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2,056,056,305
I_kwDOJ0Z1Ps56jO3x
1,718
incomplete json in api responses
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2023-12-26T05:51:29
2023-12-26T17:22:36
2023-12-26T17:22:36
NONE
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I tried both /api/chat and /api/generate endpoints which seem to produce the same results. however I'm getting invalid json on every response.
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2,651,476,994
I_kwDOJ0Z1Ps6eClQC
7,626
Role field should not be repeated in streamed response chunks
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null
1
2024-11-12T08:46:18
2024-11-18T07:52:26
null
NONE
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### What is the issue? The streamed chat-completion response from ollama's openai-compatible API repeats `"role": "assistant"` in all returned chunks. This is different to OpenAI's API which just has this in the first chunk. This breaks compatibility with the `client.beta.chat.completions.stream` helper from the openai package. See also this issue https://github.com/pydantic/logfire/pull/545#discussion_r1837660027. Ollama should omit the "role" field or return `"role": None` for all chunks after the first one. --- OpenAI chunks: "role" only in first chunk ```python from openai import OpenAI client = Client() response = client.chat.completions.create( model="gpt-4", messages=[{"role": "user", "content": "Just say: The answer is secret."}], stream=True, ) for chunk in response: print(chunk.model_dump_json(exclude_none=True)) {"id":"chatcmpl-ASgaImINIA8gwsca92CCgES2VldF8","choices":[{"delta":{"content":"","role":"assistant"},"index":0}],"created":1731400242,"model":"gpt-4-0613","object":"chat.completion.chunk"} {"id":"chatcmpl-ASgaImINIA8gwsca92CCgES2VldF8","choices":[{"delta":{"content":"The"},"index":0}],"created":1731400242,"model":"gpt-4-0613","object":"chat.completion.chunk"} {"id":"chatcmpl-ASgaImINIA8gwsca92CCgES2VldF8","choices":[{"delta":{"content":" answer"},"index":0}],"created":1731400242,"model":"gpt-4-0613","object":"chat.completion.chunk"} {"id":"chatcmpl-ASgaImINIA8gwsca92CCgES2VldF8","choices":[{"delta":{"content":" is"},"index":0}],"created":1731400242,"model":"gpt-4-0613","object":"chat.completion.chunk"} {"id":"chatcmpl-ASgaImINIA8gwsca92CCgES2VldF8","choices":[{"delta":{"content":" secret"},"index":0}],"created":1731400242,"model":"gpt-4-0613","object":"chat.completion.chunk"} {"id":"chatcmpl-ASgaImINIA8gwsca92CCgES2VldF8","choices":[{"delta":{"content":"."},"index":0}],"created":1731400242,"model":"gpt-4-0613","object":"chat.completion.chunk"} {"id":"chatcmpl-ASgaImINIA8gwsca92CCgES2VldF8","choices":[{"delta":{},"finish_reason":"stop","index":0}],"created":1731400242,"model":"gpt-4-0613","object":"chat.completion.chunk"} ``` Ollama chunks: "role" provided in every chunk ```python from openai import OpenAI client = OpenAI( base_url="http://localhost:11434/v1", api_key="ollama", ) response = client.chat.completions.create( model="llama3.1", messages=[{"role": "user", "content": "Just say: The answer is secret."}], stream=True, # stream_options={"include_usage": True}, # max_tokens=1, ) for chunk in response: print(chunk.model_dump_json(exclude_none=True)) {"id":"chatcmpl-230","choices":[{"delta":{"content":"The","role":"assistant"},"index":0}],"created":1731400290,"model":"llama3.1","object":"chat.completion.chunk","system_fingerprint":"fp_ollama"} {"id":"chatcmpl-230","choices":[{"delta":{"content":" answer","role":"assistant"},"index":0}],"created":1731400290,"model":"llama3.1","object":"chat.completion.chunk","system_fingerprint":"fp_ollama"} {"id":"chatcmpl-230","choices":[{"delta":{"content":" is","role":"assistant"},"index":0}],"created":1731400290,"model":"llama3.1","object":"chat.completion.chunk","system_fingerprint":"fp_ollama"} {"id":"chatcmpl-230","choices":[{"delta":{"content":" secret","role":"assistant"},"index":0}],"created":1731400290,"model":"llama3.1","object":"chat.completion.chunk","system_fingerprint":"fp_ollama"} {"id":"chatcmpl-230","choices":[{"delta":{"content":".","role":"assistant"},"index":0}],"created":1731400290,"model":"llama3.1","object":"chat.completion.chunk","system_fingerprint":"fp_ollama"} {"id":"chatcmpl-230","choices":[{"delta":{"content":"","role":"assistant"},"finish_reason":"stop","index":0}],"created":1731400290,"model":"llama3.1","object":"chat.completion.chunk","system_fingerprint":"fp_ollama"} ``` Using `client.beta.chat.completions.stream` with ollama results in `"role": "assistantassistant...`. openai docs: https://github.com/openai/openai-python/blob/646a579cdb305a9d3fba6c5f9a96011c5e2c2882/helpers.md#chat-completions-api ```python from openai import OpenAI client = OpenAI( base_url="http://localhost:11434/v1", api_key="ollama", ) with client.beta.chat.completions.stream( model="llama3.1", messages=[{"role": "user", "content": "Just say: The answer is secret."}], ) as stream: for event in stream: pass print(stream.get_final_completion().model_dump_json(indent=2)) { "id": "chatcmpl-653", "choices": [ { "finish_reason": "stop", "index": 0, "logprobs": null, "message": { "content": "The answer is secret.", "refusal": null, "role": "assistantassistantassistantassistantassistantassistant", "audio": null, "function_call": null, "tool_calls": [], "parsed": null } } ], "created": 1731400312, "model": "llama3.1", "object": "chat.completion", "service_tier": null, "system_fingerprint": "fp_ollama", "usage": null } ``` ### OS macOS ### GPU _No response_ ### CPU _No response_ ### Ollama version 0.4.1
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2,260,448,866
I_kwDOJ0Z1Ps6Gu7Zi
3,868
Hope to get it out on the shelves llama3-Chinese
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2024-04-24T06:23:11
2024-07-20T14:29:51
2024-07-20T14:29:51
NONE
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比如 https://github.com/UnicomAI/Unichat-llama3-Chinese
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I_kwDOJ0Z1Ps6IiAyG
4,333
`segmentation fault` when running `codellama:34b` on A100
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2024-05-11T02:48:28
2024-07-22T18:05:25
2024-07-22T18:05:25
MEMBER
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### What is the issue? CLI: ``` $ ollama run codellama:34b Error: llama runner process has terminated: signal: segmentation fault ``` Logs: ``` May 11 02:47:28 gpu ollama[27286]: time=2024-05-11T02:47:28.033Z level=INFO source=memory.go:127 msg="offload to gpu" layers.real=-1 layers.estimate=49 memory.available="39.0 GiB" memory.required.full="19.1 GiB" memory.required.partial="19.1 GiB" memory.required.kv="384.0 MiB" memory.weights.total="18.0 GiB" memory.weights.repeating="17.8 GiB" memory.weights.nonrepeating="205.1 MiB" memory.graph.full="324.0 MiB" memory.graph.partial="348.0 MiB" May 11 02:47:28 gpu ollama[27286]: time=2024-05-11T02:47:28.034Z level=INFO source=memory.go:127 msg="offload to gpu" layers.real=-1 layers.estimate=49 memory.available="39.0 GiB" memory.required.full="19.1 GiB" memory.required.partial="19.1 GiB" memory.required.kv="384.0 MiB" memory.weights.total="18.0 GiB" memory.weights.repeating="17.8 GiB" memory.weights.nonrepeating="205.1 MiB" memory.graph.full="324.0 MiB" memory.graph.partial="348.0 MiB" May 11 02:47:28 gpu ollama[27286]: time=2024-05-11T02:47:28.035Z level=INFO source=server.go:308 msg="starting llama server" cmd="/tmp/ollama944909272/runners/cuda_v11/ollama_llama_server --model /usr/share/ollama/.ollama/models/blobs/sha256-f36b668ebcd329357fac22db35f6414a1c9309307f33d08fe217bbf84b0496cc --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 49 --parallel 1 --port 36091" May 11 02:47:28 gpu ollama[27286]: time=2024-05-11T02:47:28.035Z level=INFO source=sched.go:333 msg="loaded runners" count=1 May 11 02:47:28 gpu ollama[27286]: time=2024-05-11T02:47:28.035Z level=INFO source=server.go:478 msg="waiting for llama runner to start responding" May 11 02:47:28 gpu ollama[27286]: time=2024-05-11T02:47:28.035Z level=INFO source=server.go:514 msg="waiting for server to become available" status="llm server error" May 11 02:47:28 gpu ollama[28220]: INFO [main] build info | build=1 commit="952d03d" tid="140151386750976" timestamp=1715395648 May 11 02:47:28 gpu ollama[28220]: INFO [main] system info | n_threads=6 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="140151386750976" timestamp=1715395648 total_threads=12 May 11 02:47:28 gpu ollama[28220]: INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="11" port="36091" tid="140151386750976" timestamp=1715395648 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: loaded meta data with 20 key-value pairs and 435 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-f36b668ebcd329357fac22db35f6414a1c9309307f33d08fe217bbf84b0496cc (version GGUF V2) May 11 02:47:28 gpu ollama[27286]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 0: general.architecture str = llama May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 1: general.name str = codellama May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 2: llama.context_length u32 = 16384 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 3: llama.embedding_length u32 = 8192 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 4: llama.block_count u32 = 48 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 5: llama.feed_forward_length u32 = 22016 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 7: llama.attention.head_count u32 = 64 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 10: llama.rope.freq_base f32 = 1000000.000000 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 11: general.file_type u32 = 2 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 12: tokenizer.ggml.model str = llama May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<... May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - kv 19: general.quantization_version u32 = 2 May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - type f32: 97 tensors May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - type q4_0: 337 tensors May 11 02:47:28 gpu ollama[27286]: llama_model_loader: - type q6_K: 1 tensors May 11 02:47:28 gpu ollama[27286]: llm_load_vocab: special tokens definition check successful ( 259/32000 ). May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: format = GGUF V2 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: arch = llama May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: vocab type = SPM May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_vocab = 32000 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_merges = 0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_ctx_train = 16384 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_embd = 8192 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_head = 64 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_head_kv = 8 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_layer = 48 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_rot = 128 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_embd_head_k = 128 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_embd_head_v = 128 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_gqa = 8 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_embd_k_gqa = 1024 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_embd_v_gqa = 1024 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: f_norm_eps = 0.0e+00 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: f_norm_rms_eps = 1.0e-05 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: f_clamp_kqv = 0.0e+00 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: f_logit_scale = 0.0e+00 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_ff = 22016 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_expert = 0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_expert_used = 0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: causal attn = 1 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: pooling type = 0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: rope type = 0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: rope scaling = linear May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: freq_base_train = 1000000.0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: freq_scale_train = 1 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: n_yarn_orig_ctx = 16384 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: rope_finetuned = unknown May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: ssm_d_conv = 0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: ssm_d_inner = 0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: ssm_d_state = 0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: ssm_dt_rank = 0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: model type = 34B May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: model ftype = Q4_0 May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: model params = 33.74 B May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: model size = 17.74 GiB (4.52 BPW) May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: general.name = codellama May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: BOS token = 1 '<s>' May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: EOS token = 2 '</s>' May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: UNK token = 0 '<unk>' May 11 02:47:28 gpu ollama[27286]: llm_load_print_meta: LF token = 13 '<0x0A>' May 11 02:47:28 gpu ollama[27286]: [52B blob data] May 11 02:47:28 gpu ollama[27286]: time=2024-05-11T02:47:28.286Z level=ERROR source=sched.go:339 msg="error loading llama server" error="llama runner process has terminated: signal: segmentation fault " May 11 02:47:28 gpu ollama[27286]: [GIN] 2024/05/11 - 02:47:28 | 500 | 1.242539308s | 127.0.0.1 | POST "/api/chat" May 11 02:47:30 gpu ollama[27286]: time=2024-05-11T02:47:30.881Z level=WARN source=sched.go:507 msg="gpu VRAM usage didn't recover within timeout" seconds=5.22935882 May 11 02:47:31 gpu ollama[27286]: time=2024-05-11T02:47:31.211Z level=WARN source=sched.go:507 msg="gpu VRAM usage didn't recover within timeout" seconds=5.559953212 May 11 02:47:31 gpu ollama[27286]: time=2024-05-11T02:47:31.542Z level=WARN source=sched.go:507 msg="gpu VRAM usage didn't recover within timeout" seconds=5.890430607 May 11 02:47:36 gpu ollama[27286]: time=2024-05-11T02:47:36.102Z level=WARN source=sched.go:507 msg="gpu VRAM usage didn't recover within timeout" seconds=5.221611086 May 11 02:47:36 gpu ollama[27286]: time=2024-05-11T02:47:36.434Z level=WARN source=sched.go:507 msg="gpu VRAM usage didn't recover within timeout" seconds=5.552699521 May 11 02:47:36 gpu ollama[27286]: time=2024-05-11T02:47:36.764Z level=WARN source=sched.go:507 msg="gpu VRAM usage didn't recover within timeout" seconds=5.883013969 ``` ### OS Linux ### GPU NVIDIA A100 40GB ### CPU Intel ### Ollama version 0.1.35
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llama/llama-mmap: fix missing include
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2025-01-15T20:04:49
