repo stringclasses 147 values | number int64 1 172k | title stringlengths 2 476 | body stringlengths 0 5k | url stringlengths 39 70 | state stringclasses 2 values | labels listlengths 0 9 | created_at timestamp[ns, tz=UTC]date 2017-01-18 18:50:08 2026-01-06 07:33:18 | updated_at timestamp[ns, tz=UTC]date 2017-01-18 19:20:07 2026-01-06 08:03:39 | comments int64 0 58 ⌀ | user stringlengths 2 28 |
|---|---|---|---|---|---|---|---|---|---|---|
vllm-project/vllm | 30,832 | [Performance]: DeepSeek-V3.2 on 8xH20 30 decode tokens/sec | ### Proposal to improve performance
**My Env:**
vllm 0.13.0rc2.dev178+g676db55ee
deep_gemm 2.1.1+c9f8b34
cuda. 12.9
python. 3.10.18
**command** is the same as:
vllm serve mypath/DeepSeek-V3.2 \
--tensor-parallel-size 8 \
--tokenizer-mode deepseek_v32 \
--tool-call-parser deepseek_v32 \
--enable-auto-tool-choice \
--reasoning-parser deepseek_v3
**My Question:**
The 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:
is there any wrong with this?
------------------------------------------------
Benchmarking[¶](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html#benchmarking)
We used the following script to benchmark deepseek-ai/DeepSeek-V3.2 on 8xH20.
vllm bench serve \
--model deepseek-ai/DeepSeek-V3.2 \
--dataset-name random \
--random-input 2048 \
--random-output 1024 \
--request-rate 10 \
--num-prompt 100 \
--trust-remote-code
TP8 Benchmark Output[¶](https://docs.vllm.ai/projects/recipes/en/latest/DeepSeek/DeepSeek-V3_2.html#tp8-benchmark-output)
============ Serving Benchmark Result ============
Successful requests: 100
Failed requests: 0
Request rate configured (RPS): 10.00
Benchmark duration (s): 129.34
Total input tokens: 204800
Total generated tokens: 102400
Request throughput (req/s): 0.77
Output token throughput (tok/s): 791.73
Peak output token throughput (tok/s): 1300.00
Peak concurrent requests: 100.00
Total Token throughput (tok/s): 2375.18
---------------Time to First Token----------------
Mean TTFT (ms): 21147.20
Median TTFT (ms): 21197.97
P99 TTFT (ms): 41133.00
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 99.71
Median TPOT (ms): 99.25
P99 TPOT (ms): 124.28
---------------Inter-token Latency----------------
Mean ITL (ms): 99.71
Median ITL (ms): 76.89
P99 ITL (ms): 2032.37
==================================================
### Report of performance regression
_No response_
### Misc discussion on performance
_No response_
### Your current environment (if you think it is necessary)
```text
The output of `python collect_env.py`
```
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30832 | open | [
"performance"
] | 2025-12-17T03:08:52Z | 2025-12-18T08:01:30Z | 1 | lisp2025 |
huggingface/candle | 3,247 | Parakeet V3 support? | Any plans to support Parakeet V3 by any chance? Thank you 🙏 | https://github.com/huggingface/candle/issues/3247 | open | [] | 2025-12-16T19:05:33Z | 2025-12-16T19:05:33Z | 0 | mobicham |
vllm-project/vllm | 30,798 | [Usage]: vllm offline server lora model | ### Your current environment
Hi team,
I have a question about deploying LoRA models with a vLLM offline server.
Currently, 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.
Based 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**.
Is there a way to deploy on a single vLLM server such that the models list allows choosing among these three options for inference?
1. **A**
2. **A + P**
3. **A + P + Q**
If 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**?
Thanks a lot for your help!
---
### How would you like to use vllm
I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30798 | open | [
"usage"
] | 2025-12-16T16:38:49Z | 2025-12-18T11:52:39Z | 4 | zapqqqwe |
sgl-project/sglang | 15,266 | Multi-Adapter Support for Embed Qwen3 8B Embedding Model | ### Checklist
- [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.
- [x] Please use English. Otherwise, it will be closed.
### Motivation
Hi 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 :)
### Related resources
I'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.
This is similar to how [Jina v4](https://huggingface.co/jinaai/jina-embeddings-v4) Embedding model has task specific adapters.
My adapter config looks like this -
```
{
"alpha_pattern": {},
"auto_mapping": null,
"base_model_name_or_path": "/temp/local-ssd/models/Qwen3-Embedding-8B",
"bias": "none",
"corda_config": null,
"eva_config": null,
"exclude_modules": null,
"fan_in_fan_out": false,
"inference_mode": true,
"init_lora_weights": true,
"layer_replication": null,
"layers_pattern": null,
"layers_to_transform": null,
"loftq_config": {},
"lora_alpha": 128,
"lora_bias": false,
"lora_dropout": 0.1,
"megatron_config": null,
"megatron_core": "megatron.core",
"modules_to_save": [
"classifier",
"score",
"classifier",
"score"
],
"peft_type": "LORA",
"r": 32,
"rank_pattern": {},
"revision": null,
"target_modules": [
"gate_proj",
"k_proj",
"up_proj",
"q_proj",
"down_proj",
"v_proj",
"o_proj"
],
"task_type": "SEQ_CLS",
"trainable_token_indices": null,
"use_dora": false,
"use_rslora": false
}
``` | https://github.com/sgl-project/sglang/issues/15266 | open | [] | 2025-12-16T14:14:16Z | 2025-12-16T14:14:22Z | 0 | dawnik17 |
vllm-project/vllm | 30,776 | [Usage]: Qwen3-omni's offline usage | ### Your current environment
I used the code below in vllm==0.12.0, but failed.
```
import os
import torch
from vllm import LLM, SamplingParams
from transformers import Qwen3OmniMoeProcessor
from qwen_omni_utils import process_mm_info
def build_input(processor, messages, use_audio_in_video):
text = processor.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
# print(text[0])
# print(len(text[0]))
audios, images, videos = process_mm_info(messages, use_audio_in_video=use_audio_in_video)
inputs = {
'prompt': text,
'multi_modal_data': {},
"mm_processor_kwargs": {
"use_audio_in_video": use_audio_in_video,
},
}
if images is not None:
inputs['multi_modal_data']['image'] = images
if videos is not None:
inputs['multi_modal_data']['video'] = videos
if audios is not None:
inputs['multi_modal_data']['audio'] = audios
return inputs
if __name__ == '__main__':
# vLLM engine v1 not supported yet
os.environ['VLLM_USE_V1'] = '1'
os.environ['CUDA_DEVICES'] = '0,1,2,3,4,5,6,7'
MODEL_PATH = "Qwen3-Omni-30B-A3B-Instruct"
llm = LLM(
model=MODEL_PATH, trust_remote_code=True, gpu_memory_utilization=0.95,
tensor_parallel_size=1,
limit_mm_per_prompt={'image': 3, 'video': 3, 'audio': 3},
max_num_seqs=8,
max_model_len=32768,
seed=17114,
)
sampling_params = SamplingParams(
temperature=0.6,
top_p=0.95,
top_k=20,
max_tokens=16384,
)
processor = Qwen3OmniMoeProcessor.from_pretrained(MODEL_PATH)
conversation1 = [
{
"role": "user",
"content": [
{
"type": "video",
"video": "1.mp4",
"fps": 6,
}
],
}
]
USE_AUDIO_IN_VIDEO = True
# Combine messages for batch processing
conversations = [conversation1]
inputs = [build_input(processor, messages, USE_AUDIO_IN_VIDEO) for messages in conversations]
# print(inputs[0])
outputs = llm.generate(inputs, sampling_params=sampling_params)
for i in range(len(outputs)):
print("\n\n==========\n")
print(outputs[i])
```
The error
```
Traceback (most recent call last):
File "/sft-qwen3-omni/vllm_inference.py", line 44, in <module>
llm = LLM(
^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/entrypoints/llm.py", line 334, in __init__
self.llm_engine = LLMEngine.from_engine_args(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/llm_engine.py", line 183, in from_engine_args
return cls(
^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/llm_engine.py", line 109, in __init__
self.engine_core = EngineCoreClient.make_client(
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 93, in make_client
return SyncMPClient(vllm_config, executor_class, log_stats)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 642, in __init__
super().__init__(
File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/core_client.py", line 471, in __init__
with launch_core_engines(vllm_config, executor_class, log_stats) as (
File "/usr/lib/python3.12/contextlib.py", line 144, in __exit__
next(self.gen)
File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/utils.py", line 903, in launch_core_engines
wait_for_engine_startup(
File "/usr/local/lib/python3.12/dist-packages/vllm/v1/engine/utils.py", line 960, in wait_for_engine_startup
raise RuntimeError(
RuntimeError: Engine core initialization failed. See root cause above. Failed core proc(s): {'EngineCore_DP0': 1}
[root:]$ python sft-qwen3-omni/vllm_inference.py
[2025-12-16 12:25:00] INFO vision_process.py:42: set VIDEO_TOTAL_PIXELS: 90316800
INFO 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'}
The argument `trust_remote_code` is to be used with Auto classes. It has no effect here and is ignored.
Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'mrope_interleaved', 'interleaved', 'mrope_section'}
Unrecognized keys in `rope_scaling` for 'rope_type'='default': {'interleaved', 'mrope_section'}
INFO 12-16 12:25:00 [model.py:637] Resolved architecture: Qwen3OmniMoeForConditionalGeneration
INFO 12-16 12:25:00 [model.py:1750] Using max model len 32768
INFO 12-16 12:25:00 [scheduler.py:228] Chun | https://github.com/vllm-project/vllm/issues/30776 | open | [
"bug",
"usage"
] | 2025-12-16T12:30:18Z | 2025-12-17T17:03:34Z | 50 | Auraithm |
sgl-project/sglang | 15,260 | SGLang installs newer PyTorch automatically – is there an official SGLang ↔ PyTorch compatibility guide? | Hi SGLang team, thank you for the great project!
I have a question regarding **PyTorch version compatibility and installation**.
Currently, the recommended installation command from the website is:
```bash
uv pip install "sglang" --prerelease=allow
```
However, when using this command, `pip/uv` automatically upgrades PyTorch to the latest version (e.g., torch 2.9.1).
In my environment, I am intentionally pinned to **torch 2.8.x** and would prefer not to upgrade.
At the moment, it’s not clear:
* Which **SGLang versions are compatible with which PyTorch versions**
* Whether older SGLang releases are expected to work with torch 2.8
* What the recommended installation approach is for users who need to keep a specific torch version
### **Questions**
1. Is there an **official or recommended SGLang ↔ PyTorch compatibility matrix**?
2. For users pinned to torch 2.8.x, which SGLang version is recommended?
3. Is it safe to install SGLang with `--no-deps` or a constraints file to prevent torch upgrades?
4. Would it be possible to document supported torch versions in the release notes or README?
### **Why this matters**
Many 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.
Thanks again for your work — any guidance would be greatly appreciated! | https://github.com/sgl-project/sglang/issues/15260 | open | [] | 2025-12-16T12:27:59Z | 2025-12-16T12:27:59Z | 0 | David-19940718 |
vllm-project/vllm | 30,757 | [Performance]: Async sched: Why return AsyncGPUModelRunnerOutput util func sample_tokens | ### Proposal to improve performance
Why is AsyncGPUModelRunnerOutput returned only after sample_tokens, not immediately after execute_model?
https://github.com/vllm-project/vllm/blob/0d0c929f2360cde5bae6817ad0f555641329e79d/vllm/v1/engine/core.py#L420-L422
If 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.
https://github.com/vllm-project/vllm/blob/0d0c929f2360cde5bae6817ad0f555641329e79d/vllm/v1/engine/core.py#L430-L438
### Report of performance regression
_No response_
### Misc discussion on performance
_No response_
### Your current environment (if you think it is necessary)
```text
The output of `python collect_env.py`
```
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30757 | open | [
"performance"
] | 2025-12-16T08:26:08Z | 2025-12-16T08:26:49Z | 0 | iwzbi |
vllm-project/vllm | 30,736 | [Bug] DCP/DBO: 'NoneType' error building attention_metadata during DeepSeek-V3.1 deployment dummy run | ### Your current environment
```bash
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.10.0a0+git9166f61
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.10.19 | packaged by conda-forge | (main, Oct 22 2025, 22:29:10) [GCC 14.3.0] (64-bit runtime)
Python platform : Linux-5.15.0-124-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0 : NVIDIA H200
GPU 1 : NVIDIA H200
GPU 2 : NVIDIA H200
GPU 3 : NVIDIA H200
GPU 4 : NVIDIA H200
GPU 5 : NVIDIA H200
GPU 6 : NVIDIA H200
GPU 7 : NVIDIA H200
Nvidia driver version : 570.124.06
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.11.1rc4.dev1340+gd08981aba.d20251215 (git sha: d08981aba, date: 20251215)
vLLM Build Flags:
CUDA Archs : 9.0
ROCm : Disabled
```
### 🐛 Describe the bug
When starting vllm serve with the command below, it fails during the final dummy run step and does not start successfully.
Startup Command:
```bash
vllm serve deepseek-ai/DeepSeek-V3.1-Terminus \
--enable-dbo \
--stream-interval 10 \
--api-server-count 2 \
--max-num-batched-tokens 32768 \
--max-num-seqs 256 \
--long-prefill-token-threshold 16384 \
--scheduling-policy fcfs \
--data-parallel-size 2 \
--data-parallel-size-local 2 \
--tensor-parallel-size 4 \
--decode-context-parallel-size 4 \
--data-parallel-backend mp \
--distributed-executor-backend mp \
--enable-expert-parallel \
--all2all-backend deepep_low_latency \
--max-model-len 131072 \
--gpu-memory-utilization 0.8 \
--quantization "fp8" \
--trust-remote-code \
--enable-auto-tool-choice \
--tool-call-parser "deepseek_v31" \
--chat-template dpsk-v3.1-tool-parser-vllm.jinja \
--host ${HOST} \
--port ${PORT} \
```
Error Output：
```bash
(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] WorkerProc hit an exception.
(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] Traceback (most recent call last):
(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
(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] output = func(*args, **kwargs)
(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] ^^^^^^^^^^^^^^^^^^^^^
(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
(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()
(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
(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
(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] self._capture_cudagraphs(
(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
(Worker_DP1_TP0_DCP0_EP4 pid=479) ERROR 12-15 10:54:08 [multiproc_executor.py:822] self._dummy_run(
(Worker_DP1_TP0_DCP0_EP4 pid=479) | https://github.com/vllm-project/vllm/issues/30736 | open | [
"bug",
"help wanted"
] | 2025-12-16T03:07:59Z | 2025-12-22T17:11:48Z | 3 | Butterfingrz |
huggingface/transformers.js | 1,487 | License clarification for some of the converted models | ### Question
Hello!
I 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.
Since 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:
- Are these models safe to use for commercial/personal projects?
- Is it possible to update the model cards to explicitly include the license tag?
Thanks again! | https://github.com/huggingface/transformers.js/issues/1487 | closed | [
"question"
] | 2025-12-16T00:27:16Z | 2025-12-16T19:13:09Z | null | rmahdav |
vllm-project/vllm | 30,722 | [Bug]: llama4_pythonic tool parser fails with SyntaxError on nested list parameters | ### Your current environment
I 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.
This is the fp8 model from Huggingface.
