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convert: import support for command-r models from safetensors
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working for https://huggingface.co/CohereForAI/aya-23-8B https://huggingface.co/CohereForAI/c4ai-command-r-v01
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panic: runtime error: integer divide by zero in memory.go on bad model create
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### What is the issue? I installed ollama today, the system is Ubuntu2204,I downloaded llama3.1-405b-Q2.gguf,There are 9 split files in total. Ollama create llama -f Modelfile.txt is completed successfully. The ollama list is displayed normally, but an error occurs when running.Error: Post "http://127.0.0.1:11434/api/chat": EOF Open this link and get an error 404 page not found,Open http://127.0.0.1:11434 and it will show that Ollama is running. The model file size is 151GB, my computer memory is 128G, and the video memory is 96G. Please help me, thank you. ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version ollama version is 0.3.2
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Implement tokenize and de-tokenize endpoints
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Implement endpoints to tokenize (`/api/tokenize`) and detokenize (`/api/detokenize`) text Closes #3582
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pass flags to `serve` to allow setting allowed-origins + host and port
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2023-08-07T03:41:01
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resolves: https://github.com/jmorganca/ollama/issues/300 and https://github.com/jmorganca/ollama/issues/282 example usage: ``` ollama serve --port 9999 --allowed-origins "http://foo.example.com,http://192.0.0.1" ```
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Request Support for Running Inference Through LM Studio
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ERROR: type should be string, got "https://lmstudio.ai\nhttps://github.com/lmstudio-ai/lms\n\nLM Studio is one of the most popular locally run inference platforms, which has its own inference server. Much like Ollama, LM Studio uses llama.cpp for inferences - but it also supports MLX.\n\nPlease kindly add support to use Goose with LM Studio as the inference backend. Thanks in advance! 🙏 \n\n"
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Hymba-1.5B-family of models
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https://huggingface.co/nvidia/Hymba-1.5B-Instruct https://huggingface.co/nvidia/Hymba-1.5B-Base
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2,182,552,422
PR_kwDOJ0Z1Ps5pba5V
3,084
update convert
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2024-03-12T19:48:35
2024-06-05T20:12:05
2024-03-27T21:02:34
CONTRIBUTOR
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the output of convert remains exactly the same
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I_kwDOJ0Z1Ps6RC0nU
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Gemma2 and Mistral-nemo not running on ollama
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2024-07-27T11:25:40
2024-07-28T00:14:12
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### What is the issue? [147@Clevo ~]$ ollama run mistral-nemo:12b-instruct-2407-fp16 Error: exception error loading model hyperparameters: invalid n_rot: 128, expected 160 [147@Clevo ~]$ ollama run gemma2:27b-instruct-q8_0 Error: exception error loading model architecture: unknown model architecture: 'gemma2' Can someone explain why this is happening? It is the first time models do not run on my ollama v0.1.30 ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version 0.1.30
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Generate endpoint intermittently misses final token before done
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2024-09-09T08:25:48