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Proper memory and vector headers (like in GCC 15.1) do not provide the uint32_t type, so cstdint is required. llama-mmap.h:55:20: error: ‘uint32_t’ has not been declared 55 | void write_u32(uint32_t val) const;
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detect missing GPU runners and don't report incorrect GPU info/logs
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### What is the issue? ``` $ ollama -v ollama version is 0.4.1 $ ollama run llama3.2-vision:latest $ ollama ps NAME ID SIZE PROCESSOR UNTIL llama3.2-vision:latest 38107a0cd119 12 GB 100% GPU 2 minutes from now ``` from the logs it also saying ollama offload to cuda: ``` ollama[1773]: [GIN] 2024/11/10 - 21:32:56 | 200 | 22.078108ms | 127.0.0.1 | POST "/api/show" ollama[1773]: time=2024-11-10T21:32:56.205+08:00 level=WARN source=sched.go:137 msg="mllama doesn't support parallel requests yet" ollama[1773]: time=2024-11-10T21:32:56.342+08:00 level=INFO source=sched.go:714 msg="new model will fit in available VRAM in single GPU, loading" model=/var/lib/ollama/models/blobs/sha256-11f274007f093fefeec994a5dbbb33d0733a4feb87f7ab66dcd7c1069fef0068 gpu=GPU-957abb1f-e95c-db43-ee81-b345b6e60491 parallel=1 available=16139026432 required="11.3 GiB" ollama[1773]: time=2024-11-10T21:32:56.440+08:00 level=INFO source=server.go:105 msg="system memory" total="15.4 GiB" free="11.3 GiB" free_swap="12.2 GiB" ollama[1773]: time=2024-11-10T21:32:56.442+08:00 level=INFO source=memory.go:343 msg="offload to cuda" projector.weights="1.8 GiB" projector.graph="2.8 GiB" layers.requested=-1 layers.model=41 layers.offload=41 layers.split="" memory.available="[15.0 GiB]" memory.gpu_overhead="0 B" memory.required.full="11.3 GiB" memory.required.partial="11.3 GiB" memory.required.kv="656.2 MiB" memory.required.allocations="[11.3 GiB]" memory.weights.total="5.5 GiB" memory.weights.repeating="5.1 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="258.5 MiB" memory.graph.partial="669.5 MiB" ollama[1773]: time=2024-11-10T21:32:56.443+08:00 level=INFO source=server.go:383 msg="starting llama server" cmd="/tmp/ollama1704822012/runners/cpu_avx2/ollama_llama_server --model /var/lib/ollama/models/blobs/sha256-11f274007f093fefeec994a5dbbb33d0733a4feb87f7ab66dcd7c1069fef0068 --ctx-size 2048 --batch-size 512 --n-gpu-layers 41 --mmproj /var/lib/ollama/models/blobs/sha256-ece5e659647a20a5c28ab9eea1c12a1ad430bc0f2a27021d00ad103b3bf5206f --threads 6 --no-mmap --parallel 1 --port 40225" ollama[1773]: time=2024-11-10T21:32:56.443+08:00 level=INFO source=sched.go:449 msg="loaded runners" count=1 ollama[1773]: time=2024-11-10T21:32:56.443+08:00 level=INFO source=server.go:562 msg="waiting for llama runner to start responding" ollama[1773]: time=2024-11-10T21:32:56.444+08:00 level=INFO source=server.go:596 msg="waiting for server to become available" status="llm server error" ollama[1773]: time=2024-11-10T21:32:56.446+08:00 level=INFO source=runner.go:863 msg="starting go runner" ollama[1773]: time=2024-11-10T21:32:56.446+08:00 level=INFO source=runner.go:864 msg=system info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 1 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | RISCV_VECT = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | cgo(gcc)" threads=6 ollama[1773]: time=2024-11-10T21:32:56.446+08:00 level=INFO source=.:0 msg="Server listening on 127.0.0.1:40225" ollama[1773]: llama_model_loader: loaded meta data with 27 key-value pairs and 396 tensors from /var/lib/ollama/models/blobs/sha256-11f274007f093fefeec994a5dbbb33d0733a4feb87f7ab66dcd7c1069fef0068 (version GGUF V3 (latest)) ``` but from nvidia-smi nothing in there: ``` $ nvidia-smi Sun Nov 10 21:38:22 2024 +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 560.35.03 Driver Version: 560.35.03 CUDA Version: 12.6 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+========================+======================| | 0 NVIDIA GeForce RTX 4060 Ti Off | 00000000:01:00.0 On | N/A | | 0% 35C P8 14W / 165W | 498MiB / 16380MiB | 7% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ +-----------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=========================================================================================| | 0 N/A N/A 2054 G ...nim4annni-xorg-server-21.1.13/bin/X 252MiB | | 0 N/A N/A 3315 G ...bcvgsdr9v5mjmr-picom-12.3/bin/picom 94MiB | | 0 N/A N/A 10451 G ...irefox-132.0.1/bin/.firefox-wrapped 118MiB | +-----------------------------------------------------------------------------------------+ ``` ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version 0.4.1
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4,642
docs(gpu): Add workaround for nvidia GPU unavailable
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2024-05-26T02:50:12
2024-06-06T03:51:52
2024-06-06T03:51:51
CONTRIBUTOR
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Docs: - Update docs to add workaround for Nvidia GPU becoming unavailable after a period of time idle. - Minor: Markdown formatting fixes. I see people logging issues and asking for help on Discord for this quite often, this workaround has had good success in fixing the issue for many folk. e.g. https://github.com/ollama/ollama/issues/4604
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8,170
ollama and with_structured_output fails for new langchain-ollama==0.2.2
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7
2024-12-19T10:16:24
2025-01-24T10:28:31
2024-12-20T21:45:24
NONE
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### What is the issue? ``` pip install langchain-ollama==0.2.1 pydantic ``` and ```python from langchain_ollama import ChatOllama from typing import Optional from pydantic import BaseModel, Field class Person(BaseModel): name: str age: int llm = ChatOllama( model="llama3.1:latest", base_url="http://10.103.251.101:11434", temperature=0, ).with_structured_output(Person) llm.invoke("Erick 27") ``` correctly returns `Person(name='Erick', age=27)` `pip install langchain-ollama==0.2.2 pydantic` however returns `None` for the same code ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version ollama version is 0.3.5
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I_kwDOJ0Z1Ps6kbu60
8,239
GPU is not being used on macOS when launching from CLI
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2024-12-25T11:04:31
2024-12-27T11:41:56
2024-12-27T11:41:56
NONE
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### What is the issue? On macOS, if you use Ollama, it utilizes the GPU. However, when launching via CLI, it does not. I searched for GPU flags but couldn’t find any. We need default GPU support for macOS when using the CLI. https://github.com/user-attachments/assets/26fd9f8a-94f8-458f-8482-bbb96ab40697 ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version 0.5.4
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ROCm on WSL
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15
2024-06-25T15:37:46
2025-01-23T23:15:06
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Recently, AMD released preview drivers for Windows that, alongside userspace packages for WSL, enable one to use ROCm through WSL. Ollama detection of AMD GPUs in linux, however, uses the presence of loaded amdgpu drivers and other sysfs stuff to determine various properties of the GPU. These are not available with this WSL ROCm setup, nor is rocm-smi used for querying VRAM size and its usage etc. I was wondering if it was feasible to add some detection for this setup, so it can be used anyway, even if some runtime information is not available. Is runtime knowledge of the available VRAM strictly necessary? Could a user just not make sure not to load too big of a model, and in case of failing to do so, accept that the ROCm runtime will hard error out on failing hipMallocs etc? Perhaps we could warn users in the output that this might happen.
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5,855
Remove no longer supported max vram var
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2024-07-22T16:09:01
2024-07-22T17:36:30
2024-07-22T17:35:29
COLLABORATOR
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The OLLAMA_MAX_VRAM env var was a temporary workaround for OOM scenarios. With Concurrency this was no longer wired up, and the simplistic value doesn't map to multi-GPU setups. Users can still set `num_gpu` to limit memory usage to avoid OOM if we get our predictions wrong. Fixes #5754
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404 POST "/api/chat"
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2024-08-19T02:41:49
2024-11-05T11:02:35
2024-09-02T03:05:24
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### What is the issue? At first, it started running normally, but after a while, it reported 404,and can‘t run any model. Can you help me solve it?Thx. install by:curl -fsSL https://ollama.com/install.sh **log below:** Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: llm_load_print_meta: LF token = 148848 'ÄĬ' Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: llm_load_print_meta: EOT token = 151643 '<|endoftext|>' Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: llm_load_print_meta: max token length = 256 Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: ggml_cuda_init: found 1 CUDA devices: Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: Device 0: NVIDIA A100-PCIE-40GB, compute capability 8.0, VMM: yes Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: llm_load_tensors: ggml ctx size = 0.30 MiB Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: llm_load_tensors: offloading 28 repeating layers to GPU Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: llm_load_tensors: offloading non-repeating layers to GPU Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: llm_load_tensors: offloaded 29/29 layers to GPU Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: llm_load_tensors: CPU buffer size = 292.36 MiB Aug 19 10:25:57 ecs-lcdsj ollama[1026502]: llm_load_tensors: CUDA0 buffer size = 3928.07 MiB Aug 19 10:26:01 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:26:01 | 404 | 185.499µs | ::1 | POST "/api/chat" Aug 19 10:26:02 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:26:02 | 200 | 1.273346ms | 172.17.0.2 | GET "/api/tags" Aug 19 10:26:02 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:26:02 | 200 | 88.559µs | 172.17.0.2 | GET "/api/vers> Aug 19 10:26:26 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:26:26 | 200 | 207.009µs | 127.0.0.1 | HEAD "/" Aug 19 10:26:26 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:26:26 | 200 | 1.100698ms | 127.0.0.1 | GET "/api/tags" Aug 19 10:26:33 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:26:33 | 200 | 46.933µs | 127.0.0.1 | HEAD "/" Aug 19 10:26:33 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:26:33 | 200 | 23.522263ms | 127.0.0.1 | POST "/api/show" Aug 19 10:26:44 ecs-lcdsj ollama[1026502]: time=2024-08-19T10:26:44.502+08:00 level=INFO source=server.go:627 msg="waiting for serve> Aug 19 10:26:44 ecs-lcdsj ollama[1026502]: time=2024-08-19T10:26:44.780+08:00 level=INFO source=server.go:627 msg="waiting for serve> Aug 19 10:27:01 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:27:01 | 404 | 7.051455ms | ::1 | POST "/api/chat" Aug 19 10:28:01 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:28:01 | 404 | 367.924µs | ::1 | POST "/api/chat" Aug 19 10:28:55 ecs-lcdsj systemd[1]: Stopping Ollama Service... Aug 19 10:28:55 ecs-lcdsj ollama[1026502]: time=2024-08-19T10:28:55.817+08:00 level=WARN source=server.go:600 msg="client connection> Aug 19 10:28:55 ecs-lcdsj ollama[1026502]: time=2024-08-19T10:28:55.818+08:00 level=ERROR source=sched.go:451 msg="error loading lla> Aug 19 10:28:55 ecs-lcdsj ollama[1026502]: [GIN] 2024/08/19 - 10:28:55 | 499 | 3m0s | 172.17.0.2 | POST "/api/chat" Aug 19 10:28:56 ecs-lcdsj systemd[1]: ollama.service: Succeeded. Aug 19 10:28:56 ecs-lcdsj systemd[1]: Stopped Ollama Service. Aug 19 10:28:56 ecs-lcdsj systemd[1]: Started Ollama Service. Aug 19 10:28:56 ecs-lcdsj ollama[1032507]: 2024/08/19 10:28:56 routes.go:1125: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU> Aug 19 10:28:56 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:28:56.246+08:00 level=INFO source=images.go:782 msg="total blobs: 15" Aug 19 10:28:56 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:28:56.249+08:00 level=INFO source=images.go:790 msg="total unused blob> Aug 19 10:28:56 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:28:56.249+08:00 level=INFO source=routes.go:1172 msg="Listening on [::> Aug 19 10:28:56 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:28:56.250+08:00 level=INFO source=payload.go:30 msg="extracting embedd> Aug 19 10:29:01 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:29:01.035+08:00 level=INFO source=payload.go:44 msg="Dynamic LLM libra> Aug 19 10:29:01 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:29:01.037+08:00 level=INFO source=gpu.go:204 msg="looking for compatib> Aug 19 10:29:10 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:29:10.605+08:00 level=INFO source=types.go:105 msg="inference compute"> Aug 19 10:29:10 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:29:10.606+08:00 level=INFO source=types.go:105 msg="inference compute"> Aug 19 10:29:10 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:29:10.606+08:00 level=INFO source=types.go:105 msg="inference compute"> Aug 19 10:29:10 ecs-lcdsj ollama[1032507]: time=2024-08-19T10:29:10.606+08:00 level=INFO source=types.go:105 msg="inference compute"> Aug 19 10:29:10 ecs-lcdsj ollama[1032507]: [GIN] 2024/08/19 - 10:29:10 | 404 | 13.419583ms | ::1 | POST "/api/chat" Aug 19 10:30:01 ecs-lcdsj ollama[1032507]: [GIN] 2024/08/19 - 10:30:01 | 404 | 990.349µs | ::1 | POST "/api/chat" Aug 19 10:31:01 ecs-lcdsj ollama[1032507]: [GIN] 2024/08/19 - 10:31:01 | 404 | 224.61µs | ::1 | POST "/api/chat" Aug 19 10:32:01 ecs-lcdsj ollama[1032507]: [GIN] 2024/08/19 - 10:32:01 | 404 | 15.250541ms | ::1 | POST "/api/chat" Aug 19 10:32:27 ecs-lcdsj ollama[1032507]: [GIN] 2024/08/19 - 10:32:27 | 200 | 46.654µs | 127.0.0.1 | GET "/api/vers> Aug 19 10:33:01 ecs-lcdsj ollama[1032507]: [GIN] 2024/08/19 - 10:33:01 | 404 | 959.34µs | ::1 | POST "/api/chat" Aug 19 10:34:02 ecs-lcdsj ollama[1032507]: [GIN] 2024/08/19 - 10:34:02 | 404 | 18.592866ms | ::1 | POST "/api/chat" Aug 19 10:35:01 ecs-lcdsj ollama[1032507]: [GIN] 2024/08/19 - 10:35:01 | 404 | 284.394µs | ::1 | POST "/api/chat" ### OS Linux ### GPU Nvidia ### CPU Other ### Ollama version 0.3.6
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change push to chunked uploads from monolithic
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2023-07-22T23:16:24
2023-07-23T00:31:27
2023-07-23T00:31:26
CONTRIBUTOR
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Support directly running GGUF files without importing
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2024-10-18T16:34:56
2024-12-20T04:40:13
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In llama.cpp we can directly run models with `llama-cli -m your_model.gguf ` without having to import the model, It would be great if we can do the same with ollama.