These are the vllm serve args I'm using:
VLLM Version: 0.11.0
```
--port 8002
--model /config/models/maverick
--device cuda
--tensor-parallel-size 8
--disable-log-requests
--max-num-batched-tokens 16000
--served-model-name 'llama-4-maverick-17b-128e-instruct'
--limit-mm-per-prompt image=50
--kv-cache-dtype fp8
--trust-remote-code
--enable-auto-tool-choice
--enable-chunked-prefill true
--enable-prefix-caching
--tool-call-parser llama4_pythonic
--enable-expert-parallel
--chat-template examples/tool_chat_template_llama4_pythonic.jinja
--override-generation-config '{\"attn_temperature_tuning\": true}'
--max-model-len 1000000
```
### 🐛 Describe the bug
### Description
The `llama4_pythonic` tool parser intermittently fails to parse valid tool calls, resulting in:
1. `SyntaxError` from `ast.parse()` when model output is malformed (missing closing `]`)
2. Valid pythonic syntax returned as `content` instead of being parsed into `tool_calls`
### Reproduction
**Minimal curl (run 10+ times to observe intermittent failure):**
```bash
curl -X POST https://your-vllm-endpoint/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llama-4-maverick-17b-128e-instruct",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "how do I enroll in benefits?"}
],
"tools": [{
"type": "function",
"function": {
"name": "enterprise_search",
"description": "Search enterprise knowledge base",
"parameters": {
"type": "object",
"properties": {
"query": {"type": "string"},
"rephrased_queries": {
"type": "array",
"items": {"type": "string"},
"description": "List of 2 rephrased queries"
}
},
"required": ["query", "rephrased_queries"]
}
}
}],
"tool_choice": "auto",
"max_tokens": 500,
"temperature": 0,
"top_p": 0.95
}'
```
**Observed results (10 identical requests):**
- 7/10: ✅ `finish_reason: "tool_calls"`, properly parsed
- 3/10: ❌ `finish_reason: "stop"`, pythonic syntax in `content` field, empty `tool_calls`
### Failure Modes Observed
**Mode 1: Valid pythonic not parsed**
```json
{
"finish_reason": "stop",
"message": {
"content": "[enterprise_search(query=\"Benefits enrollment\", rephrased_queries=[\"...\", \"...\"])]",
"tool_calls": []
}
}
```
Parser fails to detect valid syntax → returned as content.
**Mode 2: Model generates text after tool call**
```json
{
"content": "[enterprise_search(...)]\n\nI was unable to execute this task..."
}
```
Model mixes tool call + text, which violates parser assumption.
**Mode 3: Malformed output (missing bracket)**
```
[enterprise_search(query='...', rephrased_queries=['...', '...'])
```
Model hits `stop_reason: 200007` before completing → `ast.parse()` throws SyntaxError.
### Suspected Root Cause
***The below is suggested by Claude Opus 4.5 so take with a grain of salt.***
1. **Parser detection inconsistency** - Valid pythonic output intermittently not recognized as tool call
2. **No text-after-tool-call handling** - Parser fails when model appends text after `]`
3. **Stop token interference** - Model sometimes hits stop token (200007) mid-generation before completing brackets
4. **Nested bracket complexity** - Array parameters (`rephrased_queries`) create `[...[...]...]` nesting that may confuse detection
### Error Logs
[err.txt](https://github.com/user-attachments/files/24175232/err.txt)
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30722 | open | [
"bug"
] | 2025-12-15T21:26:24Z | 2025-12-15T21:26:24Z | 0 | mphilippnv |
huggingface/tokenizers | 1,913 | Wrong and unsuppressable print when instantiating BPE | I am running Python code that is of the form
```python
from transformers import PreTrainedTokenizerFast
from tokenizers import Tokenizer
from tokenizers.models import BPE
vocab = {"a": 5, "b": 6, "ab": 7}
merges = [("a","b")]
backend_of_backend_of_backend = BPE(vocab=vocab, merges=merges, dropout=None)
backend_of_backend = Tokenizer(model=backend_of_backend_of_backend)
backend = PreTrainedTokenizerFast(tokenizer_object=backend_of_backend)
```
The line `BPE(vocab=vocab, merges=merges, dropout=None)` has nothing to do with serialisation. Yet, when I run it, an unwanted print
```
The OrderedVocab you are attempting to save contains holes for indices [0, 1, 2, 3, 4], your vocabulary could be corrupted!
```
appears in my console, which seems to come from
https://github.com/huggingface/tokenizers/blob/f7db48f532b3d4e3c65732cf745fe62863cbe5fa/tokenizers/src/models/mod.rs#L53-L56
Not 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.
`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? | https://github.com/huggingface/tokenizers/issues/1913 | closed | [] | 2025-12-15T16:30:46Z | 2026-01-05T13:02:45Z | 4 | bauwenst |
vllm-project/vllm | 30,694 | [Feature]: CompressedTensors: NVFP4A16 not supported for MoE models | ### 🚀 The feature, motivation and pitch
NVFP4A16 (W4A16 FP4) quantization via compressed_tensors works for dense models but fails on MoE models like Qwen3-30B-A3B.
Looking 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`).
Since the Marlin kernel already supports FP4 weights + FP16 activations, is there a plan to add W4A16 MoE support for compressed_tensors?
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30694 | open | [
"feature request"
] | 2025-12-15T13:29:09Z | 2025-12-21T09:27:38Z | 2 | zhangyimi |
vllm-project/vllm | 30,685 | [Feature]: fp8 kv cache for finer-grained scaling factors (e.g., per channel). | ### 🚀 The feature, motivation and pitch
Currently, 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?
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30685 | open | [
"feature request"
] | 2025-12-15T09:32:48Z | 2025-12-15T09:32:48Z | 0 | zx-ai |
huggingface/transformers | 42,868 | sdpa_paged: How does it handle paged cache without padding? | Hi @ArthurZucker ,
I 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.
If 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.
However, 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.
Could 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?
Thanks for your time!
related PR: #38085 | https://github.com/huggingface/transformers/issues/42868 | closed | [] | 2025-12-15T08:39:00Z | 2025-12-16T03:08:27Z | 4 | jiqing-feng |
huggingface/trl | 4,692 | LLVM error during GRPO training with Apple M4 Max | 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.
How can I run GRPO on MPS?
loc("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
loc("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
LLVM ERROR: Failed to infer result type(s).
Details:
OS: Tahoe 26.2
pytorch 2.9.1
trl: 0.26.1
MLX:0.30.0
| https://github.com/huggingface/trl/issues/4692 | open | [
"🐛 bug",
"🏋 GRPO"
] | 2025-12-14T23:01:49Z | 2025-12-14T23:02:11Z | 0 | neslihaneti |
vllm-project/vllm | 30,654 | [Feature][Attention][UX]: Incorporate Features into Attention Selection | ### 🚀 The feature, motivation and pitch
SUMMARY:
* we have default attention backends by priority and a notion of which backend supports what hw
* however, certain features are not considered in this (e.g. fp8 kv cache, e.g. attention sinks)
Recent 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)
So the proposal is to:
- make sure all attention backends report what features are supported
- update the attention selector to consider these features in the selection
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30654 | open | [
"help wanted",
"good first issue",
"feature request"
] | 2025-12-14T18:04:14Z | 2025-12-30T05:38:40Z | 11 | robertgshaw2-redhat |
huggingface/diffusers | 12,838 | Merge Loras for FLUX | The issue is based on https://huggingface.co/docs/diffusers/main/using-diffusers/merge_loras
Is 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. | https://github.com/huggingface/diffusers/issues/12838 | open | [] | 2025-12-14T12:39:41Z | 2025-12-14T12:39:41Z | 0 | shrikrishnalolla |
vllm-project/vllm | 30,633 | [Installation]: How to install vLLM 0.11.0 with CUDA < 12.9 (Driver 535)? No matching wheels found | ### Your current environment
I’m trying to install vLLM 0.11.0 on a machine with NVIDIA Driver 535, and I ran into issues related to CUDA version compatibility.
Environment
OS: Linux (Ubuntu 20.04 / 22.04)
GPU: NVIDIA GPU H20
NVIDIA Driver: 535.xx
Python: 3.10
vLLM version: 0.11.0
Problem
According to the release information for vLLM 0.11.0, the available prebuilt wheels appear to target CUDA 12.9+.
However, 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.
This leads to the following questions:
Is vLLM 0.11.0 officially compatible with CUDA versions < 12.9?
If yes, what is the recommended way to install it on systems with Driver 535?
Build from source with a specific CUDA version?
Use a specific Docker image?
Pin to an older vLLM release?
Are there plans to provide prebuilt wheels for CUDA 12.1 / 12.4, or is CUDA 12.9+ now a hard requirement going forward?
What I’ve tried
Checked the GitHub Releases page for vLLM 0.11.0 — no wheels for CUDA < 12.9
Verified that upgrading CUDA to 12.9 is not possible with Driver 535
Looked for documentation on source builds for older CUDA versions, but didn’t find clear guidance
Any clarification or recommended workflow would be greatly appreciated.
Thanks in advance!
### How you are installing vllm
```sh
pip install -vvv vllm
```
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30633 | open | [
"installation"
] | 2025-12-14T04:29:41Z | 2026-01-01T16:50:50Z | 1 | whu125 |
vllm-project/vllm | 30,630 | [Usage]: SymmMemCommunicator: Device capability 10.3 not supported | ### Your current environment
```text
The output of `python collect_env.py`
```
### How would you like to use vllm
Hi, I am seeing following warning using vllm serve on B300 instances.
```
WARNING 12-13 16:31:15 [symm_mem.py:67] SymmMemCommunicator: Device capability 10.3 not supported, communicator is not available.
```
vllm launch command
```
vllm serve \
--tensor-parallel-size 4 \
--kv-cache-dtype fp8 \
--tool-call-parser glm45 \
--reasoning-parser glm45 \
--enable-auto-tool-choice \
--model zai-org/GLM-4.6-FP8'
```
I 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)).
for same config benchmarking, I am seeing same perf as H200 (slightly worse) than B300. Is B300 fully supported on vllm yet?
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30630 | open | [
"usage",
"nvidia"
] | 2025-12-14T01:00:34Z | 2025-12-18T21:17:42Z | 4 | navmarri14 |
huggingface/transformers.js | 1,484 | Should npm @xenova/transformers be deleted or marked deprecated? | ### Question
Hello,
I was surprised that none of the models I tried were supported by transformerjs, even if they were using transformerjs in their README, until I realized that I was using the old npm package.
Shouldn't this package be removed ? Or marked as deprecated in favour of huggingface's ?
Best, | https://github.com/huggingface/transformers.js/issues/1484 | open | [
"question"
] | 2025-12-13T19:49:08Z | 2025-12-17T12:21:12Z | null | matthieu-talbot-ergonomia |
huggingface/tokenizers | 1,910 | [Docs] `Visualizer` dead links | It seems like documentation for `Visualizer` is out of date and all the links return 404.
Docs: https://huggingface.co/docs/tokenizers/api/visualizer
Github Source: https://github.com/huggingface/tokenizers/blob/main/bindings/python/py_src/tokenizers/tools/visualizer.py | https://github.com/huggingface/tokenizers/issues/1910 | open | [] | 2025-12-13T19:23:33Z | 2025-12-13T19:23:33Z | 0 | dudeperf3ct |
vllm-project/vllm | 30,621 | [Feature]: Remove MXFP4 Logic From `fused_experts` | ### 🚀 The feature, motivation and pitch
SUMMARY:
* as part of effort to refactor MoE, trying to reduce cruft
* we currently only have MX emulation in vLLM
* the logic for this emulation should be moved into quark
https://github.com/vllm-project/vllm/blame/main/vllm/model_executor/layers/fused_moe/fused_moe.py#L1866-L1899
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30621 | open | [
"help wanted",
"good first issue",
"feature request"
] | 2025-12-13T18:30:30Z | 2026-01-04T14:47:45Z | 13 | robertgshaw2-redhat |
vllm-project/vllm | 30,620 | [Feature]: Remove Chunking From FusedMoE | ### 🚀 The feature, motivation and pitch
* 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
* we chunk in ~65k tokens
* this case does not happen anymore because of chunked prefill
We should remove this
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30620 | open | [
"help wanted",
"good first issue",
"feature request"
] | 2025-12-13T18:22:30Z | 2025-12-13T23:27:22Z | 3 | robertgshaw2-redhat |
vllm-project/vllm | 30,570 | [Usage]: Why is VLLM still using SSE at all for mcp? | ### Your current environment
This 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?
### How would you like to use vllm
I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30570 | open | [
"usage"
] | 2025-12-12T20:02:08Z | 2025-12-18T10:50:37Z | 1 | bags307 |
sgl-project/sglang | 14,984 | Can the source code compilation and installation of sgl-kernel support the SM86 driver for CUDA12.9 | ### Checklist
- [x] I searched related issues but found no solution.
- [ ] The bug persists in the latest version.
- [ ] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.
- [ ] 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.
- [ ] Please use English. Otherwise, it will be closed.
### Describe the bug
Encountered problem: Unable to find the. so file for sm86 when installing the latest sgl-kernel0.3.19, only sm90 and higher are available
### Reproduction
Question: The machine is a GPU driver for SM86. Can installing sgl kernel in the nvcc 12.9 container source code adapt to SM86?
### Environment
Environment: The host is SM86, the nvcc version in the Docker container is 12.9, and torch and flash attn are CU129 | https://github.com/sgl-project/sglang/issues/14984 | open | [] | 2025-12-12T10:29:50Z | 2025-12-15T09:41:18Z | 1 | zwt-1234 |
vllm-project/vllm | 30,548 | [Feature]: Support for Q.ANT Photonic Computing ? | ### 🚀 The feature, motivation and pitch
https://qant.com/
https://qant.com/wp-content/uploads/2025/11/20251111_QANT-Photonic-AI-Accelerator-Gen-2.pdf
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30548 | open | [
"feature request"
] | 2025-12-12T10:16:53Z | 2025-12-12T14:45:53Z | 2 | plitc |
huggingface/tokenizers | 1,909 | [Docs] `Encode Inputs` rendering issues | It seems like the documentation for Encode Inputs is not rendered properly.
Official URL: https://huggingface.co/docs/tokenizers/main/en/api/encode-inputs?code=python
GitHub URL: https://github.com/huggingface/tokenizers/blob/main/docs/source-doc-builder/api/encode-inputs.mdx | https://github.com/huggingface/tokenizers/issues/1909 | open | [] | 2025-12-12T09:47:48Z | 2025-12-12T09:47:48Z | 0 | ariG23498 |
vllm-project/vllm | 30,541 | [Usage]: missing dsml token "| DSML | " with DeepSeek-V3.2 tools call | ### Your current environment
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : version 4.0.3
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.9.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-5.15.0-50-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.8.93
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA H20
GPU 1: NVIDIA H20
GPU 2: NVIDIA H20
GPU 3: NVIDIA H20
GPU 4: NVIDIA H20
GPU 5: NVIDIA H20
GPU 6: NVIDIA H20
GPU 7: NVIDIA H20
Nvidia driver version : 565.57.01
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 208
On-line CPU(s) list: 0-207
Vendor ID: GenuineIntel
Model name: INTEL(R) XEON(R) PLATINUM 8563C
CPU family: 6
Model: 207
Thread(s) per core: 2
Core(s) per socket: 52
Socket(s): 2
Stepping: 2
Frequency boost: enabled
CPU max MHz: 4000.0000
CPU min MHz: 800.0000
BogoMIPS: 5200.00
Flags: 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
Virtualization: VT-x
L1d cache: 4.9 MiB (104 instances)
L1i cache: 3.3 MiB (104 instances)
L2 cache: 208 MiB (104 instances)
L3 cache: 640 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-51,104-155
NUMA node1 CPU(s): 52-103,156-207
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Vulnerable, IBPB: disabled, STIBP: disabled, PBRSB-eIBRS: Vulnerable
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.5.3
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.16.0
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu12==1.1 | https://github.com/vllm-project/vllm/issues/30541 | open | [
"usage"
] | 2025-12-12T06:47:03Z | 2025-12-12T20:59:40Z | 1 | crischeng |
vllm-project/vllm | 30,511 | Potential Deadlock? | Consider using proper synchronization primitives like threading.Event or queue.Queue.get(timeout=...) | https://github.com/vllm-project/vllm/issues/30511 | closed | [] | 2025-12-11T19:57:43Z | 2025-12-12T18:00:20Z | 1 | ChuanLi1101 |
sgl-project/sglang | 14,903 | Does the current Qwen3-VL (or Qwen3-VL-MoE) officially support TBO? | Hi team,
I noticed that Qwen3-VL and Qwen3-MoE adopt different model architectures.
When profiling the execution path, I found that:
Qwen3-MoE eventually falls back to the Qwen2-MoE implementation, which explicitly supports TBO (Two-Batch Overlap).
However, 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.
Based 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. | https://github.com/sgl-project/sglang/issues/14903 | open | [] | 2025-12-11T13:26:50Z | 2025-12-11T13:26:50Z | 0 | jerry-dream-fu |
huggingface/transformers | 42,804 | [`Quantization FP8`] Native `from_config` support | ### Feature request
Related to https://github.com/huggingface/transformers/pull/42028#discussion_r2592235170
Since 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.
### Motivation
Fp8 is becoming increasingly important
### Your contribution
👀 | https://github.com/huggingface/transformers/issues/42804 | open | [
"Feature request"
] | 2025-12-11T10:17:47Z | 2025-12-14T22:49:48Z | 3 | vasqu |
huggingface/trl | 4,679 | [SFT] High vRAM consumption during eval loop | ### Reproduction
### Unexpected behavior
When 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.
