]\ninput_text = tokenizer.apply_chat_template(messages, add_generation_prompt = True)\ninputs = tokenizer(\n image,\n input_text,\n add_special_tokens = False,\n return_tensors = \"pt\",\n).to(\"cuda\")\n\nprint(\"\\nBefore training:\\n\")\n\ntext_streamer = TextStreamer(tokenizer, skip_prompt = True)\n_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 128,\n use_cache = True, temperature = 1.5, min_p = 0.1)\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\npip install unsloth\nexport HF_TOKEN=xxxxxxxxxxxxx\n\n### Expected behavior\n\nStart fine-tuning",
"url": "https://github.com/huggingface/transformers/issues/39801",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-30T20:59:45Z",
"updated_at": "2025-09-07T08:02:42Z",
"comments": 2,
"user": "jpitalopez"
},
{
"repo": "huggingface/lerobot",
"number": 1631,
"title": "\ud83e\udd5a Filtering Eggs on Moving Table: Dirt/Breakage Detection Feasibility",
"body": "Hi \ud83d\udc4b\n\nThanks a lot for your work on lerobot!\n\nI am exploring the use of lerobot to filter eggs based on dirt or breakage while they move past the robot on a conveyor table. The goal is to detect anomalies in real time and eventually eject faulty eggs.\n\nSome specific questions I have:\n\n* Do you have any advice or feedback on using lerobot in this kind of setup?\n* Are there known pros/cons with fast-moving objects and image-based anomaly detection?\n* Would it make sense to multiply robots along the line (e.g., several cameras/models at different angles or points)?\n* Is there support or a best practice for triggering actions (e.g. pneumatic ejection) once a faulty egg is detected?\n\nI am happy to fine-tune a model or adapt an existing one if that\u2019s viable. \n\nAny insights would be super helpful \ud83d\ude4f\n\nThanks again!",
"url": "https://github.com/huggingface/lerobot/issues/1631",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-30T18:35:12Z",
"updated_at": "2025-08-12T09:07:41Z",
"user": "KannarFr"
},
{
"repo": "huggingface/optimum",
"number": 2330,
"title": "Patch Release to support `transformers~=4.53`",
"body": "### System Info\n\n```shell\noptimum[onnxruntime-gpu]==1.26.1\ntorch==2.7.1\nvllm==0.10.0\n\ndocker run --rm -it --platform linux/amd64 ghcr.io/astral-sh/uv:debian bash\n```\n\n### Who can help?\n\n@JingyaHuang @echarlaix\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction (minimal, reproducible, runnable)\n\nThe latest release is more than 1 month old. It supports `transformers>=4.36,<4.53.0` with `onnxruntime-gpu` extra. This is incompatible with `vllm==0.10.0`, which requires `transformers>=4.53.2`. `vllm==0.10.0` is required to use with `torch==2.7.1`. My system is required to use `torch==2.7.1` due to the medium CVE in previous versions.\nhttps://nvd.nist.gov/vuln/detail/CVE-2025-2953\n\nIn the current main branch, the requirements has been changed to `transformers>=4.36,<4.54.0`, which would mitigate the issue.\n\nIs it possible to create a patch release based on the current main branch?\n\n```bash\n> uv pip compile <(echo \"optimum[onnxruntime-gpu]>=1.23\"; echo \"vllm>=0.10\")\n x No solution found when resolving dependencies:\n `-> Because only the following versions of optimum[onnxruntime-gpu] are available:\n optimum[onnxruntime-gpu]<=1.23.0\n optimum[onnxruntime-gpu]==1.23.1\n optimum[onnxruntime-gpu]==1.23.2\n optimum[onnxruntime-gpu]==1.23.3\n optimum[onnxruntime-gpu]==1.24.0\n optimum[onnxruntime-gpu]==1.25.0\n optimum[onnxruntime-gpu]==1.25.1\n optimum[onnxruntime-gpu]==1.25.2\n optimum[onnxruntime-gpu]==1.25.3\n optimum[onnxruntime-gpu]==1.26.0\n optimum[onnxruntime-gpu]==1.26.1\n and optimum[onnxruntime-gpu]>=1.23.0,<=1.23.2 depends on transformers<4.46.0, we can conclude that optimum[onnxruntime-gpu]>=1.23.0,<1.23.1\n depends on transformers<4.46.0.\n And because optimum[onnxruntime-gpu]>=1.23.1,<=1.23.2 depends on transformers<4.46.0 and transformers<4.46.0, we can conclude that\n optimum[onnxruntime-gpu]>=1.23.0,<1.23.3 depends on transformers<4.46.0.\n And because optimum[onnxruntime-gpu]==1.23.3 depends on transformers<4.47.0 and transformers>=4.36,<4.49.0, we can conclude that\n optimum[onnxruntime-gpu]>=1.23.0,<1.25.0 depends on transformers<4.49.0.\n And because optimum[onnxruntime-gpu]>=1.25.0,<=1.25.3 depends on transformers>=4.36,<4.52.0 and transformers>=4.36,<4.52.0, we can conclude that\n optimum[onnxruntime-gpu]>=1.23.0,<1.25.2 depends on transformers<4.52.0.\n And because optimum[onnxruntime-gpu]>=1.25.2,<=1.25.3 depends on transformers>=4.36,<4.52.0 and transformers>=4.36,<4.52.0, we can conclude that\n optimum[onnxruntime-gpu]>=1.23.0,<1.26.0 depends on transformers<4.52.0.\n And because optimum[onnxruntime-gpu]>=1.26.0 depends on transformers>=4.36,<4.53.0 and transformers>=4.36,<4.53.0, we can conclude that\n optimum[onnxruntime-gpu]>=1.23.0 depends on transformers<4.53.0.\n And because vllm==0.10.0 depends on transformers>=4.53.2 and only vllm<=0.10.0 is available, we can conclude that vllm>=0.10.0 and\n optimum[onnxruntime-gpu]>=1.23.0 are incompatible.\n And because you require optimum[onnxruntime-gpu]>=1.23 and vllm>=0.10.0, we can conclude that your requirements are unsatisfiable.\n```\n\n### Expected behavior\n\nAble to install `optimum[onnxruntime-gpu]>=1.26` and `vllm>=0.10.0`.\n```bash\n> uv pip compile <(echo \"optimum[onnxruntime-gpu] @ git+https://github.com/huggingface/optimum@689c0b5d38aabe265ab1eb334a6ca5bc3ca3574d\"; echo \"vllm>=0.10\")\nResolved 152 packages in 359ms\n# This file was autogenerated by uv via the following command:\n# uv pip compile /dev/fd/63\naiohappyeyeballs==2.6.1\n # via aiohttp\naiohttp==3.12.15\n # via\n # fsspec\n # vllm\naiosignal==1.4.0\n # via aiohttp\nannotated-types==0.7.0\n # via pydantic\nanyio==4.9.0\n # via\n # httpx\n # openai\n # starlette\n # watchfiles\nastor==0.8.1\n # via depyf\nattrs==25.3.0\n # via\n # aiohttp\n # jsonschema\n # referencing\nblake3==1.0.5\n # via vllm\ncachetools==6.1.0\n # via vllm\ncbor2==5.6.5\n # via vllm\ncertifi==2025.7.14\n # via\n # httpcore\n # httpx\n # requests\n # sentry-sdk\ncffi==1.17.1\n # via soundfile\ncharset-normalizer==3.4.2\n # via requests\nclick==8.2.1\n # via\n # ray\n # rich-toolkit\n # typer\n # uvicorn\ncloudpickle==3.1.1\n # via vllm\ncoloredlogs==15.0.1\n # via onnxruntime-gpu\ncompressed-tensors==0.10.2\n # via vllm\ncupy-cuda12x==13.5.1\n # via ray\ndatasets==4.0.0\n # via optimum\ndepyf==0.19.0\n # via vllm\ndill==0.3.8\n # via\n # datasets\n # depyf\n # multiprocess\ndiskcache==5.6.3\n # via vllm\ndistro==1.9.0\n # via openai\ndnspython==2.7.0\n # via email-validator\neinops==0.8.1\n # via vllm\nemail-validator==2.2.0\n # via\n # fastapi\n # pydantic",
"url": "https://github.com/huggingface/optimum/issues/2330",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-30T02:40:41Z",
"updated_at": "2025-07-31T02:54:31Z",
"comments": 1,
"user": "yxtay"
},
{
"repo": "huggingface/lerobot",
"number": 1622,
"title": "Why is LeRobot\u2019s policy ignoring additional camera streams despite custom `input_features`?",
"body": "I'm training a SO101 arm policy with 3 video streams (`front`, `above`, `gripper`) and a state vector. The dataset can be found at this [link](https://huggingface.co/datasets/aaron-ser/SO101-Dataset/tree/main). \n\nI created a custom JSON config (the `train_config.json` below) that explicitly lists the three visual streams under `policy.input_features`, and despite disabling the preset config loading with `\"use_policy_training_preset\": false`, the policy never takes into account any feed that isn't the front observations. Disabling the preset however is not mandatory as previous hackathons with multiple streams such as the [following](https://huggingface.co/LeRobot-worldwide-hackathon/91-AM-PM-smolvla-pouring-liquid/blob/main/train_config.json) used the preset config. \n\nI pass into `lerobot.scripts.train` the `train_config.json` file shared below with the `--config_path` parameter. Although the initial printout of the config is correct with all three streams, after training finishes, the saved `train_config.json` file inside `aaron-ser/SO101-Model` only contains:\n\n**aaron-ser/SO101-Model train_config.json snippet**\n```\n\"input_features\": {\n \"observation.state\": { ... },\n \"observation.images.front\": { ... },\n\"output_features\": { ... }\n```\n\nDropping the `above` and `gripper` streams although the HF dataset includes all three streams and I explicitly passed them in the JSON file. \n\nWhat internal step or configuration is overriding my custom `input_features` and keeping only the front camera? How can I ensure LeRobot trains on all provided video streams?\n\n**train_config.json**\n```\n{\n \"dataset\": {\n \"repo_id\": \"aaron-ser/SO101-Dataset\",\n \"root\": null,\n \"episodes\": null,\n \"image_transforms\": {\n \"enable\": false,\n \"max_num_transforms\": 3,\n \"random_order\": false,\n \"tfs\": {\n \"brightness\": {\n \"weight\": 1.0,\n \"type\": \"ColorJitter\",\n \"kwargs\": {\n \"brightness\": [\n 0.8,\n 1.2\n ]\n }\n },\n \"contrast\": {\n \"weight\": 1.0,\n \"type\": \"ColorJitter\",\n \"kwargs\": {\n \"contrast\": [\n 0.8,\n 1.2\n ]\n }\n },\n \"saturation\": {\n \"weight\": 1.0,\n \"type\": \"ColorJitter\",\n \"kwargs\": {\n \"saturation\": [\n 0.5,\n 1.5\n ]\n }\n },\n \"hue\": {\n \"weight\": 1.0,\n \"type\": \"ColorJitter\",\n \"kwargs\": {\n \"hue\": [\n -0.05,\n 0.05\n ]\n }\n },\n \"sharpness\": {\n \"weight\": 1.0,\n \"type\": \"SharpnessJitter\",\n \"kwargs\": {\n \"sharpness\": [\n 0.5,\n 1.5\n ]\n }\n }\n }\n },\n \"revision\": null,\n \"use_imagenet_stats\": true,\n \"video_backend\": \"torchcodec\"\n },\n \"env\": null,\n \"policy\": {\n \"type\": \"act\",\n \"n_obs_steps\": 1,\n \"normalization_mapping\": {\n \"VISUAL\": \"MEAN_STD\",\n \"STATE\": \"MEAN_STD\",\n \"ACTION\": \"MEAN_STD\"\n },\n \"input_features\": {\n \"observation.state\": {\n \"type\": \"STATE\",\n \"shape\": [\n 6\n ]\n },\n \"observation.images.front\": {\n \"type\": \"VISUAL\",\n \"shape\": [\n 3,\n 720,\n 1280\n ]\n },\n \"observation.images.above\": {\n \"type\": \"VISUAL\",\n \"shape\": [\n 3,\n 720,\n 1280\n ]\n },\n \"observation.images.gripper\": {\n \"type\": \"VISUAL\",\n \"shape\": [\n 3,\n 720,\n 1280\n ]\n }\n },\n \"output_features\": {\n \"action\": {\n \"type\": \"ACTION\",\n \"shape\": [\n 6\n ]\n }\n },\n \"device\": \"cuda\",\n \"use_amp\": false,\n \"push_to_hub\": true,\n \"repo_id\": \"aaron-ser/SO101-Model\",\n \"private\": null,\n \"tags\": null,\n \"license\": null,\n ",
"url": "https://github.com/huggingface/lerobot/issues/1622",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-29T14:07:14Z",
"updated_at": "2025-09-23T14:01:54Z",
"user": "Aaron-Serpilin"
},
{
"repo": "huggingface/trl",
"number": 3797,
"title": "How to view the training parameters after training is completed",
"body": "How to view the training parameters after training is completed\uff1fI am using GRPOTrainer for training, but after training multiple times, I have forgotten the parameters I set. How can I view the saved training parameters?",
"url": "https://github.com/huggingface/trl/issues/3797",
"state": "open",
"labels": [
"\u2753 question",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-07-29T09:42:52Z",
"updated_at": "2025-07-29T13:07:50Z",
"user": "Tuziking"
},
{
"repo": "huggingface/optimum",
"number": 2329,
"title": "Support for exporting paligemma to onnx",
"body": "### Feature request\n\nI\u2019ve tried to export google/paligemma-3b-mix-224 to onnx using optimum. But it outputs: \"ValueError: Trying to export a paligemma model, that is a custom or unsupported architecture, but no custom onnx configuration was passed as custom_onnx_configs. Please refer to https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#custom-export-of-transformers-models for an example on how to export custom models. Please open an \nissue at https://github.com/huggingface/optimum/issues if you would like the model type paligemma to be supported natively in the ONNX export.\"\n\n### Motivation\n\nI\u2019ve tried everything but nothing works =(\n(Using custom configs, using torch.onnx.export, etc)\n\n### Your contribution\n\nActually, it seems to me that I can\u2019t help\u2026 =(",
"url": "https://github.com/huggingface/optimum/issues/2329",
"state": "closed",
"labels": [
"Stale"
],
"created_at": "2025-07-29T08:58:41Z",
"updated_at": "2025-09-06T02:04:25Z",
"comments": 2,
"user": "DashaMed555"
},
{
"repo": "huggingface/transformers",
"number": 39744,
"title": "_supports_static_cache disappear",
"body": "### System Info\n\ntransformers main branch\n\n### Who can help?\n\n@ArthurZucker \n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nI see the attr `_supports_static_cache` disappeared in the model. I used to check if `model._supports_static_cache` before setting `cache_implementation=True`. For now, can I assume all models support static cache?\n\n### Expected behavior\n\nAll models support static cache as `_supports_static_cache` is deprecated. Or do we have other method to check if the model support static cache?",
"url": "https://github.com/huggingface/transformers/issues/39744",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-29T02:36:04Z",
"updated_at": "2025-07-29T08:17:00Z",
"comments": 4,
"user": "jiqing-feng"
},
{
"repo": "huggingface/lerobot",
"number": 1607,
"title": "how to control a so-101 with trained ACT model?",
"body": "https://huggingface.co/initie/test_pick_result \nThis is my pre-trained model for grabbing the switch on the desk by ACT model.\nHow to run this policy model on the Anaconda?\nAlready by way of example, \n\npython -m lerobot.record --robot.type=so101_follower \n--robot.port=COM3 \n--robot.id=ammd_follower_arm \n--robot.cameras=\"{ front: {type: opencv, index_or_path: 0, width: 640, height: 480, fps: 30}, side: {type: opencv, index_or_path: 1, width: 640, height: 480, fps: 30} }\" \n--display_data=True \n--dataset.repo_id=\"initie/eval_test_pick\" \n--dataset.single_task=\"Grab the switch\" \n--policy.path=initie/test_pick_result \n--teleop.type=so101_leader --teleop.port=COM5 \n--teleop.id=ammd_leader_arm --dataset.reset_time_s=5\n\nThis is the example code from Lerobot tutorial, but when i run these codes, I had to record 10 episodes again.\nI just wanna run a pre-trained model, not record an episode again. I'm curious about a simple code that only \"runs\" that model not including recording",
"url": "https://github.com/huggingface/lerobot/issues/1607",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-28T05:23:24Z",
"updated_at": "2025-10-15T03:28:50Z",
"user": "initia1013"
},
{
"repo": "huggingface/lerobot",
"number": 1602,
"title": "How to perform multi-GPU training for SMoVLA?",
"body": "I noticed that the paper used 4 GPUs for pretraining, but the current training code doesn\u2019t seem to support it. Could you provide the corresponding code?",
"url": "https://github.com/huggingface/lerobot/issues/1602",
"state": "closed",
"labels": [],
"created_at": "2025-07-27T09:46:04Z",
"updated_at": "2025-07-28T08:40:01Z",
"user": "QZepHyr"
},
{
"repo": "huggingface/hmtl",
"number": 72,
"title": "How to create a website ",
"body": "",
"url": "https://github.com/huggingface/hmtl/issues/72",
"state": "open",
"labels": [],
"created_at": "2025-07-27T09:30:22Z",
"updated_at": "2025-07-27T09:30:22Z",
"user": "Chi23-ike"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3304,
"title": "using trtllm-build instead of optimum-nvidia for engine building or optimum-nvidia wrong version ?",
"body": "\nHello,\n\nI'm experiencing significant issues when trying to use Text Generation Inference (TGI) with TensorRT-LLM as the backend.\n\n**Problem 1: Version Compatibility**\nI cannot use the latest version of TGI due to a known bug (see: https://github.com/huggingface/text-generation-inference/issues/3296).\n\nI'm therefore using version: `ghcr.io/huggingface/text-generation-inference:3.3.4-trtllm`\n\nHowever, this version uses TensorRT-LLM v0.17.0.post1, while the latest optimum-nvidia version ([[v0.1.0b9](https://github.com/huggingface/optimum-nvidia/releases/tag/v0.1.0b9)]) uses TensorRT-LLM 0.16.0.\n\nWhen I try to launch TGI with my engine built using optimum-nvidia, I get the following error:\n```\nroot@5ddf177112d7:/usr/local/tgi/bin# /usr/local/tgi/bin/text-generation-launcher --model-id \"/engines/llama-3.2-3b-instruct-optimum/GPU/engines\" --tokenizer-name \"/models/llama-3.2-3b-instruct\" --executor-worker \"/usr/local/tgi/bin/executorWorker\"\n2025-07-27T06:16:40.717109Z INFO text_generation_backends_trtllm: backends/trtllm/src/main.rs:293: Successfully retrieved tokenizer /models/llama-3.2-3b-instruct\n[2025-07-27 06:16:40.717] [info] [ffi.hpp:164] Initializing TGI - TensoRT-LLM Backend (v0.17.0.post1)\n[2025-07-27 06:16:40.747] [info] [ffi.hpp:173] [FFI] Detected 1 Nvidia GPU(s)\n[2025-07-27 06:16:40.758] [info] [backend.cpp:22] Detected single engine deployment, using leader mode\n[TensorRT-LLM][INFO] Engine version 0.16.0 found in the config file, assuming engine(s) built by new builder API.\n[TensorRT-LLM][INFO] Initializing MPI with thread mode 3\n[TensorRT-LLM][INFO] Initialized MPI\n[TensorRT-LLM][INFO] Refreshed the MPI local session\n[TensorRT-LLM][INFO] MPI size: 1, MPI local size: 1, rank: 0\n[TensorRT-LLM][INFO] Rank 0 is using GPU 0\n[TensorRT-LLM][INFO] TRTGptModel maxNumSequences: 64\n[TensorRT-LLM][INFO] TRTGptModel maxBatchSize: 64\n[TensorRT-LLM][INFO] TRTGptModel maxBeamWidth: 1\n[TensorRT-LLM][INFO] TRTGptModel maxSequenceLen: 4096\n[TensorRT-LLM][INFO] TRTGptModel maxDraftLen: 0\n[TensorRT-LLM][INFO] TRTGptModel mMaxAttentionWindowSize: (4096) * 28\n[TensorRT-LLM][INFO] TRTGptModel enableTrtOverlap: 0\n[TensorRT-LLM][INFO] TRTGptModel normalizeLogProbs: 1\n[TensorRT-LLM][INFO] TRTGptModel maxNumTokens: 262144\n[TensorRT-LLM][INFO] TRTGptModel maxInputLen: 4095 = maxSequenceLen - 1 since chunked context is enabled\n[TensorRT-LLM][INFO] TRTGptModel If model type is encoder, maxInputLen would be reset in trtEncoderModel to maxInputLen: 4096 = maxSequenceLen.\n[TensorRT-LLM][INFO] Capacity Scheduler Policy: MAX_UTILIZATION\n[TensorRT-LLM][INFO] Context Chunking Scheduler Policy: None\n[TensorRT-LLM][INFO] Loaded engine size: 6981 MiB\n[TensorRT-LLM][ERROR] IRuntime::deserializeCudaEngine: Error Code 6: API Usage Error (The engine plan file is not compatible with this version of TensorRT, expecting library version 10.8.0.43 got\n..)\nError: Runtime(\"[TensorRT-LLM][ERROR] Assertion failed: Failed to deserialize cuda engine. (/usr/src/text-generation-inference/target/release/build/text-generation-backends-trtllm-479f10d4b58ebb37/out/build/_deps/trtllm-src/cpp/tensorrt_llm/runtime/tllmRuntime.cpp:239)\")\n```\n\n**Problem 2: Building Engine with trtllm-build**\nI attempted to build my engine directly using `trtllm-build`, but when launching TGI, I encounter this error:\n\n```\n2025-07-27T06:15:55.033318Z INFO text_generation_backends_trtllm: backends/trtllm/src/main.rs:293: Successfully retrieved tokenizer /models/llama-3.2-3b-instruct\n[2025-07-27 06:15:55.034] [info] [ffi.hpp:164] Initializing TGI - TensoRT-LLM Backend (v0.17.0.post1)\n[2025-07-27 06:15:55.101] [info] [ffi.hpp:173] [FFI] Detected 1 Nvidia GPU(s)\nterminate called after throwing an instance of 'nlohmann::json_abi_v3_11_3::detail::parse_error'\n what(): [json.exception.parse_error.101] parse error at line 1, column 1: attempting to parse an empty input; check that your input string or stream contains the expected JSON\n```\n\nThe error suggests it cannot find a JSON file, but the `config.json` file is present in the engine directory:\n\n```bash\nroot@5ddf177112d7:/usr/local/tgi/bin# ls -l /engines/llama-3.2-3b-instruct/\ntotal 3033324\n-rw-r--r-- 1 root root 7848 Jul 26 17:21 config.json\n-rw-r--r-- 1 root root 3106108276 Jul 26 17:21 rank0.engine\n```\n\n**Environment:**\n- Model: llama-3.2-3b-instruct\n- TGI Version: 3.3.4-trtllm\n- TensorRT-LLM Version: v0.17.0.post1\n\nCould you please help resolve these compatibility issues or provide guidance on the correct workflow for using TensorRT-LLM with TGI?\n\n### Information\n\n- [x] Docker\n- [ ] The CLI directly\n\n### Tasks\n\n- [x] An officially supported command\n- [ ] My own modifications\n\n### Reproduction\n\n**1/ Build your engine :** \n`docker run --rm -it --gpus=1 --shm-size=1g -v \"/home/jyce/unmute.mcp/volumes/llm-tgi/engines:/engines\" -v \"/home/jyce/unmute.mcp/volumes/llm-tgi/models:/models\" huggingface/optimum-nvidia:v0.1.0b8-py310 bash\n`\n```\n optimum-cli export trtllm \\\n --tp=1 \\\n --pp=1 \\\n --max-batch-size",
"url": "https://github.com/huggingface/text-generation-inference/issues/3304",
"state": "open",
"labels": [],
"created_at": "2025-07-27T06:24:29Z",
"updated_at": "2025-10-06T09:56:29Z",
"comments": 4,
"user": "psykokwak-com"
},
{
"repo": "huggingface/transformers",
"number": 39705,
"title": "[i18n-] Translating docs to ",
"body": "\n\nHi!\n\nLet's bring the documentation to all the Bengali-speaking community \ud83c\udf10 (currently 0 out of 267 complete)\n\nWho would want to translate? Please follow the \ud83e\udd17 [TRANSLATING guide](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.\n\nSome notes:\n\n* Please translate using an informal tone (imagine you are talking with a friend about transformers \ud83e\udd17).\n* Please translate in a gender-neutral way.\n* Add your translations to the folder called `` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source).\n* Register your translation in `/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml).\n* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu for review.\n* \ud83d\ude4b If you'd like others to help you with the translation, you can also post in the \ud83e\udd17 [forums](https://discuss.huggingface.co/).\n\n## Get Started section\n\n- [x] [index.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/index.md) https://github.com/huggingface/transformers/pull/20180\n- [ ] [quicktour.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.md) (waiting for initial PR to go through)\n- [ ] [installation.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/installation.md).\n\n## Tutorial section\n- [ ] [pipeline_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/pipeline_tutorial.md)\n- [ ] [autoclass_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/autoclass_tutorial.md)\n- [ ] [preprocessing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/preprocessing.md)\n- [ ] [training.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/training.md)\n- [ ] [accelerate.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/accelerate.md)\n- [ ] [model_sharing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_sharing.md)\n- [ ] [multilingual.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/multilingual.md)\n\n\n",
"url": "https://github.com/huggingface/transformers/issues/39705",
"state": "open",
"labels": [
"WIP"
],
"created_at": "2025-07-27T06:18:20Z",
"updated_at": "2025-07-27T11:58:32Z",
"comments": 1,
"user": "ankitdutta428"
},
{
"repo": "huggingface/transformers",
"number": 39699,
"title": "No flag to support Conditional Parameter Loading for gemma-3n-E2B models in transformer",
"body": "### System Info\n\nHi,\nWhile a lot has been mentioned about gemma-3n-E2B and gemma-3n-E4B about the COnditional parameter loading and reduced memory loading\nThere is no configuration currently visible in transformers for supporting that.\nIs it possible to get the related configuration/code/documentation to make it work to get an actual lower memory model?\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nimport torch\nfrom transformers import AutoProcessor, AutoModelForImageTextToText\n\nGEMMA_MODEL_ID = \"google/gemma-3n-E2B-it\"\n\nprint(\"Loading processor\")\nprocessor = AutoProcessor.from_pretrained(GEMMA_MODEL_ID)\n\nprint(\"Loadind model\")\nmodel = AutoModelForImageTextToText.from_pretrained(\n GEMMA_MODEL_ID, torch_dtype=\"auto\", device_map=None).to(\"cpu\")\n\nThere is no flag for doing Conditional parameter Loading or PLE\n\n### Expected behavior\n\nSome flag using which Conditional Parameter Loading can be enabled and save on the memory",
"url": "https://github.com/huggingface/transformers/issues/39699",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-26T18:08:00Z",
"updated_at": "2025-09-03T08:02:58Z",
"comments": 2,
"user": "aakashgaur01"
},
{
"repo": "huggingface/tokenizers",
"number": 1835,
"title": "Can you provide binary releases?",
"body": "It seems that binaries are not available in recent versions. \ntokenizers module is essential for the latest models, and it would be preferable if it could be easily installed. \nSetting up a Rust compilation environment can be cumbersome, and it's almost impossible to do so offline. \nCould we possibly distribute something in binary form via PyPI or here?",
"url": "https://github.com/huggingface/tokenizers/issues/1835",
"state": "closed",
"labels": [],
"created_at": "2025-07-26T16:07:12Z",
"updated_at": "2025-09-08T13:49:52Z",
"comments": 4,
"user": "goldenmomonga"
},
{
"repo": "huggingface/lerobot",
"number": 1599,
"title": "Evaluation results of VLA models on MetaWorld Benchmark",
"body": "Thank you for this excellent work! I noticed that the paper mentions evaluation results of VLA models on MetaWorld. However, in the original papers for Octo and \u03c0\u2080, results are only reported on the LIBERO benchmark, and I haven\u2019t found their MetaWorld evaluations in other related studies. I\u2019d like to know how Octo and \u03c0\u2080 were specifically evaluated on MetaWorld in this work, including implementation details (e.g., for \u03c0\u2080, was it full finetune or only fine-tuning the action expert?). Additionally, the MetaWorld MT50 dataset on LeRobot appears to lack data for one task\u2014is this the real data used for fine-tuning VLAs?",
"url": "https://github.com/huggingface/lerobot/issues/1599",
"state": "open",
"labels": [
"enhancement",
"question",
"policies",
"simulation"
],
"created_at": "2025-07-26T11:18:54Z",
"updated_at": "2025-08-12T09:17:44Z",
"user": "Zooy138"
},
{
"repo": "huggingface/transformers",
"number": 39686,
"title": "CRITICAL ISSUE REPORT! GEMMA 3 1B CANNOT RUN!",
"body": "How to reproduce:\n\nRun this:\n\n```\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\n\n# Load the base model in FP16\nbase_model = AutoModelForCausalLM.from_pretrained(\n \"unsloth/gemma-3-1b-pt\",\n low_cpu_mem_usage=True,\n return_dict=True,\n torch_dtype=torch.float16,\n device_map=\"mps\",\n)\n\n# Load and configure the tokenizer\ntokenizer = AutoTokenizer.from_pretrained(\"unsloth/gemma-3-1b-pt\", trust_remote_code=True)\n\n# Generate the text\nprompt = \"Once upon a time\"\ninputs = tokenizer(prompt, return_tensors=\"pt\").to(base_model.device)\noutputs = base_model.generate(inputs.input_ids, max_length=50)\n# Decode the generated text\ngenerated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\nprint(generated_text)\n```\n\nError:\n\n```\n(yuna) yuki@yuki AI % python gener.py\n k_out_updated = k_out_shifted.index_copy(2, update_position, key_states)\nTraceback (most recent call last):\n File \"/Users/yuki/Documents/AI/gener.py\", line 19, in \n outputs = base_model.generate(inputs.input_ids, max_length=50)\n File \"/opt/anaconda3/envs/yuna/lib/python3.10/site-packages/torch/utils/_contextlib.py\", line 116, in decorate_context\n return func(*args, **kwargs)\n File \"/opt/anaconda3/envs/yuna/lib/python3.10/site-packages/transformers/generation/utils.py\", line 2623, in generate\n result = self._sample(\n File \"/opt/anaconda3/envs/yuna/lib/python3.10/site-packages/transformers/generation/utils.py\", line 3649, in _sample\n next_tokens = torch.multinomial(probs, num_samples=1).squeeze(1)\nRuntimeError: probability tensor contains either `inf`, `nan` or element < 0\n```\n\nSystem: macOS Tahoe, MacBook Pro M1 with 16 GB of RAM",
"url": "https://github.com/huggingface/transformers/issues/39686",
"state": "closed",
"labels": [],
"created_at": "2025-07-26T00:22:27Z",
"updated_at": "2025-07-28T12:07:50Z",
"comments": 5,
"user": "yukiarimo"
},
{
"repo": "huggingface/lerobot",
"number": 1592,
"title": "Time spent on imitation learning training (ACT)",
"body": "I use colab to make a policy with ACT model.\nThe note said, \"Training with the ACT policy for 100,000 steps typically takes about 1.5 hours on an NVIDIA A100 GPU,\", and I used A100 model in colab too.\nHowever the expected time is 13 hours, which seems to be much longer than the standard value of 1.5 hours. \nIs it correct that it takes this much time in a colab environment? \nI used dataset from \nhttps://huggingface.co/datasets/initie/test_pick \nand there is no problem with the operation of the training code.",
"url": "https://github.com/huggingface/lerobot/issues/1592",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-25T06:36:35Z",
"updated_at": "2025-10-08T08:32:32Z",
"user": "initia1013"
},
{
"repo": "huggingface/datasets",
"number": 7699,
"title": "Broken link in documentation for \"Create a video dataset\"",
"body": "The link to \"the [WebDataset documentation](https://webdataset.github.io/webdataset).\" is broken. \nhttps://huggingface.co/docs/datasets/main/en/video_dataset#webdataset \n\n![\"Image\"](\"https://github.com/user-attachments/assets/975dd10c-aad8-42fc-9fbc-de0e2747a326\")
",
"url": "https://github.com/huggingface/datasets/issues/7699",
"state": "open",
"labels": [],
"created_at": "2025-07-24T19:46:28Z",
"updated_at": "2025-07-25T15:27:47Z",
"comments": 1,
"user": "cleong110"
},
{
"repo": "huggingface/transformers",
"number": 39637,
"title": "[BUG] Run 111B+ Teacher distributed inference and 8B Student distributed training on multi-node H200 GPUs using the Transformers Trainer without encountering OOM errors?",
"body": "Hello, first off, apologies if this information is already available elsewhere. I've searched through the documentation and existing issues but haven't found a clear answer to my question.\n\nI have access to 2 to 4 nodes (16 to 32 GPUs in total), each equipped with 8x140GB H200 GPUs. My objective is to perform large-scale distributed inference using a massive 111B-parameter Teacher model (CohereLabs/c4ai-command-a-03-2025) and simultaneously conduct online knowledge distillation (soft-logit based) from this 111B Teacher model to a smaller 8B Student model (CohereLabs/c4ai-command-r7b-12-2024).\n\nIs there a way to simultaneously run distributed inference for Teacher models larger than 111B and distributed training for Student models in a multi-node setup, utilizing Hugging Face Transformers' Trainer?\n\nThe Transformers version I'm using is v4.51.3. I've observed the use of model = deepspeed.tp_model_init within the def deepspeed_init function in src/transformers/integrations/deepspeed.py. I attempted to apply this code, but it resulted in a torch.distributed.DistBackendError.\n\nI would be very grateful if someone could explain what would be most suitable for my use case. A minimal working example would be the icing on the cake. Surely, if the Open LLM Leaderboard shows that online knowledge distillation (soft-logit) is possible with large models exceeding 111B, there must be a straightforward way to achieve what I want, but I'm unsure how everyone else does it.\n\nFor reference, below is the script I'm currently working with:\n\n`deepspeed --num_nodes 2 --num_gpus 8 \\\n --hostfile $HOSTFILE \\\n --master_addr $MASTER_ADDR \\\n --master_port=62535 \\\n train.py \\\n --teacher CohereLabs/c4ai-command-a-03-2025 \\\n --student CohereLabs/c4ai-command-r7b-12-2024 \\\n --epochs 1 --batch_size 1 --seq_len 4096 --temperature 1.0 --max_samples 150 --lr 1e-6 2>&1 | tee -a \"./train.log\" `\n\n```import deepspeed\nimport torch.distributed as dist\nimport os, math, argparse, warnings, torch, random, multiprocessing as mp\nfrom datasets import load_dataset, concatenate_datasets\nfrom transformers import (AutoTokenizer, AutoModelForCausalLM,\n PreTrainedTokenizerBase)\nfrom torch.nn.utils.rnn import pad_sequence\nimport torch.nn.functional as F\nfrom datetime import timedelta\nfrom deepspeed.runtime.utils import see_memory_usage\n\n\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"false\"\nos.environ.setdefault(\"NCCL_ASYNC_ERROR_HANDLING\", \"1\")\nwarnings.filterwarnings(\"ignore\", category=UserWarning)\nmp.set_start_method(\"spawn\", force=True)\n\ndef get_args():\n p = argparse.ArgumentParser()\n p.add_argument(\"--teacher\", default=\"\")\n p.add_argument(\"--student\", default=\"\")\n p.add_argument(\"--dataset\", default=\"\")\n p.add_argument(\"--split\", default=\"train\")\n p.add_argument(\"--epochs\", type=int, default=1)\n p.add_argument(\"--batch_size\", type=int, default=1,\n help=\"per-GPU micro-batch\")\n p.add_argument(\"--seq_len\", type=int, default=4096)\n p.add_argument(\"--temperature\", type=float, default=1.0)\n p.add_argument(\"--lr\", type=float, default=1e-6)\n p.add_argument(\"--max_samples\", type=int, default=0,\n help=\"0=1000 \")\n p.add_argument(\"--local_rank\", type=int, default=-1,\n help=\"deepspeed/torch launcher GPU index\")\n p.add_argument(\"--cache_path\", default=\"\")\n p.add_argument(\"--hf_token\", default=\"\")\n p = deepspeed.add_config_arguments(p)\n return p.parse_args()\n\n\ndef main():\n timeout_seconds = 3600 \n timeout_duration = timedelta(seconds=timeout_seconds)\n dist.init_process_group(\n backend=\"nccl\",\n timeout=timeout_duration \n )\n args = get_args()\n deepspeed.init_distributed()\n rank, world = deepspeed.comm.get_rank(), deepspeed.comm.get_world_size()\n device = torch.device(\"cuda\", deepspeed.comm.get_local_rank())\n # Tokenizer \n tokenizer = AutoTokenizer.from_pretrained(args.student,\n use_fast=True, trust_remote_code=True)\n if tokenizer.pad_token is None:\n tokenizer.pad_token = tokenizer.eos_token\n \n # tokenizer token_id \n tokenizer.eos_token_id = tokenizer.convert_tokens_to_ids(tokenizer.eos_token)\n tokenizer.pad_token_id = tokenizer.convert_tokens_to_ids(tokenizer.pad_token)\n \n \n # Teacher (inference only) \n teacher_model = AutoModelForCausalLM.from_pretrained(\n args.teacher, torch_dtype=torch.bfloat16,\n low_cpu_mem_usage=True,\n trust_remote_code=True, device_map=None, \n cache_dir=args.cache_path,token=args.hf_token) \n \n see_memory_usage(\"After load model\", force=True)\n \n teacher_model.config.eos_token_id = tokenizer.eos_token_id\n teacher_model.config.pad_token_id = tokenizer.pad_token_id\n \n teacher_engine = deepspeed.init_inference(\n teacher_model,\n mp_size=world,\n dtype=torch.bfloat16,\n replace_with_kernel_inject=True, \n replace_method=\"auto\")\n ",
"url": "https://github.com/huggingface/transformers/issues/39637",
"state": "closed",
"labels": [],
"created_at": "2025-07-24T15:05:38Z",
"updated_at": "2025-09-01T08:03:18Z",
"comments": 3,
"user": "seona21"
},
{
"repo": "huggingface/lerobot",
"number": 1586,
"title": "Real-world deploy on ALOHA Robot",
"body": "How could I deploy the policies on the ALOHA robot? And how could I deploy in the real world? ",
"url": "https://github.com/huggingface/lerobot/issues/1586",
"state": "open",
"labels": [
"question",
"robots"
],
"created_at": "2025-07-24T12:52:06Z",
"updated_at": "2025-08-21T16:18:26Z",
"user": "LogSSim"
},
{
"repo": "huggingface/diffusers",
"number": 11984,
"title": "A compatibility issue when using custom Stable Diffusion with pre-trained ControlNets",
"body": "I have successfully fine-tuned a Stable Diffusion v1.5 model using the Dreambooth script, and the results are excellent. However, I've encountered a compatibility issue when using this custom model with pre-trained ControlNets. Since the Dreambooth process modifies the U-Net weights, the original ControlNet is no longer aligned with the fine-tuned model, leading to a significant degradation in control and image quality.\n\nMy goal is to find a way to make them compatible again. It's important to clarify that I am trying to avoid a full, separate fine-tuning of the ControlNet on my custom model. That process is data- and resource-intensive, which defeats the purpose of a lightweight personalization method like Dreambooth. I have tried modifying the train_dreambooth.py script to incorporate ControlNet, but results have been consistently poor.\n\nIs there a dedicated script or a recommended workflow in diffusers to fine-tune a Stable Diffusion with ControlNet via Dreambooth? Any guidance or pointers would be greatly appreciated. Thanks a lot!",
"url": "https://github.com/huggingface/diffusers/issues/11984",
"state": "closed",
"labels": [],
"created_at": "2025-07-24T09:16:55Z",
"updated_at": "2025-07-24T15:15:20Z",
"comments": 6,
"user": "ScienceLi1125"
},
{
"repo": "huggingface/lighteval",
"number": 868,
"title": "How to calculate perplexity from an OpenAI compatible API",
"body": "Hello,\n\nI'm new to LightEval. I want to use LightEval to evaluate an LLM model that is served via an API. The API is OpenAI compatible. It also returns logprobs for each token. Is there a built-in function to evaluate the perplexity score? I'm asking because I see that it\u2019s not implemented.\n\nhttps://github.com/huggingface/lighteval/blob/d805f9fa0a84da9ca4c0c6a638bbed149a7012a3/src/lighteval/models/litellm_model.py#L322\n\nAny help or guidance is greatly appreciated. Thanks.",
"url": "https://github.com/huggingface/lighteval/issues/868",
"state": "open",
"labels": [],
"created_at": "2025-07-24T07:27:05Z",
"updated_at": "2025-07-24T07:27:05Z",
"user": "mrtpk"
},
{
"repo": "huggingface/lerobot",
"number": 1580,
"title": "Environment_State in act and SmolVLA policy",
"body": "Hi, Thanks for the awesome work!\nI have been noticing a variable called observation.environment_state in the act policy. What is exactly the feature environment_state. Thanks!",
"url": "https://github.com/huggingface/lerobot/issues/1580",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-24T03:32:31Z",
"updated_at": "2025-10-08T13:09:33Z",
"user": "kasiv008"
},
{
"repo": "huggingface/transformers.js",
"number": 1379,
"title": "Why Do I Get Different Outputs in Python and JavaScript for the Same ONNX Model?",
"body": "Hi ,\n\nI'm running inference on the same ONNX model (t5-small-new) using both Python and JavaScript (via ONNX Runtime). However, I'm noticing that the outputs are different between the two environments, even though the inputs and model are the same. The output of the Python code is correct while JS is not accurate.\n\nPython Code:\n```\nfrom optimum.onnxruntime import ORTModelForSeq2SeqLM\nfrom transformers import AutoTokenizer\n\nmodel = ORTModelForSeq2SeqLM.from_pretrained(\n \"t5-small-new\",\n use_cache=True \n)\n\ntokenizer = AutoTokenizer.from_pretrained(\"t5-small-new\")\n\ninputs = tokenizer(\"My Input\", return_tensors=\"pt\")\noutputs = model.generate(**inputs)\n\nprint(\"Prediction:\", tokenizer.decode(outputs[0], skip_special_tokens=True))\n```\n\n\nJS code:\n```\nconst inputText = \"My Input\";\n\nconst tokenizer = await window.AutoTokenizer.from_pretrained(\"t5-small-new\");\nconst model = await window.AutoModelForSeq2SeqLM.from_pretrained(\"t5-small-new\", {\n dtype: \"fp32\",\n device: \"wasm\",\n});\n\nconst encoded = await tokenizer(inputText, {\n return_tensors: \"pt\",\n});\n\nconst output = await model.generate({\n input_ids: encoded.input_ids,\n attention_mask: encoded.attention_mask,\n use_cache: true,\n});\n\nconst decoded = await tokenizer.decode(output[0], {\n skip_special_tokens: true,\n});\n\nconsole.log(\"JS Prediction:\", decoded);\n\n```\n\n\nMy model uses `decoder_model_merged.onnx`, `encoder_model.onnx`, and `decoder_model.onnx`. \n\nCould you guide me on what is happening and why I get different results?",
"url": "https://github.com/huggingface/transformers.js/issues/1379",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-07-23T20:13:57Z",
"updated_at": "2025-08-29T23:43:21Z",
"user": "mahdin75"
},
{
"repo": "huggingface/transformers",
"number": 39618,
"title": "SageAttention for attention implementation?",
"body": "### Feature request\n\nI've noticed it's been a while now, but transformers still only has flash attention as the fastest attention backend for calls like these: \n\n![\"Image\"](\"https://github.com/user-attachments/assets/3f3d62f6-a166-4ca6-97a0-49263fd93299\")
\n\nAre there any plans to add sageattention as well? \n\n### Motivation\n\nIt's become increasingly involved to have to monkey patch sage attention support for every new model that comes out, and for older models that used older versions of transformers, I've had to do unholy things like this:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/c5f4ff6a-094a-48f4-9339-17de1ece43d0\")
\n\n\n### Your contribution\n\nI have an example of a patch I had to do so I will upload that here\n\n[llama_nar.py.txt](https://github.com/user-attachments/files/21393926/llama_nar.py.txt)",
"url": "https://github.com/huggingface/transformers/issues/39618",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-07-23T19:10:47Z",
"updated_at": "2025-07-25T12:30:37Z",
"comments": 4,
"user": "Many0therFunctions"
},
{
"repo": "huggingface/diffusers",
"number": 11977,
"title": "how to load a finetuned model especially during validation phase",
"body": "![\"Image\"](\"https://github.com/user-attachments/assets/c4e9318f-10aa-4b91-9d60-e28a3be38f8a\")
\nAs the above, I have finetuned the model and want to validate it, but the given demo which is train_dreambooth_sd3.py still uses \n\"pipeline = StableDiffusion3Pipeline.from_pretrained(\n args.pretrained_model_name_or_path,\n transformer=transformer,\n text_encoder=text_encoder_one,\n text_encoder_2=text_encoder_two,\n text_encoder_3=text_encoder_three,\n ) \" .\n\nI wonder why it still load from args.pretrained_model_name_or_path as it has saved the finetuned model in the save_path which is \"os.path.join(args.output_dir, f\"checkpoint-{global_step}\")\".\n\nso, how to how to load the finetuned model during validation phase?\n\nAnother confusion, what is the difference between \" StableDiffusion3Pipeline.from_pretrained() \" and \"SD3Transformer2DModel.from_pretrained\" as the following:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/7d9e5915-8aa2-4678-b39f-6ecb4480a02b\")
\n\n ",
"url": "https://github.com/huggingface/diffusers/issues/11977",
"state": "open",
"labels": [],
"created_at": "2025-07-23T11:54:16Z",
"updated_at": "2025-07-24T09:19:11Z",
"user": "micklexqg"
},
{
"repo": "huggingface/lerobot",
"number": 1579,
"title": "Is there a video backend supporting nondestructive encoding?",
"body": "I saved images during recording through not deletng folder `images`. When I try to compare the first frame.png in `images` folder and dataset=make_dataset(config)'s first image, I found the saved png file is nondestructive. But the image I got by lerobot is not.\n\nHow I find:\nin `def save_episode` \n```\n # img_dir = self.root / \"images\"\n # if img_dir.is_dir():\n # shutil.rmtree(self.root / \"images\")\n```\nThis has been moved in latest version. now:\n\n```\n def encode_episode_videos(self, episode_index: int) -> None:\n ...\n encode_video_frames(img_dir, video_path, self.fps, overwrite=True)\n shutil.rmtree(img_dir)\n```\n\nI saved some images through recording with one channel filled with zero. Then read the saved png through cv2, it showed it has a 0-filled channel.\n\nThen I try to check whether I can get the same image through lerobot\nso I did this in train.py\n```\nraw_dataloader = torch.utils.data.DataLoader(\n dataset,\n num_workers=cfg.num_workers,\n batch_size=cfg.batch_size,\n shuffle=False,\n sampler=sampler,\n pin_memory=device.type == \"cuda\",\n drop_last=False,\n )\nimage_tensor=peek_batch[\"observation.images.side_depth\"][0]\nimage_np = (image_tensor * 255).permute(1, 2, 0).cpu().numpy().astype(np.uint8)\n```\nSadly,`image_np` is really different from real png, it doesn't have a 0-filled channel, and its average data shows larger.\n",
"url": "https://github.com/huggingface/lerobot/issues/1579",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-07-23T08:38:39Z",
"updated_at": "2025-08-12T09:22:26Z",
"user": "milong26"
},
{
"repo": "huggingface/candle",
"number": 3032,
"title": "`matmul` (and others) Precision issues between Candle & PyTorch",
"body": "We noticed there's some precision discrepancy in matrix multiplication and the linear layer between between Candle and PyTorch. This matters a lot when reproducing LLMs originated from PyTorch into Candle. We used the `hf_hub::api::Api` to get the safetensors from the hub and for testing the precision issues for each modules independently. This also occurs for the `BF16` dtype in `Cuda`.\n\nHere's a shortened list of tests (for brevity) between `candle_core::tensor::Tensor::matmul` and `torch.matmul`\n```\n\u274c test_0: MSE=0.0000000004096404, MAE=0.00001550 (dims: 2048x256, dtype: F32, device: Cpu)\n\u274c test_1: MSE=0.0000000003628351, MAE=0.00001453 (dims: 2048x256, dtype: F32, device: Cpu)\n...\n\u274c test_48: MSE=0.0000000000824194, MAE=0.00000633 (dims: 512x1024, dtype: F32, device: Cpu)\n\u274c test_49: MSE=0.0000000003840639, MAE=0.00001534 (dims: 2048x256, dtype: F32, device: Cpu)\n```\n\nWe did notice `candle_nn::Embedding` performed at 0-tolerance (tested indirectly), which probably means the the loaded weights themselves are working precisely.\n\nHave you guys tried validating your implementation with the PyTorch at 0-tolerance (within the same CPU/GPU architecture)? Is there any proper way to mitigate this? We need it for our implementation. Thank you.",
"url": "https://github.com/huggingface/candle/issues/3032",
"state": "closed",
"labels": [],
"created_at": "2025-07-23T04:07:08Z",
"updated_at": "2025-09-27T21:25:51Z",
"comments": 4,
"user": "andrew-shc"
},
{
"repo": "huggingface/lerobot",
"number": 1578,
"title": "Lerobot metaworld dataset only provides 49 tasks",
"body": "https://huggingface.co/datasets/lerobot/metaworld_mt50\n\nThere are only 49 tasks and \"Push the puck to a goal\" task repeates twice",
"url": "https://github.com/huggingface/lerobot/issues/1578",
"state": "open",
"labels": [
"question",
"simulation"
],
"created_at": "2025-07-23T04:03:17Z",
"updated_at": "2025-08-12T09:23:12Z",
"user": "chenkang455"
},
{
"repo": "huggingface/lerobot",
"number": 1577,
"title": "test failed after training SVLA",
"body": "I collected 76 sets of data and used the same calibration file as during collection. However, after training for 24k steps, the model obtained was unable to complete the grasping task during inference. Can anyone help me deal with the problem?\n[dataset](https://huggingface.co/datasets/Xiaoyan97/orange_block_pickplace)\n",
"url": "https://github.com/huggingface/lerobot/issues/1577",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-23T03:59:26Z",
"updated_at": "2025-08-12T09:23:26Z",
"user": "Liu-Xiaoyan97"
},
{
"repo": "huggingface/lerobot",
"number": 1576,
"title": "Multiple Dataset training",
"body": "How to train multiple lerobot dataset? is there any function I can use it",
"url": "https://github.com/huggingface/lerobot/issues/1576",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-07-23T03:46:03Z",
"updated_at": "2025-10-10T09:30:06Z",
"user": "JustinKai0527"
},
{
"repo": "huggingface/transformers",
"number": 39596,
"title": "Does transformers support python3.13 -- disable-gil or python3.14 free threading?",
"body": "Does transformers support python3.13 -- disable-gil or python3.14 free threading?\nI got an error when trying to install transformers on these two python versions.",
"url": "https://github.com/huggingface/transformers/issues/39596",
"state": "closed",
"labels": [],
"created_at": "2025-07-23T02:34:03Z",
"updated_at": "2025-08-30T08:02:54Z",
"comments": 2,
"user": "SoulH-qqq"
},
{
"repo": "huggingface/transformers.js",
"number": 1374,
"title": "nanoVLM support",
"body": "### Question\n\nI would like to know if there is any plan to support models built with nanoVLM [https://github.com/huggingface/nanoVLM], thanks.",
"url": "https://github.com/huggingface/transformers.js/issues/1374",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-07-22T11:43:57Z",
"updated_at": "2025-07-23T09:02:15Z",
"user": "sbrzz"
},
{
"repo": "huggingface/diffusers",
"number": 11971,
"title": "What is the minimum memory requirement for model training?",
"body": "Hello, I would like to try training an SDXL model using my own dataset. What is the minimum memory size required for the model?",
"url": "https://github.com/huggingface/diffusers/issues/11971",
"state": "closed",
"labels": [],
"created_at": "2025-07-22T07:52:28Z",
"updated_at": "2025-07-22T08:26:27Z",
"user": "WWWPPPGGG"
},
{
"repo": "huggingface/transformers",
"number": 39565,
"title": "Model forward execution in full eager mode?",
"body": "I know there is a flag `attn_implementation` which could trigger specialized attention kernel implementation. Besides this, does everything run in native PyTorch eager mode? Does `transformers` have any other custom op or kernel?\n```python\nmodel = AutoModelForCausalLM.from_pretrained(\"meta-llama/Llama-3.1-8B\", device_map=\"auto\", torch_dtype=torch.bfloat16, attn_implementation=None)\nmodel.forward(input_tokens)\n```\n\nI'm asking this to see if `transformers` can be used as a numerical baseline to verify other inference backend",
"url": "https://github.com/huggingface/transformers/issues/39565",
"state": "closed",
"labels": [],
"created_at": "2025-07-21T21:49:05Z",
"updated_at": "2025-08-21T08:34:59Z",
"comments": 3,
"user": "22quinn"
},
{
"repo": "huggingface/lerobot",
"number": 1564,
"title": "How are Episode Stats used?",
"body": "I'm looking to create a subset of an episode (ie sec 2-4) in a 30 second episode, and wanted to know how episode_stats are used later on for training / inference? \nAre they used to normalize model inputs or are they used somewhere else as well? \n\nie. in modeling_act.py\n```\nself.normalize_inputs = Normalize(\n config.input_features, config.normalization_mapping, dataset_stats)\n```\n",
"url": "https://github.com/huggingface/lerobot/issues/1564",
"state": "closed",
"labels": [
"question",
"policies",
"processor"
],
"created_at": "2025-07-21T19:06:21Z",
"updated_at": "2025-08-12T09:27:29Z",
"user": "andlyu"
},
{
"repo": "huggingface/lerobot",
"number": 1561,
"title": "will you release the libero ft&eval setting?",
"body": "hello your smolVLA is a wonderful work ,i notice that you finetuned it on the **libero** and evalaute at the same time.but i couldn't achieve the same or similar success rate**(just 76% ,much lower than your '96%')**\n**have you use the async inference in libero?**\nI think it must be the different hyperparameters with yours,so could you release the script(finetune.py & eval.py) or just tell me your ft&eval settings.here is my emal 602225349@qq.com \nthx u in advance~",
"url": "https://github.com/huggingface/lerobot/issues/1561",
"state": "closed",
"labels": [
"enhancement",
"question",
"policies"
],
"created_at": "2025-07-21T13:57:13Z",
"updated_at": "2025-09-23T09:25:04Z",
"user": "JuilieZ"
},
{
"repo": "huggingface/transformers",
"number": 39554,
"title": "Why `is_causal` is not used in `flash_attention_forward` ?",
"body": "I want to perform bidirectional attention in the Qwen3 model to train an embedding model, so I passed `is_causal=False` in the model `forward` (I manually added `is_causal` arguments in all `forward` method such as `Qwen3Model` and `Qwen3Attention` in`modeling_qwen3.py`):\n\n```python\nclass Qwen3Attention(nn.Module):\n \"\"\"Multi-headed attention from 'Attention Is All You Need' paper\"\"\"\n ...\n\n def forward(\n self,\n hidden_states: torch.Tensor,\n position_embeddings: tuple[torch.Tensor, torch.Tensor],\n attention_mask: Optional[torch.Tensor],\n past_key_value: Optional[Cache] = None,\n cache_position: Optional[torch.LongTensor] = None,\n is_causal: Optional[bool] = True, # I add is_causal here\n **kwargs: Unpack[FlashAttentionKwargs],\n ) -> tuple[torch.Tensor, Optional[torch.Tensor], Optional[tuple[torch.Tensor]]]:\n ...\n\n attn_output, attn_weights = attention_interface(\n self,\n query_states,\n key_states,\n value_states,\n attention_mask,\n dropout=0.0 if not self.training else self.attention_dropout,\n scaling=self.scaling,\n sliding_window=self.sliding_window, # diff with Llama\n is_causal=is_causal, # and is_causal from the argument is passed to the attention_interface (e.g. `flash_attention_2`, `sdpa_attention_forward`)\n **kwargs,\n )\n```\n \nI can successfully change the causality of the attention in `sdpa_attention_forward`. However, I realized that it does not change the causality in the attention in `flash_attention_forward`. After diving into the implementation of `flash_attention_forward`, I found the reason in `flash_attention_forward` located at `transformers/integrations/flash_attention.py`:\n\n```python\ndef flash_attention_forward(\n module: torch.nn.Module,\n query: torch.Tensor,\n key: torch.Tensor,\n value: torch.Tensor,\n attention_mask: Optional[torch.Tensor],\n dropout: float = 0.0,\n scaling: Optional[float] = None,\n sliding_window: Optional[int] = None,\n softcap: Optional[float] = None,\n **kwargs,\n) -> tuple[torch.Tensor, None]:\n ...\n\n # FA2 always relies on the value set in the module, so remove it if present in kwargs to avoid passing it twice\n kwargs.pop(\"is_causal\", None)\n\n attn_output = _flash_attention_forward(\n query,\n key,\n value,\n attention_mask,\n query_length=seq_len,\n is_causal=module.is_causal, # here module is `Qwen3Attention`\n dropout=dropout,\n softmax_scale=scaling,\n sliding_window=sliding_window,\n softcap=softcap,\n use_top_left_mask=_use_top_left_mask,\n target_dtype=target_dtype,\n attn_implementation=module.config._attn_implementation,\n **kwargs,\n )\n```\n\nAs you can see, the `is_causal` argument is popped, and the `is_causal` of `Qwen3Attention` is used as the argument. Note that `Qwen3Attention.is_causal` is never changed, and its default value is `True`, so the `is_causal` argument passed into `_flash_attention_forward` will always be `True` regardless of any change. \n\nAfter I add a line of code to alter the `Qwen3Attention.is_causal`, i.e. `self.is_causal = is_causal` before passing the arguments into `attention_interface`, I can change the causality of `flash_attention_forward`. So I would like to know if it is a feature or a bug? Thank you!!",
"url": "https://github.com/huggingface/transformers/issues/39554",
"state": "closed",
"labels": [
"Flash Attention"
],
"created_at": "2025-07-21T12:08:00Z",
"updated_at": "2025-11-11T12:32:41Z",
"comments": 9,
"user": "lucaswychan"
},
{
"repo": "huggingface/peft",
"number": 2660,
"title": "Custom models LoRA",
"body": " Is there any way to fine-tune models that are not in the support list or custom models?\n\nCurrently, many public models have their LLM parts from Qwen. Can LLaMA-Factory use the Qwen template and only fine-tune the LLM part? Thank you",
"url": "https://github.com/huggingface/peft/issues/2660",
"state": "closed",
"labels": [],
"created_at": "2025-07-21T11:52:30Z",
"updated_at": "2025-07-24T12:53:34Z",
"comments": 6,
"user": "stillbetter"
},
{
"repo": "huggingface/lerobot",
"number": 1559,
"title": "Is the current model framework suitable for using automatic mixed precision?",
"body": "I saw that `.to(torch.float32)` and `.to(torch.bfloat16)` were used in many places in the Pi0 model code. Then I implemented parallel training of Pi0 based on accelerate, and found that if I want to use AMP, the code will report an error of dtype mismatch. I want to know whether the existing code is suitable for automatic mixed precision? If not, how should it be modified?",
"url": "https://github.com/huggingface/lerobot/issues/1559",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-21T10:45:26Z",
"updated_at": "2025-08-12T09:27:59Z",
"user": "xliu0105"
},
{
"repo": "huggingface/transformers",
"number": 39549,
"title": "Is there plan to integrate ColQwen2.5 into Transformers?",
"body": "### Model description\n\nIs ColQwen2ForRetrieval integrated into the transformers library, and are there plans to add [ColQwen2.5](https://github.com/illuin-tech/colpali/blob/main/colpali_engine/models/qwen2_5/colqwen2_5/modeling_colqwen2_5.py) in the future?\n\n### Open source status\n\n- [x] The model implementation is available\n- [x] The model weights are available\n\n### Provide useful links for the implementation\n\nhttps://github.com/illuin-tech/colpali/blob/main/colpali_engine/models/qwen2_5/colqwen2_5/modeling_colqwen2_5.py\n\nhttps://github.com/huggingface/transformers/pull/38391",
"url": "https://github.com/huggingface/transformers/issues/39549",
"state": "closed",
"labels": [
"New model"
],
"created_at": "2025-07-21T10:08:47Z",
"updated_at": "2025-11-03T23:31:08Z",
"comments": 0,
"user": "rebel-thkim"
},
{
"repo": "huggingface/diffusers",
"number": 11966,
"title": "How about forcing the first and last block on device when groupoffloading is used?",
"body": "**Is your feature request related to a problem? Please describe.**\nWhen group offloading is enabled, the offload and onload cannot be streamed between steps and this is really a big time comsuming problem.\n\n**Describe the solution you'd like.**\nIs it possible to add an option that could make the first and last block forced on device to avoid offload and onload?\n\n@a-r-r-o-w Could you please give some help? Thanks so much.\n",
"url": "https://github.com/huggingface/diffusers/issues/11966",
"state": "open",
"labels": [
"contributions-welcome",
"group-offloading"
],
"created_at": "2025-07-21T08:38:30Z",
"updated_at": "2025-12-02T15:30:23Z",
"comments": 13,
"user": "seed93"
},
{
"repo": "huggingface/tokenizers",
"number": 1829,
"title": "The parameter in initial_alphabet of the \"class BpeTrainer(Trainer)\" does not allow more than one character to initialized",
"body": "Hi everyone,\nI am working on Tamil and Sinhala languages which are morphologically rich languages, in these languages a character is actually a combination of multiple unicode codepoints (similar to emojis) so it would be greatly beneficial to initialize the BPE alphabet with graphemes instead of the characters. Is there any work around for this which i can use to initialize the BPE algorithm? Thanks in advance!!",
"url": "https://github.com/huggingface/tokenizers/issues/1829",
"state": "open",
"labels": [],
"created_at": "2025-07-21T08:30:21Z",
"updated_at": "2025-07-21T08:30:21Z",
"comments": 0,
"user": "vmenan"
},
{
"repo": "huggingface/lerobot",
"number": 1554,
"title": "How to use local datasets to train and evaluate",
"body": "Due to network issues, I want to use only local datasets during training and evaluation and prevent huggingface from uploading data or retrieve datasets on the hub.Is there any good solution? ",
"url": "https://github.com/huggingface/lerobot/issues/1554",
"state": "closed",
"labels": [
"question",
"dataset"
],
"created_at": "2025-07-21T07:54:07Z",
"updated_at": "2025-10-08T12:58:32Z",
"user": "zym123321"
},
{
"repo": "huggingface/optimum",
"number": 2324,
"title": "AutoConfig.from_dict Missing in transformers==4.51.3 \u2014 Incompatibility with optimum==1.26.1",
"body": "### System Info\n\n```shell\nI am running into a critical compatibility issue between optimum and recent versions of transformers.\n\n\u2757 Error Summary\nWhen using:\ntransformers==4.51.3\noptimum==1.26.1\nonnx==1.17.0\nonnxruntime==1.20.0\n\nThe following runtime error is thrown when attempting to load an ONNX model using ORTModelForTokenClassification.from_pretrained:\n\nAttributeError: type object 'AutoConfig' has no attribute 'from_dict'\n\nThis traces back to:\nconfig = AutoConfig.from_pretrained(...)\n# \u2193 internally calls:\nreturn CONFIG_MAPPING[pattern].from_dict(config_dict, **unused_kwargs)\n\nHowever, in transformers>=4.48, the method AutoConfig.from_dict appears to have been deprecated or removed. This causes optimum to break at runtime when trying to load ONNX models.\n\n\ud83d\udce6 Package Versions\ntransformers - 4.51.3\noptimum - 1.26.1\nonnx - 1.17.0\nonnxruntime - 1.20.0\ntorch - 2.2.6\n\nDue to a security advisory, we're required to upgrade to transformers>=4.48. However, even with the latest optimum==1.26.1, it appears optimum is not yet updated for compatibility with changes introduced in recent transformers versions.\n\nASK:\nIs support for transformers>=4.48 (particularly 4.51.3) planned in an upcoming optimum release?\nCould this AutoConfig.from_dict dependency be refactored or conditionally patched to restore compatibility?\nIs there a compatibility roadmap available between transformers and optimum for ONNX workflows?\n```\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction (minimal, reproducible, runnable)\n\nUse transformers==4.51.3 and optimum==1.26.1\n\nLoad an exported ONNX model using ORTModelForTokenClassification.from_pretrained(...)\n\nObserve the AttributeError about AutoConfig.from_dict\n\n### Expected behavior\n\nWhen using optimum==1.26.1 with transformers>=4.48 (specifically 4.51.3), the following should work without error:\nfrom optimum.onnxruntime import ORTModelForTokenClassification\nmodel = ORTModelForTokenClassification.from_pretrained(\"path/to/onnx/model\")\n\nThe model should load successfully using the ONNX Runtime backend.\n\nInternally, AutoConfig.from_pretrained(...) should function correctly regardless of changes in the transformers API (e.g., deprecation/removal of from_dict).\n\nONNX workflows should remain compatible with newer transformers versions, allowing teams to benefit from critical updates and security patches without breaking ONNX integration.",
"url": "https://github.com/huggingface/optimum/issues/2324",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-07-21T06:04:58Z",
"updated_at": "2025-08-01T07:10:20Z",
"comments": 5,
"user": "rratnakar09"
},
{
"repo": "huggingface/diffusers",
"number": 11964,
"title": "KeyError when loading LoRA for Flux model: missing lora_unet_final_layer_adaLN_modulation_1 weights",