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2,161,701,266
I_kwDOJ0Z1Ps6A2PGS
2,839
keeps loading but never success
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2024-02-29T16:55:00
2024-11-06T18:00:06
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ollama run renxin_query_type_classify "hello" ![image](https://github.com/ollama/ollama/assets/16278392/a7ee2375-3493-4d41-b5a6-aa4481e53baf) I successfully generate a model from gguf file. however keeps loading but never succeed... Any idea what's happening? FROM ./model_query_type_classify.gguf PARAMETER temperature 0 SYSTEM """ classify user type """
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subprocess or pexpect rather than the API
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2023-12-11T22:34:32
2023-12-11T22:49:56
2023-12-11T22:46:01
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I find that Ollama is fast enough, but the API is very slow. I've been trying to use something like subprocess. The is program runs, but waiting for the output is torturously slow: import subprocess def run_ollama(model_name): # Build the Ollama command ollama_command = f"ollama run {model_name}" # Start Ollama as a subprocess process = subprocess.Popen(ollama_command, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True, shell=True) # Enter the interactive loop while True: # Get user input for the prompt user_input = input("Enter prompt (type 'exit' to end): ") # Check if the user wants to exit if user_input.lower() == 'exit': break # Send the user input to Ollama process.stdin.write(user_input + '\n') process.stdin.flush() # Read and print the output from Ollama output, error = process.communicate() print("Ollama Output:", output.strip()) print("Ollama Error:", error.strip()) # Close the subprocess process.stdin.close() process.stdout.close() process.stderr.close() process.terminate() if __name__ == "__main__": # Get the model name from the command line arguments import sys if len(sys.argv) != 2: print("Usage: python script.py <model_name>") sys.exit(1) model_name = sys.argv[1] # Run Ollama with the specified model run_ollama(model_name) Attempts to stream the output as it is being created have failed. Even using the pexpect module fails, I believe because of the animated prompt. Is there a way to run this as a subprocess and get the results back word by word?
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1,256
Implement tensor_split support in modelfile
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2023-11-23T14:58:47
2024-04-08T17:15:18
2024-01-25T22:13:50
NONE
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This patch allows to specify a string for --tensor-split in a modelfile, for example: PARAMETER tensor_split "25,75" This allows to adjust VRAM allocation for each model, for example, to optimize VRAM usage on each GPU or to better accommodate models which need more memory for context on the main GPU.
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PR_kwDOJ0Z1Ps5lrOJr
2,307
Fix help string for stop parameter
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2024-02-01T09:47:24
2024-05-07T23:48:35
2024-05-07T23:48:35
CONTRIBUTOR
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Changed the help prompt for setting the stop parameters, and quotes or commas are otherwise included in the stop-token: /set parameter stop "?", "!" # Invalid /set parameter stop ? ! # Valid
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Fix OpenAI `finish_reason` values when empty
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2024-05-11T22:31:27
2024-05-11T22:31:42
2024-05-11T22:31:41
MEMBER
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Fixes https://github.com/ollama/ollama/issues/4357
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Add LLAVA++ model
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2024-05-03T14:24:21
2024-05-21T21:48:43
2024-05-21T21:48:43
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There is a new version of the Amazing LLava model that uses Llama 3 or Phi-3: https://huggingface.co/collections/MBZUAI/llava-llama-3-and-phi-3-mini-662b38b972e3e3e4d8f821bb https://github.com/mbzuai-oryx/LLaVA-pp
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7,897
Audio to audio models
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2024-11-30T18:31:44
2024-11-30T18:31:44
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Hi, any plan to add audio to audio support? There are couple of open source model witch provide that
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PR_kwDOJ0Z1Ps56_i00
6,729
Feature: Add Support for Distributed Inferencing
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2024-09-10T14:24:43
2025-01-24T23:15:45
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This feature adds support for llama.cpp RPC. This allows for distributed inferencing on different devices. This Pull Request aims to implement #4643.
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1,444
Added mention of the NOPRUNE env var
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2023-12-09T01:38:51
2023-12-12T01:15:00
2023-12-12T01:15:00
CONTRIBUTOR
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OLLAMA_NOPRUNE will prevent the pruning process from running, but it isn't mentioned anywhere outside of the code and a merged PR.
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How to properly format Advanced Parameters / options in API calls?
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2023-11-07T08:10:16
2023-11-09T00:44:38
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API Documentation gives a proper example, how to use `POST /api/generate` But how to properly format the JSON object to use Advanced Parameters? Especially `options` and `system`. I tried to request the following via `POST /api/generate`. Everything behaves as expected, eg stream, but options is not workig: ` { "model": "llama2:latest", "stream": false, "prompt": "Sing a song.", "options": { "temperature": 5} } ` Happy about hints!!! Thanks a lot
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4,392
Use GTT memory in case of iGPUs to run the model efiiciently.
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2024-05-13T08:28:01
2024-11-02T18:48:53
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Models run on System memory using CPU is perfectly fine. But when using integrate GPUs which have limited VRAM locked by vendors, we have model crash due to "low vram memory" They have feature called GTT memory on linux, and Shared Memory on windows, which they can use whenever their VRAM capacity is nearly full.
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Adding avx2+avx512 to cuda runner in new ollama code
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In the old code i added avx2+512 in the gen_windows.ps1 by simply adding DGGML_AVX2=on & DGGML_AVX512=on after the DGGML_AVX=on line in the cuda build function It added a fairly decent performance boost I have added avx512 to cpu, But In the new code i cannot seem to find where to properly add it in the make files to also add it to cuda What do i need to change on what lines in which files to add this to the new ollama code?
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3,146
server: replace blob prefix separator from ':' to '-'
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2024-03-14T18:32:54
2024-03-25T16:22:07
2024-03-15T03:18:06
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This fixes issues with blob file names that contain ':' characters to be 9 rejected by file systems that do not support them.
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6,653
Loading a smaller context model after a bigger model is loaded
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### What is the issue? ## Hardware 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): 64 On-line CPU(s) list: 0-63 Vendor ID: GenuineIntel Model name: Intel(R) Xeon(R) Gold 5218 CPU @ 2.30GHz CPU family: 6 Model: 85 Thread(s) per core: 2 Core(s) per socket: 16 Socket(s): 2 3 x Quadro RTX 5000 ![Screenshot from 2024-09-05 14-53-01](https://github.com/user-attachments/assets/160c1e28-ca5e-4499-bb05-481a4528024d) ## Error The below happens when **llama3.1** is already loaded and i am loading **smollm** which are having different context length. Both model are loaded into gpu on request but results below on API request ![Screenshot from 2024-09-05 14-51-52](https://github.com/user-attachments/assets/769c484c-1399-4c65-8e93-4dd331eab024) Both model works fine concurrently using ollama cli ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version 0.3.5
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fix: pasting slash commands
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2024-01-18T01:00:22
2025-01-15T02:54:49
2025-01-15T02:54:44
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there is a bug in paste where the pasted content is written directly to the prompt buffer instead of being processed. for most content, this is fine but slash commands are processed line-by-line. aggregate status updates, e.g. "Set 'verbose' mode.", "Set system message.", to the end for aesthetics. the status message shouldn't display while in paste mode
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Ollama behind proxy can't pull new models anymore
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2025-01-13T01:38:03
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### What is the issue? Hi there, I am using Ollama on different machines (Ubuntu inside a docker container together with open web ui and on a Mac standalone) within a university that enforces the use of a proxy to access the internet. On both systems, the proxy is correctly configured and Ollama worked with it, be it via zsh ollama pull modelname or via open web ui. Now on both systems pulling new models does not work anymore. The Ollama container at the ubuntu server says: `time=2024-12-11T14:34:35.279Z level=INFO source=images.go:990 msg="request failed: Get \"http://registry.ollama.ai/v2/library/qwen2.5-coder/manifests/latest\": dial tcp 104.21.75.227:80: connect: connection refused" [GIN] 2024/12/11 - 14:34:35 | 200 | 55.03224ms | 172.31.1.5 | POST "/api/pull" ` On the Mac I get `Error: pull model manifest: Get "https://registry.ollama.ai/v2/library/qwen2.5-coder/manifests/latest": dial tcp 172.67.182.229:443: connect: connection refused` On both systems `wget https://registry.ollama.ai/v2/library/qwen2.5-coder/manifests/latest` pulls the manifest correctly. When I deactivate the LAN connection on the Mac and use WLAN to connect directly to the internet without the proxy, anything works as expected. This behavior is new to me, as I am running ollama for several months on both machines and never had these issues. Any hint is highly appreciated for the Ollama - Ollama web ui on the ubuntu server this is my docker-compose.yml which worked fine in the past `services: ollama: deploy: resources: reservations: devices: - driver: ${OLLAMA_GPU_DRIVER-nvidia} count: all capabilities: - gpu volumes: - /docker-apps/ollama-web-ui/volumes/ollama:/root/.ollama container_name: ollama pull_policy: always tty: true restart: unless-stopped image: ollama/ollama:${OLLAMA_DOCKER_TAG-latest} environment: - HTTP_PROXY=http://proxy.my-uni.tld:8080 - HTTPS_PROXY=http://proxy.my-uni.tld:8080 - NO_PROXY=localhost,127.0.0.1,.my-uni.tld ports: - 11434:11434 networks: - ollama_network open-webui: build: context: . args: OLLAMA_BASE_URL: '/ollama' dockerfile: Dockerfile image: ghcr.io/open-webui/open-webui:${WEBUI_DOCKER_TAG-main} container_name: open-webui volumes: - /docker-apps/ollama-web-ui/volumes/open-web-ui:/app/backend/data depends_on: - ollama networks: - ollama_network environment: - 'OLLAMA_BASE_URL=https://quest-gpu-06.my-uni.tld' - 'WEBUI_SECRET_KEY=xyz' - HTTP_PROXY=http://proxy.my-uni.tld:8080 - HTTPS_PROXY=http://proxy.my-uni.tld:8080 - NO_PROXY=localhost,127.0.0.1,.my-uni.tld extra_hosts: - host.docker.internal:host-gateway restart: unless-stopped ports: - 8080:8080 networks: ollama_network: external: true` ### OS Linux, macOS ### GPU Nvidia, Apple ### CPU AMD, Apple ### Ollama version 0.4.4
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api interface /api/generate I need to make sure that every question is not answered from the previous record How to do?
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api interface /api/generate I need to make sure that every question is not answered from the previous record How to do?
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could anyone help me? something is not work. use a special gpu
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### What is the issue? when i follow the instruction to install ollama with source code. i can not to finish gen.linux.sh there are error information `CMake Error at ggml/src/CMakeLists.txt:440 (find_package): By not providing "Findhip.cmake" in CMAKE_MODULE_PATH this project has asked CMake to find a package configuration file provided by "hip", but CMake did not find one. Could not find a package configuration file provided by "hip" with any of the following names: hipConfig.cmake hip-config.cmake Add the installation prefix of "hip" to CMAKE_PREFIX_PATH or set "hip_DIR" to a directory containing one of the above files. If "hip" provides a separate development package or SDK, be sure it has been installed.` i try to modify as [https://github.com/ROCm/HIP/tree/master/samples/2_Cookbook/12_cmake_hip_add_executable#including-findhip-cmake-module-in-the-project](url) but the cmakelist can not be modify, when i restart gen.linux.sh , camkelists.txt on ggml/src do not modify could anyone help me? ### OS Linux ### GPU AMD, Other ### CPU _No response_ ### Ollama version _No response_
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Windows Error:pull model manifest return wsarecv: An existing connection was forcibly closed by the remote host.