The 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.
Here's a MLFlow GPU vRAM extract showcasing this on an A100 for this 40k token scenario with Qwen3-0.6B:
<img width="1003" height="556" alt="Image" src="https://github.com/user-attachments/assets/8d909f73-6cbe-4c3e-8d6a-e6b8c6c56dbe" />
And same goes for Qwen3-4B, 40k token:
<img width="1006" height="552" alt="Image" src="https://github.com/user-attachments/assets/aa74b9c3-14eb-4c35-851f-c6802d2d420d" />
### Minimal reproduction script
Below 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.
By 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`.
You can launch the script with the following command:
```bash
python sft_example.py \
--model_name_or_path Qwen/Qwen3-0.6B \
--dataset_name trl-lib/Capybara \
--learning_rate 2.0e-4 \
--max-steps 10 \
--per_device_train_batch_size 1 \
--per_device_eval_batch_size 1 \
--eval_accumulation_steps 1 \
--gradient_accumulation_steps 1 \
--gradient_checkpointing \
--eos_token '<|im_end|>' \
--eval_strategy steps \
--eval_steps 10 \
--use_peft \
--lora_r 8 \
--lora_alpha 16 \
--use_liger \
--max_length 10000
```
```python
# Copyright 2020-2025 The HuggingFace Team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# /// script
# dependencies = [
# "trl",
# "peft",
# "trackio",
# "kernels"
# ]
# ///
import argparse
import os
from accelerate import logging
from datasets import load_dataset
from transformers import AutoConfig, AutoModelForCausalLM
from transformers.models.auto.modeling_auto import (
MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES,
)
from trl import (
DatasetMixtureConfig,
ModelConfig,
ScriptArguments,
SFTConfig,
SFTTrainer,
TrlParser,
get_dataset,
get_kbit_device_map,
get_peft_config,
get_quantization_config,
)
logger = logging.get_logger(__name__)
# Enable logging in a Hugging Face Space
os.environ.setdefault("TRACKIO_SPACE_ID", "trl-trackio")
def main(script_args, training_args, model_args, dataset_args):
################
# Model init kwargs
################
model_kwargs = dict(
revision=model_args.model_revision,
trust_remote_code=model_args.trust_remote_code,
attn_implementation=model_args.attn_implementation,
dtype=model_args.dtype,
)
quantization_config = get_quantization_config(model_args)
if quantization_config is not None:
# Passing None would not be treated the same as omitting the argument, so we include it only when valid.
model_kwargs["device_map"] = get_kbit_device_map()
model_kwargs["quantization_config"] = quantization_config
# Create model
config = AutoConfig.from_pretrained(model_args.model_name_or_path)
valid_image_text_architectures = MODEL_FOR_IMAGE_TEXT_TO_TEXT_MAPPING_NAMES.values()
if config.architectures and any(
arch in valid_image_text_architectures for arch in config.architectures
):
from transformers import AutoModelForImageTextToText
model = AutoModelForImageTextToText.from_pretrained(
model_args.model_name_or_path, **model_kwargs
)
else:
model = AutoModelForCausalLM.from_pretrained(
model_args.model_name_or_path, **model_kwargs
)
# Load the dataset
if dataset_args.datasets and script_args.dataset_name:
logger.warning(
"Both `datasets` and `dataset_name` are provided. The `datasets` argument will be used to load the "
"dataset and `dataset_name` will be ignored."
)
| https://github.com/huggingface/trl/issues/4679 | open | [
"🐛 bug",
"🏋 SFT",
"⚡ PEFT"
] | 2025-12-11T10:01:49Z | 2026-01-02T09:23:17Z | 3 | Khreas |
vllm-project/vllm | 30,477 | [Usage]: How to disable thinking for Qwen-8B | ### Your current environment
```text
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 24.04.3 LTS (x86_64)
GCC version : (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.39
==============================
PyTorch Info
==============================
PyTorch version : 2.5.1+cu121
Is debug build : False
CUDA used to build PyTorch : 12.1
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.12 (main, Nov 19 2025, 22:46:53) [Clang 21.1.4 ] (64-bit runtime)
Python platform : Linux-5.15.167.4-microsoft-standard-WSL2-x86_64-with-glibc2.39
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.1.105
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration : GPU 0: NVIDIA GeForce RTX 4090 Laptop GPU
Nvidia driver version : 546.26
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 39 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 32
On-line CPU(s) list: 0-31
Vendor ID: GenuineIntel
Model name: Intel(R) Core(TM) i9-14900HX
CPU family: 6
Model: 183
Thread(s) per core: 2
Core(s) per socket: 16
Socket(s): 1
Stepping: 1
BogoMIPS: 4838.39
Flags: 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
Virtualization: VT-x
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 768 KiB (16 instances)
L1i cache: 512 KiB (16 instances)
L2 cache: 32 MiB (16 instances)
L3 cache: 36 MiB (1 instance)
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Mitigation; Clear Register File
Vulnerability Retbleed: Mitigation; Enhanced IBRS
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.1.3.1
[pip3] nvidia-cuda-cupti-cu12==12.1.105
[pip3] nvidia-cuda-nvrtc-cu12==12.1.105
[pip3] nvidia-cuda-runtime-cu12==12.1.105
[pip3] nvidia-cudnn-cu12==9.1.0.70
[pip3] nvidia-cufft-cu12==11.0.2.54
[pip3] nvidia-curand-cu12==10.3.2.106
[pip3] nvidia-cusolver-cu12==11.4.5.107
[pip3] nvidia-cusparse-cu12==12.1.0.106
[pip3] nvidia-nccl-cu12==2.21.5
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvtx-cu12==12.1.105
[pip3] pyzmq==27.1.0
[pip3] torch==2.5.1+cu121
[pip3] torchaudio==2.5.1+cu121
[pip3] torchvision==0.20.1+cu121
[pip3] transformers==4.57.3
[pip3] triton==3.1.0
[conda] Could not collect
| https://github.com/vllm-project/vllm/issues/30477 | closed | [
"usage"
] | 2025-12-11T09:28:40Z | 2025-12-22T06:10:43Z | 3 | fancyerii |
huggingface/diffusers | 12,823 | How to use quantizer after pipeline loaded? | How to use quantizer after pipeline loaded?
- Currently
```python
# Quantization occurs at load time.
pipe = QwenImagePipeline.from_pretrained(
(
args.model_path
if args.model_path is not None
else os.environ.get(
"QWEN_IMAGE_DIR",
"Qwen/Qwen-Image",
)
),
scheduler=scheduler,
torch_dtype=torch.bfloat16,
quantization_config=quantization_config,
)
```
- What i want
```python
# Load on CPU -> Load and fuse lora -> quantize -> to GPU
``` | https://github.com/huggingface/diffusers/issues/12823 | open | [] | 2025-12-11T06:32:38Z | 2025-12-11T14:18:28Z | null | DefTruth |
huggingface/transformers | 42,794 | `decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation. | ### System Info
latest transformers
### Who can help?
@zucchini-nlp
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
```python
import torch
from transformers import pipeline
pipe = pipeline(
"document-question-answering",
model="naver-clova-ix/donut-base-finetuned-docvqa",
dtype=torch.float16,
)
image = "https://huggingface.co/spaces/impira/docquery/resolve/2359223c1837a7587402bda0f2643382a6eefeab/invoice.png"
question = "What is the invoice number?"
result = pipe(image=image, question=question)
print(result)
```
error:
```
Traceback (most recent call last):
File "/home/jiqingfe/transformers/test_dqa.py", line 13, in <module>
result = pipe(image=image, question=question)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqingfe/transformers/src/transformers/pipelines/document_question_answering.py", line 310, in __call__
return super().__call__(inputs, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqingfe/transformers/src/transformers/pipelines/base.py", line 1278, in __call__
return next(
^^^^^
File "/home/jiqingfe/transformers/src/transformers/pipelines/pt_utils.py", line 126, in __next__
item = next(self.iterator)
^^^^^^^^^^^^^^^^^^^
File "/home/jiqingfe/transformers/src/transformers/pipelines/pt_utils.py", line 271, in __next__
processed = self.infer(next(self.iterator), **self.params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqingfe/transformers/src/transformers/pipelines/base.py", line 1185, in forward
model_outputs = self._forward(model_inputs, **forward_params)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqingfe/transformers/src/transformers/pipelines/document_question_answering.py", line 468, in _forward
model_outputs = self.model.generate(**model_inputs, **generate_kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/sgl-workspace/miniforge3/lib/python3.12/site-packages/torch/utils/_contextlib.py", line 120, in decorate_context
return func(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^
File "/home/jiqingfe/transformers/src/transformers/generation/utils.py", line 2551, in generate
self._prepare_special_tokens(generation_config, kwargs_has_attention_mask, device=device)
File "/home/jiqingfe/transformers/src/transformers/generation/utils.py", line 2145, in _prepare_special_tokens
raise ValueError(
ValueError: `decoder_start_token_id` or `bos_token_id` has to be defined for encoder-decoder generation.
```
### Expected behavior
Cannot locate which PR caused this regression because too many errors recently. The transformers 4.57.3 works well on the script. | https://github.com/huggingface/transformers/issues/42794 | closed | [
"bug"
] | 2025-12-11T06:22:58Z | 2025-12-18T18:33:40Z | 1 | jiqing-feng |
vllm-project/vllm | 30,464 | [Usage]: How can I use the local pre-compiled wheel of vllm | ### Your current environment
```text
The output of `python collect_env.py`
```
### How would you like to use vllm
Every 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?
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30464 | open | [
"usage"
] | 2025-12-11T06:22:43Z | 2025-12-12T01:02:22Z | 1 | gcanlin |
huggingface/transformers | 42,791 | Add support for GPT_OSS with tp_plan or enable native tensor parallelism | ### Model description
#[https://huggingface.co/docs/transformers/main/perf_infer_gpu_multi?tp_plan=auto+plan](url)
> https://github.com/huggingface/transformers/issues/41819
There 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.
I 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:
[]([https://github.com/huggingface/transformers/issues/41819](url).)
After 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`.
`raise RuntimeError(
[rank0]: RuntimeError: aten.bmm.default: got mixed torch.Tensor and DTensor, need to convert all torch.Tensor to DTensor before calling distributed operators!`
### Open source status
- [x] The model implementation is available
- [x] The model weights are available
### Provide useful links for the implementation
_No response_ | https://github.com/huggingface/transformers/issues/42791 | open | [
"New model"
] | 2025-12-11T04:31:19Z | 2025-12-19T08:38:31Z | 1 | quic-akuruvil |
sgl-project/sglang | 14,868 | How to train vicuna EAGLE3 model? | 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.
Could you please provide an example, specifically regarding the template structure? | https://github.com/sgl-project/sglang/issues/14868 | open | [] | 2025-12-11T03:59:39Z | 2025-12-11T03:59:39Z | 0 | Sylvan820 |
vllm-project/vllm | 30,447 | [Usage]: how to load kv cache data into local file | ### Your current environment
pthon3.10+vllm0.10.0
### How would you like to use vllm
I 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.
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30447 | open | [
"usage"
] | 2025-12-11T01:43:58Z | 2025-12-12T15:11:50Z | 1 | chx725 |
vllm-project/vllm | 30,441 | [Usage]: vllm serve setup issues on B300 | ### Your current environment
The output of `python collect_env.py`
```text
Collecting environment information...
uv is set
==============================
System Info
==============================
OS : Amazon Linux 2023.9.20251208 (x86_64)
GCC version : (GCC) 11.5.0 20240719 (Red Hat 11.5.0-5)
Clang version : Could not collect
CMake version : version 3.22.2
Libc version : glibc-2.34
==============================
PyTorch Info
==============================
PyTorch version : 2.9.0+cu130
Is debug build : False
CUDA used to build PyTorch : 13.0
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python 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)
Python platform : Linux-6.1.158-180.294.amzn2023.x86_64-x86_64-with-glibc2.34
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 13.0.88
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA B300 SXM6 AC
GPU 1: NVIDIA B300 SXM6 AC
GPU 2: NVIDIA B300 SXM6 AC
GPU 3: NVIDIA B300 SXM6 AC
GPU 4: NVIDIA B300 SXM6 AC
GPU 5: NVIDIA B300 SXM6 AC
GPU 6: NVIDIA B300 SXM6 AC
GPU 7: NVIDIA B300 SXM6 AC
Nvidia driver version : 580.105.08
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 192
On-line CPU(s) list: 0-191
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8559C
CPU family: 6
Model: 207
Thread(s) per core: 2
Core(s) per socket: 48
Socket(s): 2
Stepping: 2
BogoMIPS: 4800.00
Flags: 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
Hypervisor vendor: KVM
Virtualization type: full
L1d cache: 4.5 MiB (96 instances)
L1i cache: 3 MiB (96 instances)
L2 cache: 192 MiB (96 instances)
L3 cache: 640 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-47,96-143
NUMA node1 CPU(s): 48-95,144-191
Vulnerability Gather data sampling: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
Vulnerability Srbds: Not affected
Vulnerability Tsa: Not affected
Vulnerability Tsx async abort: Not affected
Vulnerability Vms | https://github.com/vllm-project/vllm/issues/30441 | open | [
"usage"
] | 2025-12-10T23:50:27Z | 2025-12-13T02:01:04Z | 1 | navmarri14 |
sgl-project/sglang | 14,824 | Throughput degradation on Qwen3-30B-A3B with EAGLE3 | I observed a throughput degradation when trying to use EAGLE3 to speed up Qwen3-30B-A3B (on 2x H100).
I 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.
Also, 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.
(For reference, the related issue is [this](https://github.com/sgl-project/SpecForge/issues/339).)
Two quick questions:
I’m still wondering: why does EAGLE3 seem less effective on Qwen3 compared to other models?
Are there any specific tricks for training a high-quality EAGLE3 draft model for this architecture?
Thanks! 🥹🥹
| https://github.com/sgl-project/sglang/issues/14824 | open | [] | 2025-12-10T14:22:05Z | 2025-12-19T21:36:54Z | 1 | Zzsf11 |
vllm-project/vllm | 30,392 | [Bug]: Docker image v0.12.0 Fail to serve via Docker image | ### Your current environment
```text
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.9.0+cu129
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-6.6.87.2-microsoft-standard-WSL2-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA RTX A4000
GPU 1: NVIDIA RTX A4000
Nvidia driver version : 581.15
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Vendor ID: AuthenticAMD
Model name: AMD Ryzen 7 3800X 8-Core Processor
CPU family: 23
Model: 113
Thread(s) per core: 2
Core(s) per socket: 4
Socket(s): 1
Stepping: 0
BogoMIPS: 7800.02
Flags: 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
Virtualization: AMD-V
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 128 KiB (4 instances)
L1i cache: 128 KiB (4 instances)
L2 cache: 2 MiB (4 instances)
L3 cache: 16 MiB (1 instance)
NUMA node(s): 1
NUMA node0 CPU(s): 0-7
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Mitigation; untrained return thunk; SMT enabled with STIBP protection
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; IBPB conditional; STIBP always-on; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.5.3
[pip3] numpy==2.2.0
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.16.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.3.1
[pip3] | https://github.com/vllm-project/vllm/issues/30392 | open | [
"usage"
] | 2025-12-10T13:43:59Z | 2026-01-04T14:24:56Z | 7 | kuopching |
huggingface/transformers | 42,771 | FSDP of Trainer does not work well with Accelerate | ### System Info
- `transformers` version: 4.57.3
- Platform: Linux-6.6.97+-x86_64-with-glibc2.35
- Python version: 3.11.11
- Huggingface_hub version: 0.36.0
- Safetensors version: 0.7.0
- Accelerate version: 1.12.0
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.9.1+cu128 (CUDA)
- Tensorflow version (GPU?): not installed (NA)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
- Using GPU in script?: <fill in>
- GPU type: NVIDIA H100 80GB HBM3
### Who can help?
@3outeille @ArthurZucker @SunMarc
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
```python
"""
Simple example of training BERT with Transformers Trainer and FSDP
Uses random data for quick demonstration
"""
import torch
from transformers import (
BertForSequenceClassification,
BertTokenizer,
Trainer,
TrainingArguments,
)
from torch.utils.data import Dataset
# Create a simple dataset with random data
class RandomDataset(Dataset):
def __init__(self, tokenizer, num_samples=1000, max_length=128):
self.tokenizer = tokenizer
self.num_samples = num_samples
self.max_length = max_length
def __len__(self):
return self.num_samples
def __getitem__(self, idx):
# Generate random token IDs
input_ids = torch.randint(
0, self.tokenizer.vocab_size, (self.max_length,)
)
attention_mask = torch.ones(self.max_length)
labels = torch.randint(0, 2, (1,)).item() # Binary classification
return {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": labels,
}
def main():
# Initialize tokenizer and model
model_name = "bert-base-uncased"
tokenizer = BertTokenizer.from_pretrained(model_name)
model = BertForSequenceClassification.from_pretrained(
model_name, num_labels=2
)
# Create random datasets
train_dataset = RandomDataset(tokenizer, num_samples=1000)
eval_dataset = RandomDataset(tokenizer, num_samples=200)
# Configure FSDP training arguments
training_args = TrainingArguments(
output_dir="./bert_fsdp_output",
num_train_epochs=3,
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
logging_steps=50,
eval_strategy="steps",
eval_steps=100,
save_steps=200,
save_total_limit=2,
# FSDP Configuration
fsdp="full_shard auto_wrap", # Enable FSDP with full sharding
fsdp_config={
"fsdp_transformer_layer_cls_to_wrap": ["BertLayer"], # Wrap BERT layers
"fsdp_backward_prefetch": "backward_pre",
"fsdp_forward_prefetch": False,
"fsdp_use_orig_params": True,
},
# Additional settings
learning_rate=5e-5,
warmup_steps=100,
weight_decay=0.01,
logging_dir="./logs",
report_to="none", # Disable wandb/tensorboard for simplicity
)
# Initialize Trainer
trainer = Trainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
)
# Train the model
print("Starting training with FSDP...")
trainer.train()
# Save the final model
trainer.save_model("./bert_fsdp_final")
print("Training completed!")
if __name__ == "__main__":
# Note: Run this script with torchrun for multi-GPU training
# Example: torchrun --nproc_per_node=2 train_bert_fsdp.py
main()
```
torchrun --nproc_per_node=2 train_bert_fsdp.py
### Expected behavior
It will fail silently. The trace stack,
```bash
W1210 12:49:05.011000 104846 site-packages/torch/distributed/run.py:803]
W1210 12:49:05.011000 104846 site-packages/torch/distributed/run.py:803] *****************************************
W1210 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.