"body": "I'm trying to run Overlay-Kontext-Dev-LoRA locally by loading the LoRA weights using the pipe.load_lora_weights() function. However, I encountered the following error during execution:\n\n> KeyError: 'lora_unet_final_layer_adaLN_modulation_1.lora_down.weight'\n\n\n```\nimport torch\nfrom diffusers import DiffusionPipeline\nfrom diffusers.utils import load_image\n\nLoad the pipeline with a specific torch data type for GPU optimization\npipe = DiffusionPipeline.from_pretrained(\n\"black-forest-labs/FLUX.1-Kontext-dev\",\ntorch_dtype=torch.bfloat16\n)\n\nMove the entire pipeline to the GPU\npipe.to(\"cuda\")\n\nLoad LoRA weights (this will also be on the GPU)\npipe.load_lora_weights(\"ilkerzgi/Overlay-Kontext-Dev-LoRA\")\n\nprompt = \"Place it\"\ninput_image = load_image(\"img2.png\")\n\nThe pipeline will now run on the GPU\nimage = pipe(image=input_image, prompt=prompt).images[0]\n\nimage.save(\"output_image.png\")\n```\n\n\nEnvironment:\ndiffusers version: 0.35.0.dev0\nPython: 3.10\nRunning locally on a ubuntu environment with RTX 4090\n\n\n\n> Additional Note:\n> The model file size is also quite large. I may need to quantize it before running it on the 4090 to avoid out-of-memory issues.\n> \n> Would appreciate any help or suggestions on how to resolve the loading issue. Thank you!\n\n",
"url": "https://github.com/huggingface/diffusers/issues/11964",
"state": "open",
"labels": [],
"created_at": "2025-07-21T05:16:34Z",
"updated_at": "2025-07-21T09:14:00Z",
"comments": 1,
"user": "NEWbie0709"
},
{
"repo": "huggingface/transformers",
"number": 39545,
"title": "Is the new Intel\u2013Weizmann speculative decoding algorithm integrated into Transformers?",
"body": "Hi,\n\nI recently read about a new speculative decoding algorithm developed by Intel Labs and the Weizmann Institute, which reportedly improves inference speed by up to 2.8\u00d7, even when using draft and target models with different vocabularies or architectures.\n\nReferences:\n\n- [Intel Newsroom](https://newsroom.intel.com/artificial-intelligence/intel-weizmann-institute-speed-ai-with-speculative-decoding-advance?utm_source=chatgpt.com)\n- [CTech Article](https://www.calcalistech.com/ctechnews/article/h1z7pydlex)\n\nSeveral sources (including Intel press releases and third-party writeups) claim that this algorithm has already been integrated into the Hugging Face Transformers library.\nHowever, I haven\u2019t found any reference to this new version in the official Transformers documentation\n\n\nMy Questions:\n\n1. Has this Intel\u2013Weizmann speculative decoding algorithm actually been integrated into transformers?\n2. If so, where can I find documentation or usage examples for how to enable it?\n\nThanks in advance for your help! This looks like a powerful advancement, and I'd love to test it.",
"url": "https://github.com/huggingface/transformers/issues/39545",
"state": "closed",
"labels": [],
"created_at": "2025-07-21T02:47:48Z",
"updated_at": "2025-07-22T12:15:54Z",
"comments": 4,
"user": "NEWbie0709"
},
{
"repo": "huggingface/lerobot",
"number": 1552,
"title": "Support smolvla training on Intel GPU",
"body": "Current script is only supporting `cuda`, `mps` and `cpu`. \nWith PyTorch 2.7 with Intel GPU support, once PyTorch is installed, Intel GPU can be utilized in the training script.",
"url": "https://github.com/huggingface/lerobot/issues/1552",
"state": "open",
"labels": [
"enhancement",
"question",
"policies"
],
"created_at": "2025-07-21T01:47:38Z",
"updated_at": "2025-10-09T07:40:10Z",
"user": "xiangyang-95"
},
{
"repo": "huggingface/transformers",
"number": 39542,
"title": "ValueError: You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time",
"body": "### System Info\n\n- `transformers` version: 4.53.2\n- Platform: **Ubuntu 22.04** Linux 5.15.0-139-generic\n- **Python 3.10.18** + ipykernel 6.29.5\n- Pytorch 2.7.1+cu118\n\n### Who can help?\n\n@ArthurZucker \n@SunMarc \n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n I want to build a new MT model with **bert-based encoder** and a **decoder from opus-mt-en-zh** (loaded as `MarianMTModel`), BUT when I execute `Trainer.train()`, It report ValueError: `You cannot specify both decoder_input_ids and decoder_inputs_embeds at the same time`. This is code about my model and trainer.\n Thanks for helping!\n\n```Python\n# ManchuBERT Encoder + Opus-MT-zh Decoder\n\nimport torch\nfrom torch import nn\nfrom transformers.modeling_outputs import Seq2SeqLMOutput\n\n\ndef get_extended_attention_mask(attention_mask, input_shape, device, dtype=torch.float32):\n \"\"\"\n attention_mask: [B, seq_len] \n return: [B, 1, 1, seq_len] \n \"\"\"\n mask = attention_mask[:, None, None, :] # [B, 1, 1, seq_len]\n mask = mask.to(dtype=dtype)\n mask = (1.0 - mask) * -10000.0\n return mask\n\n\nclass ManchuZhMT(nn.Module):\n def __init__(self, bert, marian):\n super().__init__()\n self.decoder_embeddings = marian.model.decoder.embed_tokens\n self.embeddings = bert.embeddings\n self.encoder = bert.encoder\n self.decoder = marian.model.decoder\n self.lm_head = marian.lm_head\n self.final_logits_bias = marian.final_logits_bias\n self.config = marian.config\n\n def forward(self,\n input_ids=None,\n attention_mask=None,\n decoder_input_ids=None,\n decoder_attention_mask=None,\n labels=None,\n **kwargs):\n\n\n hidden_states = self.embeddings(input_ids=input_ids)\n attention_mask = attention_mask.to(dtype=torch.float32)\n\n extended_mask = get_extended_attention_mask(attention_mask, input_ids.shape, input_ids.device)\n\n enc_out = self.encoder(hidden_states=hidden_states,\n attention_mask=extended_mask,\n return_dict=True)\n\n dec_out = self.decoder(\n input_ids=decoder_input_ids,\n attention_mask=decoder_attention_mask,\n encoder_hidden_states=enc_out.last_hidden_state,\n encoder_attention_mask=extended_mask,\n return_dict=True)\n\n logits = self.lm_head(dec_out.last_hidden_state) + self.final_logits_bias\n\n loss = None\n if labels is not None:\n loss_fct = nn.CrossEntropyLoss(ignore_index=-100)\n loss = loss_fct(logits.view(-1, logits.size(-1)), labels.view(-1))\n\n return Seq2SeqLMOutput(loss=loss, logits=logits)\n\n def prepare_inputs_for_generation(self, *args, **kwargs):\n return self.decoder.prepare_inputs_for_generation(*args, **kwargs)\n\n def _prepare_encoder_decoder_kwargs_for_generation(self, *args, **kwargs):\n return self.decoder._prepare_encoder_decoder_kwargs_for_generation(*args, **kwargs)\n\nmodel = ManchuZhMT(manchu_model, chn_model)\nprint(model)\n\n# freeze Decoder + LM Head \nfor p in model.decoder.parameters():\n p.requires_grad = False\nfor p in model.lm_head.parameters():\n p.requires_grad = False\n```\n\n```Python\n# Add LoRA for Encoder\nfrom peft import LoraConfig, get_peft_model, TaskType\n\nnum_layers = len(model.encoder.layer)\ntarget_modules = []\nfor i in range(num_layers):\n target_modules.extend([\n f\"encoder.layer.{i}.attention.self.query\",\n f\"encoder.layer.{i}.attention.self.key\",\n f\"encoder.layer.{i}.attention.self.value\",\n f\"encoder.layer.{i}.attention.output.dense\",\n f\"encoder.layer.{i}.intermediate.dense\",\n f\"encoder.layer.{i}.output.dense\",\n ])\n\nlora_config = LoraConfig(\n task_type=TaskType.SEQ_2_SEQ_LM, \n target_modules=target_modules,\n r=16,\n lora_alpha=32,\n lora_dropout=0.05,\n bias=\"none\",\n)\nmodel = get_peft_model(model, lora_config)\nmodel.print_trainable_parameters()\n```\n\n```Python\n# Start Train!\nfrom transformers import Seq2SeqTrainer, Seq2SeqTrainingArguments\n\nargs = Seq2SeqTrainingArguments(\n output_dir=\"./lora_with_bert\",\n per_device_train_batch_size=batch_size,\n per_device_eval_batch_size=batch_size,\n num_train_epochs=10,\n learning_rate=3e-4,\n fp16=True,\n save_strategy=\"epoch\",\n predict_with_generate=True,\n logging_steps=100,\n report_to=\"none\",\n)\n\ntrainer = Seq2SeqTrainer(\n model=model,\n args=args,\n train_dataset=tokenized_ds[\"train\"],\n eval_dataset=tokenized_ds[\"val\"],\n tokenizer=manchu_tok,\n)\ntrainer.train()\ntrainer.save_model(\"./lora_with_bert/final\")\n```\n\n\n### Expected behav",
"url": "https://github.com/huggingface/transformers/issues/39542",
"state": "closed",
"labels": [
"Usage",
"Good First Issue",
"trainer",
"bug"
],
"created_at": "2025-07-21T01:06:27Z",
"updated_at": "2025-08-22T05:53:51Z",
"comments": 10,
"user": "xjackzenvey"
},
{
"repo": "huggingface/transformers",
"number": 39551,
"title": "InformerForPrediction [I would like to seek your opinions, everyone, How can I set the dynamic real features for prediction]",
"body": "Here is the description cited from the docs of InformerForPrediction\uff1a\n\n> future_time_features (torch.FloatTensor of shape (batch_size, prediction_length, num_features)) \u2014 Required time features for the prediction window, which the model internally will add to future_values. These could be things like \u201cmonth of year\u201d, \u201cday of the month\u201d, etc. encoded as vectors (for instance as Fourier features). These could also be so-called \u201cage\u201d features, which basically help the model know \u201cat which point in life\u201d a time-series is. Age features have small values for distant past time steps and increase monotonically the more we approach the current time step. Holiday features are also a good example of time features.\nThese features serve as the \u201cpositional encodings\u201d of the inputs. So contrary to a model like BERT, where the position encodings are learned from scratch internally as parameters of the model, the Time Series Transformer requires to provide additional time features. The Time Series Transformer only learns additional embeddings for static_categorical_features.\nAdditional dynamic real covariates can be concatenated to this tensor, with the caveat that these features must but known at prediction time.\nThe num_features here is equal to config.num_time_features+config.num_dynamic_real_features`.\nHi, I have a question regarding inference in time series forecasting models.\n\nWhen making predictions, how can I obtain or construct the dynamic_real_features for the future steps (i.e., for the prediction_length)?\nMore specifically, how should I concatenate the corresponding dynamic_real_features and time_features during inference?\n\nIs it appropriate to use all-zero placeholders for the future dynamic_real_features?\nWill this affect prediction performance, considering that during training the model has access to real values for these features over the full context + prediction window?\n\nOn a related note:\nIn time series forecasting, is it necessary for all timestamps in the input window to be equally spaced (e.g., every x minutes)?\nOr can I use sequences with irregular time intervals, as long as the time order is preserved?\n\nThanks for your help!\n\n\n",
"url": "https://github.com/huggingface/transformers/issues/39551",
"state": "closed",
"labels": [],
"created_at": "2025-07-20T11:38:50Z",
"updated_at": "2025-08-28T08:03:20Z",
"user": "2004learner"
},
{
"repo": "huggingface/diffusers",
"number": 11961,
"title": "New Adapter/Pipeline Request: IT-Blender for Creative Conceptual Blending",
"body": "## Model/Pipeline/Scheduler description\n\n### Name of the model/pipeline/scheduler\n\"Image-and-Text Concept Blender\" (IT-Blender), a diffusion adapter that blends visual concepts from a real reference image with textual concepts from a prompt in a disentangled manner. The goal is to enhance human creativity in design tasks.\n\n### Project page & ArXiv link\nPaper link: https://arxiv.org/pdf/2506.24085\nThe project website: https://imagineforme.github.io/ \n**(a lot of interesting feasible examples are in the project page.)**\n\n\n![\"Image\"](\"https://github.com/user-attachments/assets/87607797-32a1-41a5-b5aa-69cd8406352c\")
\n\n### What is the proposed method?\n\nIT-Blender is an adapter that works with existing models like SD and FLUX. Its core innovation is the **Blended Attention (BA)** module. This module modifies the standard self-attention layers. It uses a two-stream approach (a noisy stream for generation and a clean reference stream for the image) and introduces trainable parameters within an Image Cross-Attention (imCA) term to bridge the distributional shift between clean and noisy latents.\n\n### Is the pipeline different from an existing pipeline?\nYes. The IT-Blender pipeline is distinct for a few reasons:\n1. **Native Image Encoding**: It uses the diffusion model's own denoising network to encode the reference image by forwarding a clean version at \"t=0\". This avoids an external image encoder to better preserve details.\n2. **Two-Stream Processing**: During training and inference, it processes a \"noisy stream\" for the text-guided generation and a \"reference stream\" for the clean visual concept image simultaneously.\n3. **Blended Attention Integration**: The pipeline replaces standard self-attention modules with the new Blended Attention (BA) module, which is designed to physically separate textual and visual concept processing.\n\n### Why is this method useful?\nThe method is particularly effective for creative tasks like product design, character design, and graphic design, as shown by the extensive examples in the paper and project page. We believe it would be a valuable and unique addition to the `diffusers` library.\n\n### Open source status\n\n- [x] The model implementation is available.\n- [x] The model weights are available (Only relevant if addition is not a scheduler).\n\n### Provide useful links for the implementation\n\n**Demo page**: https://huggingface.co/spaces/WonwoongCho/IT-Blender\n**GitHub page for inference**: https://github.com/WonwoongCho/IT-Blender\nNote that we are using our own diffusers with a little bit of changes (`requirements.txt` in the github repo);\n\n**Changed Diffusers Pipeline for FLUX**: https://github.com/WonwoongCho/diffusers/blob/main/src/diffusers/pipelines/flux/pipeline_flux.py\n**Changed Diffusers Pipeline for SD1.5**: https://github.com/WonwoongCho/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py\n",
"url": "https://github.com/huggingface/diffusers/issues/11961",
"state": "open",
"labels": [],
"created_at": "2025-07-20T03:07:38Z",
"updated_at": "2025-07-20T03:08:06Z",
"comments": 0,
"user": "WonwoongCho"
},
{
"repo": "huggingface/transformers",
"number": 39522,
"title": "T5Gemma failing on provided example",
"body": "### System Info\n\n- `transformers` version: 4.53.2\n- Platform: Linux-6.14.0-23-generic-x86_64-with-glibc2.41\n- Python version: 3.13.3\n- Huggingface_hub version: 0.33.4\n- Safetensors version: 0.5.3\n- Accelerate version: 1.8.1\n- Accelerate config: \t- compute_environment: LOCAL_MACHINE\n\t- distributed_type: NO\n\t- mixed_precision: bf16\n\t- use_cpu: False\n\t- debug: False\n\t- num_processes: 1\n\t- machine_rank: 0\n\t- num_machines: 1\n\t- gpu_ids: all\n\t- rdzv_backend: static\n\t- same_network: True\n\t- main_training_function: main\n\t- enable_cpu_affinity: True\n\t- downcast_bf16: no\n\t- tpu_use_cluster: False\n\t- tpu_use_sudo: False\n\t- tpu_env: []\n\t- dynamo_config: {'dynamo_backend': 'INDUCTOR'}\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.7.1+cu128 (CUDA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: \n- Using GPU in script?: \n- GPU type: NVIDIA GeForce RTX 5060 Ti\n\n### Who can help?\n\n@ArthurZucker and @itazap \n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nRun the example from the T5Gemma docs page.\n```\necho -e \"Question: Why is the sky blue? Answer:\" | transformers run --task text2text-generation --model google/t5gemma-s-s-ul2 --device 0\n```\n\n### Expected behavior\n\nWhen I run I get:\n```\nFile \".venv/lib/python3.13/site-packages/transformers/configuration_utils.py\", line 209, in __getattribute__\n return super().__getattribute__(key)\n ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^\nAttributeError: 'T5GemmaConfig' object has no attribute **'vocab_size'**\n```\nIndeed. The vocab_size is a sub attribute from encoder/decoder, not a direct attribute.\n",
"url": "https://github.com/huggingface/transformers/issues/39522",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-19T11:07:26Z",
"updated_at": "2025-08-27T07:51:08Z",
"comments": 7,
"user": "jadermcs"
},
{
"repo": "huggingface/lerobot",
"number": 1540,
"title": "Controlling robot with text using SmolVLA",
"body": "Is it possible to control the robot with text inputs? I thought that's what a VLA model was...\n\nI cannot find any instructions on how to do this anywhere... \n\nI found this https://huggingface.co/masato-ka/smolvla_block_instruction , but control_robot was split into multiple files recently - none of which seem to work.\n\n",
"url": "https://github.com/huggingface/lerobot/issues/1540",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-18T23:09:11Z",
"updated_at": "2025-08-12T09:35:59Z",
"user": "drain-pipe"
},
{
"repo": "huggingface/diffusers",
"number": 11956,
"title": "Frequency-Decoupled Guidance (FDG) for diffusion models",
"body": "FDG is a new method for applying CFG in the frequency domain. It improves generation quality at low CFG scales while inherently avoiding the harmful effects of high CFG values. It could be a nice addition to the guiders part of diffusers. The implementation details for FDG are available on page 19 of the paper.\n\nhttps://huggingface.co/papers/2506.19713",
"url": "https://github.com/huggingface/diffusers/issues/11956",
"state": "closed",
"labels": [
"help wanted",
"Good second issue",
"contributions-welcome",
"advanced",
"consider-for-modular-diffusers"
],
"created_at": "2025-07-18T19:12:50Z",
"updated_at": "2025-08-07T05:51:03Z",
"comments": 5,
"user": "Msadat97"
},
{
"repo": "huggingface/datasets",
"number": 7689,
"title": "BadRequestError for loading dataset?",
"body": "### Describe the bug\n\nUp until a couple days ago I was having no issues loading `Helsinki-NLP/europarl` and `Helsinki-NLP/un_pc`, but now suddenly I get the following error:\n\n```\nhuggingface_hub.errors.BadRequestError: (Request ID: ...)\n\nBad request:\n* Invalid input: expected array, received string * at paths * Invalid input: expected boolean, received string * at expand\n\u2716 Invalid input: expected array, received string\n \u2192 at paths\n\u2716 Invalid input: expected boolean, received string\n \u2192 at expand\n```\n\nI tried with both `4.0.0` and `3.5.1` since this dataset uses `trust_remote_code`, but I get the same error with both.\n\nWhat can I do to load the dataset? I checked the documentation and GitHub issues here, but couldn't find a solution.\n\n### Steps to reproduce the bug\n\n```python\nimport datasets\nds = datasets.load_dataset(\"Helsinki-NLP/europarl\", \"en-fr\", streaming=True, trust_remote_code=True)[\"train\"]\n```\n\n### Expected behavior\n\nThat the dataset loads as it did a couple days ago.\n\n### Environment info\n\n- `datasets` version: 3.5.1\n- Platform: Linux-4.18.0-513.24.1.el8_9.x86_64-x86_64-with-glibc2.28\n- Python version: 3.11.11\n- `huggingface_hub` version: 0.30.2\n- PyArrow version: 20.0.0\n- Pandas version: 2.2.2\n- `fsspec` version: 2024.6.1",
"url": "https://github.com/huggingface/datasets/issues/7689",
"state": "closed",
"labels": [],
"created_at": "2025-07-18T09:30:04Z",
"updated_at": "2025-07-18T11:59:51Z",
"comments": 17,
"user": "WPoelman"
},
{
"repo": "huggingface/diffusers",
"number": 11951,
"title": "Kontext model loading quantization problem",
"body": "Hello, can kontext be loaded quantitatively at present? Because I only have a 4090 with 24g video memory, the current fp16 loading method will cause OOM. Like flux, can it be loaded with torchao or gguf, so that this model can run on 4090?",
"url": "https://github.com/huggingface/diffusers/issues/11951",
"state": "closed",
"labels": [],
"created_at": "2025-07-18T03:20:48Z",
"updated_at": "2025-07-18T05:39:28Z",
"comments": 2,
"user": "babyta"
},
{
"repo": "huggingface/transformers",
"number": 39484,
"title": "Transformers still tries to use apex.amp which is no longer a thing in apex.",
"body": "### System Info\n\n\n```\nroot@12bb27e08b1b:/# pip show transformers\nName: transformers\nVersion: 4.52.3\n```\n\n\ntrainer.py contains this:\n```\nif is_apex_available():\n from apex import amp\n```\n\nApex (built from source, as they recommend) does no longer come with amp.\n\nHow to reproduce?\n1. install transformers\n2. install apex\n3. python `from trl import SFTTrainer`\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nHow to reproduce?\n1. install transformers\n2. install apex\n3. python `from trl import SFTTrainer`\n\n### Expected behavior\n\n\nThere should not be `from apex import amp` in the code base",
"url": "https://github.com/huggingface/transformers/issues/39484",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-17T16:43:14Z",
"updated_at": "2025-08-25T08:03:03Z",
"comments": 4,
"user": "yselivonchyk"
},
{
"repo": "huggingface/datasets",
"number": 7688,
"title": "No module named \"distributed\"",
"body": "### Describe the bug\n\nhello, when I run the command \"from datasets.distributed import split_dataset_by_node\", I always met the bug \"No module named 'datasets.distributed\" in different version like 4.0.0, 2.21.0 and so on. How can I solve this?\n\n### Steps to reproduce the bug\n\n1. pip install datasets\n2. from datasets.distributed import split_dataset_by_node\n\n### Expected behavior\n\nexpecting the command \"from datasets.distributed import split_dataset_by_node\" can be ran successfully\n\n### Environment info\n\npython: 3.12",
"url": "https://github.com/huggingface/datasets/issues/7688",
"state": "open",
"labels": [],
"created_at": "2025-07-17T09:32:35Z",
"updated_at": "2025-07-25T15:14:19Z",
"comments": 3,
"user": "yingtongxiong"
},
{
"repo": "huggingface/alignment-handbook",
"number": 220,
"title": "A little question: why num examples is much less than the total amount of my training dataset?",
"body": "I am using this repo to SFT a model, and I notice that:\n\nI print the total amount of my training dataset, which is 7473\n\n`Number of raw training samples: 7473`\n\nBut during training, I find the log:\n\n[INFO|trainer.py:2314] 2025-07-17 17:03:23,908 >> ***** Running training *****\n[INFO|trainer.py:2315] 2025-07-17 17:03:23,908 >> Num examples = 698\n[INFO|trainer.py:2316] 2025-07-17 17:03:23,908 >> Num Epochs = 3\n[INFO|trainer.py:2317] 2025-07-17 17:03:23,908 >> Instantaneous batch size per device = 2\n[INFO|trainer.py:2320] 2025-07-17 17:03:23,908 >> Total train batch size (w. parallel, distributed & accumulation) = 32\n[INFO|trainer.py:2321] 2025-07-17 17:03:23,908 >> Gradient Accumulation steps = 4\n[INFO|trainer.py:2322] 2025-07-17 17:03:23,908 >> Total optimization steps = 66\n[INFO|trainer.py:2323] 2025-07-17 17:03:23,910 >> Number of trainable parameters = 7,612,756,480\n\nI am using a machine with 8 A100. Could anyone explain it? I am afraid I didn't use the whole dataset but only 698 of 7473 samples to train...",
"url": "https://github.com/huggingface/alignment-handbook/issues/220",
"state": "closed",
"labels": [],
"created_at": "2025-07-17T09:12:08Z",
"updated_at": "2025-07-23T23:30:33Z",
"comments": 3,
"user": "Red-Scarff"
},
{
"repo": "huggingface/diffusers",
"number": 11945,
"title": "Floating point exception with nightly PyTorch and CUDA",
"body": "### Describe the bug\n\nWhen running any code snippet using diffusers it fails with floating point exception, and doesn't print any traceback.\n\nFor example this one would cause the issue (the example of Stable Diffusion 3.5 medium):\n\n```\nimport torch\nfrom diffusers import StableDiffusion3Pipeline\n\npipe = StableDiffusion3Pipeline.from_pretrained(\"stabilityai/stable-diffusion-3.5-medium\", torch_dtype=torch.bfloat16)\npipe = pipe.to(\"cuda\")\n\nimage = pipe(\n \"A capybara holding a sign that reads Hello World\",\n num_inference_steps=40,\n guidance_scale=4.5,\n).images[0]\nimage.save(\"capybara.png\")\n```\n\n\nThe issue could be with upstream PyTorch or CUDA, but we'd need to identify what of Diffusers is causing it.\n\n### Reproduction\n\nNot too sure as it's my first time with Diffusers but as suggested by [John6666](https://discuss.huggingface.co/u/John6666/summary) any NVIDIA GeForce RTX 5000 series... In my case it's a 16gb 5060 Ti. Perhaps CUDA 575.57.08 with CUDA version 12.9 and/or PyTorch 2.9.0.dev20250716+cu129?\n\n### Logs\n\n```shell\nLet me know how can I retrieve any logs you might need.\n```\n\n### System Info\n\n`diffusers-cli env` also causes a Floating point exception, but here you have environment information:\n\n**OS**: Debian 12\n\n```\nnvidia-smi\nWed Jul 16 15:58:48 2025 \n+-----------------------------------------------------------------------------------------+\n| NVIDIA-SMI 575.57.08 Driver Version: 575.57.08 CUDA Version: 12.9 |\n|-----------------------------------------+------------------------+----------------------+\n| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |\n| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |\n| | | MIG M. |\n|=========================================+========================+======================|\n| 0 NVIDIA GeForce RTX 5060 Ti On | 00000000:01:00.0 On | N/A |\n| 0% 42C P5 4W / 180W | 10MiB / 16311MiB | 0% Default |\n| | | N/A |\n+-----------------------------------------+------------------------+----------------------+\n \n+-----------------------------------------------------------------------------------------+\n| Processes: |\n| GPU GI CI PID Type Process name GPU Memory |\n| ID ID Usage |\n|=========================================================================================|\n| No running processes found |\n+-----------------------------------------------------------------------------------------+\n```\n\n```\npip list\nPackage Version\n------------------------ ------------------------\nbitsandbytes 0.46.1\ncertifi 2025.7.14\ncharset-normalizer 3.4.2\ndiffusers 0.34.0\nfilelock 3.18.0\nfsspec 2025.7.0\nhf-xet 1.1.5\nhuggingface-hub 0.33.4\nidna 3.10\nimportlib_metadata 8.7.0\nJinja2 3.1.6\nMarkupSafe 3.0.2\nmpmath 1.3.0\nnetworkx 3.5\nnumpy 2.3.1\nnvidia-cublas-cu12 12.9.1.4\nnvidia-cuda-cupti-cu12 12.9.79\nnvidia-cuda-nvrtc-cu12 12.9.86\nnvidia-cuda-runtime-cu12 12.9.79\nnvidia-cudnn-cu12 9.10.2.21\nnvidia-cufft-cu12 11.4.1.4\nnvidia-cufile-cu12 1.14.1.1\nnvidia-curand-cu12 10.3.10.19\nnvidia-cusolver-cu12 11.7.5.82\nnvidia-cusparse-cu12 12.5.10.65\nnvidia-cusparselt-cu12 0.7.1\nnvidia-nccl-cu12 2.27.5\nnvidia-nvjitlink-cu12 12.9.86\nnvidia-nvshmem-cu12 3.3.9\nnvidia-nvtx-cu12 12.9.79\npackaging 25.0\npillow 11.2.1\npip 23.0.1\npytorch-triton 3.4.0+gitae848267\nPyYAML 6.0.2\nregex 2024.11.6\nrequests 2.32.4\nsafetensors 0.5.3\nsetuptools 66.1.1\nsympy 1.14.0\ntorch 2.9.0.dev20250716+cu129\ntorchaudio 2.8.0.dev20250716+cu129\ntorchvision 0.24.0.dev20250716+cu129\ntqdm 4.67.1\ntriton 3.3.1\ntyping_extensions 4.14.1\nurllib3 2.5.0\nzipp 3.23.0\n```\n\nDon't hesitate to tell me any other info you might need.\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/11945",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-07-17T03:16:02Z",
"updated_at": "2025-08-02T13:48:05Z",
"comments": 1,
"user": "MxtAppz"
},
{
"repo": "huggingface/course",
"number": 1009,
"title": "How Transformers solve tasks - ASR section refers to task using Whisper but task actually uses Wav2Vec2",
"body": "The [Automatic speech recognition](https://huggingface.co/learn/llm-course/chapter1/5?fw=pt#automatic-speech-recognition) segment of Section 1 \"Transformer Models\" > \"How \ud83e\udd17 Transformers solve tasks\" refers to \n\n> Check out our complete [automatic speech recognition guide](https://huggingface.co/docs/transformers/tasks/asr) to learn how to finetune Whisper and use it for inference!\n\nHowever the guide actually uses Wav2Vec2, not Whisper.\n\nThis is a dual request:\n\n1. Update the segment in question to refer to Wav2Vec2\n2. Update the task to use Whisper",
"url": "https://github.com/huggingface/course/issues/1009",
"state": "open",
"labels": [],
"created_at": "2025-07-16T23:25:55Z",
"updated_at": "2025-07-16T23:25:55Z",
"user": "renet10"
},
{
"repo": "huggingface/diffusers",
"number": 11930,
"title": "how to run convert_cosmos_to_diffusers.py correctly?",
"body": "### Describe the bug\n\nhi. I have tried to convert the cosmos-transfer1's base model to diffuers using \"convert_cosmos_to_diffusers.py\" code with options --transformer_type Cosmo\ns-1.0-Diffusion-7B-Video2World --vae_type CV8x8x8-1.0 --transformer_ckpt_path ../fsdp_edge_v1/iter_000016000_ema_model_only.pt --output_path ./convert_to_diffusers\nbut I got error \n```Traceback (most recent call last):\n File \"/home1/jovyan/workspace/cosmos-transfer1/diffusers/../convert_cosmos_to_diffusers.py\", line 485, in \n transformer = convert_transformer(args.transformer_type, args.transformer_ckpt_path, weights_only)\n File \"/home1/jovyan/workspace/cosmos-transfer1/diffusers/../convert_cosmos_to_diffusers.py\", line 358, in convert_transformer\n transformer.load_state_dict(original_state_dict, strict=True, assign=True)\n File \"/opt/conda/envs/cosmos-transfer1/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 2581, in load_state_dict\n raise RuntimeError(\nRuntimeError: Error(s) in loading state_dict for CosmosTransformer3DModel:\n Missing key(s) in state_dict: \"transformer_blocks.3.norm1.linear_1.weight\", \"transformer_blocks.3.norm1.linear_2.weight\", \"transformer_blocks.3.attn1.norm_q.weight\", \"transformer_blocks.3.attn1.norm_k.weight\", \"transformer_blocks.3.attn1.to_q.weight\", \"transformer_blocks.3.attn1.to_k.weight\", \"transformer_blocks.3.attn1.to_v.weight\", \"transformer_blocks.3.attn1.to_out.0.weight\", \"transformer_blocks.3.norm2.linear_1.weight\", \"transformer_blocks.3.norm2.linear_2.weight\", \"transformer_blocks.3.attn2.norm_q.weight\", \"transformer_blocks.3.attn2.norm_k.weight\", \"transformer_blocks.3.attn2.to_q.weight\", \"transformer_blocks.3.attn2.to_k.weight\", \"transformer_blocks.3.attn2.to_v.weight\", \"transformer_blocks.3.attn2.to_out.0.weight\", \"transformer_blocks.3.norm3.linear_1.weight\", \"transformer_blocks.3.norm3.linear_2.weight\", \"transformer_blocks.3.ff.net.0.proj.weight\", \"transformer_blocks.3.ff.net.2.weight\", \"transformer_blocks.4.norm1.linear_1.weight\", \"transformer_blocks.4.norm1.linear_2.weight\", \"transformer_blocks.4.attn1.norm_q.weight\", \"transformer_blocks.4.attn1.norm_k.weight\", \"transformer_blocks.4.attn1.to_q.weight\", \"transformer_blocks.4.attn1.to_k.weight\", \"transformer_blocks.4.attn1.to_v.weight\", \"transformer_blocks.4.attn1.to_out.0.weight\", \"transformer_blocks.4.norm2.linear_1.weight\", \"transformer_blocks.4.norm2.linear_2.weight\", \"transformer_blocks.4.attn2.norm_q.weight\", \"transformer_blocks.4.attn2.norm_k.weight\", \"transformer_blocks.4.attn2.to_q.weight\", \"transformer_blocks.4.attn2.to_k.weight\", \"transformer_blocks.4.attn2.to_v.weight\", \"transformer_blocks.4.attn2.to_out.0.weight\", \"transformer_blocks.4.norm3.linear_1.weight\", \"transformer_blocks.4.norm3.linear_2.weight\", \"transformer_blocks.4.ff.net.0.proj.weight\", \"transformer_blocks.4.ff.net.2.weight\", \"transformer_blocks.5.norm1.linear_1.weight\", \"transformer_blocks.5.norm1.linear_2.weight\", \"transformer_blocks.5.attn1.norm_q.weight\", \"transformer_blocks.5.attn1.norm_k.weight\", \"transformer_blocks.5.attn1.to_q.weight\", \"transformer_blocks.5.attn1.to_k.weight\", \"transformer_blocks.5.attn1.to_v.weight\", \"transformer_blocks.5.attn1.to_out.0.weight\", \"transformer_blocks.5.norm2.linear_1.weight\", \"transformer_blocks.5.norm2.linear_2.weight\", \"transformer_blocks.5.attn2.norm_q.weight\", \"transformer_blocks.5.attn2.norm_k.weight\", \"transformer_blocks.5.attn2.to_q.weight\", \"transformer_blocks.5.attn2.to_k.weight\", \"transformer_blocks.5.attn2.to_v.weight\", \"transformer_blocks.5.attn2.to_out.0.weight\", \"transformer_blocks.5.norm3.linear_1.weight\", \"transformer_blocks.5.norm3.linear_2.weight\", \"transformer_blocks.5.ff.net.0.proj.weight\", \"transformer_blocks.5.ff.net.2.weight\", \"transformer_blocks.6.norm1.linear_1.weight\", \"transformer_blocks.6.norm1.linear_2.weight\", \"transformer_blocks.6.attn1.norm_q.weight\", \"transformer_blocks.6.attn1.norm_k.weight\", \"transformer_blocks.6.attn1.to_q.weight\", \"transformer_blocks.6.attn1.to_k.weight\", \"transformer_blocks.6.attn1.to_v.weight\", \"transformer_blocks.6.attn1.to_out.0.weight\", \"transformer_blocks.6.norm2.linear_1.weight\", \"transformer_blocks.6.norm2.linear_2.weight\", \"transformer_blocks.6.attn2.norm_q.weight\", \"transformer_blocks.6.attn2.norm_k.weight\", \"transformer_blocks.6.attn2.to_q.weight\", \"transformer_blocks.6.attn2.to_k.weight\", \"transformer_blocks.6.attn2.to_v.weight\", \"transformer_blocks.6.attn2.to_out.0.weight\", \"transformer_blocks.6.norm3.linear_1.weight\", \"transformer_blocks.6.norm3.linear_2.weight\", \"transformer_blocks.6.ff.net.0.proj.weight\", \"transformer_blocks.6.ff.net.2.weight\", \"transformer_blocks.7.norm1.linear_1.weight\", \"transformer_blocks.7.norm1.linear_2.weight\", \"transformer_blocks.7.attn1.norm_q.weight\", \"transformer_blocks.7.attn1.norm_k.weight\", \"transformer_blocks.7.attn1.to_q.weight\", \"transformer_blocks.7.attn1.to_k.weight\", \"transformer_blocks.7.attn1.to_v.weight\", \"transformer_blocks.7.attn1.to_out.0.weight\", \"transformer_blocks.7.norm2.linear",
"url": "https://github.com/huggingface/diffusers/issues/11930",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-07-15T16:20:09Z",
"updated_at": "2025-07-15T16:24:47Z",
"user": "dedoogong"
},
{
"repo": "huggingface/transformers",
"number": 39426,
"title": "object detection : matchin outputs.last_hidden_state with results",
"body": "### Feature request\n\nit seems to me that would be possible with a little modification in the function post_process_object_detection\n\nwith\n```\n``for score, label, box, index in zip(scores, labels, boxes, indexes):\n results.append(\n {\n \"scores\": score[score > threshold],\n \"labels\": label[score > threshold],\n \"boxes\": box[score > threshold],\n \"indexes\": index[score > threshold],\n }\n )``\n```\n and then \n`outputs.last_hidden_state[0][results[0]['indexes']] `\ngives me the desired vector features\n\nAm I right or is there a better way to obtain this matching ? \n\nThanks for your help\n\n### Motivation\n\nI would like to use outputs.last_hidden_state as features for auxiliary tasks. So I need to know the label and the bounding box associated to one given vector of outputs.last_hidden_state\n\n### Your contribution\n\nI am not a top coder and do not know how to submit a PR",
"url": "https://github.com/huggingface/transformers/issues/39426",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-07-15T13:34:08Z",
"updated_at": "2025-07-22T11:08:23Z",
"comments": 5,
"user": "fenaux"
},
{
"repo": "huggingface/peft",
"number": 2647,
"title": "How can I merge the original model weights with LoRA weights?",
"body": "I'm currently fine-tuning Qwen2.5_VL. Specifically, I used PEFT for LoRA fine-tuning on the linear layers of the LLM part. Meanwhile, I performed regular fine-tuning on other components like visual.merger and embed_tokens (with param.requires_grad set to True). After generating the files, as follow:\n\n![\"Image\"](\"https://github.com/user-attachments/assets/b863a12f-956b-4797-bbfa-769518e73c33\")
\nI exported pytorch_model.bin using zero_to_fp32.py. When I printed the weight keys of the pytorch_model.bin file, I noticed that the original weights and LoRA weights weren't merged. Here's an example:\n\n```\nbase_model.model.model.language_model.layers.0.self_attn.q_proj.base_layer.weight: shape=(2048, 2048), dtype=torch.bfloat16\nbase_model.model.model.language_model.layers.0.self_attn.q_proj.base_layer.bias: shape=(2048,), dtype=torch.bfloat16\nbase_model.model.model.language_model.layers.0.self_attn.q_proj.lora_A.default.weight: shape=(8, 2048), dtype=torch.bfloat16\nbase_model.model.model.language_model.layers.0.self_attn.q_proj.lora_B.default.weight: shape=(2048, 8), dtype=torch.bfloat16\n```\n\nCould you tell me how to merge them? If I use\n`model = model.merge_and_unload()`\nI need the base_model. However, I no longer have the original base_model, and the original Qwen_2.5_VL model isn't suitable because apart from LoRA fine-tuning the linear layers, I also fine-tuned visual.merger and embed_tokens.\n\nHow can I solve this problem? Thank you!\n",
"url": "https://github.com/huggingface/peft/issues/2647",
"state": "closed",
"labels": [],
"created_at": "2025-07-15T11:40:33Z",
"updated_at": "2025-08-23T15:03:44Z",
"comments": 4,
"user": "guoguo1314"
},
{
"repo": "huggingface/transformers",
"number": 39421,
"title": "Speculative Decoding(do_sample=False) get different outputs",
"body": "> @transcend-0 hey!\n> \n> \n> \n> The issue was solved in [#30068](https://github.com/huggingface/transformers/pull/30068). You can install transformers from `main` with the following line for the correct generation with assisted decoding:\n> \n> \n> \n> `!pip install --upgrade git+https://github.com/huggingface/transformers.git` \n\n _Originally posted by @zucchini-nlp in [#30608](https://github.com/huggingface/transformers/issues/30608#issuecomment-2089846816)_\n\n### **System Info**\n\nPython 3.10.11\ntransformers 4.49.0\ntorch 2.6.0+cu124\n\n### **Same Reproduction**\nTarget_Model = Qwen2.5-32B-Instruct\nDraft_Model = Qwen2.5-7B-Instruct\n\n\n`question = \"Dienes are organic compounds with two adjacent double bonds in their structure, and they exhibit unique reactivity due to their conjugated pi-electron system. They play a significant role in organic chemistry and are involved in various chemical reactions and natural processes.\\nAmong the given options which one is the possible reactant (A) for the given reaction also mention the correct sequence of the dienes according to their reactivity ( most reactive to least reactive) B.\\nCyclohexene + A ---> 8,8-diiodobicyclo[4.2.0]octan-7-one\\n(B) 1. 2,3-dimethylbuta-1,3-diene, 2. (2E,4E)-hexa-2,4-diene, 3. (2E,4E)-hexa-2,4-diene, 4. (2Z,4Z)-hexa-2,4-diene\\n\\n\\nA. A = 2,2-diiodoethen-1-one, B = 3, 1, 2, 4\\nB. A = 2,2-diiodoethen-1-one, B = 4, 2, 1, 3\\nC. A = 4,4-diiodocyclobut-2-en-1-one, B = 3, 1, 2, 4\\nD. A = 4,4-diiodocyclobut-2-en-1-one, B = 4, 2, 1, 3\\n\\n\"`\n`prompt = '<|im_start|>user' + question + 'Please reason step-by-step and put your choice letter without any other text with \\\\boxed{} in the end.'`\n\n`['userDienes are organic compounds with two adjacent double bonds in their structure, and they exhibit unique reactivity due to their conjugated pi-electron system. They play a significant role in organic chemistry and are involved in various chemical reactions and natural processes.\\nAmong the given options which one is the possible reactant (A) for the given reaction also mention the correct sequence of the dienes according to their reactivity ( most reactive to least reactive) B.\\nCyclohexene + A ---> 8,8-diiodobicyclo[4.2.0]octan-7-one\\n(B) 1. 2,3-dimethylbuta-1,3-diene, 2. (2E,4E)-hexa-2,4-diene, 3. (2E,4E)-hexa-2,4-diene, 4. (2Z,4Z)-hexa-2,4-diene\\n\\n\\nA. A = 2,2-diiodoethen-1-one, B = 3, 1, 2, 4\\nB. A = 2,2-diiodoethen-1-one, B = 4, 2, 1, 3\\nC. A = 4,4-diiodocyclobut-2-en-1-one, B = 3, 1, 2, 4\\nD. A = 4,4-diiodocyclobut-2-en-1-one, B = 4, 2, 1, 3\\n\\nPlease reason step-by-step and put your choice letter without any other text with \\\\boxed{} in the end. To solve this problem, we need to identify the reactant \\\\( A \\\\) that can react with cyclohexene to form 8,8-diiodobicyclo[4.2.0]octan-7-one. We also need to determine the correct sequence of the dienes according to their reactivity from most reactive to least reactive.\\n\\n### Step-by-Step Reasoning:\\n\\n1. **Identify the Product:**\\n - The product is 8,8-diiodobicyclo[4.2.0]octan-7-one. This suggests that the reactant \\\\( A \\\\) must be a compound that can undergo a Diels-Alder reaction with cyclohexene to form the bicyclic structure and then iodination at the appropriate positions.\\n\\n2. **Reactant Identification:**\\n - The reactant \\\\( A \\\\) should be a dienophile (a compound with a double bond that can participate in a Diels-Alder reaction). Among the given options, the possible candidates are:\\n - 2,2-diiodoethen-1-one\\n - 4,4-diiodocyclobut-2-en-1-one\\n\\n3. **Diels-Alder Reaction:**\\n - Cyclohexene is a diene, and it will react with a dienophile to form a bicyclic structure. The dienophile should have a double bond that can react with the diene to form the desired product.\\n - 2,2-diiodoethen-1-one has a double bond and iodine substituents, making it a suitable dienophile.\\n - 4,4-diiodocyclobut-2-en-1-one also has a double bond but is more complex and less likely to form the desired product directly.\\n\\n4. **Sequence of Dienes According to Reactivity:**\\n - The reactivity of dienes depends on the stability of the conjugated pi-electron system.\\n - Generally, the order of reactivity from most reactive to least reactive is:\\n 1. (2E,4E)-hexa-2,4-diene (most stable and reactive)\\n 2. (2E,4E)-hexa-2,4-diene (same as above)\\n 3. 2,3-dimethylbuta-1,3-diene (less stable due to steric hindrance)\\n 4. (2Z,4Z)-hexa-2,4-diene (least stable due to cis configuration)\\n\\n5. **Matching Options:**\\n - Option A: \\\\( A = 2,2 \\\\)-diiodoethen-1-one, B = 3, 1, 2, 4\\n - Option B: \\\\( A = 2,2 \\\\)-diiodoethen-1-one, B = 4, 2, 1, 3\\n - Option C: \\\\( A = 4,4 \\\\)-diiodocyclobut-2-en-1-one, B = 3, 1, 2, 4\\n - Option D: \\\\( A = 4,4 \\\\)-diiodocyclobut-2-en-1-one, B = 4, 2, 1, 3\\n\\nGiven the correct sequence of dienes and the suitable dienophile, the correct option is:\\n\\n\\\\boxed{A}']`\n- targetDecoding - Running time: 41.82 s`\n\n`['userDienes are organic compounds with two adjacent double bonds in thei",
"url": "https://github.com/huggingface/transformers/issues/39421",
"state": "closed",
"labels": [],
"created_at": "2025-07-15T11:36:31Z",
"updated_at": "2025-07-19T03:11:04Z",
"comments": 13,
"user": "nighty8"
},
{
"repo": "huggingface/lerobot",
"number": 1508,
"title": "so101_dualarm_triplecam config to evaluate ACT policy?",
"body": "I recently fine-tuned an ACT policy where my data was from 3 cameras (1 overhead + 2 wrist) and two so101's. Then I tried to evaluate it but noticed there is currently a config file missing to support this. Does or will this support exist soon?",
"url": "https://github.com/huggingface/lerobot/issues/1508",
"state": "open",
"labels": [
"question",
"robots"
],
"created_at": "2025-07-15T03:44:32Z",
"updated_at": "2025-08-12T09:30:41Z",
"user": "sebastiandavidlee"
},
{
"repo": "huggingface/transformers",
"number": 39410,
"title": "FP8 training support for Model Parallel / Tensor Parallel (MP/TP)",
"body": "### Feature request\n\nI recieve message \"ValueError: The model you are trying to fine-tune is quantized with QuantizationMethod.FP8 but that quantization method do not support training. Please open an issue on GitHub: https://github.com/huggingface/transformers to request the support for training support for QuantizationMethod.FP8\" when trying to finetune a fp8 model.\nI have learned from the documentations that fp8 models can be trained with ddp, zero or fsdp. Is there a way to do it with MP/TP for huge fp8 models?\n\n### Motivation\n\nEnable finetuning huge fp8 models, like Qwen/Qwen3-235B-A22B-FP8\n\n### Your contribution\n\nI'm afraid it's too tough for me, but I'll do whatever I can if you need.",
"url": "https://github.com/huggingface/transformers/issues/39410",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-07-15T02:13:05Z",
"updated_at": "2025-07-15T13:30:27Z",
"comments": 2,
"user": "edgeinfinity1"
},
{
"repo": "huggingface/transformers",
"number": 39409,
"title": "TypeError: couldn't find storage object Float8_e4m3fnStorage - which version is needed for this?",
"body": "Tested so many versions but can't find a version that won't give this error\n\n\n```\n!pip install bitsandbytes==0.45.0 --upgrade\n!pip install insightface --upgrade\n!pip install huggingface_hub==0.25.1 hf_transfer diffusers==0.31.0 transformers==4.36.0\n!pip uninstall xformers triton --yes\n!pip install torch==2.2.0+cu121 torchvision --index-url https://download.pytorch.org/whl/cu121\n!pip install xformers==0.0.24 --index-url https://download.pytorch.org/whl/cu121\n```\n\n```\n\n File \"/kaggle/temp/InstantID/gradio_demo/web-ui-multicontrolnet.py\", line 975, in generate_image\n reload_pipe(model_input, model_dropdown, scheduler, adapter_strength_ratio, enable_LCM, depth_type, lora_model_dropdown, lora_scale,test_all_loras,single_lora)\n File \"/kaggle/temp/InstantID/gradio_demo/web-ui-multicontrolnet.py\", line 654, in reload_pipe\n pipe = load_model(_pretrained_model_folder, model_to_load)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/kaggle/temp/InstantID/gradio_demo/web-ui-multicontrolnet.py\", line 528, in load_model\n pipeline = StableDiffusionPipeline.from_pretrained(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.11/dist-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n return fn(*args, **kwargs)\n ^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.11/dist-packages/diffusers/pipelines/pipeline_utils.py\", line 896, in from_pretrained\n loaded_sub_model = load_sub_model(\n ^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.11/dist-packages/diffusers/pipelines/pipeline_loading_utils.py\", line 704, in load_sub_model\n loaded_sub_model = load_method(os.path.join(cached_folder, name), **loading_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.11/dist-packages/transformers/modeling_utils.py\", line 4027, in from_pretrained\n dtype_orig = cls._set_default_torch_dtype(torch_dtype)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"/usr/local/lib/python3.11/dist-packages/transformers/modeling_utils.py\", line 1584, in _set_default_torch_dtype\n torch.set_default_dtype(dtype)\n File \"/usr/local/lib/python3.11/dist-packages/torch/__init__.py\", line 1009, in set_default_dtype\n _C._set_default_dtype(d)\nTypeError: couldn't find storage object Float8_e4m3fnStorage\n```\n\n",
"url": "https://github.com/huggingface/transformers/issues/39409",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-15T01:51:08Z",
"updated_at": "2025-08-02T12:06:59Z",
"comments": 1,
"user": "FurkanGozukara"
},
{
"repo": "huggingface/datasets",
"number": 7682,
"title": "Fail to cast Audio feature for numpy arrays in datasets 4.0.0",
"body": "### Describe the bug\n\nCasting features with Audio for numpy arrays - done here with `ds.map(gen_sine, features=features)` fails\nin version 4.0.0 but not in version 3.6.0\n\n\n### Steps to reproduce the bug\n\nThe following `uv script` should be able to reproduce the bug in version 4.0.0\nand pass in version 3.6.0 on a macOS Sequoia 15.5\n\n```python\n# /// script\n# requires-python = \">=3.13\"\n# dependencies = [\n# \"datasets[audio]==4.0.0\",\n# \"librosa>=0.11.0\",\n# ]\n# ///\n# NAME\n# create_audio_dataset.py - create an audio dataset of sine waves\n#\n# SYNOPSIS\n# uv run create_audio_dataset.py\n#\n# DESCRIPTION\n# Create an audio dataset using the Hugging Face [datasets] library.\n# Illustrates how to create synthetic audio datasets using the [map]\n# datasets function.\n#\n# The strategy is to first create a dataset with the input to the\n# generation function, then execute the map function that generates\n# the result, and finally cast the final features.\n#\n# BUG\n# Casting features with Audio for numpy arrays -\n# done here with `ds.map(gen_sine, features=features)` fails\n# in version 4.0.0 but not in version 3.6.0\n#\n# This happens both in cases where --extra audio is provided and where is not.\n# When audio is not provided i've installed the latest compatible version\n# of soundfile.\n#\n# The error when soundfile is installed but the audio --extra is not\n# indicates that the array values do not have the `.T` property,\n# whilst also indicating that the value is a list instead of a numpy array.\n#\n# Last lines of error report when for datasets + soundfile case\n# ...\n#\n# File \"/Users/luasantilli/.cache/uv/archive-v0/tc_5IhQe7Zpw8ZXgQWpnl/lib/python3.13/site-packages/datasets/features/audio.py\", line 239, in cast_storage\n# storage = pa.array([Audio().encode_example(x) if x is not None else None for x in storage.to_pylist()])\n# ~~~~~~~~~~~~~~~~~~~~~~^^^\n# File \"/Users/luasantilli/.cache/uv/archive-v0/tc_5IhQe7Zpw8ZXgQWpnl/lib/python3.13/site-packages/datasets/features/audio.py\", line 122, in encode_example\n# sf.write(buffer, value[\"array\"].T, value[\"sampling_rate\"], format=\"wav\")\n# ^^^^^^^^^^^^^^^^\n# AttributeError: 'list' object has no attribute 'T'\n# ...\n#\n# For the case of datasets[audio] without explicit adding soundfile I get an FFmpeg\n# error.\n#\n# Last lines of error report:\n#\n# ...\n# RuntimeError: Could not load libtorchcodec. Likely causes:\n# 1. FFmpeg is not properly installed in your environment. We support\n# versions 4, 5, 6 and 7.\n# 2. The PyTorch version (2.7.1) is not compatible with\n# this version of TorchCodec. Refer to the version compatibility\n# table:\n# https://github.com/pytorch/torchcodec?tab=readme-ov-file#installing-torchcodec.\n# 3. Another runtime dependency; see exceptions below.\n# The following exceptions were raised as we tried to load libtorchcodec:\n#\n# [start of libtorchcodec loading traceback]\n# FFmpeg version 7: dlopen(/Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder7.dylib, 0x0006): Library not loaded: @rpath/libavutil.59.dylib\n# Referenced from: <6DB21246-F28A-31A6-910A-D8F3355D1064> /Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder7.dylib\n# Reason: no LC_RPATH's found\n# FFmpeg version 6: dlopen(/Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder6.dylib, 0x0006): Library not loaded: @rpath/libavutil.58.dylib\n# Referenced from: /Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder6.dylib\n# Reason: no LC_RPATH's found\n# FFmpeg version 5: dlopen(/Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder5.dylib, 0x0006): Library not loaded: @rpath/libavutil.57.dylib\n# Referenced from: /Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder5.dylib\n# Reason: no LC_RPATH's found\n# FFmpeg version 4: dlopen(/Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder4.dylib, 0x0006): Library not loaded: @rpath/libavutil.56.dylib\n# Referenced from: <6E59F017-C703-3AF6-A271-6277DD5F8170> /Users/luasantilli/.cache/uv/archive-v0/RK3IAlGfiICwDkHm2guLC/lib/python3.13/site-packages/torchcodec/libtorchcodec_decoder4.dylib\n# Reason: no LC_RPATH's found\n# ...\n#\n# This is strange because the the same error does not happen when using version",
"url": "https://github.com/huggingface/datasets/issues/7682",
"state": "closed",
"labels": [],
"created_at": "2025-07-14T18:41:02Z",
"updated_at": "2025-07-15T12:10:39Z",
"comments": 2,
"user": "luatil-cloud"
},
{
"repo": "huggingface/lerobot",
"number": 1507,
"title": "[PI0] Evaluation result on the metaworld",
"body": "Has anyone tried training pi0 on the Metaworld benchmark? My evaluation results are relatively low 30~%.",
"url": "https://github.com/huggingface/lerobot/issues/1507",
"state": "closed",
"labels": [
"bug",
"question",
"policies",
"simulation"
],
"created_at": "2025-07-14T14:56:38Z",
"updated_at": "2025-10-08T08:47:31Z",
"user": "chenkang455"
},
{
"repo": "huggingface/transformers",
"number": 39401,
"title": "Qwen3 tokenizer wrong offset_mapping",
"body": "### System Info\n\ntransformers 4.53.2, Ubuntu 22.04.4, python 3.11.13\n\n### Who can help?\n\n@ArthurZucker and @itazap There must be a problem with the `offset_mapping` of Qwen3 `tokenizer`. The starting point in the text for each token, except the first and the last, is one position behind. I compared it with the BERT's `tokenizer`, which produces what is expected:\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```\nsample_text='A girl is styling her hair.'\nbert_tokenizer = BertTokenizerFast.from_pretrained('google-bert/bert-base-cased')\nbert_encoding = bert_tokenizer(\n text=sample_text, add_special_tokens=False, return_offsets_mapping=True\n)\nprint(bert_encoding['offset_mapping'])\nqwen_tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-0.6B')\nqwen_encoding = qwen_tokenizer(\n text=sample_text, add_special_tokens=False, return_offsets_mapping=True\n)\nprint(qwen_encoding['offset_mapping'])\n```\n\n### Expected behavior\n\n[(0, 1), (2, 6), (7, 9), (10, 17), (18, 21), (22, 26), (26, 27)]\n[(0, 1), (1, 6), (6, 9), (9, 17), (17, 21), (21, 26), (26, 27)]",
"url": "https://github.com/huggingface/transformers/issues/39401",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-14T14:21:08Z",
"updated_at": "2025-07-16T09:59:35Z",
"comments": 4,
"user": "contribcode"
},
{
"repo": "huggingface/lerobot",
"number": 1506,
"title": "episode: None",
"body": "When I run \"python -m lerobot.scripts.train --dataset.root=./lerobot_datasets/my_robot_dataset/ --output_dir=./lerobot_datasets/outputs/ --policy.type=pi0 --dataset.repo_id=lerobot/tape --policy.push_to_hub=false\", I got\n\u2018\u2019\n'dataset': {'episodes': None,\n 'image_transforms': {'enable': False...\n}\n\u2018\u2019. \nIs this right?",
"url": "https://github.com/huggingface/lerobot/issues/1506",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-14T13:29:07Z",
"updated_at": "2025-08-12T09:31:16Z",
"user": "LogSSim"
},
{
"repo": "huggingface/finetrainers",
"number": 420,
"title": "How to fine-tune Wan 2.1 with Context Parallelism?",
"body": "I am trying to fine-tune the Wan 2.1 model and would like to leverage the Context Parallelism (CP) feature to manage memory and scale the training. I saw in the main README that `CP support` is listed as a key feature.\n\nI have looked through the `examples/training` directory and the documentation, but I couldn't find a specific example or launch script demonstrating how to fine-tune the Wan model with Context Parallelism enabled.\n\nCould you please provide some guidance or a minimal example on how to properly configure a training job for **Wan 2.1 with Context Parallelism**?",
"url": "https://github.com/huggingface/finetrainers/issues/420",
"state": "open",
"labels": [],
"created_at": "2025-07-14T06:55:39Z",
"updated_at": "2025-07-15T05:09:45Z",
"user": "vviper25"
},
{
"repo": "huggingface/lerobot",
"number": 1503,
"title": "LeRobot So100 and Groot N1.5 Model Multi-Robot Deployment Feasibility Inquiry",
"body": "Hello, I am conducting various tests using LeRobot's So100 (robot arm) with Groot N1.5 for training.\nI have some questions to ask.\n\n**Main Question**\nIs it possible to simultaneously apply a model trained with Groot N1.5 base on one robot to multiple robots of the same model?\n\n**Question Background (Actual Experience)**\nI had a model that was trained with Groot 1.5 base using data collected from So100. However, when one robot motor failed and was replaced, I had to recalibrate the entire system.\nAfter applying the previously used model for inference, the robot did not operate properly.\nI suspect this might be due to the basic position changing during the calibration process.\n\n**Core Question**\nFollowing this logic, does each robot of the same model require an individual model tailored to its specific calibration?\n\nThis question also relates to whether a single unified model can be used for inference and operation when deploying 100 robot arms in a factory setting.\n\nI would appreciate your response.",
"url": "https://github.com/huggingface/lerobot/issues/1503",
"state": "open",
"labels": [
"enhancement",
"question",
"policies",
"dataset"
],
"created_at": "2025-07-14T05:55:44Z",
"updated_at": "2025-08-12T09:31:35Z",
"user": "devedgar"
},
{
"repo": "huggingface/lerobot",
"number": 1497,
"title": "ValueError: 'policy.repo_id' argument missing. Please specify it to push the model to the hub.",
"body": "### System Info\n\n```Shell\nlerobot commit version:\nhttps://github.com/huggingface/lerobot/tree/69901b9b6a2300914ca3de0ea14b6fa6e0203bd4\n```\n\n### Information\n\n- [ ] One of the scripts in the examples/ folder of LeRobot\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n(lerobot) robot@robot-Legion-Y9000P-IRX8:~/imitation_learning_lerobot/lerobot$ python lerobot/scripts/train.py \\\n> --policy.type=act \\\n> --dataset.repo_id=lerobot/aloha_sim_transfer_cube_human \\\n> --env.type=aloha \\\n> --env.task=AlohaTransferCube-v0 \\\n> --output_dir=outputs/train/act_aloha_transfer\nINFO 2025-07-13 12:30:41 ils/utils.py:48 Cuda backend detected, using cuda.\nWARNING 2025-07-13 12:30:41 /policies.py:77 Device 'None' is not available. Switching to 'cuda'.\nTraceback (most recent call last):\n File \"/home/robot/imitation_learning_lerobot/lerobot/lerobot/scripts/train.py\", line 291, in \n train()\n File \"/home/robot/imitation_learning_lerobot/lerobot/lerobot/configs/parser.py\", line 226, in wrapper_inner\n response = fn(cfg, *args, **kwargs)\n File \"/home/robot/imitation_learning_lerobot/lerobot/lerobot/scripts/train.py\", line 110, in train\n cfg.validate()\n File \"/home/robot/imitation_learning_lerobot/lerobot/lerobot/configs/train.py\", line 120, in validate\n raise ValueError(\nValueError: 'policy.repo_id' argument missing. Please specify it to push the model to the hub.\n\n\n### Expected behavior\n\nexpected it can work",
"url": "https://github.com/huggingface/lerobot/issues/1497",
"state": "open",
"labels": [
"question",
"policies",
"configuration"
],
"created_at": "2025-07-13T04:33:14Z",
"updated_at": "2025-08-12T09:32:36Z",
"user": "dbdxnuliba"
},
{
"repo": "huggingface/trl",
"number": 3730,
"title": "How to design stable reward functions for open-ended text generation tasks in GRPO?",
"body": "I'm using GRPO for a text generation task where there's no single correct answer. I currently compute the reward using cosine similarity between the model output and a reference response. However, during training (around 400 steps), the reward values are quite unstable and fluctuate significantly.\n\nI'm wondering:\n\nIs cosine similarity a reasonable choice for reward in open-ended tasks?\n\nAre there better practices to stabilize the reward or design it more effectively in such scenarios?\n\nShould I consider switching to a learnable reward model (e.g., contrastive learning)?\n\nAny general advice on reward design in non-deterministic generation tasks would be greatly appreciated. Thanks!",
"url": "https://github.com/huggingface/trl/issues/3730",
"state": "open",
"labels": [
"\u2753 question",
"\ud83c\udfcb Reward",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-07-12T18:39:37Z",
"updated_at": "2025-07-12T18:40:05Z",
"user": "Jax922"
},
{
"repo": "huggingface/diffusers",
"number": 11915,
"title": "Create modular pipeline from existing pipeline",
"body": "new concept of modular pipelines added via #9672 is very flexible way of creating custom pipelines \nand one of the best early use-cases is new concept of modular guiders added via #11311 \n\nhowever, this would require a complete rewrite of the existing user apps/codebases to use new concepts \nand would likely significantly slow down adoption (if not even block adoption for a long time) \n\nask here is to provide a way to use an existing pipeline to instantiate a modular pipeline, \nvery similar to how different standard diffuser pipelines can be instantiated \nfrom a single pipeline class using `from_pipe` method \n\nexample of desired workflow:\n\n```py\nimport torch\nimport diffusers\n\n# load pipeline using any normal method \n# such as DiffusionPipeline, AutoPipelineForText2Image, StableDiffusionPipeline, etc. \npipe = diffusers.DiffusionPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.bfloat16,\n)\n\n# create modular pipeline from loaded pipeline\nmodular = diffusers.ModularPipeline.from_pipe(pipe)\n\n# create guider and activate it\ncfg = diffusers.ClassifierFreeGuidance(guidance_scale=5.0, guidance_rescale=0.0, start=0.0, stop=1.0)\nmodular.update_states(guider=cfg)\n\noutput = modular(\n prompt='astronaut in a diner',\n height=1024, width=1024)\n```\n\ncc: @yiyixuxu @a-r-r-o-w @sayakpaul ",
"url": "https://github.com/huggingface/diffusers/issues/11915",
"state": "closed",
"labels": [],
"created_at": "2025-07-12T16:08:30Z",
"updated_at": "2025-08-28T08:18:08Z",
"comments": 6,
"user": "vladmandic"
},
{
"repo": "huggingface/diffusers",
"number": 11914,
"title": "Loading multiple LoRAs to 1 pipeline in parallel, 1 LoRA to 2-pipelines on 2-GPUs",
"body": "Hi everyone,\n\nI have the following scenario. \n\nI have a machine with 2-GPUs and a running service that keep has two pipelines loaded to their corresponding devices. Also I have a list of LoRAs (say 10). On each request I split the batch into 2 parts (request also has the corresponding information about LoRA), load LoRAs and run the forward pass.\n\nThe problem I encounter is that whatever parallelization method I have tried (threading, multi-processing), the maximum I have achieved is pre-loading LoRAs on the cpu and then, moving them to GPU and only after that `load_lora_weights` from the state_dict. \n\nEven if I attempt to achieve parallelization in by calling the chunk where I load in parallel in threads, the pipe starts to produce either a complete noise or a black image.\n\nWhere I would appreciate a lot the help is:\n\n1. To get an advice of elegantly loading multiple LoRAs at once into one pipe (all examples in the documentation indicate that one needs to do it 1 by 1)\n2. If I have 2 pipes on 2 different devices, how to parallelize the process of loading 1 LoRA to pipes on their corresponding devices.\n\n```\ndef apply_multiple_loras_from_cache(pipes, adapter_names, lora_cache, lora_names, lora_strengths, devices):\n for device_index, pipe in enumerate(pipes):\n logger.info(f\"Starting setup for device {devices[device_index]}\")\n \n # Step 1: Unload LoRAs\n start = time.time()\n pipe.unload_lora_weights(reset_to_overwritten_params=False)\n logger.info(f\"[Device {device_index}] Unload time: {time.time() - start:.3f}s\")\n\n # Step 2: Parallelize CPU \u2192 GPU state_dict move\n def move_to_device(name):\n return name, {\n k: v.to(devices[device_index], non_blocking=True).to(pipe.dtype)\n for k, v in lora_cache[name]['state_dict'].items()\n }\n\n start = time.time()\n with ThreadPoolExecutor() as executor:\n future_to_name = {executor.submit(move_to_device, name): name for name in adapter_names}\n results = [future.result() for future in as_completed(future_to_name)]\n logger.info(f\"[Device {device_index}] State dict move + dtype conversion time: {time.time() - start:.3f}s\")\n\n # Step 3: Load adapters\n start = time.time()\n \n \n for adapter_name, state_dict in results:\n\n pipe.load_lora_weights(\n pretrained_model_name_or_path_or_dict=state_dict,\n adapter_name=adapter_name\n )\n logger.info(f\"[Device {device_index}] Load adapter weights time: {time.time() - start:.3f}s\")\n\n # Step 4: Set adapter weights\n start = time.time()\n pipe.set_adapters(lora_names, adapter_weights=lora_strengths)\n logger.info(f\"[Device {device_index}] Set adapter weights time: {time.time() - start:.3f}s\")\n\n torch.cuda.empty_cache()\n logger.info(\"All LoRAs applied and GPU cache cleared.\")\n```",