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i can't down the model: [app.log](https://github.com/ollama/ollama/files/14589977/app.log) [server.log](https://github.com/ollama/ollama/files/14589978/server.log) `PS C:\Users\heimu\AppData\Local\Ollama> ollama pull llama2 pulling manifest Error: pull model manifest: Get "https://ollama.com/token?nonce=A-QmGZFS0za-Kv0GKrDy3Q&scope=repository%!A(MISSING)library%!F(MISSING)llama2%!A(MISSING)pull&service=ollama.com&ts=1710339130": read tcp 192.168.247.214:56798->34.120.132.20:443: wsarecv: An existing connection was forcibly closed by the remote host. PS C:\Users\heimu\AppData\Local\Ollama>`
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docs: generate chat response `loadDuration` missing
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in the documentation https://github.com/jmorganca/ollama/blob/main/docs/api.md#response-6 loadDuration is listed as a return value but does not get returned by the api <img width="981" alt="image" src="https://github.com/jmorganca/ollama/assets/4961248/bb0dbc37-c2cf-48ff-8c8d-be2ffcfa5115">
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website content and design update
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2023-07-18T19:58:33
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Adding instructions when user doesn't have sudo privileges
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Adding instructions when user doesn't have sudo privileges according to https://github.com/ollama/ollama/issues/2111
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6,287
UHD intel GPU Accelerate
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2024-08-09T16:03:25
2024-08-28T02:52:37
2024-08-09T18:36:38
NONE
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### What is the issue? Why Ollama use CPU, but not utilizing intel UHD integrated GPU ? (Computer with not Nvidia GPU) ### OS Linux ### GPU Intel ### CPU Intel ### Ollama version _No response_
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https://api.github.com/repos/ollama/ollama/issues/6287/timeline
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https://api.github.com/repos/ollama/ollama/issues/199
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https://github.com/ollama/ollama/issues/199
1,819,047,422
I_kwDOJ0Z1Ps5sbHX-
199
nous-hermes and parameters
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2023-07-24T19:58:34
2023-08-23T17:46:45
2023-08-23T17:46:45
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Hello, If I want to generate my model, this example with temperature and num_ctx paramters crashing "ollama" application. FROM nous-hermes # sets the temperature to 1 [higher is more creative, lower is more coherent] # sets the context size to 4096 PARAMETER temperature 2 PARAMETER num_ctx 4096 Here is all result when app crashed. alivardar@Alis-MacBook-Air ModelFiles % ollama serve [GIN-debug] [WARNING] Creating an Engine instance with the Logger and Recovery middleware already attached. [GIN-debug] [WARNING] Running in "debug" mode. Switch to "release" mode in production. - using env: export GIN_MODE=release - using code: gin.SetMode(gin.ReleaseMode) [GIN-debug] GET / --> github.com/jmorganca/ollama/server.Serve.func1 (3 handlers) [GIN-debug] POST /api/pull --> github.com/jmorganca/ollama/server.PullModelHandler (3 handlers) [GIN-debug] POST /api/generate --> github.com/jmorganca/ollama/server.GenerateHandler (3 handlers) [GIN-debug] POST /api/create --> github.com/jmorganca/ollama/server.CreateModelHandler (3 handlers) [GIN-debug] POST /api/push --> github.com/jmorganca/ollama/server.PushModelHandler (3 handlers) [GIN-debug] GET /api/tags --> github.com/jmorganca/ollama/server.ListModelsHandler (3 handlers) [GIN-debug] DELETE /api/delete --> github.com/jmorganca/ollama/server.DeleteModelHandler (3 handlers) 2023/07/24 22:54:48 routes.go:237: Listening on 127.0.0.1:11434 llama.cpp: loading model from /Users/alivardar/.ollama/models/blobs/sha256:d1735b93e1dc503f1045ccd6c8bd73277b18ba892befd1dc29e9b9a7822ed998 llama_model_load_internal: format = ggjt v3 (latest) llama_model_load_internal: n_vocab = 32001 llama_model_load_internal: n_ctx = 4096 llama_model_load_internal: n_embd = 5120 llama_model_load_internal: n_mult = 256 llama_model_load_internal: n_head = 40 llama_model_load_internal: n_layer = 40 llama_model_load_internal: n_rot = 128 llama_model_load_internal: freq_base = 10000.0 llama_model_load_internal: freq_scale = 1 llama_model_load_internal: ftype = 2 (mostly Q4_0) llama_model_load_internal: n_ff = 13824 llama_model_load_internal: model size = 13B llama_model_load_internal: ggml ctx size = 0.09 MB llama_model_load_internal: mem required = 9132.72 MB (+ 1608.00 MB per state) llama_new_context_with_model: kv self size = 3200.00 MB ggml_metal_init: allocating ggml_metal_init: using MPS ggml_metal_init: loading '/Applications/Ollama.app/Contents/Resources/ggml-metal.metal' ggml_metal_init: loaded kernel_add 0x152f0a280 ggml_metal_init: loaded kernel_mul 0x152f0a880 ggml_metal_init: loaded kernel_mul_row 0x152f0aeb0 ggml_metal_init: loaded kernel_scale 0x152f0b3d0 ggml_metal_init: loaded kernel_silu 0x152f0b8f0 ggml_metal_init: loaded kernel_relu 0x152f0be10 ggml_metal_init: loaded kernel_gelu 0x152f0c330 ggml_metal_init: loaded kernel_soft_max 0x152f0c9e0 ggml_metal_init: loaded kernel_diag_mask_inf 0x152f0d040 ggml_metal_init: loaded kernel_get_rows_f16 0x152f0d6c0 ggml_metal_init: loaded kernel_get_rows_q4_0 0x152f0dd40 ggml_metal_init: loaded kernel_get_rows_q4_1 0x152f0e530 ggml_metal_init: loaded kernel_get_rows_q2_K 0x152f0ebb0 ggml_metal_init: loaded kernel_get_rows_q3_K 0x152f0f230 ggml_metal_init: loaded kernel_get_rows_q4_K 0x152f0f8b0 ggml_metal_init: loaded kernel_get_rows_q5_K 0x152f0ff30 ggml_metal_init: loaded kernel_get_rows_q6_K 0x152f105b0 ggml_metal_init: loaded kernel_rms_norm 0x152f10c70 ggml_metal_init: loaded kernel_norm 0x152f11320 ggml_metal_init: loaded kernel_mul_mat_f16_f32 0x152f11cf0 ggml_metal_init: loaded kernel_mul_mat_q4_0_f32 0x152f123b0 ggml_metal_init: loaded kernel_mul_mat_q4_1_f32 0x152f12a70 ggml_metal_init: loaded kernel_mul_mat_q2_K_f32 0x152f13150 ggml_metal_init: loaded kernel_mul_mat_q3_K_f32 0x152f139d0 ggml_metal_init: loaded kernel_mul_mat_q4_K_f32 0x152f14090 ggml_metal_init: loaded kernel_mul_mat_q5_K_f32 0x152f14730 ggml_metal_init: loaded kernel_mul_mat_q6_K_f32 0x152f14dd0 ggml_metal_init: loaded kernel_rope 0x152f154f0 ggml_metal_init: loaded kernel_alibi_f32 0x152f16010 ggml_metal_init: loaded kernel_cpy_f32_f16 0x152f168a0 ggml_metal_init: loaded kernel_cpy_f32_f32 0x152f17130 ggml_metal_init: loaded kernel_cpy_f16_f16 0x152f179c0 ggml_metal_init: recommendedMaxWorkingSetSize = 10922.67 MB ggml_metal_init: hasUnifiedMemory = true ggml_metal_init: maxTransferRate = built-in GPU llama_new_context_with_model: max tensor size = 87.89 MB ggml_metal_add_buffer: allocated 'data ' buffer, size = 6984.06 MB, ( 6984.52 / 10922.67) ggml_metal_add_buffer: allocated 'eval ' buffer, size = 1040.00 MB, ( 8024.52 / 10922.67) ggml_metal_add_buffer: allocated 'kv ' buffer, size = 3202.00 MB, (11226.52 / 10922.67), warning: current allocated size is greater than the recommended max working set size ggml_metal_add_buffer: allocated 'scr0 ' buffer, size = 597.00 MB, (11823.52 / 10922.67), warning: current allocated size is greater than the recommended max working set size ggml_metal_add_buffer: allocated 'scr1 ' buffer, size = 512.00 MB, (12335.52 / 10922.67), warning: current allocated size is greater than the recommended max working set size ggml_metal_graph_compute: command buffer 0 failed with status 5 GGML_ASSERT: ggml-metal.m:1023: false SIGABRT: abort PC=0x1a1500724 m=5 sigcode=0 signal arrived during cgo execution goroutine 19 [syscall]: runtime.cgocall(0x102d99e8c, 0x1400018d278) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/cgocall.go:157 +0x54 fp=0x1400018d240 sp=0x1400018d200 pc=0x102888c64 github.com/jmorganca/ollama/llama._Cfunc_llama_eval(0x153813c00, 0x14000471e28, 0x1, 0x0, 0x8) _cgo_gotypes.go:210 +0x38 fp=0x1400018d270 sp=0x1400018d240 pc=0x102d87448 github.com/jmorganca/ollama/llama.New.func4(0x102fc2de0?, {0x14000471e28, 0x1, 0x0?}, {0xffffffffffffffff, 0x0, 0x1000, 0x200, 0x1, 0x0, ...}) /Users/jmorgan/workspace/ollama/llama/llama.go:141 +0x7c fp=0x1400018d2c0 sp=0x1400018d270 pc=0x102d8826c github.com/jmorganca/ollama/llama.New({0x1400028ce00, 0x6d}, {0xffffffffffffffff, 0x0, 0x1000, 0x200, 0x1, 0x0, 0x0, 0x1, ...}) /Users/jmorgan/workspace/ollama/llama/llama.go:141 +0x288 fp=0x1400018d480 sp=0x1400018d2c0 pc=0x102d88028 github.com/jmorganca/ollama/server.GenerateHandler(0x14000498300) /Users/jmorgan/workspace/ollama/server/routes.go:54 +0x5c0 fp=0x1400018d6e0 sp=0x1400018d480 pc=0x102d939f0 github.com/gin-gonic/gin.(*Context).Next(...) /Users/jmorgan/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174 github.com/gin-gonic/gin.CustomRecoveryWithWriter.func1(0x14000498300) /Users/jmorgan/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/recovery.go:102 +0x7c fp=0x1400018d730 sp=0x1400018d6e0 pc=0x102d7eb3c github.com/gin-gonic/gin.(*Context).Next(...) /Users/jmorgan/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174 github.com/gin-gonic/gin.LoggerWithConfig.func1(0x14000498300) /Users/jmorgan/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/logger.go:240 +0xac fp=0x1400018d8e0 sp=0x1400018d730 pc=0x102d7ddbc github.com/gin-gonic/gin.(*Context).Next(...) /Users/jmorgan/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/context.go:174 github.com/gin-gonic/gin.(*Engine).handleHTTPRequest(0x1400047a820, 0x14000498300) /Users/jmorgan/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/gin.go:620 +0x54c fp=0x1400018da70 sp=0x1400018d8e0 pc=0x102d7cecc github.com/gin-gonic/gin.(*Engine).ServeHTTP(0x1400047a820, {0x103014f10?, 0x14000443420}, 0x14000498200) /Users/jmorgan/go/pkg/mod/github.com/gin-gonic/gin@v1.9.1/gin.go:576 +0x1d4 fp=0x1400018dab0 sp=0x1400018da70 pc=0x102d7c7d4 net/http.serverHandler.ServeHTTP({0x103012e70?}, {0x103014f10, 0x14000443420}, 0x14000498200) /opt/homebrew/Cellar/go/1.20.5/libexec/src/net/http/server.go:2936 +0x2d8 fp=0x1400018db60 sp=0x1400018dab0 pc=0x102b06408 net/http.(*conn).serve(0x140001a0900, {0x103015588, 0x1400049a060}) /opt/homebrew/Cellar/go/1.20.5/libexec/src/net/http/server.go:1995 +0x560 fp=0x1400018dfa0 sp=0x1400018db60 pc=0x102b02100 net/http.(*Server).Serve.func3() /opt/homebrew/Cellar/go/1.20.5/libexec/src/net/http/server.go:3089 +0x30 fp=0x1400018dfd0 sp=0x1400018dfa0 pc=0x102b06c30 runtime.goexit() /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/asm_arm64.s:1172 +0x4 fp=0x1400018dfd0 sp=0x1400018dfd0 pc=0x1028ec074 created by net/http.(*Server).Serve /opt/homebrew/Cellar/go/1.20.5/libexec/src/net/http/server.go:3089 +0x520 goroutine 1 [IO wait]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:381 +0xe4 fp=0x14000337860 sp=0x14000337840 pc=0x1028bbee4 runtime.netpollblock(0x140003378f8?, 0x296fca4?, 0x1?) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/netpoll.go:527 +0x158 fp=0x140003378a0 sp=0x14000337860 pc=0x1028b5408 internal/poll.runtime_pollWait(0x12aae7a18, 0x72) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/netpoll.go:306 +0xa0 fp=0x140003378d0 sp=0x140003378a0 pc=0x1028e5c40 internal/poll.