W1210 12:49:05.011000 104846 site-packages/torch/distributed/run.py:803] *****************************************
Some weights of BertForSequenceClassification were not initialized from the model checkpoint at bert-base-uncased and are newly initialized: ['classifier.bias', 'classifier.weight']
You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.
Some weights of BertForSequenceClassification were not initialized from the model check | https://github.com/huggingface/transformers/issues/42771 | open | [
"bug"
] | 2025-12-10T12:54:49Z | 2025-12-11T07:07:19Z | 2 | gouchangjiang |
vllm-project/vllm | 30,381 | [Usage]: | ### Your current environment
```text
The output of `python collect_env.py`
```
### How would you like to use vllm
I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30381 | closed | [
"usage"
] | 2025-12-10T09:27:51Z | 2025-12-10T09:28:26Z | 0 | tobeprozy |
vllm-project/vllm | 30,380 | [Usage]: 大家一般怎么使用vllm/tests的？ | ### Your current environment
anywhere
### How would you like to use vllm
I don't know how to use vllm test.
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30380 | open | [
"usage"
] | 2025-12-10T09:27:46Z | 2025-12-10T13:19:18Z | 1 | tobeprozy |
vllm-project/vllm | 30,379 | [Usage]: how to use vllm/tests/？ | ### Your current environment
大家一般怎么使用[vllm](https://github.com/vllm-project/vllm/tree/main)/[tests](https://github.com/vllm-project/vllm/tree/main/tests)的？
### How would you like to use vllm
I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30379 | closed | [
"usage"
] | 2025-12-10T09:25:52Z | 2025-12-10T09:26:25Z | 0 | tobeprozy |
vllm-project/vllm | 30,375 | [Bug]: [TPU] ShapeDtypeStruct error when loading custom safetensors checkpoint on TPU v5litepod | ### Your current environment
<details>
<summary>The output of <code>python collect_env.py</code></summary>
PyTorch version: 2.9.0+cu128
vLLM version: 0.12.0 (vllm-tpu)
JAX version: 0.8.0
Python version: 3.12.8 (main, Jan 14 2025, 22:49:14) [Clang 19.1.6]
TPU: v5litepod-4 (4 chips, single host)
OS: Amazon Linux 2023 (container)
Container runtime: Podman with --privileged --net=host
Additional packages:
- tpu_inference (bundled with vllm-tpu)
- flax (from tpu_inference deps)
- orbax-checkpoint: 0.11.28
- safetensors: 0.4.5
- transformers: 4.57.3</details>
### 🐛 Describe the bug
vLLM-TPU fails to load a **local HuggingFace checkpoint** (safetensors format) on TPU v5litepod with this error:
```
TypeError: Argument 'model.states[0][6]' of shape bfloat16[128] of type <class 'jax._src.core.ShapeDtypeStruct'> is not a valid JAX type.
```
**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.
Loading from **HuggingFace Hub works fine** (e.g., `Qwen/Qwen3-0.6B`), but loading the **exact same model architecture from a local directory fails**.
### How to reproduce the bug
**Minimal reproduction:**
from vllm import LLM
# This WORKS:
model = LLM("Qwen/Qwen3-0.6B", tensor_parallel_size=4, dtype="bfloat16")
# This FAILS with ShapeDtypeStruct error:
model = LLM(
model="/path/to/local/checkpoint", # Contains model.safetensors + config.json
tensor_parallel_size=4,
dtype="bfloat16",
trust_remote_code=True,
)**Checkpoint directory contents:**
```
/path/to/local/checkpoint/
├── config.json # Valid Qwen3 config with "architectures": ["Qwen3ForCausalLM"]
├── model.safetensors # bfloat16 weights (~1.2GB for Qwen3-0.6B)
├── tokenizer.json
├── tokenizer_config.json
├── special_tokens_map.json
├── vocab.json
└── merges.txt
```
**Context:** The checkpoint was converted from MaxText/Orbax format using orbax-checkpoint + safetensors libraries. The weights are valid (verified with `safetensors.torch.load_file()`).
### Full error traceback
```
File "/pm_env/.venv/lib/python3.12/site-packages/tpu_inference/models/common/model_loader.py", line 345, in get_model
return get_flax_model(vllm_config, rng, mesh, is_draft_model)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/pm_env/.venv/lib/python3.12/site-packages/tpu_inference/models/common/model_loader.py", line 219, in get_flax_model
jit_model = _get_nnx_model(model_class, vllm_config, rng, mesh)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/pm_env/.venv/lib/python3.12/site-packages/tpu_inference/models/common/model_loader.py", line 200, in _get_nnx_model
jit_model = create_jit_model(
^^^^^^^^^^^^^^^^^
File "/pm_env/.venv/lib/python3.12/site-packages/flax/nnx/transforms/compilation.py", line 431, in __call__
pure_args_out, pure_kwargs_out, pure_out = self.jitted_fn(
^^^^^^^^^^^^^^^
TypeError: Argument 'model.states[0][6]' of shape bfloat16[128] of type <class 'jax._src.core.ShapeDtypeStruct'> is not a valid JAX type.
```
### What I tried
| Attempt | Result |
|---------|--------|
| Load from HuggingFace Hub | ✅ Works |
| Load local checkpoint (safetensors) | ❌ ShapeDtypeStruct error |
| Use float32 dtype | ❌ Same error |
| Use bfloat16 dtype | ❌ Same error |
| Set `VLLM_USE_V1=0` | ❌ Still uses v1 engine on TPU |
| Add `pytorch_model.bin` alongside safetensors | ❌ Same error |
### Expected behavior
vLLM should load the weights from the local safetensors file and initialize the model, exactly like it does when loading from HuggingFace Hub.
### Analysis
Looking at the traceback, the issue is in `tpu_inference/models/common/model_loader.py`:
1. `get_flax_model()` creates the model architecture
2. `_get_nnx_model()` calls `create_jit_model()`
3. At this point, `model.states[0][6]` is still a `ShapeDtypeStruct` placeholder instead of actual weight data
4. JIT compilation fails because it can't compile shape placeholders
It 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.
### Additional context
- We're building an RL environment for LLM evaluation that needs to load custom finetuned checkpoints
- JetStream/MaxText can load the same Orbax checkpoints without issues
- The safetensors file was verified to contain valid tensors with correct shapes
- This blocks our ability to use vLLM's logprobs-based evaluation on TPU
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30375 | open | [
"bug"
] | 2025-12-10T08:12:57Z | 2025-12-11T05:34:19Z | 1 | Baltsat |
sgl-project/sglang | 14,800 | How should we set piecewise-cuda-graph-max-tokens according to TP DP and chunked-prefill-size? | How should we set piecewise-cuda-graph-max-tokens according to TP DP and chunked-prefill-size?
For TP only, should we set piecewise-cuda-graph-max-tokens = chunked-prefill-size?
and for DP attention DP<=TP, should we set piecewise-cuda-graph-max-tokens = chunked-prefill-size/DP?
Thanks. | https://github.com/sgl-project/sglang/issues/14800 | open | [] | 2025-12-10T07:26:36Z | 2025-12-10T07:26:36Z | 0 | llc-kc |
sgl-project/sglang | 14,783 | [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'? | ### Checklist
- [x] I searched related issues but found no solution.
- [ ] The bug persists in the latest version.
- [ ] Issues without environment info and a minimal reproducible demo are hard to resolve and may receive no feedback.
- [ ] 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.
- [ ] Please use English. Otherwise, it will be closed.
### Describe the bug
(sglang-latest) [root:trinity-asr]$ bash test.sh
/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.
import pkg_resources
INFO 12-10 11:48:25 [importing.py:53] Triton module has been replaced with a placeholder.
INFO 12-10 11:48:26 [__init__.py:243] No platform detected, vLLM is running on UnspecifiedPlatform
WARNING 12-10 11:48:27 [_logger.py:72] Failed to import from vllm._C with ModuleNotFoundError("No module named 'vllm._C'")
/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.
warnings.warn(f"Only CUDA, HIP and XPU support AWQ currently.")
/usr/local/Ascend/thirdparty/sglang/sglang_diffusion_ascend/python/sglang/srt/layers/quantization/gguf.py:46: UserWarning: Only CUDA support GGUF q uantization currently.
warnings.warn(f"Only CUDA support GGUF q uantization currently.")
[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.
[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', | https://github.com/sgl-project/sglang/issues/14783 | closed | [
"npu"
] | 2025-12-10T03:54:50Z | 2025-12-13T12:28:26Z | 1 | rsy-hub4121 |
huggingface/transformers | 42,757 | cannot import name 'is_offline_mode' from 'huggingface_hub' | ### System Info
- transformers-5.0.0
- huggingface_hub-1.2.1
```
ImportError: cannot import name 'is_offline_mode' from 'huggingface_hub' (/root/miniconda3/envs/transformers/lib/python3.10/site-packages/huggingface_hub/__init__.py)
```
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
from transformers import AutoModel, AutoProcessor, AutoTokenizer
### Expected behavior
how to fix ? | https://github.com/huggingface/transformers/issues/42757 | closed | [
"bug"
] | 2025-12-10T02:43:43Z | 2025-12-23T17:15:20Z | 0 | dollarser |
vllm-project/vllm | 30,359 | [RFC] [QeRL]: Online Quantization and Model Reloading | ### Motivation.
## What is Quantized Model Reloading and Why is it Useful?
vLLM 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 – Quantization-enhanced Reinforcement Learning for LLMs](https://arxiv.org/html/2510.11696v1).
These 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.
## Existing Features in vLLM
vLLM already has some pathways for enabling these kinds of workflows. However, the current implementations have caveats which can make usage difficult.
### Weight Reloading
After 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 “restore” 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.
### Online Quantization
There are two styles of online quantization implemented in vLLM. Originally, there was on the “offline” 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).
Recently, @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.
## Design Considerations
### Nomenclature
- “Checkpoint format” refers to the format in which weights are loaded from disk or provided by a user.
- “Model format” refers to the state of the model after `init` but before weights are processed with `process_weights_after_loading` . The mapping between “checkpoint format” and “model format” is implemented by `model.load_weights`.
- “Kernel format” refers to the state of the model after `process_weights_after_loading`
- In the case that checkpoint format is unquantized, but the kernel format is quantized, we call this “online quantization”, where unquantized weights are quantized by vLLM during/after loading.
### Model Cuda Graph
After 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.
Regenerating the model cuda graph is far too slow for the required cadence of model reloading (on the order of a few seconds).
### Memory Requirements
An 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.
Additionally, 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.
### Existing Quantized Reloading Scripts
Although 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.
1. [MoonshotAI](https://github.com/MoonshotAI/checkpoint-engine/blob/44d5670b0e6aed5b9cd6c16e970c09f3dc888ad0/checkpoint_engine/worker.py#L167)
2. [Verl](https://github.com/volcengine/verl/blob/f332fc814718b9ea7968f6d264211460d4e90fff/verl/utils/vllm/vllm_fp8_utils.py#L209)
3. Periodic Labs, which calls `model.load_weights` with subsets | https://github.com/vllm-project/vllm/issues/30359 | open | [
"RFC"
] | 2025-12-09T21:24:20Z | 2025-12-19T18:19:22Z | 8 | kylesayrs |
vllm-project/vllm | 30,358 | [Bug]: NIXL PD disaggregate with host_buffer has accuracy issue - Prefill scheduled num_block mismatch at update_state_after_alloc and request_finished | ### Your current environment
vllm-commit-id: 73a484caa1ad320d6e695f098c25c479a71e6774
Tested with A100
### 🐛 Describe the bug
How to reproduce
```
PREFILL_BLOCK_SIZE=16 DECODE_BLOCK_SIZE=16 bash tests/v1/kv_connector/nixl_integration/run_accuracy_test.sh --kv_buffer_device cpu
```
accuracy is ~0.3 much lower than expected 0.4 with Qwen0.6
---
What is the issue
I found that the num_blocks sent to `update_state_after_alloc` and `request_finished` sometimes is not match.
`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.
`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.
However, based print logs, sometimes, block_ids in `scheduler.schedule` `update_state_after_alloc` is shorter than `scheduler._connector_finished` `request_finished` sometimes.
Example as below
```
📊 Found 1320 unique Request IDs.
FINAL SUMMARY
✅ Consistent Requests : 1085 => num_blocks are same at `update_state_after_alloc` and `request_finished`
❌ Mismatched Requests : 235 => num_blocks is less in `update_state_after_alloc` than `request_finished`
```
```
================================================================================
🔴 MISMATCH DETECTED: cmpl-25c7397c-5686-4b70-a569-29ef04c7b4f9-0
First Block Count: 44
Last Block Count : 71
--- Raw Lines for Context ---
[0;36m(EngineCore_DP0 pid=417455)[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]
[0;36m(EngineCore_DP0 pid=417455)[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]
--------------------------------------------------------------------------------
🔴 MISMATCH DETECTED: cmpl-d77aa1e2-a55a-4a7e-8435-f3bfaaf7c7ed-0
First Block Count: 26
Last Block Count : 84
--- Raw Lines for Context ---
[0;36m(EngineCore_DP0 pid=417455)[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]
[0;36m(EngineCore_DP0 pid=417455)[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]
--------------------------------------------------------------------------------
🔴 MISMATCH DETECTED: cmpl-3ccca907-6af5-41fd-acdf-8a0bd0b48322-0
First Block Count: 71
Last Block Count : 82
--- Raw Lines for Context ---
[0;36m(EngineCore_DP0 pid=417455)[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]
[0;36m(EngineCore_DP0 pid=417455)[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, | https://github.com/vllm-project/vllm/issues/30358 | open | [
"bug"
] | 2025-12-09T20:15:48Z | 2025-12-10T17:07:38Z | 3 | xuechendi |
huggingface/datasets | 7,900 | `Permission denied` when sharing cache between users | ### Describe the bug
We 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.
It looks like this was supported in the past (see #6589)?
Is there a correct way to share caches across users?