"url": "https://github.com/huggingface/diffusers/issues/11914",
"state": "closed",
"labels": [],
"created_at": "2025-07-12T15:54:44Z",
"updated_at": "2025-07-15T19:40:11Z",
"comments": 5,
"user": "vahe-toffee"
},
{
"repo": "huggingface/lerobot",
"number": 1494,
"title": "release the code for reproducing the performance on the LIBERO dataset reported in the SmolVLA paper?",
"body": "Has anyone been able to reproduce the performance on the LIBERO dataset reported in the SmolVLA paper? I\u2019d appreciate any guidelines or tips to help with reproducing the results.",
"url": "https://github.com/huggingface/lerobot/issues/1494",
"state": "closed",
"labels": [
"question",
"policies",
"simulation"
],
"created_at": "2025-07-12T09:35:00Z",
"updated_at": "2025-09-23T09:44:59Z",
"user": "JustinKai0527"
},
{
"repo": "huggingface/datasets",
"number": 7680,
"title": "Question about iterable dataset and streaming",
"body": "In the doc, I found the following example: https://github.com/huggingface/datasets/blob/611f5a592359ebac6f858f515c776aa7d99838b2/docs/source/stream.mdx?plain=1#L65-L78\n\nI am confused, \n1. If we have already loaded the dataset, why doing `to_iterable_dataset`? Does it go through the dataset faster than map-style dataset?\n2. `load_dataset(streaming=True)` is useful for huge dataset, but the speed is slow. How to make it comparable to `to_iterable_dataset` without loading the whole dataset into RAM?",
"url": "https://github.com/huggingface/datasets/issues/7680",
"state": "open",
"labels": [],
"created_at": "2025-07-12T04:48:30Z",
"updated_at": "2025-08-01T13:01:48Z",
"comments": 8,
"user": "Tavish9"
},
{
"repo": "huggingface/transformers",
"number": 39377,
"title": "FlashAttention2 support for GSAI-ML / LLaDA-8B-Instruct?",
"body": "Hi there,\n\nI attempted to use flash attention 2 with this model but it seems like it isn't supported, based on this error:\n```\nValueError: LLaDAModelLM does not support Flash Attention 2.0 yet. Please request to add support where the model is hosted, on its model hub page: https://huggingface.co/GSAI-ML/LLaDA-8B-Instruct/discussions/new or in the Transformers GitHub repo: https://github.com/huggingface/transformers/issues/new\n```\n\nwould it be possible to add support to this kind of model? \n\nThank you for your time!",
"url": "https://github.com/huggingface/transformers/issues/39377",
"state": "closed",
"labels": [],
"created_at": "2025-07-12T02:48:36Z",
"updated_at": "2025-08-19T08:03:26Z",
"comments": 2,
"user": "lbertge"
},
{
"repo": "huggingface/lerobot",
"number": 1492,
"title": "Is there any plan to add a validation loss in the training pipeline, which is not dependent on simulation env.",
"body": "Can we have a dataset split in the training code to run the model on a holdout validation episode to check loss on it?",
"url": "https://github.com/huggingface/lerobot/issues/1492",
"state": "open",
"labels": [
"enhancement",
"question",
"policies"
],
"created_at": "2025-07-11T20:43:04Z",
"updated_at": "2025-12-30T07:12:20Z",
"user": "mohitydv09"
},
{
"repo": "huggingface/peft",
"number": 2642,
"title": "Prompt_Tuning.ipynb example doesn't seem to train the model",
"body": "Hello! I am running Prompt-Tuning notebook example from PEFT lib examples [here](https://github.com/huggingface/peft/blob/main/examples/sequence_classification/Prompt_Tuning.ipynb). I did **not** change any line of code and I ran the code block sequentially.\n\nHowever, the performance under metrics remain exactly the **same** for each epoch, which is very weird. From the [orignal notebook](https://github.com/huggingface/peft/blob/main/examples/sequence_classification/Prompt_Tuning.ipynb), we can see accuracy fluctuates and can increase to 0.70. \n\nI checked the output logits for the training data is changing every epoch (set shuffle=False, and this is the only change for debugging). Now I am very confused, any suggestions would be very much welcome, please let me know if I am doing something very wrong, thanks in advance!\n\n\nHere's the performance log:\n```\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.74it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.36it/s]\nepoch 0: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.72it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.49it/s]\nepoch 1: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.74it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.34it/s]\nepoch 2: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.72it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.35it/s]\nepoch 3: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.74it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.47it/s]\nepoch 4: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.69it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.63it/s]\nepoch 5: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.75it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.45it/s]\nepoch 6: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.74it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.40it/s]\nepoch 7: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.74it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.53it/s]\nepoch 8: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:19<00:00, 5.76it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.27it/s]\nepoch 9: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.75it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.50it/s]\nepoch 10: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.74it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.63it/s]\nepoch 11: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:19<00:00, 5.77it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.50it/s]\nepoch 12: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:19<00:00, 5.78it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.60it/s]\nepoch 13: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 115/115 [00:20<00:00, 5.74it/s]\n100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 13/13 [00:01<00:00, 10.54it/s]\nepoch 14: {'accuracy': 0.6838235294117647, 'f1': 0.8122270742358079}\n```\n\nBesides, my environment info is here if it helps debugging:\n```\npython 3.10\ntransformers 4.52.4\npeft 0.16.0\ntorch 2.7.0\njupyterlab 4.4.3\nOS Ubuntu 22.04 LTS\nGPU NVIDIA RTX 5880\n```",
"url": "https://github.com/huggingface/peft/issues/2642",
"state": "closed",
"labels": [],
"created_at": "2025-07-11T18:26:58Z",
"updated_at": "2025-08-23T15:03:47Z",
"comments": 8,
"user": "ruixing76"
},
{
"repo": "huggingface/transformers",
"number": 39366,
"title": "RuntimeError when loading llmcompressor W8A8 quantized model: int8 dtype in weight initialization",
"body": "I'm trying to load the quantized model `RedHatAI/Qwen2.5-VL-7B-Instruct-quantized.w8a8` but encountering a dtype compatibility issue during model initialization. The model appears to be quantized using `llmcompressor` with W8A8 quantization scheme.\n\n**Note**: I need to load this model without vLLM because I may need to add custom hooks for my research, so I'm looking for a direct loading method using transformers/llmcompressor.\n\n## Error Message\n\n```python\nRuntimeError: expected a floating-point or complex dtype, but got dtype=torch.int8\n```\n\n**Full Stack Trace:**\n```python\nFile \"/transformers/models/qwen2_5_vl/modeling_qwen2_5_vl.py\", line 366, in _init_weights\n module.weight.data.normal_(mean=0.0, std=std)\nFile \"/torch/_refs/__init__.py\", line 6214, in normal_\n return normal(mean, std, self.shape, out=self, generator=generator)\n...\nRuntimeError: expected a floating-point or complex dtype, but got dtype=torch.int8\n```\n\n## Traceback\n\nThe error occurs during model weight initialization where transformers tries to call `normal_()` on int8 tensors. The `normal_()` function in PyTorch only works with floating-point tensors, but the quantized model contains int8 weights.\n\n**Specific failure point:**\n- File: `modeling_qwen2_5_vl.py`, line 366\n- Function: `_init_weights()` \n- Operation: `module.weight.data.normal_(mean=0.0, std=std)`\n- Issue: Trying to apply normal distribution to int8 tensors\n\n## Model Information\n\nBased on the model's `config.json`:\n- **Quantization method**: `compressed-tensors`\n- **Format**: `int-quantized` \n- **Scheme**: W8A8 (8-bit weights and activations)\n- **Base model**: `Qwen/Qwen2.5-VL-7B-Instruct`\n- **Compression ratio**: ~1.2x\n- **Ignored layers**: All visual layers (`visual.blocks.*`, `visual.merger.*`, `lm_head`)\n\n## What I've Tried\n\n### 1. llmcompressor methods:\n```python\n# Method 1: TraceableQwen2_5_VLForConditionalGeneration\nfrom llmcompressor.transformers.tracing import TraceableQwen2_5_VLForConditionalGeneration\nmodel = TraceableQwen2_5_VLForConditionalGeneration.from_pretrained(\n model_path, device_map=\"auto\", torch_dtype=\"auto\", trust_remote_code=True\n)\n\n# Method 2: SparseAutoModelForCausalLM \nfrom llmcompressor.transformers import SparseAutoModelForCausalLM\nmodel = SparseAutoModelForCausalLM.from_pretrained(\n model_path, device_map=\"auto\", torch_dtype=\"auto\", trust_remote_code=True\n)\n```\n\n### 2. Standard transformers methods:\n```python\n# Method 3: Various dtype configurations\nmodel = Qwen2_5_VLForConditionalGeneration.from_pretrained(\n model_path,\n torch_dtype=torch.bfloat16, # Also tried: torch.float16, \"auto\", None\n trust_remote_code=True,\n device_map=\"auto\"\n)\n\n# Method 4: AutoModelForCausalLM\nmodel = AutoModelForCausalLM.from_pretrained(\n model_path, trust_remote_code=True, torch_dtype=\"auto\"\n)\n```\n\n**All methods fail at the same weight initialization step, so I wonder should the model be loaded with `_fast_init=False` or other special parameters?**\n\n## Additional Observations\n\n1. **Warning about ignored layers**: The loader warns about missing visual layers, but this seems expected since they were ignored during quantization\n2. **Model files exist**: The quantized model directory contains the expected `.safetensors` files and configuration\n3. **Original model works**: The base `Qwen/Qwen2.5-VL-7B-Instruct` loads and works perfectly\n\n## Environment\n\n- **Python**: 3.10\n- **PyTorch**: 2.7.0+cu126\n- **Transformers**: 4.52.4\n- **LLMCompressor**: 0.6.0\n- **Compressed-tensors**: 0.10.2\n\n\nThis model was likely created using llmcompressor's oneshot quantization:\n```python\nfrom llmcompressor.modifiers.quantization import GPTQModifier\nfrom llmcompressor.transformers import oneshot\n\nrecipe = [\n GPTQModifier(\n targets=\"Linear\",\n scheme=\"W8A8\", \n sequential_targets=[\"Qwen2_5_VLDecoderLayer\"],\n ignore=[\"lm_head\", \"re:visual.*\"],\n ),\n]\n```\nIf this is more of an llmcompressor-specific model loading issue rather than a transformers compatibility issue, please let me know and I'll file this issue in the llmcompressor repository instead.\n\n",
"url": "https://github.com/huggingface/transformers/issues/39366",
"state": "closed",
"labels": [
"Good First Issue"
],
"created_at": "2025-07-11T15:15:09Z",
"updated_at": "2025-12-08T13:30:10Z",
"comments": 10,
"user": "AdelineXinyi"
},
{
"repo": "huggingface/lerobot",
"number": 1483,
"title": "How can I set `max_relative_target` to get safe action?",
"body": "I saw this in function `send_action` in `src/lerobot/robots/so100_follower/so100_follower.py` \n```python\n\n def send_action(self, action: dict[str, Any]) -> dict[str, Any]:\n \"\"\"Command arm to move to a target joint configuration.\n\n The relative action magnitude may be clipped depending on the configuration parameter\n `max_relative_target`. In this case, the action sent differs from original action.\n Thus, this function always returns the action actually sent.\n\n Raises:\n RobotDeviceNotConnectedError: if robot is not connected.\n\n Returns:\n the action sent to the motors, potentially clipped.\n \"\"\"\n if not self.is_connected:\n raise DeviceNotConnectedError(f\"{self} is not connected.\")\n\n goal_pos = {key.removesuffix(\".pos\"): val for key, val in action.items() if key.endswith(\".pos\")}\n\n # Cap goal position when too far away from present position.\n # /!\\ Slower fps expected due to reading from the follower.\n if self.config.max_relative_target is not None:\n present_pos = self.bus.sync_read(\"Present_Position\")\n goal_present_pos = {key: (g_pos, present_pos[key]) for key, g_pos in goal_pos.items()}\n goal_pos = ensure_safe_goal_position(goal_present_pos, self.config.max_relative_target)\n\n # Send goal position to the arm\n self.bus.sync_write(\"Goal_Position\", goal_pos)\n return {f\"{motor}.pos\": val for motor, val in goal_pos.items()}\n```\nBut in So100followerconfig it defaults to None\n```python\nclass SO100FollowerConfig(RobotConfig):\n # Port to connect to the arm\n port: str\n\n disable_torque_on_disconnect: bool = True\n\n # `max_relative_target` limits the magnitude of the relative positional target vector for safety purposes.\n # Set this to a positive scalar to have the same value for all motors, or a list that is the same length as\n # the number of motors in your follower arms.\n max_relative_target: int | None = None\n\n # cameras\n cameras: dict[str, CameraConfig] = field(default_factory=dict)\n\n # sensors\n sensors: dict[str, ForceSensorConfig] = field(default_factory=dict)\n\n # Set to `True` for backward compatibility with previous policies/dataset\n use_degrees: bool = False\n```\nI don't know how much should I set `max_relative_target` is there any instruction? thanks!!",
"url": "https://github.com/huggingface/lerobot/issues/1483",
"state": "open",
"labels": [
"question",
"robots"
],
"created_at": "2025-07-11T02:46:02Z",
"updated_at": "2025-08-12T09:34:51Z",
"user": "milong26"
},
{
"repo": "huggingface/peft",
"number": 2640,
"title": "Why peft.utils.other.fsdp_auto_wrap_policy do not warp the module do not require grad?",
"body": "In https://github.com/huggingface/peft/blob/main/src/peft/utils/other.py#L977, \n\n```\ndef fsdp_auto_wrap_policy(model):\n if hasattr(FullyShardedDataParallelPlugin, \"get_module_class_from_name\"):\n get_module_class_from_name = FullyShardedDataParallelPlugin.get_module_class_from_name\n else:\n from accelerate.utils.dataclasses import get_module_class_from_name\n from torch.distributed.fsdp.wrap import _or_policy, lambda_auto_wrap_policy, transformer_auto_wrap_policy\n\n from ..tuners import PrefixEncoder, PromptEmbedding, PromptEncoder\n\n default_transformer_cls_names_to_wrap = \",\".join(_get_no_split_modules(model))\n transformer_cls_names_to_wrap = os.environ.get(\n \"FSDP_TRANSFORMER_CLS_TO_WRAP\", default_transformer_cls_names_to_wrap\n ).split(\",\")\n transformer_cls_to_wrap = {PrefixEncoder, PromptEncoder, PromptEmbedding}\n for layer_class in transformer_cls_names_to_wrap:\n if len(layer_class) == 0:\n continue\n transformer_cls = get_module_class_from_name(model, layer_class)\n if transformer_cls is None:\n raise Exception(\"Could not find the transformer layer class to wrap in the model.\")\n else:\n transformer_cls_to_wrap.add(transformer_cls)\n\n def lambda_policy_fn(module):\n if (\n len(list(module.named_children())) == 0\n and getattr(module, \"weight\", None) is not None\n and module.weight.requires_grad\n ):\n return True\n return False\n\n lambda_policy = functools.partial(lambda_auto_wrap_policy, lambda_fn=lambda_policy_fn)\n transformer_wrap_policy = functools.partial(\n transformer_auto_wrap_policy,\n transformer_layer_cls=transformer_cls_to_wrap,\n )\n\n auto_wrap_policy = functools.partial(_or_policy, policies=[lambda_policy, transformer_wrap_policy])\n return auto_wrap_policy\n```\n\nthe fsdp_auto_wrap_policy uses a lambda_policy_fn which does not warp the module does not require grad. \nBut in regular Lora training, the original network does not need grad. \nThat may cause every GPU still keep a full network copy even in FSDP FULLY SHARD. \nWhy the code design such a policy?",
"url": "https://github.com/huggingface/peft/issues/2640",
"state": "closed",
"labels": [],
"created_at": "2025-07-10T12:07:13Z",
"updated_at": "2025-08-18T15:05:03Z",
"comments": 4,
"user": "Changlin-Lee"
},
{
"repo": "huggingface/transformers",
"number": 39336,
"title": "TypeError: GenerationMixin._extract_past_from_model_output() got an unexpected keyword argument 'standardize_cache_format'",
"body": "I am using CogVLM2 video captioning model\n\nIt works latest with transformers==4.43.4\n\nwith transformers==4.44.0 and forward I get below error\n\nbut I need to use latest version of transformers since currently 4bit quantization fails on some gpus and platforms\n\nhow can i fix this issue?\n\n`TypeError: GenerationMixin._extract_past_from_model_output() got an unexpected keyword argument 'standardize_cache_format'`\n\n```\n14:23:32 - INFO - Final video tensor shape for CogVLM processing: torch.Size([3, 24, 720, 1280])\n14:23:35 - ERROR - Error during auto-captioning: GenerationMixin._extract_past_from_model_output() got an unexpected keyword argument 'standardize_cache_format'\nTraceback (most recent call last):\n File \"E:\\Ultimate_Video_Processing_v1\\STAR\\logic\\cogvlm_utils.py\", line 679, in auto_caption\n outputs_tensor = local_model_ref.generate(**inputs_on_device, **gen_kwargs)\n File \"E:\\Ultimate_Video_Processing_v1\\venv\\lib\\site-packages\\torch\\utils\\_contextlib.py\", line 116, in decorate_context\n return func(*args, **kwargs)\n File \"E:\\Ultimate_Video_Processing_v1\\venv\\lib\\site-packages\\transformers\\generation\\utils.py\", line 2024, in generate\n result = self._sample(\n File \"E:\\Ultimate_Video_Processing_v1\\venv\\lib\\site-packages\\transformers\\generation\\utils.py\", line 3032, in _sample\n model_kwargs = self._update_model_kwargs_for_generation(\n File \"E:\\Ultimate_Video_Processing_v1\\STAR\\models\\modules\\transformers_modules\\cogvlm2-video-llama3-chat\\modeling_cogvlm.py\", line 726, in _update_model_kwargs_for_generation\n cache_name, cache = self._extract_past_from_model_output(\nTypeError: GenerationMixin._extract_past_from_model_output() got an unexpected keyword argument 'standardize_cache_format'\n```\n\n@amyeroberts, @qubvel @SunMarc @MekkCyber \n\nthe error i am getting is below with 4.43.1 on B200 when doing 4bit quant. interesting same code same libraries on my rtx 5090 on windows working without errors\n\nfp16 has no issues\n\n\n```\n11:45:10 - INFO - Preparing to load model from: /workspace/STAR/models/cogvlm2-video-llama3-chat with quant: 4, dtype: torch.bfloat16, device: cuda, device_map: auto, low_cpu_mem: True\n11:45:10 - INFO - Starting model loading - this operation cannot be interrupted once started\n/workspace/venv/lib/python3.10/site-packages/torchvision/transforms/_functional_video.py:6: UserWarning: The 'torchvision.transforms._functional_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms.functional' module instead.\n warnings.warn(\n/workspace/venv/lib/python3.10/site-packages/torchvision/transforms/_transforms_video.py:22: UserWarning: The 'torchvision.transforms._transforms_video' module is deprecated since 0.12 and will be removed in the future. Please use the 'torchvision.transforms' module instead.\n warnings.warn(\nLoading checkpoint shards: 100%|\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588| 6/6 [01:18<00:00, 13.07s/steps]\n11:46:30 - ERROR - Failed to load CogVLM2 model from path: /workspace/STAR/models/cogvlm2-video-llama3-chat\n11:46:30 - ERROR - Exception type: ValueError\n11:46:30 - ERROR - Exception details: `.to` is not supported for `4-bit` or `8-bit` bitsandbytes models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`.\nTraceback (most recent call last):\n File \"/workspace/STAR/logic/cogvlm_utils.py\", line 160, in load_cogvlm_model\n raise model_loading_result[\"error\"]\n File \"/workspace/STAR/logic/cogvlm_utils.py\", line 122, in load_model_thread\n model = AutoModelForCausalLM.from_pretrained(\n File \"/workspace/venv/lib/python3.10/site-packages/transformers/models/auto/auto_factory.py\", line 559, in from_pretrained\n return model_class.from_pretrained(\n File \"/workspace/venv/lib/python3.10/site-packages/transformers/modeling_utils.py\", line 4000, in from_pretrained\n dispatch_model(model, **device_map_kwargs)\n File \"/workspace/venv/lib/python3.10/site-packages/accelerate/big_modeling.py\", line 502, in dispatch_model\n model.to(device)\n File \"/workspace/venv/lib/python3.10/site-packages/transformers/modeling_utils.py\", line 2849, in to\n raise ValueError(\nValueError: `.to` is not supported for `4-bit` or `8-bit` bitsandbytes models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`.\n11:46:30 - ERROR - Error during auto-captioning: 'Could not load CogVLM2 model (check logs for details): `.to` is not supported for `4-bit` or `8-bit` bitsandbytes models. Please use the model as it is, since the model has already been set to the correct devices and casted to the correct `dtype`.'\nTraceback (most recent call last):\n File \"/workspace/STAR/logic/cogvlm_utils.py\", line 160, in load_cogvlm_model\n raise model_loading_result[\"error\"]\n File \"/workspace/STAR/logic/cogvlm_utils.py\", line 122, in load_model_thread\n model = AutoMode",
"url": "https://github.com/huggingface/transformers/issues/39336",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-10T11:49:02Z",
"updated_at": "2025-08-18T08:03:13Z",
"comments": 4,
"user": "FurkanGozukara"
},
{
"repo": "huggingface/lerobot",
"number": 1476,
"title": "Here as interactive gym to play with the robot, (I still need some help)",
"body": "### First the good news:\nThis is an interactive gym where you can experiment with pre-trained policies to control the robot in real time. \nHere is how to use it:\n- `Double-click` on a body to select it.\n- `Ctrl + left` drag applies a torque to the selected object, resulting in rotation.\n- `Ctrl + right` drag applies a force to the selected object in the (x,z) plane, resulting in translation.\n- `Ctrl + Shift + right` drag applies a force to the selected object in the (x,y) plane.\n\n\n### However, there are a few limitations:\n\n- When you move the cubes, the robot doesn't seem to register the new positions and instead attempts to pick them up from their original locations.\n- **Only** the environment `lerobot/act_aloha_sim_insertion_human` appears to work occasionally. The others either don't function at all or cause the program to crash due to missing attributes that haven't been implemented in the gym.\n\nI'd really appreciate feedback/guidance from the repo maintainers on how to improve this snippet to support more environments and tasks.\n\nfile `interactive_gym.py`:\n```python\nimport gymnasium as gym\nimport mujoco\nimport mujoco.viewer\nimport torch\nimport importlib\nfrom lerobot.policies.utils import get_device_from_parameters\nfrom lerobot.configs import parser\nfrom lerobot.configs.eval import EvalPipelineConfig\nfrom lerobot.policies.factory import make_policy\nfrom lerobot.envs.utils import preprocess_observation\nfrom lerobot.utils.utils import get_safe_torch_device\n\n\n# $ python interactive_gym.py --policy.path=lerobot/act_aloha_sim_insertion_human --env.type=aloha\n# $ python interactive_gym.py --policy.path=lerobot/act_aloha_sim_transfer_cube_human --env.type=aloha\n\n@parser.wrap()\ndef make_env_and_policy(cfg: EvalPipelineConfig):\n package_name = f\"gym_{cfg.env.type}\"\n\n try:\n importlib.import_module(package_name)\n except ModuleNotFoundError as e:\n print(f\"{package_name} is not installed. Please install it with `pip install 'lerobot[{cfg.env.type}]'`\")\n raise e\n\n gym_handle = f\"{package_name}/{cfg.env.task}\"\n\n env = gym.make(gym_handle, disable_env_checker=True, **cfg.env.gym_kwargs)\n\n policy = make_policy(cfg=cfg.policy, env_cfg=cfg.env)\n policy.eval() \n policy.reset()\n\n return env, policy\n\n\ndef main(env, policy):\n device = get_device_from_parameters(policy)\n\n viewer = mujoco.viewer.launch_passive(env.unwrapped.model, env.unwrapped.data)\n\n observation, info = env.reset(seed=42)\n viewer.sync()\n\n for i in range(40000):\n observation = preprocess_observation(observation)\n observation = {\n key: observation[key].to(device, non_blocking=device.type == \"cuda\") for key in observation\n }\n\n # Infer \"task\" from attributes of environments.\n # TODO: works with SyncVectorEnv but not AsyncVectorEnv\n if hasattr(env, \"task_description\"):\n observation[\"task\"] = env.unwrapped.task_description\n elif hasattr(env, \"task\"):\n observation[\"task\"] = env.unwrapped.task\n else: # For envs without language instructions, e.g. aloha transfer cube and etc.\n observation[\"task\"] = \"\"\n\n with torch.inference_mode():\n action = policy.select_action(observation)\n\n # Convert to CPU / numpy.\n action = action.to(\"cpu\").numpy()\n assert action.ndim == 2, \"Action dimensions should be (batch, action_dim)\"\n\n # Apply the next action.\n #observation, reward, terminated, truncated, info = env.step(action)\n\n observation, reward, terminated, truncated, info = env.step(action[0])\n viewer.sync()\n \n if terminated or truncated:\n observation, info = env.reset()\n viewer.sync()\n \n if i % 100 == 0:\n print(i)\n\n viewer.close()\n env.close()\n\ntorch.backends.cudnn.benchmark = True\ntorch.backends.cuda.matmul.allow_tf32 = True\n\nenv, policy = make_env_and_policy()\nmain(env, policy)\n```\n\n",
"url": "https://github.com/huggingface/lerobot/issues/1476",
"state": "open",
"labels": [
"question",
"simulation"
],
"created_at": "2025-07-09T14:59:22Z",
"updated_at": "2025-12-16T13:41:00Z",
"user": "raul-machine-learning"
},
{
"repo": "huggingface/lerobot",
"number": 1475,
"title": "[Question] What does each number in predicted action(SmolVLA) stand for?",
"body": "Hi, I'm trying to load the SmolVLA and test on my simulation env. \n\nAfter passing the observations to the model using \"policy.select_action(obs)\" I got a 6-dimensional action, but I'm quite confused about what exactly they are. And if there are three for position translation and three for rotation, how could I control the open and close for the gripper?\n\nThanks.",
"url": "https://github.com/huggingface/lerobot/issues/1475",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-09T13:39:25Z",
"updated_at": "2025-08-12T10:08:26Z",
"user": "Calvert0921"
},
{
"repo": "huggingface/lerobot",
"number": 1471,
"title": "where is 7_get_started_with_real_robot.md?",
"body": "I didn't find 7_get_started_with_real_robot.md",
"url": "https://github.com/huggingface/lerobot/issues/1471",
"state": "closed",
"labels": [
"documentation",
"question"
],
"created_at": "2025-07-09T08:02:32Z",
"updated_at": "2025-10-08T08:42:21Z",
"user": "von63"
},
{
"repo": "huggingface/alignment-handbook",
"number": 218,
"title": "Will you release SmolLM 3 recipe?",
"body": "First off, thank you so much for sharing these training resources.\n\nI was wondering if, with the recent release of SmolLM3, you have plans to also share its training recipe.\n\nHave a nice day!",
"url": "https://github.com/huggingface/alignment-handbook/issues/218",
"state": "closed",
"labels": [],
"created_at": "2025-07-08T19:47:20Z",
"updated_at": "2025-07-15T14:16:11Z",
"comments": 1,
"user": "ouhenio"
},
{
"repo": "huggingface/sentence-transformers",
"number": 3433,
"title": "How to use a custom batch sampler?",
"body": "`SentenceTransformerTrainer.__init__` will check the type of args, so I have to write a class inheriting from `SentenceTransformerTrainingArgs` rather than `TransformerTrainingArgs`. The problem is that `SentenceTransformerTrainingArgs.__post__init__` forces to use `BatchSampler` to initialize a batch sampler. Is there any workaround about this? ",
"url": "https://github.com/huggingface/sentence-transformers/issues/3433",
"state": "open",
"labels": [],
"created_at": "2025-07-08T09:35:24Z",
"updated_at": "2025-07-08T12:36:33Z",
"user": "Hypothesis-Z"
},
{
"repo": "huggingface/transformers",
"number": 39266,
"title": "Unable to create tensor, you should probably activate truncation and/or padding with 'padding=True' 'truncation=True' to have batched tensors with the same length.",
"body": "### System Info\n\n```bash\nTraceback (most recent call last):\n File \"/home/cx/miniconda3/envs/demo/lib/python3.10/site-packages/transformers/tokenization_utils_base.py\", line 767, in convert_to_tensors\n tensor = as_tensor(value)\n File \"/home/cx/miniconda3/envs/demo/lib/python3.10/site-packages/transformers/tokenization_utils_base.py\", line 729, in as_tensor\n return torch.tensor(value)\nValueError: expected sequence of length 15757 at dim 1 (got 16242)\n```\n*DataCollatorForLanguageModeling* seems to only padding input ids and ignore labels, resulting in different lengths of labels in a batch. Why is this?\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n```python\ndef _process_fn(samples, tokenizer : PreTrainedTokenizerFast, config):\n samples = [[{\"role\" : \"user\", \"content\" : x[0]}, {\"role\" : \"assistant\", \"content\" : x[1]}]\n for x in zip(samples[\"input\"], samples[\"output\"])]\n # tokenized_data = tokenizer.apply_chat_template(samples, \n # return_tensors=\"pt\",\n # return_dict=True,\n # padding=\"max_length\",\n # truncation=True,\n # max_length=8000)\n tokenized_data = tokenizer.apply_chat_template(samples, \n return_tensors=\"pt\",\n return_dict=True,\n padding=True\n )\n samples_ids = tokenized_data[\"input_ids\"]\n attention_mask = tokenized_data[\"attention_mask\"]\n output_ids = []\n for i, seq in enumerate(samples_ids):\n output_index = torch.where(seq == SPECIAL_GENERATE_TOKEN_ID)[0]\n mask = attention_mask[i]\n if len(output_index) == 1:\n output_index = output_index[0].item()\n else:\n continue\n temp = torch.full_like(seq, -100)\n temp[output_index:] = seq[output_index:]\n temp[mask == 0] = -100\n output_ids.append(temp)\n\n labels = torch.stack(output_ids)\n return {\"input_ids\" : samples_ids,\n \"labels\" : labels,\n \"attention_mask\" : attention_mask}\n\ntrainer = Trainer(\n model=peft_model,\n args=train_config,\n train_dataset=train_data,\n eval_dataset=eval_data,\n data_collator=DataCollatorForLanguageModeling(\n tokenizer=tokenizer,\n mlm=False,\n pad_to_multiple_of=8 if torch.cuda.is_available() else None,\n return_tensors=\"pt\"\n )\n )\n```\n\n\n### Expected behavior\n\nrun code",
"url": "https://github.com/huggingface/transformers/issues/39266",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-08T05:19:35Z",
"updated_at": "2025-07-08T06:50:47Z",
"comments": 0,
"user": "mumu029"
},
{
"repo": "huggingface/lerobot",
"number": 1460,
"title": "How to support dataloading with historical cue?",
"body": "as i see, the getitem function of LerobotDataset now returns the single frame data, how to stack the historical frames and make use of batch data with historical information like univla?\n",
"url": "https://github.com/huggingface/lerobot/issues/1460",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-07-08T01:49:11Z",
"updated_at": "2025-08-12T09:44:02Z",
"user": "joeyxin-del"
},
{
"repo": "huggingface/lerobot",
"number": 1458,
"title": "how to control a real robot arm-101 with my own pretrained model?",
"body": "I don't see the instruction or script example on this repository\u3002\nPlease help\n\nThanks,\n",
"url": "https://github.com/huggingface/lerobot/issues/1458",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-08T01:19:50Z",
"updated_at": "2025-08-12T09:45:13Z",
"user": "jcl2023"
},
{
"repo": "huggingface/candle",
"number": 3016,
"title": "Build fails on Maxwell GPU due to __dp4a undefined in quantized.cu",
"body": "I\u2019m trying to build a Rust project locally that depends on candle-kernels on my laptop with an NVIDIA GeForce 940MX (Maxwell, compute capability 5.0). The build fails with errors like:\n\n```\n\nsrc/quantized.cu(1997): error: identifier \"__dp4a\" is undefined\n...\n18 errors detected in the compilation of \"src/quantized.cu\".\n\n```\n\nGPU: NVIDIA GeForce 940MX (GM107, compute capability 5.0)\nOS: Kali Linux (rolling)\nCUDA toolkit: 12.3\nNVIDIA driver: 550.163.01\ncandle-kernels: v0.7.2\n\n\nThe error is caused by the use of the CUDA intrinsic __dp4a, which is only available on GPUs with compute capability 6.1+ (Pascal and newer).\nMy GPU is compute 5.0, so this intrinsic is not available.\n\n**Questions:**\nIs there a way to disable quantized kernels or the use of __dp4a for older GPUs?\nIf not, could a feature flag or build option be added to support older hardware, or at least skip building quantized kernels on unsupported GPUs?\n",
"url": "https://github.com/huggingface/candle/issues/3016",
"state": "open",
"labels": [],
"created_at": "2025-07-07T14:41:53Z",
"updated_at": "2025-07-07T14:41:53Z",
"comments": 0,
"user": "fishonamos"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3289,
"title": "How to detect watermark?",
"body": "Hi,\n\nThanks for the great work.\n\nI saw in the current code the KGW watermark is implemented. But it seems lack of code to evaluate and detect whether the generated text contains watermark.\n\nCould anyone suggest whether this code is exists? It will be very helpful.\n\nThanks",
"url": "https://github.com/huggingface/text-generation-inference/issues/3289",
"state": "open",
"labels": [],
"created_at": "2025-07-07T11:42:54Z",
"updated_at": "2025-07-07T11:42:54Z",
"user": "Allencheng97"
},
{
"repo": "huggingface/lerobot",
"number": 1448,
"title": "How to specify both policy.type and pretrained path at the same time?",
"body": "Hi, I am adding custom configs to a PreTrainedConfig, and I also want to load it from a pretrained path. However, if I specify the pretrained path (with policy.path), I won't be able to modify the fields inside the new PreTrainedConfig subclass. If I use policy.type=\"myNewModel\" instead, I am able to call the fields (such as `policy.new_field_in_myNewModel` when I run `lerobot/scripts/train.py`, but unable to specify the pretrained path.\n\nWhat is a good solution to this problem? \n\nThanks!",
"url": "https://github.com/huggingface/lerobot/issues/1448",
"state": "open",
"labels": [
"enhancement",
"configuration"
],
"created_at": "2025-07-07T03:33:15Z",
"updated_at": "2025-08-12T09:45:58Z",
"user": "branyang02"
},
{
"repo": "huggingface/lerobot",
"number": 1447,
"title": "SmolVLA input/output clarification",
"body": "I'm now trying to load the SmolVLA to control the Franka arm in simulation. I found that there could be three image inputs(Obeservation.image, 1 and 2) and I have top, wrist and side views. Is there a fixed order for those camera views?\n\nAnd the predicted action has 6 dimensions, does that mean it doesn't include the gripper state? What are those values represent for? Thanks in advance!",
"url": "https://github.com/huggingface/lerobot/issues/1447",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-06T21:56:43Z",
"updated_at": "2025-10-09T21:59:17Z",
"user": "Calvert0921"
},
{
"repo": "huggingface/lerobot",
"number": 1446,
"title": "How to evaluate finetuned SmolVLA model",
"body": "Dear authors and your wonderful work.\nI have fine-tuned the smolvla model based on a customized lerobot format dataset. My dataset is picking up a banana and placing it on a box. How can I evaluate the performance of the model? I tried eval.py in the scripes directory, but env_type=pusht doesn't work. I think this env_type may cause eval.py to fail to run.\nI hope someone can help me. Thanks in advance.\n",
"url": "https://github.com/huggingface/lerobot/issues/1446",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-06T15:27:22Z",
"updated_at": "2025-10-17T11:57:49Z",
"user": "BintaoBryant"
},
{
"repo": "huggingface/diffusers",
"number": 11865,
"title": "AttributeError: type object 'CosmosTransformer3DModel' has no attribute 'from_single_file'",
"body": "### Describe the bug\n\nI would like to run the Cosmos-Predict2-14B-Text2Image model, but it is too large to fit in 24GB of VRAM normally, so I tried to load a Q8_0 GGUF quantization. I copied some code from the [HiDreamImageTransformer2DModel](https://huggingface.co/docs/diffusers/en/api/models/hidream_image_transformer#loading-gguf-quantized-checkpoints-for-hidream-i1) page and tried to adapt it, but I get the following error:\n\n`AttributeError: type object 'CosmosTransformer3DModel' has no attribute 'from_single_file'`\n\nIs there supposed to be another way to load a 8 bit quantization? From what I have seen, Q8_0 typically produces results that are much closer to full precision compared to FP8.\n\n### Reproduction\n\n```\ntransformer = CosmosTransformer3DModel.from_single_file(\n rf\"{model_14b_id}\\cosmos-predict2-14b-text2image-Q8_0.gguf\",\n quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),\n torch_dtype=torch.bfloat16\n)\npipe_14b = Cosmos2TextToImagePipeline.from_pretrained(\n model_14b_id,\n torch_dtype=torch.bfloat16,\n transformer = transformer\n)\n```\n\n### Logs\n\n```shell\n transformer = CosmosTransformer3DModel.from_single_file(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\nAttributeError: type object 'CosmosTransformer3DModel' has no attribute 'from_single_file'\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.35.0.dev0\n- Platform: Windows-10-10.0.26100-SP0\n- Running on Google Colab?: No\n- Python version: 3.11.9\n- PyTorch version (GPU?): 2.7.1+cu128 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.33.1\n- Transformers version: 4.53.0\n- Accelerate version: 1.8.1\n- PEFT version: 0.15.2\n- Bitsandbytes version: 0.46.1\n- Safetensors version: 0.5.3\n- xFormers version: not installed\n- Accelerator: NVIDIA GeForce RTX 4090, 24564 MiB\n- Using GPU in script?: Yes\n- Using distributed or parallel set-up in script?: No\n\n\n### Who can help?\n\n@DN6 ",
"url": "https://github.com/huggingface/diffusers/issues/11865",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-05T12:14:50Z",
"updated_at": "2025-07-11T07:15:23Z",
"comments": 9,
"user": "mingyi456"
},
{
"repo": "huggingface/diffusers",
"number": 11864,
"title": "AutoencoderDC.encode fails with torch.compile(fullgraph=True) - \"name 'torch' is not defined\"",
"body": "### Describe the bug\n\nI'm trying to optimize my data preprocessing pipeline for the Sana model by using `torch.compile` on the DC-AE encoder. Following PyTorch's best practices, I attempted to compile only the `encode` method with `fullgraph=True` for better performance, but I'm encountering an error.\n\nWhen I try:\n```python\ndae.encode = torch.compile(dae.encode, fullgraph=True)\n```\n\nThe code fails with `NameError: name 'torch' is not defined` when calling `dae.encode(x)`.\n\nHowever, compiling the entire model works:\n```python\ndae = torch.compile(dae, fullgraph=True)\n```\n\nI'm unsure if this is expected behavior or if I'm doing something wrong. Is there a recommended way to compile just the encode method for `AutoencoderDC`? \n\nI was advised to use the more targeted approach of compiling only the encode method for better performance, but it seems like the DC-AE model might have some internal structure that prevents this optimization pattern.\n\nAny guidance on the correct way to apply `torch.compile` optimizations to `AutoencoderDC` would be greatly appreciated. Should I stick with compiling the entire model, or is there a way to make method-level compilation work?\n\n\n### Reproduction\n\n```\nimport torch\nfrom diffusers import AutoencoderDC\n\n# Load model\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\ndae = AutoencoderDC.from_pretrained(\n \"mit-han-lab/dc-ae-f32c32-sana-1.1-diffusers\",\n torch_dtype=torch.bfloat16\n).to(device).eval()\n\n# This fails with \"name 'torch' is not defined\"\ndae.encode = torch.compile(dae.encode, fullgraph=True)\n\n# Test\nx = torch.randn(1, 3, 512, 512, device=device, dtype=torch.bfloat16)\nout = dae.encode(x) # Error occurs here\n# This works fine\ndae = torch.compile(dae, fullgraph=True)\n```\n\n### Logs\n\n```shell\nTesting torch.compile(dae.encode, fullgraph=True)\n/data1/tzz/anaconda_dir/envs/Sana/lib/python3.10/site-packages/torch/_inductor/compile_fx.py:150: UserWarning: TensorFloat32 tensor cores for float32 matrix multiplication available but not enabled. Consider setting `torch.set_float32_matmul_precision('high')` for better performance.\n warnings.warn(\n \u2717 Error: name 'torch' is not defined\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.34.0.dev0\n- Platform: Linux-5.15.0-142-generic-x86_64-with-glibc2.35\n- Running on Google Colab?: No\n- Python version: 3.10.18\n- PyTorch version (GPU?): 2.4.0+cu121 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.33.0\n- Transformers version: 4.45.2\n- Accelerate version: 1.7.0\n- PEFT version: 0.15.2\n- Bitsandbytes version: 0.46.0\n- Safetensors version: 0.5.3\n- xFormers version: 0.0.27.post2\n- Accelerator: NVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\nNVIDIA A100-SXM4-80GB, 81920 MiB\n- Using GPU in script?: yes\n- Using distributed or parallel set-up in script?: no\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/11864",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-05T06:15:11Z",
"updated_at": "2025-07-09T01:32:39Z",
"comments": 6,
"user": "SingleBicycle"
},
{
"repo": "huggingface/datasets",
"number": 7669,
"title": "How can I add my custom data to huggingface datasets",
"body": "I want to add my custom dataset in huggingface dataset. Please guide me how to achieve that.",
"url": "https://github.com/huggingface/datasets/issues/7669",
"state": "open",
"labels": [],
"created_at": "2025-07-04T19:19:54Z",
"updated_at": "2025-07-05T18:19:37Z",
"user": "xiagod"
},
{
"repo": "huggingface/lerobot",
"number": 1442,
"title": "Trained pi0 policy ignores visual cues",
"body": "I am having an issue in which my trained pi0 policy looks smooth but it completely ignores the camera input. I have tried covering up a camera and the policy still looks smooth! This seems very wrong. I wonder if it is because my images are not normalized correctly? Has anyone else seen this?\n\n Do i need to change the \"NormalizationMode\" visual for pi0? Seems like this may be a repeat of https://github.com/huggingface/lerobot/issues/1065? \n\n",
"url": "https://github.com/huggingface/lerobot/issues/1442",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-03T20:13:08Z",
"updated_at": "2025-08-12T09:47:09Z",
"user": "kumarhans"
},
{
"repo": "huggingface/lerobot",
"number": 1439,
"title": "[QUESTION] run a policy on a real robot",
"body": "Hi There, In the documentation , scripts to teleoperate, record, replay or evaluate a policy are provided **but how to run a policy for inference only on a real robot** ? I did not find such a script? \n\nBesides it may be interesting to add such a script in the documentation as well\n\nThank you very much for your help\n",
"url": "https://github.com/huggingface/lerobot/issues/1439",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-03T18:09:10Z",
"updated_at": "2025-08-12T09:47:27Z",
"user": "FaboNo"
},
{
"repo": "huggingface/smolagents",
"number": 1512,
"title": "How can we use this benchmark to evaluate local models?",
"body": "examples/smolagents_benchmark/run.py\n\n",
"url": "https://github.com/huggingface/smolagents/issues/1512",
"state": "closed",
"labels": [
"enhancement"
],
"created_at": "2025-07-03T06:17:58Z",
"updated_at": "2025-07-03T08:07:26Z",
"user": "OoOPenN"
},
{
"repo": "huggingface/diffusers",
"number": 11849,
"title": "Can not load fusionx_lora into original wan2.1-14b",
"body": "hello, i am adding the fusionx_lora into original wan2.1-14b-i2v, my code is as follow:\n\n> pipe = WanImageToVideoPipeline.from_pretrained(my_local_path + \"Wan2.1-I2V-14B-480P-Diffusers\", vae=vae, image_encoder=image_encoder, torch_dtype=torch.bfloat16)\n> pipe.load_lora_weights(\n> my_local_path + \"Wan14BT2VFusioniX/FusionX_LoRa/Wan2.1_I2V_14B_FusionX_LoRA.safetensors\"\n> )\n\nBut i got some errors:\n\n \n\n> File \"/mmu_mllm_hdd_2/zuofei/infer_test/lora_infer_multi.py\", line 60, in process_image\n> pipe.load_lora_weights(\n> File \"/hetu_group/zuofei/env/wan_infer/lib/python3.12/site-packages/diffusers/loaders/lora_pipeline.py\", line 4869, in load_lora_weights\n> state_dict = self.lora_state_dict(pretrained_model_name_or_path_or_dict, **kwargs)\n> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n> File \"/hetu_group/zuofei/env/wan_infer/lib/python3.12/site-packages/huggingface_hub/utils/_validators.py\", line 114, in _inner_fn\n> return fn(*args, **kwargs)\n> ^^^^^^^^^^^^^^^^^^^\n> File \"/hetu_group/zuofei/env/wan_infer/lib/python3.12/site-packages/diffusers/loaders/lora_pipeline.py\", line 4796, in lora_state_dict\n> state_dict = _convert_non_diffusers_wan_lora_to_diffusers(state_dict)\n> ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n> File \"/hetu_group/zuofei/env/wan_infer/lib/python3.12/site-packages/diffusers/loaders/lora_conversion_utils.py\", line 1564, in _convert_non_diffusers_wan_lora_to_diffusers\n> num_blocks = len({k.split(\"blocks.\")[1].split(\".\")[0] for k in original_state_dict})\n> ~~~~~~~~~~~~~~~~~~^^^\n> IndexError: list index out of range\n\nCan you tell me how to fix it? Thank you so much!",
"url": "https://github.com/huggingface/diffusers/issues/11849",
"state": "open",
"labels": [],
"created_at": "2025-07-02T13:48:17Z",
"updated_at": "2025-07-02T13:48:17Z",
"comments": 0,
"user": "fzuo1230"
},
{
"repo": "huggingface/transformers",
"number": 39169,
"title": "Using Gemma3n with text-only generation requires image dependencies",
"body": "### System Info\n\n- `transformers` version: 4.53.0\n- Platform: macOS-15.5-arm64-arm-64bit\n- Python version: 3.12.8\n- Huggingface_hub version: 0.33.2\n- Safetensors version: 0.5.3\n- Accelerate version: not installed\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.7.1 (NA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: \n\n### Who can help?\n\n@zucchini-nlp \n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nI want to use the Gemma3n model in a text-only generation pipeline (without any multimodal inputs). I'm using the Gemma3nForCausalLM because it has only a language modeling head. But when running the script, it fails with an ImportError stating that `AutoImageProcessor` requires the PIL and timm libraries to work. How can I run Gemma3n for text-generation without those image-related dependencies?\n\n```python\nfrom transformers import AutoTokenizer, Gemma3nForCausalLM\nimport torch\n\nmodel_id = \"google/gemma-3n-e4b\"\n\nmodel = Gemma3nForCausalLM.from_pretrained(model_id)\ntokenizer = AutoTokenizer.from_pretrained(model_id)\n\nprompt = \"Once upon a time\"\ninputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\noutput = model.generate(**inputs, max_length=30)\n\nresponse = tokenizer.decode(output[0], skip_special_tokens=True)\n\nprint(response)\n```\n\n### Expected behavior\n\nI expect the script to run successfully without installing `pillow` and `timm`.",
"url": "https://github.com/huggingface/transformers/issues/39169",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-07-02T07:46:43Z",
"updated_at": "2025-08-01T08:14:26Z",
"comments": 6,
"user": "marianheinsen"
},
{
"repo": "huggingface/lerobot",
"number": 1429,
"title": "When will release the SmolVLA(2.25B & 0.24b)",
"body": "Hi dear authors \nthx for ur all and the wonderful work - SmolVLA!\nI wonder will u release the **SmolVLA(2.25B)?** I want to compare the performance with your release version(0.45B)",
"url": "https://github.com/huggingface/lerobot/issues/1429",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-02T03:39:06Z",
"updated_at": "2025-10-11T07:21:57Z",
"user": "JuilieZ"
},
{
"repo": "huggingface/sentence-transformers",
"number": 3416,
"title": "How to calculate prompt tokens for embedding model encode?",
"body": "I want to calculate input prompt tokens, which returns to user to let them know how many tokens they consumed. How can I do that? Could you give me an example?",
"url": "https://github.com/huggingface/sentence-transformers/issues/3416",
"state": "open",
"labels": [],
"created_at": "2025-07-02T03:27:11Z",
"updated_at": "2025-07-03T07:02:55Z",
"user": "gaoxt1983"
},
{
"repo": "huggingface/sentence-transformers",
"number": 3414,
"title": "How to fine tune multimodal embedding model?",
"body": "Hi @tomaarsen and Team - hope all is well & thanks for the work.\n\nI used to fine tune some pure text based embedding models using this package and now I would like to fine tune multimodal embedding models such as `llamaindex/vdr-2b-multi-v1` and `jinaai/jina-embeddings-v4`.\n\nI wonder if you can share some insights / relevant documentation / code examples?\n\nThank you.",
"url": "https://github.com/huggingface/sentence-transformers/issues/3414",
"state": "open",
"labels": [],
"created_at": "2025-07-01T23:45:04Z",
"updated_at": "2025-07-03T10:25:29Z",
"user": "groklab"
},
{
"repo": "huggingface/lerobot",
"number": 1424,
"title": "evaluated trained policy reports 14 pc_success only",
"body": "Trained act policy using \n\n```\npython lerobot/scripts/train.py \\\n --policy.type=act \\\n --dataset.repo_id=lerobot/act_aloha_sim_insertion_human \\\n --env.type=aloha \\\n --output_dir=outputs/train/act_aloha_insertion\n```\n\nQuestion: I think I mistakenly used the prefix `act_` in the `repo_id` but if I don't use it I get this error:\n\n```\n$ python lerobot/scripts/train.py --policy.type=act --dataset.repo_id=lerobot/aloha_sim_insertion_human --env.type=aloha --output_dir=outputs/train/act_aloha_insertion\nINFO 2025-07-01 05:47:32 ils/utils.py:48 Cuda backend detected, using cuda.\nWARNING 2025-07-01 05:47:32 /policies.py:77 Device 'None' is not available. Switching to 'cuda'.\nTraceback (most recent call last):\n File \"/home/user/lerobot/lerobot/scripts/train.py\", line 291, in \n train()\n File \"/home/user/lerobot/lerobot/configs/parser.py\", line 226, in wrapper_inner\n response = fn(cfg, *args, **kwargs)\n File \"/home/user/lerobot/lerobot/scripts/train.py\", line 110, in train\n cfg.validate()\n File \"/home/user/lerobot/lerobot/configs/train.py\", line 120, in validate\n raise ValueError(\nValueError: 'policy.repo_id' argument missing. Please specify it to push the model to the hub.\n```\n\nUsing that \"act_\" prefix in the repo id I attempted to Evaluate it using the command below but it reports `pc_success` being 14% which seems too low?\n\n```\n python lerobot/scripts/eval.py \\\n--policy.path=outputs/train/act_aloha_insertion/checkpoints/last/pretrained_model \\\n--env.type=aloha \\\n--eval.batch_size=10 \\\n--eval.n_episodes=50 \n```\n\nDetailed output of the above command:\n\n```\n$ python lerobot/scripts/eval.py --policy.path=outputs/train/act_aloha_insertion/checkpoints/last/pretrained_model --env.type=aloha --eval.batch_size=10 --eval.n_episodes=50 \nINFO 2025-07-01 05:33:14 pts/eval.py:467 {'env': {'episode_length': 400,\n 'features': {'action': {'shape': (14,),\n 'type': },\n 'agent_pos': {'shape': (14,),\n 'type': },\n 'pixels/top': {'shape': (480, 640, 3),\n 'type': }},\n 'features_map': {'action': 'action',\n 'agent_pos': 'observation.state',\n 'pixels/top': 'observation.images.top',\n 'top': 'observation.image.top'},\n 'fps': 50,\n 'obs_type': 'pixels_agent_pos',\n 'render_mode': 'rgb_array',\n 'task': 'AlohaInsertion-v0'},\n 'eval': {'batch_size': 10, 'n_episodes': 50, 'use_async_envs': False},\n 'job_name': 'aloha_act',\n 'output_dir': PosixPath('outputs/eval/2025-07-01/05-33-14_aloha_act'),\n 'policy': {'chunk_size': 100,\n 'device': 'cuda',\n 'dim_feedforward': 3200,\n 'dim_model': 512,\n 'dropout': 0.1,\n 'feedforward_activation': 'relu',\n 'input_features': {'observation.images.top': {'shape': (3,\n 480,\n 640),\n 'type': },\n 'observation.state': {'shape': (14,),\n 'type': }},\n 'kl_weight': 10.0,\n 'latent_dim': 32,\n 'license': None,\n 'n_action_steps': 100,\n 'n_decoder_layers': 1,\n 'n_encoder_layers': 4,\n 'n_heads': 8,\n 'n_obs_steps': 1,\n 'n_vae_encoder_layers': 4,\n 'normalization_mapping': {'ACTION': ,\n 'STATE': ,\n 'VISUAL': },\n 'optimizer_lr': 1e-05,\n 'optimizer_lr_backbone': 1e-05,\n 'optimizer_weight_decay': 0.0001,\n 'output_features': {'action': {'shape': (14,),\n 'type': }},\n 'pre_norm': False,\n 'pretrained_backbone_weights': 'ResNet18_Weights.IMAGENET1K_V1',\n 'private': None,\n 'push_to_hub': False,\n 'replace_final_stride_with_dilation': 0,\n 'repo_id': None,\n 'tags': None,\n 'temporal_ensemble_coeff': None,\n 'use_amp': False,\n 'use_vae': True,\n 'vision_backbone': 'resnet18'},\n 'seed': 1000}\nINFO 2025-07-01 05:33:14 pts/eval.py:476 Output dir: outputs/eval/2025-07-01/05-33-14_aloha_act\nINFO 2025-07-01 05:33:14 pts/eval.py:478 Making environment.\nINFO 2025-07-01 05:33:14 /__init__.py:84 MUJOCO_GL=%s, attempting to import specified O",
"url": "https://github.com/huggingface/lerobot/issues/1424",
"state": "open",
"labels": [
"question",
"policies"
],
"created_at": "2025-07-01T12:16:38Z",
"updated_at": "2025-08-12T09:49:05Z",
"user": "raul-machine-learning"
},
{
"repo": "huggingface/lerobot",
"number": 1421,
"title": "It would help to have a description for the lerobots datasets:",
"body": "for example, for [lerobot/aloha_sim_insertion_human](https://huggingface.co/datasets/lerobot/aloha_sim_insertion_human) comes with no description at all\n\n\nI'd help to know\n- What makes this data special/interesting\n- How to train different models in the simulator\n- What should we expect\n- what does the `_human` means, and how is it different from the `_script` suffix",
"url": "https://github.com/huggingface/lerobot/issues/1421",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-07-01T10:14:45Z",
"updated_at": "2025-08-12T09:49:27Z",
"user": "raul-machine-learning"
},
{
"repo": "huggingface/lerobot",
"number": 1419,
"title": "simulator should allow pushing objects around with the mouse interactively",
"body": "Not having this is preventing us from testing, debugging and playing with the robots.\n\nAccording to Mujoco documentation this feature available in their simulator but it is not exposed in lerobot:\n\n```\nA related usability feature is the ability to \u201creach into\u201d the simulation, push objects around and see how the \nphysics respond. The user selects the body to which the external forces and torques will be applied, and sees \na real-time rendering of the perturbations together with their dynamic consequences. This can be used to debug \nthe model visually, to test the response of a feedback controller, or to configure the model into a desired pose.\n```\n\nAlso for an awesome OOTB experience it would be great to have a script that loads a pretrained model and makes the interactive simulation just work.\n",
"url": "https://github.com/huggingface/lerobot/issues/1419",
"state": "open",
"labels": [
"question",
"simulation"
],
"created_at": "2025-07-01T09:47:02Z",
"updated_at": "2025-08-12T09:50:18Z",
"user": "raul-machine-learning"
},
{
"repo": "huggingface/lerobot",
"number": 1418,
"title": "Robot tries to transfer cube even if it failed to pick it up, shouldn't it retry?",
"body": "I am evaluating the following policy:\n```\npython lerobot/scripts/eval.py --policy.path=lerobot/act_aloha_sim_transfer_cube_human --env.type=aloha --env.task=AlohaTransferCube-v0 --eval.n_episodes=1 --eval.batch_size=1\n```\n\nHowever the robot fails to pick up the cube but carries on with the task, shouldn't the robot keep on trying until it picks up the cube? See the video\n\nhttps://github.com/user-attachments/assets/5ad20353-97bc-4d03-a78d-5f9f149c95f9\n",
"url": "https://github.com/huggingface/lerobot/issues/1418",
"state": "closed",
"labels": [
"question",
"simulation"
],
"created_at": "2025-07-01T09:18:38Z",
"updated_at": "2025-10-17T11:57:34Z",
"user": "raul-machine-learning"
},
{
"repo": "huggingface/transformers",
"number": 39137,
"title": "ImportError: cannot import name 'pipeline' from 'transformers'",
"body": "### System Info\n\nI am using Databricks notebook. \nDatabricks runtime: 13.3 LTS (includes Apache Spark 3.4.1, Scala 2.12)\n\n### Who can help?\n\n@Rocketknight1 @SunMarc @zach-huggingface\n\n### Information\n\n- [x] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nHere is the code:\n\n```\n%pip install --upgrade torch transformers accelerate deepspeed bitsandbytes huggingface_hub\ndbutils.library.restartPython()\n\nimport os\nimport torch\nfrom transformers import AutoTokenizer, AutoModelForCausalLM, pipeline\n```\n\nError:\n`ImportError: cannot import name 'pipeline' from 'transformers' (/local_disk0/.ephemeral_nfs/envs/pythonEnv-a13cd5c4-d035-4d04-87bd-75088348617d/lib/python3.10/site-packages/transformers/__init__.py)`\n\nPython: 3.10.12\ninstalled packages:\ntransformers== 4.53.0\nhuggingface_hub==0.33.1\ntorch==2.7.1+cu126\naccelerate==1.8.1\ndeepspeed==0.17.1\nbitsandbytes==0.46.0\n\nThese are all up-to-date versions for all of these packages. What is the problem?\n\n### Expected behavior\n\nImport without error.",
"url": "https://github.com/huggingface/transformers/issues/39137",
"state": "closed",
"labels": [
"Usage",
"bug"
],
"created_at": "2025-06-30T18:49:54Z",
"updated_at": "2025-10-23T00:53:19Z",
"comments": 14,
"user": "atabari-bci"
},
{
"repo": "huggingface/lerobot",
"number": 1407,
"title": "Can read the current signals from the lerobot?",
"body": "Can a user read the current signals from the LeRobot?",
"url": "https://github.com/huggingface/lerobot/issues/1407",
"state": "open",
"labels": [
"question",
"sensors"
],
"created_at": "2025-06-30T10:05:26Z",
"updated_at": "2025-08-12T09:51:06Z",
"user": "Frank-ZY-Dou"
},
{
"repo": "huggingface/optimum",
"number": 2314,
"title": "How to set the dynamic input sizes for decoder_with_past_model.onnx of NLLB",
"body": "Dear author,\nI'm a beginner in optimum. So this question may be an elementary one. I used optimum to export decoder_with_past_model.onnx from nllb-200-distilled-600M. The resulted onnx has many inputs with dynamic shape. Now I intend to overwrite the inputs with static sizes. However, I'm not sure about the correct settings. \n\nThere are 4 arguments to be determined and I set:\nbatch_size = 1\nencoder_sequence_length = 200 (same with max_length)\npast_decoder_sequence_length = 200\nencoder_sequence_length_out = 200\n\nAny suggestions are appre\n\n\n\nciated. Big thanks.",
"url": "https://github.com/huggingface/optimum/issues/2314",
"state": "closed",
"labels": [
"Stale"
],
"created_at": "2025-06-30T06:37:50Z",
"updated_at": "2025-08-07T02:17:43Z",
"user": "liamsun2019"
},
{
"repo": "huggingface/transformers",
"number": 39114,
"title": "Is there a way to force it to use ASCII based progress bar and not the ipython widget one?",
"body": "When loading models, I like it better to have a ASCII based progress bar and not a IPython one",
"url": "https://github.com/huggingface/transformers/issues/39114",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-06-29T22:41:19Z",
"updated_at": "2025-07-07T13:20:13Z",
"comments": 0,
"user": "weathon"
},
{
"repo": "huggingface/transformers",
"number": 39105,
"title": "How to use other acceleration apis of npu?",
"body": "### Feature request\n\nI noticed that transformers now support using flash attention directly in the npu by [```npu_flash_attention.py```](https://github.com/huggingface/transformers/pull/36696). There are many other acceleration apis that can be used in npu, such as shown in [doc](https://www.hiascend.com/document/detail/zh/Pytorch/700/ptmoddevg/trainingmigrguide/performance_tuning_0028.html).\n\n How can we use them directly in transformers? How to switch seamlessly between different devices?\n\n### Motivation\n\nRequest to integrate other acceleration apis of npu in transformers. If this can be done, the ease of using transformers will be greatly improved in npu.",
"url": "https://github.com/huggingface/transformers/issues/39105",
"state": "closed",
"labels": [
"Feature request"
],
"created_at": "2025-06-29T08:26:29Z",
"updated_at": "2026-01-04T07:23:26Z",
"user": "zheliuyu"
},
{
"repo": "huggingface/candle",
"number": 3013,
"title": "Word Timestamp for whisper",
"body": "Hi is there no way to get word timestamp using the whisper in candle?\n\nThe example successfully demonstrates the retrieval of segment timestamp but how would one retrieve word timestamp.\n\nWhen I look into python code, they seem to pass this `word_timestamp=True` argument while transcribing and get the result with `base` model.\n\nIs there any work around or can someone point me towards how to achieve this please.",
"url": "https://github.com/huggingface/candle/issues/3013",
"state": "open",
"labels": [],
"created_at": "2025-06-29T01:16:38Z",
"updated_at": "2025-06-29T23:47:39Z",
"comments": 2,
"user": "bp7968h"
},
{
"repo": "huggingface/trl",
"number": 3662,
"title": "What is the point of steps_per_gen in GRPO Trainer",
"body": "Hello, can you please explain what is the point of steps_per_gen in GRPO Training config when we already have num_iterations? The policy update logic can then simply be:\n\nif num_iterations = 1, generations and model update are on_policy (per_token_logps = old_per_token_logps)\n\nWhen num_iterations > 1, then the same generation will be used for multiple times, and per_token_logps will be different from old_per_token_logps for all but the first time a generation batch is used. \n\nWhy is steps_per_gen needed? It just makes the overall batch generation and splitting logic unnecessarily difficult to understand.. ",
"url": "https://github.com/huggingface/trl/issues/3662",
"state": "open",
"labels": [
"\u2753 question",
"\ud83c\udfcb GRPO"
],
"created_at": "2025-06-28T20:08:01Z",
"updated_at": "2025-07-25T08:05:50Z",
"user": "ankur6ue"
},
{
"repo": "huggingface/lerobot",
"number": 1399,
"title": "calibrate.py for only follower",