(*pollDesc).wait(0x14000478600?, 0x0?, 0x0) /opt/homebrew/Cellar/go/1.20.5/libexec/src/internal/poll/fd_poll_runtime.go:84 +0x28 fp=0x14000337900 sp=0x140003378d0 pc=0x10296b2e8 internal/poll.(*pollDesc).waitRead(...) /opt/homebrew/Cellar/go/1.20.5/libexec/src/internal/poll/fd_poll_runtime.go:89 internal/poll.(*FD).Accept(0x14000478600) /opt/homebrew/Cellar/go/1.20.5/libexec/src/internal/poll/fd_unix.go:614 +0x250 fp=0x140003379b0 sp=0x14000337900 pc=0x10296fd90 net.(*netFD).accept(0x14000478600) /opt/homebrew/Cellar/go/1.20.5/libexec/src/net/fd_unix.go:172 +0x28 fp=0x14000337a70 sp=0x140003379b0 pc=0x1029af2f8 net.(*TCPListener).accept(0x1400012ed68) /opt/homebrew/Cellar/go/1.20.5/libexec/src/net/tcpsock_posix.go:148 +0x28 fp=0x14000337aa0 sp=0x14000337a70 pc=0x1029c48f8 net.(*TCPListener).Accept(0x1400012ed68) /opt/homebrew/Cellar/go/1.20.5/libexec/src/net/tcpsock.go:297 +0x2c fp=0x14000337ae0 sp=0x14000337aa0 pc=0x1029c3a6c net/http.(*onceCloseListener).Accept(0x140001a0900?) <autogenerated>:1 +0x30 fp=0x14000337b00 sp=0x14000337ae0 pc=0x102b2a3b0 net/http.(*Server).Serve(0x1400039cff0, {0x103014d00, 0x1400012ed68}) /opt/homebrew/Cellar/go/1.20.5/libexec/src/net/http/server.go:3059 +0x304 fp=0x14000337c30 sp=0x14000337b00 pc=0x102b068d4 github.com/jmorganca/ollama/server.Serve({0x103014d00, 0x1400012ed68}) /Users/jmorgan/workspace/ollama/server/routes.go:242 +0x294 fp=0x14000337ca0 sp=0x14000337c30 pc=0x102d95aa4 github.com/jmorganca/ollama/cmd.RunServer(0x14000449200?, {0x102deac46?, 0x0?, 0x0?}) /Users/jmorgan/workspace/ollama/cmd/cmd.go:384 +0x114 fp=0x14000337d20 sp=0x14000337ca0 pc=0x102d98ea4 github.com/spf13/cobra.(*Command).execute(0x14000449200, {0x10348e290, 0x0, 0x0}) /Users/jmorgan/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:940 +0x5c8 fp=0x14000337e60 sp=0x14000337d20 pc=0x102bacb18 github.com/spf13/cobra.(*Command).ExecuteC(0x14000448900) /Users/jmorgan/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:1068 +0x35c fp=0x14000337f20 sp=0x14000337e60 pc=0x102bad26c github.com/spf13/cobra.(*Command).Execute(...) /Users/jmorgan/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:992 github.com/spf13/cobra.(*Command).ExecuteContext(0x14000054768?, {0x103015518?, 0x14000120010?}) /Users/jmorgan/go/pkg/mod/github.com/spf13/cobra@v1.7.0/command.go:985 +0x50 fp=0x14000337f40 sp=0x14000337f20 pc=0x102bace00 main.main() /Users/jmorgan/workspace/ollama/main.go:10 +0x34 fp=0x14000337f70 sp=0x14000337f40 pc=0x102d99c54 runtime.main() /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:250 +0x248 fp=0x14000337fd0 sp=0x14000337f70 pc=0x1028bbab8 runtime.goexit() /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/asm_arm64.s:1172 +0x4 fp=0x14000337fd0 sp=0x14000337fd0 pc=0x1028ec074 goroutine 2 [force gc (idle)]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:381 +0xe4 fp=0x14000054fa0 sp=0x14000054f80 pc=0x1028bbee4 runtime.goparkunlock(...) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:387 runtime.forcegchelper() /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:305 +0xb8 fp=0x14000054fd0 sp=0x14000054fa0 pc=0x1028bbd28 runtime.goexit() /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/asm_arm64.s:1172 +0x4 fp=0x14000054fd0 sp=0x14000054fd0 pc=0x1028ec074 created by runtime.init.6 /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:293 +0x24 goroutine 3 [GC sweep wait]: runtime.gopark(0x0?, 0x0?, 0x0?, 0x0?, 0x0?) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:381 +0xe4 fp=0x14000055760 sp=0x14000055740 pc=0x1028bbee4 runtime.goparkunlock(...) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:387 runtime.bgsweep(0x0?) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/mgcsweep.go:278 +0xa4 fp=0x140000557b0 sp=0x14000055760 pc=0x1028a8bc4 runtime.gcenable.func1() /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/mgc.go:178 +0x28 fp=0x140000557d0 sp=0x140000557b0 pc=0x10289d6d8 runtime.goexit() /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/asm_arm64.s:1172 +0x4 fp=0x140000557d0 sp=0x140000557d0 pc=0x1028ec074 created by runtime.gcenable /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/mgc.go:178 +0x74 goroutine 4 [GC scavenge wait]: runtime.gopark(0x1400007c000?, 0x102ee86e8?, 0x1?, 0x0?, 0x0?) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:381 +0xe4 fp=0x14000055f50 sp=0x14000055f30 pc=0x1028bbee4 runtime.goparkunlock(...) /opt/homebrew/Cellar/go/1.20.5/libexec/src/runtime/proc.go:387 runtime.(*scavengerState).park(0x1033d2d60) 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I_kwDOJ0Z1Ps6n-Nqe
8,657
running ollama deepseek-r1:1.5b on windows stuck for whole day
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2025-01-29T12:40:29
2025-01-29T13:44:51
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optimize for low end device, I'm using should windows with 8gb ram and 4gb nvidia gtx 1650 graphics card and im unable to run PS deepseek-r1:1.5b In command line it stuck at 0% for whole day ```bash C:\Users\ADITYA> ollama run deepseek-r1:1.5b pulling manifest pulling aabd4debf0c8... 0% ▕ ▏ 0 B/1.1 GB ``` ![Image](https://github.com/user-attachments/assets/e4f25ad8-1780-46c3-abad-59004c3c1e71)
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8,267
examples: remove codified examples
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2024-12-29T22:10:00
2025-01-13T19:26:25
2025-01-13T19:26:23
CONTRIBUTOR
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This PR aims to streamline the examples and to have outgoing links to community frameworks instead. Closes #8117
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5,090
`amdgpu version file missing` when running via systemd
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Previous issue was closed as fixed but the bug still exists. Hi, this doesn't happen to me when running ollama as root directly in a shell, but it happens when I start ollama as a service (regardless of the user): ``` amnesia λ ~/ sudo systemctl status ollama ● ollama.service - Ollama Service Loaded: loaded (/etc/systemd/system/ollama.service; enabled; preset: disabled) Active: active (running) since Sun 2024-06-16 16:47:04 PDT; 39s ago Main PID: 7273 (ollama) Tasks: 18 (limit: 38365) Memory: 561.0M (peak: 615.8M) CPU: 5.644s CGroup: /system.slice/ollama.service └─7273 /usr/local/bin/ollama serve Jun 16 16:47:37 dead ollama[7273]: time=2024-06-16T16:47:37.252-07:00 level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"10.3.0\"" Jun 16 16:47:37 dead ollama[7273]: time=2024-06-16T16:47:37.506-07:00 level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sy> Jun 16 16:47:37 dead ollama[7273]: time=2024-06-16T16:47:37.511-07:00 level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"10.3.0\"" Jun 16 16:47:37 dead ollama[7273]: time=2024-06-16T16:47:37.714-07:00 level=WARN source=sched.go:511 msg="gpu VRAM usage didn't recover within timeout" seconds=5.045401852 Jun 16 16:47:37 dead ollama[7273]: time=2024-06-16T16:47:37.749-07:00 level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sy> Jun 16 16:47:37 dead ollama[7273]: time=2024-06-16T16:47:37.753-07:00 level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"10.3.0\"" Jun 16 16:47:37 dead ollama[7273]: time=2024-06-16T16:47:37.964-07:00 level=WARN source=sched.go:511 msg="gpu VRAM usage didn't recover within timeout" seconds=5.295260429 Jun 16 16:47:38 dead ollama[7273]: time=2024-06-16T16:47:38.007-07:00 level=WARN source=amd_linux.go:48 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sy> Jun 16 16:47:38 dead ollama[7273]: time=2024-06-16T16:47:38.012-07:00 level=INFO source=amd_linux.go:304 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION="\"10.3.0\"" Jun 16 16:47:38 dead ollama[7273]: time=2024-06-16T16:47:38.214-07:00 level=WARN source=sched.go:511 msg="gpu VRAM usage didn't recover within timeout" seconds=5.545797232 ``` But somehow: `amnesia λ ~/ sudo ROCR_VISIBLE_DEVICES=0 HSA_OVERRIDE_GFX_VERSION="10.3.0" OLLAMA_DEBUG=1 ollama serve` Works fine and I can chat without issue. Here's my service file, please note I have tried with both the ollama user and the root user (and the ollama user is properly configured/in render & video group): ``` [Unit] Description=Ollama Service After=network-online.target [Service] ExecStart=/usr/local/bin/ollama serve User=root Group=root Restart=always RestartSec=3 Environment="PATH=/usr/local/bin:/usr/bin:/usr/local/sbin:/usr/lib/jvm/default/bin:/usr/bin/site_perl:/usr/bin/vendor_perl:/usr/bin/core_perl:/opt/rocm/bin:/usr/local/lib/baresip/modules" Environment="ROCR_VISIBLE_DEVICES=0" Environment="HSA_OVERRIDE_GFX_VERSION=\"10.3.0\"" [Install] WantedBy=default.target ``` Both in the shell & run as a service they report using the same GPU (id=0, 6700XT): `level=INFO source=amd_linux.go:71 msg="inference compute" id=0 library=rocm compute=gfx1031 driver=0.0 name=1002:73df total="12.0 GiB" available="12.0 GiB"` _Originally posted by @pulpocaminante in https://github.com/ollama/ollama/issues/4427#issuecomment-2171941948_
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WSL2 Ubuntu 22.04 GPU "CUDA error 100" ggml-cuda.cu:5522 ggml-cuda.cu:4883 no CUDA-capable device is detected
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2023-10-02T23:45:31
2024-01-21T09:53:28
2023-10-03T23:44:47
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Thank you so much for ollama and the wsl2 support, I already wrote a vuejs frontend and it works great with CPU. I want GPU on WSL. I installed CUDA like recomended from nvidia with wsl2 (cuda on windows). I ran the following: go generate ./... go build . I got a ollama that runs with CPU but not with GPU. In journalctl | grep cuda I see: /home/y/Dev/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:5522: no CUDA-capable device is detected Everytime I run any model in ollama I get that error. I tried with mistral and with my own gpu-mistral that had num_gpu 50 and the same with num_gpu 1000. It doesn't matter. I am able to create the models with num_gpu. When I run ollama/llm/llama.cpp/gguf/build/cuda/bin/server or ggml/...../server it used to have the same error I am still facing with ollama now. I adjusted my environmennt variables and now the error doesn't show anymore for those built "server" binaries. ollama still shows the error. I adjusted my enviroment variables like this: **cat /etc/*/environment_variables.sh** ``` export CUDA_PATH="/usr/local/cuda-12.2/bin" export LD_LIBRARY_PATH="/mnt/c/Windows/System32/lxss/lib:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:/usr/lib/x86_64-linux-gnu" export PATH=/usr/local/cuda-12.2/bin${PATH:+:${PATH}} ``` It works fine for the server binaries of gguf and ggml (the error 100 was no longer present there). Info: There is no more text from ehre on apart of console commands and their output. **./Dev/ollama/llm/llama.cpp/gguf/build/cuda/bin/server** ``` ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 2060 with Max-Q Design, compute capability 7.5 {"timestamp":1696288901,"level":"INFO","function":"main","line":1294,"message":"build info","build":1267,"commit":"bc9d3e3"} {"timestamp":1696288901,"level":"INFO","function":"main","line":1296,"message":"system info","n_threads":8,"total_threads":16,"system_info":"AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | "} error loading model: failed to open models/7B/ggml-model-f16.gguf: No such file or directory llama_load_model_from_file: failed