### Steps to reproduce the bug
1. Create a directory `/models/hf_hub_shared_experiment` with read/write permissions for two different users
2. For each user run the script below
```python
import os
os.environ["HF_HOME"] = "/models/hf_hub_shared_experiment"
os.environ["HF_DATASETS_CACHE"] = "/models/hf_hub_shared_experiment/data"
import datasets
import transformers
DATASET = "tatsu-lab/alpaca"
MODEL = "meta-llama/Llama-3.2-1B-Instruct"
model = transformers.AutoModelForCausalLM.from_pretrained(MODEL)
tokenizer = transformers.AutoTokenizer.from_pretrained(MODEL)
dataset = datasets.load_dataset(DATASET)
```
The first user is able to download and use the model and dataset. The second user gets these errors:
```
$ python ./experiment_with_shared.py
Could 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'
Could 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'
Could 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'
Could 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'
Traceback (most recent call last):
File "/home/user2/.venv/experiment_with_shared.py", line 17, in <module>
dataset = datasets.load_dataset(DATASET)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/home/user2/.venv/lib/python3.12/site-packages/datasets/load.py", line 1397, in load_dataset
builder_instance = load_dataset_builder(
^^^^^^^^^^^^^^^^^^^^^
File "/home/user2/.venv/lib/python3.12/site-packages/datasets/load.py", line 1171, in load_dataset_builder
builder_instance: DatasetBuilder = builder_cls(
^^^^^^^^^^^^
File "/home/user2/.venv/lib/python3.12/site-packages/datasets/builder.py", line 390, in __init__
with FileLock(lock_path):
File "/home/user2/.venv/lib/python3.12/site-packages/filelock/_api.py", line 377, in __enter__
self.acquire()
File "/home/user2/.venv/lib/python3.12/site-packages/filelock/_api.py", line 333, in acquire
self._acquire()
File "/home/user2/.venv/lib/python3.12/site-packages/filelock/_unix.py", line 45, in _acquire
fd = os.open(self.lock_file, open_flags, self._context.mode)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
PermissionError: [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'
```
### Expected behavior
The second user should be able to read the shared cache files.
### Environment info
$ datasets-cli env
- `datasets` version: 4.4.1
- Platform: Linux-6.8.0-88-generic-x86_64-with-glibc2.39
- Python version: 3.12.3
- `huggingface_hub` version: 0.36.0
- PyArrow version: 22.0.0
- Pandas version: 2.3.3
- `fsspec` version: 2025.10.0 | https://github.com/huggingface/datasets/issues/7900 | open | [] | 2025-12-09T16:41:47Z | 2025-12-16T15:39:06Z | 2 | qthequartermasterman |
sgl-project/sglang | 14,746 | Cannot join SGL slack Channel | same issue with [#3929](https://github.com/sgl-project/sglang/issues/3929) and [#11983](https://github.com/sgl-project/sglang/issues/11983)
Can we get a new invitation link? Thanks a lot! | https://github.com/sgl-project/sglang/issues/14746 | closed | [] | 2025-12-09T15:43:51Z | 2025-12-10T08:33:01Z | 2 | alphabetc1 |
huggingface/transformers | 42,740 | how to train trocr with transformers 4.57+? | 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? | https://github.com/huggingface/transformers/issues/42740 | open | [] | 2025-12-09T14:07:50Z | 2026-01-05T06:46:34Z | null | cqray1990 |
huggingface/transformers | 42,739 | How about adding local kernel loading to `transformers.KernelConfig()` | ### Feature request
As title.
### Motivation
Currently, 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.
### Your contribution
`huggingface/kernels` already has `LocalLayerRepository` built in. Maybe we should consider adding it to `KernelConfig()`. | https://github.com/huggingface/transformers/issues/42739 | closed | [
"Feature request"
] | 2025-12-09T12:22:41Z | 2025-12-17T01:21:57Z | null | zheliuyu |
huggingface/peft | 2,945 | Return base model state_dict with original keys | ### Feature request
TL;DR: `from peft import get_base_model_state_dict`
Hi!
I'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.
A 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.
### Your contribution
I 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). | https://github.com/huggingface/peft/issues/2945 | open | [] | 2025-12-09T11:23:52Z | 2025-12-09T17:06:13Z | 6 | dvmazur |
vllm-project/vllm | 30,325 | [Performance]: Can we enable triton_kernels on sm120 | ### Proposal to improve performance
Since PR (https://github.com/triton-lang/triton/pull/8498) had been merged, we may enable triton_kernels on sm120.
https://github.com/vllm-project/vllm/blob/67475a6e81abea915857f82e6f10d80b03b842c9/vllm/model_executor/layers/quantization/mxfp4.py#L153-L160
Although 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.
@zyongye Does this idea make sense?
### Report of performance regression
_No response_
### Misc discussion on performance
_No response_
### Your current environment (if you think it is necessary)
```text
The output of `python collect_env.py`
```
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30325 | open | [
"performance"
] | 2025-12-09T09:21:04Z | 2025-12-10T10:16:18Z | 2 | ijpq |
vllm-project/vllm | 30,296 | [Usage]: Is it possible to configure P2P kv-cache in multi-machine and multi-gpu scenarios? | ### Your current environment
```text
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : version 4.1.3
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.9.0+cu129
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-5.15.0-126-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA L20
GPU 1: NVIDIA L20
GPU 2: NVIDIA L20
GPU 3: NVIDIA L20
GPU 4: NVIDIA L20
GPU 5: NVIDIA L20
GPU 6: NVIDIA L20
GPU 7: NVIDIA L20
Nvidia driver version : 550.90.07
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.5.2
[pip3] numpy==2.2.0
[pip3] nvidia-cublas-cu12==12.9.1.4
[pip3] nvidia-cuda-cupti-cu12==12.9.79
[pip3] nvidia-cuda-nvrtc-cu12==12.9.86
[pip3] nvidia-cuda-runtime-cu12==12.9.79
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.16.0
[pip3] nvidia-cufft-cu12==11.4.1.4
[pip3] nvidia-cufile-cu12==1.14.1.1
[pip3] nvidia-curand-cu12==10.3.10.19
[pip3] nvidia-cusolver-cu12==11.7.5.82
[pip3] nvidia-cusparse-cu12==12.5.10.65
[pip3] nvidia-cusparselt-cu12==0.7.1
[pip3] nvidia-cutlass-dsl==4.2.1
[pip3] nvidia-ml-py==13.580.82
[pip3] nvidia-nccl-cu12==2.27.5
[pip3] nvidia-nvjitlink-cu12==12.9.86
[pip3] nvidia-nvshmem-cu12==3.3.20
[pip3] nvidia-nvtx-cu12==12.9.79
[pip3] pyzmq==27.1.0
[pip3] torch==2.9.0+cu129
[pip3] torchaudio==2.9.0+cu129
[pip3] torchvision==0.24.0+cu129
[pip3] transformers==4.57.1
[pip3] triton==3.5.0
[conda] Could not collect
==============================
vLLM Info
==============================
ROCM Version : Could not collect
vLLM Version : 0.11.2
vLLM Build Flags:
CUDA Archs: Not Set; ROCm: Disabled
GPU Topology:
GPU0 GPU1 GPU2 GPU3 GPU4 GPU5 GPU6 GPU7 CPU Affinity NUMA Affinity GPU NUMA ID
GPU0 X PIX PIX PIX SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU1 PIX X PIX PIX SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU2 PIX PIX X PIX SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU3 PIX PIX PIX X SYS SYS SYS SYS 0-55,112-167 0 N/A
GPU4 SYS SYS SYS SYS X PIX PIX PIX 56-111,168-223 1 N/A
GPU5 SYS SYS SYS SYS PIX X PIX PIX 56-111,168-223 1 N/A
GPU6 SYS SYS SYS SYS PIX PIX X PIX 56-111,168-223 1 N/A
GPU7 SYS SYS SYS SYS PIX PIX PIX X 56-111,168-223 1 N/A
==============================
Environment Variables
==============================
NVIDIA_VISIBLE_DEVICES=all
NVIDIA_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>= | https://github.com/vllm-project/vllm/issues/30296 | open | [
"usage"
] | 2025-12-09T03:29:48Z | 2025-12-09T03:29:48Z | 0 | lululu-1997 |
huggingface/trl | 4,641 | Further improving `GRPOTrainer` doc to include Qwen SAPO in Loss Types | ### Feature request
Hello,
I'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.
I'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.
<img width="1196" height="694" alt="Image" src="https://github.com/user-attachments/assets/7cfb33d3-bb39-4420-8da1-bd482f28f52e" />
*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)*
### Motivation
Compared 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.
Since 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.
### Your contribution
I'd like to submit a PR if you think this is something useful for readers/users. | https://github.com/huggingface/trl/issues/4641 | closed | [
"📚 documentation",
"✨ enhancement",
"🏋 GRPO"
] | 2025-12-08T20:06:59Z | 2025-12-12T17:28:06Z | 1 | casinca |
huggingface/transformers | 42,713 | mulitmodal forward pass for ministral 3 family | ### System Info
https://github.com/huggingface/transformers/blob/main/src/transformers/models/ministral3/modeling_ministral3.py#L505
seems 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 ?
### Who can help?
@Cyrilvallez
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
please implement https://github.com/huggingface/transformers/blob/main/src/transformers/models/gemma3/modeling_gemma3.py#L1174 for ministral family as well with multimodal capabilities
### Expected behavior
need multimodal capabilities using ministral for finetuning ministral for sequence classification like gemma 3 4b
https://github.com/huggingface/transformers/blob/main/src/transformers/models/gemma3/modeling_gemma3.py#L1174 | https://github.com/huggingface/transformers/issues/42713 | closed | [
"bug"
] | 2025-12-08T18:46:14Z | 2025-12-15T11:21:08Z | 4 | rishavranaut |
vllm-project/vllm | 30,271 | [Usage]: Qwen 3 VL Embedding | ### Your current environment
Hi 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?
I've tried to initialize the model this way but it doesn't work:
```
model = LLM(
model="Qwen/Qwen3-VL-8B-Instruct",
task="embed",
trust_remote_code=True,
)
```
### How would you like to use vllm
I want to run inference of a [specific model](put link here). I don't know how to integrate it with vllm.
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30271 | closed | [
"usage"
] | 2025-12-08T17:26:41Z | 2025-12-09T07:18:35Z | 2 | MingFengC |
huggingface/optimum | 2,390 | Request for input shapes to be specified | ### Feature request
Currently,
optimum-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?
An example would be:
`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]`
### Motivation
Specifying 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
### Your contribution
Yes if possible or the green light is given that this is allowed.
Some modifications to the optimum_cli.py file [here](https://github.com/huggingface/optimum/blob/0227a1ce9652b1b02da5a510bf513c585608f8c2/optimum/commands/optimum_cli.py#L179)
would probably be needed | https://github.com/huggingface/optimum/issues/2390 | open | [] | 2025-12-08T15:24:04Z | 2025-12-20T19:38:02Z | 3 | danielliuce |
huggingface/transformers | 42,698 | parse_response must not accept detokenized text | ### System Info
[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.
Once 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 `<tool call start>rm -rf .<tool call end>` 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))
Also 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.
It 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.
I 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.
### Who can help?
_No response_
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
If a model produces following token sequences:
`["<tool call start>", "rm -rf /", "<tool call end>"]`
`["<", "tool ", "call ", "start", ">", "rm -rf /", "<", "tool ", "call ", "end", ">"]`
They both are detokenized to the same "<tool call start>rm -rf .<tool call end>". 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.
### Expected behavior
[parse_response](https://github.com/huggingface/transformers/blob/5ee9ffe386c5ecc77d8009ab648b8c4c109931ea/src/transformers/tokenization_utils_base.py#L3525) must return tool call for `["<tool call start>", "rm -rf /", "<tool call end>"]` but a plain text for `["<", "tool ", "call ", "start", ">", "rm -rf /", "<", "tool ", "call ", "end", ">"]` . | https://github.com/huggingface/transformers/issues/42698 | open | [
"bug"
] | 2025-12-08T12:20:39Z | 2025-12-08T15:59:19Z | 2 | kibergus |
vllm-project/vllm | 30,248 | [Feature]: any plan to support Relaxed Acceptance in v1? | ### 🚀 The feature, motivation and pitch
[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)
There 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?
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30248 | open | [
"feature request"
] | 2025-12-08T08:45:20Z | 2025-12-09T10:18:22Z | 4 | chengda-wu |
vllm-project/vllm | 30,246 | [Usage]: How to disable reasoning for gpt-oss-120b | ### Your current environment
```
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04.2) 11.4.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.9.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.11.13 (main, Jun 5 2025, 13:12:00) [GCC 11.2.0] (64-bit runtime)
Python platform : Linux-5.15.0-160-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : Could not collect
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA L20
GPU 1: NVIDIA L20
GPU 2: NVIDIA L20
GPU 3: NVIDIA L20
GPU 4: NVIDIA L20
GPU 5: NVIDIA L20
GPU 6: NVIDIA L20
GPU 7: NVIDIA L20
Nvidia driver version : 535.274.02
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 96
On-line CPU(s) list: 0-95
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 5418Y
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 24
Socket(s): 2
Stepping: 8
CPU max MHz: 3800.0000
CPU min MHz: 800.0000
BogoMIPS: 4000.00
Flags: 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
Virtualization: VT-x
L1d cache: 2.3 MiB (48 instances)
L1i cache: 1.5 MiB (48 instances)
L2 cache: 96 MiB (48 instances)
L3 cache: 90 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-23,48-71
NUMA node1 CPU(s): 24-47,72-95
Vulnerability Gather data sampling: Not affected
Vulnerability Indirect target selection: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; PBRSB- | https://github.com/vllm-project/vllm/issues/30246 | open | [
"usage"
] | 2025-12-08T08:23:58Z | 2025-12-08T08:23:58Z | 0 | WiiliamC |
huggingface/transformers | 42,690 | How to run Phi4MultimodalProcessor | ### System Info
transformers version: 4.57.1
python version: 3.9
### Who can help?
_No response_
### Information
- [x] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [x] My own task or dataset (give details below)
### Reproduction
[Phi4MultiModal example](https://huggingface.co/docs/transformers/model_doc/phi4_multimodal)
### Expected behavior
I just run [the example](https://huggingface.co/docs/transformers/model_doc/phi4_multimodal) but there is an error raised. | https://github.com/huggingface/transformers/issues/42690 | open | [
"bug"
] | 2025-12-08T03:27:02Z | 2025-12-09T12:30:27Z | null | wcrzlh |
vllm-project/vllm | 30,222 | [Bug]: gpt-oss response api: streaming + code interpreter has bugs | ### Your current environment
<details>
<summary>The output of <code>python collect_env.py</code></summary>
```text
Your output of `python collect_env.py` here
```
</details>
### 🐛 Describe the bug
Gpt-oss in streaming mode cannot see internal code interpreter output
the problem is with https://github.com/vllm-project/vllm/blob/af0444bf40b7db2f3fb9fe1508d25ceba24cac87/vllm/entrypoints/context.py#L720-L732
I can see that tool call result is not appended to message.
My basic testing code looks like this
```python
stream = client.responses.create(
model="vllm-model",
input=[{"role": "user", "content": "what is 123^456 mod 1000000007? use python tool to solve this problem"}],
tools=[{"type": "code_interpreter", "container": {"type": "auto"}}],
max_output_tokens=32768,
temperature=1.0,
reasoning={"effort": "high"},
stream=True,
instructions=system_prompt,
extra_body={
"min_p": 0.02,
"stop_token_ids": stop_token_ids,
"chat_template_kwargs": {"enable_thinking": True},
}
)
current_tool_code = ""
for event in stream:
generation_idx += 1
# Reasoning text
if event.type == "response.reasoning_text.delta":
delta = event.delta
reasoning_response += delta
text_response += delta
print(delta, end="", flush=True) # Real-time output
# Message text
elif event.type == "response.output_text.delta":
delta = event.delta
text_response += delta
print(delta, end="", flush=True)
# Tool call events
elif event.type == "response.code_interpreter_call_code.delta":
current_tool_code += event.delta
elif event.type == "response.code_interpreter_call_code.done":
tool_calls_log.append({
"code": event.code,
"type": "code_interpreter"
})
current_tool_code = ""
print(event.code)
elif event.type == "response.completed":
# Final event - could extract full response here if needed
pass
```
model response (ignore the pretty looking, it is just another version for visualization)
```bash
============================================================
💭 REASONING:
We 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.
📝 CODE EXECUTED:
pow(123, 456, 1000000007)
------------------------------------------------------------
💭 REASONING:
Let's see result.