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### What is the issue? When using the generate endpoint it intermittently misses the last token right before the "done" message ```JSON {"model":"adrienbrault/nous-hermes2theta-llama3-8b:q8_0","created_at":"2024-09-09T08:04:47.463348938Z","response":" Bear","done":false} {"model":"adrienbrault/nous-hermes2theta-llama3-8b:q8_0","created_at":"2024-09-09T08:04:47.475993178Z","response":",","done":false} {"model":"adrienbrault/nous-hermes2theta-llama3-8b:q8_0","created_at":"2024-09-09T08:04:47.488651949Z","response":" Elephant","done":false} {"model":"adrienbrault/nous-hermes2theta-llama3-8b:q8_0","created_at":"2024-09-09T08:04:47.50131158Z","response":",","done":false} {"model":"adrienbrault/nous-hermes2theta-llama3-8b:q8_0","created_at":"2024-09-09T08:04:47.51400078Z","response":" Gor","done":false} {"model":"adrienbrault/nous-hermes2theta-llama3-8b:q8_0","created_at":"2024-09-09T08:04:47.539481043Z","response":"","done":true,"done_reason":"stop","total_duration":8790953777,"load_duration":8080650494," ``` In the above example the token that should be the end of "Gorilla" is not emitted before the done response and we just get "Gor". here's the curl command to reproduce this ```sh curl -H 'Host: 127.0.0.1:11434' -H 'Content-Type: application/json' -H 'Connection: Keep-Alive' --compressed -H 'Accept-Language: en-GB,*' -H 'User-Agent: Mozilla/5.0' -X POST http://127.0.0.1:11434/a pi/generate -d '{"model": "adrienbrault/nous-hermes2theta-llama3-8b:q8_0", "prompt": "\n<|im_start|>user\nYou will think of a number. Then you will list that many animals. Do not write any other words only the animal. Be terse in your response.<|im_end|>\n<|im_start|>assistant", "raw": true, "stream": true, "keep_alive": -1, "options": {"seed": 99, "num_predict": 1024, "num_ctx": 4096, "stop": ["<end>", "user :", "assistant:"], "num_batch": 1, "temperature": 0.5, "top_k": 40, "top_p": 0.9}}' ``` I have only seen this with one model so far (adrienbrault/nous-hermes2theta-llama3-8b:q8_0) so the model may well be a factor however I don't get this problem with the chat endpoint for that model but I do get it with the generate endpoint. I'm using raw mode and stream=true ### OS Linux ### GPU Nvidia ### CPU AMD ### Ollama version 0.3.9
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Phi-3-mini-128k no load
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model download: `https://huggingface.co/PrunaAI/Phi-3-mini-128k-instruct-GGUF-Imatrix-smashed` modfile: ``` FROM ./Phi-3-mini-128k-instruct.Q4_K_M.gguf PARAMETER num_ctx 65536 PARAMETER num_keep 4 PARAMETER stop <|user|> PARAMETER stop <|assistant|> PARAMETER stop <|system|> PARAMETER stop <|end|> PARAMETER stop <|endoftext|> TEMPLATE """ {{ if .System }}<|system|> {{ .System }}<|end|> {{ end }}{{ if .Prompt }}<|user|> {{ .Prompt }}<|end|> {{ end }}<|assistant|> {{ .Response }}<|end|> """ ``` error: `Error: llama runner process no longer running: 3221226505`
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Fix message truncation logic and ensure at least one system message i…
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### Changes: - Fixed the message truncation logic to ensure that at least one `system` message is included. - Adjusted the message handling to always include the last message, even if the context window is exceeded. ### Motivation: - This change ensures that the context window truncation logic respects the system messages and guarantees that at least one `system` message is always included in the prompt. ### Related Issue(s): - https://github.com/ollama/ollama/issues/6176
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I_kwDOJ0Z1Ps6FYbvE
3,597
there is Massive Text Embedding Benchmark (MTEB) Leaderboard,could u support those mod?
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2024-04-11T13:49:28
2024-04-15T19:14:10
2024-04-15T19:14:09
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### What model would you like? ![image](https://github.com/ollama/ollama/assets/104901283/1f59958f-cf01-4fda-a4c6-37d53de011f0) ![image](https://github.com/ollama/ollama/assets/104901283/08202971-6f82-4b15-ba37-1482ddea5f05) https://huggingface.co/spaces/mteb/leaderboard
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2,002,340,291
I_kwDOJ0Z1Ps53WUnD
1,207
it is possible to have multiple ssh on linux (due to ollama running as a service)
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2023-11-20T14:33:47
2023-12-05T23:24:52