to load model llama_init_from_gpt_params: error: failed to load model 'models/7B/ggml-model-f16.gguf' {"timestamp":1696288901,"level":"ERROR","function":"loadModel","line":265,"message":"unable to load model","model":"models/7B/ggml-model-f16.gguf"} ``` **./Dev/ollama/llm/llama.cpp/ggml/build/cuda/bin/server** ``` ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 2060 with Max-Q Design, compute capability 7.5 {"timestamp":1696288885,"level":"INFO","function":"main","line":1190,"message":"build info","build":1009,"commit":"9e232f0"} {"timestamp":1696288885,"level":"INFO","function":"main","line":1192,"message":"system info","n_threads":8,"total_threads":16,"system_info":"AVX = 1 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | VSX = 0 | "} error loading model: failed to open models/7B/ggml-model.bin: No such file or directory llama_load_model_from_file: failed to load model llama_init_from_gpt_params: error: failed to load model 'models/7B/ggml-model.bin' {"timestamp":1696288885,"level":"ERROR","function":"loadModel","line":261,"message":"unable to load model","model":"models/7B/ggml-model.bin"} ``` **cmake --version** cmake version 3.27.6 **go version** go version go1.21.1 linux/amd64 **gcc --version** gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0 **echo $PATH** (full env output with full path output at bottom) /usr/local/cuda-12.2/bin /usr/lib/wsl/lib /mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.2/bin /mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.2/libnvvp /mnt/c/program files/python311/scripts/ /mnt/c/program files/python311/ /mnt/c/program files/nvidia corporation/nvidia nvdlisr /mnt/c/Program Files (x86)/NVIDIA Corporation/PhysX/Common /mnt/c/Program Files/NVIDIA Corporation/Nsight Compute 2023.2.2/ **Other relevant environment variables:** (full env at bottom) LD_LIBRARY_PATH=/mnt/c/Windows/System32/lxss/lib:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:/usr/lib/x86_64-linux-gnu WSL2_GUI_APPS_ENABLED=1 WSL_DISTRO_NAME=Ubuntu-22.04 CUDA_PATH=/usr/local/cuda-12.2/bin **journalctl | grep cuda** ``` Oct 03 01:10:56 c1 unknown: /usr/lib/wsl/lib/libcuda.so.1 is not a symbolic link Oct 03 01:12:30 c1 ollama[884]: CUDA error 100 at /home/y/Dev/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:5522: no CUDA-capable device is detected Oct 03 01:15:33 c1 ollama[1138]: CUDA error 100 at /home/y/Dev/ollama/llm/llama.cpp/ggml/ggml-cuda.cu:4883: no CUDA-capable device is detected ``` **nvcc --version** ``` nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2023 NVIDIA Corporation Built on Tue_Aug_15_22:02:13_PDT_2023 Cuda compilation tools, release 12.2, V12.2.140 Build cuda_12.2.r12.2/compiler.33191640_0 ``` **nvidia-smi** Tue Oct 3 01:18:06 2023 +---------------------------------------------------------------------------------------+ | NVIDIA-SMI 535.112 Driver Version: 537.42 CUDA Version: 12.2 | |-----------------------------------------+----------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | | | MIG M. | |=========================================+======================+======================| | 0 NVIDIA GeForce RTX 2060 ... On | 00000000:01:00.0 Off | N/A | | N/A 54C P8 4W / 65W | 12MiB / 6144MiB | 0% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+ +---------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=======================================================================================| | No running processes found | +---------------------------------------------------------------------------------------+ **env** ``` SHELL=/bin/bash NVM_INC=/home/y/.nvm/versions/node/v20.6.1/include/node WSL2_GUI_APPS_ENABLED=1 WSL_DISTRO_NAME=Ubuntu-22.04 NAME=c1 PWD=/home/y LOGNAME=y HOME=/home/y LANG=C.UTF-8 WSL_INTEROP=/run/WSL/391_interop LS_COLORS=rs=0:di=01;34:ln=01;36:mh=00:pi=40;33:so=01;35:do=01;35:bd=40;33;01:cd=40;33;01:or=40;31;01:mi=00:su=37;41:sg=30;43:ca=30;41:tw=30;42:ow=34;42:st=37;44:ex=01;32:*.tar=01;31:*.tgz=01;31:*.arc=01;31:*.arj=01;31:*.taz=01;31:*.lha=01;31:*.lz4=01;31:*.lzh=01;31:*.lzma=01;31:*.tlz=01;31:*.txz=01;31:*.tzo=01;31:*.t7z=01;31:*.zip=01;31:*.z=01;31:*.dz=01;31:*.gz=01;31:*.lrz=01;31:*.lz=01;31:*.lzo=01;31:*.xz=01;31:*.zst=01;31:*.tzst=01;31:*.bz2=01;31:*.bz=01;31:*.tbz=01;31:*.tbz2=01;31:*.tz=01;31:*.deb=01;31:*.rpm=01;31:*.jar=01;31:*.war=01;31:*.ear=01;31:*.sar=01;31:*.rar=01;31:*.alz=01;31:*.ace=01;31:*.zoo=01;31:*.cpio=01;31:*.7z=01;31:*.rz=01;31:*.cab=01;31:*.wim=01;31:*.swm=01;31:*.dwm=01;31:*.esd=01;31:*.jpg=01;35:*.jpeg=01;35:*.mjpg=01;35:*.mjpeg=01;35:*.gif=01;35:*.bmp=01;35:*.pbm=01;35:*.pgm=01;35:*.ppm=01;35:*.tga=01;35:*.xbm=01;35:*.xpm=01;35:*.tif=01;35:*.tiff=01;35:*.png=01;35:*.svg=01;35:*.svgz=01;35:*.mng=01;35:*.pcx=01;35:*.mov=01;35:*.mpg=01;35:*.mpeg=01;35:*.m2v=01;35:*.mkv=01;35:*.webm=01;35:*.webp=01;35:*.ogm=01;35:*.mp4=01;35:*.m4v=01;35:*.mp4v=01;35:*.vob=01;35:*.qt=01;35:*.nuv=01;35:*.wmv=01;35:*.asf=01;35:*.rm=01;35:*.rmvb=01;35:*.flc=01;35:*.avi=01;35:*.fli=01;35:*.flv=01;35:*.gl=01;35:*.dl=01;35:*.xcf=01;35:*.xwd=01;35:*.yuv=01;35:*.cgm=01;35:*.emf=01;35:*.ogv=01;35:*.ogx=01;35:*.aac=00;36:*.au=00;36:*.flac=00;36:*.m4a=00;36:*.mid=00;36:*.midi=00;36:*.mka=00;36:*.mp3=00;36:*.mpc=00;36:*.ogg=00;36:*.ra=00;36:*.wav=00;36:*.oga=00;36:*.opus=00;36:*.spx=00;36:*.xspf=00;36: WAYLAND_DISPLAY=wayland-0 NVM_DIR=/home/y/.nvm LESSCLOSE=/usr/bin/lesspipe %s %s TERM=xterm-256color LESSOPEN=| /usr/bin/lesspipe %s USER=y CUDA_PATH=/usr/local/cuda-12.2/bin DISPLAY=:0 SHLVL=1 NVM_CD_FLAGS= LD_LIBRARY_PATH=/mnt/c/Windows/System32/lxss/lib:/usr/local/cuda/lib64:/usr/local/cuda/lib64/stubs:/usr/lib/x86_64-linux-gnu XDG_RUNTIME_DIR=/run/user/1000/ WSLENV= XDG_DATA_DIRS=/usr/local/share:/usr/share:/var/lib/snapd/desktop PATH=/home/y/.local/bin:/home/y/.nvm/versions/node/v20.6.1/bin:/usr/local/cuda-12.2/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/usr/lib/wsl/lib:/mnt/c/Program Files/WindowsApps/CanonicalGroupLimited.Ubuntu22.04LTS_2204.2.47.0_x64__79rhkp1fndgsc:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.2/bin:/mnt/c/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v12.2/libnvvp:/mnt/c/program files/python311/scripts/:/mnt/c/program files/python311/:/mnt/c/program files/common files/oracle/java/javapath:/mnt/c/windows/system32:/mnt/c/windows:/mnt/c/windows/system32/wbem:/mnt/c/windows/system32/windowspowershell/v1.0/:/mnt/c/windows/system32/openssh/:/mnt/c/program files/dotnet/:/mnt/c/programdata/chocolatey/bin:/mnt/c/program files/microsoft vs code/bin:/mnt/c/program files/putty/:/mnt/c/program files/nvidia corporation/nvidia nvdlisr:/mnt/c/program files (x86)/vim/vim82/:/mnt/c/windows/system32/openssh/:/mnt/c/program files/nodejs/:/mnt/c/program files/process lasso/:/mnt/c/Program Files/PowerShell/7/:/mnt/c/Program Files (x86)/NVIDIA Corporation/PhysX/Common:/mnt/c/Program Files/NVIDIA Corporation/Nsight Compute 2023.2.2/:/mnt/c/Users/User/AppData/Local/Microsoft/WindowsApps:/mnt/c/Windows/Microsoft.NET/Framework/v4.0.30319/:/mnt/c/Program Files (x86)/Vim/vim82/vim.exe:/mnt/c/Program Files/Java/jdk-19/bin/java.exe:/mnt/c/Users/User/AppData/Roaming/npm:/mnt/c/Users/User/AppData/Local/GitHubDesktop/bin:/mnt/c/Program Files (x86)/Nmap:/snap/bin DBUS_SESSION_BUS_ADDRESS=unix:path=/run/user/1000/bus NVM_BIN=/home/y/.nvm/versions/node/v20.6.1/bin HOSTTYPE=x86_64 PULSE_SERVER=unix:/mnt/wslg/PulseServer _=/usr/bin/env ``` So am I missing something? Thank you for any hints!
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msg="CPU does not have AVX or AVX2, disabling GPU support."
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2024-03-03T20:57:38
2024-03-06T16:49:29
2024-03-06T16:49:28
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winserver2022 old cpuX5675 GPU RTX3070 CUDA11.8
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2,815,799,891
I_kwDOJ0Z1Ps6n1bJT
8,636
Upload compressed package file, unable to decompress and error reported
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2025-01-28T14:13:01
2025-01-29T23:29:46
2025-01-29T23:29:45
NONE
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### What is the issue? Thanks for this great program, I love it! However, I uploaded a compressed package containing the project source code in the dialog interface, and an error occurred when the program was run. Can this problem be solved? ![Image](https://github.com/user-attachments/assets/2c6d9c78-5512-4b4e-a60e-04b98efb60ea) ### OS _No response_ ### GPU _No response_ ### CPU _No response_ ### Ollama version _No response_
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2,223,395,529
I_kwDOJ0Z1Ps6EhlLJ
3,477
Support CLIP in LLaVA to provide services externally
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2024-04-03T16:35:03
2024-04-19T15:41:21
null
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### What are you trying to do? I am new to ollama (including llama.cpp, of course), so my questions may be a bit silly. My use case is to serve both CLIP and LLaVA (which combines clip and mistral) at the same time. LLaVA can run perfectly on ollama. But I need to open another service for CLIP. What I want to ask is 1. Can ollama support the CLIP embedding interface? The current embedding interface seems to only support text. 2. Since the frozen CLIP included in LLaVA running on ollama, can it be directly reused instead of loading two copies in memory? ### How should we solve this? _No response_ ### What is the impact of not solving this? _No response_ ### Anything else? _No response_
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2,815,526,057
I_kwDOJ0Z1Ps6n0YSp
8,629
Choose path to install on Windows
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2025-01-28T12:31:56
2025-01-28T21:31:28
2025-01-28T21:31:28
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The title is pretty self-expanatory. I would be nice to chose a folder where ollama is being installed on windows instead of defaulting to C:\Users\...
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5,995
return tool calls finish reason for openai
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2024-07-26T20:46:29
2024-07-30T08:51:13
2024-07-29T20:56:57
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Ollama is amazing!!
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2024-07-26T12:41:49
2024-08-25T18:42:26
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This really isn't an issue but I just wanted to say that everyone who works on and maintains this project is doing incredible work! Thank you so much for all of the countless hours and hard work you put into making Ollama. I was a little shocked to see the project has over 900 issues at the time of this writing and I just want to hopefully encourage you all. You truly are doing important work and this project means a lot to a lot of people! God speed. Cheers, Robert
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fix(cmd): history in alt prompt
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2024-01-05T23:58:04
2024-01-08T21:48:35
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using up/down arrows (for history) messes up multiline string inputs by replacing the alt prefix `...` with the default prefix `>>>`
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模型下载最后1%速度骤降,导致下载时间超长。The download speed suddenly drops at the last 1%, resulting in an extremely long download time.