💭 REASONING:
It printed something? Wait, no output visible yet. We may need to capture the output. Let's assign.
📝 CODE EXECUTED:
result = pow(123, 456, 1000000007)
result
------------------------------------------------------------
💭 REASONING:
It returned something? Let's see.
💭 REASONING:
It didn't print, but the value is stored. We should print the result.
📝 CODE EXECUTED:
print(result)
------------------------------------------------------------
💭 REASONING:
565291922
So answer is 565291922. Provide box.
📄 FINAL ANSWER:
The value of \(123^{456} \bmod 1000000007\) is
\[
\boxed{565291922}
\]
============================================================
✅ RESPONSE COMPLETED
Tool output tokens: 82
============================================================
```
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30222 | open | [
"bug"
] | 2025-12-08T01:32:35Z | 2025-12-08T09:49:55Z | 4 | jordane95 |
vllm-project/vllm | 30,211 | [Bug]: How to make vLLM support multi stream torch compile and each stream capture cuda graph. | ### Your current environment
<details>
<summary>The output of <code>python collect_env.py</code></summary>
```text
Your output of `python collect_env.py` here
```
</details>
### 🐛 Describe the bug
SGLang now supports multi stream torch compile and each stream capture cuda graph. The code link is
https://github.com/sgl-project/sglang/blob/main/python/sglang/srt/model_executor/cuda_graph_runner.py#L500-#L506
If I want to make vLLM support that. My code on vLLM bypass the vLLM backend and make it like sglang
```
import torch._dynamo.config
import torch._inductor.config
torch._inductor.config.coordinate_descent_tuning = True
torch._inductor.config.triton.unique_kernel_names = True
torch._inductor.config.freezing = True
torch._inductor.config.fx_graph_cache = False # Experimental feature to reduce compilation times, will be on by default in future
from vllm.model_executor.custom_op import CustomOp
def _to_torch(model: torch.nn.Module, reverse: bool, num_tokens: int):
for sub in model._modules.values():
# sub.enter_torch_compile(num_tokens=num_tokens)
# if isinstance(sub, torch.nn.Module):
# _to_torch(sub, reverse, num_tokens)
if isinstance(sub, CustomOp):
if reverse:
sub.leave_torch_compile()
else:
sub.enter_torch_compile(num_tokens=num_tokens)
if isinstance(sub, torch.nn.Module):
_to_torch(sub, reverse, num_tokens)
@contextmanager
def patch_model(
model: torch.nn.Module,
enable_compile: bool,
num_tokens: int,
# tp_group: GroupCoordinator,
):
"""Patch the model to make it compatible with with torch.compile"""
backup_ca_comm = None
current_stream = torch.cuda.current_stream()
with torch.cuda.stream(current_stream):
print(f"patch_model, the current_stream:{current_stream.cuda_stream}", flush = True)
try:
if enable_compile:
_to_torch(model, reverse=False, num_tokens=num_tokens)
# backup_ca_comm = tp_group.ca_comm
# Use custom-allreduce here.
# We found the custom allreduce is much faster than the built-in allreduce in torch,
# even with ENABLE_INTRA_NODE_COMM=1.
# tp_group.ca_comm = None
wrapped_forward = model.forward # 🔥 只改这里
with torch.no_grad():
compiled = torch.compile(wrapped_forward, mode="max-autotune-no-cudagraphs", dynamic=False)
yield compiled
# yield torch.compile(
# model.forward,
# mode="max-autotune-no-cudagraphs",
# dynamic=False,)
# yield torch.compile(
# torch.no_grad()(model.forward),
# mode="reduce-overhead",
# dynamic=_is_hip and get_bool_env_var("SGLANG_TORCH_DYNAMIC_SHAPE"),
# )
else:
yield model.forward
finally:
if enable_compile:
_to_torch(model, reverse=True, num_tokens=num_tokens)
@torch.inference_mode()
def _my_dummy_run(
self,
num_tokens: int,
run_decode_phase:bool=False,
stream_idx: int = 0,
) -> torch.Tensor:
# Set num_scheduled_tokens based on num_tokens and max_num_seqs
# for dummy run with LoRA so that the num_reqs collectively
# has num_tokens in total.
with torch.cuda.stream(torch.cuda.current_stream()):
assert num_tokens <= self.scheduler_config.max_num_batched_tokens
max_num_reqs = self.scheduler_config.max_num_seqs
num_reqs = max_num_reqs if num_tokens >= max_num_reqs else num_tokens
min_tokens_per_req = num_tokens // num_reqs
num_scheduled_tokens_list = [min_tokens_per_req] * num_reqs
num_scheduled_tokens_list[-1] += num_tokens % num_reqs
assert sum(num_scheduled_tokens_list) == num_tokens
assert len(num_scheduled_tokens_list) == num_reqs
num_scheduled_tokens = np.array(num_scheduled_tokens_list,
dtype=np.int32)
with self.maybe_dummy_run_with_lora(self.lora_config,
num_scheduled_tokens):
model = self.model
if self.is_multimodal_model:
input_ids = None
inputs_embeds = self.inputs_embeds[:num_tokens]
else:
input_ids = self.input_ids[:num_tokens]
inputs_embeds = None
if self.uses_mrope:
positions = self.mrope_positions[:, :num_tokens]
else:
positions = self.positions[:num_tokens]
if get_pp_group().is_first_rank:
intermediate_tensors = None
else:
| https://github.com/vllm-project/vllm/issues/30211 | open | [
"bug",
"feature request",
"nvidia"
] | 2025-12-07T15:12:04Z | 2025-12-15T05:39:39Z | 3 | lambda7xx |
vllm-project/vllm | 30,193 | [Bug]: Behavioral Difference in hidden_states[-1] between vLLM and Transformers for Qwen3VLForConditionalGeneration | ### Your current environment
- vLLM Version: 0.11.2
- Transformers Version: 4.57
- Model: Qwen3VLForConditionalGeneration
### 🐛 Describe the bug
I 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).
In 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.
Is 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)?
I don't have minimal demo, but I change the origin code to test.
Because the`forward` method of `Qwen3VLForConditionalGeneration` has the following code:
```python
hidden_states = self.language_model.model(
input_ids=input_ids,
positions=positions,
intermediate_tensors=intermediate_tensors,
inputs_embeds=inputs_embeds,
# args for deepstack
deepstack_input_embeds=deepstack_input_embeds,
)
```
The type of `self.language_model.model` is `Qwen3LLMModel`.
I introduced an environment variable:`LAST_HIDDEN_STATE_NOT_NORM` before return of `Qwen3LLMModel` 's `forward` method:
```python
if os.environ.get("LAST_HIDDEN_STATE_NOT_NORM", "0") == "1":
return hidden_states + residual
if not get_pp_group().is_last_rank:
return IntermediateTensors(
{"hidden_states": hidden_states, "residual": residual}
)
hidden_states, _ = self.norm(hidden_states, residual)
return hidden_states
```
When `LAST_HIDDEN_STATE_NOT_NORM=1` is set, hidden states output exactly match Transformers' behavior.
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30193 | closed | [
"bug"
] | 2025-12-07T04:50:11Z | 2025-12-16T03:24:00Z | 3 | guodongxiaren |
huggingface/transformers | 42,674 | Missing imports for DetrLoss and DetrHungarianMatcher | Previously, I was able to import these classes as
```
from transformers.models.detr.modeling_detr import DetrLoss, DetrObjectDetectionOutput, DetrHungarianMatcher
```
In 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?
Thank you for assistance / information | https://github.com/huggingface/transformers/issues/42674 | open | [] | 2025-12-06T15:32:14Z | 2026-01-06T08:02:43Z | 1 | sammlapp |
vllm-project/vllm | 30,163 | [Usage]: Help Running NVFP4 model on 2x DGX Spark with vLLM + Ray (multi-node) | ### Your current environment
# Help: Running NVFP4 model on 2x DGX Spark with vLLM + Ray (multi-node)
## Hardware
- **2x DGX Spark** (GB10 GPU each, sm_121a / compute capability 12.1)
- Connected via 200GbE ConnectX-7/Ethernet
- Driver: 580.95.05, Host CUDA: 13.0
## Goal
Run `lukealonso/GLM-4.6-NVFP4` (357B MoE model, NVFP4 quantization) across both nodes using vLLM with Ray distributed backend.
## What I've Tried
### 1. `nvcr.io/nvidia/vllm:25.11-py3` (NGC)
- vLLM 0.11.0
- **Error:** `FlashInfer kernels unavailable for ModelOptNvFp4FusedMoE on current platform`
- NVFP4 requires vLLM 0.12.0+
### 2. `vllm/vllm-openai:nightly-aarch64` (vLLM 0.11.2.dev575)
- With `VLLM_USE_FLASHINFER_MOE_FP4=1`
- **Error:** `ptxas fatal: Value 'sm_121a' is not defined for option 'gpu-name'`
- Triton's bundled ptxas 12.8 doesn't support GB10
### 3. `vllm/vllm-openai:v0.12.0-aarch64` (vLLM 0.12.0)
- Fixed ptxas with symlink: `ln -sf /usr/local/cuda/bin/ptxas /usr/local/lib/python3.12/dist-packages/triton/backends/nvidia/bin/ptxas`
- Triton compilation passes ✅
- **Error:** `RuntimeError: [FP4 gemm Runner] Failed to run cutlass FP4 gemm on sm120. Error: Error Internal`
### 4. Tried both parallelism modes:
- `--tensor-parallel-size 2` → same CUTLASS error
- `--pipeline-parallel-size 2` → same CUTLASS error
### 5. `--enforce-eager` flag
- Not fully tested yet
## Environment Details
| Component | Version |
|-----------|---------|
| Host Driver | 580.95.05 |
| Host CUDA | 13.0 |
| Container CUDA | 12.9 |
| Container ptxas | 12.9.86 (supports sm_121a ✅) |
| Triton bundled ptxas | 12.8 (NO sm_121a ❌) |
| PyTorch | 2.9.0+cu129 |
## The Blocking Error
vLLM correctly loads weights (41/41 shards), then during profile_run:
```
INFO [flashinfer_utils.py:289] Flashinfer TRTLLM MOE backend is only supported on SM100 and later, using CUTLASS backend instead
INFO [modelopt.py:1142] Using FlashInfer CUTLASS kernels for ModelOptNvFp4FusedMoE.
...
RuntimeError: [FP4 gemm Runner] Failed to run cutlass FP4 gemm on sm120. Error: Error Internal
```
FlashInfer detects GB10 is not SM100 (B200), falls back to CUTLASS - but CUTLASS FP4 also fails.
## Key Question
**Are CUTLASS FP4 GEMM kernels compiled for GB10 (sm_121a)?**
Is there:
1. A vLLM build with CUTLASS kernels for sm_121?
2. A way to force Marlin FP4 fallback on GB10?
3. Recommended Docker image for DGX Spark + NVFP4?
I see NVFP4 models tested on:
- B200 (sm_100) ✅
- H100/A100 with Marlin FP4 fallback ✅
But GB10 is **sm_121** (Blackwell desktop/workstation variant). The error says `sm120` which seems wrong - GB10 should be sm_121a.
## References
- [ GLM-4.6-NVFP4](https://huggingface.co/lukealonso/GLM-4.6-NVFP4)(https://huggingface.co/lukealonso/GLM-4.6-NVFP4)
- [Firworks/GLM-4.5-Air-nvfp4](https://huggingface.co/Firworks/GLM-4.5-Air-nvfp4)
Thanks!
| https://github.com/vllm-project/vllm/issues/30163 | open | [
"usage"
] | 2025-12-06T00:24:52Z | 2025-12-07T16:22:40Z | 2 | letsrock85 |
huggingface/accelerate | 3,876 | Why TP can't be used with pure DP? | 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?
Please share any documentation, code reference and justification for the same.
What to do inorder to do TP+DP? | https://github.com/huggingface/accelerate/issues/3876 | open | [] | 2025-12-05T16:11:22Z | 2025-12-26T10:07:09Z | 3 | quic-meetkuma |
huggingface/lerobot | 2,589 | Clarification on XVLA folding checkpoint | Hi Lerobot team, great work on the XVLA release!
I have tried finetuning on my custom dataset and have a few clarifications:
1. 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)?
- 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`
- The `observation.state` shape also do not match
2. 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?
Thanks! | https://github.com/huggingface/lerobot/issues/2589 | open | [
"question",
"policies"
] | 2025-12-05T11:42:46Z | 2025-12-22T08:43:05Z | null | brycegoh |
vllm-project/vllm | 30,129 | [Feature]: About video input for qwen3vl | ### 🚀 The feature, motivation and pitch
I 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.
However, 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?
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30129 | open | [
"feature request"
] | 2025-12-05T10:32:06Z | 2025-12-19T03:32:30Z | 4 | lingcco |
huggingface/sentence-transformers | 3,585 | How to choose negative instance when using MultipleNegativesRankingLoss train embedding model? | 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
`embeddings = [self.model(sentence_feature)["sentence_embedding"] for sentence_feature in sentence_features]
`
I 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?
Here is my simple training script, I didn't add negative part in dataset,
```
import os
os.environ["CUDA_VISIBLE_DEVICES"] = "0,1,2,3"
import json
import torch
from sentence_transformers import (
SentenceTransformer,
SentenceTransformerTrainer,
SentenceTransformerTrainingArguments,
InputExample,
)
from sentence_transformers.losses import MultipleNegativesRankingLoss
from sentence_transformers.training_args import BatchSamplers
from datasets import load_dataset, Dataset
def train_embedding_model():
train_epo = 3
save_path = f"/app/raw_model/tmp"
data_path = "/app/emb_train_1205.json"
model = SentenceTransformer(
"/app/download_models/Qwen3-Embedding-0.6B",
model_kwargs={
"attn_implementation": "flash_attention_2",
"torch_dtype": "auto"
}
)
model.tokenizer.padding_side = "left"
model.tokenizer.pad_token = model.tokenizer.eos_token
model.tokenizer.model_max_length = 2048
dataset = load_dataset("json", data_files=data_path)
'''
DatasetDict({
train: Dataset({
features: ['question', 'positive'],
num_rows: 4000
})
})
'''
loss = MultipleNegativesRankingLoss(model)
args = SentenceTransformerTrainingArguments(
output_dir=save_path,
num_train_epochs=train_epo,
per_device_train_batch_size=8,
per_device_eval_batch_size=1,
learning_rate=5e-5,
warmup_ratio=0.1,
fp16=True, # Set to False if you get an error that your GPU can't run on FP16
bf16=False, # Set to True if you have a GPU that supports BF16
batch_sampler=BatchSamplers.NO_DUPLICATES, # MultipleNegativesRankingLoss benefits from no duplicate samples in a batch
optim='adamw_torch_fused',
logging_steps=5,
)
trainer = SentenceTransformerTrainer(
model=model,
args=args,
train_dataset=dataset['train'], # dataset['train'], train_dataset
eval_dataset=dataset['train'], # dataset['train'], train_dataset
loss=loss,
)
trainer.train()
model.save_pretrained(save_path)
```
Besides， can I manually add a list of negatives directly into the dataset while still using the MultipleNegativesRankingLoss? | https://github.com/huggingface/sentence-transformers/issues/3585 | open | [] | 2025-12-05T09:50:26Z | 2025-12-09T11:49:26Z | null | 4daJKong |
vllm-project/vllm | 30,124 | [Bug]: How to run DeepSeek-V3.2 on 2 H100 nodes? |
### 🐛 Describe the bug
How to run DeepSeek-V3.2 on 2 H100 nodes?
I only found the cmd for H200/B200:
vllm serve deepseek-ai/DeepSeek-V3.2 -tp 8
but it does not work in multi-node scenarios (e.g., 2 H100 nodes).
So what should the cmd be for two H100 nodes?
how should params --tp/--dp/--pp be configured?