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I guess still there is an issue in the push function this is my repo https://ollama.ai/eramax/nous-capybara-7b-1.9 the ssh pub key shown at `cat ~/.ollama/id_ed25519.pub` is already set and added to my profile *md is the directory ```bash ➜ md llm -v ollama version 0.1.10 ➜ md l .0644 root root 4.8 GB Wed Nov 15 17:32:01 2023 🗋 Capybara-7B-V1.9-Q5_K_M.gguf .0644 root root 139 B Sat Nov 18 01:31:40 2023 🗋 Modelfile ➜ md llm create eramax/nous-capybara-7b-1.9:Q5_K_M -f Modelfile transferring context creating model layer creating template layer creating parameters layer creating config layer using already created layer sha256:08323667b50ceb4ddf208f475b6101857c26688cf413e80329f174fe34f53e9a using already created layer sha256:a8ac3515452d80041d2c3ed2ebf79f2b9a1ac4468e201a1b661ceb90c20c1a93 writing layer sha256:f4c99b0ffe2c4d82a82fcc83294c8603984598f5a77d2e1ddaedabc50bbf9ad6 writing layer sha256:e6d5ee0679e5d1afe5b2b66a38ebc0f8475801b210aea9734e626bb63f00f9bf writing manifest success ➜ md llm ls NAME ID SIZE MODIFIED eramax/nous-capybara-7b-1.9:Q5_K_M 6a898ba40903 5.1 GB 3 seconds ago ➜ md llm run eramax/nous-capybara-7b-1.9:Q5_K_M >>> who are you I am a helpful AI-powered digital assistant. ➜ md llm push eramax/nous-capybara-7b-1.9:Q5_K_M retrieving manifest Error: unable to push eramax/nous-capybara-7b-1.9, make sure this namespace exists and you are authorized to push to it ➜ md llm push eramax/nous-capybara-7b-1.9 retrieving manifest couldn't retrieve manifest Error: stat /usr/share/ollama/.ollama/models/manifests/registry.ollama.ai/eramax/nous-capybara-7b-1.9/latest: no such file or directory ➜ md llm cp eramax/nous-capybara-7b-1.9:Q5_K_M eramax/nous-capybara-7b-1.9 copied 'eramax/nous-capybara-7b-1.9:Q5_K_M' to 'eramax/nous-capybara-7b-1.9' ➜ md llm ls NAME ID SIZE MODIFIED eramax/nous-capybara-7b-1.9:Q5_K_M 6a898ba40903 5.1 GB 9 minutes ago eramax/nous-capybara-7b-1.9:latest 6a898ba40903 5.1 GB 4 seconds ago ➜ md llm push eramax/nous-capybara-7b-1.9 retrieving manifest Error: unable to push eramax/nous-capybara-7b-1.9, make sure this namespace exists and you are authorized to push to it ➜ md ```
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5,349
Ollama stderr returns info logs
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2024-06-28T01:17:58
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### What is the issue? Ollama is outputting regular logs that should be in `stdout` but they are outputting in `stderr` when running it through a subprocess, these logs should be outputting via stdout and only errors through stderr This is for all supported OS. ### OS Linux, macOS, Windows, Docker, WSL2 ### GPU Nvidia, AMD, Intel, Apple ### CPU Intel, AMD, Apple ### Ollama version 0.1.47
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2,271,490,246
I_kwDOJ0Z1Ps6HZDDG
4,052
Unable to create gguf file for my finetuned mixtral8x7b model
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2024-04-30T13:31:42
2024-05-07T17:24:27
2024-05-07T17:24:07
NONE
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### What is the issue? I am trying to create a gguf file for my finetuned mixtral model but it keeps throwing following error: Command :` python llm/llama.cpp/convert.py /home/raft_mixtral_2epochs_v1 --outtype q8_0 --outfile converted.bin` Error: ``` raise FileNotFoundError(f"Can't find model in directory {path}") FileNotFoundError: Can't find model in directory /home/raft_mixtral_2epochs_v1 ``` Below are the content's of my raft_mixtral_2epochs_v1 folder : ![Screenshot from 2024-04-30 19-01-05](https://github.com/ollama/ollama/assets/129523872/fff3327a-5e96-4991-8ecb-c2fc0c539f32) ### OS _No response_ ### GPU _No response_ ### CPU _No response_ ### Ollama version 0.1.32
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2,155,110,949
PR_kwDOJ0Z1Ps5n9zZc
2,770
expand user home dir in OLLAMA_MODELS
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2024-02-26T20:59:23
2024-11-21T18:23:47
2024-11-21T18:23:47
CONTRIBUTOR
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This allows the `OLLAMA_MODELS` env var to contain a tilde, the same way other paths can be specified in ollama models. Ex: `OLLAMA_MODELS="~/models" ollama serve` now puts models in the proper location
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391
Min device that llama 70b require?
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2023-08-22T00:48:36
2023-08-22T00:58:48
2023-08-22T00:58:48
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Love Ollama which made my intel mac can run llama 7b 😄. Just wonder what kind of mac device are required to run llama 2 70B? Will M2 ultra with 64G vRam be satisfying? Thx.
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7,811