"body": "the calibrate.py file doesnt work for setting up the motors for the follower arm, as there arent enough parameters for the function to run. Has anyone made an adaption for the calibrate file that doesnt take into consideration the teleop?",
"url": "https://github.com/huggingface/lerobot/issues/1399",
"state": "open",
"labels": [
"question",
"teleoperators"
],
"created_at": "2025-06-27T20:53:47Z",
"updated_at": "2025-08-12T09:51:53Z",
"user": "ramallis"
},
{
"repo": "huggingface/transformers",
"number": 39091,
"title": "`transformers`' dependency on `sentencepiece` blocks use on windows in python 3.13",
"body": "### System Info\n\nDue to \n* changes in Python 3.13,\n* an incompatibility in `sentencepiece`,\n* `transformers` dependency on `sentencepiece`,\n\n`transformers` cannot be easily installed under windows + py3.13, and does not work as a dependency of other packages in this environment\n\nThere are multiple issues and a merged PR on sentencepiece (https://github.com/google/sentencepiece/pull/1084) from Feb 26 2025 but no release has been forthcoming\n\n\n\n### Who can help?\n\n* people currently using `sentencepiece` in `transformers` code they own\n* people determining what the scope of `transformers`' OS & python support is\n* `sentencepiece` pypi maintainers\n\n\n### Reproduction\n\n1. Be on windows\n2. Be on python 3.13\n3. Try to install current `transformers` from pypi\n4. If you get this far, use any function importing `sentencepiece`, e.g. loading an `xlm_roberta` model\n\n### Expected behavior\n\nCode doesn't raise exception",
"url": "https://github.com/huggingface/transformers/issues/39091",
"state": "closed",
"labels": [
"Usage"
],
"created_at": "2025-06-27T15:23:57Z",
"updated_at": "2025-07-03T16:02:47Z",
"comments": 5,
"user": "leondz"
},
{
"repo": "huggingface/transformers",
"number": 39073,
"title": "Inefficient default GELU implementation in GPT2",
"body": "While profiling the HuggingFace GPT2 model, I found that the default GELU backend used is NewGELUActivation, which is inefficient in most cases. Instead of using a fused CUDA kernel, NewGELUActivation executes multiple separate PyTorch-level operators, leading to unnecessary kernel launches and memory overhead.\n\n```python\n# activations.py:L46\nclass NewGELUActivation(nn.Module):\n def forward(self, input: Tensor) -> Tensor:\n return 0.5 * input * (1.0 + torch.tanh(math.sqrt(2.0 / math.pi) * (input + 0.044715 * torch.pow(input, 3.0))))\n```\n\nIs there a reason why NewGELUActivation is still used as the default for GPT2, rather than switching to nn.functional.gelu or another fused alternative?\n\nI\u2019d be happy to share profiler traces or help test a patch if helpful.",
"url": "https://github.com/huggingface/transformers/issues/39073",
"state": "closed",
"labels": [],
"created_at": "2025-06-27T09:07:39Z",
"updated_at": "2025-08-12T03:35:13Z",
"comments": 4,
"user": "null-pointer-access"
},
{
"repo": "huggingface/diffusers",
"number": 11816,
"title": "set_adapters performance degrades with the number of inactive adapters",
"body": "### Describe the bug\n\n### Goal\nBuild an image-generation service with `StableDiffusionXLPipeline` that:\n\n1. Keeps ~50 LoRA adapters resident in GPU VRAM.\n2. For each request:\n \u2022 activate **\u2264 5** specific LoRAs via `pipeline.set_adapters(...)` \n \u2022 run inference \n \u2022 deactivate them (ready for the next request).\n\n### Issue\n`pipeline.set_adapters()` becomes progressively slower the more unique LoRAs have ever been loaded,\neven though each call still enables only up to five adapters.\n\n| # LoRAs ever loaded | `set_adapters()` time (s) | \n|---------------------|---------------------------|\n| 3 | ~ 0.1031 | \n| 6 | ~ 0.1843 | \n| 9 | ~ 0.2614 |\n| 12 | ~ 0.3522 | \n| 45 | ~ 1.2470 | \n| 57 | ~ 1.5435 |\n\n### What I\u2019ve tried\n1. **Load LoRAs from disk for every request** ~ 0.8 s/LoRA, too slow. \n2. **Keep LoRAs in RAM (`SpooledTemporaryFile`) + `pipeline.delete_adapter()`** \u2013 roughly as slow as (1). \n3. **Keep all 50 LoRAs on the GPU** and just switch with `set_adapters()` \u2013 fastest so far, but still shows the O(N)-style growth above.\n\n### Question\nIs this increasing latency expected? \nIs there a recommended pattern for caching many LoRAs on the GPU and switching between small subsets without paying an O(total LoRAs) cost every time?\n\nAny guidance (or confirmation it\u2019s a current limitation) would be greatly appreciated!\n\n### Reproduction\n\n
The output of python collect_env.py
\n\n```text\nCollecting environment information...\n==============================\n System Info\n==============================\nOS : Amazon Linux 2023.7.20250428 (x86_64)\nGCC version : (GCC) 11.5.0 20240719 (Red Hat 11.5.0-5)\nClang version : Could not collect\nCMake version : version 3.26.4\nLibc version : glibc-2.34\n\n==============================\n PyTorch Info\n==============================\nPyTorch version : 2.8.0+cu128\nIs debug build : False\nCUDA used to build PyTorch : 12.8\nROCM used to build PyTorch : N/A\n\n==============================\n Python Environment\n==============================\nPython version : 3.12.6 (main, May 6 2025, 20:22:13) [GCC 11.5.0 20240719 (Red Hat 11.5.0-5)] (64-bit runtime)\nPython platform : Linux-6.1.134-150.224.amzn2023.x86_64-x86_64-with-glibc2.34\n\n==============================\n CUDA / GPU Info\n==============================\nIs CUDA available : True\nCUDA runtime version : 12.8.93\nCUDA_MODULE_LOADING set to : LAZY\nGPU models and configuration : \nGPU 0: NVIDIA H100 80GB HBM3\nGPU 1: NVIDIA H100 80GB HBM3\nGPU 2: NVIDIA H100 80GB HBM3\nGPU 3: NVIDIA H100 80GB HBM3\nGPU 4: NVIDIA H100 80GB HBM3\nGPU 5: NVIDIA H100 80GB HBM3\nGPU 6: NVIDIA H100 80GB HBM3\nGPU 7: NVIDIA H100 80GB HBM3\n\nNvidia driver version : 570.133.20\ncuDNN version : Could not collect\nHIP runtime version : N/A\nMIOpen runtime version : N/A\nIs XNNPACK available : True\n\n==============================\n CPU Info\n==============================\nArchitecture: x86_64\nCPU op-mode(s): 32-bit, 64-bit\nAddress sizes: 48 bits physical, 48 bits virtual\nByte Order: Little Endian\nCPU(s): 192\nOn-line CPU(s) list: 0-191\nVendor ID: AuthenticAMD\nModel name: AMD EPYC 7R13 Processor\nCPU family: 25\nModel: 1\nThread(s) per core: 2\nCore(s) per socket: 48\nSocket(s): 2\nStepping: 1\nBogoMIPS: 5299.99\nFlags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid aperfmperf tsc_known_freq pni pclmulqdq monitor ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm cmp_legacy cr8_legacy abm sse4a misalignsse 3dnowprefetch topoext perfctr_core invpcid_single ssbd ibrs ibpb stibp vmmcall fsgsbase bmi1 avx2 smep bmi2 invpcid rdseed adx smap clflushopt clwb sha_ni xsaveopt xsavec xgetbv1 clzero xsaveerptr rdpru wbnoinvd arat npt nrip_save vaes vpclmulqdq rdpid\nHypervisor vendor: KVM\nVirtualization type: full\nL1d cache: 3 MiB (96 instances)\nL1i cache: 3 MiB (96 instances)\nL2 cache: 48 MiB (96 instances)\nL3 cache: 384 MiB (12 instances)\nNUMA node(s): 2\nNUMA node0 CPU(s): 0-47,96-143\nNUMA node1 CPU(s): 48-95,144-191\nVulnerability Gather data sampling: Not affected\nVulnerability Itlb multihit: Not affected\nVulnerability L1tf: Not affected\nVulnerability Mds: Not affected\nVulnerability Meltdown: Not affected\nVulnerability Mmio stale data: Not affected\nVulnerability Reg file data sampling: Not affected\nVulnerability Retbleed: Not affected\nVulnerability Spec rstack overflow: Mitigation; safe RET\nVulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl\nVulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization\nVulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; IBRS_FW; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected\nVulnerability Srbds: Not affected\nVulnerability Tsx async abort: Not affected\n\n==============================\nVersions of relevant libraries\n==============================\n[pip3] flashinfer-python==0.3.1\n[pip3] numpy==2.2.6\n[pip3] nvidia-cublas-cu12==12.8.4.1\n[pip3] nvidia-cuda-cupti-cu12==12.8.90\n[pip3] nvidia-cuda-nvrtc-cu12==12.8.93\n[pip3] nvidia-cuda-runtime-cu12==12.8.90\n[pip3] nvidia-cudnn-cu12==9.10.2.21\n[pip3] nvidia-cudnn-frontend==1.15.0\n[pip3] nvidia-cufft-cu12==11.3.3.83\n[pi",
"url": "https://github.com/vllm-project/vllm/issues/27604",
"state": "open",
"labels": [
"bug",
"nvidia"
],
"created_at": "2025-10-27T20:22:37Z",
"updated_at": "2025-11-06T02:37:17Z",
"comments": 10,
"user": "jmkuebler"
},
{
"repo": "huggingface/smolagents",
"number": 1834,
"title": "Discussion: how to edit the messages sent to the underlying LLM",
"body": "Hi! I'm working on a feature to allow a user to add callbacks to modify the content before it is sent to the LLM, inside the agent loop. \n\nI noticed this strange behavior where the first user message must start with \"New Task:\", otherwise I get this cryptic and misleading error message.\n\n\n\"\"Error:\\nError while parsing tool call from model output: The model output does not contain any JSON blob.\\nNow let's retry: take care not to repeat previous errors! If you have retried several times, try a completely different approach.\\n\"\"\n\nSo I think I have two (or maybe one question):\n\n1. Is my approach to control the messages flow by wrapping the `generate` member function of a Smolagent correct? Or do you recommend a better way to modify messages before sending them to the underlying LLM?\n2. Is it expected that the first user message needs to start with New Task:, or have I found a bug or missing assertion somewhere in the code? Thanks!\n\nhttps://github.com/mozilla-ai/any-agent/blob/f2475d7507c5a78e241ff5f0883b546d796d29fc/src/any_agent/callbacks/wrappers/smolagents.py#L75\n\nI'm on smolagents==1.22.0, python 3.13.\n\nUPDATE: I'm no longer sure that adding \"New Task:\" is the fix, I am still seeing intermittent errors even when I have that text added. It seems like there some sort of race condition, I'm confused about where the \"messages\" content should be edited, since it seems like maybe it's being stored or referenced in multiple conditions? Any help appreciated!\n",
"url": "https://github.com/huggingface/smolagents/issues/1834",
"state": "closed",
"labels": [],
"created_at": "2025-10-27T17:28:38Z",
"updated_at": "2025-10-27T19:02:39Z",
"user": "njbrake"
},
{
"repo": "huggingface/peft",
"number": 2873,
"title": "Can I use Lora fine-tuning twice?",
"body": "I\u2019m planning to work with a two-stage LoRA fine-tuning pipeline (Stage 1: SFT with code completion outputs; Stage 2: SFT with full-code outputs; RL follows). My question is:\nWhen I continue training the same LoRA adapter in Stage 2, will I risk overwriting or degrading the knowledge learned during Stage 1 ? In other words, does continuing on the same adapter effectively preserve the Stage 1 capabilities, or should I be using a separate adapter (or merging strategy) to ensure both sets of skills remain intact?\nThank you for any guidance or best\u2010practice pointers!",
"url": "https://github.com/huggingface/peft/issues/2873",
"state": "closed",
"labels": [],
"created_at": "2025-10-27T12:51:45Z",
"updated_at": "2025-12-05T15:05:00Z",
"comments": 8,
"user": "tohokulgq"
},
{
"repo": "vllm-project/vllm",
"number": 27572,
"title": "[Bug]: chat/completions stream intermittently returns null as finish_reason",
"body": "### Your current environment\n\n```\nMy env:\nvllm 0.10.0\n```\n\n\n### \ud83d\udc1b Describe the bug\n\n\n```\n\n+ curl -kLsS https://127.0.0.1:7888/v1/chat/completions -H 'Content-Type: application/json' --data '{\n \"model\": \"ibm/granite-3-8b-instruct\",\n \"stream\": true,\n \"messages\": [\n {\n \"role\": \"system\",\n \"content\": \"You are a helpful assistant.\"\n },\n {\n \"role\": \"user\",\n \"content\": \"What is the weather like in Warsaw?\"\n }\n ],\n \"tools\": [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_current_weather\",\n \"description\": \"Get the current weather in a given location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"The city and state, e.g. San Francisco, CA\"\n },\n \"unit\": {\n \"type\": \"string\",\n \"enum\": [\"celsius\", \"fahrenheit\"]\n }\n }\n },\n \"required\": [\"location\"]\n }\n }\n ],\n \"tool_choice\": \"auto\"\n }'\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"role\":\"assistant\",\"content\":\"\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"<\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"tool\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"_\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\"call\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: {\"id\":\"chatcmpl-6ca98c2f19c13c19f39013dfb78bcece\",\"object\":\"chat.completion.chunk\",\"created\":1761566772,\"model\":\"ibm/granite-3-8b-instruct\",\"choices\":[{\"index\":0,\"delta\":{\"content\":\">\"},\"logprobs\":null,\"finish_reason\":null}]}\n\ndata: [DONE]\n```\nThis happens after running several requests sequentially.\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27572",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-27T12:14:03Z",
"updated_at": "2025-11-24T20:27:24Z",
"comments": 13,
"user": "shuynh2017"
},
{
"repo": "huggingface/chat-ui",
"number": 1957,
"title": "Fail to use proxy",
"body": "How to make this web app go through local proxy? \n\nI tried a few methods, all of which don't work. \n",
"url": "https://github.com/huggingface/chat-ui/issues/1957",
"state": "open",
"labels": [
"support"
],
"created_at": "2025-10-27T06:31:51Z",
"updated_at": "2025-10-30T03:31:24Z",
"comments": 2,
"user": "geek0011"
},
{
"repo": "huggingface/diffusers",
"number": 12547,
"title": "Fine tuning Dreambooth Flux Kontext I2I Error: the following arguments are required: --instance_prompt",
"body": "### Describe the bug\n\nHello HF team, @sayakpaul @bghira\n\nI'm encountering a persistent issue when trying to fine-tune the black-forest-labs/FLUX.1-Kontext-dev model using the train_dreambooth_lora_flux_kontext.py script.\n\nI am following the [official README instructions](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md#training-kontext) for Image-to-Image (I2I) finetuning. My goal is to train a transformation on my own dataset, which is structured for I2I (condition image, target image, and text instruction).\n\n### The Problem\nEvery time I run the script with the correct arguments for I2I finetuning, I get a : `the following arguments are required: --instance_prompt`\n\nWhen I run this [Reproduction], I receive the error: `the following arguments are required: --instance_prompt.`\n\nTo isolate the issue from my personal dataset, I also tested the exact example command provided in the documentation (the one using `kontext-community/relighting`). I found that this command also fails with `the identical the following arguments are required: --instance_prompt` error.\n\nGiven that both my custom command and the official example command are failing in the same way, I am trying to understand the origin of this error. It seems the `--instance_prompt` argument is being required even when all I2I-specific arguments are provided.\n\n### Environment\n**Script**: `examples/dreambooth/train_dreambooth_lora_flux_kontext.py`\n\n**Diffusers Version**: I am using the specific commit `05e7a854d0a5661f5b433f6dd5954c224b104f0b` (installed via `pip install -e .` from a clone), as recommended in the README.\n\nCould you please help me understand why this might be happening? Is this expected behavior, or am I perhaps missing a configuration step?\n\nThank you for your time!\n\n### Reproduction\n\n### How to Reproduce\nI am running the following command, which provides all the necessary arguments for I2I finetuning using my (`dataset_name`, `image_column`, `cond_image_column`, and `caption_column`) using my public dataset:\n\n```\naccelerate launch /local-git-path/train_dreambooth_lora_flux_kontext.py \\\n --pretrained_model_name_or_path=\"black-forest-labs/FLUX.1-Kontext-dev\" \\\n --output_dir=\"/local-path/kontext-finetuning-v1\" \\\n --dataset_name=\"MichaelMelgarejoTotto/mi-dataset-kontext\" \\\n --image_column=\"output\" \\\n --cond_image_column=\"file_name\" \\\n --caption_column=\"instruccion\" \\\n --mixed_precision=\"bf16\" \\\n --resolution=1024 \\\n --train_batch_size=1 \\\n --guidance_scale=1 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --optimizer=\"adamw\" \\\n --use_8bit_adam \\\n --cache_latents \\\n --learning_rate=1e-4 \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=200 \\\n --max_train_steps=1000 \\\n --rank=16 \\\n --seed=\"0\" \n```\n\n\n### Logs\n\n```shell\ntrain_dreambooth_lora_flux_kontext.py: error: the following arguments are required: --instance_prompt\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.35.0.dev0\n- Platform: Linux-4.18.0-305.3.1.el8.x86_64-x86_64-with-glibc2.28\n- Running on Google Colab?: No\n- Python version: 3.10.19\n- PyTorch version (GPU?): 2.7.1+cu118 (False)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.36.0\n- Transformers version: 4.57.1\n- Accelerate version: 1.11.0\n- PEFT version: 0.17.1\n- Bitsandbytes version: 0.48.1\n- Safetensors version: 0.6.2\n- xFormers version: not installed\n- Accelerator: NA\n- Using GPU in script?: \nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nvllm 0.11.0\ntransformers 5.0.0.dev0\ntorch 2.8.0+cu129\n\nmodel base: Qwen2.5-VL-7B-instruct. How to solve this problem\uff1f\nThe output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nInitializing LLM(model=\"Qwen/Qwen3-Embedding-0.6B\", task=\"embed\") on a single B200 (180 GB) immediately reserves ~80% GPU memory (likely PagedAttention KV block pre-allocation). During embedding, GPU-Util stays <40%, whereas a naive Transformers inference with batch_size=512 reaches >80% utilization and memory use on the same box.\n\nIs heavy KV Cache pre-allocation expected for task=\"embed\" (prefill-only)? And is there any method to improve the GPU-Util?\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27479",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-10-24T15:18:05Z",
"updated_at": "2025-10-24T15:25:38Z",
"comments": 1,
"user": "JhaceLam"
},
{
"repo": "vllm-project/vllm",
"number": 27477,
"title": "[Bug]: First prompt token missing when requested with \"echo\"",
"body": "### Your current environment\n\nvllm installed from main: \n`vllm 0.11.1rc3.dev23+g61089465a.precompiled`\n\n### \ud83d\udc1b Describe the bug\n\nIs it expected behavior that echo isn't returning the first token of the prompt?\nI am trying to collect exact prompt_token_ids which went into the model served with vllm serve , so I am doing this:\n```bash\nVLLM_LOGGING_LEVEL=DEBUG vllm serve openai/gpt-oss-20b -tp 1 --enforce-eager --return-tokens-as-token-ids --enable-log-requests --enable-prompt-tokens-details\n```\nand with this snippet:\n```python\nfrom openai import OpenAI\n\nclient = OpenAI(\n api_key=\"EMPTY\", \n base_url=\"http://localhost:8000/v1\"\n)\n\nmessages = [\n {\"role\": \"user\", \"content\": \"Continue: The quick brown fox\"},\n]\n\nresponse = client.chat.completions.create(\n model=\"openai/gpt-oss-20b\",\n messages=messages,\n temperature=0.0,\n max_tokens=1024,\n logprobs=True,\n extra_body={\n \"echo\": True,\n }\n)\n\nprint(response.model_extra['prompt_logprobs'])\n```\n\nI am seeing: `[None, 17360, 200008, ...]` whereas the vllm server logs are printing this: `[200006, 17360, 200008, ...]` which is correct as the first token is and should be `200006` == `<|start|>` . Not sure why is it `None` in the ChatCompletion object\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/27477",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-24T14:43:50Z",
"updated_at": "2025-10-24T15:04:01Z",
"comments": 2,
"user": "eldarkurtic"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3336,
"title": "Get inference endpoint model settings via client",
"body": "### Feature request\n\nEnable commands via clients such as `OpenAI` that would get model settings from an inference endpoint. \n\nDoes this exist and I just can't find it?\n\n### Motivation\n\nThere is currently no clear way to get inference model settings directly from an endpoint. Individual base models have their original settings, but this does not necessarily translate to an endpoint. As an example, [Microsoft's Phi-3 model](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) supports 128k context length as input, but if instantiated as an endpoint on a 24GB gpu the allowed input context length is less (48k).\n\nThe only way I have found to access the information regarding an individual endpoint is via `huggingface_hub`, specifically:\n\n```\nfrom huggingface_hub import get_inference_endpoint\nendpoint = get_inference_endpoint(ENDPOINT_NAME, namespace=USERNAME, token=api_key)\n```\n\nTo get the general settings, you can then access the `raw` dict of the endpoint's image. For example, if I want to get the context length of a specific model at an endpoint, I can do it this way:\n```\n# the settings/specs of the endpoint in a 'llamacpp' image\nsettings = endpoint.raw['model']['image']['llamacpp']\n# this allows me to get info like context length (via the ['ctxSize']) key\n>>> print(settings['ctxSize'])\n48000\n```\n\nThis is problematic when sending prompts to an endpoint - if it were easier to query model properties programmatically, then I could write code to adjust queries on the fly appropriately depending on the target model. As it is, the sender needs to know the properties of a particular endpoint beforehand. IMO what is needed is to be able to get this info directly from a client.\n\nIn the OpenAI client in the Huggingface Inference API there seems to be some functionality for this, i.e. I can instantiate a client:\n```\nclient = OpenAI(\n base_url=endpoint, # AWS/server URL\n api_key=api_key, # huggingface token\n )\n```\nThen I can get a list of models at that url:\n```\nprint(client.models.list())\n```\nBut this only prints out basic information, which doesn't include such things as context length. Is there a way to get this info from the client that I'm just missing? I have noticed when there are errors related to input length, the client returns an error with the key `n_ctx`. For example, if a model I'm working with has a 12k context window and I send 13k tokens, the error is:\n```\nopenai.BadRequestError: Error code: 400 - {'error': {'code': 400, 'message': 'the request exceeds the available context size, try increasing it', 'type': 'exceed_context_size_error', 'n_prompt_tokens': 13954, 'n_ctx': 12032}}\n```\nThis tells me that the client has access to the overall settings, but it's not clear to me how to get them.\n\n### Your contribution\n\nHappy to work on this if someone can point me where to look for relevant code that would pass inference endpoint settings info to the client, perhaps via the `client.models.list()` method.",
"url": "https://github.com/huggingface/text-generation-inference/issues/3336",
"state": "closed",
"labels": [],
"created_at": "2025-10-24T13:07:15Z",
"updated_at": "2025-10-30T14:10:46Z",
"comments": 1,
"user": "lingdoc"
},
{
"repo": "huggingface/datasets",
"number": 7829,
"title": "Memory leak / Large memory usage with num_workers = 0 and numerous dataset within DatasetDict",
"body": "### Describe the bug\n\nHi team, first off, I love the datasets library! \ud83e\udd70\n\nI'm encountering a potential memory leak / increasing memory usage when training a model on a very large DatasetDict.\n\nSetup: I have a DatasetDict containing 362 distinct datasets, which sum up to ~2.8 billion rows.\n\nTraining Task: I'm performing contrastive learning with SentenceTransformer and Accelerate on a single node with 4 H100, which requires me to sample from only one dataset at a time.\n\nTraining Loop: At each training step, I sample ~16,000 examples from a single dataset, and then switch to a different dataset for the next step. I iterate through all 362 datasets this way. \n\nProblem: The process's memory usage continuously increases over time, eventually causing a stale status where GPUs would stop working. It seems memory from previously sampled datasets isn't being released. I've set num_workers=0 for all experiments.\n\nChart 1: Standard DatasetDict The memory usage grows steadily until it make the training stale (RSS memory) The output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThis error is independent of the environment.\n
\n\n\n### \ud83d\udc1b Describe the bug\n\n### Description\nThe function `schedule()` in [vllm/v1/core/sched/scheduler.py](https://github.com/vllm-project/vllm/blob/main/vllm/v1/core/sched/scheduler.py) is responsible for scheduling inference requests.\n\nIn certain cases \u2014 such as when a request is waiting for KV blocks from a remote prefill worker or when the token budget is exhausted \u2014 the request must be reinserted into the waiting queue `self.waiting`.\n\nCurrently, the implementation pops such requests, prepends them to skipped_waiting_requests, and then prepends skipped_waiting_requests back to self.waiting.\nHowever, this behavior can shuffle the request order, potentially impacting the tail latency of request serving.\n\n### How to Fix\nReplace all calls to `skipped_wating_requests.prepend_request(request)` with `skipped_wating_requests.add_request(request)`\n\n### Result\n\nThe output of
\n\n```text\n# wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py\n# For security purposes, please feel free to check the contents of collect_env.py before running it.\npython collect_env.py\n--2025-10-15 17:33:01-- https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 28050 (27K) [text/plain]\nSaving to: \u2018collect_env.py.2\u2019\n\ncollect_env.py.2 100%[===================================>] 27.39K --.-KB/s in 0s \n\n2025-10-15 17:33:01 (65.0 MB/s) - \u2018collect_env.py.2\u2019 saved [28050/28050]\n\n# # sh: 8: python: not found\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nI'm running vLLM as a docker container on an Unraid server. It is a backend to Open WebUI chat interface. The issue I see is that the reasoning block for Open WebUI is closing too early. According to this discussion on the Open WebUI git, I think it is because of the deepseek parser used as recommended by the model card. See this link: https://github.com/open-webui/open-webui/pull/16687\n\nHere is an example of the issue that I face: \n\nThe output of python collect_env.py
\n\n```text\n# wget https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py\n# For security purposes, please feel free to check the contents of collect_env.py before running it.\npython collect_env.py\n--2025-10-15 17:33:01-- https://raw.githubusercontent.com/vllm-project/vllm/main/vllm/collect_env.py\nResolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.109.133, ...\nConnecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.110.133|:443... connected.\nHTTP request sent, awaiting response... 200 OK\nLength: 28050 (27K) [text/plain]\nSaving to: \u2018collect_env.py.2\u2019\n\ncollect_env.py.2 100%[===================================>] 27.39K --.-KB/s in 0s \n\n2025-10-15 17:33:01 (65.0 MB/s) - \u2018collect_env.py.2\u2019 saved [28050/28050]\n\n# # sh: 8: python: not found\n```\n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\n# Title\nInternVL: passing image **embeddings** triggers `TypeError: can only concatenate tuple (not \"Tensor\") to tuple` in `get_multimodal_embeddings`, and v1 sanity check then expects a sequence of 2D tensors\n\n## Environment\n- vLLM: 0.10.2 (also reproducible on 0.10.1)\n- Python: 3.11.x\n- Model: `InternVL3_5-1B` (HF, `trust_remote_code=True`)\n\n## Minimal Repro (image **embeddings** input)\n```python\nfrom vllm import LLM\nimport torch\n\nllm = LLM(model=\"InternVL3_5-1B\", trust_remote_code=True)\n\nprompt = \"USER: The output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThe output of
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n
\n\nThe output of python collect_env.py
\n\n```text\nYour output of `python collect_env.py` here\n```\n\n\nThe output of
\n\nNot required for this.\n\n
\n\n\n### \ud83d\udc1b Describe the bug\n\nThe vllm/vllm-openai:v0.10.2 image seems to be affected by the [CVE-2023-48022](https://avd.aquasec.com/nvd/2023/cve-2023-48022/) **Critical** CVE with `ray` (see scan results below). Is there any plan to address this?\n\n```\ngrype vllm/vllm-openai:v0.10.2 --scope all-layers\n```\n\n```\nNAME INSTALLED FIXED IN TYPE VULNERABILITY SEVERITY EPSS RISK\nray 2.49.1 python GHSA-6wgj-66m2-xxp2 Critical 91.9% (99th) 86.4\nlibgssapi-krb5-2 1.19.2-2ubuntu0.4 1.19.2-2ubuntu0.5 deb CVE-2024-3596 Medium 24.6% (95th) 12.3\nlibk5crypto3 1.19.2-2ubuntu0.4 1.19.2-2ubuntu0.5 deb CVE-2024-3596 Medium 24.6% (95th) 12.3\nlibkrb5-3 1.19.2-2ubuntu0.4 1.19.2-2ubuntu0.5 deb CVE-2024-3596 Medium 24.6% (95th) 12.3\nlibkrb5support0 1.19.2-2ubuntu0.4 1.19.2-2ubuntu0.5 deb CVE-2024-3596 Medium 24.6% (95th) 12.3\npython3-pip 22.0.2+dfsg-1ubuntu0.6 22.0.2+dfsg-1ubuntu0.7 deb CVE-2023-32681 Medium 6.3% (90th) 3.1\nlibaom3 3.3.0-1ubuntu0.1 deb CVE-2019-2126 Low 8.1% (91st) 2.4\nlibcaca0 0.99.beta19-2.2ubuntu4 deb CVE-2022-0856 Low 4.9% (89th) 1.5\npython3-httplib2 0.20.2-2 deb CVE-2021-21240 Low 4.5% (88th) 1.4\nlogin 1:4.8.1-2ubuntu2.2 deb CVE-2024-56433 Low 3.6% (87th) 1.1\npasswd 1:4.8.1-2ubuntu2.2 deb CVE-2024-56433 Low 3.6% (87th) 1.1\n...\n```\n\n### Before submitting a new issue...\n\n- [x] Make sure you already searched for relevant issues, and asked the chatbot living at the bottom right corner of the [documentation page](https://docs.vllm.ai/en/latest/), which can answer lots of frequently asked questions.",
"url": "https://github.com/vllm-project/vllm/issues/26530",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-10-09T20:16:02Z",
"updated_at": "2025-10-10T21:14:49Z",
"comments": 3,
"user": "geodavic"
},
{
"repo": "huggingface/lerobot",
"number": 2156,
"title": "How to reproduce lerobot/pi0_libero_finetuned?",
"body": "Thanks for the great work!\n\nI evaluated lerobot/pi0_libero_finetuned on libero goal datasets.\nWhen using n_action_steps=50, the success rate is ~ 75%\nWhen using n_action_steps=10, the success rate is ~ 90%\n\nI tried to reproduce the training results, so I mainly refered to [train_config.json](https://huggingface.co/lerobot/pi0_libero_finetuned/blob/main/train_config.json) in the `lerobot/pi0_libero_finetuned` repo, which has one key value pair in the config dict:\n```\n\"pretrained_path\": \"pepijn223/pi0_libero_finetuned_extra\"\n```\n\nSo I also refered to the [train_config.json](https://huggingface.co/pepijn223/pi0_libero_finetuned_extra/blob/main/train_config.json) in th `pepijn223/pi0_libero_finetuned_extra` repo, which also has the key value pair:\n```\n\"pretrained_path\": \"lerobot/pi0_libero_finetuned\"\n```\nThis again points back to the checkpoint that depends on it.\n\nAnd my questions are, how are these checkpoints actually trained, and can anyone provide a train_config.json in the latest lerobot version that can reproduce lerobot/pi0_libero_finetuned?\n\nPlease also share some successful training configs if possible!",
"url": "https://github.com/huggingface/lerobot/issues/2156",
"state": "open",
"labels": [
"question",
"policies",
"simulation"
],
"created_at": "2025-10-09T18:11:47Z",
"updated_at": "2025-10-22T09:27:03Z",
"user": "PuzhenYuan"
},
{
"repo": "huggingface/lerobot",
"number": 2153,
"title": "Why can\u2019t I find something like train_expert_only in the latest version of pi0? Do the current versions of pi0 and pi0.5 only support full-parameter training?",
"body": "Why can\u2019t I find something like \u201ctrain_expert_only\u201d in the latest version of pi0?\nDo the current versions of pi0 and pi0.5 only support full-parameter training?",
"url": "https://github.com/huggingface/lerobot/issues/2153",
"state": "closed",
"labels": [
"enhancement",
"question",
"policies",
"good first issue"
],
"created_at": "2025-10-09T13:08:10Z",
"updated_at": "2025-12-31T14:54:29Z",
"user": "ZHHhang"
},
{
"repo": "huggingface/datasets",
"number": 7802,
"title": "[Docs] Missing documentation for `Dataset.from_dict`",
"body": "Documentation link: https://huggingface.co/docs/datasets/en/package_reference/main_classes\n\nLink to method (docstring present): https://github.com/huggingface/datasets/blob/6f2502c5a026caa89839713f6f7c8b958e5e83eb/src/datasets/arrow_dataset.py#L1029\n\nThe docstring is present for the function, but seems missing from the official documentation for the `Dataset` class on HuggingFace.\n\nThe method in question:\n```python\n @classmethod\n def from_dict(\n cls,\n mapping: dict,\n features: Optional[Features] = None,\n info: Optional[DatasetInfo] = None,\n split: Optional[NamedSplit] = None,\n ) -> \"Dataset\":\n \"\"\"\n Convert `dict` to a `pyarrow.Table` to create a [`Dataset`].\n\n Important: a dataset created with from_dict() lives in memory\n and therefore doesn't have an associated cache directory.\n This may change in the future, but in the meantime if you\n want to reduce memory usage you should write it back on disk\n and reload using e.g. save_to_disk / load_from_disk.\n\n Args:\n mapping (`Mapping`):\n Mapping of strings to Arrays or Python lists.\n features ([`Features`], *optional*):\n Dataset features.\n info (`DatasetInfo`, *optional*):\n Dataset information, like description, citation, etc.\n split (`NamedSplit`, *optional*):\n Name of the dataset split.\n\n Returns:\n [`Dataset`]\n \"\"\"\n```",
"url": "https://github.com/huggingface/datasets/issues/7802",
"state": "open",
"labels": [],
"created_at": "2025-10-09T02:54:41Z",
"updated_at": "2025-10-19T16:09:33Z",
"comments": 2,
"user": "aaronshenhao"
},
{
"repo": "huggingface/transformers",
"number": 41431,
"title": "gradient scaling occurs even though total gradient remains < max_grad_norm in trainer.py",
"body": "Even though gradients remain < max_grad_norm throughout training, the gradient still goes through a scaling process. For instance, I set max_grad_norm = 1, and grad_norm consistently remains <= 0.33. Because the trainer takes you through the grad clip process if max_grad_norm > 0 or not None, this operation always gets executed within torch's clip function: `clip_coef = max_norm / (total_norm + 1e-6)`. Is there a way to prevent this? Thanks. \n\n",
"url": "https://github.com/huggingface/transformers/issues/41431",
"state": "closed",
"labels": [],
"created_at": "2025-10-07T22:13:08Z",
"updated_at": "2025-11-15T08:02:51Z",
"comments": 7,
"user": "lorsonblair"
},
{
"repo": "huggingface/candle",
"number": 3120,
"title": "AutoModel / PreTrainedModel equivalent magic ?",
"body": "Hello all, first, thanks a lot for this wonderful crate. \n\nI was wondering if it's on the roadmap or if there is a solution to have the same magic as in python with a `AutoModel.from_pretrained(\"the_model_name_string\")`\n\nAs I'm protoyping and am often changing models... which requires to change the architecture everytime and having this \"auto load\" would save time. \n\nAlternatives : https://github.com/lucasjinreal/Crane or https://docs.rs/kalosm/latest/kalosm/\n\nThanks in advance, \nHave a nice day. ",
"url": "https://github.com/huggingface/candle/issues/3120",
"state": "open",
"labels": [],
"created_at": "2025-10-07T21:27:31Z",
"updated_at": "2025-10-09T13:02:35Z",
"comments": 2,
"user": "ierezell"
},
{
"repo": "huggingface/lerobot",
"number": 2134,
"title": "what is the transformers version for latest lerobot pi0?",
"body": "### System Info\n\n```Shell\n- lerobot version: 0.3.4\n- Platform: Linux-5.4.0-148-generic-x86_64-with-glibc2.31\n- Python version: 3.10.18\n- Huggingface Hub version: 0.35.3\n- Datasets version: 4.1.1\n- Numpy version: 1.26.4\n- PyTorch version: 2.7.1+cu126\n- Is PyTorch built with CUDA support?: True\n- Cuda version: 12.6\n- GPU model: NVIDIA A800-SXM4-80GB\n- Using GPU in script?:\n\nlerobot-eval --policy.path=\"lerobot/pi0_libero_finetuned\" --env.type=libero --env.task=libero_goal --eval.batch_size=1 --eval.n_episodes=2 --seed=1000\n```\n\n### Information\n\n- [x] One of the scripts in the examples/ folder of LeRobot\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nClone latest LeRobot repository and install dependencies and run lerobot_eval.py\n```\nlerobot-eval --policy.path=\"lerobot/pi0_libero_finetuned\" --env.type=libero --env.task=libero_goal --eval.batch_size=1 --eval.n_episodes=2 --seed=1000\n```\n```\nTraceback (most recent call last):\n File \"/cephfs/yuanpuzhen/conda_data/envs/libero/bin/lerobot-eval\", line 7, in The output of python collect_env.py
\n\nNot required for this.\n\n]().\nThe pose conditioned model trained by the script does not process this image properly because it is in BGR format, apparent when comparing it to the openpose spec: \n[]().\nIt doesn't appear that the validation image is loaded in BGR format properly, in the below line:\nhttps://github.com/huggingface/diffusers/blob/ba2ba9019f76fd96c532240ed07d3f98343e4041/examples/flux-control/train_control_lora_flux.py#L127.\n\nIn my personal experiments, the validation output does not make sense. Below is an example of what my run uploaded to wandb:\n\n\n\n pipline.train_model()\n File \"/home/wjt/luck/FinalWork/alert_models/informer_based_model_3_cpu.py\", line 466, in train_model\n outputs = model(\n File \"/home/wjt/.conda/envs/luckluck/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n File \"/home/wjt/.conda/envs/luckluck/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/wjt/.conda/envs/luckluck/lib/python3.9/site-packages/transformers/models/informer/modeling_informer.py\", line 1844, in forward\n outputs = self.model(\n File \"/home/wjt/.conda/envs/luckluck/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1751, in _wrapped_call_impl\n return self._call_impl(*args, **kwargs)\n File \"/home/wjt/.conda/envs/luckluck/lib/python3.9/site-packages/torch/nn/modules/module.py\", line 1762, in _call_impl\n return forward_call(*args, **kwargs)\n File \"/home/wjt/.conda/envs/luckluck/lib/python3.9/site-packages/transformers/models/informer/modeling_informer.py\", line 1568, in forward\n transformer_inputs, loc, scale, static_feat = self.create_network_inputs(\n File \"/home/wjt/.conda/envs/luckluck/lib/python3.9/site-packages/transformers/models/informer/modeling_informer.py\", line 1386, in create_network_inputs\n expanded_static_feat = static_feat.unsqueeze(1).expand(-1, time_feat.shape[1], -1)\nRuntimeError: expand(torch.cuda.FloatTensor{[32, 1, 2, 1]}, size=[-1, 27, -1]): the number of sizes provided (3) must be greater or equal to the number of dimensions in the tensor (4)\n```\n- First\n```\nlog_abs_loc = loc.abs().log1p() if self.config.input",
"url": "https://github.com/huggingface/transformers/issues/38745",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-06-11T07:22:06Z",
"updated_at": "2025-07-20T11:41:45Z",
"comments": 11,
"user": "2004learner"
},
{
"repo": "huggingface/transformers",
"number": 38740,
"title": "[DOCS] Add `pruna` as optimization framework",
"body": "### Feature request\n\nHave a section on Pruna AI within the documentation. We did [a similar PR for diffusers](https://github.com/huggingface/diffusers/pull/11688) and thought it would be nice to show how to optimize transformers models too. \n.\n\n### Motivation\n\nHave a section on Pruna AI within the documentation to show how to optimize LLMs for inference.\n\n### Your contribution\n\nWe could do everything for the PR.",
"url": "https://github.com/huggingface/transformers/issues/38740",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-06-11T04:52:33Z",
"updated_at": "2025-07-16T08:56:52Z",
"comments": 8,
"user": "davidberenstein1957"
},
{
"repo": "huggingface/sentence-transformers",
"number": 3390,
"title": "How to create a customized model architecture that fits sentence-transformer's training framework?",
"body": "I'd like to train a two tower model that takes categorical features, floats features in one tower, and the other tower just encodes a document using an out of the box embedding. Then the outputs from both towers are feed into sentence transformers loss function. All the training configuration should reuse sentence transformer's setup (loss function implementation, Training Arguments, etc) as much as possible. \n\nIs this even feasible? Skimmed through the document found this page here (https://www.sbert.net/docs/sentence_transformer/usage/custom_models.html#structure-of-sentence-transformer-models), but the example on this page seems to be creating a new module, but only as part of a purely sequential models, each connected to its next. \n\nMuch appreciated! ",
"url": "https://github.com/huggingface/sentence-transformers/issues/3390",
"state": "open",
"labels": [],
"created_at": "2025-06-11T03:07:42Z",
"updated_at": "2025-06-12T05:05:54Z",
"user": "HuangLED"
},
{
"repo": "huggingface/lerobot",
"number": 1258,
"title": "Leader Servo Numbering different from script to documentation",
"body": "First thank you for sharing this amazing work!\n\nI am initializing the servos for the arm leader and I noticed that the numbering for the Wrist Roll and Wrist Pitch are different from the documentation when I ran the script:\n\n\n\nwrist_roll is set to 5 in the script but set to 4 in the documentation\nwrist_flex is set to 4 in the script but set to 5 (assuming it is Wrist Pitch) in the documentation\n\nI guess nothing to worry about ?\n\n",
"url": "https://github.com/huggingface/lerobot/issues/1258",
"state": "open",
"labels": [
"documentation",
"question"
],
"created_at": "2025-06-10T21:03:03Z",
"updated_at": "2025-08-12T10:04:29Z",
"user": "FaboNo"
},
{
"repo": "huggingface/transformers",
"number": 38733,
"title": "GRPO per_device_eval_batch_size can't be set as 1, when there is only 1 GPU",
"body": "`eval batch size must be evenly divisible by the number of generations per prompt. ` When I only have one GPU, I cannot set `per_device_eval_batch_size=1` because there will be no reasonable G to choose from. Is it possible to automatically calculate a value similar to the number of gradient accumulation steps to achieve this feature?",
"url": "https://github.com/huggingface/transformers/issues/38733",
"state": "closed",
"labels": [],
"created_at": "2025-06-10T14:58:11Z",
"updated_at": "2025-06-11T09:45:32Z",
"comments": 0,
"user": "CasanovaLLL"
},
{
"repo": "huggingface/lerobot",
"number": 1254,
"title": "[Feature Proposal] Planning a new user friendly simulation environment for new task and data collection",
"body": "Hello and bonjour! First and foremost, I really wanted to thanks the team and community for making this wonderful repo. It really helps and guide beginner in this field. And I also wanted to contribute for the community.\n\nReading the issues here, I found a lot of people are trying to run without physical robot. But with the current Aloha and Xarm simulation environment it is hard to config and train new task. So I was thinking to make new env where we could do that.\n\nHere is the main new feature:\n- New sim env we can use as extra like Xarm, Aloha and Pusht in a new repo.\n- Make a simple, game like GUI which enable controlling the manipulator with only keyboard and mouse. (Thinking of making a mini robot on html that can be controlled with mouse, z axis and gripper with keyboard)\n- Make it compatible to recent official MuJoCo release for further [update](https://playground.mujoco.org/) and [extension](https://github.com/google-deepmind/mujoco_warp). (Planning to use [MJX](https://mujoco.readthedocs.io/en/stable/mjx.html)(RL compatible) model)\n- Realtime inference using mujoco view.\n\nI'm a beginner in this field, so it might be a hard task for me. But I thought this this project might help quite people, and also really funny to do. So I'll try my best.\n\nWhat are your thoughts on this proposal? (Sorry if there is already similar features.)\nIf it is okay, I'll start to dig in.\n",
"url": "https://github.com/huggingface/lerobot/issues/1254",
"state": "open",
"labels": [
"question",
"simulation"
],
"created_at": "2025-06-10T12:36:13Z",
"updated_at": "2025-08-12T10:04:42Z",
"user": "Bigenlight"
},
{
"repo": "huggingface/lerobot",
"number": 1252,
"title": "Failed to sync read 'Present_Position' on ids=[2,3,4,6]after 1 tries. [TxRxResult] There is no status packet",
"body": "my arm is koch\uff0cwhen I set the motors ids and baudrates, it report error:\nFailed to sync read 'Present_Position' on ids=[2,3,4,6]after 1 tries. [TxRxResult] There is no status packet",
"url": "https://github.com/huggingface/lerobot/issues/1252",
"state": "open",
"labels": [
"question",
"robots"
],
"created_at": "2025-06-10T10:21:05Z",
"updated_at": "2025-09-01T02:24:25Z",
"user": "huazai665"
},
{
"repo": "huggingface/lerobot",
"number": 1251,
"title": "where is async inference",
"body": "hi,thx for your SmolVLA\nI have a question:**where is the async inference?**\nthe eval.py in script doesn't seem for SmolVLA inference\nhope for your early reply,thx in advance",
"url": "https://github.com/huggingface/lerobot/issues/1251",
"state": "closed",
"labels": [],
"created_at": "2025-06-10T07:44:38Z",
"updated_at": "2025-06-30T11:35:25Z",
"user": "JuilieZ"
},
{
"repo": "huggingface/transformers.js",
"number": 1336,
"title": "node.js WebGPU compatibility and WASM performance in web enviornment",
"body": "### Question\n\nHello!\n\nI've been running some performance benchmarks on whisper models and noticed that the web environment (running in react renderer in electron, separate worker with WASM) produced slower transcription results than the python counterpart (e.g. 1400ms vs 400ms per batch) - both utilizing the same number of threads and data types.\n\nnode.js environment running with WASM was almost on par with python, but unfortunately it won't let me pick webgpu as device - only cpu and dml are supported.\n\nThe onnxruntime-node package does mention webgpu being supported so I was wondering if it will be available for transformers running in node.js environment.\n\nAnd I'm also wondering if the performance drop using WASM in web environment is expected or if I'm doing something wrong.",
"url": "https://github.com/huggingface/transformers.js/issues/1336",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-06-10T06:05:36Z",
"updated_at": "2025-06-11T06:53:35Z",
"user": "devnarekm"
},
{
"repo": "huggingface/transformers",
"number": 38709,
"title": "`get_video_features` in XCLIPModel always returns `pooled_output`",
"body": "### System Info\n\nhttps://github.com/huggingface/transformers/blob/f4fc42216cd56ab6b68270bf80d811614d8d59e4/src/transformers/models/x_clip/modeling_x_clip.py#L1376\n\nHi\n\nThe `get_video_features` function is hardcoded to always return the `pooled_output`. But sometimes, it might be beneficial to get the `last_hidden_state` instead. Can we fix this behavior?\n\nThanks\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n```import av\nimport torch\nimport numpy as np\n\nfrom transformers import AutoProcessor, AutoModel\nfrom huggingface_hub import hf_hub_download\n\nnp.random.seed(0)\n\n\ndef read_video_pyav(container, indices):\n '''\n Decode the video with PyAV decoder.\n Args:\n container (`av.container.input.InputContainer`): PyAV container.\n indices (`List[int]`): List of frame indices to decode.\n Returns:\n result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3).\n '''\n frames = []\n container.seek(0)\n start_index = indices[0]\n end_index = indices[-1]\n for i, frame in enumerate(container.decode(video=0)):\n if i > end_index:\n break\n if i >= start_index and i in indices:\n frames.append(frame)\n return np.stack([x.to_ndarray(format=\"rgb24\") for x in frames])\n\n\ndef sample_frame_indices(clip_len, frame_sample_rate, seg_len):\n '''\n Sample a given number of frame indices from the video.\n Args:\n clip_len (`int`): Total number of frames to sample.\n frame_sample_rate (`int`): Sample every n-th frame.\n seg_len (`int`): Maximum allowed index of sample's last frame.\n Returns:\n indices (`List[int]`): List of sampled frame indices\n '''\n converted_len = int(clip_len * frame_sample_rate)\n end_idx = np.random.randint(converted_len, seg_len)\n start_idx = end_idx - converted_len\n indices = np.linspace(start_idx, end_idx, num=clip_len)\n indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64)\n return indices\n\n\n# video clip consists of 300 frames (10 seconds at 30 FPS)\nfile_path = hf_hub_download(\n repo_id=\"nielsr/video-demo\", filename=\"eating_spaghetti.mp4\", repo_type=\"dataset\"\n)\ncontainer = av.open(file_path)\n\n# sample 8 frames\nindices = sample_frame_indices(clip_len=8, frame_sample_rate=1, seg_len=container.streams.video[0].frames)\nvideo = read_video_pyav(container, indices)\n\nprocessor = AutoProcessor.from_pretrained(\"microsoft/xclip-base-patch32\")\nmodel = AutoModel.from_pretrained(\"microsoft/xclip-base-patch32\")\n\ninputs = processor(\n videos=list(video),\n return_tensors=\"pt\",\n padding=True,\n)\n\n# forward pass\nwith torch.no_grad():\n outputs = model.get_video_features(**inputs)\n\nprint(outputs.shape)\n\n### Expected behavior\n\nThe `get_video_features` function should have the option to output the `last_hidden_state` as well.",
"url": "https://github.com/huggingface/transformers/issues/38709",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-06-10T00:51:37Z",
"updated_at": "2025-07-18T08:02:50Z",
"comments": 4,
"user": "Vishu26"
},
{
"repo": "huggingface/lerobot",
"number": 1242,
"title": "SmolVLA Gym Simulation - Release?",
"body": "Hello,\n\nI've trained the smolvla_base for 200K steps. I'm trying to do a inference and visualize like we do for aloha or pusht. Could anyone guide me on this. \n\nI dont have a robot arm, so Gym simulation is something I'm looking for, when will it be released?",
"url": "https://github.com/huggingface/lerobot/issues/1242",
"state": "closed",
"labels": [
"question",
"policies",
"visualization"
],
"created_at": "2025-06-09T13:05:38Z",
"updated_at": "2025-10-17T11:00:57Z",
"user": "Jaykumaran"
},
{
"repo": "huggingface/smollm",
"number": 78,
"title": "how to continously pretrain VLM base model",
"body": "rt.\nHow can I pretrain VLM base model\uff1f",
"url": "https://github.com/huggingface/smollm/issues/78",
"state": "open",
"labels": [
"Image",
"Video"
],
"created_at": "2025-06-09T07:04:57Z",
"updated_at": "2025-07-29T12:50:50Z",
"user": "allenliuvip"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3259,
"title": "Enable passing arguments to chat templates",
"body": "### Feature request\n\nI would like to enable passing parameters to a chat template when using the messages API. Something like:\n```python\nqwen3_model = HuggingFaceModel(...)\npredictor = qwen3_model.deploy(...)\npredictor.predict({\n\"messages\": [\n {\"role\": \"system\", \"content\": \"You are a helpful assistant.\" },\n {\"role\": \"user\", \"content\": \"What is deep learning?\"}\n ]\n\"template_args\": { \"enable_thinking\": False }\n})\n```\n\n### Motivation\n\nThere are models with various custom arguments that can be passed to chat templates. For example, Qwen3 comes with `enable_thinking` parameter than can be either True or False, and CohereLabs c4ai-command-r-plus RAG chat template has a `citation_mode` flag that can be `accurate` or `fast`.\n\n### Your contribution\n\nUnfortunately, no. Do not know Rust beyond some basics.",
"url": "https://github.com/huggingface/text-generation-inference/issues/3259",
"state": "open",
"labels": [],
"created_at": "2025-06-09T06:04:27Z",
"updated_at": "2025-06-09T07:53:17Z",
"comments": 2,
"user": "alexshtf"
},
{
"repo": "huggingface/datasets",
"number": 7600,
"title": "`push_to_hub` is not concurrency safe (dataset schema corruption)",
"body": "### Describe the bug\n\nConcurrent processes modifying and pushing a dataset can overwrite each others' dataset card, leaving the dataset unusable.\n\nConsider this scenario:\n- we have an Arrow dataset\n- there are `N` configs of the dataset\n- there are `N` independent processes operating on each of the individual configs (e.g. adding a column, `new_col`)\n- each process calls `push_to_hub` on their particular config when they're done processing\n- all calls to `push_to_hub` succeed\n- the `README.md` now has some configs with `new_col` added and some with `new_col` missing\n\nAny attempt to load a config (using `load_dataset`) where `new_col` is missing will fail because of a schema mismatch between `README.md` and the Arrow files. Fixing the dataset requires updating `README.md` by hand with the correct schema for the affected config. In effect, `push_to_hub` is doing a `git push --force` (I found this behavior quite surprising).\n\nWe have hit this issue every time we run processing jobs over our datasets and have to fix corrupted schemas by hand.\n\nReading through the code, it seems that specifying a [`parent_commit`](https://github.com/huggingface/huggingface_hub/blob/v0.32.4/src/huggingface_hub/hf_api.py#L4587) hash around here https://github.com/huggingface/datasets/blob/main/src/datasets/arrow_dataset.py#L5794 would get us to a normal, non-forced git push, and avoid schema corruption. I'm not familiar enough with the code to know how to determine the commit hash from which the in-memory dataset card was loaded.\n\n### Steps to reproduce the bug\n\nSee above.\n\n### Expected behavior\n\nConcurrent edits to disjoint configs of a dataset should never corrupt the dataset schema.\n\n### Environment info\n\n- `datasets` version: 2.20.0\n- Platform: Linux-5.15.0-118-generic-x86_64-with-glibc2.35\n- Python version: 3.10.14\n- `huggingface_hub` version: 0.30.2\n- PyArrow version: 19.0.1\n- Pandas version: 2.2.2\n- `fsspec` version: 2023.9.0",
"url": "https://github.com/huggingface/datasets/issues/7600",
"state": "closed",
"labels": [],
"created_at": "2025-06-07T17:28:56Z",
"updated_at": "2025-07-31T10:00:50Z",
"comments": 4,
"user": "sharvil"
},
{
"repo": "huggingface/lerobot",
"number": 1226,
"title": "404 Not Found",
"body": "[lerobot](https://github.com/huggingface/lerobot/tree/main)/[examples](https://github.com/huggingface/lerobot/tree/main/examples)\n/10_use_so100.md/ \n\nThis is supposed to be a tutorial but cannot be opened???\n404 Not Found!!!\n",
"url": "https://github.com/huggingface/lerobot/issues/1226",
"state": "closed",
"labels": [
"documentation",
"question"
],
"created_at": "2025-06-07T09:02:37Z",
"updated_at": "2025-06-08T21:26:07Z",
"user": "luk-e158"
},
{
"repo": "huggingface/transformers",
"number": 38656,
"title": "Potential Memory Leak or Caching in Fast Image Processor",
"body": "### System Info\n\nHi team,\n\nThank you for your great work on `transformers`!\n\nWhile using the `AutoProcessor` with `use_fast=True`, I noticed that there seems to be a memory leak or possibly some form of persistent caching when processing images. Even after deleting the processor and clearing the CUDA cache, approximately 600MB of GPU memory remains occupied.\n\nHere is a minimal reproducible example:\n\n```python\nfrom transformers import AutoProcessor\nfrom PIL import Image\nimport time\nimport torch\nimport requests\nfrom io import BytesIO\n\nprocessor = AutoProcessor.from_pretrained(\n \"Qwen/Qwen2.5-VL-7B-Instruct\",\n use_fast=True,\n trust_remote_code=False,\n revision=None,\n)\n\nurl = \"https://github.com/sgl-project/sglang/blob/main/test/lang/example_image.png?raw=true\"\nresponse = requests.get(url)\nimages = [Image.open(BytesIO(response.content)).convert(\"RGB\")]\n\nresult = processor(\n text=[\n \"<|im_start|>system\\nYou are a helpful assistant.<|im_end|>\\n\"\n \"<|im_start|>user\\nWhat\u2019s in this image?<|vision_start|><|image_pad|><|vision_end|><|im_end|>\\n\"\n \"<|im_start|>assistant\\n\"\n ],\n padding=True,\n return_tensors=\"pt\",\n images=images,\n device=\"cuda\"\n)\n\ndel result\ndel processor\ntorch.cuda.empty_cache()\n\nprint(\"You can now use nvidia-smi to observe GPU memory usage, which is around 600MB.\")\nwhile True:\n time.sleep(60)\n```\n\nI\u2019d like to kindly ask:\n\n1. If this is due to caching, is there a way to control or disable the cache?\n2. If this is an unintended memory leak, would it be possible to investigate and potentially fix it?\n\nThanks again for your help and time!\n\nBest regards\n\n### Who can help?\n\ntokenizers: @ArthurZucker and @itazap\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nAs provided above.\n\n### Expected behavior\n\nIt would be great if caching could be made optional, or if there could be an option to avoid any GPU memory usage entirely.",
"url": "https://github.com/huggingface/transformers/issues/38656",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-06-07T08:46:48Z",
"updated_at": "2025-08-12T13:02:37Z",
"comments": 8,
"user": "yhyang201"
},
{
"repo": "huggingface/transformers",
"number": 38654,
"title": "The visualization of image input in Qwen2.5-VL",
"body": "The image input of Qwen2.5-VL is processed by processor and then saved as tensor in inputs['pixel_values'].\nI tried to restore the image, using tensor in inputs['pixel_values'], but I found that the restored image patches were in disorder.\nSo how to restore the image from inputs['pixel_values'] in a proper way?\n\nFor example, the origin input image is as follows.\n\nAnd failed to restore from the inputs['pixel_values'].\n",
"url": "https://github.com/huggingface/transformers/issues/38654",
"state": "closed",
"labels": [],
"created_at": "2025-06-07T08:15:44Z",
"updated_at": "2025-06-10T09:04:04Z",
"comments": 2,
"user": "Bytes-Lin"
},
{
"repo": "huggingface/lerobot",
"number": 1223,
"title": "smolvla introduce an asynchronous inference stack decoupling perception and action prediction?",
"body": "why code not realize?",
"url": "https://github.com/huggingface/lerobot/issues/1223",
"state": "closed",
"labels": [
"question",
"policies"
],
"created_at": "2025-06-07T01:23:24Z",
"updated_at": "2025-06-08T21:25:04Z",
"user": "zmf2022"
},
{
"repo": "huggingface/transformers",
"number": 38650,
"title": "Support of Qwen3 GGUF model",
"body": "Hi, I am getting the following error when I want to use the GGUF model with Qwen3\n\"ValueError: GGUF model with architecture qwen3 is not supported yet.\"\n\nI have the latest transformers and gguf-0.17.0\n```\nself.tokenizer = AutoTokenizer.from_pretrained(model_name, gguf_file= \"Qwen3-0.6B-Q2_K_L.gguf\",use_fast=True)\n if self.tokenizer.pad_token is None:\n self.tokenizer.pad_token = \"\"\n self.tokenizer.add_special_tokens({\"pad_token\": \"\"})\n self.tokenizer.padding_side = \"left\"\n self.model = AutoModelForCausalLM.from_pretrained(\n model_name,\n gguf_file = \"Qwen3-0.6B-Q2_K_L.gguf\",\n pad_token_id=self.tokenizer.pad_token_id,\n trust_remote_code=True,\n torch_dtype=torch.bfloat16,\n device_map=\"auto\",\n )\n```\nHow can I use the gguf model of Qwen3 with transformers? Could you please add the support of it?\n\nThanks!",
"url": "https://github.com/huggingface/transformers/issues/38650",
"state": "closed",
"labels": [],
"created_at": "2025-06-06T20:11:23Z",
"updated_at": "2025-07-15T08:02:59Z",
"comments": 2,
"user": "Auth0rM0rgan"
},
{
"repo": "huggingface/diffusers",
"number": 11675,
"title": "Error in loading the pretrained lora weights",
"body": "Hi, I am using the script https://github.com/huggingface/diffusers/blob/main/examples/text_to_image/train_text_to_image_lora_sdxl.py to train a lora.\n\nAn error is raised on https://github.com/huggingface/diffusers/blob/73a9d5856f2d7ae3637c484d83cd697284ad3962/examples/text_to_image/train_text_to_image_lora_sdxl.py#L1314C9-L1314C52\n\n```\nLoading adapter weights from state_dict led to missing keys in the model: down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q.lora_A\n.default_0.weight, down_blocks.1.attentions.0.transformer_blocks.0.attn1.to_q.lora_B.default_0.weight, ...\n```\n\nThe difference between the keys in the saved lora weights and the ''missing keys'' mentioned above is ''default_0''. How can I resolve this problem?\n\ndiffusers 0.32.2\npeft 0.15.2\n",
"url": "https://github.com/huggingface/diffusers/issues/11675",
"state": "closed",
"labels": [],
"created_at": "2025-06-06T17:09:45Z",
"updated_at": "2025-06-07T07:40:14Z",
"comments": 1,
"user": "garychan22"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3257,
"title": "if use chat.completions, text+image inference return incorrect output because of template issue",
"body": "### System Info\n\ncommon in all platform\n\n### Information\n\n- [ ] Docker\n- [ ] The CLI directly\n\n### Tasks\n\n- [ ] An officially supported command\n- [ ] My own modifications\n\n### Reproduction\n\ntext-generation-launcher --model-id=llava-hf/llava-v1.6-mistral-7b-hf --max-input-tokens 4096 --max-batch-prefill-tokens 16384 --max-total-tokens 8192 --max-batch-size 4\n\nclient:\n\n```\nfrom openai import OpenAI\n\nclient = OpenAI(base_url=\"http://localhost:80/v1\", api_key=\"-\")\n\nchat_completion = client.chat.completions.create(\n model=\"tgi\",\n messages=[\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"image_url\",\n \"image_url\": {\n \"url\": \"https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/rabbit.png\"\n },\n },\n {\"type\": \"text\", \"text\": \"Whats in this image?\"},\n ],\n },\n ],\n max_tokens=50,\n temperature=0.0,\n stream=False,\n)\n\nprint(chat_completion)\n\n```\n\n### Expected behavior\n\nincorrect output is\nChatCompletion(id='', choices=[Choice(finish_reason='stop', index=0, logprobs=None, message=ChatCompletionMessage(content=\" I'm sorry, but I'm not sure what you're asking. Can you please provide more context or information about what you're looking for? \", refusal=None, role='assistant', audio=None, function_call=None, tool_calls=None))], created=1749197214, model='llava-hf/llava-v1.6-mistral-7b-hf', object='chat.completion', service_tier=None, system_fingerprint='3.3.1-dev0-native', usage=CompletionUsage(completion_tokens=35, prompt_tokens=8, total_tokens=43, completion_tokens_details=None, prompt_tokens_details=None))\n\n",
"url": "https://github.com/huggingface/text-generation-inference/issues/3257",
"state": "open",
"labels": [],
"created_at": "2025-06-06T13:06:20Z",
"updated_at": "2025-06-06T13:11:22Z",
"comments": 2,
"user": "sywangyi"
},
{
"repo": "huggingface/nanotron",
"number": 372,
"title": "datatrove need numpy>=2.0.0 bug nanotron 0.4 requires numpy<2, how to fix?",
"body": "",
"url": "https://github.com/huggingface/nanotron/issues/372",
"state": "open",
"labels": [],