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30124 | open | [
"bug"
] | 2025-12-05T09:40:45Z | 2025-12-14T08:57:52Z | 2 | XQZ1120 |
vllm-project/vllm | 30,121 | [Feature]: Could you please provide Chinese documentation for vLLM? 😊 | ### 🚀 The feature, motivation and pitch
Could you please provide Chinese documentation for vLLM? 😊
### Alternatives
Could you please provide Chinese documentation for vLLM? 😊
### Additional context
Could you please provide Chinese documentation for vLLM? 😊
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30121 | open | [
"feature request"
] | 2025-12-05T08:13:46Z | 2025-12-08T04:31:05Z | 4 | moshilangzi |
huggingface/transformers | 42,641 | Cannot inference llava-next with transformers==4.57.1 on dtype="auto" bug | ### System Info
```
- `transformers` version: 4.57.1
- Platform: Linux-5.15.0-161-generic-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.35.3
- Safetensors version: 0.6.2
- Accelerate version: 1.10.1
- Accelerate config: not found
- DeepSpeed version: not installed
- PyTorch version (accelerator?): 2.8.0+cpu (NA)
- Tensorflow version (GPU?): 2.18.0 (False)
- Flax version (CPU?/GPU?/TPU?): not installed (NA)
- Jax version: not installed
- JaxLib version: not installed
- Using distributed or parallel set-up in script?: <fill in>
```
### Who can help?
@zucchini-nlp
### Information
- [ ] The official example scripts
- [x] My own modified scripts
### Tasks
- [x] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
```
from transformers import LlavaNextProcessor, LlavaNextForConditionalGeneration
import torch
from PIL import Image
import requests
processor = LlavaNextProcessor.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf")
model = LlavaNextForConditionalGeneration.from_pretrained("llava-hf/llava-v1.6-mistral-7b-hf", dtype="auto", low_cpu_mem_usage=True)
# prepare image and text prompt, using the appropriate prompt template
url = "https://github.com/haotian-liu/LLaVA/blob/1a91fc274d7c35a9b50b3cb29c4247ae5837ce39/images/llava_v1_5_radar.jpg?raw=true"
image = Image.open(requests.get(url, stream=True).raw)
# Define a chat history and use `apply_chat_template` to get correctly formatted prompt
# Each value in "content" has to be a list of dicts with types ("text", "image")
conversation = [
{
"role": "user",
"content": [
{"type": "text", "text": "What is shown in this image?"},
{"type": "image"},
],
},
]
prompt = processor.apply_chat_template(conversation, add_generation_prompt=True)
inputs = processor(images=image, text=prompt, return_tensors="pt")
# autoregressively complete prompt
output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))
```
### Expected behavior
📝 Transformers GitHub Issue: Translation
Here is the translated text for your GitHub issue, including the title and body.
Title
Cannot inference llava-next with transformers==4.57.1 on dtype="auto" bug
Body
I 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.
The issue stems from the model's `config.json` having different `torch_dtype` values for the overall model and the text configuration:
```
"text_config": {
"_name_or_path": "mistralai/Mistral-7B-Instruct-v0.2",
// ... other config values
"torch_dtype": "bfloat16",
"vocab_size": 32064
},
"torch_dtype": "float16",
```
When 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"`).
This difference in data types then causes an error during inference, specifically within the `forward` pass of the `LlavaNextForConditionalGeneration` model:
```
File "MY_ENV/.venv/lib/python3.10/site-packages/transformers/models/llava_next/modeling_llava_next.py", line 687, in forward
logits = self.lm_head(hidden_states[:, slice_indices, :])
File "MY_ENV/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1773, in _wrapped_call_impl
return self._call_impl(*args, **kwargs)
File "MY_ENV/.venv/lib/python3.10/site-packages/torch/nn/modules/module.py", line 1784, in _call_impl
return forward_call(*args, **kwargs)
File "MY_ENV/.venv/lib/python3.10/site-packages/torch/nn/modules/linear.py", line 125, in forward
return F.linear(input, self.weight, self.bias)
RuntimeError: expected m1 and m2 to have the same dtype, but got: c10::BFloat16 != c10::Half
```
This `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`.
Is there a plan to support loading LLaVA-Next models with `dtype="auto"` given their current configuration structure? | https://github.com/huggingface/transformers/issues/42641 | open | [
"bug"
] | 2025-12-05T04:39:35Z | 2025-12-23T11:08:56Z | 5 | rebel-seinpark |
vllm-project/vllm | 30,098 | [Doc]: Misleading Logic & Docstring in `block_quant_to_tensor_quant` (Block FP8) | ### 📚 The doc issue
The 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.
### Problematic Function
The function is currently implemented as:
```python
def block_quant_to_tensor_quant(
x_q_block: torch.Tensor,
x_s: torch.Tensor,
) -> tuple[torch.Tensor, torch.Tensor]:
"""This function converts block-wise quantization to tensor-wise
quantization. The inputs are block-wise quantization tensor `x_q_block`,
block-wise quantization scale and the block size.
The outputs are tensor-wise quantization tensor and tensor-wise
quantization scale. Note only float8 is supported for now.
"""
x_dq_block = group_broadcast(x_q_block, x_s)
x_q_tensor, scale = input_to_float8(x_dq_block, dtype=x_q_block.dtype)
return x_q_tensor, scale
```
### Observation and Impact
- 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.
- 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.
### Suggest a potential alternative/fix
The function should be either documented clearly as a kernel preparation helper OR refactored to ensure numerical correctness when used as a conversion API.
**1. Fix Documentation/Name (If intent is kernel prep):**
* Rename the function to something like `_prepare_block_quant_for_fused_kernel`.
* Add a warning that this function does not perform dequantization.
**2. Implement Safe Logic Dispatch (If intent is a robust conversion API):**
The 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.
The safe logic is in v.0.9.2:
```python
# Safe path required for correctness on general backends
x_dq_block = scaled_dequantize(x_q_block, x_s)
x_q_tensor, scale = input_to_float8(x_dq_block, dtype=x_q_block.dtype)
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30098 | closed | [
"documentation"
] | 2025-12-05T02:12:07Z | 2025-12-24T17:22:50Z | 0 | xqoasis |
huggingface/transformers | 42,638 | Routing Replay for MoEs | ### Feature request
RecentRL approaches for training MoE models increasingly rely on **Routing Replay**, as described in the following papers:
- https://huggingface.co/papers/2507.18071
- https://huggingface.co/papers/2510.11370
- https://huggingface.co/papers/2512.01374
Without 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.
AFAICT, Transformers currently does not expose a way to override router logits or manually control expert selection at inference/training time.
I imagine something along the following lines (minimal example):
```python
from transformers import AutoModelForCausalLM
import torch
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B-Instruct-2507", device_map="auto", dtype="auto")
input_ids = torch.tensor([[1, 2, 3, 4]], device="cuda")
# Standard forward pass, retrieving router logits
outputs = model(input_ids, output_router_logits=True)
# Forward pass with router logits injected (enabling Routing Replay)
model(input_ids, router_logits=outputs.router_logits)
```
## Alternative
If we decide not to implement this feature, it would be nice to provide an example showing how to _patch_ a MoE to enable this.
### Motivation
See above.
### Your contribution
I think I can do it. | https://github.com/huggingface/transformers/issues/42638 | open | [
"Feature request"
] | 2025-12-04T23:58:14Z | 2025-12-05T16:29:05Z | 2 | qgallouedec |
vllm-project/vllm | 30,084 | [Performance]: Should I expect linear scaling with pure DP? | ### Proposal to improve performance
_No response_
### Report of performance regression
_No response_
### Misc discussion on performance
I 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):
DP1 deployment:
```
vllm serve ${MODEL_NAME} \
--port 8000 \
--trust-remote-code
```
DP8 deployment:
```
vllm serve ${MODEL_NAME} \
--port 8000 \
--trust-remote-code \
--data-parallel-size 8 \
--data-parallel-size-local 8
```
My benchmark roughly looks like this:
```
for rate in [10, 20, ... 100, 200, ... 1000, 2000, ... 100000]:
vllm bench serve \
--host "$HOST" \
--model Qwen/Qwen2.5-32B-Instruct \
--dataset-name random \
--random-input-len 128 \
--random-output-len 128 \
--num-prompts 10000 \
--request-rate "$rate" \
--ignore-eos
```
Should I expect ~8x scaling? Result show only ~4x (duration, request throughput, tokens throughput, etc...)
<img width="1789" height="3490" alt="Image" src="https://github.com/user-attachments/assets/81feb936-73d6-49c3-949e-dfbd6d7ba7d7" />
cc @KeitaW @amanshanbhag
### Your current environment (if you think it is necessary)
```text
The output of `python collect_env.py`
```
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30084 | open | [
"performance"
] | 2025-12-04T19:52:45Z | 2025-12-16T04:09:24Z | 7 | pbelevich |
vllm-project/vllm | 30,082 | [Usage]: Turn off reasoning for Kimi-K2-Thinking? | ### Your current environment
```text
Output of collect_env.py-
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : version 4.1.3
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.9.0+cu129
Is debug build : False
CUDA used to build PyTorch : 12.9
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.12 (main, Oct 10 2025, 08:52:57) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-4.18.0-553.56.1.el8_10.x86_64-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.9.86
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA H200
GPU 1: NVIDIA H200
GPU 2: NVIDIA H200
GPU 3: NVIDIA H200
GPU 4: NVIDIA H200
GPU 5: NVIDIA H200
GPU 6: NVIDIA H200
GPU 7: NVIDIA H200
Nvidia driver version : 550.163.01
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 192
On-line CPU(s) list: 0-191
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8468
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 48
Socket(s): 2
Stepping: 8
CPU max MHz: 3800.0000
CPU min MHz: 800.0000
BogoMIPS: 4200.00
Flags: 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
L1d cache: 4.5 MiB (96 instances)
L1i cache: 3 MiB (96 instances)
L2 cache: 192 MiB (96 instances)
L3 cache: 210 MiB (2 instances)
NUMA node(s): 8
NUMA node0 CPU(s): 0-11,96-107
NUMA node1 CPU(s): 12-23,108-119
NUMA node2 CPU(s): 24-35,120-131
NUMA node3 CPU(s): 36-47,132-143
NUMA node4 CPU(s): 48-59,144-155
NUMA node5 CPU(s): 60-71,156-167
NUMA node6 CPU(s): 72-83,168-179
NUMA node7 CPU(s): 84-95,180-191
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spect | https://github.com/vllm-project/vllm/issues/30082 | open | [
"usage"
] | 2025-12-04T19:32:13Z | 2025-12-08T23:02:58Z | 2 | vikrantdeshpande09876 |
vllm-project/vllm | 30,075 | [Feature]: Default eplb num_redundant_experts to the lowest valid value if unspecified | ### 🚀 The feature, motivation and pitch
EPLB 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.
As a consequence, it would also streamline templating the same config to work across multiple EP sizes for the default case.
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30075 | open | [
"help wanted",
"good first issue",
"feature request"
] | 2025-12-04T18:19:03Z | 2025-12-20T21:00:23Z | 4 | smarterclayton |
vllm-project/vllm | 30,058 | [Feature]: Multi-Adapter Support for Embed Qwen3 8B Embedding Model | ### 🚀 The feature, motivation and pitch
Hi 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 :)
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30058 | open | [
"feature request"
] | 2025-12-04T12:05:15Z | 2025-12-04T19:42:04Z | 4 | dawnik17 |
huggingface/accelerate | 3,873 | How to specify accelerate launch yaml config item when running with torchrun | 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.
What are the equivalent of these configurations when using torchrun? | https://github.com/huggingface/accelerate/issues/3873 | open | [] | 2025-12-04T07:27:43Z | 2026-01-03T15:07:19Z | null | WhoisZihan |
huggingface/lerobot | 2,580 | How can the leader arm be synchronized to follow the follower arm during inference? | https://github.com/huggingface/lerobot/issues/2580 | open | [] | 2025-12-04T07:22:07Z | 2025-12-11T02:53:11Z | null | zhoushaoxiang | |
vllm-project/vllm | 30,023 | [Feature]: Support qwen3next with GGUF? | ### 🚀 The feature, motivation and pitch
With v0.11.0, `vllm` report:
```
vllm | (APIServer pid=1) ValueError: GGUF model with architecture qwen3next is not supported yet.
```
https://huggingface.co/Qwen/Qwen3-Next-80B-A3B-Thinking-GGUF
I did a simple dig for this, seems the vllm has support of `Qwen3-Next` as architecture is `qwen3_next`.
But the `Qwen` set it as `qwen3next`.
### Alternatives
_No response_
### Additional context
_No response_
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/30023 | open | [
"feature request"
] | 2025-12-04T03:40:26Z | 2025-12-18T05:31:57Z | 0 | zeerd |
vllm-project/vllm | 29,998 | [Bug]: cannot send two POST to /v1/chat/completions endpoint with identic tool function name with model GPT-OSS-120B | ### Your current environment
<details>
<summary>The bug is reproducible with docker image vllm/vllm-openai:v0.12.0</summary>
```yaml
services:
vllm-gptoss-large:
image: vllm/vllm-openai:v0.12.0
restart: always
shm_size: '64gb'
deploy:
resources:
reservations:
devices:
- driver: nvidia
device_ids: ['0', '1']
capabilities: [gpu]
volumes:
- ./data/hf:/data
environment:
- HF_TOKEN=${HF_TOKEN}
ports:
- 8000:8000
command: ["openai/gpt-oss-120b",
"--tool-call-parser","openai",
"--enable-auto-tool-choice",
"--reasoning-parser","openai_gptoss",
"--tensor-parallel-size","2",
"--port","8000",
"--api-key", "${VLLM_API_KEY}",
"--download_dir", "/data"]
```
</details>
### 🐛 Describe the bug
This 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.
```bash
#!/bin/bash
curl -X POST http://localhost:8000/v1/chat/completions \
-H "Authorization: Bearer ${VLLM_API_KEY}" \
-H "Content-Type: application/json" \
-d '{
"model": "openai/gpt-oss-120b",
"stream": false,
"messages": [
{
"role": "system",
"content": "Be a helpful assistant."
},
{
"role": "user",
"content": "Hi"
},
{
"role": "assistant",
"content": "How can I help you?"
},
{
"role": "user",
"content": "Do you like Monty Python?"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "CHANGE-NAME-BEFORE-SENDING",
"description": "Use this tool if you need to extract information from a website.",
"parameters": {
"type": "object",
"properties": {
"url": {
"type": "string",
"description": "The URL to search or extract information from."
}
},
"required": ["url"]
}
}
}
]
}'
```
The 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.
<img width="2962" height="274" alt="Image" src="https://github.com/user-attachments/assets/115672b2-f85f-43ec-b89c-d3a0daae7d81" />
This is quite weird, because when you call it with python SDK, it is working fine, e.g.
```python
from openai import OpenAI
from dotenv import load_dotenv
import os
load_dotenv()
client = OpenAI(
api_key=os.getenv("API_KEY"),
base_url="http://localhost:8000/v1",
)
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {"type": "string"},
"description": "Location and state, e.g., 'San Francisco, CA'"
},
"required": ["location"]
},
},
}
]
response = client.chat.completions.create(
model="openai/gpt-oss-120b",
messages=[{"role": "user", "content": "How is the weather in Berlin? use the tool get_weather."}],
tools=tools,
tool_choice="auto",
stream=False
)
print(response.choices[0].message)
```
In 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
Here you can also see that chat windows freeze when a tool is attached and a user is asking the second question.
The 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:
```python
from openai import OpenAI
from dotenv import load_dotenv
import os
load_dotenv()
client = OpenAI(
api_key=os.getenv("API_KEY"),
base_url=f"https://{os.getenv('API_DOMAIN')}/v1",
)
tools = [{
"type": "function",
"function": {
"name": "get_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "Location and state, e.g., 'San Francisco, CA'"
},
},
"required": ["locatio | https://github.com/vllm-project/vllm/issues/29998 | open | [
"bug"
] | 2025-12-03T21:41:35Z | 2025-12-19T15:53:43Z | 14 | pd-t |
huggingface/transformers | 42,589 | Incorrect tokenization `tokenizers` for escaped strings / Mismatch with `mistral_common` | ### System Info
```
In [3]: mistral_common.__version__
Out[3]: '1.8.6'
```
```
In [4]: import transformers; transformers.__version__
Out[4]: '5.0.0.dev0'
```
```
In [5]: import tokenizers; tokenizers.__version__
Out[5]: '0.22.1'
```
### Who can help?
@ArthurZucker @itazap
### Information
- [ ] The official example scripts
- [ ] My own modified scripts
### Tasks
- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)
### Reproduction
```py
from transformers import AutoTokenizer
from mistral_common.tokens.tokenizers.mistral import MistralTokenizer
from mistral_common.protocol.instruct.request import ChatCompletionRequest
req = ChatCompletionRequest(messages=[
{'role': 'system', 'content': ''},
{'role': 'user', 'content': 'hey'},
{'role': 'assistant', 'content': 'ju\x16'},
{'role': 'user', 'content': 'hey'},
])
tokenizer_orig = MistralTokenizer.from_hf_hub("mistralai/Ministral-3-3B-Instruct-2512")
tokenizer_hf = AutoTokenizer.from_pretrained("mistralai/Ministral-3-3B-Instruct-2512")
orig_tokens = tokenizer_orig.encode_chat_completion(req).tokens
orig_text = tokenizer_orig.encode_chat_completion(req).text
print("Expected")
print(orig_text)
print(orig_tokens)
hf_tokens = tokenizer_hf.apply_chat_template(req.to_openai()["messages"])
hf_text = tokenizer_hf.convert_ids_to_tokens(hf_tokens)
print("HF")
print(hf_tokens)
print(hf_text)
```
gives:
```
Expected
<s>[SYSTEM_PROMPT][/SYSTEM_PROMPT][INST]hey[/INST]ju</s>[INST]hey[/INST]
[1, 17, 18, 3, 74058, 4, 5517, 1022, 2, 3, 74058, 4]
HF
[1, 17, 18, 3, 74058, 4, 5517, 1022, 1032, 2, 3, 74058, 4]
['<s>', '[SYSTEM_PROMPT]', '[/SYSTEM_PROMPT]', '[INST]', 'hey', '[/INST]', 'ju', 'Ė', 'Ġ', '</s>', '[INST]', 'hey', '[/INST]']
```
As 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.