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### What is the issue? 模型无论大小,每次下载前面99%都最大的速度可以达到29MB/s。但是最后1%速度就只有几百 KB/s。很奇怪。是进度条有问题,还是啥bug?这在我本地电脑和线上服务器都出现了。 Regardless of model size, in most cases, the download speed reaches 29MB/s for about 99% of the time before completion. However, the last 1% takes only a few hundred KB/s. This is quite strange. Is it an issue with the progress bar or some bug? This phenomenon has been observed on both my local computer and online server. ![image](https://github.com/ollama/ollama/assets/37319319/be1b24f1-922c-4478-8548-74704686e573) ![image](https://github.com/ollama/ollama/assets/37319319/9a8b4e0c-4dc6-4156-bd46-744befb3795e) ![image](https://github.com/ollama/ollama/assets/37319319/87782acf-2caa-40d5-a84a-b19eab38333e) ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version 0.1.32
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PR_kwDOJ0Z1Ps5mrdNr
2,468
Update llama.cpp submodule to `099afc6`
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Run LLM directly in Golang App without Ollama Server
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2024-10-31T16:04:31
2024-11-29T17:07:31
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Hello everyone, I would like to know if it is possible to run an all-minilm LLM model directly in my Golang App without having to make calls to the Ollama Server http://localhost:11434/api I would like to take a small "all-minilm" model and use the //go:embed model/* to already have the tool embedded in the Golang binary. The idea would be to simplify the use and infrastructure so as not to need to set up an extra Docker Container or VM, not to mention avoiding network latency. I would like to use Ollama as a library and import the necessary parts in my Golang App. I couldn't find any examples, if there is someone more experienced and can help me, I would appreciate it.
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Add action for publishing package to WinGet
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2024-11-23T19:35:49
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## Description This PR proposes to add a GitHub action for submitting the latest stable release to WinGet as it gets published. [microsoft/winget-create](https://github.com/microsoft/winget-create) is used as the tool for submitting the latest package. ## Steps needed from maintainers If the maintainers approve of these changes, they will need to do the following before merging this PR: 1. Fork [microsoft/winget-pkgs](https://github.com/microsoft/winget-pkgs) under a personal or bot account. 2. Create a [public access token (classic)](https://github.com/microsoft/winget-create?tab=readme-ov-file#github-personal-access-token-classic-permissions) with `public_repo` scope from the user account where the fork exists. 3. Create a repo secret in this repo with the name `WINGET_GITHUB_TOKEN` For reference, maintainers may see similar implemented actions in the following repos: [PowerToys](https://github.com/microsoft/PowerToys/blob/main/.github/workflows/package-submissions.yml), [Terminal](https://github.com/microsoft/terminal/blob/main/.github/workflows/winget.yml), [DevHome](https://github.com/microsoft/devhome/blob/main/.github/workflows/winget-submission.yml), [Oh-my-posh](https://github.com/JanDeDobbeleer/oh-my-posh/blob/main/.github/workflows/winget.yml)
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go-1.21 fails to build ollama: C source files not allowed when not using cgo or SWIG: gpu_info_cpu.c gpu_info_cuda.c gpu_info_rocm.c
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``` ===> Building for ollama-0.1.25 (cd /usr/ports/misc/ollama/work/github.com/ollama/ollama@v0.1.25; for t in ./cmd; do out=$(/usr/bin/basename $(echo ${t} | /usr/bin/sed -Ee 's/^[^:]*:([^:]+).*$/\1/' -e 's/^\.$/ollama/')); pkg=$(echo ${t} | /usr/bin/sed -Ee 's/^([^:]*).*$/\1/' -e 's/^ollama$/./'); echo "===> Building ${out} from ${pkg}"; /usr/bin/env XDG_DATA_HOME=/usr/ports/misc/ollama/work XDG_CONFIG_HOME=/usr/ports/misc/ollama/work XDG_CACHE_HOME=/usr/ports/misc/ollama/work/.cache HOME=/usr/ports/misc/ollama/work PATH=/usr/local/libexec/ccache:/usr/ports/misc/ollama/work/.bin:/home/yuri/.cargo/bin:/home/yuri/bin:/sbin:/bin:/usr/sbin:/usr/bin:/usr/local/sbin:/usr/local/bin PKG_CONFIG_LIBDIR=/usr/ports/misc/ollama/work/.pkgconfig:/usr/local/libdata/pkgconfig:/usr/local/share/pkgconfig:/usr/libdata/pkgconfig MK_DEBUG_FILES=no MK_KERNEL_SYMBOLS=no SHELL=/bin/sh NO_LINT=YES PREFIX=/usr/local LOCALBASE=/usr/local CC="cc" CFLAGS="-O2 -pipe -fstack-protector-strong -fno-strict-aliasing " CPP="cpp" CPPFLAGS="" LDFLAGS=" -fstack-protector-strong " LIBS="" CXX="c++" CXXFLAGS="-O2 -pipe -fstack-protector-strong -fno-strict-aliasing " CCACHE_DIR="/tmp/.ccache" BSD_INSTALL_PROGRAM="install -s -m 555" BSD_INSTALL_LIB="install -s -m 0644" BSD_INSTALL_SCRIPT="install -m 555" BSD_INSTALL_DATA="install -m 0644" BSD_INSTALL_MAN="install -m 444" CGO_ENABLED=1 CGO_CFLAGS="-I/usr/local/include" CGO_LDFLAGS="-L/usr/local/lib" GOAMD64= GOARM= GOTMPDIR="/usr/ports/misc/ollama/work" GOPATH="/usr/ports/distfiles/go/misc_ollama" GOBIN="/usr/ports/misc/ollama/work/bin" GO111MODULE=on GOFLAGS=-modcacherw GOSUMDB=sum.golang.org GOMAXPROCS=7 GOPROXY=off /usr/local/bin/go121 build -buildmode=exe -v -trimpath -ldflags=-s -buildvcs=false -mod=vendor -o /usr/ports/misc/ollama/work/bin/${out} ${pkg}; done) ===> Building cmd from ./cmd package github.com/jmorganca/ollama/cmd imports github.com/jmorganca/ollama/server imports github.com/jmorganca/ollama/gpu: C source files not allowed when not using cgo or SWIG: gpu_info_cpu.c gpu_info_cuda.c gpu_info_rocm.c *** Error code 1 ```
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Support for hyenadna-large-1m-seqlen-hf
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Hi, any plans to support hyenadna? it has 1 million tokens! https://huggingface.co/LongSafari/hyenadna-large-1m-seqlen-hf
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is it possible to use ollama as a library , not through network
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2024-03-19T03:58:11
2024-03-21T13:42:48
2024-03-21T13:42:47
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### What are you trying to do? i want to know if it's possible to call ollama function like some library, for example by dll in windows so other developer can develop some gui or related software with this library not to run ollama as a stand-alone server ### How should we solve this? it's easier to build related gui or other third party software based on ollama ### What is the impact of not solving this? _No response_ ### Anything else? _No response_
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:link: Please add HF (HuggingFace) model link to `duckdb-nsql` :duck:
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2024-01-26T21:40:29
2024-01-27T09:26:19
2024-01-27T06:25:50
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# :grey_question: About Recently, [`duckdb-nsql`](https://ollama.ai/library/duckdb-nsql) has been added to `ollama` library: - https://github.com/ollama/ollama/issues/2193 ![image](https://github.com/ollama/ollama/assets/5235127/efb2ee93-cff5-41ad-ad22-747842014d77) **:point_right: ... but the page is lacking the HuggingFace model page.** # :dart: Documentation request Please add the following model [`motherduckdb/DuckDB-NSQL-7B-v0.1`](https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1) url to [`duckdb-nsql` `ollama` page](https://ollama.ai/library/duckdb-nsql): https://huggingface.co/motherduckdb/DuckDB-NSQL-7B-v0.1 # :moneybag: Benefits - Better documentation - Better indexation of `ollama` models
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Ability to preload embedding model
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2024-08-10T01:30:27
2024-08-13T17:19:57
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The "empty request" trick to preload a model does not currently work for embedding models. Source: https://github.com/ollama/ollama/blob/main/docs/faq.md#how-can-i-preload-a-model-into-ollama-to-get-faster-response-times and #2431 ``` $ curl http://localhost:11434/api/embed -d '{"model": "all-minilm:latest"}' {"error":"invalid input type"}% ``` ``` ollama --version ollama version is 0.3.4 ```
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Custom-llama issue
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### What is the issue? Hello, I have a problem, or I misunderstood something. If I put several SYSTEM tags in my custom-llama3 file, only the last one is taken. How can I give him more information? Example I would like SYSTEM You are a helpful AI assistant named Droid but also that they can know the location of the smoking area in my building, or that to contact the Sales department they must contact 00*******? thank you in advance for your help and sorry if my question is stupid. ### OS Windows ### GPU Other ### CPU Intel ### Ollama version 0.1.39
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To configure Ollama to run multiple models simultaneously
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2024-09-26T15:46:40
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if the design of Ollama can support calling multiple models simultaneously. For example, can it be used in evaluation scenarios? Thanks for your support.
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Ollama on FreeBSD
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2023-11-12T20:07:58
2024-11-08T22:12:37
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Hello there: is there any chance to get ollama working on freebsd please??
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Refactor chat prompt templating
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2024-02-12T07:03:05
2024-02-12T23:06:58
2024-02-12T23:06:57
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This refactors the chat prompt processing to be a little easier to follow. It also fully deprecates `.First` in favor of the chat endpoint Fixes https://github.com/ollama/ollama/issues/2443 Fixes https://github.com/ollama/ollama/issues/2438
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ollama -v return 2version one is 0.0.0 the other is client version 0.3.14
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2024-11-04T17:59:44
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### What is the issue? i am using ubuntu20.04;install ollama through curl -fsSL https://ollama.com/install.sh | sh; when input ollama -v return ollama version is 0.0.0 Warning: client version is 0.3.14 btw, when using ollama, i notice my gpu is not used and the response is so slow ### OS Linux ### GPU Nvidia ### CPU _No response_ ### Ollama version 0.3.14
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fix json encoder
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2024-03-06T19:48:35
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Note that CGO must be enabled in dev docs
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llama3.2-vision doesn't utilize my GPU.
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### What is the issue? I bought a new pc with 4070 Super to do some AI tasks using Ollama, but when I tried to run llama3.2-vision it just didn't utilize my GPU and only utilize my CPU, llama3.2 does utilize my GPU, so why is that? thank you. ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.5.4
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Update .gitignore
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2023-07-22T14:03:26
2023-07-22T16:40:38
2023-07-22T16:40:38
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2,124,200,380
PR_kwDOJ0Z1Ps5mUxPw
2,403
Ensure the libraries are present
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2024-02-08T01:28:22
2024-02-08T01:55:33
2024-02-08T01:55:31
COLLABORATOR
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When we store our libraries in a temp dir, a reaper might clean them when we are idle, so make sure to check for them before we reload.
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PR_kwDOJ0Z1Ps5sRyQc
3,579
fix ci
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2024-04-10T18:27:10
2024-04-10T18:37:02
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api improvements
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2023-10-02T18:59:10
2024-01-10T13:14:47
2023-10-05T16:38:04
NONE
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its a stream of objects that are separated with a newline. often times new lines are returned in the response, so that breaks just splitting on new lines. I think the split should be on something else. Also it seems like there should be an api endpoint that just returns the whole response in a string. thoughts?
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3,149
fix: clip memory leak
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2024-03-14T19:47:41
2024-03-14T20:34:16
2024-03-14T20:34:15
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this change patches llama.cpp and fixes two bugs 1. llama_server_context never calls clip_free 2. clip_free does not fully free its context
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1,212
enable metal for fp32, q5_0, q5_1
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2023-11-20T21:48:29
2023-11-20T21:56:41
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recent llama.cpp update added kernels for fp32, q5_0, and q5_1 resolves #1200
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GPU runs at maximum load with 2 models
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### What is the issue? Hi, i use ollama as the provider for the Continue extension for VSCode for tab autocompletion. Since the last update I have the problem that my GPU runs at maximum load as soon as 2 models are running at the same time. Even if they are only very small models (e.g. nomic-embed-text for embeddings and qwen2.5-coder:0.5b as tab autocomplete). The load remains at 100% until I stop one of the two models. Before the last update, I had no problems using larger models (e.g. qwen2.5-coder:3b). My OS is Manjaro Linux with kernel 6.12.4, my hardware is an AMD Ryzen 9 9950X processor and an AMD Radeon RX 7900 XTX. ### OS Linux ### GPU AMD ### CPU AMD ### Ollama version 0.5.4
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V0.1.48 The model is loaded into the GPU Memory but runs on the CPU
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2024-07-01T03:38:43
2024-07-03T07:26:48
2024-07-03T07:26:48
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### What is the issue? ollama run gemma2:27b ![image](https://github.com/ollama/ollama/assets/28422636/313df4c1-141f-4c61-aeeb-3525cc6fd975) The same goes for loading other models. It was normal in the previous version. I did not change any environment variables. ### OS Windows ### GPU Nvidia ### CPU Intel ### Ollama version 0.1.48
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CLI run output not standard output
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2023-09-30T08:04:16
2023-10-02T18:52:16
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Hello, I've been on this for quite some time now, and I'm sorry if I'm misinformed. To me, it seems like even when I use the command line argument style input such as `ollama run mistral "Here is my prompt"` (as mentioned here https://github.com/jmorganca/ollama#pass-in-prompt-as-arguments ), the output isn't clean text. When I run that command manually, while it should be just straight text with newline characters, instead it is doing some other characters to always fit the width of the terminal that called the command. Here's an example of `ollama run mistral "Here is my prompt" > out.txt`. It is adding some strange characters in the output. <img width="256" alt="image" src="https://github.com/jmorganca/ollama/assets/304560/6e8152e9-b5c8-448c-9d7c-9d2fc8661924"> I think this has to do with how Ollama handles terminal commands, similar to when you use interactive chat mode. I would expect that the little loading ascii icon should not show when I'm using it as a standard command line tool (passing in the prompt directly vs chat mode). If my understanding is correct here, and you're in agreement that we shouldn't be using the fancy terminal features and instead just outputing as STDOUT when it is finished processing, I'm happy to take a swing at creating a PR to fix this case. Thank you!