Add Observability section and OpenLIT in README
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2024-11-24T02:01:49
2024-11-24T02:09:10
2024-11-24T02:03:12
CONTRIBUTOR
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Adding OpenLIT to the README as an integration. Did not find a proper category so added `Observability`
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2,067
Use `gzip` for embedded files
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2024-01-19T04:55:36
2024-01-19T18:23:05
2024-01-19T18:23:04
MEMBER
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"Error loading llama server" when using a T5ForConditionalGeneration architucture model, converted to GGUF format
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### What is the issue? With the help of https://huggingface.co/spaces/ggml-org/gguf-my-repo I made the https://huggingface.co/iG8R/t5_translate_en_ru_zh_large_1024_v2-Q8_0-GGUF model which was successfully imported into `ollama`. But when I try to use it, I always get the following error, while all other models work almost perfectly: ``` GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\src\llama.cpp:14882: strcmp(embd->name, "result_norm") == 0 time=2024-07-26T23:33:10.669+03:00 level=INFO source=server.go:617 msg="waiting for server to become available" status="llm server error" time=2024-07-26T23:33:10.934+03:00 level=ERROR source=sched.go:443 msg="error loading llama server" error="llama runner process has terminated: exit status 0xc0000409" [GIN] 2024/07/26 - 23:33:10 | 500 | 9.3372066s | 127.0.0.1 | POST "/v1/chat/completions" ``` Here is the full log: ``` time=2024-07-26T23:33:09.450+03:00 level=WARN source=memory.go:115 msg="model missing blk.0 layer size" time=2024-07-26T23:33:09.451+03:00 level=INFO source=sched.go:701 msg="new model will fit in available VRAM in single GPU, loading" model=H:\OllamaModels\blobs\sha256-cca50b43a8d0071238d9cb22864768dec5a8146f0b9969b83e69a076e267b17e gpu=GPU-60a344b3-0290-00b9-ed05-6b799407d228 parallel=4 available=10883338240 required="702.5 MiB" time=2024-07-26T23:33:09.451+03:00 level=WARN source=memory.go:115 msg="model missing blk.0 layer size" time=2024-07-26T23:33:09.451+03:00 level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=25 layers.offload=25 layers.split="" memory.available="[10.1 GiB]" memory.required.full="702.5 MiB" memory.required.partial="702.5 MiB" memory.required.kv="48.0 MiB" memory.required.allocations="[702.5 MiB]" memory.weights.total="48.0 MiB" memory.weights.repeating="17179869184.0 GiB" memory.weights.nonrepeating="67.5 MiB" memory.graph.full="128.0 MiB" memory.graph.partial="128.0 MiB" time=2024-07-26T23:33:09.455+03:00 level=INFO source=server.go:383 msg="starting llama server" cmd="f:\\Ollama\\ollama_runners\\cuda_v11.3\\ollama_llama_server.exe --model H:\\OllamaModels\\blobs\\sha256-cca50b43a8d0071238d9cb22864768dec5a8146f0b9969b83e69a076e267b17e --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 25 --no-mmap --parallel 4 --port 62423" time=2024-07-26T23:33:09.459+03:00 level=INFO source=sched.go:437 msg="loaded runners" count=1 time=2024-07-26T23:33:09.459+03:00 level=INFO source=server.go:583 msg="waiting for llama runner to start responding" time=2024-07-26T23:33:09.461+03:00 level=INFO source=server.go:617 msg="waiting for server to become available" status="llm server error" INFO [wmain] build info | build=3440 commit="d94c6e0c" tid="17472" timestamp=1722025989 INFO [wmain] system info | n_threads=4 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 0 | " tid="17472" timestamp=1722025989 total_threads=8 INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="7" port="62423" tid="17472" timestamp=1722025989 llama_model_loader: loaded meta data with 33 key-value pairs and 558 tensors from H:\OllamaModels\blobs\sha256-cca50b43a8d0071238d9cb22864768dec5a8146f0b9969b83e69a076e267b17e (version GGUF V3 (latest)) llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. llama_model_loader: - kv 0: general.architecture str = t5 llama_model_loader: - kv 1: general.type str = model llama_model_loader: - kv 2: general.name str = T5_Translate_En_Ru_Zh_Large_1024_V2 llama_model_loader: - kv 3: general.size_label str = 851M llama_model_loader: - kv 4: general.license str = apache-2.0 llama_model_loader: - kv 5: general.tags arr[str,1] = ["translation"] llama_model_loader: - kv 6: general.languages arr[str,3] = ["ru", "zh", "en"] llama_model_loader: - kv 7: general.datasets arr[str,1] = ["ccmatrix"] llama_model_loader: - kv 8: t5.context_length u32 = 512 llama_model_loader: - kv 9: t5.embedding_length u32 = 1024 llama_model_loader: - kv 10: t5.feed_forward_length u32 = 2816 llama_model_loader: - kv 11: t5.block_count u32 = 24 llama_model_loader: - kv 12: t5.attention.head_count u32 = 16 llama_model_loader: - kv 13: t5.attention.key_length u32 = 64 llama_model_loader: - kv 14: t5.attention.value_length u32 = 64 llama_model_loader: - kv 15: t5.attention.layer_norm_epsilon f32 = 0.000001 llama_model_loader: - kv 16: t5.attention.relative_buckets_count u32 = 32 llama_model_loader: - kv 17: t5.attention.layer_norm_rms_epsilon f32 = 0.000001 llama_model_loader: - kv 18: t5.decoder_start_token_id u32 = 0 llama_model_loader: - kv 19: general.file_type u32 = 7 llama_model_loader: - kv 20: tokenizer.ggml.model str = t5 llama_model_loader: - kv 21: tokenizer.ggml.pre str = default llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,65100] = ["<pad>", "</s>", "<unk>", ",", "▁"... llama_model_loader: - kv 23: tokenizer.ggml.scores arr[f32,65100] = [0.000000, 0.000000, 0.000000, -3.144... llama_model_loader: - kv 24: tokenizer.ggml.token_type arr[i32,65100] = [3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... llama_model_loader: - kv 25: tokenizer.ggml.add_space_prefix bool = true llama_model_loader: - kv 26: tokenizer.ggml.remove_extra_whitespaces bool = true llama_model_loader: - kv 27: tokenizer.ggml.precompiled_charsmap arr[u8,237561] = [0, 180, 2, 0, 0, 132, 0, 0, 0, 0, 0,... llama_model_loader: - kv 28: tokenizer.ggml.eos_token_id u32 = 1 llama_model_loader: - kv 29: tokenizer.ggml.padding_token_id u32 = 0 llama_model_loader: - kv 30: tokenizer.ggml.add_bos_token bool = false llama_model_loader: - kv 31: tokenizer.ggml.add_eos_token bool = true llama_model_loader: - kv 32: general.quantization_version u32 = 2 llama_model_loader: - type f32: 122 tensors llama_model_loader: - type f16: 2 tensors llama_model_loader: - type q8_0: 434 tensors time=2024-07-26T23:33:09.722+03:00 level=INFO source=server.go:617 msg="waiting for server to become available" status="llm server loading model" llm_load_vocab: special tokens cache size = 103 llm_load_vocab: token to piece cache size = 0.5577 MB llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = t5 llm_load_print_meta: vocab type = UGM llm_load_print_meta: n_vocab = 65100 llm_load_print_meta: n_merges = 0 llm_load_print_meta: vocab_only = 0 llm_load_print_meta: n_ctx_train = 512 llm_load_print_meta: n_embd = 1024 llm_load_print_meta: n_layer = 24 llm_load_print_meta: n_head = 16 llm_load_print_meta: n_head_kv = 16 llm_load_print_meta: n_rot = 64 llm_load_print_meta: n_swa = 0 llm_load_print_meta: n_embd_head_k = 64 llm_load_print_meta: n_embd_head_v = 64 llm_load_print_meta: n_gqa = 1 llm_load_print_meta: n_embd_k_gqa = 1024 llm_load_print_meta: n_embd_v_gqa = 1024 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-06 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: f_logit_scale = 0.0e+00 llm_load_print_meta: n_ff = 2816 llm_load_print_meta: n_expert = 0 llm_load_print_meta: n_expert_used = 0 llm_load_print_meta: causal attn = 1 llm_load_print_meta: pooling type = 0 llm_load_print_meta: rope type = -1 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 10000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_ctx_orig_yarn = 512 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: ssm_d_conv = 0 llm_load_print_meta: ssm_d_inner = 0 llm_load_print_meta: ssm_d_state = 0 llm_load_print_meta: ssm_dt_rank = 0 llm_load_print_meta: model type = 780M llm_load_print_meta: model ftype = Q8_0 llm_load_print_meta: model params = 850.68 M llm_load_print_meta: model size = 862.32 MiB (8.50 BPW) llm_load_print_meta: general.name = T5_Translate_En_Ru_Zh_Large_1024_V2 llm_load_print_meta: EOS token = 1 '</s>' llm_load_print_meta: UNK token = 2 '<unk>' llm_load_print_meta: PAD token = 0 '<pad>' llm_load_print_meta: LF token = 4 '▁' llm_load_print_meta: max token length = 48 ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no ggml_cuda_init: found 1 CUDA devices: Device 0: NVIDIA GeForce RTX 3080 Ti, compute capability 8.6, VMM: yes llm_load_tensors: ggml ctx size = 0.44 MiB llm_load_tensors: offloading 24 repeating layers to GPU llm_load_tensors: offloading non-repeating