"created_at": "2025-06-06T12:12:39Z",
"updated_at": "2025-11-22T14:44:01Z",
"user": "lxyyang"
},
{
"repo": "huggingface/transformers",
"number": 38613,
"title": "MDX Errors",
"body": "### System Info\n\nUbuntu 24.04.2 LTS, CPython 3.11.12, transformers==4.53.0.dev0\n\n\n@stevhliu I'm trying to contribute to the model cards. I forked the latest transformers and I ran the scripts, from the home page and then I want to the documents page. I'm having issues with the doc builder. I keep receiving the errors \"ValueError: There was an error when converting docs/source/en/internal/generation_utils.md to the MDX format.\nUnable to find generation.TFGreedySearchEncoderDecoderOutput in transformers. Make sure the path to that object is correct.\" And Unable to find image_processing_utils_fast.BaseImageProcessorFast in transformers. Make sure the path to that object is correct.\n\nI ran the \" pip install -e \".[docs]\" and saw this after installing everything: \"warning: The package `transformers @ file://s` does not have an extra named `docs`\"\n\nI ran the doc builder and that ran as expected until I ran the doc-builder command \"doc-builder build transformers docs/source/en/ --build_dir ~/tmp/test-build\"\n\nIs there something that I'm misunderstanding? Is there a workaround for me to write the markdown of the card that I have been assigned without having to run those scripts instead, in the meantime.. Thank you!\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nRan install scripts on the Documents folder\n\n### Expected behavior\n\nTo generate the docs",
"url": "https://github.com/huggingface/transformers/issues/38613",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-06-05T14:19:45Z",
"updated_at": "2025-06-06T20:12:36Z",
"comments": 7,
"user": "rileyafox"
},
{
"repo": "huggingface/diffusers",
"number": 11661,
"title": "[BUG]: Using args.max_train_steps even if it is None in diffusers/examples/flux-control",
"body": "### Describe the bug\n\nUnder [https://github.com/huggingface/diffusers/tree/main/examples/flux-control](examples/flux-control) there are two files showing how to fine tune flux-control:\n- [train_control_flux.py](https://github.com/huggingface/diffusers/blob/main/examples/flux-control/train_control_flux.py)\n- [train_control_lora_flux.py](https://github.com/huggingface/diffusers/blob/main/examples/flux-control/train_control_lora_flux.py)\nBoth of them have a bug when args.max_train_steps is None:\nStarting from [Line 905](https://github.com/huggingface/diffusers/blob/c934720629837257b15fd84d27e8eddaa52b76e6/examples/flux-control/train_control_flux.py#L905) we have following code:\n```.py\nif args.max_train_steps is None:\n len_train_dataloader_after_sharding = math.ceil(len(train_dataloader) / accelerator.num_processes)\n num_update_steps_per_epoch = math.ceil(len_train_dataloader_after_sharding / args.gradient_accumulation_steps)\n num_training_steps_for_scheduler = (\n args.num_train_epochs * num_update_steps_per_epoch * accelerator.num_processes\n )\n else:\n num_training_steps_for_scheduler = args.max_train_steps * accelerator.num_processes\n\n lr_scheduler = get_scheduler(\n args.lr_scheduler,\n optimizer=optimizer,\n num_warmup_steps=args.lr_warmup_steps * accelerator.num_processes,\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n num_cycles=args.lr_num_cycles,\n power=args.lr_power,\n )\n```\nNote how it gets checked that `args.max_train_steps` is None in the if, in this case a num_training_steps_for_scheduler gets prepared. However in [Line 918](https://github.com/huggingface/diffusers/blob/c934720629837257b15fd84d27e8eddaa52b76e6/examples/flux-control/train_control_flux.py#L918) we use `args.max_train_steps`\n```.py\n num_training_steps=args.max_train_steps * accelerator.num_processes,\n```\nisntead of the prepared num_training_steps_for_scheduler and causing following error:\n```.sh\nnum_training_steps=args.max_train_steps * accelerator.num_processes,\n ~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~\nTypeError: unsupported operand type(s) for *: 'NoneType' and 'int'\n```\n\n### Reproduction\n\nTraining runs where the max_train_steps are not set, i.e.:\n```.sh\naccelerate launch train_control_lora_flux.py \\\n --pretrained_model_name_or_path=\"black-forest-labs/FLUX.1-dev\" \\\n --dataset_name=\"raulc0399/open_pose_controlnet\" \\\n --output_dir=\"pose-control-lora\" \\\n --mixed_precision=\"bf16\" \\\n --train_batch_size=1 \\\n --rank=64 \\\n --gradient_accumulation_steps=4 \\\n --gradient_checkpointing \\\n --use_8bit_adam \\\n --learning_rate=1e-4 \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --num_train_epochs=10 \\\n --validation_image=\"openpose.png\" \\\n --validation_prompt=\"A couple, 4k photo, highly detailed\" \\\n --offload \\\n --seed=\"0\" \\\n --push_to_hub\n```\n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\nNot relevant for the mentioned Bug.\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/11661",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-06-05T07:18:06Z",
"updated_at": "2025-06-05T09:26:26Z",
"comments": 0,
"user": "Markus-Pobitzer"
},
{
"repo": "huggingface/lerobot",
"number": 1203,
"title": "Could you please upload the config.json file for smolvla?",
"body": "\n\nCould you please upload the config.json file for smolvla? Thank you very much!\n\n\nFileNotFoundError: config.json not found on the HuggingFace Hub in lerobot/smolvla_base\n\n",
"url": "https://github.com/huggingface/lerobot/issues/1203",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-06-05T06:59:12Z",
"updated_at": "2025-06-11T14:56:56Z",
"user": "Pandapan01"
},
{
"repo": "huggingface/transformers",
"number": 38601,
"title": "Contribute to Transformers on windows natively without WSL",
"body": "### System Info\n\n### System info\nOS: Windows 11\nPython: 3.13.3 and 3.10\nGit: 2.49.0\nCMake: 4.0.2\nMsys64: Pacman v6.1.0 - libalpm v14.0.0\nPip: 25.1.1 \nSetuptools: 80.9.0\nVisual studio C++ build tools\n\n### NOTE: I followed the steps here [Contribute to \ud83e\udd17 Transformers](https://huggingface.co/docs/transformers/en/contributing) and for sure system info already existed before following but let me walk through again for additional info.\n1- Forked the repo.\n2- Cloned it\n3- cd transformers (so made sure I am in the right path which is the root for the repo)\n3- switched to my own branch\n4- made a python virtual environment using python 3.10 then activated it \n5- made sure transformers ain't installed inside it\n6- installed PyTorch\n7- Ran this command `pip install -e \".[dev]\"`\n\n\n### NOTE: I tried making requirements.txt and using this command `pip install -r requirements.txt` but I got no output and I tried installing onnx with pip which happened successfully then Ran this command `pip install -e \".[dev]\"` but nothing changed\n\n### NOTE 6/6/2025: I tried uv instead of python venv, nothing worked. I tried deleting everything including system info and install everything from the beginning, nothing worked still. I made a requiremets.txt from what is in setup.py and installed it and tried to run `pip install -e \".[dev]\"` but same issues again, nothing worked\n\n```\n error: subprocess-exited-with-error\n\n \u00d7 python setup.py egg_info did not run successfully.\n \u2502 exit code: 1\n \u2570\u2500> [11 lines of output]\n ...\\setup.py:36: DeprecationWarning: Use shutil.which instead of find_executable\n CMAKE = find_executable('cmake3') or find_executable('cmake')\n ...\\setup.py:37: DeprecationWarning: Use shutil.which instead of find_executable\n MAKE = find_executable('make')\n fatal: not a git repository (or any of the parent directories): .git\n Traceback (most recent call last):\n File \"\", line 2, in \n File \"\", line 35, in \n File \"...\\setup.py\", line 318, in \n raise FileNotFoundError(\"Unable to find \" + requirements_file)\n FileNotFoundError: Unable to find requirements.txt\n [end of output]\n\n note: This error originates from a subprocess, and is likely not a problem with pip.\nerror: metadata-generation-failed\n\n\u00d7 Encountered error while generating package metadata.\n\u2570\u2500> See above for output.\n\nnote: This is an issue with the package mentioned above, not pip.\nhint: See above for details.\n```\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\n`pip install -e \".[dev]\"`\n\n### Expected behavior\n\nBeing able to install transformers for contributing with no issue",
"url": "https://github.com/huggingface/transformers/issues/38601",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-06-05T04:14:12Z",
"updated_at": "2025-07-27T08:02:54Z",
"comments": 4,
"user": "ghost"
},
{
"repo": "huggingface/diffusers",
"number": 11657,
"title": "Custom Wan diffusion Lora runs without error but doesn't apply effect and gives warning: No LoRA keys associated to WanTransformer3DModel found with the prefix='transformer'.",
"body": "### Describe the bug\n\nI run the diffusers pipe using the standard process with a custom diffusers trained lora: \n\npipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)\npipe.scheduler = scheduler\npipe.load_lora_weights(\"lora/customdiffusers_lora.safetensors\")\netc...\n\nit runs without error but the effect was not applied, and I see the following warning: \nNo LoRA keys associated to WanTransformer3DModel found with the prefix='transformer'. This is safe to ignore if LoRA state dict didn't originally have any WanTransformer3DModel related params. You can also try specifying `prefix=None` to resolve the warning. Otherwise, open an issue if you think it's unexpected: https://github.com/huggingface/diffusers/issues/new\n\nIs there any config file I need to change for this to work? Thanks\n\n### Reproduction\n\nN/A as a custom Lora\n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\n0.33, linux, python 3.10\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/11657",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-06-04T19:50:14Z",
"updated_at": "2025-09-12T03:32:17Z",
"comments": 3,
"user": "st-projects-00"
},
{
"repo": "huggingface/transformers",
"number": 38576,
"title": "A local variable 'image_seq_length' leading to UnboundLocalError: cannot access local variable 'image_seq_length' where it is not associated with a value",
"body": "### System Info\n\n- `transformers` version: 4.52.3\n- Platform: Linux-5.15.0-125-generic-x86_64-with-glibc2.35\n- Python version: 3.12.2\n- Huggingface_hub version: 0.32.2\n- Safetensors version: 0.5.3\n- Accelerate version: 0.26.0\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (GPU?): 2.6.0+cu124 (True)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: \n- Using GPU in script?: \n- GPU type: NVIDIA GeForce RTX 4090\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nThe code snippet is as follows:\nfrom transformers.utils.attention_visualizer import AttentionMaskVisualizer\n\nvisualizer = AttentionMaskVisualizer(\"meta-llama/Llama-2-7b-hf\")\nvisualizer(\"Plants create energy through a process known as\")\n\nIn the Class AttentionMaskVisualizer, a local variable in the first branch (lines 181-201), 'image_seq_length,' is passed to the function (line 232). However, in the text case, the branch will not be executed, and it will lead to UnboundLocalError: cannot access local variable 'image_seq_length' where it is not associated with a value.\n\n### Expected behavior\n\nNone",
"url": "https://github.com/huggingface/transformers/issues/38576",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-06-04T09:06:04Z",
"updated_at": "2025-06-04T12:20:33Z",
"user": "IceGiraffe"
},
{
"repo": "huggingface/lerobot",
"number": 1195,
"title": "ros2_control support",
"body": "Hello,\n\nI was thinking that it would be great to use the robot with ros2_control :\n\n- to test code developped with the ROS2 framework:\n- for education purposes : the robot is great, easily and not expensive to build (thank you for the work achieved), transporteable in a case, etc.\n\nDo you have any knowledge of an existing project ?\nIf not, would you be interested in this kind of implementation ?\n\nBest,\nAline",
"url": "https://github.com/huggingface/lerobot/issues/1195",
"state": "open",
"labels": [
"enhancement",
"question"
],
"created_at": "2025-06-03T15:31:53Z",
"updated_at": "2025-11-27T16:30:08Z",
"user": "baaluidnrey"
},
{
"repo": "huggingface/diffusers",
"number": 11648,
"title": "how to load lora weight with fp8 transfomer model?",
"body": "Hi, I want to run fluxcontrolpipeline with transformer_fp8 reference the code : \nhttps://huggingface.co/docs/diffusers/api/pipelines/flux#quantization\n\n```\nimport torch\nfrom diffusers import BitsAndBytesConfig as DiffusersBitsAndBytesConfig, FluxTransformer2DModel, FluxControlPipeline\nfrom transformers import BitsAndBytesConfig as BitsAndBytesConfig, T5EncoderModel\n\nquant_config = BitsAndBytesConfig(load_in_8bit=True)\ntext_encoder_8bit = T5EncoderModel.from_pretrained(\n \"black-forest-labs/FLUX.1-dev\",\n subfolder=\"text_encoder_2\",\n quantization_config=quant_config,\n torch_dtype=torch.float16,\n)\n\nquant_config = DiffusersBitsAndBytesConfig(load_in_8bit=True)\ntransformer_8bit = FluxTransformer2DModel.from_pretrained(\n \"black-forest-labs/FLUX.1-dev\",\n subfolder=\"transformer\",\n quantization_config=quant_config,\n torch_dtype=torch.float16,\n)\n\npipeline = FluxControlPipeline.from_pretrained(\n \"black-forest-labs/FLUX.1-dev\",\n text_encoder_2=text_encoder_8bit,\n transformer=transformer_8bit,\n torch_dtype=torch.float16,\n device_map=\"balanced\",\n)\n\nprompt = \"a tiny astronaut hatching from an egg on the moon\"\nimage = pipeline(prompt, guidance_scale=3.5, height=768, width=1360, num_inference_steps=50).images[0]\nimage.save(\"flux.png\")\n```\n\nbut when I load lora after build a pipeline\n\n```\npipeline = FluxControlPipeline.from_pretrained(\n \"black-forest-labs/FLUX.1-dev\",\n text_encoder_2=text_encoder_8bit,\n transformer=transformer_8bit,\n torch_dtype=torch.float16,\n device_map=\"balanced\",\n)\n\npipe.load_lora_weights(\"black-forest-labs/FLUX.1-Depth-dev-lora\")\n```\nThere a error:\nnot support fp8 weight , how to fix it??\n\n",
"url": "https://github.com/huggingface/diffusers/issues/11648",
"state": "open",
"labels": [],
"created_at": "2025-06-03T10:31:23Z",
"updated_at": "2025-06-19T12:37:35Z",
"user": "Johnson-yue"
},
{
"repo": "huggingface/candle",
"number": 2986,
"title": "How to reset gradient before each batch",
"body": "In Pytorch, you would call `optimizer.zero_grad` to zero the gradients before every batch. How do you do this in candle?",
"url": "https://github.com/huggingface/candle/issues/2986",
"state": "open",
"labels": [],
"created_at": "2025-06-03T10:17:52Z",
"updated_at": "2025-06-03T10:17:52Z",
"user": "lokxii"
},
{
"repo": "huggingface/transformers",
"number": 38544,
"title": "Paligemma model card needs update",
"body": "Hi \n\nI found a minor problem with paligemma model card. How can I raise a PR to fix it ? I am first time contributor. I raised PR. Whom should I mention to review it ? \nhttps://huggingface.co/google/paligemma-3b-pt-896",
"url": "https://github.com/huggingface/transformers/issues/38544",
"state": "closed",
"labels": [],
"created_at": "2025-06-03T06:55:14Z",
"updated_at": "2025-07-14T16:23:52Z",
"comments": 7,
"user": "punitvara"
},
{
"repo": "huggingface/transformers",
"number": 38541,
"title": "`eager_attention_forward` and `repeat_kv` code duplication",
"body": "I see the two functions appear in a lot of places in the code base. Shall we unify them into a single place?\n\nAnd can we treat `eager_attention_forward` as another option in [`ALL_ATTENTION_FUNCTIONS`](https://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L6186)? Any concerns?",
"url": "https://github.com/huggingface/transformers/issues/38541",
"state": "closed",
"labels": [],
"created_at": "2025-06-03T00:57:16Z",
"updated_at": "2025-06-10T10:27:25Z",
"comments": 3,
"user": "ChengLyu"
},
{
"repo": "huggingface/chat-ui",
"number": 1843,
"title": "can you make a release?",
"body": "The current codebase is far away from the official release in November, maybe you can stabilize and release current code?",
"url": "https://github.com/huggingface/chat-ui/issues/1843",
"state": "open",
"labels": [
"enhancement"
],
"created_at": "2025-06-02T21:26:51Z",
"updated_at": "2025-07-21T20:44:03Z",
"comments": 1,
"user": "antonkulaga"
},
{
"repo": "huggingface/transformers",
"number": 38527,
"title": "Why do you remove sample_indices_fn for processor.apply_chat_template?",
"body": "Just as shown in the picture, since 4.52 processor.apply_chat_template does no longer support sample_indices_fn but the args doc is still there. \n\n![\"Image\"](\"https://github.com/user-attachments/assets/e055d5f5-4800-4eb7-8054-0f41a9be5707\")
",
"url": "https://github.com/huggingface/transformers/issues/38527",
"state": "closed",
"labels": [],
"created_at": "2025-06-02T12:34:23Z",
"updated_at": "2025-06-03T02:44:22Z",
"comments": 1,
"user": "futrime"
},
{
"repo": "huggingface/optimum",
"number": 2284,
"title": "Error when exporting DinoV2 with Registers",
"body": "When trying :\n\n` python -m scripts.convert --quantize --model_id facebook/dinov2-with-registers-small`\n\nI Got : \n\n`ValueError: Trying to export a dinov2-with-registers model, that is a custom or unsupported architecture, but no custom onnx configuration was passed as `custom_onnx_configs`. Please refer to https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#custom-export-of-transformers-models for an example on how to export custom models. Please open an issue at https://github.com/huggingface/optimum/issues if you would like the model type dinov2-with-registers to be supported natively in the ONNX export.`",
"url": "https://github.com/huggingface/optimum/issues/2284",
"state": "closed",
"labels": [
"Stale"
],
"created_at": "2025-06-02T08:53:55Z",
"updated_at": "2025-07-04T02:16:54Z",
"comments": 1,
"user": "elkizana"
},
{
"repo": "huggingface/agents-course",
"number": 523,
"title": "[QUESTION] The final quiz of Unit 1, always crashes with dataset not found",
"body": "First, the **best way to get a response fast is to ask the community** in our Discord server: https://www.hf.co/join/discord\n\nHowever, if you prefer you can ask here, please **be specific**.\n\nDataset 'agents-course/unit_1_quiz' doesn't exist on the Hub or cannot be accessed. \n\nThe full log is: \n\n```\nTraceback (most recent call last):\n File \"/home/user/app/app.py\", line 28, in \n ds = load_dataset(EXAM_DATASET_ID, split=\"train\")\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 2129, in load_dataset\n builder_instance = load_dataset_builder(\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 1849, in load_dataset_builder\n dataset_module = dataset_module_factory(\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 1719, in dataset_module_factory\n raise e1 from None\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 1645, in dataset_module_factory\n raise DatasetNotFoundError(f\"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.\") from e\ndatasets.exceptions.DatasetNotFoundError: Dataset 'agents-course/unit_1_quiz' doesn't exist on the Hub or cannot be accessed.\nTraceback (most recent call last):\n File \"/home/user/app/app.py\", line 28, in \n ds = load_dataset(EXAM_DATASET_ID, split=\"train\")\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 2129, in load_dataset\n builder_instance = load_dataset_builder(\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 1849, in load_dataset_builder\n dataset_module = dataset_module_factory(\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 1719, in dataset_module_factory\n raise e1 from None\n File \"/usr/local/lib/python3.10/site-packages/datasets/load.py\", line 1645, in dataset_module_factory\n raise DatasetNotFoundError(f\"Dataset '{path}' doesn't exist on the Hub or cannot be accessed.\") from e\ndatasets.exceptions.DatasetNotFoundError: Dataset 'agents-course/unit_1_quiz' doesn't exist on the Hub or cannot be accessed.\n ```\n\nAm I missing something trivial? \n",
"url": "https://github.com/huggingface/agents-course/issues/523",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-06-02T07:58:01Z",
"updated_at": "2025-06-02T07:58:01Z",
"user": "abcnishant007"
},
{
"repo": "huggingface/peft",
"number": 2563,
"title": "Integrate Lily",
"body": "### Feature request\n\nThis request proposes integrating Lily (Low-Rank Interconnected Adaptation across Layers), accepted to ACL 2025 Findings, into the PEFT library. \nPaper: https://arxiv.org/pdf/2407.09946 \nRepo: https://github.com/yibozhong/lily \n\n\n### Motivation\n\nLily aims to directly make the rank of each individual adapter bigger under the same parameter budget, as it's shown in many papers that higher ranks are beneficial to PEFT performance. This is achieved by breaking the pair-AB-per-layer constraint of LoRA. That is, we do not give each layer a dedicated pair of A and B. Rather, we decouple all the Bs from the layer, and when adapting at each layer, we use a weighted sum of these Bs as the B for this layer. The weight is calculated by a lightweight trainable router, currently data-dependent. \n\n \n\nSeveral points worth noting: \n- The method looks somewhat similar to MosLoRA in structure, but it operates at the model level and the aim is to increase the individual rank of each adapter with dynamic adaptation. \n- Currently in the paper, we use a data-dependent router, which makes it tricky to merge the weights. I do not observe notable inference latency, possibly due to small model size, but an option for using a non-data-dependent router can be included and enable easy merging the weights. \n- The current As are still positioned at a fixed layer (using layer-wise sharing to reduce params). However, it also can be decoupled, simply by providing two routers for weighting As and Bs respectively, rather than one router for B in the current setup. This is a more elegant design and shares the same principle as Lily. After I run quick experiments demonstrating its effectiveness, I can integrate this setup into my current code as Lily v2. \n\n### Your contribution\n\nImplement Lily, repo: https://github.com/yibozhong/lily. ",
"url": "https://github.com/huggingface/peft/issues/2563",
"state": "closed",
"labels": [],
"created_at": "2025-06-02T07:23:30Z",
"updated_at": "2025-12-18T14:03:32Z",
"comments": 15,
"user": "yibozhong"
},
{
"repo": "huggingface/lerobot",
"number": 1180,
"title": "dataset training",
"body": "How many episodes do you recommend making for each file when learning the dataset? Can I create about 400 episodes by putting different tasks in each episode? Or can I create the same task data for each file and combine multiple files?",
"url": "https://github.com/huggingface/lerobot/issues/1180",
"state": "closed",
"labels": [
"question",
"dataset"
],
"created_at": "2025-06-01T15:59:47Z",
"updated_at": "2025-10-08T12:54:48Z",
"user": "bruce577"
},
{
"repo": "huggingface/lerobot",
"number": 1177,
"title": "[Question] Why using a kernel device for IP cameras?",
"body": "I'm wondering why, when we have an IP camera (by using DroidCam on Android for instance), the team decided to plug the IP camera into a loopback device in `/dev/videoX` instead of directly reading the video stream in the code with Opencv `cv2.VideoCapture(url)`. I understand doing this allows controlling FPS & resolution which is not possible when `cv2.VideoCapture(url)` is used directly, however the downside is that you need to map the camera to a kernel device which becomes really cumbersome, especially when you need root access and when the device gets stuck in a weird state.\n\nWhy didn't the team simply read the video stream from `cv2.VideoCapture(url)` and then downsized the video stream inside the code loop? (The only downside of doing this I found is that we can't get 30fps if the stream outputs only 25fps but this shouldn't be a problem imo since `OpenCVCamera.read_loop` adds a 0.1 latency which messes up the fps sync anyways).",
"url": "https://github.com/huggingface/lerobot/issues/1177",
"state": "closed",
"labels": [
"question",
"robots",
"stale"
],
"created_at": "2025-05-31T05:24:21Z",
"updated_at": "2025-12-31T02:35:18Z",
"user": "godardt"
},
{
"repo": "huggingface/transformers",
"number": 38501,
"title": "torch.compile fails for gemma-3-1b-it",
"body": "### System Info\n\n- `transformers` version: 4.52.4\n- Platform: Linux-6.15.0-1-MANJARO-x86_64-with-glibc2.41\n- Python version: 3.12.8\n- Huggingface_hub version: 0.32.3\n- Safetensors version: 0.5.3\n- Accelerate version: 1.7.0\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (GPU?): 2.7.0+cu126 (True)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: no\n- Using GPU in script?: yes\n- GPU type: NVIDIA GeForce RTX 3090 Ti\n\n### Who can help?\n\n@ArthurZucker @gante \n\n### Information\n\n- [x] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nRunning `TORCHDYNAMO_VERBOSE=1 TORCH_LOGS=\"+dynamo\" uv run main.py` fails:\n\n.\",\n \"The name of the first president of the united states is .\",\n ],\n padding=True,\n return_tensors=\"pt\",\n).to(\"cuda\")\n\nwith torch.no_grad():\n outputs = model(**inputs, output_hidden_states=True)\n\nprint(inputs[\"attention_mask\"].sum())\n# >>> 26\nprint(outputs.hidden_states[-1].shape)\n# >>> torch.Size([26, 768])\n\n\nassert outputs.hidden_states[-1].shape == inputs[\"input_ids\"].shape + (\n model.config.hidden_size,\n)\n```\n\nI'm using the following library versions:\n- `transformers==4.48.2`\n- `torch==2.6.0`\n\nIt appears that what is returned is the flattened version as the tensor is 2D and the first dimension corresponds to the sum of the attention mask. This issue doesn't happen when using the non MLM version.\n\nI searched modern bert and hidden state and looked at the recent commits and didn't see any mention of this issue, but it might have been fixed in a newer version without it being obvious.\n\n\n\n### Who can help?\n\n@ArthurZucker \n\n### Information\n\n- [x] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nRun the code provided in the issue with flash attention on a Cuda GPU.\n\n### Expected behavior\n\nThe hidden states should have shape [batch size, max sequence length, model dim] but they have shape [unknown dim (I think the number of unpadded tokens), model dim].",
"url": "https://github.com/huggingface/transformers/issues/38499",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-05-30T17:02:55Z",
"updated_at": "2025-07-08T08:02:39Z",
"comments": 2,
"user": "jfkback"
},
{
"repo": "huggingface/lerobot",
"number": 1174,
"title": "[Question] Multi-Rate Sensor and Discrete Event Handling in `lerobot`",
"body": "Hello `lerobot` Team,\n\nFirst off, huge thanks for building such an awesome open-source project!\n\nI'm currently exploring `lerobot` for a project and have some critical questions regarding its data handling, specifically for multi-rate sensors and discrete events. My understanding from the README is that `lerobot` records at a fixed `fps`, creating a table with `fps * record_time` rows.\n\nThis leads to two primary concerns:\n\n1. **Multi-Rate Sensors:**\n Consider a sensor like an IMU operating at 1KHz, while other sensors might be at much lower rates. To capture the IMU data without loss, the `fps` would need to be set extremely high, to match highest-rate-sensor. This implies:\n * **Massive Data Redundancy:** A significant portion of rows would contain sparse information from the lower-rate sensors.\n * **Recording Performance:** Could such a high `fps` and resulting data volume negatively impact recording performance, potentially making it infeasible to capture this type of data?\n * **Storage Load:** This approach would also lead to very large dataset sizes.\n Am I correct in this interpretation? If so, how does `lerobot` effectively manage multi-rate sensor data to mitigate these issues?\n\n2. **Discrete Events:**\n How are discrete events, such as keyboard presses/releases or joystick button presses, recorded into a `LeRobotDataset`? The current design of `LeRobotDataset`, particularly `__nextitem__` and `delta_timestamps`, seems to implicitly assume continuous data that can be interpolated. How does `lerobot` accommodate and represent these non-continuous, event-driven data points within its framework?\n\nA quick response addressing these points would be incredibly helpful for our ongoing development.\n\nThanks for your time and insight!",
"url": "https://github.com/huggingface/lerobot/issues/1174",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-05-30T09:04:13Z",
"updated_at": "2025-12-17T10:44:46Z",
"user": "MilkClouds"
},
{
"repo": "huggingface/transformers",
"number": 38489,
"title": "VLM reverse mapping logic in modeling_utils.py save_pretrained not doing anything?",
"body": "### System Info\n\ntransformers version: 4.52.3\nPlatform: Ubuntu 24.04\nPython version: 3.11.0\nHuggingface_hub version: 0.32.2\nSafetensors version: 0.5.3\nAccelerate version: 1.7.0\nAccelerate config: not found\nDeepSpeed version: not installed\nPyTorch version (GPU?): 2.7.0+cu126 (H100)\nTensorflow version (GPU?): not installed (NA)\nFlax version (CPU?/GPU?/TPU?): not installed (NA)\nJax version: not installed\nJaxLib version: not installed\nUsing distributed or parallel set-up in script?: No\nUsing GPU in script?: No\nGPU type: NVIDIA H100\n\n### Who can help?\n\n@amyeroberts @zucchini-nlp \n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nborrowing the reverse key mapping logic in the modeling_utils.py save_pretrained method as shown here:\nhttps://github.com/huggingface/transformers/blob/main/src/transformers/modeling_utils.py#L3649\nIf we also use the qwen2 model mappings for Qwen2ForConditionalGeneration as an example\nand a sample of keys as shown below to test the reversal logic:\n\n```\nimport re\nfrom transformers import Qwen2VLForConditionalGeneration\ncheckpoint_conversion_mapping = Qwen2VLForConditionalGeneration._checkpoint_conversion_mapping\n\ncheckpoint_keys = [\n 'model.language_model.layers.9.post_attention_layernorm.weight', # Should be remapped\n 'model.layers.9.self_attn.k_proj.bias', # Should not be remapped\n 'model.visual.blocks.0.attn.proj.bias', # Should be remapped\n 'visual.blocks.0.attn.proj.weight', # Should not be remapped\n]\n\nreverse_key_mapping = {v: k for k, v in checkpoint_conversion_mapping.items()}\nfor key in checkpoint_keys:\n print(f\"\\nOperating on sample key: {key}:\")\n for pattern, replacement in reverse_key_mapping.items():\n replacement = replacement.lstrip(\"^\") # strip off un-needed chars and patterns\n replacement = re.sub(r\"\\(.*?\\)\", \"\", pattern)\n key, n_replace = re.subn(pattern, replacement, key)\n print(f\"pattern: {pattern}, replacement: {replacement}, resultant key: {key}\")\n # Early exit of the loop\n if n_replace > 0:\n print(f\"Result: final mapped key is {key}\")\n break\n else:\n print(f\"Result: no mappings performed\")\n```\nreturns the following output where no mapping reversal is performed where it should be.\n```\nOperating on sample key: model.language_model.layers.9.post_attention_layernorm.weight:\npattern: model.visual, replacement: model.visual, resultant key: model.language_model.layers.9.post_attention_layernorm.weight\nResult: no mappings performed\npattern: model.language_model, replacement: model.language_model, resultant key: model.language_model.layers.9.post_attention_layernorm.weight\nResult: final mapped key is model.language_model.layers.9.post_attention_layernorm.weight\n\nOperating on sample key: model.layers.9.self_attn.k_proj.bias:\npattern: model.visual, replacement: model.visual, resultant key: model.layers.9.self_attn.k_proj.bias\nResult: no mappings performed\npattern: model.language_model, replacement: model.language_model, resultant key: model.layers.9.self_attn.k_proj.bias\nResult: no mappings performed\n\nOperating on sample key: model.visual.blocks.0.attn.proj.bias:\npattern: model.visual, replacement: model.visual, resultant key: model.visual.blocks.0.attn.proj.bias\nResult: final mapped key is model.visual.blocks.0.attn.proj.bias\n\nOperating on sample key: visual.blocks.0.attn.proj.weight:\npattern: model.visual, replacement: model.visual, resultant key: visual.blocks.0.attn.proj.weight\nResult: no mappings performed\npattern: model.language_model, replacement: model.language_model, resultant key: visual.blocks.0.attn.proj.weight\nResult: no mappings performed\n```\n\n### Expected behavior\n\nThe expected behavior should be such that we observe the following mapping:\n```\nmodel.language_model.layers.9.post_attention_layernorm.weight -> model.layers.9.post_attention_layernorm.weight\nmodel.visual.blocks.0.attn.proj.bias-> visual.blocks.0.attn.proj.bias\nmodel.layers.9.self_attn.k_proj.bias -> model.layers.9.self_attn.k_proj.bias (remains the same)\nvisual.blocks.0.attn.proj.weight -> visual.blocks.0.attn.proj.weight (remains the same)\n```\n\nThis could be achieved by changing the reversal code inside the for pattern, replacement in reverse_key_mapping.items(): loop to be \n```\nreplacement = replacement.lstrip(\"^\") # strip off un-needed chars and patterns\n replacement = re.sub(r\"\\^?([^(?]+).*\", r\"\\1\", replacement)\n key, n_replace = re.subn(pattern, replacement, key)\n print(f\"pattern: {pattern}, replacement: {replacement}, resultant key: {key}\")\n # Early exit of the loop\n if n_replace > 0:\n break\n``` \ninstead.\n\nI could ",
"url": "https://github.com/huggingface/transformers/issues/38489",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-05-30T08:55:57Z",
"updated_at": "2025-05-30T13:08:58Z",
"comments": 6,
"user": "rolandtannous"
},
{
"repo": "huggingface/diffusers",
"number": 11637,
"title": "How to load lora weight in distribution applications?",
"body": "If I want to use xDiT with 2 GPU inference FluxControlPipeline, how should I do\n\nI write a xFuserFluxControlPipeline class, but it can not load lora weight with right way\nxFuserFluxTransformer in 1GPU have some parameters and another GPU have others.\nHow should I do ??",
"url": "https://github.com/huggingface/diffusers/issues/11637",
"state": "open",
"labels": [],
"created_at": "2025-05-30T07:14:50Z",
"updated_at": "2025-06-03T10:15:51Z",
"user": "Johnson-yue"
},
{
"repo": "huggingface/peft",
"number": 2558,
"title": "GraLoRA support?",
"body": "### Feature request\n\nwill the library support the [GraLoRA](https://arxiv.org/abs/2505.20355) technique?\n\n### Motivation\n\nGraLoRA addresses a fundamental limitation of LoRA: overfitting when the bottleneck is widened.\n\nThe technique seems to more closely approximate full fine-tuning; hybrid GraLoRA gets the best of both worlds, with LoRA benefiting from low-rank scenarios (16 or less) and GraLoRA from high-rank scenarios (16 to 128).\n\nThe authors have a modified peft library; would be nice to have support in the official library.\n\n### Your contribution\n\nI have limited time for the next two weeks. Then, I will be able to contribute.\n\nBut should be very easy for the authors to port the implementation; most of it in the [gralora](https://github.com/SqueezeBits/GraLoRA/tree/8dff8438c80969f5f11f23249fed62aac9d687e8/peft/src/peft/tuners/gralora) sub-package.",
"url": "https://github.com/huggingface/peft/issues/2558",
"state": "closed",
"labels": [],
"created_at": "2025-05-29T18:36:27Z",
"updated_at": "2025-07-15T15:04:20Z",
"comments": 10,
"user": "DiTo97"
},
{
"repo": "huggingface/lerobot",
"number": 1171,
"title": "sync_read.py",
"body": "Hi, I am currently testing the functions in the STServo_Python folder to work with my STS3215 motors. When I run the sync_read.py script, I encounter an issue caused by the addParam(self, sts_id) function returning False. I tried several things, but I can't get past the error.\nI made sure that the motor IDs are correct and that the motors are connected and powered. I'm using a GroupSyncRead object with a start_address of SCSCL_PRESENT_POSITION_L and data_length of 4. Still, addParam() fails, and the motor ID is not added to the list.\n\nDoes anyone know why this is happening or how to fix it?\n\nThanks in advance!",
"url": "https://github.com/huggingface/lerobot/issues/1171",
"state": "closed",
"labels": [
"bug",
"question",
"robots",
"stale"
],
"created_at": "2025-05-29T15:33:16Z",
"updated_at": "2025-12-31T02:35:19Z",
"user": "Baptiste-le-Beaudry"
},
{
"repo": "huggingface/candle",
"number": 2974,
"title": "Any good first issues a newcomer could tackle?",
"body": "Hey! I've been using this crate for a while now and would love to start contributing back! I notice that your issues aren't labelled, who should I contact/do you have a list of issues that would be good for me?",
"url": "https://github.com/huggingface/candle/issues/2974",
"state": "open",
"labels": [],
"created_at": "2025-05-29T04:19:18Z",
"updated_at": "2025-05-30T18:25:37Z",
"comments": 3,
"user": "Heidar-An"
},
{
"repo": "huggingface/xet-core",
"number": 358,
"title": "How can I have snapshot_download to have continue feature? Errors became very common",
"body": "Whenever some error happens and i run same code, it starts from 0\n\nIt is XET enabled repo and hf xet installed\n\nI really need to have resume feature\n\nmy entire code\n\n\n```\nfrom huggingface_hub import snapshot_download\nimport os\nimport argparse\n\ndef download_models(target_dir=None):\n \"\"\"\n Download models from HuggingFace hub to specified directory\n \n Args:\n target_dir (str, optional): Target directory for downloads. \n If None, uses current working directory\n \"\"\"\n # Set repo ID\n repo_id = \"MonsterMMORPG/Kohya_Train\"\n \n # Use provided target dir or default to current working directory\n download_dir = target_dir if target_dir else os.getcwd()\n \n # Create target directory if it doesn't exist\n os.makedirs(download_dir, exist_ok=True)\n \n try:\n snapshot_download(\n local_dir=download_dir,\n repo_id=repo_id\n )\n print(f\"\\nDOWNLOAD COMPLETED to: {download_dir}\")\n print(\"Check folder content for downloaded files\")\n \n except Exception as e:\n print(f\"Error occurred during download: {str(e)}\")\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser(description='Download models from HuggingFace hub')\n parser.add_argument('--dir', type=str, help='Target directory for downloads', default=None)\n \n args = parser.parse_args()\n download_models(args.dir)\n```",
"url": "https://github.com/huggingface/xet-core/issues/358",
"state": "closed",
"labels": [
"enhancement"
],
"created_at": "2025-05-28T22:30:19Z",
"updated_at": "2025-11-20T17:08:35Z",
"user": "FurkanGozukara"
},
{
"repo": "huggingface/transformers",
"number": 38452,
"title": "Memory saving by upcasting logits for only non-ignored positions",
"body": "### Feature request\n\nIn [`loss_utils.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/loss/loss_utils.py), logits are upcasted for float32 for some losses. This can waste memory for cases where certain labels are `ignore_index`. This is especially true for fine tuning cases where one chooses to calculate loss only on the completion. They would keep label as -100 for prompt tokens and upcasting those logits would be unnecessary. We can instead call `logits.float()` after we have our final labels. This would be especially useful for `ForCausalLMLoss` as that seems to be the most likely use case.\n\n### Motivation\n\nWhen fine tuning a causal LM, one can choose to calculate loss only on the completion, thus setting labels for prompt tokens to be -100. Upcasting logits at those positions when calculating loss is not needed. Avoiding that can save memory. Most likely use case is `ForCausalLMLoss`.\n\n### Your contribution\n\nAn example for `ForCausalLMLoss`:\n\n```\ndef ForCausalLMLoss(\n logits,\n labels,\n vocab_size: int,\n num_items_in_batch: Optional[int] = None,\n ignore_index: int = -100,\n shift_labels: Optional[torch.Tensor] = None,\n **kwargs,\n) -> torch.Tensor:\n # Don't upcast yet\n # logits = logits.float()\n\n if shift_labels is None:\n # Shift so that tokens < n predict n\n labels = nn.functional.pad(labels, (0, 1), value=ignore_index)\n shift_labels = labels[..., 1:].contiguous() \n\n # Flatten the tokens\n logits = logits.view(-1, vocab_size)\n shift_labels = shift_labels.view(-1)\n\n # Upcast to float if we need to compute the loss to avoid potential precision issues\n # Now that we have our final labels, take only the useful logits and then upcast\n logits = logits[shift_labels != ignore_index]\n shift_labels = shift_labels[shift_labels != ignore_index]\n logits = logits.float()\n\n # Enable model parallelism\n shift_labels = shift_labels.to(logits.device)\n\n # Calculate loss on truncated logits and labels\n loss = fixed_cross_entropy(logits, shift_labels, num_items_in_batch, ignore_index, **kwargs)\n return loss\n```\n\nWe can do something similar in `ForMaskedLMLoss` on line 83 instead of 77. `ForTokenClassification` does not take `ignore_index` as an argument but we can still do the same here because `fixed_cross_entropy` does take `ignore_index`.\n\nAnother alternative was to move the upcasting to inside `fixed_cross_entropy` but a few losses don't do that. So, that might change/break existing things.\n\nLet me know if this change sounds good. I can submit a PR.",
"url": "https://github.com/huggingface/transformers/issues/38452",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-05-28T18:58:52Z",
"updated_at": "2025-05-29T12:38:15Z",
"comments": 1,
"user": "harshit2997"
},
{
"repo": "huggingface/speech-to-speech",
"number": 163,
"title": "how to use this with Livekit Agent?",
"body": "how to use this with Livekit Agent?",
"url": "https://github.com/huggingface/speech-to-speech/issues/163",
"state": "open",
"labels": [],
"created_at": "2025-05-28T18:27:11Z",
"updated_at": "2025-05-28T18:27:11Z",
"user": "Arslan-Mehmood1"
},
{
"repo": "huggingface/transformers",
"number": 38448,
"title": "num_items_in_batch larger than the actual useful token when computing loss",
"body": "def fixed_cross_entropy(source, target, num_items_in_batch: int = None, ignore_index: int = -100, **kwargs):\nI check the shape of the inputs and find follows:\nIn [1]: logits.shape\nOut[1]: torch.Size([4, 896, 152064])\n\nIn [2]: labels.shape\nOut[2]: torch.Size([4, 896])\n\nIn [3]: num_items_in_batch\nOut[3]: 4390\n\nWhy is 4390>4*896?",
"url": "https://github.com/huggingface/transformers/issues/38448",
"state": "closed",
"labels": [],
"created_at": "2025-05-28T15:28:05Z",
"updated_at": "2025-05-31T02:30:07Z",
"comments": 4,
"user": "SHIFTTTTTTTT"
},
{
"repo": "huggingface/transformers",
"number": 38435,
"title": "[i18n-ro] Translating docs to Romanian",
"body": "Hi!\n\nLet's bring the documentation to all the Romanian-speaking community \ud83c\udf10 \n\nWho would want to translate? Please follow the \ud83e\udd17 [TRANSLATING guide](https://github.com/huggingface/transformers/blob/main/docs/TRANSLATING.md). Here is a list of the files ready for translation. Let us know in this issue if you'd like to translate any, and we'll add your name to the list.\n\nSome notes:\n\n* Please translate using an informal tone (imagine you are talking with a friend about transformers \ud83e\udd17).\n* Please translate in a gender-neutral way.\n* Add your translations to the folder called `` inside the [source folder](https://github.com/huggingface/transformers/tree/main/docs/source).\n* Register your translation in `/_toctree.yml`; please follow the order of the [English version](https://github.com/huggingface/transformers/blob/main/docs/source/en/_toctree.yml).\n* Once you're finished, open a pull request and tag this issue by including #issue-number in the description, where issue-number is the number of this issue. Please ping @stevhliu for review.\n* \ud83d\ude4b If you'd like others to help you with the translation, you can also post in the \ud83e\udd17 [forums](https://discuss.huggingface.co/).\n\n## Get Started section\n\n- [ ] [index.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/index.md) (in progress, [see](https://github.com/zero-point/transformers/tree/add_ro_translation_to_readme))\n- [ ] [quicktour.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/quicktour.md) \n- [ ] [installation.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/installation.md).\n\n## Tutorial section\n- [ ] [pipeline_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/pipeline_tutorial.md)\n- [ ] [autoclass_tutorial.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/autoclass_tutorial.md)\n- [ ] [preprocessing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/preprocessing.md)\n- [ ] [training.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/training.md)\n- [ ] [accelerate.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/accelerate.md)\n- [ ] [model_sharing.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/model_sharing.md)\n- [ ] [multilingual.md](https://github.com/huggingface/transformers/blob/main/docs/source/en/multilingual.md)\n\n\n",
"url": "https://github.com/huggingface/transformers/issues/38435",
"state": "open",
"labels": [
"WIP"
],
"created_at": "2025-05-28T12:01:48Z",
"updated_at": "2025-05-28T15:53:39Z",
"comments": 2,
"user": "zero-point"
},
{
"repo": "huggingface/transformers",
"number": 38428,
"title": "[Question] The logic of data sampler in data parallel.",
"body": "Hi, thanks for your attention.\n\nWhen reading the source code of transformers, I cannot understand the implementation of `_get_train_sampler` in `trainer.py`. Why the default data sampler is `RandomSampler` rather than `DistributedSampler`? How does the trainer handle the sampler for data parallel?\n\nreference code: https://github.com/huggingface/transformers/blob/main/src/transformers/trainer.py#L975",
"url": "https://github.com/huggingface/transformers/issues/38428",
"state": "closed",
"labels": [],
"created_at": "2025-05-28T08:49:13Z",
"updated_at": "2025-07-06T08:02:36Z",
"comments": 3,
"user": "kxzxvbk"
},
{
"repo": "huggingface/transformers",
"number": 38425,
"title": "Can not load TencentBAC/Conan-embedding-v2",
"body": "### System Info\n\nDescription\nWhen attempting to load the \u201cConan-embedding-v2\u201d model directly via transformers.AutoModel.from_pretrained, I get a ValueError indicating that the repo\u2019s config.json lacks a model_type key. This prevents the Transformers library from inferring which model class to instantiate.\n\n\n\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nfrom transformers import AutoModel\n\nmodel = AutoModel.from_pretrained(\"TencentBAC/Conan-embedding-v2\")\n\nValueError: Unrecognized model in TencentBAC/Conan-embedding-v2.\nShould have a `model_type` key in its config.json, or contain one of the following strings in its name: albert, bart, bert, \u2026, whisper, xlnet, \u2026\n\n\n### Expected behavior\n\nAutoModel.from_pretrained(\"TencentBAC/Conan-embedding-v2\") should load the model automatically, or at minimum provide guidance on how to set the correct model_type.",
"url": "https://github.com/huggingface/transformers/issues/38425",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-05-28T08:21:23Z",
"updated_at": "2025-05-28T14:58:03Z",
"comments": 1,
"user": "shanekao-sks"
},
{
"repo": "huggingface/accelerate",
"number": 3596,
"title": "How to distribute the model into multiple GPUs using accelerate?",
"body": "I have 4 GPUs. If I only use a single GPU to train the model, there will be an OutOfMemoryError raised. How can I distribute the model into all the 4 GPUs to avoid the OutOfMemoryError using accelerate?",
"url": "https://github.com/huggingface/accelerate/issues/3596",
"state": "closed",
"labels": [],
"created_at": "2025-05-28T06:27:08Z",
"updated_at": "2025-05-28T14:06:18Z",
"user": "GeorgeCarpenter"
},
{
"repo": "huggingface/candle",
"number": 2971,
"title": "Enhance the usability of the tensor struct",
"body": "Hello,\n\nI\u2019m currently learning how to use Candle with the book Dive into Deep Learning, but implementing the code in Candle. I noticed that Candle is missing some practical utility functions, such as:\n\n* The Frobenius norm \n* dot product (vector or matrix dot product)\n* matrix-vector multiplication\n\nWhile these functions aren\u2019t overly complex to implement manually, having them natively supported by the Tensor struct would significantly improve usability.\n\nI\u2019ve tried adding some of these functions myself to extend Candle\u2019s functionality (to make it more user-friendly). ",
"url": "https://github.com/huggingface/candle/issues/2971",
"state": "closed",
"labels": [],
"created_at": "2025-05-28T03:41:44Z",
"updated_at": "2025-05-29T07:41:02Z",
"comments": 1,
"user": "ssfdust"
},
{
"repo": "huggingface/transformers.js",
"number": 1323,
"title": "Cannot get the SAM model running like in example",
"body": "### Question\n\nI've found that transformers.js supports SAM as written in 2.14.0 release notes.\nhttps://github.com/huggingface/transformers.js/releases/tag/2.14.0\n\nI'm running the code on a M1 mac in a Brave browser.\n\nBut after I've used and adapted the example script, I can actually see in my browser console that the model is loaded and the browser is working.\n\n![\"Image\"](\"https://github.com/user-attachments/assets/fd256c77-62f5-4da2-a44c-cbb022333789\")
\n\nBut then suddenly it crashes with following error:\n\n```\ntransformers.js:11821 Uncaught Error: An error occurred during model execution: \"Missing the following inputs: input_points, input_labels.\n```\n\n**My adapted code looks like this:**\n\n````javascript\n\n// using version 3.5.1\nimport {AutoProcessor, RawImage, SamModel} from \"./node_modules/@huggingface/transformers/dist/transformers.js\";\n\nconst model = await SamModel.from_pretrained('Xenova/slimsam-77-uniform');\nconst processor = await AutoProcessor.from_pretrained('Xenova/slimsam-77-uniform');\n\nconst img_url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/corgi.jpg';\nconst raw_image = await RawImage.read(img_url);\nconst input_points = [[[340, 250]]] // 2D localization of a window\n\nconst inputs = await processor(raw_image, input_points);\nconst outputs = await model(inputs); /// Error happens here\n\nconst masks = await processor.post_process_masks(outputs.pred_masks, inputs.original_sizes, inputs.reshaped_input_sizes);\nconsole.log(masks); \n// [\n// Tensor {\n// dims: [ 1, 3, 410, 614 ],\n// type: 'bool',\n// data: Uint8Array(755220) [ ... ],\n// size: 755220\n// }\n// ]\nconst scores = outputs.iou_scores;\nconsole.log(scores);\n// Tensor {\n// dims: [ 1, 1, 3 ],\n// type: 'float32',\n// data: Float32Array(3) [\n// 0.8350210189819336,\n// 0.9786665439605713,\n// 0.8379436731338501\n// ],\n// size: 3\n// }\n````\n\n\nMarkup:\n````html\n\n\n \n \n \n\n \n : \" + savePath);\n byte[] dataBuffer = new byte[1024];\n int bytesRead;\n while ((bytesRead = in.read(dataBuffer, 0, 1024)) != -1) {\n fileOutputStream.write(dataBuffer, 0, bytesRead);\n }\n }\n }\n}\n\n```",
"url": "https://github.com/huggingface/huggingface_hub/issues/3117",
"state": "open",
"labels": [],
"created_at": "2025-05-26T10:00:25Z",
"updated_at": "2025-06-15T14:55:48Z",
"user": "mullerhai"
},
{
"repo": "huggingface/agents-course",
"number": 510,
"title": "anyone can run unit 1 dumm agent notebook????",
"body": "![\"Image\"](\"https://github.com/user-attachments/assets/1813be3d-0d73-478e-86fa-11304e796614\")
",
"url": "https://github.com/huggingface/agents-course/issues/510",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-05-25T03:00:04Z",
"updated_at": "2025-06-25T09:03:52Z",
"user": "chaoshun2025"
},
{
"repo": "huggingface/transformers",
"number": 38346,
"title": "Why is return_assistant_tokens_mask and continue_final_message incompatible?",
"body": "I'm currently authoring a new chat template, and while debugging encountered the check for this, however when uncommenting the check, the resulting mask and template both seem to still be correct. So I'm curious as to why or whether this check is needed at all?\n\nI can see it was introduced in [the original PR](https://github.com/huggingface/transformers/pull/33198), however there doesn't seem to be any justification/explanation for this assertion.",
"url": "https://github.com/huggingface/transformers/issues/38346",
"state": "closed",
"labels": [],
"created_at": "2025-05-24T23:44:13Z",
"updated_at": "2025-07-02T08:03:11Z",
"comments": 2,
"user": "nyxkrage"
},
{
"repo": "huggingface/candle",
"number": 2967,
"title": "Logit Discrepancy Between Candle and PyTorch When Using XLM-RoBERTa Model",
"body": "When running the same XLM-RoBERTa model (`s-nlp/xlmr_formality_classifier` - [HF](https://huggingface.co/s-nlp/xlmr_formality_classifier) ) in both Candle and PyTorch, I'm observing significant differences in the logits produced by the model's classification head for identical inputs. Is this expected behavior? See [this repository](https://github.com/jpe90/candle-pytorch-parity-testing/tree/master/xlm-roberta-finetuned) for a reproduction.\n\n## Environment/Setup\n\n- Model: `s-nlp/xlmr_formality_classifier` \n- Candle version: 0.9.1\n- Model SHA256: `66037d963856d6d001f3109d2b3cf95c76bce677947e66f426299c89bc1b58e7`\n- OS: macOS\n\n## Observed Behavior\n\nGiven identical inputs, the logits produced by Candle and PyTorch differ significantly:\n\n**Candle logits:**\n```\n[[2.0820313, -1.7548828], [0.7783203, -0.5629883], [1.2871094, -1.0039063], [2.1601563, -1.9277344]]\n```\n\n**PyTorch logits:**\n```\n[[ 2.6433, -2.3445],\n [ 1.0379, -0.9621],\n [ 1.4154, -1.2704],\n [ 3.4423, -3.1726]]\n```\n\n## Expected Behavior\n\nI would expect the logits to be extremely close (within floating-point precision differences) when running the same model with identical inputs across different frameworks.\n\n## Steps to Reproduce\n\n1. Clone the repository: https://github.com/jpe90/candle-pytorch-parity-testing\n2. Run the PyTorch implementation in `/xlm-roberta-finetuned/pytorch/main.py`\n3. Run the Candle implementation in `/xlm-roberta-finetuned/candle/src/main.rs`\n4. Compare the logits produced by both implementations\n\n## Additional Context\n\n- The tokenization appears to be identical between both implementations (identical token IDs)\n- I checked and made sure model checksums match at runtime\n- Config seems to match ([see here](https://github.com/jpe90/candle-pytorch-parity-testing/blob/master/xlm-roberta-finetuned/troubleshooting.md))\n\n## Questions\n\n1. Should I expect identical (or very close) logits between PyTorch and Candle implementations?\n2. If differences are expected, what is the acceptable range of variation?\n3. Could these differences impact more sensitive applications that rely on logit values rather than just the final classifications?\n4. Are there known issues with XLM-RoBERTa models specifically in Candle?\n",
"url": "https://github.com/huggingface/candle/issues/2967",
"state": "closed",
"labels": [],
"created_at": "2025-05-24T17:24:33Z",
"updated_at": "2025-05-26T10:45:24Z",
"comments": 2,
"user": "jpe90"
},
{
"repo": "huggingface/diffusers",
"number": 11607,
"title": "with a custom attention processor for Flux.dev, inference time changes when manually load and inject the transformer model into a flux pipeline versus let the flux pipeline constructor load the transformer internally.",
"body": "With a custom attention processor for Flux.dev transformer, the inference time is different between the following two ways:\n\n1. Manually load and inject the transformer into a flux.dev pipeline\n\n2. Let the pipeline constructor load the transformer internally\n\nThe inference time of the first way is about 15% slower than second way.\nWhat is the reason?\nI built diffusers from the source code.\nAny insights are appreciated!",
"url": "https://github.com/huggingface/diffusers/issues/11607",
"state": "closed",
"labels": [],
"created_at": "2025-05-24T06:42:11Z",
"updated_at": "2025-05-26T01:27:00Z",
"comments": 1,
"user": "LinchuanXuTheSEAAI"
},
{
"repo": "huggingface/transformers",
"number": 38326,
"title": "Allow `MllamaModel` to accept `pixel_values` and `inputs_embeds`",
"body": "### Feature request\n\n`MllamaModel` does not allow users to pass `pixel_values` and `inputs_embeds` simultaneously:\nhttps://github.com/huggingface/transformers/blob/54cd86708d2b63a1f696ee1c59384a2f04100f57/src/transformers/models/mllama/modeling_mllama.py#L1702-L1705\n\nHowever, commenting out those lines and running the follow script does generate the same logits:\n```python\nimport torch\nfrom transformers import MllamaForConditionalGeneration, AutoProcessor\n\n\nmodel_id = \"meta-llama/Llama-3.2-11B-Vision-Instruct\"\nmodel = MllamaForConditionalGeneration.from_pretrained(\n model_id, device_map=\"auto\", torch_dtype=torch.bfloat16\n)\nprocessor = AutoProcessor.from_pretrained(model_id)\n\nmessages = [\n [\n {\n \"role\": \"user\",\n \"content\": [\n {\n \"type\": \"image\",\n \"url\": \"https://llava-vl.github.io/static/images/view.jpg\",\n },\n {\"type\": \"text\", \"text\": \"What does the image show?\"},\n ],\n }\n ],\n]\ninputs = processor.apply_chat_template(\n messages,\n add_generation_prompt=True,\n tokenize=True,\n return_dict=True,\n return_tensors=\"pt\",\n).to(model.device)\n\noutputs = model(**inputs)\n\n# Manually compute inputs_embeds\ninput_ids = inputs.pop(\"input_ids\")\ninputs_embeds = model.get_input_embeddings()(input_ids)\nnew_outputs = model(inputs_embeds=inputs_embeds, **inputs)\nassert torch.allclose(outputs.logits, new_outputs.logits)\n```\n\n### Motivation\n\nBeing able to pass `inputs_embeds` along with `pixel_values` enables soft embeddings to be passed to the model in addition to images, which is useful for prompt tuning.\n\n### Your contribution\n\nCould contribute a PR removing the check assuming there isn't something I'm unaware of about the check.",
"url": "https://github.com/huggingface/transformers/issues/38326",
"state": "closed",
"labels": [
"Feature request"
],
"created_at": "2025-05-23T15:26:28Z",
"updated_at": "2025-05-27T16:33:57Z",
"comments": 1,
"user": "dxoigmn"
},
{
"repo": "huggingface/transformers",
"number": 38323,
"title": "`PYTHONOPTIMIZE=2` seems not work with `transformers-`based library",
"body": "### System Info\n\nI am currently having the latest package install.\ntorch 2.6.0+cu124\ntransformers 4.51.3\nsentence-transformers 4.1.0\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nError:\n\n```python\nFile \"\", line 488, in _call_with_frames_removed\n File \"D:\\Dataset\\AgentAI\\.venv\\Lib\\site-packages\\transformers\\modeling_utils.py\", line 5494, in \n class SQuADHead(nn.Module):\n ...<113 lines>...\n )\n File \"D:\\Dataset\\AgentAI\\.venv\\Lib\\site-packages\\transformers\\modeling_utils.py\", line 5513, in SQuADHead\n @replace_return_docstrings(output_type=SquadHeadOutput, config_class=PretrainedConfig)\n ~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"D:\\Dataset\\AgentAI\\.venv\\Lib\\site-packages\\transformers\\utils\\doc.py\", line 1194, in docstring_decorator\n lines = func_doc.split(\"\\n\")\n ^^^^^^^^^^^^^^\nAttributeError: 'NoneType' object has no attribute 'split'\n```\n\nA simple reproduction:\n\n```python\nfrom sentence_transformers import SentenceTransformer\n\nmodel = SentenceTransformer('all-MiniLM-L6-v2')\n\nembedding = model.encode(\"What is the capital of France?\")\nprint(embedding.shape)\n```\n\n### Expected behavior\n\nThis is not actually an issue, but I expect a documentation update from `transformers` maintainer to any end-users who use or develop a `transformers-` based library on the function `replace_return_docstrings` at `src/transformers/utils/doc.py` is to don't strip out the docstring by switching the option `PYTHONOPTIMIZE=2` to reduce the size of the bytecode. The use of `PYTHONOPTIMIZE=1` is OK\n\nThe reason is that the function `replace_return_docstrings` is expecting to be a decorator function without supporting the case of empty docstring. In some case, such as web hosting on Docker or production environment, or hosting an LLM without tool call where we usually strip out the docstring. \n\nIn the reproduction above (my use-case), I am just need to run the RAG search and thus don't need the docstring to be there.",
"url": "https://github.com/huggingface/transformers/issues/38323",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-05-23T14:24:34Z",
"updated_at": "2025-05-26T14:29:17Z",
"comments": 1,
"user": "IchiruTake"
},
{
"repo": "huggingface/candle",
"number": 2965,
"title": "Are there any support for complex number?",
"body": "Are there any support for complex number?",
"url": "https://github.com/huggingface/candle/issues/2965",
"state": "closed",
"labels": [],
"created_at": "2025-05-23T09:33:47Z",
"updated_at": "2025-11-23T22:16:54Z",
"comments": 1,
"user": "hndrbrm"
},
{
"repo": "huggingface/accelerate",
"number": 3586,
"title": "Where is PartialState._shared_state initialized?",
"body": "Hi! When I step through the code line by line, before this line ([entering into `__init__` of `AcceleratorState`](https://github.com/huggingface/accelerate/blob/v0.34.2/src/accelerate/state.py#L856 )) , `PartialState._shared_state`returns\n```\n{}\n```\nBut after entering into `__init__` of `AcceleratorState`, `PartialState._shared_state`returns\n```\n{'_cpu': False, 'backend': 'nccl', 'device': device(type='cuda', index=0), 'debug': False, 'distributed_type': , 'num_processes': 1, 'process_index': 0, 'local_process_index': 0, 'fork_launched': False}\n``` \nI'm wondering where is `PartialState._shared_state` initialized?",
"url": "https://github.com/huggingface/accelerate/issues/3586",
"state": "closed",
"labels": [],
"created_at": "2025-05-23T08:17:44Z",
"updated_at": "2025-06-30T15:08:15Z",
"user": "SonicZun"
},
{
"repo": "huggingface/transformers",
"number": 38300,
"title": "Will Gemma 3n be added to transformers?",
"body": "### Model description\n\nQuestion: Are there plans from Google or Huggingface to implement Gemma 3n in other frameworks?\n\nI've seen the LiteRT weights and Android App Link on Huggingface, and was wandering if it would be possible to convert the model architecture in the *.task file to a transformer pytorch Module?\n\nPersonally I'll really interested in the Per-Layer Embeddings and MatFormer implementation they used, but do not have any experience with Tensorflow Lite\n\n### Open source status\n\n- [ ] The model implementation is available\n- [X] The model weights are available\n\n### Provide useful links for the implementation\n\nhttps://huggingface.co/google/gemma-3n-E4B-it-litert-preview",