**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`.
This is especially important for "long-log" parsing tasks that often have escaped strings.
It's def an edge case, but would still be very nice to fix.
### Expected behavior
Align encoding. | https://github.com/huggingface/transformers/issues/42589 | closed | [
"bug"
] | 2025-12-03T10:57:35Z | 2025-12-16T10:45:35Z | 5 | patrickvonplaten |
huggingface/diffusers | 12,781 | Impossible to log into Huggingface/Diffusers Discord | ### Describe the bug
When trying to verify my Discord/Huggingface account, no matter what I do, I end up with this message:
<img width="512" height="217" alt="Image" src="https://github.com/user-attachments/assets/d1d0f18b-c80f-4862-abde-fb49ee505ddd" />
Has the HF Discord died? If that is the case, what alternatives are there?
I 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?
### Reproduction
Try to log-in in to Discord.
### Logs
```shell
-
```
### System Info
-
### Who can help?
_No response_ | https://github.com/huggingface/diffusers/issues/12781 | closed | [
"bug"
] | 2025-12-03T09:42:55Z | 2025-12-04T15:11:42Z | 4 | tin2tin |
vllm-project/vllm | 29,944 | [Usage]:It seems that the prefix cache has not brought about any performance benefits. | ### Your current environment
```
root@ubuntu:/vllm-workspace# python3 collect_env.py
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : version 4.1.0
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.8.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.11 (main, Jun 4 2025, 08:56:18) [GCC 11.4.0] (64-bit runtime)
Python platform : Linux-5.15.0-25-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.8.93
CUDA_MODULE_LOADING set to : LAZY
GPU models and configuration :
GPU 0: NVIDIA H20
GPU 1: NVIDIA H20
GPU 2: NVIDIA H20
GPU 3: NVIDIA H20
GPU 4: NVIDIA H20
GPU 5: NVIDIA H20
GPU 6: NVIDIA H20
GPU 7: NVIDIA H20
Nvidia driver version : 550.127.08
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 224
On-line CPU(s) list: 0-223
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Platinum 8480+
CPU family: 6
Model: 143
Thread(s) per core: 2
Core(s) per socket: 56
Socket(s): 2
Stepping: 8
Frequency boost: enabled
CPU max MHz: 2001.0000
CPU min MHz: 800.0000
BogoMIPS: 4000.00
Flags: 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
Virtualization: VT-x
L1d cache: 5.3 MiB (112 instances)
L1i cache: 3.5 MiB (112 instances)
L2 cache: 224 MiB (112 instances)
L3 cache: 210 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-55,112-167
NUMA node1 CPU(s): 56-111,168-223
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.3.1
[pip3] numpy==2.2.6
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
[pip3] nvidia-cudnn-cu12==9.10.2.21
[pip3] nvidia-cudnn-frontend==1.14.1
[pip3] nvidia-cufft-cu12==11.3.3.83
[pip3] nvidia-cufile-cu | https://github.com/vllm-project/vllm/issues/29944 | open | [
"usage"
] | 2025-12-03T07:03:49Z | 2025-12-03T07:04:37Z | 0 | wenba0 |
vllm-project/vllm | 29,940 | [Usage]: QWen2-Audio-7B support | ### Your current environment
We 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?
Thank you very much！
### 🐛 Describe the bug
Refer to Whisper's demo
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/29940 | closed | [
"usage"
] | 2025-12-03T06:04:07Z | 2025-12-04T14:23:05Z | 1 | freedom-cui |
huggingface/datasets | 7,893 | push_to_hub OOM: _push_parquet_shards_to_hub accumulates all shard bytes in memory | ## Summary
Large dataset uploads crash or hang due to memory exhaustion. This appears to be the root cause of several long-standing issues.
### Related Issues
This is the root cause of:
- #5990 - Pushing a large dataset on the hub consistently hangs (46 comments, open since 2023)
- #7400 - 504 Gateway Timeout when uploading large dataset
- #6686 - Question: Is there any way for uploading a large image dataset?
### Context
Discovered while uploading the [Aphasia Recovery Cohort (ARC)](https://openneuro.org/datasets/ds004884) neuroimaging dataset (~270GB, 902 sessions) to HuggingFace Hub using the `Nifti()` feature.
Working implementation with workaround: [arc-aphasia-bids](https://github.com/The-Obstacle-Is-The-Way/arc-aphasia-bids)
## Root Cause
In `_push_parquet_shards_to_hub` (arrow_dataset.py), the `additions` list accumulates every `CommitOperationAdd` with full Parquet bytes in memory:
```python
additions = []
for shard in shards:
parquet_content = shard.to_parquet_bytes() # ~300 MB per shard
shard_addition = CommitOperationAdd(path_or_fileobj=parquet_content)
api.preupload_lfs_files(additions=[shard_addition])
additions.append(shard_addition) # THE BUG: bytes stay in memory forever
```
For a 902-shard dataset: **902 × 300 MB = ~270 GB RAM requested → OOM/hang**.
The bytes are held until the final `create_commit()` call, preventing garbage collection.
## Reproduction
```python
from datasets import load_dataset
# Any large dataset with embedded files (Image, Audio, Nifti, etc.)
ds = load_dataset("imagefolder", data_dir="path/to/large/dataset")
ds.push_to_hub("repo-id", num_shards=500) # Watch memory grow until crash
```
## Workaround
Process one shard at a time, upload via `HfApi.upload_file(path=...)`, delete before next iteration:
```python
from huggingface_hub import HfApi
import pyarrow.parquet as pq
api = HfApi()
for i in range(num_shards):
shard = ds.shard(num_shards=num_shards, index=i, contiguous=True)
# Write to disk, not memory
shard.to_parquet(local_path)
# Upload from file path (streams from disk)
api.upload_file(
path_or_fileobj=str(local_path),
path_in_repo=f"data/train-{i:05d}-of-{num_shards:05d}.parquet",
repo_id=repo_id,
repo_type="dataset",
)
# Clean up before next iteration
local_path.unlink()
del shard
```
Memory usage stays constant (~1-2 GB) instead of growing linearly.
## Suggested Fix
After `preupload_lfs_files` succeeds for each shard, release the bytes:
1. Clear `path_or_fileobj` from the `CommitOperationAdd` after preupload
2. Or write to temp file and pass file path instead of bytes
3. Or commit incrementally instead of batching all additions
## Environment
- datasets version: main branch (post-0.22.0)
- Platform: macOS 14.x ARM64
- Python: 3.13
- PyArrow: 18.1.0
- Dataset: 902 shards, ~270 GB total embedded NIfTI files | https://github.com/huggingface/datasets/issues/7893 | closed | [] | 2025-12-03T04:19:34Z | 2025-12-05T22:45:59Z | 2 | The-Obstacle-Is-The-Way |
vllm-project/vllm | 29,920 | [Feature]: Add support for fused fp8 output to FlashAttention 3 | ### 🚀 The feature, motivation and pitch
On 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.
vLLM already maintains our own fork of FA, adding output quant support should be pretty non-intrusive. Subtasks:
- vllm-flash-attn:
- add `output_scale` parameter to attention forward functions
- plumb parameter through all layers of the interface
- compare branching at runtime/compile-time for performance and binary size (Hopper)
- vllm:
- integrate new FA version
- add support for attention+quant fusion to FA attention backend
- check FA version, hardware version
- should be as easy as modifying the `supports_fused_output_quant` method and plumbing `output_scale` from `FlashAttentionImpl.forward()` to the kernel call
### Additional context
cc @LucasWilkinson
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/29920 | open | [
"help wanted",
"performance",
"feature request",
"torch.compile"
] | 2025-12-02T20:16:31Z | 2026-01-05T20:53:11Z | 4 | ProExpertProg |
vllm-project/vllm | 29,917 | [Feature]: VLLM_DISABLE_COMPILE_CACHE should be a config flag | ### 🚀 The feature, motivation and pitch
`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
Offline @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
### Alternatives
none
### Additional context
n/a
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/29917 | open | [
"help wanted",
"feature request",
"torch.compile"
] | 2025-12-02T20:06:01Z | 2025-12-05T05:19:12Z | 6 | zou3519 |
huggingface/inference-playground | 102 | How to know when a model is outdated ? | I'm testing https://huggingface.co/chat/models/openai/gpt-oss-20b and there I asked this:
```
do you know any github repository created in 2025?
<p>Sure! Here are a few GitHub repositories that were created in 2025 (all with their public “created date” and a short description):</p>
Repository | Created | Short description | Link
open‑thoughts/open‑thoughts | 2025‑01‑27 | An open‑source platform for curating datasets that can be used to train and evaluate reasoning‑oriented language models. | https://github.com/open-thoughts/open-thoughts
...
```
But on https://huggingface.co/playground:
```
do you know any github repository created in 2025?
I don’t have any information about repositories that were created in 2025. My training data only goes up to September 2023, so I can’t see or reference anything that was added to GitHub after that date. If you’re looking for recent projects, you could search GitHub directly or use the GitHub API to filter repositories by creation date.
```
I'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 .
I've also downloaded the https://huggingface.co/openai/gpt-oss-20b and running locally it doesn't know anything from 2025.
**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 .**
**How/Where can we get the version running here https://huggingface.co/chat/models/openai/gpt-oss-20b ?** | https://github.com/huggingface/inference-playground/issues/102 | open | [] | 2025-12-02T17:10:51Z | 2025-12-02T17:10:51Z | null | mingodad |
vllm-project/vllm | 29,875 | [Usage]: Is there a way to inject the grammar into the docker directly | ### Your current environment
```text
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
Clang version : Could not collect
CMake version : version 3.28.0
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.9.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.10.19 | packaged by conda-forge | (main, Oct 22 2025, 22:29:10) [GCC 14.3.0] (64-bit runtime)
Python platform : Linux-6.8.0-1030-azure-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : Could not collect
CUDA_MODULE_LOADING set to :
GPU models and configuration : GPU 0: NVIDIA H100 NVL
Nvidia driver version : 535.247.01
cuDNN version : Probably one of the following:
/usr/lib/x86_64-linux-gnu/libcudnn.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_adv.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_cnn.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_engines_precompiled.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_engines_runtime_compiled.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_graph.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_heuristic.so.9.10.2
/usr/lib/x86_64-linux-gnu/libcudnn_ops.so.9.10.2
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 48 bits physical, 48 bits virtual
Byte Order: Little Endian
CPU(s): 40
On-line CPU(s) list: 0-39
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9V84 96-Core Processor
CPU family: 25
Model: 17
Thread(s) per core: 1
Core(s) per socket: 40
Socket(s): 1
Stepping: 1
BogoMIPS: 4800.05
Flags: 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
Hypervisor vendor: Microsoft
Virtualization type: full
L1d cache: 1.3 MiB (40 instances)
L1i cache: 1.3 MiB (40 instances)
L2 cache: 40 MiB (40 instances)
L3 cache: 160 MiB (5 instances)
NUMA node(s): 1
NUMA node0 CPU(s): 0-39
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Vulnerable: Safe RET, no microcode
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Retpolines; STIBP disabled; RSB filling; PBRSB-eIBRS Not affected; BHI Not affected
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
==============================
Versions of relevant libraries
==============================
[pip3] flashinfer-python==0.5.2
[pip3] numpy==1.26.4
[pip3] nvidia-cublas-cu12==12.8.4.1
[pip3] nvidia-cuda-cupti-cu12==12.8.90
[pip3] nvidia-cuda-nvrtc-cu12==12.8.93
[pip3] nvidia-cuda-runtime-cu12==12.8.90
| https://github.com/vllm-project/vllm/issues/29875 | open | [
"usage"
] | 2025-12-02T12:30:56Z | 2025-12-03T11:53:43Z | 1 | chwundermsft |
vllm-project/vllm | 29,871 | [Usage]: Extremly low token input speed for DeepSeek-R1-Distill-Llama-70B | ### Your current environment
<details>
<summary>The output of <code>python collect_env.py</code></summary>
```text
Collecting environment information...
==============================
System Info
==============================
OS : Ubuntu 22.04.5 LTS (x86_64)
GCC version : (GCC) 14.2.0
Clang version : Could not collect
CMake version : Could not collect
Libc version : glibc-2.35
==============================
PyTorch Info
==============================
PyTorch version : 2.9.0+cu128
Is debug build : False
CUDA used to build PyTorch : 12.8
ROCM used to build PyTorch : N/A
==============================
Python Environment
==============================
Python version : 3.12.4 (main, Aug 29 2025, 09:21:27) [GCC 14.2.0] (64-bit runtime)
Python platform : Linux-5.15.0-118-generic-x86_64-with-glibc2.35
==============================
CUDA / GPU Info
==============================
Is CUDA available : True
CUDA runtime version : 12.8.61
CUDA_MODULE_LOADING set to :
GPU models and configuration :
GPU 0: NVIDIA H100 80GB HBM3
GPU 1: NVIDIA H100 80GB HBM3
GPU 2: NVIDIA H100 80GB HBM3
GPU 3: NVIDIA H100 80GB HBM3
GPU 4: NVIDIA H100 80GB HBM3
GPU 5: NVIDIA H100 80GB HBM3
GPU 6: NVIDIA H100 80GB HBM3
GPU 7: NVIDIA H100 80GB HBM3
Nvidia driver version : 570.158.01
cuDNN version : Could not collect
HIP runtime version : N/A
MIOpen runtime version : N/A
Is XNNPACK available : True
==============================
CPU Info
==============================
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 52 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 192
On-line CPU(s) list: 0-191
Vendor ID: AuthenticAMD
Model name: AMD EPYC 9654 96-Core Processor
CPU family: 25
Model: 17
Thread(s) per core: 1
Core(s) per socket: 96
Socket(s): 2
Stepping: 1
Frequency boost: enabled
CPU max MHz: 3707.8120
CPU min MHz: 1500.0000
BogoMIPS: 4793.01
Flags: 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
Virtualization: AMD-V
L1d cache: 6 MiB (192 instances)
L1i cache: 6 MiB (192 instances)
L2 cache: 192 MiB (192 instances)
L3 cache: 768 MiB (24 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0-95
NUMA node1 CPU(s): 96-191
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Reg file data sampling: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Mitigation; safe RET
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced / Automatic IBRS; IBPB conditional; STIBP disabled; RSB filling; PBRSB-eIB | https://github.com/vllm-project/vllm/issues/29871 | open | [
"usage"
] | 2025-12-02T11:25:25Z | 2025-12-02T15:30:53Z | 2 | muelphil |
vllm-project/vllm | 29,866 | [Doc]: | ### 📚 The doc issue
# Installation des bibliothèques XAI
!pip install shap
!pip install lime
!pip install alibi
!pip install interpret
!pip install dalex
!pip install eli5
### Suggest a potential alternative/fix
# Installation des bibliothèques XAI
!pip install shap
!pip install lime
!pip install alibi
!pip install interpret
!pip install dalex
!pip install eli5
### Before submitting a new issue...
- [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. | https://github.com/vllm-project/vllm/issues/29866 | closed | [
"documentation"
] | 2025-12-02T10:43:04Z | 2025-12-02T10:50:10Z | 0 | hassaballahmahamatahmat5-cpu |
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