layers to GPU llm_load_tensors: offloaded 25/25 layers to GPU llm_load_tensors: CUDA_Host buffer size = 67.55 MiB llm_load_tensors: CUDA0 buffer size = 794.79 MiB llama_new_context_with_model: n_ctx = 8192 llama_new_context_with_model: n_batch = 512 llama_new_context_with_model: n_ubatch = 512 llama_new_context_with_model: flash_attn = 0 llama_new_context_with_model: freq_base = 10000.0 llama_new_context_with_model: freq_scale = 1 llama_kv_cache_init: CUDA0 KV buffer size = 768.00 MiB llama_new_context_with_model: KV self size = 768.00 MiB, K (f16): 384.00 MiB, V (f16): 384.00 MiB llama_new_context_with_model: CUDA_Host output buffer size = 1.01 MiB llama_new_context_with_model: CUDA0 compute buffer size = 1046.00 MiB llama_new_context_with_model: CUDA_Host compute buffer size = 291.00 MiB llama_new_context_with_model: graph nodes = 1350 llama_new_context_with_model: graph splits = 50 GGML_ASSERT: C:\a\ollama\ollama\llm\llama.cpp\src\llama.cpp:14882: strcmp(embd->name, "result_norm") == 0 time=2024-07-26T23:33:10.669+03:00 level=INFO source=server.go:617 msg="waiting for server to become available" status="llm server error" time=2024-07-26T23:33:10.934+03:00 level=ERROR source=sched.go:443 msg="error loading llama server" error="llama runner process has terminated: exit status 0xc0000409" [GIN] 2024/07/26 - 23:33:10 | 500 | 9.3372066s | 127.0.0.1 | POST "/v1/chat/completions" ``` ### OS Windows ### GPU Nvidia ### CPU Intel ### Ollama version 0.3.0
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1,833,465,017
I_kwDOJ0Z1Ps5tSHS5
258
Ollama running in Dockerfile
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2023-08-02T16:00:14
2023-12-12T21:49:23
2023-09-07T13:31:57
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@jmorganca @mxyng I got ./ollama serve to work in docker. The only issue is that I am not able to pull down the files for other models like llama2 via the commad ./ollama pull llama2. I have tested the same configuration on ubuntu and works fine. Just inside the docker I get the following issue: ```Error: Post "http://127.0.0.1:11434/api/pull": dial tcp 127.0.0.1:11434: connect: connection refused``` Here is my Dockerfile: ``` ROM ubuntu:latest RUN apt-get update RUN apt-get install -y git WORKDIR /home RUN (cd /home; git clone https://github.com/jmorganca/ollama.git) RUN apt-get install -y wget RUN wget https://golang.org/dl/go1.20.7.linux-amd64.tar.gz -O go.tar.gz RUN apt-get install -y gcc RUN apt-get install -y g++ RUN tar -C /usr/local -xzf go.tar.gz ENV PATH=$PATH:/usr/local/go/bin RUN go version RUN rm go.tar.gz WORKDIR /home/ollama RUN go build . RUN ./ollama pull llama2 EXPOSE 11434 RUN ./ollama serve & ``` and it only fails on the line RUN ./ollama pull llama2 and I have no clue where the issue is coming from:
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2,328,191,658
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4,753
FROM is not recognized
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2024-05-31T16:24:21
2024-06-24T16:43:36
2024-06-24T16:43:36
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### What is the issue? I followed the instructions to make a gguf model work but FROM doesn't work ``` C:\Users\Armaguedin\Documents\dev\python\text-generation-webui\models>ollama Usage: ollama [flags] ollama [command] Available Commands: serve Start ollama create Create a model from a Modelfile show Show information for a model run Run a model pull Pull a model from a registry push Push a model to a registry list List models ps List running models cp Copy a model rm Remove a model help Help about any command Flags: -h, --help help for ollama -v, --version Show version information Use "ollama [command] --help" for more information about a command. C:\Users\Armaguedin\Documents\dev\python\text-generation-webui\models>FROM ./c4ai-command-r-v01-Q4_K_M.gguf 'FROM' n’est pas reconnu en tant que commande interne ou externe, un programme exécutable ou un fichier de commandes. ``` ### OS Windows ### GPU Nvidia ### CPU Intel ### Ollama version 0.1.39
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2,518,937,874
I_kwDOJ0Z1Ps6WI_ES
6,746
add support for Reflection-Llama-3.1
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2024-09-11T08:11:48
2024-09-11T23:57:44
2024-09-11T23:57:44
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any idea that we add Reflection-Llama-3.1 model?