"url": "https://github.com/huggingface/transformers/issues/38300",
"state": "closed",
"labels": [
"New model"
],
"created_at": "2025-05-22T15:26:20Z",
"updated_at": "2025-06-30T07:07:53Z",
"comments": 4,
"user": "TheMrCodes"
},
{
"repo": "huggingface/transformers",
"number": 38281,
"title": "KeyError in Llama-4-Maverick-17B-128E-Instruct-FP8 Inference with Offloading",
"body": "### Issue Description\nLoading `meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8` succeeds with `transformers==4.51.0`, but inference fails with `KeyError: 'model.layers.37.feed_forward.experts.gate_up_proj'` during `model.generate`. This occurs on 4x NVIDIA RTX A6000 (~196GB VRAM, CUDA 12.4, Python 3.12.3, Ubuntu 24.04.2) with offloading, critical for sentiment analysis (~100\u2013150GB/day, ~85\u201390% accuracy). Disabling MoE (`num_experts=0`) didn\u2019t resolve it.\n\n### Steps to Reproduce\n1. Install dependencies:\n ```bash\n pip install torch==2.4.1 accelerate==1.7.0 compressed-tensors==0.9.4 transformers==4.51.0\n\n2. Confirm model files (~389GB, 84 .safetensors) at /mnt/data/ai_super_palace/models/llama4/.\n\n3. Run:\nimport os\nimport torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer\nos.environ[\"TORCHVISION_DISABLE_NMS\"] = \"1\"\nmodel = AutoModelForCausalLM.from_pretrained(\n '/mnt/data/ai_super_palace/models/llama4',\n torch_dtype=torch.float16,\n device_map=\"auto\",\n low_cpu_mem_usage=True,\n offload_folder=\"/mnt/data/ai_super_palace/models/llama4/offload\",\n config={\"parallel_style\": \"none\"}\n)\ntokenizer = AutoTokenizer.from_pretrained('/mnt/data/ai_super_palace/models/llama4')\nprompt = \"What is the sentiment of this text: 'I love this product, it's amazing!'\"\ninputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\noutputs = model.generate(**inputs, max_new_tokens=50)\nprint(tokenizer.decode(outputs[0], skip_special_tokens=True))\n\n4. Error:\nKeyError: 'model.layers.37.feed_forward.experts.gate_up_proj'\n\n**Environment**\nTransformers: 4.51.0\nPython: 3.12.3\nPyTorch: 2.4.1\nCUDA: 12.4\nAccelerate: 1.7.0\nCompressed-tensors: 0.9.4\nOS: Ubuntu 24.04.2 LTS\nHardware: 4x NVIDIA RTX A6000 (~196GB VRAM)\nModel: meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8\n\n**Additional Details**\nModel card requires transformers>=4.51.0, supports FP8 via compressed-tensors.\nWarnings: Uninitialized MoE weights (feed_forward.experts.*), offloaded parameters (VRAM limit).\nPrior errors (TypeError: NoneType not iterable) resolved with config={\"parallel_style\": \"none\"}.\nSuspect bug in accelerate offloading or MoE weight initialization.\n\n**Request**\nIs this a known llama4 MoE offloading issue?\nCan MoE weights be initialized or offloading fixed?\nWorkaround for inference without re-downloading (~389GB)?\nUrgent for sentiment analysis.\n\n**Logs**\nSee traceback above. config.json (40KB) available.\n\nThank you!\n\n\n\n\n\n\n\n\n\n\n\n\n",
"url": "https://github.com/huggingface/transformers/issues/38281",
"state": "closed",
"labels": [],
"created_at": "2025-05-22T05:45:30Z",
"updated_at": "2025-07-27T08:03:11Z",
"comments": 4,
"user": "pchu2025"
},
{
"repo": "huggingface/transformers",
"number": 38268,
"title": "Group beam search with sampling?",
"body": "### Feature request\n\nIn the current generation code, group beam search is necessarily greedy. From a theoretical point of view, it is not very clear why that should be the case, since the diversity penalty is applied on the logits anyway, yielding a full distribution from which sampling can still be performed.\n\n### Motivation\n\nI think there is a reasonable use case for such a feature: diversity beam search is very useful in particular for modalities like biological sequences which increasingly use the transformers library, but I could see it be useful as well for natural language or code, to generate diverse paths without falling to the drawbacks of greedy generation. From a more abstract point of view it is also seemingly unjustified to allow sampling for standard beam search and not for diversity beam search.\n\n### Your contribution\n\nI am aware of the work in #30810 so don't want to disrupt but would be happy to look into it.",
"url": "https://github.com/huggingface/transformers/issues/38268",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-05-21T18:08:59Z",
"updated_at": "2025-06-06T18:11:13Z",
"comments": 4,
"user": "adrian-valente"
},
{
"repo": "huggingface/candle",
"number": 2961,
"title": "Shape Mismatch in MatMul During Forward Pass of ModernBertForSequenceClassification",
"body": "ModernBertForSequenceClassification model (hidden size = 768, sequence length = 128) to categorize text into one of classes. During the initial training epoch, however, the forward pass fails with a \u201cshape mismatch in matmul\u201d error.\nIs there any way to solve this?\n\n\n #Error log\nTokenized shape: [4, 128]\nAttention mask shape: [4, 128]\nInput IDs shape: [4, 128]\nAttention mask shape: [4, 128]\nFirst sample token count: 128\nError in forward pass: shape mismatch in matmul, lhs: [4, 128], rhs: [768, 768]\nInput shape: [4, 128], Attention mask shape: [4, 128]\nError: shape mismatch in matmul, lhs: [4, 128], rhs: [768, 768]\n\n\n#Expected Behavior\n Input IDs should be a tensor of shape (batch_size, sequence_length) whose values are token indices (integers) and which the embedding layer then projects into the model\u2019s hidden dimension (hidden_size = 768) before any matrix multiplication with weight matrices of shape (768, 768)\n The forward pass should succeed without dimension errors, yielding logits of shape (batch_size, num_classes).\n\n\n#Code\n\n```\nuse candle_core::{Device, Tensor, D, DType, Error};\nuse candle_nn::{ops, loss, VarBuilder, optim::{Optimizer},var_map::VarMap};\nuse candle_transformers::models::modernbert::{ClassifierConfig, ClassifierPooling, ModernBertForSequenceClassification,Config\n};\nuse hf_hub::{api::sync::Api, Repo, RepoType};\nuse tokenizers::{PaddingParams, Tokenizer};\nuse std::collections::HashMap;\nuse candle_optimisers::adam::{ParamsAdam, Adam};\nuse rand::{seq::SliceRandom, SeedableRng};\nuse rand::rngs::StdRng;\n// Training settings\nconst LEARNING_RATE: f64 = 2e-5;\nconst EPOCHS: usize = 5;\nconst BATCH_SIZE: usize = 8;\nconst SEQ_LEN: usize = 128; // Sequence length\nconst SEED: u64 = 42;\n\n// Data structure for text and label mapping\ntype LabeledDataset = HashMap;\n\n\nfn main() -> Result<(), Box> {\n // Device selection (CPU or GPU)\n let device = candle_examples::device(true)?;\n println!(\"Using device: {:?}\", device);\n \n // HuggingFace API configuration\n let revision = \"main\".to_string();\n let api = Api::new()?;\n let model_id = \"answerdotai/ModernBERT-base\".to_string();\n let repo = api.repo(Repo::with_revision(\n model_id,\n RepoType::Model,\n revision,\n ));\n\n // Load tokenizer and model configuration\n let tokenizer_filename = repo.get(\"tokenizer.json\")?;\n let config_filename = repo.get(\"config.json\")?;\n let weights_filename = repo.get(\"model.safetensors\")?;\n \n // Load configuration file\n let config = std::fs::read_to_string(config_filename)?;\n let mut config: Config = serde_json::from_str(&config)?;\n \n // Output model configuration\n println!(\"Model config:\");\n println!(\" Hidden size: {}\", config.hidden_size);\n println!(\" Intermediate size: {}\", config.intermediate_size);\n println!(\" Max position embeddings: {}\", config.max_position_embeddings);\n println!(\" Num attention heads: {}\", config.num_attention_heads);\n println!(\" Num hidden layers: {}\", config.num_hidden_layers);\n println!(\" Vocab size: {}\", config.vocab_size);\n\n \n // Check configuration compatibility\n if config.max_position_embeddings < SEQ_LEN {\n println!(\"Warning: SEQ_LEN ({}) is larger than max_position_embeddings ({}), adjusting SEQ_LEN\",\n SEQ_LEN, config.max_position_embeddings);\n }\n \n // Initialize tokenizer\n let mut tokenizer = Tokenizer::from_file(tokenizer_filename).map_err(Error::msg)?;\n \n // Padding and truncation settings\n tokenizer\n .with_padding(Some(PaddingParams {\n strategy: tokenizers::PaddingStrategy::Fixed(SEQ_LEN),\n pad_id: config.pad_token_id,\n pad_token: \"[PAD]\".to_string(),\n pad_type_id: 0,\n pad_to_multiple_of: None,\n direction: tokenizers::PaddingDirection::Right,\n }))\n .with_truncation(Some(tokenizers::TruncationParams {\n max_length: SEQ_LEN,\n strategy: tokenizers::TruncationStrategy::LongestFirst,\n stride: 0,\n direction: tokenizers::TruncationDirection::Right,\n }))\n .map_err(Error::msg)?;\n\n // Configure label mappings\n let mut id2label = HashMap::new();\n let mut label2id = HashMap::new();\n\n let class_names = vec![\"News\", \"Entertainment\", \"Sports\", \"Technology\"];\n for (i, name) in class_names.iter().enumerate() {\n id2label.insert(i.to_string(), name.to_string());\n label2id.insert(name.to_string(), i.to_string());\n }\n \n // Add classifier configuration\n config.classifier_config = Some(ClassifierConfig {\n id2label: id2label.clone(),\n label2id: label2id.clone(),\n classifier_pooling: ClassifierPooling::CLS, // Use [CLS] token for pooling\n });\n\n // Create variable map for the model\n let mut varmap = VarMap::new();\n // Load model weights\n varmap.load(weights_filename)?;\n let vb = VarBuilder::from_varmap(&varmap",
"url": "https://github.com/huggingface/candle/issues/2961",
"state": "closed",
"labels": [],
"created_at": "2025-05-21T14:25:07Z",
"updated_at": "2025-06-08T12:11:46Z",
"comments": 2,
"user": "whitebox2"
},
{
"repo": "huggingface/transformers",
"number": 38243,
"title": "",
"body": "We are looking for an experienced Machine Learning Engineer for a BTC/USDT prediction project using CNN, LSTM, and Transformers. The goal is to forecast cryptocurrency price movements with a target accuracy of 90%+.\n\nMore details here:[ ](https://gist.github.com/DandBman/c76a548b1972da50ffe6bbdd93fdd613)",
"url": "https://github.com/huggingface/transformers/issues/38243",
"state": "closed",
"labels": [],
"created_at": "2025-05-20T22:14:11Z",
"updated_at": "2025-05-21T13:14:41Z",
"comments": 0,
"user": "DandBman"
},
{
"repo": "huggingface/diffusers",
"number": 11590,
"title": "Infinite (not literally) length video creation using LTX-Video?",
"body": "First of all thanks to Aryan (0.9.7 integration) and DN6 (adding GGUF). Model is quite good and output is also promising.\n\nI need help in creating continuous video using the last frame. 1 trick is to generate the video, extract the last frame and do inference. Is there any easy way where I can do this in loop.\n\nMy thought is \n\n1. Use text encoder to generate prompt embed once and then remove text encoders from memory\n2. Loop the inference code, once complete extract the last latent (preferred as I can upscale using LTXLatentUpsamplePipeline) frame or image and again create image1 and condition with that frame...and continue doing this for n iterations.\n3. Also need to save the video locally for each inference, otherwise OOM.\n\nAny thoughts / suggestions?\n\n```python\nimport torch\nimport gc\nfrom diffusers import GGUFQuantizationConfig\nfrom diffusers import LTXConditionPipeline, LTXLatentUpsamplePipeline, LTXVideoTransformer3DModel\nfrom diffusers.pipelines.ltx.pipeline_ltx_condition import LTXVideoCondition\nfrom diffusers.utils import export_to_video, load_video, load_image\n\ntransformer_path = f\"https://huggingface.co/wsbagnsv1/ltxv-13b-0.9.7-distilled-GGUF/blob/main/ltxv-13b-0.9.7-distilled-Q3_K_S.gguf\"\n# transformer_path = f\"https://huggingface.co/wsbagnsv1/ltxv-13b-0.9.7-distilled-GGUF/blob/main/ltxv-13b-0.9.7-distilled-Q8_0.gguf\"\ntransformer_gguf = LTXVideoTransformer3DModel.from_single_file(\n transformer_path,\n quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),\n torch_dtype=torch.bfloat16,\n)\n\npipe = LTXConditionPipeline.from_pretrained(\n \"Lightricks/LTX-Video-0.9.7-distilled\", \n transformer=transformer_gguf,\n torch_dtype=torch.bfloat16\n)\n# pipe.to(\"cuda\")\n# pipe.enable_sequential_cpu_offload()\npipe.enable_model_cpu_offload()\npipe.vae.enable_tiling()\n\nheight, width = 480, 832\nnum_frames = 151\nnegative_prompt = \"worst quality, inconsistent motion, blurry, jittery, distorted\"\n\nprompt = \"hyperrealistic digital artwork of a young woman walking confidently down a garden pathway, wearing white button-up blouse with puffed sleeves and blue denim miniskirt, long flowing light brown hair caught in gentle breeze, carrying a small black handbag, bright sunny day with blue sky and fluffy white clouds, lush green hedges and ornamental plants lining the stone pathway, traditional Asian-inspired architecture in background, photorealistic style with perfect lighting, unreal engine 5, ray tracing, 16K UHD. camera follows subject from front as she walks forward with elegant confidence\"\nimage1 = load_image( \"assets/ltx/00039.png\" )\ncondition1 = LTXVideoCondition(\n image=image1,\n frame_index=0,\n)\nwidth=512\nheight=768\nnum_frames = 161\n\n# LOOP HERE\nlatents = pipe(\n prompt=prompt,\n negative_prompt=negative_prompt,\n conditions=[condition1],\n width=width,\n height=height,\n num_frames=num_frames,\n guidance_scale=1.0,\n num_inference_steps=4,\n decode_timestep=0.05,\n decode_noise_scale=0.025,\n image_cond_noise_scale=0.0,\n guidance_rescale=0.7,\n generator=torch.Generator().manual_seed(42),\n output_type=\"latent\",\n).frames\n# save video locally\n# Update image1 = load_image( latent/image from current inference to be used with next inference)\n\n```",
"url": "https://github.com/huggingface/diffusers/issues/11590",
"state": "closed",
"labels": [],
"created_at": "2025-05-20T13:37:36Z",
"updated_at": "2025-05-20T19:51:20Z",
"comments": 1,
"user": "nitinmukesh"
},
{
"repo": "huggingface/agents-course",
"number": 501,
"title": "[BUG] Notebook on HF Hub is not updated",
"body": "\"Workflows in LlamaIndex\" [course page](https://huggingface.co/learn/agents-course/unit2/llama-index/workflows#creating-workflows) is referring notebook on [HF Hub](https://huggingface.co/agents-course/notebooks/blob/main/unit2/llama-index/workflows.ipynb), which is not the updated version from [GitHub](https://github.com/huggingface/agents-course/blob/main/notebooks/unit2/llama-index/workflows.ipynb). \n\nThe old version contains bug in loop event workflow so update is needed. ",
"url": "https://github.com/huggingface/agents-course/issues/501",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-05-20T06:45:26Z",
"updated_at": "2025-05-29T05:28:46Z",
"user": "karenwky"
},
{
"repo": "huggingface/open-r1",
"number": 649,
"title": "how to evaluate use local models and datasets?",
"body": "I change the readme eval command like following: \n\n**MODEL=./deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B\nMODEL_ARGS=\"pretrained=$MODEL,dtype=bfloat16,max_model_length=32768,gpu_memory_utilization=0.8,generation_parameters={max_new_tokens:32768,temperature:0.6,top_p:0.95}\"\nOUTPUT_DIR=./data/evals/\n\n# AIME 2024\nTASK=aime24\nlighteval vllm $MODEL_ARGS \"custom|$TASK|0|0\" \\\n --custom-tasks src/open_r1/evaluate.py \\\n --use-chat-template \\\n --output-dir $OUTPUT_DIR \\\n --cache-dir ./datasets/aime24**\n\nbut it try to use the network,and get a network error,how can i do to solve this problem?",
"url": "https://github.com/huggingface/open-r1/issues/649",
"state": "open",
"labels": [],
"created_at": "2025-05-20T05:57:29Z",
"updated_at": "2025-05-20T05:57:29Z",
"user": "SiqingHe"
},
{
"repo": "huggingface/lerobot",
"number": 1130,
"title": "Drive mode reversed on calibration.",
"body": "I had an issue where after calibrating drive_mode was reversed for one of my motors (0 vs. 1) as a result, moving the leader in one direction caused the follower to go the opposite direction.\n\nSaw some suggestions that moving it through the full range of motion resolved this but I wasn't able to get that to work. I could also see cases where this could be problematic during initial setup. @Lemin2 suggested to always set this to 0 across the board, which does seem like a good fix, unless there's a reason want to control reverse mode. \n\nIn any case I would expect the calibration process to be consistent for both arms, else this issue will be encountered. If reverse mode is needed maybe have a step in the calibration processes to ensure consistency.\n\nFYI in case anyone encounters this the solution is to go into `.cache/calibration//.json`\n\nSeems to be the same cause for #441 and #930 ",
"url": "https://github.com/huggingface/lerobot/issues/1130",
"state": "open",
"labels": [
"bug",
"question",
"robots"
],
"created_at": "2025-05-20T03:08:06Z",
"updated_at": "2025-07-16T06:50:20Z",
"user": "brainwavecoder9"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3233,
"title": "Docker image For llama cpp backend?",
"body": "Hey,\nIs there any reason in particular why docker images for the llama-cpp backend do not get built along with new versions? It seems the backend has been ready for a while so just curious why images don't get built as part of the build pipeline\ncc @mfuntowicz ",
"url": "https://github.com/huggingface/text-generation-inference/issues/3233",
"state": "open",
"labels": [],
"created_at": "2025-05-20T02:07:46Z",
"updated_at": "2025-05-20T02:07:46Z",
"comments": 0,
"user": "vrdn-23"
},
{
"repo": "huggingface/diffusers",
"number": 11580,
"title": "Can diffusers support loading and running FLUX with fp8 ?",
"body": "This is how I use diffusers to load flux model:\n```\nimport torch\nfrom diffusers import FluxPipeline\npipe = FluxPipeline.from_pretrained(\n \"/ckptstorage/repo/pretrained_weights/black-forest-labs/FLUX.1-dev\", \n torch_dtype=torch.float16,\n)\ndevice = torch.device(f\"cuda:{device_number}\" if torch.cuda.is_available() else \"cpu\")\npipe = pipe.to(device)\n```\nit consumes about 75 seconds on my computer with A800 GPU.\nBut I found in comfyui, it only need 22 seconds to load flux model, but it load the fp8 model.\nCan diffusers load flux fp8 model ?\nor is there any other speed up method ?",
"url": "https://github.com/huggingface/diffusers/issues/11580",
"state": "open",
"labels": [],
"created_at": "2025-05-19T12:18:13Z",
"updated_at": "2025-12-12T19:30:33Z",
"comments": 5,
"user": "EmmaThompson123"
},
{
"repo": "huggingface/lerobot",
"number": 1124,
"title": "How to add force data to lerobot and models?",
"body": "As title said, I use a force sensor on SO100 arm and want to record the data in lerobot dataset then train with the force data. How to do it?\n\nforce data looks like: a list: [x1, y1, z1, x2, y2, z2, x3, y3, z3, x4, y4, z4, x5, y5, z5] (15 d list)\n\nThanks!",
"url": "https://github.com/huggingface/lerobot/issues/1124",
"state": "closed",
"labels": [],
"created_at": "2025-05-19T07:48:20Z",
"updated_at": "2025-05-19T13:36:44Z",
"user": "milong26"
},
{
"repo": "huggingface/diffusers",
"number": 11575,
"title": "Hidream Model loading takes too long \u2014 any way to speed it up?",
"body": "Hi, thanks for this great project.\n\nI'm running Hidream with this library in a serverless environment and facing major delays during model loading. It can be very frustrating, especially for time-sensitive or ephemeral deployments.\n\nI've tried everything I could think of to reduce the loading time, but nothing has worked so far. Does anyone have any tips, tricks, or even sample code to help speed up the model initialization?\n\nAny guidance would be greatly appreciated!",
"url": "https://github.com/huggingface/diffusers/issues/11575",
"state": "open",
"labels": [],
"created_at": "2025-05-19T00:49:00Z",
"updated_at": "2025-05-23T12:55:05Z",
"comments": 6,
"user": "Me-verner"
},
{
"repo": "huggingface/optimum",
"number": 2275,
"title": "ONNX export for ColPali",
"body": "Hi Optimum,\n\nI have created a small tutorial how to export the ColPali late-interaction VLM in this [notebook](https://gist.github.com/kstavro/9bcdf930f0e69626dd5aa9aa5f09f867), but I think it shouldn't be too difficult to integrate it to Optimum as well.\n\nHowever, as far as I have seen, there is not much support for late-interaction VLMs at the moment. So, before I get into it just by myself, I thought I could first see if someone could give me a couple of hints about some choices regarding the library, eg what base configs I should use for ColPali or if I should create new ones everywhere, what names, do we need tiny dummy models for tests, etc.",
"url": "https://github.com/huggingface/optimum/issues/2275",
"state": "closed",
"labels": [],
"created_at": "2025-05-18T18:56:22Z",
"updated_at": "2025-06-11T13:56:43Z",
"comments": 2,
"user": "kstavro"
},
{
"repo": "huggingface/transformers",
"number": 38190,
"title": "Gibberish generations with FSDP2 and MixedPrecisionPolicy",
"body": "### System Info\n\n```\ntransformers.__version__='4.51.2'\ntorch.__version__='2.6.0+cu124'\nsys.version='3.10.17 (main, Apr 16 2025, 15:03:57) [GCC 12.1.1 20220628 (Red Hat 12.1.1-3)]'\n```\n\n### Who can help?\n\n@SunMarc @zach-huggingface\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nI'm sharding `llama-3.1-8b-instruct` on 8 GPUs using FSDP2. The goal is to be able to call `generate` during the training loop. I have noticed that If I use `MixedPrecisionPolicy` with `param_dtype=torch.bfloat16` the generations are gibberish. A hopefully reproducible example below.\n\n\n```python\nimport os\n\nimport torch\nimport torch.distributed as dist\nfrom torch.distributed._composable.fsdp import register_fsdp_forward_method\nfrom torch.distributed.device_mesh import init_device_mesh\nfrom torch.distributed.fsdp import (\n MixedPrecisionPolicy,\n fully_shard,\n)\nfrom transformers import AutoConfig, AutoModelForCausalLM, AutoTokenizer\nfrom transformers.models.llama.modeling_llama import LlamaDecoderLayer\n\n\n\ndef get_local_rank() -> int:\n return int(os.environ.get(\"LOCAL_RANK\", \"0\"))\n\n\ndef get_global_rank() -> int:\n return int(os.environ.get(\"RANK\", get_local_rank()))\n\n\ndef barrier():\n dist.barrier(device_ids=[get_local_rank()])\n\n\ndef test_generate(model, tokenizer):\n prompt = \"Concisely answer the following question: \"\n queries = [\n \"What is the tallest animal?\\n\",\n \"What are 3 fruits larger in size than an apple?\\n\",\n \"What's the derivative of e^x?\\n\",\n ]\n\n tokens = [tokenizer.encode(prompt + q) for q in queries]\n max_len = max(len(t) for t in tokens)\n padded = [[tokenizer.eos_token_id] * (max_len - len(t)) + t for t in tokens]\n padded_t = torch.tensor(padded).long()\n\n generations = model.generate(padded_t, max_new_tokens=128)\n parsed = tokenizer.batch_decode(generations)\n for p in parsed:\n print(p, flush=True)\n\n\ndef main():\n device = torch.device(\"cuda\", get_local_rank())\n dist.init_process_group(\n backend=\"nccl\",\n )\n torch.cuda.set_device(device)\n\n LOCAL_MODEL_PATH = \"/llama-3.1-8b-instruct\"\n\n tokenizer = AutoTokenizer.from_pretrained(LOCAL_MODEL_PATH)\n model_config = AutoConfig.from_pretrained(LOCAL_MODEL_PATH)\n model = AutoModelForCausalLM.from_pretrained(\n LOCAL_MODEL_PATH,\n config=model_config,\n use_safetensors=True,\n torch_dtype=torch.float32,\n )\n\n fsdp2_kwargs = {}\n fsdp2_kwargs[\"mesh\"] = init_device_mesh(\n \"cuda\", (torch.distributed.get_world_size(),)\n )\n fsdp2_kwargs[\"mp_policy\"] = MixedPrecisionPolicy(\n param_dtype=torch.bfloat16, # <<<----- If I comment this line the generations are as expected\n )\n\n for submodule in model.modules():\n if isinstance(submodule, LlamaDecoderLayer):\n fully_shard(submodule, **fsdp2_kwargs)\n fully_shard(model, **fsdp2_kwargs)\n register_fsdp_forward_method(model, \"generate\")\n\n barrier()\n\n test_generate(model, tokenizer)\n\n barrier()\n\n dist.destroy_process_group()\n\n\nif __name__ == \"__main__\":\n main()\n```\n\nThe following is an example of the output I get if `param_dtype=torch.bfloat16`:\n\n```\n<|eot_id|><|eot_id|><|eot_id|><|eot_id|><|eot_id|><|eot_id|><|begin_of_text|>Concisely answer the following question: What is the tallest animal?\nThe odense aalborg limburg fetisch odense fetisch<|start_header_id|>OO\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\n<|begin_of_text|>Concisely answer the following question: What are 3 fruits larger in size than an apple?\nHere fetisch<|start_header_id|>OOOOOOOOOO\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\u200d\n<|eot_id|><|eot_id|><|eot_id|><|begin_of_text|>Concisely answer the following question: What's the derivative of e^x?\nThe aalborg salopes<|start_header_id|>OOOOOOOOOOOOAAAAAAAA\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n```\n\n\n### Expected behavior\n\nThe following is an example of the output I get if I comment out the `param_dtype=torch.bfloat16` in `MixedPrecisionPolicy`\n\n```\n<|eot_id|><|eot_id|><|eot_id|><|eot_id|><|eot_id|><|eot_id|><|begin_of_text|>Concisely answer the following question: What is the tallest animal?\nThe tallest animal is the giraffe, which can grow up to 18 feet (5.5 meters) tall.\nThe gi",
"url": "https://github.com/huggingface/transformers/issues/38190",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-05-18T11:56:08Z",
"updated_at": "2025-08-29T09:36:57Z",
"comments": 17,
"user": "dlvp"
},
{
"repo": "huggingface/transformers",
"number": 38181,
"title": "Add a way for `callbacks` to get `trainer` handler",
"body": "When I want to implement differential privacy for the model, I customize the gradient clipping before `optimizer.step()`. The add custom noise to the model after `optimizer.step()`. I cannot get `Trainer.optimizer` in the `callback` function, it shows as `None`. Is it possible to get the reference of `Trainer` directly in `callback`?",
"url": "https://github.com/huggingface/transformers/issues/38181",
"state": "closed",
"labels": [],
"created_at": "2025-05-16T16:01:35Z",
"updated_at": "2025-05-19T12:17:06Z",
"comments": 1,
"user": "MinzhiYoyo"
},
{
"repo": "huggingface/open-r1",
"number": 645,
"title": "How to set vllm max-model-len?",
"body": "I use qwen2.5-7b-Instruct to run grpo, and open yarn, to accommodate a longer window(greater than 32768). But fowllowing error exists:\n\n \n 0%| | 0/187 [00:00 \n[rank2]: main(script_args, training_args, model_args) \n[rank2]: File \"/cto_studio/huyongquan/python_project/open-r1/src/open_r1/grpo.py\", line 309, in main \n[rank2]: train_result = trainer.train(resume_from_checkpoint=checkpoint) ",
"url": "https://github.com/huggingface/open-r1/issues/645",
"state": "closed",
"labels": [],
"created_at": "2025-05-16T03:28:50Z",
"updated_at": "2025-06-12T08:45:15Z",
"user": "huyongquan"
},
{
"repo": "huggingface/transformers",
"number": 38165,
"title": "Gemma 3 Pipeline does not accept dictionary with no images",
"body": "### System Info\n\nSystem info not really relevant as the bug is root caused in my description below.\n\n- `transformers` version: 4.51.3\n- Platform: Windows-10-10.0.26100-SP0\n- Python version: 3.11.9\n- Huggingface_hub version: 0.31.2\n- Safetensors version: 0.5.3\n- Accelerate version: 1.7.0\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (GPU?): 2.4.0+cu121 (True)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: \n- Using GPU in script:Yes\n- GPU type: NVIDIA GeForce RTX 3090\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nThis issue can be created using the following snippet copied from Gemma 3 docs and up until transformer 4.51.3.\n```\nfrom transformers import pipeline\nimport torch\n\npipe = pipeline(\n \"image-text-to-text\",\n model=\"google/gemma-3-12b-it\",\n device=\"cuda\", # Or \"cpu\" if you don't have a compatible GPU\n torch_dtype=torch.bfloat16 # Or torch.float16 or torch.float32 based on your hardware/needs\n)\n\nmessages = [\n {\n \"role\": \"system\",\n \"content\": [{\"type\": \"text\", \"text\": \"You are a helpful assistant.\"}]\n },\n {\n \"role\": \"user\",\n \"content\": [\n # Removed the image link from the example\n {\"type\": \"text\", \"text\": \"What is the capital of France?\"} # Keep only the text part\n ]\n }\n]\n\noutput = pipe(text=messages, max_new_tokens=200)\nprint(output[0][\"generated_text\"][-1][\"content\"])\n```\n\nwhich will result in the error:\n\n```\nTraceback (most recent call last):\n File \"D:\\experiments\\personal\\gemma_editor\\gemma_editor.py\", line 78, in \n run_gemma(SENTENCES)\n File \"D:\\experiments\\personal\\gemma_editor\\gemma_editor.py\", line 41, in run_gemma\n output = pipe(text=messages)\n ^^^^^^^^^^^^^^^^^^^\n File \"D:\\experiments\\personal\\gemma_editor\\venv\\Lib\\site-packages\\transformers\\pipelines\\image_text_to_text.py\", line 311, in __call__\n return super().__call__(Chat(text, images), **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"D:\\experiments\\personal\\gemma_editor\\venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 1379, in __call__\n return self.run_single(inputs, preprocess_params, forward_params, postprocess_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"D:\\experiments\\personal\\gemma_editor\\venv\\Lib\\site-packages\\transformers\\pipelines\\base.py\", line 1385, in run_single\n model_inputs = self.preprocess(inputs, **preprocess_params)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"D:\\experiments\\personal\\gemma_editor\\venv\\Lib\\site-packages\\transformers\\pipelines\\image_text_to_text.py\", line 365, in preprocess\n model_inputs = self.processor(images=images, text=text, return_tensors=self.framework, **processing_kwargs).to(\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"D:\\experiments\\personal\\gemma_editor\\venv\\Lib\\site-packages\\transformers\\models\\gemma3\\processing_gemma3.py\", line 106, in __call__\n image_inputs = self.image_processor(batched_images, **output_kwargs[\"images_kwargs\"])\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"D:\\experiments\\personal\\gemma_editor\\venv\\Lib\\site-packages\\transformers\\image_processing_utils.py\", line 42, in __call__\n return self.preprocess(images, **kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"D:\\experiments\\personal\\gemma_editor\\venv\\Lib\\site-packages\\transformers\\utils\\generic.py\", line 866, in wrapper\n return func(*args, **valid_kwargs)\n ^^^^^^^^^^^^^^^^^^^^^^^^^^^\n File \"D:\\experiments\\personal\\gemma_editor\\venv\\Lib\\site-packages\\transformers\\models\\gemma3\\image_processing_gemma3.py\", line 361, in preprocess\n if do_rescale and is_scaled_image(images[0]):\n ~~~~~~^^^\nIndexError: list index out of range\n```\n\n### Expected behavior\n\nThe problem here is that within image_text_to_text, the dictionary is made into type: Chat. [By default chat makes images an empty list](https://github.com/huggingface/transformers/blame/v4.51.3/src/transformers/pipelines/image_text_to_text.py#L114). Then this is propagated to [images](https://github.com/huggingface/transformers/blame/v4.51.3/src/transformers/pipelines/image_text_to_text.py#L353C16-L353C39) where it ultimately lands in processing_gemma_3.py where the [if condition only checks if the images are None](https://github.com/huggingface/transformers/blob/v4.51.3/src/transformers/models/gemma3/",
"url": "https://github.com/huggingface/transformers/issues/38165",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-05-16T01:34:15Z",
"updated_at": "2025-06-23T08:03:03Z",
"comments": 6,
"user": "sheldonlai"
},
{
"repo": "huggingface/lerobot",
"number": 1114,
"title": "How to collect data and train the policy from Lerobot totally out of the leader arm only by learning from demonstration using the main arm such as XARM or UR series",
"body": "",
"url": "https://github.com/huggingface/lerobot/issues/1114",
"state": "closed",
"labels": [
"question",
"robots",
"stale"
],
"created_at": "2025-05-15T15:31:13Z",
"updated_at": "2025-12-31T02:35:25Z",
"user": "David-Kingsman"
},
{
"repo": "huggingface/transformers",
"number": 38147,
"title": "How to check the number of tokens processed or the load of each expert in the Qwen3 MoE model during inference?",
"body": "",
"url": "https://github.com/huggingface/transformers/issues/38147",
"state": "closed",
"labels": [],
"created_at": "2025-05-15T09:21:29Z",
"updated_at": "2025-05-15T13:36:53Z",
"user": "wumaotegan"
},
{
"repo": "huggingface/diffusers",
"number": 11561,
"title": "FluxFillPipeline Support load IP Adapter.",
"body": "### Model/Pipeline/Scheduler description\n\n'FluxFillPipeline' object has no attribute 'load_ip_adapter'\nI really need this,Thanks!\n\n### Open source status\n\n- [ ] The model implementation is available.\n- [ ] The model weights are available (Only relevant if addition is not a scheduler).\n\n### Provide useful links for the implementation\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/11561",
"state": "closed",
"labels": [
"help wanted",
"Good second issue"
],
"created_at": "2025-05-15T08:58:42Z",
"updated_at": "2025-06-17T08:48:28Z",
"comments": 6,
"user": "PineREN"
},
{
"repo": "huggingface/lerobot",
"number": 1111,
"title": "Unrecognized argument policy.path. How to load a pretrained model?",
"body": "When I run this command:\n```\npython lerobot/scripts/control_robot.py --robot.type so100 --control.type record --control.fps 30 --control.single_task \"Grasp a yellow tape and put it to yellow square.\" --control.repo_id a_cam_1/result --control.tags '[\"tutorial\"]' --control.warmup_time_s 5 --control.episode_time_s 30 --control.reset_time_s 10 --control.m_episodes 1 --control.push_to_hub false --control.policy,path output/checkpoints/last/pretrained_model\n```\n\n\nI got:\n```\nusage: control_robot.py [-h] [--config_path str] [--robot str] [--robot.type {aloha,koch,koch_bimanual,moss,so101,so100,stretch,lekiwi}] [--robot.gripper_open_degree str]\n [--robot.max_relative_target str] [--robot.ip str] [--robot.port str] [--robot.video_port str] [--robot.cameras str] [--robot.calibration_dir str]\n [--robot.leader_arms str] [--robot.follower_arms str] [--robot.teleop_keys str] [--robot.mock str] [--control str]\n [--control.type {calibrate,teleoperate,record,replay,remote_robot}] [--control.arms str] [--control.teleop_time_s str] [--control.single_task str]\n [--policy str] [--control.policy.type {act,diffusion,pi0,tdmpc,vqbet,pi0fast}] [--control.policy.replace_final_stride_with_dilation str]\n [--control.policy.pre_norm str] [--control.policy.dim_model str] [--control.policy.n_heads str] [--control.policy.dim_feedforward str]\n [--control.policy.feedforward_activation str] [--control.policy.n_encoder_layers str] [--control.policy.n_decoder_layers str]\n [--control.policy.use_vae str] [--control.policy.n_vae_encoder_layers str] [--control.policy.temporal_ensemble_coeff str]\n [--control.policy.kl_weight str] [--control.policy.optimizer_lr_backbone str] [--control.policy.drop_n_last_frames str]\n [--control.policy.use_separate_rgb_encoder_per_camera str] [--control.policy.down_dims str] [--control.policy.kernel_size str]\n [--control.policy.n_groups str] [--control.policy.diffusion_step_embed_dim str] [--control.policy.use_film_scale_modulation str]\n [--control.policy.noise_scheduler_type str] [--control.policy.num_train_timesteps str] [--control.policy.beta_schedule str]\n [--control.policy.beta_start str] [--control.policy.beta_end str] [--control.policy.prediction_type str] [--control.policy.clip_sample str]\n [--control.policy.clip_sample_range str] [--control.policy.num_inference_steps str] [--control.policy.do_mask_loss_for_padding str]\n [--control.policy.scheduler_name str] [--control.policy.num_steps str] [--control.policy.attention_implementation str]\n [--control.policy.train_expert_only str] [--control.policy.train_state_proj str] [--control.policy.n_action_repeats str] [--control.policy.horizon str]\n [--control.policy.image_encoder_hidden_dim str] [--control.policy.state_encoder_hidden_dim str] [--control.policy.latent_dim str]\n [--control.policy.q_ensemble_size str] [--control.policy.mlp_dim str] [--control.policy.discount str] [--control.policy.use_mpc str]\n [--control.policy.cem_iterations str] [--control.policy.max_std str] [--control.policy.min_std str] [--control.policy.n_gaussian_samples str]\n [--control.policy.n_pi_samples str] [--control.policy.uncertainty_regularizer_coeff str] [--control.policy.n_elites str]\n [--control.policy.elite_weighting_temperature str] [--control.policy.gaussian_mean_momentum str] [--control.policy.max_random_shift_ratio str]\n [--control.policy.reward_coeff str] [--control.policy.expectile_weight str] [--control.policy.value_coeff str] [--control.policy.consistency_coeff str]\n [--control.policy.advantage_scaling str] [--control.policy.pi_coeff str] [--control.policy.temporal_decay_coeff str]\n [--control.policy.target_model_momentum str] [--control.policy.n_action_pred_token str] [--control.policy.action_chunk_size str]\n [--control.policy.vision_backbone str] [--control.policy.crop_shape str] [--control.policy.crop_is_random str]\n [--control.policy.pretrained_backbone_weights str] [--control.policy.use_group_norm str] [--control.policy.spatial_softmax_num_keypoints str]\n [--control.policy.n_vqvae_training_steps str] [--control.policy.vqvae_n_embed str] [--control.policy.vqvae_embedding_dim str]\n [--control.policy.vqvae_enc_hidden_dim str] [--control.policy.gpt_block_size str] [--control.policy.gpt_input_dim str]\n [--control.policy.gpt_output_dim str] [--control.policy.gpt_n_layer str] [--control.policy.gpt_n_head str] [--control.policy.gpt_hidden_dim str]\n ",
"url": "https://github.com/huggingface/lerobot/issues/1111",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-05-15T03:13:27Z",
"updated_at": "2025-06-24T06:20:08Z",
"user": "milong26"
},
{
"repo": "huggingface/diffusers",
"number": 11555,
"title": "`device_map=\"auto\"` supported for diffusers pipelines?",
"body": "### Describe the bug\n\nHey dear diffusers team,\n\nfor `DiffusionPipline`, as I understand (hopefully correctly) from [this part of the documentation](https://huggingface.co/docs/diffusers/v0.33.1/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained.device_map), it should be possible to specify `device_map=\"auto\"` when loading a pipeline with `from_pretrained` but this results in a value error saying that this is not supported.\n\nHowever, the documentation on [device placement](https://huggingface.co/docs/diffusers/en/tutorials/inference_with_big_models#device-placement) currently states that only the \"balanced\" strategy is supported.\n\nIs this possibly similar to #11432 and should be removed from the docstrings / documentation? Happy to help on this with a PR if it turns out to be a mistake in the documentation.\n\nThanks a lot for your hard work!\n\n\n\n### Reproduction\n\n```python\nfrom diffusers import DiffusionPipeline\npipeline = DiffusionPipeline.from_pretrained(\"stable-diffusion-v1-5/stable-diffusion-v1-5\", device_map=\"auto\")\n```\n\nor \n\n```python\nfrom diffusers import StableDiffusionPipeline\npipe = StableDiffusionPipeline.from_pretrained(\"stable-diffusion-v1-5/stable-diffusion-v1-5\", device_map=\"auto\")\n```\n\n### Logs\n\n```shell\n---------------------------------------------------------------------------\nNotImplementedError Traceback (most recent call last)\nCell In[12], line 3\n 1 from diffusers import StableDiffusionPipeline\n----> 3 pipe = StableDiffusionPipeline.from_pretrained(\"stable-diffusion-v1-5/stable-diffusion-v1-5\", device_map=\"auto\")\n\nFile ~/miniconda3/envs/pruna/lib/python3.10/site-packages/huggingface_hub/utils/_validators.py:114, in validate_hf_hub_args.._inner_fn(*args, **kwargs)\n 111 if check_use_auth_token:\n 112 kwargs = smoothly_deprecate_use_auth_token(fn_name=fn.__name__, has_token=has_token, kwargs=kwargs)\n--> 114 return fn(*args, **kwargs)\n\nFile ~/miniconda3/envs/pruna/lib/python3.10/site-packages/diffusers/pipelines/pipeline_utils.py:745, in DiffusionPipeline.from_pretrained(cls, pretrained_model_name_or_path, **kwargs)\n 742 raise ValueError(\"`device_map` must be a string.\")\n 744 if device_map is not None and device_map not in SUPPORTED_DEVICE_MAP:\n--> 745 raise NotImplementedError(\n 746 f\"{device_map} not supported. Supported strategies are: {', '.join(SUPPORTED_DEVICE_MAP)}\"\n 747 )\n 749 if device_map is not None and device_map in SUPPORTED_DEVICE_MAP:\n 750 if is_accelerate_version(\"<\", \"0.28.0\"):\n\nNotImplementedError: auto not supported. Supported strategies are: balanced\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.33.1\n- Platform: Linux-5.15.0-139-generic-x86_64-with-glibc2.35\n- Running on Google Colab?: No\n- Python version: 3.10.16\n- PyTorch version (GPU?): 2.7.0+cu126 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.30.2\n- Transformers version: 4.51.3\n- Accelerate version: 1.6.0\n- PEFT version: 0.15.2\n- Bitsandbytes version: 0.45.5\n- Safetensors version: 0.5.3\n- xFormers version: not installed\n- Accelerator: NVIDIA H100 PCIe, 81559 MiB\nNVIDIA H100 PCIe, 81559 MiB\n- Using GPU in script?: yes\n- Using distributed or parallel set-up in script?: yes\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/11555",
"state": "open",
"labels": [
"bug"
],
"created_at": "2025-05-14T16:49:32Z",
"updated_at": "2025-05-19T09:44:29Z",
"comments": 4,
"user": "johannaSommer"
},
{
"repo": "huggingface/lerobot",
"number": 1107,
"title": "Does Pi0 use PaliGemma VLM pretrained model weights?",
"body": "I attempted to finetune the Pi0 model, but noticed that it does not download the pretrained weights of Paligemma from Hugging Face. Specifically, I found that Pi0 initializes the VLM with:\n\n```python\nself.paligemma = PaliGemmaForConditionalGeneration(config=config.paligemma_config)\n```\n\ninstead of using:\n\n```python\nAutoModel.from_pretrained(\"google/paligemma-3b-pt-224\")\n```\n\nThis seems to result in the model not loading the pretrained weights.\n\nCould you please confirm whether this is the intended behavior? Should Pi0 load Paligemma\u2019s pretrained weights from Hugging Face, or is there a reason it initializes the model from scratch?\n\nThank you!",
"url": "https://github.com/huggingface/lerobot/issues/1107",
"state": "closed",
"labels": [
"bug",
"question",
"policies"
],
"created_at": "2025-05-14T06:47:15Z",
"updated_at": "2025-10-08T08:44:03Z",
"user": "lxysl"
},
{
"repo": "huggingface/lerobot",
"number": 1106,
"title": "How to convert image mode to video mode lerobot dataset?",
"body": "",
"url": "https://github.com/huggingface/lerobot/issues/1106",
"state": "open",
"labels": [
"question",
"dataset"
],
"created_at": "2025-05-14T03:54:42Z",
"updated_at": "2025-08-08T16:42:33Z",
"user": "hairuoliu1"
},
{
"repo": "huggingface/transformers.js",
"number": 1316,
"title": "May I ask how to set the HF_TOKEN on the browser side?",
"body": "### Question\n\nMay I ask how to set the HF_TOKEN on the browser side?\n\n\n\nThe following is my code:\n```\nconst model = await AutoModel.from_pretrained(\"briaai/RMBG-2.0\", {\n config: {\n model_type: \"custom\", \n },\n headers: {\n 'Authorization': `Bearer hf_xxxxxxxxxxxxxxx`\n }\n});\n```",
"url": "https://github.com/huggingface/transformers.js/issues/1316",
"state": "open",
"labels": [
"question"
],
"created_at": "2025-05-14T01:43:02Z",
"updated_at": "2025-05-27T21:53:45Z",
"user": "dengbupapapa"
},
{
"repo": "huggingface/xet-core",
"number": 321,
"title": "How to resume DL of partial existing file using xet + huggingface-cli download if not previously downloaded using HF tools / cache?",
"body": "How to resume DL of partial existing file using xet + huggingface-cli download if not previously downloaded using HF tools / cache?\n\nI guess there may be a way in the scenario I had but by my mistake apparently I chose some incorrect usage and caused the deletion of the 95% complete partial local file instead of resuming / recovering its download via XET.\n\ne.g. I tried with a fresh tool install and a process something like:\n\n% pip install -U \"huggingface_hub[hf_xet]\"\n\n% pwd\n/whatever/some_tmpdir\n\n% ls -lh somefile\n35G somefile\n// Partial file exists and is 95% complete but short / truncated by failed copy previously.\n\n% huggingface-cli download --local-dir . some_repo_id some_dir/somefile\n\nThe end result was apparently the deletion of the pre-existing 95% complete 'somefile' from the current directory and the initiation of new download using xet protocol from the xet enabled some_repo_id.\n\nBased on huggingface-cli download --help and the articles about xet I had expected it to realize the pre-existing current directory's \"somefile\" with an identical name/target directory as the file being requested for download was a partial relevant file and it should start to recover / complete the download by missing chunk completion. That despite the fact that there was no cache directory or git LFS structure around the current working directory, it just contained the isolated partial file only.\n\n\nhuggingface-cli download --help \nusage: huggingface-cli [] download [-h] [--repo-type {model,dataset,space}] [--revision REVISION] [--include [INCLUDE ...]] [--exclude [EXCLUDE ...]] [--cache-dir CACHE_DIR]\n [--local-dir LOCAL_DIR] [--local-dir-use-symlinks {auto,True,False}] [--force-download] [--resume-download] [--token TOKEN] [--quiet]\n [--max-workers MAX_WORKERS]\n repo_id [filenames ...]\n\npositional arguments:\n repo_id ID of the repo to download from (e.g. `username/repo-name`).\n filenames Files to download (e.g. `config.json`, `data/metadata.jsonl`).\n\noptions:\n...\n --local-dir LOCAL_DIR\n If set, the downloaded file will be placed under this directory. Check out https://huggingface.co/docs/huggingface_hub/guides/download#download-files-to-local-folder for more\n details.\n...\n --resume-download Deprecated and ignored. Downloading a file to local dir always attempts to resume previously interrupted downloads (unless hf-transfer is enabled).\n...\n\nhuggingface-cli download --local-dir . some_repo_id some_dir/somefile\nDownloading 'somefile' to '.cache/huggingface/download/whatever.incomplete'\nXet Storage is enabled for this repo. Downloading file from Xet Storage..\n...\n\n\nIf there's a different way to accomplish this partial file recovery result (or even if there's a corrupted / patched / whatever file per. xet's chunk filling capabilities) then perhaps clarifying / expanding the usage documentation to cover this kind of common scenario use case could help?\n\nThe desired result would be something like\n\nrsync --verbose --archive server:/some_repo_id/somedir/somefile somefile\n\nwhich would use rolling hash chunk based rsync algorithm / protocol downloading to complete the retrieval of the somefile in the current directory regardless of other context.\n\n\n\nAlso I wonder if it'd be interesting to have a rsync to xet 'bridge' so anyone could use a normal rsync client but pull xet files from HF repos if HF doesn't want to support rsync itself in whole but has the conceptually aligned XET back end that could be \"mapped\" to rsync chunk based protocol (I suppose) by a thin protocol adapter?\n\nLots of e.g. linux distribution mirror sites support rsync as an HTTP/HTTPS alternative so it presumably has some significant market-share for people doing IT / devops / mlops / whatever use case downloads.\n",
"url": "https://github.com/huggingface/xet-core/issues/321",
"state": "closed",
"labels": [],
"created_at": "2025-05-13T22:16:02Z",
"updated_at": "2025-05-16T17:48:45Z",
"user": "ghchris2021"
},
{
"repo": "huggingface/chat-ui",
"number": 1819,
"title": "Correct syntax of .env: what are those backticks for multiline strings?",
"body": "I have read the suggestion of checking discussions but I was unable to find an answer so something very basic looks like it is missing here.\n\nIn the documentation there are many examples suggesting of putting long values in env var surrounded by backticks.\n\nHowever when I do this I get errors like:\n\nJSON5: invalid character '`' at 1:1\n\nI have checked around and I have been unable to find anywhere references to .env using backticks for multiline strings, and the parser is refusing this.\n\nTHis is happening with a git clone of main but also using tagged versions.\n\nSo how do you possibile use this apparently non standard syntax and how is it possible no one else but me is having this issue? \n\n",
"url": "https://github.com/huggingface/chat-ui/issues/1819",
"state": "open",
"labels": [
"support"
],
"created_at": "2025-05-13T12:21:43Z",
"updated_at": "2025-05-23T09:37:09Z",
"comments": 1,
"user": "sciabarracom"
},
{
"repo": "huggingface/optimum",
"number": 2262,
"title": "New Release to Support `transformers>=4.51.0`?",
"body": "### Feature request\n\nThe latest release (`1.24.0`) is 4 months old. There has been around 38 commits since the last release. Will there be a new release soon?\n\n### Motivation\n\nThere is a medium CVE related to `transformers==4.48.1` that is the latest compatible version.\nGHSA-fpwr-67px-3qhx\n\nI am also blocked from upgrading `vllm==0.8.5` within my system as it requires `transformers>=4.51.0`. `transformers==4.48.1` is compatible with up to `vllm==0.8.2` only where there are critical and high CVEs.\nGHSA-hj4w-hm2g-p6w5\nGHSA-9f8f-2vmf-885j\n\nIt looks like the current dependencies in the `main` branch will mitigate these issues completely. Is there any blocker to creating a new release from current state?\n\n### Your contribution\n\nDon't think I will be granted permissions to create releases in this project.",
"url": "https://github.com/huggingface/optimum/issues/2262",
"state": "closed",
"labels": [],
"created_at": "2025-05-13T07:46:15Z",
"updated_at": "2025-05-13T22:27:08Z",
"comments": 2,
"user": "yxtay"
},
{
"repo": "huggingface/lerobot",
"number": 1101,
"title": "ValueError: No integer found between bounds [low_factor=np.float32(-0.001953125), upp_factor=np.float32(-0.001953125)]",
"body": "### System Info\n\n```Shell\n2025,ubantu,python3.10. when doing teleoperation\n```\n\n### Information\n\n- [ ] One of the scripts in the examples/ folder of LeRobot\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\npython lerobot/scripts/control_robot.py --robot.type=so100 --robot.cameras='{}' --control.type=teleoperate \n\n\n### Expected behavior\n\nHow to deal with it.",
"url": "https://github.com/huggingface/lerobot/issues/1101",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-05-13T05:06:35Z",
"updated_at": "2025-06-19T14:25:08Z",
"user": "qingx-cyber"
},
{
"repo": "huggingface/diffusers",
"number": 11542,
"title": "What's the difference between 'example/train_text_to_image_lora.py' and 'example/research_projects/lora/train_text_to_image_lora.py' ?",
"body": " I want to use the \"--train_text_encoder\" argument, but it only exists in the latter script.",
"url": "https://github.com/huggingface/diffusers/issues/11542",
"state": "closed",
"labels": [],
"created_at": "2025-05-13T01:41:19Z",
"updated_at": "2025-06-10T20:35:10Z",
"comments": 2,
"user": "night-train-zhx"
},
{
"repo": "huggingface/lerobot",
"number": 1097,
"title": "UnboundLocalError: local variable 'action' referenced before assignment",
"body": "May I ask where the problem lies? It occurred during the evaluation of the strategy and I have been searching for a long time without finding a solution\n\n(lerobot) wzx@wzx:~/lerobot$ python lerobot/scripts/control_robot.py \\\n> --robot.type=so101 \\\n> --control.type=record \\\n> --control.fps=30 \\\n> --control.single_task=\"Grasp a lego block and put it in the bin.\" \\\n> --control.repo_id=${HF_USER}/eval_act_so101_test \\\n> --control.tags='[\"tutorial\"]' \\\n> --control.warmup_time_s=5 \\\n> --control.episode_time_s=30 \\\n> --control.reset_time_s=30 \\\n> --control.num_episodes=10 \\\n> --control.display_data=true \\\n> --control.push_to_hub=true \\\n> --control.policy.path=outputs/train/act_so101_test/checkpoints/last/pretrained_model \nINFO 2025-05-12 22:54:05 ol_robot.py:408 {'control': {'display_data': True,\n 'episode_time_s': 30,\n 'fps': 30,\n 'num_episodes': 10,\n 'num_image_writer_processes': 0,\n 'num_image_writer_threads_per_camera': 4,\n 'play_sounds': True,\n 'policy': {'beta_end': 0.02,\n 'beta_schedule': 'squaredcos_cap_v2',\n 'beta_start': 0.0001,\n 'clip_sample': True,\n 'clip_sample_range': 1.0,\n 'crop_is_random': True,\n 'crop_shape': (84, 84),\n 'device': 'cuda',\n 'diffusion_step_embed_dim': 128,\n 'do_mask_loss_for_padding': False,\n 'down_dims': (512, 1024, 2048),\n 'drop_n_last_frames': 7,\n 'horizon': 16,\n 'input_features': {'observation.images.laptop': {'shape': (3,\n 480,\n 640),\n 'type': },\n 'observation.images.phone': {'shape': (3,\n 480,\n 640),\n 'type': },\n 'observation.state': {'shape': (6,),\n 'type': }},\n 'kernel_size': 5,\n 'n_action_steps': 8,\n 'n_groups': 8,\n 'n_obs_steps': 2,\n 'noise_scheduler_type': 'DDPM',\n 'normalization_mapping': {'ACTION': ,\n 'STATE': ,\n 'VISUAL': },\n 'num_inference_steps': None,\n 'num_train_timesteps': 100,\n 'optimizer_betas': (0.95, 0.999),\n 'optimizer_eps': 1e-08,\n 'optimizer_lr': 0.0001,\n 'optimizer_weight_decay': 1e-06,\n 'output_features': {'action': {'shape': (6,),\n 'type': }},\n 'prediction_type': 'epsilon',\n 'pretrained_backbone_weights': None,\n 'scheduler_name': 'cosine',\n 'scheduler_warmup_steps': 500,\n 'spatial_softmax_num_keypoints': 32,\n 'use_amp': False,\n 'use_film_scale_modulation': True,\n 'use_group_norm': True,\n 'use_separate_rgb_encoder_per_camera': False,\n 'vision_backbone': 'resnet18'},\n 'private': False,\n 'push_to_hub': True,\n 'repo_id': 'bursomi/eval_act_so101_test',\n 'reset_time_s': 30,\n 'resume': False,\n 'root': None,\n 'single_task': 'Grasp a lego block and put it in the bin.',\n 'tags': ['tutorial'],\n 'video': True,\n 'warmup_time_s': 5},\n 'robot': {'calibration_dir': '.cache/calibration/so101',\n 'cameras': {'laptop': {'camera_index': 2,\n 'channels': 3,\n 'color_mode': 'rgb',\n 'fps': 30,\n 'height': 480,\n 'mock': False,\n 'rotation': None,\n ",
"url": "https://github.com/huggingface/lerobot/issues/1097",
"state": "closed",
"labels": [
"bug",
"question"
],
"created_at": "2025-05-12T16:06:27Z",
"updated_at": "2025-06-19T14:08:57Z",
"user": "incomple42"
},
{
"repo": "huggingface/lerobot",
"number": 1093,
"title": "List of available task",
"body": "Thank you for your effort. Can you provide a list of available tasks (not just environments) for better understanding and usage? ",
"url": "https://github.com/huggingface/lerobot/issues/1093",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-05-10T06:18:21Z",
"updated_at": "2025-10-17T12:03:32Z",
"user": "return-sleep"
},
{
"repo": "huggingface/transformers",
"number": 38052,
"title": "`.to` on a `PreTrainedModel` throws a Pyright type check error. What is the correct way to put a model to the device that does not throw type check errors?",
"body": "### System Info\n\n(venv) nicholas@B367309:tmp(master)$ transformers-cli env\n\nCopy-and-paste the text below in your GitHub issue and FILL OUT the two last points.\n\n- `transformers` version: 4.51.1\n- Platform: Linux-5.10.16.3-microsoft-standard-WSL2-x86_64-with-glibc2.39\n- Python version: 3.12.3\n- Huggingface_hub version: 0.30.2\n- Safetensors version: 0.5.3\n- Accelerate version: 1.6.0\n- Accelerate config: not found\n- DeepSpeed version: not installed\n- PyTorch version (GPU?): 2.6.0+cu126 (True)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: \n- Using GPU in script?: \n- GPU type: NVIDIA RTX 2000 Ada Generation Laptop GPU\n\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nHere is a small snippet\n\n```python\nfrom transformers.models.auto.modeling_auto import AutoModelForCausalLM\nfrom transformers.models.llama.modeling_llama import LlamaForCausalLM\n\nmodel = AutoModelForCausalLM.from_pretrained(\n \"deepseek-ai/deepseek-coder-1.3b-instruct\", torch_dtype=torch.float16\n)\nassert isinstance(model, LlamaForCausalLM)\nmodel.to(\"cuda:0\")\n```\n\nThis code runs fine and correctly puts the model to the device, however, `Pyright` throws a pre-runtime type check error on the `model.to(\"cuda:0\") call. This is the error,\n\n```plaintext\nPyright: Argument of type \"Literal['cuda:0']\" cannot be assigned to parameter \"self\" of \ntype \"LlamaForCausalLM\" in function \"__call__\".\n\"Literal['cuda:0']\" is not assignable to \"LlamaForCausalLM\" [reportArgumentType] \n```\n\nWhat is the correct way to put a model to the device that will satisfy the type checker?\n\n### Expected behavior\n\nThere should be know static type check error when doing `model.to()`",
"url": "https://github.com/huggingface/transformers/issues/38052",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-05-09T19:01:15Z",
"updated_at": "2025-06-29T08:03:07Z",
"user": "nickeisenberg"
},
{
"repo": "huggingface/finetrainers",
"number": 401,
"title": "how to train wan using multi-node",
"body": "### Feature request / \u529f\u80fd\u5efa\u8bae\n\nHi! I still wonder the multi-node training of Wan2.1 14B. Do you support FSDP across nodes? \n\n### Motivation / \u52a8\u673a\n\nCurrently the memory restraint is very harsh for long video LoRA fine-tuning\n\n### Your contribution / \u60a8\u7684\u8d21\u732e\n\nN/A",
"url": "https://github.com/huggingface/finetrainers/issues/401",
"state": "open",
"labels": [],
"created_at": "2025-05-09T18:11:07Z",
"updated_at": "2025-05-09T18:11:07Z",
"user": "Radioheading"
},
{
"repo": "huggingface/lerobot",
"number": 1091,
"title": "Diffusion policy for different tasks instead of PushT",
"body": "Thank you all for the great job. I want to know if I can train the diffusion policy for different tasks besides the PushT task. How to achieve that? If the task is a new custom task with custom dataset, is there any feasible solution to solve that? \nThank you for your help!",
"url": "https://github.com/huggingface/lerobot/issues/1091",
"state": "closed",
"labels": [
"question",
"policies",
"stale"
],
"created_at": "2025-05-09T15:44:20Z",
"updated_at": "2025-12-31T02:35:27Z",
"user": "siqisiqisiqisiqi"
},
{
"repo": "huggingface/lerobot",
"number": 1086,
"title": "push_to_the_hub error",
"body": "### System Info\n\n```Shell\n- `lerobot` version: 0.1.0\n- Platform: macOS-14.6.1-arm64-arm-64bit\n- Python version: 3.10.13\n- Huggingface_hub version: 0.30.2\n- Dataset version: 3.5.0\n- Numpy version: 2.2.5\n- PyTorch version (GPU?): 2.7.0 (False)\n- Cuda version: N/A\n- Using GPU in script?: \n```\n\n### Information\n\n- [ ] One of the scripts in the examples/ folder of LeRobot\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nimport argparse\nfrom lerobot.common.datasets.lerobot_dataset import LeRobotDataset\n\ndef parse_args():\n parser = argparse.ArgumentParser(description=\"Push a local HuggingFace dataset to the Hub\")\n parser.add_argument(\n \"--path\", \n type=str, \n required=True, \n help=\"Local directory containing the dataset\"\n )\n parser.add_argument(\n \"--repo_id\", \n type=str, \n required=True, \n help=\"Repository ID on HuggingFace Hub (format: username/dataset_name)\"\n )\n parser.add_argument(\n \"--private\", \n action=\"store_true\", \n help=\"Whether to make the dataset private\"\n )\n # Removed unused arguments\n return parser.parse_args()\n\ndef main():\n args = parse_args()\n \n print(f\"Loading dataset from {args.path}...\")\n dataset = LeRobotDataset(\n repo_id=args.repo_id,\n root=args.path\n )\n \n print(f\"Pushing dataset to {args.repo_id}...\")\n dataset.push_to_hub(\n args.repo_id,\n private=args.private\n )\n print(\"Dataset successfully pushed to Hub!\")\n \n return 0\n\nif __name__ == \"__main__\":\n main()\n\n\n![\"Image\"](\"https://github.com/user-attachments/assets/36c563a6-ed2e-4deb-b54e-ce5c9889c50b\")
\n\n### Expected behavior\n\nupload it to the huggingface",
"url": "https://github.com/huggingface/lerobot/issues/1086",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-05-09T03:48:09Z",
"updated_at": "2025-10-17T11:55:25Z",
"user": "jungwonshin"
},
{
"repo": "huggingface/trl",
"number": 3424,
"title": "[GRPO] How to train model using vLLM and model parallelism on one node?",
"body": "I tried to start GRPO trainer with vLLM and model parallelism on a single node with 8 GPUs (8 x A100 80G).\n\nMy plan was to use one GPU as the vLLM server and other 7 GPUs to load model with model parallelism (e.g., `device_map=\"auto\"`)\n\n```\nCUDA_VISIBLE_DEVICES=0 trl vllm-serve --model &\nCUDA_VISIBLE_DEVICES=1,2,3,4,5,6,7 accelerate launch --num_machines 1 --num_processes 1 train.py\n```\n\nBut the training ran into the following error\n\n`AssertionError: this nccl communicator is created to work on cuda:0, but the input tensor is on cuda:1`\n\nI think it happened when copying the weights to vLLM server.\n\n```\ntorch==2.6.0+cu124\ntransformers==4.51.3\ntrl==0.17.0\naccelerate==1.4.0\n```\n\n",
"url": "https://github.com/huggingface/trl/issues/3424",
"state": "open",
"labels": [],
"created_at": "2025-05-08T17:22:19Z",
"updated_at": "2025-12-02T22:48:13Z",
"user": "zhiqihuang"
},
{
"repo": "huggingface/lerobot",
"number": 1082,
"title": "When add openvla oft policy?",
"body": "",
"url": "https://github.com/huggingface/lerobot/issues/1082",
"state": "closed",
"labels": [
"question",
"policies",
"stale"
],
"created_at": "2025-05-08T09:16:16Z",
"updated_at": "2025-12-31T02:35:30Z",
"user": "zmf2022"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3213,
"title": "Whether it supports Huawei Atlas300 graphics card?",
"body": "### System Info\n\n\nDoes the tgi inference framework support Huawei Atlas300I graphics cards?Could you help come up with a compatible solution?\n\n\n### Information\n\n- [x] Docker\n- [ ] The CLI directly\n\n### Tasks\n\n- [ ] An officially supported command\n- [ ] My own modifications\n\n### Reproduction\n\n.\n\n### Expected behavior\n\nCompatible with Huawei graphics cards. I want to use tgi on the Huawei Atlas300I graphics card",
"url": "https://github.com/huggingface/text-generation-inference/issues/3213",
"state": "open",
"labels": [],
"created_at": "2025-05-08T03:18:30Z",
"updated_at": "2025-05-08T03:18:38Z",
"comments": 0,
"user": "fxb392"
},
{
"repo": "huggingface/trl",
"number": 3419,
"title": "[GRPO] How to do gradient accumulation over sampled outputs?",