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Ollama Utilizing Only CPU Instead of GPU on MacBook Pro M1 Pro
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2024-01-14T07:18:33
2024-06-29T17:51:50
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Description I've encountered an issue where Ollama, when running any llm is utilizing only the CPU instead of the GPU on my MacBook Pro with an M1 Pro chip. This results in less efficient model performance than expected. Environment MacBook Pro with M1 Pro chip MacOS version: Sonoma 14.2.1 Ollama version: 1.20 No specific error messages are observed. All dependencies and drivers are up to date. I would appreciate any guidance or updates regarding this issue. If there are any configurations or settings I might be missing, please let me know. <img width="852" alt="Screenshot 2024-01-14 at 08 00 10" src="https://github.com/jmorganca/ollama/assets/77242455/ee3c0398-37e9-4473-af5b-a3b3253d1662"> PS. the image was taken when running dolphin-mixtral Thanks!
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Fetch `q4_k` models from hugging face
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2023-07-03T16:25:37
2023-07-08T03:26:50
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On macOS, metal only supports 4-bit and 16-bit quantization
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Using ollama version 0.3.3, downloading all models will result in errors.
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2024-08-05T08:17:10
2024-08-30T12:32:54
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### What is the issue? pulling manifest Error: pull model manifest: Get "https://registry.ollama.ai/v2/library/gemma2/manifests/2b": net/http: TLS handshake timeout. Before version 0.3.0, there were no similar issues. Starting from the update to 0.3.1, I tried many methods, but none of them worked, including using VPN and reinstalling Olama. ### OS Linux ### GPU Other ### CPU Intel ### Ollama version 0.3.3
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Multiple windows instances with different ports
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1
2024-07-15T17:44:03
2024-07-16T02:51:39
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COLLABORATOR
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### What is the issue? When you set an alternate port for OLLAMA_HOST, the CLI will spawn a new app, and create multiple tray instances that have no way to tell which one represents which port. ### OS Windows ### GPU _No response_ ### CPU _No response_ ### Ollama version _No response_
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2,344,553,985
PR_kwDOJ0Z1Ps5yAx3F
4,965
fix: skip removing layers that no longer exist
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2024-06-10T18:18:33
2024-06-10T18:40:04
2024-06-10T18:40:03
CONTRIBUTOR
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some models, such as `wizardcoder:34b-python`, incorrectly includes the config layer as an item in layers. this causes `RemoveLayers` to try to remove the same layer more than once, failing the second time since it's already removed ```json { "schemaVersion": 2, "mediaType": "application/vnd.docker.distribution.manifest.v2+json", "config": { "mediaType": "application/vnd.docker.container.image.v1+json", "digest": "sha256:a168bedb9a09640289c5174690a6221adae48b75dc431a219923f052ef20d0af", "size": 456 }, "layers": [ { "mediaType": "application/vnd.ollama.image.model", "digest": "sha256:c8ad30822293b3c24f02265a42e2879b9725bd19a54d048fbe2c38487ee0ea84", "size": 19052059872 }, { "mediaType": "application/vnd.ollama.image.template", "digest": "sha256:2d836d77287d85ac3d2ea87f4d765db6aaabc98543442072111b3d9831cdf9f1", "size": 61 }, { "mediaType": "application/vnd.ollama.image.system", "digest": "sha256:8fadb9ad1206f2f130b54004893a2a7f76b1ff41a78049d69d797df2ee67fe94", "size": 106 }, { "mediaType": "application/vnd.ollama.image.params", "digest": "sha256:bf6237d30a42b25b196a7a178dc566e113cf2f193aa11e7302c6d61880be6028", "size": 30 }, { "mediaType": "application/vnd.docker.container.image.v1+json", "digest": "sha256:a168bedb9a09640289c5174690a6221adae48b75dc431a219923f052ef20d0af", "size": 456 } ] } ``` resolves #4898
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8,491
MacApp fails to build when building from source
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2025-01-20T00:32:59
2025-01-20T00:33:36
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### What is the issue? I cloned the repo and was building the macapp and it fails to build. Can't find webpack.main.config There's a webpack.main.config.ts file but that's not the file referenced. I tried to fix it myself and fell down a rabbit hole. I'm just bringing this to the attention of whomever is maintaining it. ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version current head
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I_kwDOJ0Z1Ps6HJWBd
3,993
Add support for EMO-2B
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2024-04-28T06:21:00
2024-04-28T06:21:00
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Gguf model https://huggingface.co/Abhaykoul/EMO-2B-GGUF Full model https://huggingface.co/OEvortex/EMO-2B
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1,376
install: fix rocky kernel packages
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2023-12-04T19:20:27
2023-12-04T22:23:44
2023-12-04T22:23:43
CONTRIBUTOR
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package names for rocky-linux are slightly different
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请问下如何将模型也封装进ollama的docker镜像中
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2024-05-20T12:48:17
2024-05-20T14:48:27
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请问下如何将qwen:32b模型也封装进ollama的docker镜像中
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strange tool response
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2024-07-29T09:42:35
2024-08-15T21:44:53
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### What is the issue? The prompt was "call fibonacci function to determine 7 element of the sequence". I wonder if there should be 2 tool calls. ``` { "model": "llama3.1:8b", "created_at": "2024-07-29T09:32:02.5425761Z", "message": { "role": "assistant", "content": "", "tool_calls": [ { "function": { "name": "fibonacci", "arguments": { "n": null } } }, { "function": { "name": "fibonacci", "arguments": { "n": 7 } } } ] }, "done_reason": "stop", "done": true, "total_duration": 17912684000, "load_duration": 4394720300, "prompt_eval_count": 132, "prompt_eval_duration": 583579000, "eval_count": 85, "eval_duration": 12932451000 } ``` ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.3.0
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2,647,535,085
I_kwDOJ0Z1Ps6dzi3t
7,602
Ollama ps to report actual number of layers instead of percentage.
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2024-11-10T19:02:27
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Can we have Ollama to report how many layers out of total layers are offloaded to cpu instead of percentage? I think This would be more useful information than just percentage when setting num_gpu parameter. Also you can see how many layers a model has. Thanks!
null
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PR_kwDOJ0Z1Ps51Bp_X
5,617
OpenAI: Update Docs to Include Tools
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2024-07-10T22:39:15
2024-07-25T22:34:07
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I_kwDOJ0Z1Ps5-5_1M
2,449
Log request/responses payload
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2024-02-11T19:26:43
2024-10-01T12:28:46
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In order to debug low-level details during development, it would be very useful to be able to see the payload of requests and responses. Is there a way to enable this?
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2,374
disable rocm builds
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2024-02-06T17:29:51
2024-02-06T17:41:04
2024-02-06T17:41:03
CONTRIBUTOR
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rocm builds are failing because of disk space issues. disable them temporarily until larger runners resolves #2373
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Ollama hung after 30 minute of use
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2023-12-11T02:28:13
2024-05-05T01:11:36
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I'm running Ollama on my mac M1 and I'm trying to use the 7b models for processing batches of questions / answers. I noticed that after a while ollama just hang and the process stay there forever. Is there a way to know what's going on? I did not find a way to get to the logs. Thank you in advance
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max retries exceeded: http status 502 Bad Gateway while pushing a model
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### What is the issue? I have spend nearly a whole day to push, but fail. are there anyway to set the time of re-try? please cancel limitation of re-tries. or is that possible pushing is resumable? taozhiyu@603e5f4a42f1 Q8 % ollama push taozhiyuai/openbiollm-llama-3-70b:q8_0 retrieving manifest retrieving manifest retrieving manifest retrieving manifest retrieving manifest retrieving manifest retrieving manifest retrieving manifest retrieving manifest retrieving manifest retrieving manifest pushing cf7fb8dac551... 13% ▕█████ ▏ 10 GB/ 74 GB 4.3 MB/s 4h13m Error: max retries exceeded: http status 502 Bad Gateway: <html> <head><title>502 Bad Gateway</title></head> <body> <center><h1>502 Bad Gateway</h1></center> <hr><center>cloudflare</center> </body> </html> ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version up to date
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2,084,797,403
I_kwDOJ0Z1Ps58Q3vb
2,022
List available models
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2024-01-16T20:14:24
2024-11-21T17:26:22
2024-11-21T17:26:22
CONTRIBUTOR
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Hi. The API allows me to list the local models. Is there a way to list all available models (those we can find in the website of ollama? I need that for the models zoo to make it easy for users of lollms with ollama backend to install the models. I prefer this rather than having to scrape the website to get the latest list of models.
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Intel GPU in Docker container crashes
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2024-08-09T12:14:56
2024-08-09T19:14:44
2024-08-09T19:14:44
NONE
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### What is the issue? with the error `panic: runtime error: invalid memory address or nil pointer dereference` Docker Compose File: https://bin.minionflo.net/p/E9gFhE.yaml Log: https://bin.minionflo.net/p/QyrT8Z.txt ### OS Docker on Linux ### GPU Intel ### CPU AMD ### Ollama version 0.3.4
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2,474,477,204
PR_kwDOJ0Z1Ps54y56d
6,432
Split rocm back out of bundle
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2024-08-20T00:12:54
2024-08-20T14:26:41
2024-08-20T14:26:38
COLLABORATOR
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We're [over budget for github's maximum release artifact size](https://github.com/ollama/ollama/actions/runs/10461795539/job/28973022210) with rocm + 2 cuda versions. This splits rocm back out as a discrete artifact, but keeps the layout so it can be extracted into the same location as the main bundle.
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I_kwDOJ0Z1Ps54ehYl
1,346
Set conversation or chat history/context in CLI
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2023-12-01T17:03:50
2023-12-27T15:09:54
2023-12-27T15:09:54
NONE
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Thank you for making this! I tried the `/set history` command within the CLI and expected it to work. I would like to use the CLI as a chatbot itself having access to conversation history (a window of messages if not whole). What is the process to set the conversation history as context in `Openhermes-mistral` specifically? Here is the bug: <img width="1070" alt="image" src="https://github.com/jmorganca/ollama/assets/53078775/c6033c8b-5054-41a8-9977-9a0e0ab8b130">
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I_kwDOJ0Z1Ps6JBdDm
4,458
Confirm GPU usage command
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2024-05-15T21:16:25
2024-05-16T21:11:45
2024-05-15T22:53:09
NONE
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Would like to have a way to to confirm if a GPU is actually being utilized. Maybe some kind of command or option when running a given model to test/log individual machine performance.
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Bump llama.cpp commit to 6b91b1e which includes Intel GPU support (iGPU, Arc, Max, Flex)
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2024-02-06T00:44:53
2024-02-13T21:52:10
2024-02-13T21:52:10
NONE
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llama.cpp has added support for Intel GPUs. commit ID: [6b91b1e0a92ac2e4e269eec6361ca53a61ced6c6](https://github.com/ggerganov/llama.cpp/commit/6b91b1e0a92ac2e4e269eec6361ca53a61ced6c6) *Task* 1. Bump llama.cpp commit if feasible 2. Then update Dockerfile with with Intel GPU support for one-click deployment or as reference to bare metal deployment. **Reference for dockerfile implementation** llama.cpp guidelines on Intel GPU support via SYCL lib. - https://github.com/ggerganov/llama.cpp/blob/master/README-sycl.md
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Ollama serve fails silently when an input is too long
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2024-02-21T21:05:18
2024-03-12T02:02:12
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CONTRIBUTOR
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When I use `ollama serve` and provide a context of ~30k tokens with a mistral model that has a max context window of 32768, the server doesn't show any error and proceeds to return as usual. That gave me the impression that it successfully took in the entire context. But after digging a bit deeper, I see it's not. ![SCR-20240221-lpyt](https://github.com/ollama/ollama/assets/4860545/8caef175-f97d-4304-9f19-1a8103770427) So when I do this below it started working fine ``` ollama run <model> /set parameter num_ctx 32768 /save ``` Perhaps it's because there are flags to set with `ollama serve` which I don't know about after reading the docs. Is there a better way to set the context window for `ollama serve`? In my mind, the expected behavior is to show an error message when the input is exceeding the set context window length. LM Studio does this <img width="1426" alt="SCR-20240221-lsnn" src="https://github.com/ollama/ollama/assets/4860545/ee4f2408-bbce-4fb8-bd74-6306aca08b3c"> Please let me know if it's because I'm not using it with the right flags or if this is a legit concern.
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