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Build failure with v0.0.18
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2023-09-06T22:31:32
2023-09-07T03:34:28
2023-09-07T03:08:32
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Hello 👋 . I'm a maintainer for the [Homebrew](https://brew.sh) project. While packaging v0.0.18 of ollama, we're encountering a build failure. Here is the error: ```shell go build -trimpath -o=/home/linuxbrew/.linuxbrew/Cellar/ollama/0.0.18/bin/ollama -ldflags=-s -w go: downloading github.com/spf13/cobra v1.7.0 go: downloading github.com/chzyer/readline v1.5.1 go: downloading github.com/dustin/go-humanize v1.0.1 go: downloading github.com/olekukonko/tablewriter v0.0.5 go: downloading golang.org/x/crypto v0.10.0 go: downloading github.com/mattn/go-runewidth v0.0.14 go: downloading github.com/mitchellh/colorstring v0.0.0-20190213212951-d06e56a500db go: downloading golang.org/x/term v0.10.0 go: downloading github.com/gin-contrib/cors v1.4.0 go: downloading github.com/gin-gonic/gin v1.9.1 go: downloading golang.org/x/exp v0.0.0-20230817173708-d852ddb80c63 go: downloading gonum.org/v1/gonum v0.13.0 go: downloading github.com/spf13/pflag v1.0.5 go: downloading github.com/rivo/uniseg v0.2.0 go: downloading golang.org/x/sys v0.11.0 go: downloading github.com/pbnjay/memory v0.0.0-20210728143218-7b4eea64cf58 go: downloading github.com/gin-contrib/sse v0.1.0 go: downloading github.com/mattn/go-isatty v0.0.19 go: downloading golang.org/x/net v0.10.0 go: downloading github.com/go-playground/validator/v10 v10.14.0 go: downloading github.com/pelletier/go-toml/v2 v2.0.8 go: downloading github.com/ugorji/go/codec v1.2.11 go: downloading google.golang.org/protobuf v1.30.0 go: downloading gopkg.in/yaml.v3 v3.0.1 go: downloading github.com/gabriel-vasile/mimetype v1.4.2 go: downloading github.com/go-playground/universal-translator v0.18.1 go: downloading github.com/leodido/go-urn v1.2.4 go: downloading golang.org/x/text v0.10.0 go: downloading github.com/go-playground/locales v0.14.1 llm/ggml_llama.go:31:12: pattern llama.cpp/ggml/build/*/bin/*: no matching files found ``` Relates to https://github.com/Homebrew/homebrew-core/pull/141639
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Ollama
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2024-07-14T17:53:02
2024-07-15T02:24:20
2024-07-14T23:07:23
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grammar: surgically wrenching gbnf from system messages
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2024-11-05T16:53:53
2024-12-05T00:33:51
2024-12-05T00:33:51
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Some people have reached out to me re: my comment from earlier https://github.com/ollama/ollama/issues/6237#issuecomment-2428338129 so I'd decided it might be worth a shot. To re-cap: this pull request implements wrenching GBNF's (only one at a time!) from the system prompt. I know a bunch of pull requests to similar effect already exist. However, I also believe that the prior efforts are completely misguided. What they should be doing instead is parsing the system prompt for **\`\`\`gbnf** code blocks. This approach does not impact the API surface, and it would also allow for dynamically generating the grammar on the fly from _any_ existing Ollama client. More details in the linked comment. ```bash set GRAMMAR ' ```gbnf root ::= (expr "=" ws term "\n")+ expr ::= term ([-+*/] term)* term ::= ident | num | "(" ws expr ")" ws ident ::= [a-z] [a-z0-9_]* ws num ::= [0-9]+ ws ws ::= [ \t\n]* ```' curl http://ollama.lan/chat -d '{ "model": "llama3.2", "messages": [ { "role": "system", "content": "You are helpful assistant.\n$GRAMMAR" }, { "role": "user", "content": "why is the sky blue?" } ] }' ``` I've so far only committed the bare-bones portion (i.e. vanilla GBNF only) and some tests I had lying around—due to obvious reasons. I don't believe a `jsonschema` and/or `openapi` function-calling implementation is necessary, as it may as well be done trivially on the client-side, but hey, that may be fun for all I care. Otherwise, happy to bestow my genius upon this world.
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Error: llama runner exited, you may not have enough available memory to run this model
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2023-12-16T19:41:46
2024-01-08T21:42:04