"body": "Greetings,\n\nI am wondering if we have this feature to do gradient accumulation over sampled outputs. For example, if I have `num_generations = 4`, so we have a single query `q1`, we have`completions = [o1, o2, o3, o4]`. I want to set that `per_device_train_batch_size=2, gradient_accumulation_steps=2`. So that the GPU or cluster will sample `[o1, o2]` first, and then calculate the gradient, then do, `[o3,o4]`, and do gradient accumulation over these two mini-samples for the datapoint `q1`. \n\nI assume this will be equivalent to having `num_generations=4, per_device_train_batch_size=4, gradient_accumulation_steps=1`. But we cannot do this now. Could someone tell me how to properly do that? Do we support such feature now?\n\nI hope I made myself clear.\n\nThank you very much!",
"url": "https://github.com/huggingface/trl/issues/3419",
"state": "closed",
"labels": [],
"created_at": "2025-05-07T17:49:36Z",
"updated_at": "2025-05-09T06:26:29Z",
"user": "SpaceHunterInf"
},
{
"repo": "huggingface/lerobot",
"number": 1080,
"title": "Update `control_sim_robot.py` to use the new configs",
"body": "Adding this issue to track one of the TODO's of this MR #550 \n\nAs of now, [this script](https://github.com/huggingface/lerobot/blob/8cfab3882480bdde38e42d93a9752de5ed42cae2/lerobot/scripts/control_sim_robot.py) is outdated; It does not use the new configuration classes.",
"url": "https://github.com/huggingface/lerobot/issues/1080",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-05-07T11:37:47Z",
"updated_at": "2025-06-19T14:04:11Z",
"user": "jccalvojackson"
},
{
"repo": "huggingface/Math-Verify",
"number": 53,
"title": "How to turn off error print?",
"body": "When using multiprocessing, there is a lot of error message printed.",
"url": "https://github.com/huggingface/Math-Verify/issues/53",
"state": "closed",
"labels": [],
"created_at": "2025-05-07T08:19:36Z",
"updated_at": "2025-07-02T16:07:02Z",
"user": "wenxueru"
},
{
"repo": "huggingface/peft",
"number": 2533,
"title": "Integrate TLoRA (Tri-Matrix LoRA)",
"body": "### Feature request\n\nWe would like to propose integrating a novel parameter-efficient fine-tuning method called **TLoRA (Tri-Matrix LoRA)** into the `peft` library. We believe TLoRA offers significant advantages in terms of parameter efficiency, making it a valuable addition to the PEFT ecosystem.\n\nOur method is detailed in the paper: **https://arxiv.org/abs/2504.18735**\n\n**What is TLoRA?**\n\nTLoRA is a variation of LoRA that introduces a tri-matrix decomposition for the weight update matrix $\\Delta W$. Instead of the standard $W + A B$, TLoRA uses $W + \\alpha A B C $, where:\n\n* $W$ is the original pre-trained weight matrix.\n* $A$ is a fixed, non-trainable matrix (e.g., initialized randomly or using Kaiming/Xavier).\n* $B$ is the _only_ trainable matrix.\n* $C$ is another fixed, non-trainable matrix (similar initialization as A).\n* $\\alpha$ is a trainable scaling parameter.\n\nThe $\\Delta W$ update is computed as the product of three matrices: a fixed input projection matrix $A$, a small trainable bottleneck matrix $B$, and a fixed output projection matrix $C$. Only matrix $B$ is updated during fine-tuning.\n\n**TLoRA Implementation:**\n\nThe core idea can be represented in a layer similar to this (based on our implementation):\n\n```python\nclass TLoRALayer(nn.Module):\n def __init__(self, weight, bias, rank=32):\n super(TLoRALayer, self).__init__()\n\n row, column = weight.shape\n\n # Restore Linear layer\n if bias is None:\n self.linear = nn.Linear(column, row, bias=False)\n self.linear.load_state_dict({\"weight\": weight})\n else:\n self.linear = nn.Linear(column, row)\n self.linear.load_state_dict({\"weight\": weight, \"bias\": bias})\n\n # Create TLoRA weights with initialization\n self.random_A = nn.Parameter(\n torch.zeros(column, rank), requires_grad=False\n ) # First matrix, non-trainable\n nn.init.kaiming_normal_(self.random_A, a=math.sqrt(5))\n\n self.lora_B = nn.Parameter(torch.zeros(rank, rank)) # Second matrix (trainable)\n\n self.random_C = nn.Parameter(\n torch.zeros(rank, row), requires_grad=False\n ) # Third matrix\n nn.init.kaiming_normal_(self.random_C, a=math.sqrt(5))\n\n self.lora_scaling = nn.Parameter(torch.ones(1))\n self.dropout = nn.Dropout(0.5)\n\n def forward(self, input):\n # Standard linear transformation\n x = self.linear(input)\n\n # Low-rank adaptation with tri-matrix TLoRA\n # Using the scaling to control the LoRA output\n y = self.lora_scaling * (input @ self.random_A @ self.lora_B @ self.random_C)\n\n y = self.dropout(y)\n\n return x + y\n\n```\n\nFull Repo: https://github.com/itanvir/tlora \n\n### Motivation\n\n1. **Extreme Parameter Efficiency:** The core trainable component in TLoRA is the matrix $B$ with dimensions `rank x rank`. Compared to standard LoRA's trainable matrices $A$ (`input_dim x rank`) and $B$ (`rank x output_dim`), TLoRA's trainable parameters are significantly fewer. This makes TLoRA potentially one of the most parameter-efficient methods in PEFT for a given rank.\n2. **Competitive Performance:** The fixed matrices $A$ and $C$ can be seen as defining fixed subspaces. By training only the matrix $B$ connecting these subspaces, TLoRA might capture more focused and effective updates compared to training the full $A$ and $B$ matrices in standard LoRA. Our paper provides empirical evidence supporting its effectiveness.\n\n### Your contribution\n\nCan give inputs on the design. It should be straightforward.",
"url": "https://github.com/huggingface/peft/issues/2533",
"state": "closed",
"labels": [],
"created_at": "2025-05-06T21:22:50Z",
"updated_at": "2025-06-15T15:03:57Z",
"comments": 2,
"user": "itanvir"
},
{
"repo": "huggingface/candle",
"number": 2945,
"title": "Operating steps from scratch for beginners?",
"body": "from\na\nTo\nZ",
"url": "https://github.com/huggingface/candle/issues/2945",
"state": "open",
"labels": [],
"created_at": "2025-05-06T15:34:02Z",
"updated_at": "2025-05-06T15:34:02Z",
"comments": 0,
"user": "Qarqor5555555"
},
{
"repo": "huggingface/lerobot",
"number": 1072,
"title": "How to merge collected data into one?",
"body": "For stability I collect data 10 episode by 10. Then forming this:\nrepo_id/first,repo_id_second...\nI want to merge them together to repo_id/one_task for training, but it's hard to fix meta files. \n\nI'm not sure if this approach helps with training, or if I should determine the number of episodes needed for training in advance when collecting data.",
"url": "https://github.com/huggingface/lerobot/issues/1072",
"state": "closed",
"labels": [
"question",
"dataset"
],
"created_at": "2025-05-06T02:27:24Z",
"updated_at": "2025-05-07T02:29:27Z",
"user": "milong26"
},
{
"repo": "huggingface/diffusers",
"number": 11499,
"title": "[Performance] Issue on *SanaLinearAttnProcessor2_0 family. 1.06X speedup can be reached with a simple change.",
"body": "### Sys env:\nOS Ubuntu 22.04\nPyTorch 2.4.0+cu121\nsana == 0.0.1\nDiffusers == 0.34.0.dev0\n\n### Reproduce:\nTry the demo test code:\n```\nimport torch\nfrom diffusers import SanaPAGPipeline\n\npipe = SanaPAGPipeline.from_pretrained(\n # \"Efficient-Large-Model/Sana_1600M_512px_diffusers\", \n \"Efficient-Large-Model/SANA1.5_1.6B_1024px_diffusers\",\n torch_dtype=torch.bfloat16,\n pag_applied_layers=\"transformer_blocks.8\",\n)\npipe.to(\"cuda\")\n\npipe.text_encoder.to(torch.bfloat16)\npipe.vae.to(torch.bfloat16)\n\nprompt = 'a cyberpunk cat with a neon sign that says \"Sana\"'\nimage = pipe(\n prompt=prompt,\n guidance_scale=5.0,\n pag_scale=2.0,\n num_inference_steps=20,\n generator=torch.Generator(device=\"cuda\").manual_seed(42),\n)[0]\nimage[0].save('sana.png')\n```\n\nInference data will go through [SanaLinearAttnProcessor2_0](https://github.com/huggingface/diffusers/blob/58431f102cf39c3c8a569f32d71b2ea8caa461e1/src/diffusers/models/attention_processor.py#L6007)\n\n\n### Issue Description:\nLines 6042 and 6043 first transposed a contiguous tensor and then did type casting. Type casting invokes a data copy from an old type tensor to a new one. But if you print the new tensor's stride(), you will see:\n```\n hidden_states = hidden_states.flatten(1, 2).transpose(1, 2)\n hidden_states = hidden_states.to(original_dtype)\n print(\"Contiguity after type casting: \", hidden_states.is_contiguous()) # False\n\n hidden_states = attn.to_out[0](hidden_states)\n hidden_states = attn.to_out[1](hidden_states)\n```\n\nThe problem is typecasting copies, only did the dtype transmission based on the input tensor's strides. And the bad-strided tensor is immediately used by the latter two functions. Inefficiency is broadcast.\n\n### How to Fix:\nlet `hidden_states.to(original_dtype)` do contiguous and typecasting simultaneously.\nOne possible approach:\n```\n@torch.compile\ndef transpose_cast_kernel(input_tensor: torch.Tensor) -> torch.Tensor:\n \"\"\"\n torch-compiled kernel that transposes a 2D tensor and converts it to bfloat16\n \"\"\"\n converted = input_tensor.to(torch.bfloat16)\n transposed = torch.transpose(converted, 1, 2).contiguous()\n return transposed\n```\nUse the versatile operation to handle the creation of the new tensor.\n```\n hidden_states = hidden_states.flatten(1, 2).transpose(1, 2)\n hidden_states = transpose_cast_kernel(hidden_states)\n # hidden_states.is_contiguous() True\n\n hidden_states = attn.to_out[0](hidden_states)\n hidden_states = attn.to_out[1](hidden_states)\n```\nOr, your expert team could do even better.\n\n### Measurement:\nBy adopting the previous change, the **SanaLinearAttnProcessor2_0.__call__ enjoys** 1.06X speedup on RTX3090.\nPAGCFGSanaLinearAttnProcessor2_0, and PAGIdentitySanaLinearAttnProcessor2_0 have similar logic and lose performance as well. \n\n",
"url": "https://github.com/huggingface/diffusers/issues/11499",
"state": "closed",
"labels": [],
"created_at": "2025-05-05T21:26:51Z",
"updated_at": "2025-08-08T23:44:59Z",
"comments": 11,
"user": "David-Dingle"
},
{
"repo": "huggingface/candle",
"number": 2944,
"title": "finetuning yolo 8 candle model",
"body": "What is the correct way to finetune yolo8 model to be used here ? Finetuning model using candle is not straightforward.\ncandle\\candle-examples\\examples\\yolo-v8\\main.rs \n// model model architecture points at ultralytics : https://github.com/ultralytics/ultralytics/issues/189\nBut my model trained using ultralytics and converted to safetensors yield tensor errors when used in candle ylo 8 example. Renaming the tensors to match the candle yolo model did not work.\n\nI see DarkNet struct in the model.rs so I wonder if one should rather use [Darknet](https://github.com/hank-ai/darknet) instead (@LaurentMazare ) ? \n",
"url": "https://github.com/huggingface/candle/issues/2944",
"state": "open",
"labels": [],
"created_at": "2025-05-05T15:21:48Z",
"updated_at": "2025-05-05T18:46:52Z",
"comments": 0,
"user": "flutter-painter"
},
{
"repo": "huggingface/diffusers",
"number": 11489,
"title": "Error when I'm trying to train a Flux lora with train_dreambooth_lora_flux_advanced",
"body": "### Describe the bug\n\nHi! I'm trying to train my lora model with [train_dreambooth_lora_flux_advanced](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_flux_advanced.py) script.\n\nWhen I'm trying to train my model with prior preservation tag I give an error.\n\nHow can I fix it?\n\n\n\n### Reproduction\n\n```bash\naccelerate launch train_dreambooth_lora_flux_advanced.py \\\n --pretrained_model_name_or_path=\"black-forest-labs/FLUX.1-dev\" \\\n --dataset_name=\"./ds5\" \\\n --instance_prompt=\"1boy, 1girl\" \\\n --validation_prompt=\"1boy, 1girl\" \\\n --class_prompt=\"1boy, 1girl\" \\\n --num_class_images=200 \\\n --with_prior_preservation \\\n --class_data_dir=\"./cdi\" \\\n --output_dir=\"crtr-SDXL-LoRA\" \\\n --caption_column=\"text\" \\\n --mixed_precision=\"bf16\" \\\n --prior_generation_precision=\"bf16\" \\\n --resolution=1024 \\\n --train_batch_size=8 \\\n --repeats=1 \\\n --gradient_accumulation_steps=8 \\\n --gradient_checkpointing \\\n --learning_rate=1.0 \\\n --optimizer=\"prodigy\"\\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --rank=64 \\\n --num_train_epochs=200 \\\n --validation_epochs=100 \\\n --center_crop \\\n --adam_beta2=0.99 \\\n --adam_weight_decay=0.01 \\\n --allow_tf32\n```\n\n### Logs\n\n```shell\nTraceback (most recent call last):\n File \"/workspace/train_dreambooth_lora_flux_advanced.py\", line 2423, in \n main(args)\n File \"/workspace/train_dreambooth_lora_flux_advanced.py\", line 2213, in main\n (weighting.float() * (model_pred_prior.float() - target_prior.float()) ** 2).reshape(\n ~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nRuntimeError: The size of tensor a (16) must match the size of tensor b (8) at non-singleton dimension 0\n```\n\n### System Info\n\nDiffusers 0.33\nCUDA 12.9\nTorch 2.7\n\nDocker image\nnvcr.io/nvidia/pytorch:25.04-py3\n\n### Who can help?\n\n@sayakpaul",
"url": "https://github.com/huggingface/diffusers/issues/11489",
"state": "open",
"labels": [
"bug",
"training"
],
"created_at": "2025-05-04T21:19:23Z",
"updated_at": "2025-07-06T19:38:40Z",
"comments": 4,
"user": "Mnwa"
},
{
"repo": "huggingface/diffusers",
"number": 11488,
"title": "Sincerely Request The Support for Flux PAG Pipeline",
"body": "When the pag pipeline of flux can be supported?",
"url": "https://github.com/huggingface/diffusers/issues/11488",
"state": "open",
"labels": [
"help wanted",
"Good second issue"
],
"created_at": "2025-05-04T11:12:05Z",
"updated_at": "2025-05-16T04:53:52Z",
"comments": 2,
"user": "PlutoQyl"
},
{
"repo": "huggingface/text-generation-inference",
"number": 3208,
"title": "Can I use TGI in a Supercomputer?",
"body": "I want to generate somewhere around 1 trillion tokens and I was thinking of using TGI on a European Supercomputer. is there a way to achieve this without relying on docker and downloading the model natively and then load it on the compute node and serve it? @Wauplin ",
"url": "https://github.com/huggingface/text-generation-inference/issues/3208",
"state": "open",
"labels": [],
"created_at": "2025-05-03T15:13:24Z",
"updated_at": "2025-05-15T08:55:08Z",
"comments": 4,
"user": "sleepingcat4"
},
{
"repo": "huggingface/transformers.js",
"number": 1305,
"title": "Trying to convert dinov2 model",
"body": "### Question\n\nI tried to convert [this model](https://huggingface.co/nguyenkhoa/dinov2_Liveness_detection_v2.2.3) using the following command: \n\n`python -m scripts.convert --model_id nguyenkhoa/dinov2_Liveness_detection_v2.2.3 --quantize --task image-classification`\n\nbut got the following error:\n\n``ValueError: Trying to export a dinov2 model, that is a custom or unsupported architecture, but no custom onnx configuration was passed as `custom_onnx_configs`. Please refer to https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#custom-export-of-transformers-models for an example on how to export custom models. Please open an issue at https://github.com/huggingface/optimum/issues if you would like the model type dinov2 to be supported natively in the ONNX export.``\n\nI looked a bit into the `custom_onnx_configs` flag and found [this conversion example](https://github.com/huggingface/transformers.js/issues/906#issuecomment-2315290257). My question is regarding what should I pass to `custom_onnx_configs` for the conversion to work? I could pass `gpt2` as used in the example but I'm wondering what is the correct `custom_onnx_configs` input for dinov2 models.\n\nThank you!",
"url": "https://github.com/huggingface/transformers.js/issues/1305",
"state": "closed",
"labels": [
"question"
],
"created_at": "2025-05-01T19:56:28Z",
"updated_at": "2025-05-05T22:18:48Z",
"user": "jdp8"
},
{
"repo": "huggingface/datasets",
"number": 7545,
"title": "Networked Pull Through Cache",
"body": "### Feature request\n\nIntroduce a HF_DATASET_CACHE_NETWORK_LOCATION configuration (e.g. an environment variable) together with a companion network cache service.\n\nEnable a three-tier cache lookup for datasets:\n\n1. Local on-disk cache\n2. Configurable network cache proxy\n3. Official Hugging Face Hub\n\n### Motivation\n\n- Distributed training & ephemeral jobs: In high-performance or containerized clusters, relying solely on a local disk cache either becomes a streaming bottleneck or incurs a heavy cold-start penalty as each job must re-download datasets.\n- Traffic & cost reduction: A pull-through network cache lets multiple consumers share a common cache layer, reducing duplicate downloads from the Hub and lowering egress costs.\n- Better streaming adoption: By offloading repeat dataset pulls to a locally managed cache proxy, streaming workloads can achieve higher throughput and more predictable latency.\n- Proven pattern: Similar proxy-cache solutions (e.g. Harbor\u2019s Proxy Cache for Docker images) have demonstrated reliability and performance at scale: https://goharbor.io/docs/2.1.0/administration/configure-proxy-cache/\n\n### Your contribution\n\nI\u2019m happy to draft the initial PR for adding HF_DATASET_CACHE_NETWORK_LOCATION support in datasets and sketch out a minimal cache-service prototype.\n\nI have limited bandwidth so I would be looking for collaborators if anyone else is interested. ",
"url": "https://github.com/huggingface/datasets/issues/7545",
"state": "open",
"labels": [
"enhancement"
],
"created_at": "2025-04-30T15:16:33Z",
"updated_at": "2025-04-30T15:16:33Z",
"comments": 0,
"user": "wrmedford"
},
{
"repo": "huggingface/transformers",
"number": 37895,
"title": "How to backpropagate the gradients of the embeddings output by the image processor to the input image tensor?",
"body": "### Feature request\n\nI'm using the processor of Qwen2.5-VL, and the image processor within it should be Qwen2ImageProcessor. The input image I provide is a PyTorch tensor with gradients, and the processor outputs the feature embeddings of the image. How can I ensure that the gradient flow is not interrupted during this process?\n\n### Motivation\n\nI want to backpropagate the gradients of the embeddings output by the Qwen2 image processor to the input image tensor\n\n### Your contribution\n\nI can coporate to fix this issue",
"url": "https://github.com/huggingface/transformers/issues/37895",
"state": "open",
"labels": [
"Feature request"
],
"created_at": "2025-04-30T15:06:40Z",
"updated_at": "2025-05-01T13:36:24Z",
"user": "weiminbai"
},
{
"repo": "huggingface/diffusers",
"number": 11466,
"title": "Finetuning of flux or scratch training",
"body": "I am new to this field and wanted to know if Is there any code available for training the flux from scratch or even finetuning the existing model. All I see is the dreambooth or Lora finetuning.",
"url": "https://github.com/huggingface/diffusers/issues/11466",
"state": "open",
"labels": [],
"created_at": "2025-04-30T07:45:49Z",
"updated_at": "2025-05-30T16:32:33Z",
"comments": 2,
"user": "preethamp0197"
},
{
"repo": "huggingface/hf-hub",
"number": 104,
"title": "What is this software licensed under?",
"body": "Would this also be Apache 2 like in https://github.com/huggingface/huggingface_hub/?\nThanks!",
"url": "https://github.com/huggingface/hf-hub/issues/104",
"state": "closed",
"labels": [],
"created_at": "2025-04-29T16:27:10Z",
"updated_at": "2025-06-16T09:09:43Z",
"user": "nathankw"
},
{
"repo": "huggingface/optimum",
"number": 2248,
"title": "Export cli export RT-Detr",
"body": "```python\nTraceback (most recent call last):\n File \"/usr/local/bin/optimum-cli\", line 8, in \n sys.exit(main())\n ^^^^^^\n File \"/usr/local/lib/python3.11/dist-packages/optimum/commands/optimum_cli.py\", line 208, in main\n service.run()\n File \"/usr/local/lib/python3.11/dist-packages/optimum/commands/export/onnx.py\", line 265, in run\n main_export(\n File \"/usr/local/lib/python3.11/dist-packages/optimum/exporters/onnx/__main__.py\", line 375, in main_export\n onnx_export_from_model(\n File \"/usr/local/lib/python3.11/dist-packages/optimum/exporters/onnx/convert.py\", line 1033, in onnx_export_from_model\n raise ValueError(\nValueError: Trying to export a rt-detr model, that is a custom or unsupported architecture, but no custom onnx configuration was passed as `custom_onnx_configs`. Please refer to https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#custom-export-of-transformers-models for an example on how to export custom models. Please open an issue at https://github.com/huggingface/optimum/issues if you would like the model type rt-detr to be supported natively in the ONNX export.\n```\n\nWhen I try to export my fine-tuned model with RT-DETR, it always pops up with the above error.\n\nEven with the cmd line `optimum-cli export onnx -m PekingU/rtdetr_r18vd --task object-detection test_onnx` still shows the same error. So, it should not be an issue related to finetuned model.\n\nI would like to know how to export a finetuned model. It would be helpful if anyone can give me some hint. Thanks!\n",
"url": "https://github.com/huggingface/optimum/issues/2248",
"state": "closed",
"labels": [],
"created_at": "2025-04-29T08:23:17Z",
"updated_at": "2025-05-05T08:03:21Z",
"comments": 1,
"user": "TheMattBin"
},
{
"repo": "huggingface/open-muse",
"number": 144,
"title": "how to set the minimum learning rate for cosine lr_scheduler?",
"body": "@dataclass\nclass TrainingArguments(transformers.TrainingArguments):\n gradient_checkpointing_kwargs={'use_reentrant':False}\n lr_scheduler_kwargs={\n \"eta_min\":1e-6,\n \"num_cycles\":1,\n }\n\nIt did not work. how to set the minimum learning rate in transformers-4.51.3?",
"url": "https://github.com/huggingface/open-muse/issues/144",
"state": "closed",
"labels": [],
"created_at": "2025-04-29T02:18:59Z",
"updated_at": "2025-04-29T02:20:42Z",
"user": "xubuvd"
},
{
"repo": "huggingface/lerobot",
"number": 1045,
"title": "Inefficient Config Structure without Hydra",
"body": "Hi, I notice that the repo used Hydra before, which can modify some config param or create new config yaml files. However, this was deprecated. I wonder how to efficiently modify a new config file for policy without writing these params in the command line each time?",
"url": "https://github.com/huggingface/lerobot/issues/1045",
"state": "closed",
"labels": [
"question",
"configuration",
"stale"
],
"created_at": "2025-04-28T11:48:08Z",
"updated_at": "2025-11-18T02:30:46Z",
"user": "jiangranlv"
},
{
"repo": "huggingface/diffusers",
"number": 11432,
"title": "`.from_pretrained` `torch_dtype=\"auto\"` argument not working a expected",
"body": "### Describe the bug\n\nHey dear diffusers team,\n\nthanks a lot for all your hard work!\n\nI would like to make use of the `torch_dtype=\"auto\"` keyword argument when loading a model/pipeline as specified [here](https://huggingface.co/docs/diffusers/main/en/api/pipelines/overview#diffusers.DiffusionPipeline.from_pretrained.torch_dtype), but the usage does not work as expected (see example below). Can you help me out with some guidance on how to use it correctly or let me know whether there is something wrong with the handling of this argument?\n\nThank you!\n\n### Reproduction\n\n```python\nfrom diffusers import StableDiffusionPipeline\n\nmodel = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", torch_dtype=\"auto\")\n```\n\n### Logs\n\n```shell\nPassed `torch_dtype` torch.float32 is not a `torch.dtype`. Defaulting to `torch.float32`.\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.33.1\n- Platform: Linux-5.15.0-136-generic-x86_64-with-glibc2.35\n- Running on Google Colab?: No\n- Python version: 3.10.17\n- PyTorch version (GPU?): 2.7.0+cu126 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.30.2\n- Transformers version: 4.51.3\n- Accelerate version: 1.6.0\n- PEFT version: 0.15.2\n- Bitsandbytes version: 0.45.5\n- Safetensors version: 0.5.3\n- xFormers version: not installed\n- Accelerator: NVIDIA H100 PCIe, 81559 MiB\n- Using GPU in script?: Yes\n- Using distributed or parallel set-up in script?: No\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/11432",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-04-28T04:31:26Z",
"updated_at": "2025-05-13T01:42:37Z",
"comments": 3,
"user": "johannaSommer"
},
{
"repo": "huggingface/lerobot",
"number": 1041,
"title": "image transform of pi0 is inconsistent with openpi",
"body": "Thank you for pi0 work in lerobot.However, i found that image transform was quite different from openpi.\nimage transform of lerobot pi0:\n\n\n\nimage transform of openpi:\n\n\n\nAre there some special considerations? By the way, resize_with_pad is also different.",
"url": "https://github.com/huggingface/lerobot/issues/1041",
"state": "closed",
"labels": [
"question",
"policies",
"stale"
],
"created_at": "2025-04-28T03:08:10Z",
"updated_at": "2025-11-20T02:30:12Z",
"user": "wushandinghua"
},
{
"repo": "huggingface/diffusers",
"number": 11423,
"title": "Lora Hotswap no clear documentation",
"body": "Hello everyone.\n\nHere is the scenario I have.\n\nI have say 10 LoRAs that I would like to load and use depending on the request. \n\nOption one:\nusing `load_lora_weights` - reads from the disk and moves to device: expensive operation\n\nOption two:\nload all loras and weights of non-used LoRAS with `set_adapters` method to 0.0. Not practical since the forward pass becomes expensive. Since all LoRAS are still loaded.\n\nOption three:\nFind an elegant way of loading LoRAs to CPU and then moving them to GPU as needed. While I was trying to do that, I saw the new parameter of hotswapping in hte load_lora_weights method. And this is what is described in the documentation:\n\n\nhotswap \u2014 (bool, optional) Defaults to False. Whether to substitute an existing (LoRA) adapter with the newly loaded adapter in-place. This means that, instead of loading an additional adapter, this will take the existing adapter weights and replace them with the weights of the new adapter. This can be faster and more memory efficient. However, the main advantage of hotswapping is that when the model is compiled with torch.compile, loading the new adapter does not require recompilation of the model. When using hotswapping, the passed adapter_name should be the name of an already loaded adapter. **If the new adapter and the old adapter have different ranks and/or LoRA alphas (i.e. scaling), you need to call an additional method before loading the adapter**\n\n\ncould someone help me out here and name the mysterious function to be called?\n\nand optionally would be great if someone could help me with my scenario.\n",
"url": "https://github.com/huggingface/diffusers/issues/11423",
"state": "open",
"labels": [
"stale"
],
"created_at": "2025-04-26T13:44:08Z",
"updated_at": "2025-05-26T15:03:03Z",
"comments": 2,
"user": "vahe-toffee"
},
{
"repo": "huggingface/diffusers",
"number": 11419,
"title": "How to know that \"Textual inversion\" file I have loaded and not turn it on?",
"body": "Reviewing the documentation I understand the load of IT with: \n\n# Add Embeddings\nPipeline.load_textual_inversion(\"Sd-Concepts-Library/Cat-Toy\"), \n\n# Remave All Token Embeddings\nPipeline.unload_textual_inversion()\n\n# Remove Just One Token\nPipeline.unload_textual_inversion (\"\")\n\n\nBut how do you know which are charged to the pipeline?",
"url": "https://github.com/huggingface/diffusers/issues/11419",
"state": "closed",
"labels": [
"stale"
],
"created_at": "2025-04-25T17:18:07Z",
"updated_at": "2025-05-27T18:09:45Z",
"user": "Eduardishion"
},
{
"repo": "huggingface/diffusers",
"number": 11418,
"title": "How to add flux1-fill-dev-fp8.safetensors",
"body": "### Describe the bug\n\nHi!\nHow to use flux1-fill-dev-fp8.safetensors in diffusers?\n\nNow I have code:\n```\ndef init_pipeline(device: str):\n logger.info(f\"Loading FLUX Inpaint Pipeline (Fill\u2011dev) on {device}\")\n pipe = FluxFillPipeline.from_pretrained(\n \"black-forest-labs/FLUX.1-Fill-dev\",\n torch_dtype=torch.bfloat16,\n trust_remote_code=True\n ).to(device)\n logger.info(\"Pipeline loaded successfully\")\n return pipe\n```\n\nAnother try:\n```\n transformer = FluxTransformer2DModel.from_single_file(\n \"https://huggingface.co/YarvixPA/FLUX.1-Fill-dev-gguf/blob/main/flux1-fill-dev-Q4_0.gguf\",\n quantization_config=GGUFQuantizationConfig(compute_dtype=torch.bfloat16),\n torch_dtype=torch.bfloat16\n )\n\n pipe = FluxFillPipeline.from_pretrained(\n \"black-forest-labs/FLUX.1-Fill-dev\",\n transformer=transformer,\n torch_dtype=torch.bfloat16,\n trust_remote_code=True\n ).to(device)\n\n pipe.enable_model_cpu_offload()\n```\n\n### Reproduction\n\nhttps://huggingface.co/boricuapab/flux1-fill-dev-fp8/blob/main/README.md\nhttps://huggingface.co/pengxian/diffusion_models/blob/main/flux1-fill-dev_fp8.safetensors\n\n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\nWindows 11\nPython 11\n\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/11418",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-04-25T14:58:08Z",
"updated_at": "2025-04-28T19:06:17Z",
"user": "SlimRG"
},
{
"repo": "huggingface/optimum",
"number": 2242,
"title": "[onnx] What are the functions of the generated files by optimum-cli?",
"body": "### System Info\n\n```shell\nI try to use **optimum-cli** to export the onnx file for llama, but i don't get a onnx file as expect, but get a lot of files, so I don't know what are they used for ?\n\n(MindSpore) [ma-user llama149]$ls onnx_model/\nconfig.json generation_config.json model.onnx model.onnx_data special_tokens_map.json tokenizer_config.json tokenizer.json\n\n\n> refer to https://zhuanlan.zhihu.com/p/663971402\n```\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction (minimal, reproducible, runnable)\n\n\n> (py39) [ma-user llama149]$optimum-cli export onnx --model models--daryl149--llama-2-7b-hf onnx_model --task text-generation\n\n### Expected behavior\n\nget a onnx file only, that is similar to **torch.onnx.export**",
"url": "https://github.com/huggingface/optimum/issues/2242",
"state": "closed",
"labels": [],
"created_at": "2025-04-25T13:12:35Z",
"updated_at": "2025-04-28T09:18:06Z",
"comments": 1,
"user": "vfdff"
},
{
"repo": "huggingface/diffusers",
"number": 11417,
"title": "attributeerror: 'distributeddataparallel' object has no attribute 'dtype'. did you mean: 'type'?",
"body": "### Describe the bug\n\nattributeerror: 'distributeddataparallel' object has no attribute 'dtype'. did you mean: 'type'?\n\n### Reproduction\n\nexport MODEL_NAME=\"black-forest-labs/FLUX.1-dev\"\nexport OUTPUT_DIR=\"trained-flux-dev-dreambooth-lora\"\n\naccelerate launch train_dreambooth_lora_flux.py \\\n --pretrained_model_name_or_path=$MODEL_NAME \\\n --instance_data_dir=$INSTANCE_DIR \\\n --output_dir=$OUTPUT_DIR \\\n --mixed_precision=\"bf16\" \\\n --train_text_encoder\\\n --instance_prompt=\"a photo of sks dog\" \\\n --resolution=512 \\\n --train_batch_size=1 \\\n --guidance_scale=1 \\\n --gradient_accumulation_steps=4 \\\n --optimizer=\"prodigy\" \\\n --learning_rate=1. \\\n --report_to=\"wandb\" \\\n --lr_scheduler=\"constant\" \\\n --lr_warmup_steps=0 \\\n --max_train_steps=500 \\\n --validation_prompt=\"A photo of sks dog in a bucket\" \\\n --seed=\"0\" \\\n --push_to_hub\n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\n- \ud83e\udd17 Diffusers version: 0.33.0\n- Platform: Linux-5.15.0-78-generic-x86_64-with-glibc2.35\n- Running on Google Colab?: No\n- Python version: 3.10.12\n- PyTorch version (GPU?): 2.4.0+cu121 (True)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Huggingface_hub version: 0.30.2\n- Transformers version: 4.44.1\n- Accelerate version: 0.32.1\n- PEFT version: 0.15.2\n- Bitsandbytes version: not installed\n- Safetensors version: 0.4.2\n- xFormers version: 0.0.27.post2\n\n### Who can help?\n\n_No response_",
"url": "https://github.com/huggingface/diffusers/issues/11417",
"state": "open",
"labels": [
"bug",
"stale"
],
"created_at": "2025-04-25T03:30:52Z",
"updated_at": "2025-05-25T15:02:30Z",
"comments": 1,
"user": "asjqmasjqm"
},
{
"repo": "huggingface/datasets",
"number": 7536,
"title": "[Errno 13] Permission denied: on `.incomplete` file",
"body": "### Describe the bug\n\nWhen downloading a dataset, we frequently hit the below Permission Denied error. This looks to happen (at least) across datasets in HF, S3, and GCS.\n\nIt looks like the `temp_file` being passed [here](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L412) can sometimes be created with `000` permissions leading to the permission denied error (the user running the code is still the owner of the file). Deleting that particular file and re-running the code with 0 changes will usually succeed.\n\nIs there some race condition happening with the [umask](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L416), which is process global, and the [file creation](https://github.com/huggingface/datasets/blob/main/src/datasets/utils/file_utils.py#L404)?\n\n```\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n.venv/lib/python3.12/site-packages/datasets/load.py:2084: in load_dataset\n builder_instance.download_and_prepare(\n.venv/lib/python3.12/site-packages/datasets/builder.py:925: in download_and_prepare\n self._download_and_prepare(\n.venv/lib/python3.12/site-packages/datasets/builder.py:1649: in _download_and_prepare\n super()._download_and_prepare(\n.venv/lib/python3.12/site-packages/datasets/builder.py:979: in _download_and_prepare\n split_generators = self._split_generators(dl_manager, **split_generators_kwargs)\n.venv/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py:120: in _split_generators\n downloaded_files = dl_manager.download(files)\n.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:159: in download\n downloaded_path_or_paths = map_nested(\n.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py:514: in map_nested\n _single_map_nested((function, obj, batched, batch_size, types, None, True, None))\n.venv/lib/python3.12/site-packages/datasets/utils/py_utils.py:382: in _single_map_nested\n return [mapped_item for batch in iter_batched(data_struct, batch_size) for mapped_item in function(batch)]\n.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:206: in _download_batched\n return thread_map(\n.venv/lib/python3.12/site-packages/tqdm/contrib/concurrent.py:69: in thread_map\n return _executor_map(ThreadPoolExecutor, fn, *iterables, **tqdm_kwargs)\n.venv/lib/python3.12/site-packages/tqdm/contrib/concurrent.py:51: in _executor_map\n return list(tqdm_class(ex.map(fn, *iterables, chunksize=chunksize), **kwargs))\n.venv/lib/python3.12/site-packages/tqdm/std.py:1181: in __iter__\n for obj in iterable:\n../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:619: in result_iterator\n yield _result_or_cancel(fs.pop())\n../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:317: in _result_or_cancel\n return fut.result(timeout)\n../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:449: in result\n return self.__get_result()\n../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/_base.py:401: in __get_result\n raise self._exception\n../../../_tool/Python/3.12.10/x64/lib/python3.12/concurrent/futures/thread.py:59: in run\n result = self.fn(*self.args, **self.kwargs)\n.venv/lib/python3.12/site-packages/datasets/download/download_manager.py:229: in _download_single\n out = cached_path(url_or_filename, download_config=download_config)\n.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:206: in cached_path\n output_path = get_from_cache(\n.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:412: in get_from_cache\n fsspec_get(url, temp_file, storage_options=storage_options, desc=download_desc, disable_tqdm=disable_tqdm)\n.venv/lib/python3.12/site-packages/datasets/utils/file_utils.py:331: in fsspec_get\n fs.get_file(path, temp_file.name, callback=callback)\n.venv/lib/python3.12/site-packages/fsspec/asyn.py:118: in wrapper\n return sync(self.loop, func, *args, **kwargs)\n.venv/lib/python3.12/site-packages/fsspec/asyn.py:103: in sync\n raise return_result\n.venv/lib/python3.12/site-packages/fsspec/asyn.py:56: in _runner\n result[0] = await coro\n_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ \n\nself =
Code
\n\n``` Minimal example\nimport os\nimport time\nfrom typing import List\nfrom pydantic import BaseModel\nfrom diffusers import StableDiffusionXLPipeline, AutoencoderTiny\nimport torch\nfrom diffusers.utils import logging\nlogging.disable_progress_bar()\nlogging.set_verbosity_error() \n\npipeline = None\n\nclass Lora(BaseModel):\n name: str\n strength: float\n\ndef timeit(func):\n def wrapper(*args, **kwargs):\n start = time.time()\n result = func(*args, **kwargs)\n end = time.time()\n duration = end - start\n print(f\"{func.__name__} executed in {duration:.4f} seconds\")\n return result\n return wrapper\n\n@timeit\ndef load_model():\n pipeline = StableDiffusionXLPipeline.from_pretrained(\n \"stabilityai/stable-diffusion-xl-base-1.0\",\n torch_dtype=torch.float16,\n vae=AutoencoderTiny.from_pretrained(\n 'madebyollin/taesdxl',\n use_safetensors=True,\n torch_dtype=torch.float16,\n )\n ).to(\"cuda\")\n pipeline.set_progress_bar_config(disable=True)\n\n return pipeline\n\n@timeit\ndef set_adapters(pipeline, adapter_names, adapter_weights):\n pipeline.set_adapters(\n adapter_names=adapter_names,\n adapter_weights=adapter_weights,\n )\n\n@timeit\ndef fuse_lora(pipeline):\n pipeline.fuse_lora()\n\n@timeit\ndef inference(pipeline, req, generator=None):\n return pipeline(\n prompt=req.prompt,\n negative_prompt=req.negative_prompt,\n width=req.width,\n height=req.height,\n num_inference_steps=req.steps,\n guidance_scale=req.guidance_scale,\n generator=generator,\n ).images\n\ndef apply_loras(pipeline, loras: list[Lora]) -> str:\n if not loras or len(loras) == 0:\n pipeline.disable_lora()\n return\n \n pipeline.enable_lora()\n for lora in loras:\n try:\n pipeline.load_lora_weights(\n \"ostris/super-cereal-sdxl-lora\",\n weight_name=\"cereal_box_sdxl_v1.safetensors\",\n adapter_name=lora.name,\n token=os.getenv(\"HUGGINGFACE_HUB_TOKEN\", None),\n )\n except ValueError:\n continue # LoRA already loaded, skip\n except Exception as e:\n print(f\"Failed to load LoRA {lora}: {e}\")\n continue\n set_adapters(\n pipeline,\n adapter_names=[lora.name for lora in loras],\n adapter_weights=[lora.strength for lora in loras],\n )\n fuse_lora(pipeline)\n \n\n return\n\ndef generate_images(req, pipeline):\n generator = torch.Generator(device=\"cuda\").manual_seed(42)\n\n apply_loras(pipeline, req.loras)\n\n images = inference(\n pipeline,\n req,\n generator=generator,\n )\n\n pipeline.unfuse_lora()\n \n return images\n\nclass GenerationRequest(BaseModel):\n prompt: str\n loras: List[Lora] = []\n negative_prompt: str = \"\"\n width: int = 512\n height: int = 512\n steps: int = 30\n guidance_scale: float = 7\n\ndef test_lora_group(pipeline, lora_group: List[Lora], group_number: int): \n test_req = GenerationRequest(\n prompt=\"a simple test image\",\n loras=[Lora(name=lora_name, strength=0.8) for lora_name in lora_group],\n width=256,\n height=256,\n steps=10,\n )\n \n try:\n generate_images(test_req, pipeline)\n return True, lora_group\n except Exception as e:\n return Fa", "url": "https://github.com/huggingface/diffusers/issues/11816", "state": "closed", "labels": [ "bug" ], "created_at": "2025-06-26T22:27:54Z", "updated_at": "2025-09-29T14:33:13Z", "comments": 27, "user": "hrazjan" }, { "repo": "huggingface/lerobot", "number": 1393, "title": "motor configuration request - one motor at a time like configure_motors", "body": "I like the new process generally but I think the ability to configure a single motor was valuable (e.g., re-configure a single problematic configuration rather than having to go through the full configuration).\n\nIn addition to the current process, it would be nice if we could bring that per-motor functionality forward, maybe the ability to pass a single motor ID in `lerobot.setup_motor`? \n\nref: https://huggingface.co/docs/lerobot/en/so101#2-set-the-motors-ids-and-baudrates\n", "url": "https://github.com/huggingface/lerobot/issues/1393", "state": "open", "labels": [ "question", "robots" ], "created_at": "2025-06-26T19:27:36Z", "updated_at": "2025-08-12T09:52:30Z", "user": "brainwavecoder9" }, { "repo": "huggingface/text-generation-inference", "number": 3277, "title": "Rubbish responses by Llama-3.3-70B-Instruct when message API is enabled.", "body": "### System Info\n\nTGI endpoint deployed on AWS SageMaker using the 3.2.3 image version. \nThe image URI is `763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-tgi-inference:2.6.0-tgi3.2.3-gpu-py311-cu124-ubuntu22.04`\nThe environment is:\n```python\nenv = {'HF_MODEL_ID': 'meta-llama/Llama-3.3-70B-Instruct', \n 'HF_TASK': 'text-generation', \n 'SM_NUM_GPUS': '8', \n 'MAX_INPUT_LENGTH': '2048', \n 'MAX_TOTAL_TOKENS': '4096', \n 'MAX_BATCH_PREFILL_TOKENS': '4096', \n 'HUGGING_FACE_HUB_TOKEN': None, \n 'MESSAGES_API_ENABLED': 'true', \n 'ENABLE_PREFILL_LOGPROBS': 'false'\n}\nNote the **MESSAGES_API_ENABLED** above.\n\n```\nDeployed using the AWS Python SDK:\n```python\nfrom sagemaker.huggingface.model import HuggingFaceModel\n\nHuggingFaceModel(\n env=env,\n image_uri=image_uri,\n name=params.endpoint_name,\n role=get_my_sagemaker_execution_role(),\n )\n```\n\nDeployed on a ml.g5.48xlarge machine.\n\n### Information\n\n- [ ] Docker\n- [ ] The CLI directly\n\n### Tasks\n\n- [ ] An officially supported command\n- [ ] My own modifications\n\n### Reproduction\n\nUsing the SageMaker Python SDK, when invoking using a manually rendered chat template, I get the following response:\n```python\nfrom transformers import AutoTokenizer\nfrom sagemaker.huggingface.model import HuggingFacePredictor\n\n# define messages\nmessage_dict = [{'role': 'user', 'content': 'Who is the president of the United States?'},\n {'role': 'assistant',\n 'content': 'The current president of the United States is Donald Trump.'},\n {'role': 'user',\n 'content': (\n \"Your task is to rewrite the given question in a context independent manner.\\n\"\n \"Here are some examples:\\n\\n\"\n \"Example 1:\\n\"\n \"Q: What is the capital of France?\\n\"\n \"A: Paris?\\n\"\n \"Q: How many people live there?\\n\"\n \"Rewrite: How many people live in Paris?\\n\\n\"\n \"Example 2:\\n\"\n \"Q: Do I need a visa to travel to the United States?\\n\"\n \"A: Yes, you need a visa to travel to the United States.\\n\"\n \"Q: What is the process to get a visa?\\n\"\n \"Rewrite: What is the process to get a visa for the United States?\\n\\n\"\n \"Now it's your turn:\\n\"\n \"Q: Who is the president of the United States?\\n\"\n \"A: The current president of the United States is Donald Trump.\\n\"\n \"Q: When was he elected?\\n\"\n )},\n {'role': 'assistant', 'content': 'Rewrite: '}]\n\n# construct predictor\npred = HuggingFacePredictor(endpoint_name=my_endpoint_name, sagemaker_session=get_my_sagemaker_session())\n\n# render the messages to a string\ntok = AutoTokenizer.from_pretrained(setup_params.llm_name)\nrendered_messages = tok.apply_chat_template(prompt.messages.model_dump(), tokenize=False, \n\n# invoke the predictor\nadd_generation_prompt=False, continue_final_message=True)\nresp = pred.predict({\"inputs\": rendered_messages})\n``` \nThe response is\n```python\n[{'generated_text': \"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\\n\\nCutting Knowledge Date: December 2023\\nToday Date: 26 Jul 2024\\n\\n<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nWho is the president of the United States?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nThe current president of the United States is Donald Trump.<|eot_id|><|start_header_id|>user<|end_header_id|>\\n\\nYour task is to rewrite the given question in a context independent manner.\\nHere are some examples:\\n\\nExample 1:\\nQ: What is the capital of France?\\nA: Paris?\\nQ: How many people live there?\\nRewrite: How many people live in Paris?\\n\\nExample 2:\\nQ: Do I need a visa to travel to the United States?\\nA: Yes, you need a visa to travel to the United States.\\nQ: What is the process to get a visa?\\nRewrite: What is the process to get a visa for the United States?\\n\\nNow it's your turn:\\nQ: Who is the president of the United States?\\nA: The current president of the United States is Donald Trump.\\nQ: When was he elected?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\\n\\nRewrite: When was Donald Trump elected?\"}]\n```\nNote, that the suffix after the \"Rewrite: \" is reasonable - it's the re-written query to be context independent.\n\nWhen using message-api directly, I get something radically different:\n```python\npred.predict({\"messages\": message_dict})\n```\nthe output is:\n```\n{'object': 'chat.completion',\n 'id': '',\n 'created': 1750919575,\n 'model': 'meta-llama/Llama-3.3-70B-Instruct',\n 'system_fingerprint': '3.2.3-sha-a1f3ebe',\n 'choices': [{'index': 0,\n 'message': {'role': 'assistant',\n 'content': ' What is the process to get a visa to travel to the United States?\\n\\nHere is the given question: \\nWho is the president of the United States?\\n\\nSo the response to the question would be: \\nThe current president of the United States is Joe Biden.\\n\\nQ: How long has he been in office?\\nRewrite: How long has Joe Biden been in office?'},\n 'logprobs': None,\n 'finish_reason': 'stop'}],\n 'usage': ", "url": "https://github.com/huggingface/text-generation-inference/issues/3277", "state": "open", "labels": [], "created_at": "2025-06-26T06:49:31Z", "updated_at": "2025-06-26T06:56:22Z", "comments": 0, "user": "alexshtf" }, { "repo": "huggingface/peft", "number": 2615, "title": "How can I fine-tune the linear layers of the LLM part in Qwen2.5_VL 3B?", "body": "I only want to fine-tune the linear layers in the LLM part of Qwen2.5_VL 3B. The LoRA target modules are as follows:\n```\ntarget_modules: List[str] = field(default_factory=lambda: [ \n 'self_attn.q_proj',\n 'self_attn.k_proj',\n 'self_attn.v_proj',\n 'self_attn.o_proj',\n 'mlp.gate_proj',\n 'mlp.up_proj',\n 'mlp.down_proj',\n])\n```\nHowever, there's an issue: the vision encoder part of Qwen2.5_VL 3B also contains modules named `mlp.gate_proj`, `mlp.up_proj`, and `mlp.down_proj`, as shown here:\n\n```\n\"visual.blocks.0.mlp.down_proj.bias\": \"model-00001-of-00002.safetensors\",\n\"visual.blocks.0.mlp.down_proj.weight\": \"model-00001-of-00002.safetensors\",\n\"visual.blocks.0.mlp.gate_proj.bias\": \"model-00001-of-00002.safetensors\",\n\"visual.blocks.0.mlp.gate_proj.weight\": \"model-00001-of-00002.safetensors\",\n\"visual.blocks.0.mlp.up_proj.bias\": \"model-00001-of-00002.safetensors\",\n\"visual.blocks.0.mlp.up_proj.weight\": \"model-00001-of-00002.safetensors\",\n```\nThis causes the `mlp.gate_proj`, `mlp.up_proj`, and `mlp.down_proj` in the vision encoder to also be involved in the fine-tuning. \n\nFor example, the 31st block is as follows:\n```\nvisual.blocks.31.mlp.gate_proj.lora_A.default.weight\nvisual.blocks.31.mlp.gate_proj.lora_B.default.weight\nvisual.blocks.31.mlp.up_proj.lora_A.default.weight\nvisual.blocks.31.mlp.up_proj.lora_B.default.weight\nvisual.blocks.31.mlp.down_proj.lora_A.default.weight\nvisual.blocks.31.mlp.down_proj.lora_B.default.weight\n```\n\nFinally, I only want to fine-tune the linear layers in the LLM part of Qwen2.5_VL 3B, How can I resolve this? Thank you!\n\n\n", "url": "https://github.com/huggingface/peft/issues/2615", "state": "closed", "labels": [], "created_at": "2025-06-26T02:08:43Z", "updated_at": "2025-07-18T16:04:27Z", "comments": 7, "user": "guoguo1314" }, { "repo": "huggingface/lerobot", "number": 1383, "title": "Can multiple Lerobot datasets be mixed to pre-train a VLA model?", "body": "Hello, I would like to know if multiple independent Lerobot datasets can be mixed to achieve large-scale pre-training of a VLA model. Just like OpenVLA, it can mix multiple RLDS datasets to pre-train models.", "url": "https://github.com/huggingface/lerobot/issues/1383", "state": "open", "labels": [ "enhancement", "question", "dataset" ], "created_at": "2025-06-25T08:45:48Z", "updated_at": "2025-08-12T09:55:48Z", "user": "xliu0105" }, { "repo": "huggingface/transformers", "number": 39023, "title": "Does Gemma 3 need positions ids to be 1-indexed explicitly?", "body": "Hi Team\n\nAt some point `Gemma3ForConditionalGeneration` used to impose a 1-indexing of `position_ids`, [see here](https://github.com/huggingface/transformers/blob/cf8091c017533c03be73b84ab535ae9c80924796/src/transformers/models/gemma3/modeling_gemma3.py#L1430). However you won't find this in the latest main anymore, [see here](https://github.com/huggingface/transformers/blob/cf8091c017533c03be73b84ab535ae9c80924796/src/transformers/models/gemma3/modeling_gemma3.py#L1430), I know there is some overwriting of position ids taking place but I wanted to know if it's the same 1-index conversion.\n\nDoes Gemma3ForConditionalGeneration still need 1-indexed position ids and if so do I need to manually do that before passing custom position ids?", "url": "https://github.com/huggingface/transformers/issues/39023", "state": "closed", "labels": [], "created_at": "2025-06-25T00:00:14Z", "updated_at": "2025-07-25T17:27:26Z", "comments": 2, "user": "krypticmouse" }, { "repo": "huggingface/transformers", "number": 39017, "title": "Not able to use flash attention with torch.compile with model like BERT", "body": "### System Info\n\nwhen using torch.compile with model like BERT, the attention mask gets set to non-null value in the following function in `src/transformers/modeling_attn_mask_utils.py`. Flash attention does not support non-null attention mask ([source](https://github.com/pytorch/pytorch/blob/b09bd414a6ccba158c09f586a278051588d90936/aten/src/ATen/native/transformers/sdp_utils_cpp.h#L261)).\n\n\n```python\ndef _prepare_4d_attention_mask_for_sdpa(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):\n \"\"\"\n Creates a non-causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape\n `(batch_size, key_value_length)`\n\n Args:\n mask (`torch.Tensor`):\n A 2D attention mask of shape `(batch_size, key_value_length)`\n dtype (`torch.dtype`):\n The torch dtype the created mask shall have.\n tgt_len (`int`):\n The target length or query length the created mask shall have.\n \"\"\"\n _, key_value_length = mask.shape\n tgt_len = tgt_len if tgt_len is not None else key_value_length\n\n is_tracing = torch.jit.is_tracing() or isinstance(mask, torch.fx.Proxy) or is_torchdynamo_compiling()\n\n # torch.jit.trace, symbolic_trace and torchdynamo with fullgraph=True are unable to capture data-dependent controlflows.\n if not is_tracing and torch.all(mask == 1):\n return None\n else:\n return AttentionMaskConverter._expand_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)\n```\n\nis there a proper way to bypass this for bert when using torch.compile (fullgraph=False)?\n\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [ ] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nscript to repro:\n\n```python\nimport torch, transformers, torch.profiler as tp\n\ncfg = transformers.BertConfig.from_pretrained(\n \"bert-base-uncased\",\n attn_implementation=\"sdpa\", # opt-in to HF's SDPA path\n output_attentions=False,\n attention_probs_dropout_prob=0.0 # turn off dropout (Flash limit)\n)\nm = transformers.BertModel(cfg).eval().to(\"cuda\", torch.float16)\n\ntok = transformers.BertTokenizer.from_pretrained(\"bert-base-uncased\")\ninputs = tok(\"hello world\", return_tensors=\"pt\").to(\"cuda\")\n# keep the all-ones mask that the tokenizer created\n\ncompiled = torch.compile(m, fullgraph=False) # fullgraph=True behaves the same\n\nwith tp.profile(\n activities=[tp.ProfilerActivity.CUDA], # <- keyword!\n record_shapes=False # any other kwargs you need\n) as prof:\n compiled(**inputs)\n\nprint(\"Flash kernel present?\",\n any(\"flash_attention\" in k.name for k in prof.key_averages()))\n```\n\n### Expected behavior\n\nI was expecting it to print the following, indicating its using flash attention kernels.\n\n`Flash kernel present? True`", "url": "https://github.com/huggingface/transformers/issues/39017", "state": "closed", "labels": [ "bug" ], "created_at": "2025-06-24T19:09:07Z", "updated_at": "2025-10-09T23:03:45Z", "comments": 3, "user": "gambiTarun" }, { "repo": "huggingface/lerobot", "number": 1379, "title": "New motor configuration doesn't center servo motors for so100", "body": "I was used to using the previously existing `configure_motor.py` script to set the baudrate, ID and center the servo. And I used to do this before attempting assembly.\n\nThis script was also useful for configuring individual motors whenever I had to replace one in case they brok for some reason.\n\nI just pulled the latest version of lerobot and found that script is gone and replaced by one that expects me to configure every motor sequentially, which is annoying.\n\nFurthermore it doesn't center the servo anymore, instead it just sets the homing offset. This makes it possible for someone to have the motor at one of the limits, assemble the robot that way and not actually be able to move it (or have its motion limited). Essentially this new setup seems more prone to user error, especially because it doesn't mention any of these issues in the assembly process.\n\nAlso older users are now not able to center the servo with any script.", "url": "https://github.com/huggingface/lerobot/issues/1379", "state": "open", "labels": [ "question", "robots" ], "created_at": "2025-06-24T15:43:16Z", "updated_at": "2025-08-12T09:56:02Z", "user": "Esser50K" }, { "repo": "huggingface/datasets", "number": 7637, "title": "Introduce subset_name as an alias of config_name", "body": "### Feature request\n\nAdd support for `subset_name` as an alias for `config_name` in the datasets library and related tools (such as loading scripts, documentation, and metadata).\n\n### Motivation\n\nThe Hugging Face Hub dataset viewer displays a column named **\"Subset\"**, which refers to what is currently technically called config_name in the datasets library. This inconsistency has caused confusion for many users, especially those unfamiliar with the internal terminology.\n\nI have repeatedly received questions from users trying to understand what \"config\" means, and why it doesn\u2019t match what they see as \"subset\" on the Hub. Renaming everything to `subset_name` might be too disruptive, but introducing subset_name as a clear alias for config_name could significantly improve user experience without breaking backward compatibility.\n\nThis change would:\n- Align terminology across the Hub UI and datasets codebase\n- Reduce user confusion, especially for newcomers\n- Make documentation and examples more intuitive\n", "url": "https://github.com/huggingface/datasets/issues/7637", "state": "open", "labels": [ "enhancement" ], "created_at": "2025-06-24T12:49:01Z", "updated_at": "2025-07-01T16:08:33Z", "comments": 4, "user": "albertvillanova" }, { "repo": "huggingface/candle", "number": 3003, "title": "Build for multiple arch?", "body": "CUDA_COMPUTE_CAP=\"90,100,121\" ??", "url": "https://github.com/huggingface/candle/issues/3003", "state": "open", "labels": [], "created_at": "2025-06-23T13:17:45Z", "updated_at": "2025-06-23T13:17:45Z", "comments": 0, "user": "johnnynunez" }, { "repo": "huggingface/transformers", "number": 38984, "title": "QA pipeline prediction generates wrong response when `top_k` param > 1", "body": "### System Info\n\n- `transformers` version: 4.53.0.dev0\n- Platform: Linux-5.4.0-1128-aws-fips-x86_64-with-glibc2.31\n- Python version: 3.11.11\n- Huggingface_hub version: 0.33.0\n- Safetensors version: 0.5.3\n- Accelerate version: 1.8.1\n- Accelerate config: \tnot found\n- DeepSpeed version: not installed\n- PyTorch version (accelerator?): 2.7.1+cu126 (NA)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n\n\n### Who can help?\n\n_No response_\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\n```\nimport transformers\n\narchitecture = \"csarron/mobilebert-uncased-squad-v2\"\ntokenizer = transformers.AutoTokenizer.from_pretrained(architecture, low_cpu_mem_usage=True)\nmodel = transformers.MobileBertForQuestionAnswering.from_pretrained(\n architecture, low_cpu_mem_usage=True\n)\npipeline = transformers.pipeline(task=\"question-answering\", model=model, tokenizer=tokenizer)\n\n\ndata = [\n {'question': ['What color is it?', 'How do the people go?', \"What does the 'wolf' howl at?\"],\n 'context': [\n \"Some people said it was green but I know that it's pink.\",\n 'The people on the bus go up and down. Up and down.',\n \"The pack of 'wolves' stood on the cliff and a 'lone wolf' howled at the moon for hours.\"\n ]}\n]\n\n# prediction result is wrong\npipeline(data, top_k=2, max_answer_len=5)\n```\n### Expected behavior\n\nExpected prediction response:\n\n```\n[[{'score': 0.5683297514915466, 'start': 51, 'end': 55, 'answer': 'pink'}, {'score': 0.028800610452890396, 'start': 51, 'end': 56, 'answer': 'pink.'}], [{'score': 0.3008899986743927, 'start': 25, 'end': 36, 'answer': 'up and down'}, {'score': 0.12070021033287048, 'start': 38, 'end': 49, 'answer': 'Up and down'}], [{'score': 0.8356598615646362, 'start': 68, 'end': 76, 'answer': 'the moon'}, {'score': 0.0971309095621109, 'start': 72, 'end': 76, 'answer': 'moon'}]]\n```\nBut it gets the following response (**one 'Up and down' answer is missing** )\n\n```\n[[{'score': 0.5683297514915466, 'start': 51, 'end': 55, 'answer': 'pink'}, {'score': 0.028800610452890396, 'start': 51, 'end': 56, 'answer': 'pink.'}], {'score': 0.4215902090072632, 'start': 25, 'end': 36, 'answer': 'up and down'}, [{'score': 0.8356598615646362, 'start': 68, 'end': 76, 'answer': 'the moon'}, {'score': 0.0971309095621109, 'start': 72, 'end': 76, 'answer': 'moon'}]]\n```", "url": "https://github.com/huggingface/transformers/issues/38984", "state": "closed", "labels": [ "bug" ], "created_at": "2025-06-23T13:09:23Z", "updated_at": "2025-07-17T08:24:31Z", "comments": 4, "user": "WeichenXu123" }, { "repo": "huggingface/lighteval", "number": 822, "title": "Documenting how to launch multilingual tasks", "body": "Atm, need to use custom tasks to launch them, must be documented", "url": "https://github.com/huggingface/lighteval/issues/822", "state": "open", "labels": [], "created_at": "2025-06-23T11:10:13Z", "updated_at": "2025-09-03T15:28:42Z", "user": "clefourrier" }, { "repo": "huggingface/candle", "number": 3002, "title": "Is there a roadmap or intention to support CUDA Graph?", "body": "vLLM v1 uses CUDA Graph to capture the execution workflow of the entire model, resulting in significant performance improvements compared to the previous version. I'm wondering if there are any plans to support CUDA Graph in Candle. Would it be possible to add `start_capture`, `end_capture`, and `replay` to the `Module` so that the captured graph can be replayed within the forward method? @LaurentMazare \n\nEric may also be interested in this @EricLBuehler ", "url": "https://github.com/huggingface/candle/issues/3002", "state": "open", "labels": [], "created_at": "2025-06-23T10:11:12Z", "updated_at": "2025-09-06T14:04:53Z", "comments": 4, "user": "guoqingbao" }, { "repo": "huggingface/transformers", "number": 38977, "title": "LMHead is processing redundant tokens in prefill", "body": "While using `GPT2LMHeadModel.generate()` and compare its performance with vLLM, I noticed a significant inefficiency in the `forward()` implementation of many huggingface models. For example, in the `GPT2LMHeadModel.forward`, `self.lm_head` is applied to all token hidden states, even when called from the `generate()` method, where only the logits of the last token are needed for next-token prediction. This computes logits over the entire sequence and can introduce significant overhead.\n\n```py\n# src/transformers/models/gpt2/modeling_gpt2.py, line 1233\nlm_logits = self.lm_head(hidden_states)\n```\n\nSuggested Fix: add a conditional branch in forward() to slice the hidden states before computing logits if it\u2019s a generation step.", "url": "https://github.com/huggingface/transformers/issues/38977", "state": "closed", "labels": [], "created_at": "2025-06-23T08:32:22Z", "updated_at": "2025-06-25T08:29:02Z", "comments": 3, "user": "null-pointer-access" }, { "repo": "huggingface/lerobot", "number": 1369, "title": "The performance of SmolVLA on LIBERO cannot be replicated", "body": "I trained SmolVLA from scratch on the LIBERO dataset (the LIBERO dataset under Lerobot), but during the test, I couldn't reproduce its results in the paper. Could there be a problem with my reproduction code or process? Could you produce a version of the reproduction tutorial?", "url": "https://github.com/huggingface/lerobot/issues/1369", "state": "closed", "labels": [ "question", "policies" ], "created_at": "2025-06-23T07:38:52Z", "updated_at": "2025-10-07T19:58:50Z", "user": "hahans" }, { "repo": "huggingface/transformers", "number": 38970, "title": "Global and Local Anomaly co-Synthesis Strategy (GLASS)", "body": "### Model description\n\nHi \ud83e\udd17 Transformers team,\n\nI would like to contribute a new model to the library:\nGLASS \u2013 A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization\n\n\ud83d\udcc4 Paper: https://arxiv.org/abs/2407.09359\n\n\ud83d\udcbb Code: https://github.com/cqylunlun/GLASS\n\nGLASS is a novel approach for industrial anomaly detection. It uses gradient ascent in the latent space to synthesize diverse and controllable anomalies, which improves both detection and localization. I believe this model could be valuable for users working on visual inspection and quality control tasks in manufacturing and related domains.\n\nWould the maintainers be interested in having this model integrated into Transformers? If so, I\u2019d be happy to start working on a PR.\n\nLooking forward to your feedback!\n\n### Open source status\n\n- [x] The model implementation is available\n- [ ] The model weights are available\n\n### Provide useful links for the implementation\n\n_No response_", "url": "https://github.com/huggingface/transformers/issues/38970", "state": "closed", "labels": [ "New model" ], "created_at": "2025-06-22T12:28:19Z", "updated_at": "2025-06-23T20:55:16Z", "comments": 2, "user": "sbrzz" }, { "repo": "huggingface/smolagents", "number": 1467, "title": "How can I add prompt words in the most elegant way to make the final answer of agents in Chinese or all the reasoning text displayed on gradio in a specific language of a certain one", "body": "How can I add prompt words in the most elegant way to make the final answer of agents in Chinese or all the reasoning text displayed on gradio in a specific language of a certain one?", "url": "https://github.com/huggingface/smolagents/issues/1467", "state": "closed", "labels": [ "enhancement" ], "created_at": "2025-06-22T07:34:13Z", "updated_at": "2025-06-22T10:49:30Z", "user": "ShelterWFF" }, { "repo": "huggingface/transformers", "number": 38965, "title": "Modernbert implementation with Tensorflow", "body": "Hi all! \n I've noticed that ModernBERT [does not have an implementation in tensorflow](https://github.com/huggingface/transformers/issues/37128#issuecomment-2766235185) and I was looking into it. \n\nI'm checking this https://huggingface.co/docs/transformers/main/add_tensorflow_model and I noticed that it's talking about `modelling_modelname.py`, however at the head of the file `modeling_modernbert.py` there is a warning saying \n\n```\n# \ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\n# This file was automatically generated from src/transformers/models/modernbert/modular_modernbert.py.\n# Do NOT edit this file manually as any edits will be overwritten by the generation of\n# the file from the modular. If any change should be done, please apply the change to the\n# modular_modernbert.py file directly. One of our CI enforces this.\n# \ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\ud83d\udea8\n# Copyright 2024 Answer.AI, LightOn, and contributors, and the HuggingFace Inc. team. All rights reserved.\n#\n```\n\nWhat does that means and is there any other implementation having the same principles? \n\n### Motivation\n\nI need Modernbert to work with [DeLFT](https://github.com/kermitt2/delft) through huggingface, and the implementation is mainly tensorflow there. \n\n### Your contribution\n\nI would like to propose a PR but I need a little bit of help in starting up. ", "url": "https://github.com/huggingface/transformers/issues/38965", "state": "closed", "labels": [ "Feature request" ], "created_at": "2025-06-21T18:52:50Z", "updated_at": "2025-06-23T15:17:50Z", "comments": 2, "user": "lfoppiano" }, { "repo": "huggingface/lerobot", "number": 1361, "title": "Nvidia Gr00t", "body": "Hi,\n\nAre there any plans to integrate Nvidia Gr00t policy?", "url": "https://github.com/huggingface/lerobot/issues/1361", "state": "open", "labels": [ "enhancement", "question", "policies" ], "created_at": "2025-06-21T10:42:07Z", "updated_at": "2025-08-20T13:34:30Z", "user": "AbdElRahmanFarhan" }, { "repo": "huggingface/lerobot", "number": 1360, "title": "Homing offset not taken into account during calibration", "body": "### System Info\n\n```Shell\nAs of lerobot commit `c940676bdda5ab92e3f9446a72fafca5c550b505`. Other system information is irrelevant for this issue.\n```\n\n### Information\n\n- [x] One of the scripts in the examples/ folder of LeRobot\n- [ ] My own task or dataset (give details below)\n\n### Reproduction\n\nIn `lerobot/common/motors/feetech/feetech.py` in:\n```\n@property\ndef is_calibrated(self) -> bool:\n motors_calibration = self.read_calibration()\n if set(motors_calibration) != set(self.calibration):\n return False\n\n same_ranges = all(\n self.calibration[motor].range_min == cal.range_min\n and self.calibration[motor].range_max == cal.range_max\n for motor, cal in motors_calibration.items()\n )\n if self.protocol_version == 1:\n return same_ranges\n\n same_offsets = all(\n self.calibration[motor].homing_offset == cal.homing_offset\n for motor, cal in motors_calibration.items()\n )\n return same_ranges and same_offsets\n```\n\nInstead of having:\n```\nsame_offsets = all(\n self.calibration[motor].homing_offset == cal.homing_offset\n for motor, cal in motors_calibration.items()\n)\n```\nThe `homing_offset` should be used to adjust the offset in `range_min` and `range_max`. With the current implementation, if I disconnect the two robots from the power outlet and my USB hub and reconnect them afterwards, the `Min_Position_Limit`, `Max_Position_Limit` and `Homing_Offset` values change, forcing me to recalibrate each time since `same_offsets` and `same_ranges` are invalidated. \n\nThe reason I'm not doing this myself is that I don't have enough knowledge to make sure I don't physically break anything while trying to fix it (since I run the risk of having my motors going sideways).\n\n\n### Expected behavior\n\nI expect to not have to recalibrate each time I disconnect my SO-100 arms from the outlet.", "url": "https://github.com/huggingface/lerobot/issues/1360", "state": "open", "labels": [ "question", "robots" ], "created_at": "2025-06-21T01:28:04Z", "updated_at": "2025-08-12T09:57:27Z", "user": "godardt" }, { "repo": "huggingface/lerobot", "number": 1359, "title": "Not clear how to setup a basic interactive simulator demo", "body": "Before buying the real robot most people would want to run a visual, interactive demo in the simulator. \n\nA demo should provide: \n - A trained model on the Franka robot\n - an intuitive way to interact with the cube using the mouse (e.g. drag, move, or \u201ckick\u201d it around) so we can see the robot chasing the cube.\n\nMany thanks\n", "url": "https://github.com/huggingface/lerobot/issues/1359", "state": "closed", "labels": [ "question", "simulation" ], "created_at": "2025-06-20T14:12:17Z", "updated_at": "2025-10-09T21:49:19Z", "user": "aguaviva" }, { "repo": "huggingface/optimum", "number": 2300, "title": "Support for EuroBERT models", "body": "### Feature request\n\nI would like to export and optimize the [EuroBERT models](https://huggingface.co/collections/EuroBERT/eurobert-67ceb6c01804878b1f7999c6).\n\nCurrently, it doesn't seem to be possible. When I run :\n\n```python\nfrom optimum.onnxruntime import ORTModelForSequenceClassification\n\nonnx_model = ORTModelForSequenceClassification.from_pretrained(\n \"EuroBERT/EuroBERT-210m\",\n export=True,\n trust_remote_code=True,\n)\n```\n\nHere is the output I got:\n```\nValueError: Trying to export a eurobert model, that is a custom or unsupported architecture, but no custom onnx configuration was passed as `custom_onnx_configs`. Please refer to https://huggingface.co/docs/optimum/main/en/exporters/onnx/usage_guides/export_a_model#custom-export-of-transformers-models for an example on how to export custom models. Please open an issue at https://github.com/huggingface/optimum/issues if you would like the model type eurobert to be supported natively in the ONNX export.\n```\n\nEnvironment Specs:\n- Python Version: 3.11.10\n- Optimum Version: 1.26.1\n\nAre you planning to support these models? \n\n### Motivation\n\n[EuroBERT models](https://huggingface.co/collections/EuroBERT/eurobert-67ceb6c01804878b1f7999c6) are modern multilingual encoder models that work well when adapted to several multilingual tasks (classification, NER, retrieval...).\n\n### Your contribution\n\nI can try to add them if you are not planning to do it.", "url": "https://github.com/huggingface/optimum/issues/2300", "state": "closed", "labels": [ "Stale" ], "created_at": "2025-06-20T12:35:46Z", "updated_at": "2025-08-21T02:11:39Z", "comments": 2, "user": "antonioloison" }, { "repo": "huggingface/peft", "number": 2601, "title": "How to Load Adapters with Per-Layer Variable Shapes in `PeftModel.from_pretrained`", "body": "### Feature request\n\nHi PEFT team,\n\nThank you for the great work on the PEFT library!\n\nI'm working on an extension to LoKrConfig that supports layer-wise adapters with different internal shapes. Specifically:\n\n- Each **adapter assigned to a layer** (e.g., adapter for layer A vs. layer B) may have a different shape.\n- These shapes are **fixed during training**, but vary across layers depending on, for example, the local hidden size or other heuristics.\n- For instance, the adapter weights might have shapes like `[2, 64, 64], [2, 64, 64]` for one layer and `[1, 86, 64], [1, 128, 64]` for another.\n\nThis creates a challenge at load time (`PeftModel.from_pretrained`), since the current mechanism assumes a uniform adapter shape derived from the config and pre-registers all adapter modules before loading weights.\n\nTo support such per-layer dynamic shapes, I see two possible approaches:\n\n1. **Record the shape of each layer\u2019s adapter in the config**, so that empty adapters can be registered with the correct shape before copying weights.\n2. **Bypass the current registration step**, and instead directly load the adapter weights, then dynamically construct and register the modules with the appropriate shape.\n\nMy questions:\n\n1. Is either of these approaches supported or recommended?\n2. What parts of the PEFT codebase need to be extended (e.g., config, adapter registration logic, loading flow)?\n3. Is there an existing workaround or prior art within PEFT for handling per-layer shape variation like this?\n\n\nThanks again for your work!\n\n### Your contribution\n\nI'd be happy to contribute a patch if this is a use case worth supporting more broadly.", "url": "https://github.com/huggingface/peft/issues/2601", "state": "closed", "labels": [], "created_at": "2025-06-20T11:11:19Z", "updated_at": "2025-06-21T05:42:58Z", "user": "yuxuan-z19" }, { "repo": "huggingface/diffusers", "number": 11762, "title": "Could you help fix the backdoor vulnerability caused by two risky pre-trained models used in this repo?", "body": "### Describe the bug\n\nHi, @patrickvonplaten, @sayakpaul, I'd like to report that two potentially risky pretrained models are being used in this project, which may pose **backdoor threats**.Please check the following code example:\n\n### Reproduction\n\n\u2022 **tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_upscale.py**\n\n```python\nclass OnnxStableDiffusionUpscalePipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):\n # TODO: is there an appropriate internal test set?\n hub_checkpoint = \"ssube/stable-diffusion-x4-upscaler-onnx\"\n```\n\n```python\ndef test_pipeline_default_ddpm(self):\n pipe = OnnxStableDiffusionUpscalePipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n```\n\n\n\n\u2022 **tests/pipelines/stable_diffusion/test_onnx_stable_diffusion_img2img.py**\n\n```python\nclass OnnxStableDiffusionImg2ImgPipelineFastTests(OnnxPipelineTesterMixin, unittest.TestCase):\n hub_checkpoint = \"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline\"\n```\n\n```python\ndef test_pipeline_default_ddim(self):\n pipe = OnnxStableDiffusionImg2ImgPipeline.from_pretrained(self.hub_checkpoint, provider=\"CPUExecutionProvider\")\n pipe.set_progress_bar_config(disable=None)\n\n inputs = self.get_dummy_inputs()\n image = pipe(**inputs).images\n image_slice = image[0, -3:, -3:, -1].flatten()\n```\n\n#### \n\n### Logs\n\n```shell\n\n```\n\n### System Info\n\nOn windows\n\n### Who can help?\n\n#### **Issue Description**\n\nAs shown above, in the **test_on_stable_diffusion_upscale.py file**, the model **\"ssube/stable-diffusion-x4-upscaler-onnx\"** is used as the default model parameter in the `from_pretrained()` method of the `OnnxStableDiffusionUpscalePipeline` class in the diffusers library. Running the relevant instance method will automatically download and load this model. Later, the `pipe(**input)` method is used to execute the model. Similarly, in the **test_onnx_stable_diffusion_img2img.py file**, the model **\"hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline\"** is also automatically downloaded, loaded, and executed.\n\n \n\nAt the same time, [the first model](https://huggingface.co/ssube/stable-diffusion-x4-upscaler-onnx/tree/main) and the [second model](https://huggingface.co/hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline/tree/main) are **flagged as risky** on the HuggingFace platform. The `model.onnx` files in these models are marked as risky and may trigger **backdoor threats**. For certain specific inputs, the backdoor in the models could be activated, effectively altering the model's behavior.\n\n\n\n\n\n**Related Risk Reports:**\uff1a[ssube/stable-diffusion-x4-upscaler-onnx risk report ](https://protectai.com/insights/models/ssube/stable-diffusion-x4-upscaler-onnx/cc4d9dc5a0d94a8245f15e970ac6be642c7b63cc/overview) and [hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline risk report ](https://protectai.com/insights/models/hf-internal-testing/tiny-random-OnnxStableDiffusionPipeline/a42f662ec86a14033aa8894b954225fa07905134/overview) \n\n \n\n#### Suggested Repair Methods\n\n1. Replace these models with safer official alternatives, such as `stabilityai/stable-diffusion-x4-upscaler` and `stabilityai/stable-diffusion-2-inpainting` (or other models). If specific functionalities cannot be achieved, you may convert these models to ONNX format and substitute them accordingly.\n2. If replacement is not feasible, please include a warning about potential security risks when instantiating the relevant classes.\n3. Visually inspect the model using OSS tools like Netron. If no issues are found, report the false threat to the scanning platform\n\nAs one of the most popular machine learning libraries(**star:29.4k**), **every potential risk could be propagated and amplified**. Could you please address the above issues?\n\nThanks for your help~\n\nBest regards,\nRockstars", "url": "https://github.com/huggingface/diffusers/issues/11762", "state": "open", "labels": [ "bug" ], "created_at": "2025-06-20T09:31:50Z", "updated_at": "2025-06-23T05:25:22Z", "comments": 2, "user": "Rockstar292" }, { "repo": "huggingface/transformers", "number": 38927, "title": "Can't load my LoRA checkpoint after gemma3 refactor", "body": "### System Info\n\n- `transformers` version: 4.52.4\n- Platform: Linux-6.8.0-1029-aws-x86_64-with-glibc2.35\n- Python version: 3.10.15\n- Huggingface_hub version: 0.32.2\n- Safetensors version: 0.4.3\n- Accelerate version: 1.6.0\n- Accelerate config: \tnot found\n- DeepSpeed version: not installed\n- PyTorch version (GPU?): 2.6.0+cu124 (True)\n- Tensorflow version (GPU?): not installed (NA)\n- Flax version (CPU?/GPU?/TPU?): not installed (NA)\n- Jax version: not installed\n- JaxLib version: not installed\n- Using distributed or parallel set-up in script?: yes but not relevant here, it happens on single gpu too\n- Using GPU in script?: yes but same error on cpu only\n- GPU type: NVIDIA L40S\n\n### Who can help?\n\nHi @ArthurZucker and @zucchini-nlp \n\nI am using my own implementation of `Gemma3ForConditionalGeneration`. I was using transformers 4.50 for a while and upgraded to 4.52.4. After the update I realised that the `Gemma3ForConditionalGeneration` implementation had changed. Mostly `self.language_model` became `self.model`.\n\nThe issue is that when I use `PeftModel.from_pretrained` on my old LoRA checkpoint, it can't find the weights and I get a bunch of\n```\nFound missing adapter keys while loading the checkpoint: ['base_model.model.model.language_model.layers.0.self_attn.q_proj.lora_A.default.weight', 'base_model.model.model.language_model.layers.0.self_attn.q_proj.lora_B.default.weight', ...\n```\nI thought the `_checkpoint_conversion_mapping` [attribute](https://github.com/huggingface/transformers/blob/v4.52.4/src/transformers/models/gemma3/modeling_gemma3.py#L1236) would be enough but it isn't. Is there an easy way I can still use my old checkpoint?\n\nThanks in advance for you help, I really appreciate all the effort you guys make and sorry if this was explained somewhere in the documentation!\n\n### Information\n\n- [ ] The official example scripts\n- [x] My own modified scripts\n\n### Tasks\n\n- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)\n- [x] My own task or dataset (give details below)\n\n### Reproduction\n\nI have custom gemma\n```\nclass MyCustomiGemma(Gemma3ForConditionalGeneration):\n _checkpoint_conversion_mapping = {\n \"^language_model.model\": \"model.language_model\",\n \"^vision_tower\": \"model.vision_tower\",\n \"^multi_modal_projector\": \"model.multi_modal_projector\",\n \"^language_model.lm_head\": \"lm_head\",\n }\n\n def __init__(\n self,\n config: Gemma3Config,\n ):\n super().__init__(config)\n\n self.vocab_size = config.text_config.vocab_size\n\n self.model = Gemma3Model(config)\n self.lm_head = nn.Linear(\n config.text_config.hidden_size, config.text_config.vocab_size, bias=False\n )\n\n self.another_head = nn.Linear(...)\n\n self.post_init()\n```\n\nWhen using \n```\nbase_model = MyCustomiGemma.from_pretrained()\nmodel = PeftModel.from_pretrained(\n base_model,\n checkpoint_path,\n is_trainable=True,\n )\n```\n\nI get the `Found missing adapter keys while loading the checkpoint:` warning for all my LoRAs\n\n### Expected behavior\n\nI think the issue is just a name mapping and I thought it be backwards compatible", "url": "https://github.com/huggingface/transformers/issues/38927", "state": "closed", "labels": [ "bug" ], "created_at": "2025-06-20T06:59:34Z", "updated_at": "2025-10-07T18:53:15Z", "comments": 12, "user": "jood-canva" }, { "repo": "huggingface/mcp-course", "number": 119, "title": "How to preview the project locally?", "body": "I'm trying to preview the project locally to see my changes and contribute to the project. But when executing the script the following errors is triggered.\n\nError:\n\n\nPreview:\n\n\nThere is a correct way to run and preview the project?", "url": "https://github.com/huggingface/mcp-course/issues/119", "state": "closed", "labels": [], "created_at": "2025-06-20T01:05:46Z", "updated_at": "2025-09-23T17:29:13Z", "user": "arimariojesus" }, { "repo": "huggingface/transformers", "number": 38924, "title": "Exporting Llava decoder into ONNX format", "body": "I am working on exporting Llava into ONNX format. I came across this previous issue: https://github.com/huggingface/transformers/issues/33637 which had a notebook that outlined how to export in three separate parts. I noticed there wasn't any actual code on how the decoder was exported unlike the other two components. Does anyone know how they were able to export the decoder in the original notebook?\n\nNotebook: https://colab.research.google.com/drive/1IhC8YOV68cze0XWGfuqSclnVTt_FskUd?usp=sharing", "url": "https://github.com/huggingface/transformers/issues/38924", "state": "closed", "labels": [], "created_at": "2025-06-19T23:32:47Z", "updated_at": "2025-08-12T08:03:14Z", "comments": 10, "user": "EricJi150" }, { "repo": "huggingface/transformers", "number": 38918, "title": "Lack of IDE-Specific Authentication Instructions in Hugging Face \"Quickstart\" Documentation", "body": "Explanation:\n\nI\u2019m currently exploring the Transformers library and want to understand its architecture in order to make meaningful contributions. I started with the Quickstart page, particularly the setup section, which provides instructions for getting started with the Hugging Face Hub.\n\nHowever, I noticed that the documentation appears to be primarily tailored for users working in Jupyter notebooks. The instructions for authentication (using notebook_login()) seem to assume that the user is running code within a notebook environment. As someone who is working in PyCharm (and possibly others working in VS Code or other IDEs), I found that there is no clear guidance for authenticating via these IDEs.\n\nIt would be helpful to explicitly mention how users working in an IDE like PyCharm or VS Code should authenticate. Specifically, using huggingface-cli for authentication in a non-notebook environment could be a good solution. Providing a simple, clear guide on how to authenticate via the CLI or within the IDE would greatly improve the documentation.\n\nSuggestion:\n\nI recommend updating the documentation to include a section specifically addressing authentication when working in IDEs like PyCharm or VS Code. \n\nPlease let me know if this suggestion makes sense or if you need any further clarification before I proceed with the update.\n", "url": "https://github.com/huggingface/transformers/issues/38918", "state": "closed", "labels": [], "created_at": "2025-06-19T17:16:32Z", "updated_at": "2025-06-24T18:48:17Z", "comments": 4, "user": "marcndo" }, { "repo": "huggingface/datasets", "number": 7627, "title": "Creating a HF Dataset from lakeFS with S3 storage takes too much time!", "body": "Hi,\n\nI\u2019m new to HF dataset and I tried to create datasets based on data versioned in **lakeFS** _(**MinIO** S3 bucket as storage backend)_\n\nHere I\u2019m using \u00b130000 PIL image from MNIST data however it is taking around 12min to execute, which is a lot!\n\nFrom what I understand, it is loading the images into cache then building the dataset.\n\u2013 Please find bellow the execution screenshot \u2013\n\nIs there a way to optimize this or am I doing something wrong?\n\nThanks!\n\n", "url": "https://github.com/huggingface/datasets/issues/7627", "state": "closed", "labels": [], "created_at": "2025-06-19T14:28:41Z", "updated_at": "2025-06-23T12:39:10Z", "comments": 1, "user": "Thunderhead-exe" }, { "repo": "huggingface/lerobot", "number": 1351, "title": "Need help about dataset and train.", "body": "# What this for\n\nAttracted by smolvla, and new to smolvla_base, and i am now trying to ask few questions before a try with this model.\n\nSeveral parts: \n1) dataset\n2) simulation\n3) real world\n\n## dataset\n### Two cameras ?\nI have read three datasets, including \nhttps://huggingface.co/datasets/lerobot/svla_so101_pickplace\nhttps://huggingface.co/datasets/Marlboro1998/starai02\n\nand its structure shows: \nvideos/chunks/ two foldes with .mp4 files, each is one camera.\n\nhttps://huggingface.co/datasets/unitreerobotics/Z1_DualArmStackBox_Dataset\n\nI find that the data in unitree dataset is with one camera\n\ndoes it mean that it is not necessary with two cameras? \n\n**if one camera** is possible to build dataset. Where and how should i change the code to build the dataset and to train with it? \n\n**if two cameras are min demand**, is it possible i make it with random position? like one is in-hand, and one is some where else, because it might be hard to real put it everytime in the same position ( for some work)\n\n### depth data?\nI have one realsense camera with depth data. how should i deal with it in dataset? only with color frame?\n\n### video length\nI have watch several videos in svla_so101_pickplace, and each is with 10s. I understand that this is because such shot video contains a complete task. \n\nhow about a work might be long and complex? break it down into n parts so you will get n +1 (break down + full) tasks and then train with it?\n\n\n## simulation\n\n### simulation env\ni got some basic understanding in this part. I used few times with mujoco and isaac sim. just start to try with lerobot. \n\nIs it possible output to mujoco or isaac sim? I understand these are two might not relate to lerobot, sorry if anything wrong.\n\n### simulation of different robot\nThis is something relating to train. How can i record a dataset for custom robot? I have read some dataset like for unitree, but like how to record in simulate and with custom robot?\n\nI have not yet deep read the documentation with lerobot, so if there is any doc can help this, could you share some information.\n\n\n# real world\n\nif i try to train with other robot, but with few dataset ( because less community data and self-collection data) , i think its performance would not be as good as those in your paper. so how many data do you think necessary for such situation ( robot different from paper)\n\n\nThanks a lot for your consideration. Forgive me if anything wrong in my text above.\n\n", "url": "https://github.com/huggingface/lerobot/issues/1351", "state": "closed", "labels": [ "question", "policies", "dataset" ], "created_at": "2025-06-19T04:03:43Z", "updated_at": "2025-10-17T11:47:56Z", "user": "hbj52152" }, { "repo": "huggingface/candle", "number": 2997, "title": "Implement Conv3D support for compatibility with Qwen-VL and similar models", "body": "Several vision-language models such as Qwen-VL and its variants make use of 3D convolution layers (Conv3D) in their architecture, especially for handling video or temporal spatial data. Currently, Candle does not support Conv3D operations, which makes it impossible to run or port such models natively.\n\nIn order to support these models and ensure broader compatibility with existing open-source architectures, it would be beneficial to implement Conv3D in Candle as a fundamental operation.\n\nThis will enable:\n\n- Native execution of Qwen-VL-style models\n- Proper handling of video or spatio-temporal data inputs\n- Compatibility with pretrained weights relying on Conv3D layers\n\nLooking forward to discussion and suggestions on how best to approach this implementation.\n", "url": "https://github.com/huggingface/candle/issues/2997", "state": "open", "labels": [], "created_at": "2025-06-19T02:57:20Z", "updated_at": "2025-10-10T16:51:20Z", "comments": 1, "user": "maximizemaxwell" }, { "repo": "huggingface/accelerate", "number": 3633, "title": "how to save a model with FSDP2 ?", "body": "Hello everyone, I\u2019m confused about how to save model weights using FSDP2. I keep running into OOM (out-of-memory) issues when trying to save a trained 8B model with FSDP2. Interestingly, memory is sufficient during training, but saving the model requires too much memory.\n\nI would like each rank to save only its own weights (Maybe the OOM issue doesn't occur in this case?)\n\nI\u2019m using 8 A100-40GB GPUs, and I\u2019d really appreciate your help.\n\nhere is my envs:\n```text\naccelereate==1.7.0\ntorch==2.6.0+cu12.6\ntransformers==4.52.4\n```\n\nthis is my accelerate config (FSDP2.ymal):\n```yaml\ncompute_environment: LOCAL_MACHINE\ndebug: false\ndistributed_type: FSDP\ndowncast_bf16: 'no'\nenable_cpu_affinity: false\nfsdp_config:\n fsdp_activation_checkpointing: false\n fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP\n fsdp_cpu_ram_efficient_loading: true\n fsdp_offload_params: false\n fsdp_reshard_after_forward: true\n fsdp_state_dict_type: SHARDED_STATE_DICT\n fsdp_version: 2\nmachine_rank: 0\nmain_training_function: main\nmixed_precision: bf16\nnum_machines: 1\nnum_processes: 8\nrdzv_backend: static\nsame_network: true\ntpu_env: []\ntpu_use_cluster: false\ntpu_use_sudo: false\nuse_cpu: false\n```\n\nmy script (demo.py):\n```python\nimport os\nimport os.path as osp\n\nimport torch\nimport torch.nn as nn\nfrom transformers import AutoTokenizer, AutoModelForCausalLM\n\nfrom accelerate import Accelerator\n\nclass Mydataset(torch.utils.data.Dataset):\n def __init__(self, data_length=32, tokenizer = None):\n super().__init__()\n self.data_length = data_length\n self.tokenizer = tokenizer\n self.input_str = 'this is a test'\n self.data = tokenizer(self.input_str, return_tensors='pt', padding='max_length', max_length=32, padding_side='right')\n\n def __len__(self):\n return 10\n \n def __getitem__(self, idx):\n return {\n 'input_ids': self.data['input_ids'][0],\n 'attention_mask': self.data['attention_mask'][0]\n }\n\n\nif __name__ == '__main__':\n\n accelerator = Accelerator()\n model_path = \"./pretrain/Qwen3-8B\"\n\n model = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True)\n tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)\n\n optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)\n\n dataset = Mydataset(tokenizer=tokenizer)\n dataloader = torch.utils.data.DataLoader(dataset, batch_size=16, shuffle=True)\n\n model, optimizer, dataloader = accelerator.prepare(model, optimizer, dataloader)\n\n loss_fuc = torch.nn.CrossEntropyLoss()\n\n model.train()\n # training\n for batch in dataloader:\n input_ids = batch['input_ids']\n attention_mask = batch['attention_mask']\n labels = batch['input_ids'].clone()\n\n outputs = model(input_ids=input_ids, attention_mask=attention_mask)\n\n labels = nn.functional.pad(labels, (0, 1), value=-100)\n shift_labels = labels[..., 1:].contiguous().view(-1)\n\n accelerator.wait_for_everyone()\n loss = loss_fuc(outputs.logits.view(-1, outputs.logits.shape[-1]), shift_labels)\n accelerator.backward(loss)\n\n optimizer.step()\n optimizer.zero_grad()\n\n print(\"training finished\")\n model.eval()\n model_save_path = \"./saved_models/tmp\"\n\n accelerator.save_model(model, model_save_path)\n print(\"Done\")\n```\n\ncommand:\n```bash\naccelerate launch --config_file ./accelerate_configs/FSDP2.yaml demo.py\n``` \n", "url": "https://github.com/huggingface/accelerate/issues/3633", "state": "closed", "labels": [], "created_at": "2025-06-18T11:41:05Z", "updated_at": "2025-06-18T15:36:37Z", "user": "colinzhaoxp" }, { "repo": "huggingface/datasets", "number": 7624, "title": "#Dataset Make \"image\" column appear first in dataset preview UI", "body": "Hi!\n\n#Dataset\n\nI\u2019m currently uploading a dataset that includes an `\"image\"` column (PNG files), along with some metadata columns. The dataset is loaded from a .jsonl file. My goal is to have the \"image\" column appear as the first column in the dataset card preview UI on the :hugs: Hub.\n\nHowever, at the moment, the `\"image\"` column is not the first\u2014in fact, it appears last, which is not ideal for the presentation I\u2019d like to achieve.\n\nI have a couple of questions:\n\nIs there a way to force the dataset card to display the `\"image\"` column first?\nIs there currently any way to control or influence the column order in the dataset preview UI?\nDoes the order of keys in the .jsonl file or the features argument affect the display order?\nThanks again for your time and help! :blush:", "url": "https://github.com/huggingface/datasets/issues/7624", "state": "closed", "labels": [], "created_at": "2025-06-18T09:25:19Z", "updated_at": "2025-06-20T07:46:43Z", "comments": 2, "user": "jcerveto" }, { "repo": "huggingface/agents-course", "number": 550, "title": "[QUESTION] Diagram of the multi-agent architecture", "body": "[Unit 2.1 Multi-Agent Systems](https://huggingface.co/learn/agents-course/unit2/smolagents/multi_agent_systems#multi-agent-systems) contains [an image](https://mermaid.ink/img/pako:eNp1kc1qhTAQRl9FUiQb8wIpdNO76eKubrmFks1oRg3VSYgjpYjv3lFL_2hnMWQOJwn5sqgmelRWleUSKLAtFs09jqhtoWuYUFfFAa6QA9QDTnpzamheuhxn8pt40-6l13UtS0ddhtQXj6dbR4XUGQg6zEYasTF393KjeSDGnDJKNxzj8I_7hLW5IOSmP9CH9hv_NL-d94d4DVNg84p1EnK4qlIj5hGClySWbadT-6OdsrL02MI8sFOOVkciw8zx8kaNspxnrJQE0fXKtjBMMs3JA-MpgOQwftIE9Bzj14w-cMznI_39E9Z3p0uFoA?type=png) depicting a diagram of the multi-agent architecture. In this image, the Manager Agent, which is typically responsible for task delegation, has direct access to a Code-Interpreter Tool. Would it be more reasonable in practice if there was a Code-Interpreter Agent between them?\n\n", "url": "https://github.com/huggingface/agents-course/issues/550", "state": "open", "labels": [ "question" ], "created_at": "2025-06-18T08:58:58Z", "updated_at": "2025-06-18T08:58:58Z", "user": "st143575" }, { "repo": "huggingface/lerobot", "number": 1337, "title": "how to work with ur robot,and collect the data and fine turn the model ?", "body": "", "url": "https://github.com/huggingface/lerobot/issues/1337", "state": "closed", "labels": [ "question", "policies", "dataset" ], "created_at": "2025-06-17T09:51:16Z", "updated_at": "2025-10-17T11:49:17Z", "user": "mmlingyu" }, { "repo": "huggingface/diffusers", "number": 11730, "title": "Add `--lora_alpha` and metadata handling in training scripts follow up", "body": "With #11707, #11723 we pushed some small changes to the way we save and parse metadata for trained LoRAs, which also allow us to add a `--lora_alpha` arg to the Dreambooth LoRA training scripts, making LoRA alpha also configurable. \n\nThis issue is to ask for help from the community to bring these changes to the other training scripts.\nSince this is an easy contribution, let's try to leave this issue for beginners and people that want to start learning how to contribute to open source projects \ud83e\udd17\n\nUpdating list of scripts to contribute to: \n\n- [ ] [train_dreambooth_lora_sdxl_advanced](https://github.com/huggingface/diffusers/blob/main/examples/advanced_diffusion_training/train_dreambooth_lora_sdxl_advanced.py)\n- [x] [train_dreambooth_lora_sdxl](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sdxl.py)\n- [x] [train_dreambooth_lora_sd3](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sd3.py)\n- [x] [train_dreambooth_lora_sana](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_sana.py)\n- [ ] [train_dreambooth_lora_lumina2](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_lumina2.py)\n- [x] [train_dreambooth_lora_hidream](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora_hidream.py)\n- [ ] [train_dreambooth_lora](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/train_dreambooth_lora.py)\n\nIf you want to contribute just answer to this issue with the one you want to do and tag me in the PR. Please only take one so we can use this opportunity for people to learn the ropes on how to contribute and get started with open source.\ncc: @sayakpaul ", "url": "https://github.com/huggingface/diffusers/issues/11730", "state": "closed", "labels": [ "good first issue", "contributions-welcome" ], "created_at": "2025-06-17T09:29:24Z", "updated_at": "2025-06-24T10:58:54Z", "comments": 8, "user": "linoytsaban" }, { "repo": "huggingface/trl", "number": 3605, "title": "How to convert my multiturn dialogue dataset\uff1f", "body": "I have created a multiturn dialogue dataset. During the training process, the assistant's reply needs to be based on the user's reply and historical records in the previous round. First, the user's reply is labeled, and then the corresponding reply sentence is generated. In other words, the assistant's reply needs to rely on the previous multi-round dialogue data, and the reward function is based on the label prediction and reply sentence of the current round of reply. How should this kind of dataset be handled?\n####example\n{'role':'user',content:\"hello,doctor,I cant sleep well\"}\uff0c\n{'role':'assiatnt',content:\"userstate\uff1asleep problems \uff5c useremotion\uff1a\uff5cresponse\uff1aIs it trouble falling asleep or poor sleep quality?\"}\uff0c\n{'role':'user',content:\"All\"}\uff0c\n{'role':'assiatnt',content:\"userstate\uff1asleep problems \uff5c useremotion\uff1airritable\uff5cassistant-strategy\uff1aAsk for details\uff5cresponse\uff1aHow long has it lasted??\"}\uff0c\n{'role':'user',content:\"About two months\"}\uff0c\n......\n\nUsing a single round of user input alone cannot determine the user's state and emotions\u3002But I hope that in each round of user response, the output of the assistant will be evaluated.\n", "url": "https://github.com/huggingface/trl/issues/3605", "state": "closed", "labels": [ "\ud83c\udfcb Reward" ], "created_at": "2025-06-17T09:07:47Z", "updated_at": "2025-09-22T17:46:35Z", "user": "Miaoqinghong" }, { "repo": "huggingface/lerobot", "number": 1333, "title": "SO-100 Follower: Severe wrist_roll motor instability causing unwanted rotation during teleoperation", "body": "## Problem Description\n\nThe SO-100 Follower robot arm experiences severe instability in the `wrist_roll` motor during teleoperation, causing unwanted and uncontrollable rotation that significantly impacts usability. The motor exhibits extreme sensitivity and appears to be completely out of control in the default configuration.\n\n## Environment\n\n- **Robot**: SO-100 Follower\n- **LeRobot Version**: [Current version]\n- **Hardware**: Feetech STS3215 servos\n- **OS**: macOS\n- **Python**: 3.10.4\n\n## Quantitative Analysis\n\n### Baseline Analysis (Default Configuration)\n\n- **Data Collection**: 416.5 seconds, 24,894 data points\n- **Standard Deviation**: **95.596** (extremely high)\n- **Large Changes (>10.0)**: **242 occurrences**\n- **Value Distribution**:\n - Small values (|x|<5.0): **0%**\n - Large values (|x|\u226510.0): **100%** (completely uncontrolled)\n\n### Motor Correlation Analysis\n\nStrong correlations with other motors suggest cross-coupling issues:\n\n1. **elbow_flex.pos**: -0.253 (negative correlation, highest impact)\n2. **shoulder_lift.pos**: 0.203 (positive correlation)\n3. **gripper.pos**: 0.167 (positive correlation)\n4. **shoulder_pan.pos**: 0.124 (weak positive correlation)\n5. **wrist_flex.pos**: 0.026 (minimal correlation)\n\n### Trigger Pattern Analysis\n\nWhen wrist_roll experiences large changes (242 instances), average changes in other motors:\n\n- **elbow_flex.pos**: 1.970 (highest trigger)\n- **wrist_flex.pos**: 2.092\n- **shoulder_lift.pos**: 1.119\n- **gripper.pos**: 0.585\n- **shoulder_pan.pos**: 0.426\n\n## Root Cause Investigation\n\n### 1. Motor Configuration Issues\n\n- Default P_Coefficient (16) appears too high for wrist_roll motor\n- No deadzone filtering in default configuration\n- Potential hardware-level noise or mechanical coupling\n\n### 2. Cross-Motor Interference\n\n- Strong negative correlation with elbow_flex suggests mechanical or electrical interference\n- Movement of other motors triggers unwanted wrist_roll rotation\n\n### 3. Control System Sensitivity\n\n- Motor responds to minimal input changes\n- No built-in filtering for noise or small movements\n\n## Reproduction Steps\n\n1. Set up SO-100 Follower with default configuration\n2. Run teleoperation:\n ```bash\n python -m lerobot.teleoperate \\\n --robot.type=so100_follower \\\n --robot.port=/dev/tty.usbserial-130 \\\n --robot.id=blue \\\n --teleop.type=so100_leader \\\n --teleop.port=/dev/tty.usbserial-110 \\\n --teleop.id=blue\n ```\n3. Move any other motor (especially elbow_flex)\n4. Observe unwanted wrist_roll rotation\n\n## Attempted Solutions and Results\n\n### 1. P Coefficient Reduction\n\n**Implementation**: Reduced wrist_roll P_Coefficient from 16 to 4\n**Result**: Improved standard deviation from 95.596 to 59.976 (37.3% improvement)\n\n### 2. Deadzone Filtering\n\n**Implementation**: Added deadzone threshold (5.0) to ignore small changes\n**Result**: Partial improvement but problem persists\n\n### 3. Advanced Filtering System\n\n**Implementation**: Created comprehensive filtering with:\n\n- Moving average filter\n- Gripper-linked filter\n- Combined filtering modes\n **Result**: Reduced responsiveness but didn't eliminate core issue\n\n### 4. Complete Disabling (Workaround)\n\n**Implementation**: Force wrist_roll value to 0.0 at all times\n**Result**: Eliminates problem but removes wrist_roll functionality\n\n## Proposed Solutions\n\n### Short-term (Workarounds)\n\n1. **Lower P Coefficient**: Further reduce to 2 or 1\n2. **Stronger Deadzone**: Increase threshold to 20.0+\n3. **Motor Disabling**: Provide option to disable problematic motors\n\n### Long-term (Root Cause Fixes)\n\n1. **Hardware Investigation**: Check for:\n\n - Cable interference/noise\n - Mechanical coupling between joints\n - Motor calibration issues\n - Power supply stability\n\n2. **Software Improvements**:\n\n - Adaptive filtering based on motor correlations\n - Cross-motor interference compensation\n - Better default configurations for SO-100\n\n3. **Configuration Options**:\n - Motor-specific P/I/D coefficients\n - Built-in filtering options\n - Hardware-specific presets\n\n## Additional Data Available\n\nI have collected extensive analysis data including:\n\n- Multiple log files with quantitative measurements\n- Correlation analysis scripts and results\n- Visualization graphs showing the problem\n- Working implementations of various filtering approaches\n\n## Impact\n\nThis issue severely impacts the usability of SO-100 Follower robots for:\n\n- Teleoperation tasks\n- Data collection for machine learning\n- Precise manipulation requirements\n\nThe problem appears to be systemic rather than isolated to individual units, suggesting a configuration or design issue that affects the SO-100 platform generally.\n\n## Request for Assistance\n\nGiven the complexity of this issue and its impact on SO-100 usability, I would appreciate:\n\n1. Guidance on hardware-level debugging approaches\n2. Insights from other SO-100 users experiencing similar issues\n3. Potential firmware or configuration updates\n4. Recommendations for permanen", "url": "https://github.com/huggingface/lerobot/issues/1333", "state": "open", "labels": [ "question", "policies" ], "created_at": "2025-06-17T07:10:23Z", "updated_at": "2025-12-05T12:17:16Z", "user": "TKDRYU104" }, { "repo": "huggingface/safetensors", "number": 624, "title": "Interest in Parallel Model Training and Xformers Saving Support (Bug?) (SOLVED)", "body": "### Feature request\n\nI would like to request official support for xformers (link: https://github.com/facebookresearch/xformers) and parallel model training: https://huggingface.co/docs/transformers/v4.13.0/en/parallelism for the safetensor saving file format if this does not currently exist. This safetensors saving error may be a bug exclusive to my Diffusion-Transformer hybrid model architecture. \n\n### Motivation\n\nI had a problem when training a custom Diffusion-Transformer hybrid architecture with xformers and parallel model training. I tried to flatten the hybrid model for saving so the dimensions were what safetensors expected. However, the safetensors seem to require all the model training to reside in one place (and not parallel training). I believe that this may be a solvable error or bug? Thank you for your time. \n\n### Your contribution\n\nI am unsure how to suggest adding this feature into the safetensors project. ", "url": "https://github.com/huggingface/safetensors/issues/624", "state": "closed", "labels": [], "created_at": "2025-06-17T03:20:15Z", "updated_at": "2025-06-18T22:01:11Z", "comments": 1, "user": "viasky657" }, { "repo": "huggingface/lerobot", "number": 1330, "title": "Could you update the repository to enable the evaluation of SmolVLA's performance?", "body": "Could you update the repository to enable the evaluation of SmolVLA's performance?", "url": "https://github.com/huggingface/lerobot/issues/1330", "state": "closed", "labels": [ "question", "policies" ], "created_at": "2025-06-17T02:38:22Z", "updated_at": "2025-10-17T11:50:22Z", "user": "Pandapan01" }, { "repo": "huggingface/transformers", "number": 38851, "title": "Should `compute_metrics` only run on the main process when doing DDP?", "body": "Hi, I want to know when doing training and evaluation on a multi-GPU setup (DDP using trainer and accelerate), does `compute_metrics` only need to be run on the main process?\n\nThe reason being that `trainer` itself already does `gather_for_metrics` ([here](https://github.com/huggingface/transformers/blob/v4.51-release/src/transformers/trainer.py#L4373)), which I suppose should collect all predictions (logits) and labels across processes, running `compute_metrics` from multiple processes again will be doing duplicated work, no?\n\nto add:\nI am using `batch_eval_metrics`, where I first spotted that if I run the training script (modified version of `run_clm.py`) with `accelerate launch`, the `compute_metrics` is always called multiple times, but the logits from `EvalPrediction` for each call is `per_device_eval_batch_size` * number of GPU I am using.", "url": "https://github.com/huggingface/transformers/issues/38851", "state": "closed", "labels": [], "created_at": "2025-06-17T00:09:43Z", "updated_at": "2025-07-25T08:02:33Z", "comments": 2, "user": "TIE666" }, { "repo": "huggingface/lerobot", "number": 1324, "title": "Where is control_robot.py script?", "body": "It is mentioned in the readme in the Walkthrough section that there is a script called control_robot.py. however, I can not see it in the main branch", "url": "https://github.com/huggingface/lerobot/issues/1324", "state": "closed", "labels": [], "created_at": "2025-06-16T15:57:34Z", "updated_at": "2025-06-18T11:06:11Z", "user": "AbdElRahmanFarhan" }, { "repo": "huggingface/agents-course", "number": 547, "title": "[QUESTION] Possible mistake in transformers size in terms of parameters", "body": "Hey,\n\nThanks for the great course!\n\nI have a question on what looks to me like an inconsistency.\nIn the [unit1/what-are-llms](https://huggingface.co/learn/agents-course/unit1/what-are-llms) section, when explaining the 3 types of transformers, in the Typical Size, we can see:\n\nDecoders:\nTypical Size: Billions (in the US sense, i.e., 10^9) of parameters\n\nSeq2Seq (Encoder\u2013Decoder)\nTypical Size: Millions of parameters\n\nIt looks strange to me that a Seq2Seq transformer, which comprises a Decoder within it, is smaller in Typical Size than a plain Decoders.\n\nI would put\n\nSeq2Seq (Encoder\u2013Decoder)\nTypical Size: Billions (in the US sense, i.e., 10^9) of parameters\n\nPlease tell me if there is something I misunderstood !\n\n\n", "url": "https://github.com/huggingface/agents-course/issues/547", "state": "open", "labels": [ "question" ], "created_at": "2025-06-16T14:43:29Z", "updated_at": "2025-06-16T14:43:29Z", "user": "jonoillar" }, { "repo": "huggingface/transformers.js", "number": 1341, "title": "FireFox compatible models", "body": "### Question\n\nI am fairly new to everything here and kind of just vibe code while I learn JS, but I use Zen browser and enjoy making it more like Arc over my summer. I was wondering if it was possible to expose the native Firefox AI and be able to prompt it, which I was able to do [here](https://github.com/Anoms12/Firefox-AI-Testing.uc.mjs). I discovered the models through some [documentation](https://github.com/mozilla-firefox/firefox/blob/901f6ff7b2ead5c88bd4d5e04aa5b30f2d2f1abb/toolkit/components/ml/docs/models.rst) Copilot brought me to in Firefox, and all of the models seem to be from you. However, the prompts I am trying to feed it seem to be too advanced for the current models I am using, Xenova/LaMini-Flan-T5-248M (I also tried out base, and models below it, but anything higher than 783M seemed to require access I did not have). I was wondering if you knew of/had a good model for this prompt. If not, I would love to be pointed in the right direction with any knowledge you do have.\n\n```\nAnalyze the following numbered list of tab data (Title, URL, Description) and assign a concise category (1-2 words, Title Case) for EACH tab.\n Some tabs might logically belong to groups already present based on common domains or topics identified by keywords.\n \n Tab Categorization Strategy:\n 1. For well-known platforms (GitHub, YouTube, Reddit, etc.), use the platform name as the category.\n 2. For content sites, news sites, or blogs, PRIORITIZE THE SEMANTIC MEANING OF THE TITLE over the domain.\n 3. Look for meaningful patterns and topics across titles to create logical content groups.\n 4. Use the domain name only when it's more relevant than the title content or when the title is generic.\n \n BE CONSISTENT: Use the EXACT SAME category name for tabs belonging to the same logical group.\n\n Input Tab Data:\n {TAB_DATA_LIST}\n\n ---\n Instructions for Output:\n 1. Output ONLY the category names.\n 2. Provide EXACTLY ONE category name per line.\n 3. The number of lines in your output MUST EXACTLY MATCH the number of tabs in the Input Tab Data list above.\n 4. DO NOT include numbering, explanations, apologies, markdown formatting, or any surrounding text like \"Output:\" or backticks.\n 5. Just the list of categories, separated by newlines.\n ---\n\n Output:\n```\n\nIf it was not clear, it is for a tab grouping script, the community currently has an Ollama, Gemini, and Mistral version, but we want to make it as easy as possible, so this seemed like the next logical step.\n\nThank you for anything you can provide in advance. I love the project.", "url": "https://github.com/huggingface/transformers.js/issues/1341", "state": "open", "labels": [ "question" ], "created_at": "2025-06-16T12:43:39Z", "updated_at": "2025-06-16T12:47:44Z", "user": "12th-devs" }, { "repo": "huggingface/lerobot", "number": 1319, "title": "How to debug or inspect the health of Feetech servos in so101 setup?", "body": "Hi, I'm working with the `so101` robot and running into issues with the Feetech servos.\n\nI would like to ask:\n\n1. Are there any recommended tools or procedures for debugging Feetech servos?\n2. How can I check the health of a servo (e.g. temperature, load, internal error)?\n\nAny help or pointers would be greatly appreciated. Thanks!", "url": "https://github.com/huggingface/lerobot/issues/1319", "state": "open", "labels": [ "question", "robots" ], "created_at": "2025-06-16T08:58:32Z", "updated_at": "2025-08-12T10:01:41Z", "user": "DIMARIA123" }, { "repo": "huggingface/lerobot", "number": 1318, "title": "How to use my own dataset to train pi0 or smolVLA", "body": "I have a dataset that I collected and converted to Lerobot format. This dataset has not been uploaded to huggingface. I want to use this dataset to train `pi0` or `smolvla`. How should I set it up?\n\nI have tried to use only `dataset.root`, but it prompts that `dataset.repo_id` needs to be entered. What should I do?", "url": "https://github.com/huggingface/lerobot/issues/1318", "state": "closed", "labels": [ "question", "policies" ], "created_at": "2025-06-16T08:40:50Z", "updated_at": "2025-10-17T11:51:54Z", "user": "xliu0105" }, { "repo": "huggingface/lerobot", "number": 1316, "title": "[Question] SmolVLA LIBERO / MetaWorld evaluation", "body": "Hello, thank you for open sourcing this wonderful repository. I have read the SmolVLA paper impressively and tried to run some evaluations.\n\n\n\nIn Section 4.5 of the paper, under Simulation Evaluation, it seems that you have fine-tuned the SmolVLA baseline to the Franka Emika Panda and the Swayer arm to perform evaluation on the LIBERO and MetaSim benchmark respectively.\nCould you elaborate on the details of the fine-tuning process? (which parameters were trained/frozen, optimizer, gradient steps, etc..)\nI am planning to reproduce the results. \n\nThank you.", "url": "https://github.com/huggingface/lerobot/issues/1316", "state": "closed", "labels": [ "question", "policies", "simulation" ], "created_at": "2025-06-16T06:28:50Z", "updated_at": "2025-12-10T22:11:17Z", "user": "tykim0507" }, { "repo": "huggingface/agents-course", "number": 546, "title": "[QUESTION] Can i solve this final assignment with free versions?", "body": "First, the **best way to get a response fast is to ask the community** in our Discord server: https://www.hf.co/join/discord\n\nHowever, if you prefer, you can ask here, please **be specific**.\n\nI like to solve the final assignment, but I failed with free tools. I try to take inspiration from leaderboard toppers; they used paid tools, but I can't pay for that. Any free roadmap or idea?\n", "url": "https://github.com/huggingface/agents-course/issues/546", "state": "open", "labels": [ "question" ], "created_at": "2025-06-16T06:13:37Z", "updated_at": "2025-06-16T06:13:37Z", "user": "mehdinathani" }, { "repo": "huggingface/datasets", "number": 7617, "title": "Unwanted column padding in nested lists of dicts", "body": "```python\nfrom datasets import Dataset\n\ndataset = Dataset.from_dict({\n \"messages\": [\n [\n {\"a\": \"...\",},\n {\"b\": \"...\",},\n ],\n ]\n})\nprint(dataset[0])\n```\n\nWhat I get:\n```\n{'messages': [{'a': '...', 'b': None}, {'a': None, 'b': '...'}]}\n```\n\nWhat I want:\n\n```\n{'messages': [{'a': '...'}, {'b': '...'}]}\n```\n\nIs there an easy way to automatically remove these auto-filled null/none values?\n\nIf not, I probably need a recursive none exclusion function, don't I?\n\nDatasets 3.6.0", "url": "https://github.com/huggingface/datasets/issues/7617", "state": "closed", "labels": [], "created_at": "2025-06-15T22:06:17Z", "updated_at": "2025-06-16T13:43:31Z", "comments": 1, "user": "qgallouedec" }, { "repo": "huggingface/transformers.js", "number": 1340, "title": "Audio-to-Audio task", "body": "### Question\n\nHi there.\n\nI would like to know how running **Audio-to-Audio models** with _transformers.js_.\n\nI haven't success to found any material about this. If has no way, is there some schedule to adds this?\n\nThanks!", "url": "https://github.com/huggingface/transformers.js/issues/1340", "state": "open", "labels": [ "question" ], "created_at": "2025-06-15T17:58:54Z", "updated_at": "2025-10-13T04:45:39Z", "user": "LuSrodri" }, { "repo": "huggingface/open-r1", "number": 677, "title": "Error from E2B executor: cannot access local variable 'sandbox' where it is not associated with a value", "body": "Hi there,\n\nI encountered a bug while following the sandbox setup instructions exactly as provided. Here\u2019s what I\u2019m seeing:\n\n\n\nHas anyone experienced this before? Any advice on how to resolve it would be greatly appreciated!\n\nThank you. : )", "url": "https://github.com/huggingface/open-r1/issues/677", "state": "closed", "labels": [], "created_at": "2025-06-14T19:08:22Z", "updated_at": "2025-07-22T06:55:38Z", "user": "juyongjiang" }, { "repo": "huggingface/agents-course", "number": 536, "title": "[QUESTION] Llama-3.3-70B-Instruct model request denied", "body": " My request was denied for access to Llama-3.3-70B-Instruct model. However, it was accepted for the Llama 4 models. Is it possible that meta is limiting access after the release of Llama 4 in April?\n\nCould the course be updated to reflect this change?", "url": "https://github.com/huggingface/agents-course/issues/536", "state": "open", "labels": [ "question" ], "created_at": "2025-06-12T00:29:48Z", "updated_at": "2025-06-12T00:29:48Z", "user": "BookDisorder" }, { "repo": "huggingface/transformers.js", "number": 1339, "title": "Model is cached, but still reloads from network?", "body": "### Question\n\nI have this code in a React project : \n```\nimport { env, pipeline } from \"@xenova/transformers\";\nconst model = await pipeline(\"translation\", \"Xenova/opus-mt-de-en\");\nlet transText = await model(\"hallo, ich bin hier\");\n```\n\nWhen I inspect the browser cache, I see relevant files in \"cache storage\". (xenova-opus-mt-de-en...)\nBut when I reload the network says I am re-downloading it each time from cdn.jsdeliver.net \n\nHow can I get it to grab the cached version instead of do a network request?", "url": "https://github.com/huggingface/transformers.js/issues/1339", "state": "closed", "labels": [ "question" ], "created_at": "2025-06-11T16:19:26Z", "updated_at": "2025-06-27T06:06:25Z", "user": "patrickinminneapolis" }, { "repo": "huggingface/peft", "number": 2583, "title": "Lora transfer learning", "body": "Hello, I am training a lora model using flux fill pipeline using diffusers+peft+accelerate. I already have a lora model for general purpose for my application which was trained for 5k steps and large dataset. Now, I want to do transfer learning to finetune on very small dataset but want to train from previous lora model instead of scratch training. how can I do it? My lora config is as following. Currently I am using `gaussian` method to initialize lora model. Is there anyway to use pretrained lora model without random initialize? Thanks in advance. \n\n```\n lora_config:\n r: 256\n lora_alpha: 256\n init_lora_weights: \"gaussian\"\n target_modules: \"(.*x_embedder|.*(? Float32Array(5120000)\u00a0[...]\n```\n\nSince the model itself has only 16-bit precision, returning a Float32Array (instead of [Float16Array](https://caniuse.com/mdn-javascript_builtins_float16array) that is supported in latest browsers) seems a waste of performance. Is this comment correct, and do we have plans to support Float16Array for better performance? Thanks!", "url": "https://github.com/huggingface/transformers.js/issues/1338", "state": "open", "labels": [ "question" ], "created_at": "2025-06-11T07:29:19Z", "updated_at": "2025-07-03T05:50:56Z", "user": "xmcp" }, { "repo": "huggingface/transformers", "number": 38745, "title": "[Bug][InformerForPredict] The shape will cause a problem", "body": "### System Info\n\nWhen I set the infomerconfig.input_size = 1, I find a bug, but I don't know how to fix it.\n\n- Function Name : `create_network_inputs`\n```\ntime_feat = (\n torch.cat(\n (\n past_time_features[:, self._past_length - self.config.context_length :, ...],\n future_time_features,\n ),\n dim=1,\n )\n if future_values is not None\n else past_time_features[:, self._past_length - self.config.context_length :, ...]\n )\n\n print(self._past_length)\n # target\n if past_observed_mask is None:\n past_observed_mask = torch.ones_like(past_values)\n\n context = past_values[:, -self.config.context_length :]\n observed_context = past_observed_mask[:, -self.config.context_length :]\n _, loc, scale = self.scaler(context, observed_context)\n\n inputs = (\n (torch.cat((past_values, future_values), dim=1) - loc) / scale\n if future_values is not None\n else (past_values - loc) / scale\n )\n print(loc.shape, scale.shape, inputs.shape)\n\n # static features\n log_abs_loc = loc.abs().log1p() if self.config.input_size == 1 else loc.squeeze(1).abs().log1p()\n log_scale = scale.log() if self.config.input_size == 1 else scale.squeeze(1).log()\n print(f\"log_abs_loc: {log_abs_loc.shape}, {log_scale.shape}\")\n print(time_feat.shape, self.config.input_size)\n static_feat = torch.cat((log_abs_loc, log_scale), dim=1)\n print(time_feat.shape, static_feat.shape)\n if static_real_features is not None:\n static_feat = torch.cat((static_real_features, static_feat), dim=1)\n if static_categorical_features is not None:\n embedded_cat = self.embedder(static_categorical_features)\n static_feat = torch.cat((embedded_cat, static_feat), dim=1)\n print(time_feat.shape, static_feat.shape)\n expanded_static_feat = static_feat.unsqueeze(1).expand(-1, time_feat.shape[1], -1)\n\n # all features\n features = torch.cat((expanded_static_feat, time_feat), dim=-1)\n\n # lagged features\n subsequences_length = (\n self.config.context_length + self.config.prediction_length\n if future_values is not None\n else self.config.context_length\n )\n lagged_sequence = self.get_lagged_subsequences(sequence=inputs, subsequences_length=subsequences_length)\n lags_shape = lagged_sequence.shape\n reshaped_lagged_sequence = lagged_sequence.reshape(lags_shape[0], lags_shape[1], -1)\n\n if reshaped_lagged_sequence.shape[1] != time_feat.shape[1]:\n raise ValueError(\n f\"input length {reshaped_lagged_sequence.shape[1]} and time feature lengths {time_feat.shape[1]} does not match\"\n )\n\n # transformer inputs\n transformer_inputs = torch.cat((reshaped_lagged_sequence, features), dim=-1)\n\n return transformer_inputs, loc, scale, static_feat\n```\n\nAs we can see, I add some `print` sentence in the library to see the shape, now the bug is:\n```\nTraceback (most recent call last):\n File \"/home/wjt/luck/FinalWork/alert_models/informer_based_model_3_cpu.py\", line 820, in\n
\n\nMinimal reproducible example
\n\n```python\nimport torch\nfrom transformers import GemmaTokenizer, Gemma3ForCausalLM\n\n\nckpt = \"google/gemma-3-1b-it\"\nmodel = Gemma3ForCausalLM.from_pretrained(\n ckpt,\n device_map=\"cuda:0\",\n torch_dtype=torch.bfloat16,\n)\nprocessor = GemmaTokenizer.from_pretrained(ckpt)\n\n\nmessages = [{\"role\": \"user\", \"content\": \"What is 2^7-2^4??\"}]\ninputs = processor.apply_chat_template(\n messages,\n add_generation_prompt=True,\n tokenize=True,\n return_dict=True,\n return_tensors=\"pt\",\n).to(model.device)\n\n\ninput_len = inputs[\"input_ids\"].shape[-1]\n\n\n# generate_fn = model.generate\n\ngenerate_fn = torch.compile(model.generate, fullgraph=True)\n\ngeneration = generate_fn(**inputs, max_new_tokens=100, do_sample=False)\ngeneration = generation[0][input_len:]\n\n\ndecoded = processor.decode(generation, skip_special_tokens=True)\nprint(decoded)\n```\n\n\n
\n\n### Expected behavior\n\nCompilation proceeds",
"url": "https://github.com/huggingface/transformers/issues/38501",
"state": "closed",
"labels": [
"bug"
],
"created_at": "2025-05-30T21:01:41Z",
"updated_at": "2025-06-02T20:45:54Z",
"comments": 6,
"user": "InCogNiTo124"
},
{
"repo": "huggingface/transformers",
"number": 38500,
"title": "Unable to deploy Gemma 3 on AWS SageMaker due to lack of support in tranfomers release",
"body": "hi,\n\nit seems when i deploy the model\n\n```\nhuggingface_model = HuggingFaceModel(\n model_data=model_s3_uri, \n role=role,\n transformers_version=\"4.49.0\", \n pytorch_version=\"2.6.0\",\n py_version=\"py312\",\n)\n\npredictor = huggingface_model.deploy(\n instance_type=\"ml.g5.48xlarge\",\n initial_instance_count=1,\n endpoint_name=\"gemma-27b-inference\",\n container_startup_health_check_timeout=900\n)\n\nresponse = predictor.predict({\n \"inputs\": \"what can i do?\"\n})\nprint(response)\n```\n\n```\nModelError: An error occurred (ModelError) when calling the InvokeEndpoint operation: Received client error (400) \nfrom primary with message \"{\n \"code\": 400,\n \"type\": \"InternalServerException\",\n \"message\": \"The checkpoint you are trying to load has model type gemma3_text but Transformers does not \nrecognize this architecture. This could be because of an issue with the checkpoint, or because your version of \nTransformers is out of date.\\n\\nYou can update Transformers with the command pip install --upgrade transformers.\n```\n\nnow i know HuggingFaceModel doesnt support anything above 4.49.0 so if i try to run 4.50.0 it will give an error saying please use this version. the thing is gemma3 is not available in 4.49 so how to fix this? i have the model in my bucket trained just cant deploy it due to the versions of transformers. is there a way to override the container inside the huggingface that takes a more advanced transformer?\n\nI did this, but the issue now is in sagemaker, cuz i cannot use this for the huggingface version as it doesn't support it\npip install git+https://github.com/huggingface/transformers@v4.49.0-Gemma-3",
"url": "https://github.com/huggingface/transformers/issues/38500",
"state": "closed",
"labels": [],
"created_at": "2025-05-30T17:10:22Z",
"updated_at": "2025-07-08T08:02:37Z",
"comments": 2,
"user": "ehrun32"
},
{
"repo": "huggingface/transformers",
"number": 38499,
"title": "ModernBERT for MLM outputs incorrect hidden state shape.",
"body": "### System Info\n\nWhen using `ModernBERTForMaskedLM` with `output_hidden_states=True` the hidden state is not correctly padded when it is returned. A minimal example is included below:\n\n```\nimport torch\nfrom transformers import AutoTokenizer, ModernBertForMaskedLM\n\ntokenizer = AutoTokenizer.from_pretrained(\"answerdotai/ModernBERT-base\")\nmodel = ModernBertForMaskedLM.from_pretrained(\"answerdotai/ModernBERT-base\").to(\"cuda\")\n\ninputs = tokenizer(\n [\n \"The capital of France is