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Hi, When I have run a modell and try to communicate with it, I always get same response, no matter which model (or small or big)... ' Error: llama runner exited, you may not have enough available memory to run this model ' Any clues on this one? My host is running ubuntu 20.04 on proxmox with approx 56 gb memory free, nvidia m40 24 gb gpu ' free total used free shared buff/cache available Mem: 58212660 641572 54462900 5692 3108188 56950236 Swap: 8388604 0 8388604 ' ' nvidia-smi Sat Dec 16 19:39:44 2023 +---------------------------------------------------------------------------------------+ | NVIDIA-SMI 535.129.03 Driver Version: 535.129.03 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 Tesla M40 24GB Off | 00000000:01:00.0 Off | 0 | | N/A 37C P8 16W / 250W | 0MiB / 23040MiB | 0% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+ +---------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=======================================================================================| | No running processes found | +---------------------------------------------------------------------------------------+ ' Seems ollama finds the gpu: journalctl: ` Dec 16 18:30:05 tesla ollama[2245]: 2023/12/16 18:30:05 llama.go:300: 22939 MB VRAM available, loading up to 150 GPU layers Dec 16 18:30:05 tesla ollama[2245]: 2023/12/16 18:30:05 llama.go:436: starting llama runner Dec 16 18:30:05 tesla ollama[2245]: 2023/12/16 18:30:05 llama.go:494: waiting for llama runner to start responding Dec 16 18:30:05 tesla ollama[2245]: ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no Dec 16 18:30:05 tesla ollama[2245]: ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes Dec 16 18:30:05 tesla ollama[2245]: ggml_init_cublas: found 1 CUDA devices: Dec 16 18:30:05 tesla ollama[2245]: Device 0: Tesla M40 24GB, compute capability 5.2 Dec 16 18:30:05 tesla ollama[2326]: {"timestamp":1702751405,"level":"INFO","function":"main","line":2652,"message":"build info","build":441,"commit":"948ff1> Dec 16 18:30:05 tesla ollama[2326]: {"timestamp":1702751405,"level":"INFO","function":"main","line":2655,"message":"system info","n_threads":8,"n_threads_ba> Dec 16 18:30:05 tesla ollama[2245]: llama_model_loader: loaded meta data with 19 key-value pairs and 291 tensors from /usr/share/ollama/.ollama/models/blobs> --- --- Dec 16 18:31:49 tesla ollama[2245]: llm_load_tensors: ggml ctx size = 0.12 MiB Dec 16 18:31:49 tesla ollama[2245]: llm_load_tensors: using CUDA for GPU acceleration Dec 16 18:31:49 tesla ollama[2245]: llm_load_tensors: mem required = 70.43 MiB Dec 16 18:31:49 tesla ollama[2245]: llm_load_tensors: offloading 32 repeating layers to GPU Dec 16 18:31:49 tesla ollama[2245]: llm_load_tensors: offloading non-repeating layers to GPU Dec 16 18:31:49 tesla ollama[2245]: llm_load_tensors: offloaded 33/33 layers to GPU Dec 16 18:31:49 tesla ollama[2245]: llm_load_tensors: VRAM used: 3577.56 MiB ` Loading a modell works fine, but error comes when trying to communicate, happens with any modell, even the smallest. Error: llama runner exited, you may not have enough available memory to run this model journalctl: ' Dec 16 18:31:50 tesla ollama[2245]: .................................................................................................. Dec 16 18:31:50 tesla ollama[2245]: llama_new_context_with_model: n_ctx = 4096 Dec 16 18:31:50 tesla ollama[2245]: llama_new_context_with_model: freq_base = 10000.0 Dec 16 18:31:50 tesla ollama[2245]: llama_new_context_with_model: freq_scale = 1 Dec 16 18:31:51 tesla ollama[2245]: llama_kv_cache_init: VRAM kv self = 2048.00 MB Dec 16 18:31:51 tesla ollama[2245]: llama_new_context_with_model: KV self size = 2048.00 MiB, K (f16): 1024.00 MiB, V (f16): 1024.00 MiB Dec 16 18:31:51 tesla ollama[2245]: llama_build_graph: non-view tensors processed: 676/676 Dec 16 18:31:51 tesla ollama[2245]: llama_new_context_with_model: compute buffer total size = 291.32 MiB Dec 16 18:31:51 tesla ollama[2245]: llama_new_context_with_model: VRAM scratch buffer: 288.00 MiB Dec 16 18:31:51 tesla ollama[2245]: llama_new_context_with_model: total VRAM used: 5913.57 MiB (model: 3577.56 MiB, context: 2336.00 MiB) Dec 16 18:31:51 tesla ollama[2588]: {"timestamp":1702751511,"level":"INFO","function":"main","line":3035,"message":"HTTP server listening","hostname":"127.0> Dec 16 18:31:51 tesla ollama[2588]: {"timestamp":1702751511,"level":"INFO","function":"log_server_request","line":2596,"message":"request","remote_addr":"12> Dec 16 18:31:51 tesla ollama[2245]: 2023/12/16 18:31:51 llama.go:508: llama runner started in 2.201689 seconds Dec 16 18:31:51 tesla ollama[2245]: [GIN] 2023/12/16 - 18:31:51 | 200 | 2.311479662s | 127.0.0.1 | POST "/api/generate" Dec 16 18:32:14 tesla ollama[2588]: {"timestamp":1702751534,"level":"INFO","function":"log_server_request","line":2596,"message":"request","remote_addr":"12> Dec 16 18:32:14 tesla ollama[2245]: 2023/12/16 18:32:14 llama.go:577: loaded 0 images **Dec 16 18:32:14 tesla ollama[2245]: cuBLAS error 15 at /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:8448** Dec 16 18:32:14 tesla ollama[2245]: current device: 0 **Dec 16 18:32:14 tesla ollama[2245]: GGML_ASSERT: /go/src/github.com/jmorganca/ollama/llm/llama.cpp/gguf/ggml-cuda.cu:8448: !"cuBLAS error"** Dec 16 18:32:14 tesla ollama[2245]: 2023/12/16 18:32:14 llama.go:451: signal: aborted (core dumped) Dec 16 18:32:14 tesla ollama[2245]: 2023/12/16 18:32:14 llama.go:525: llama runner stopped successfully Dec 16 18:32:14 tesla ollama[2245]: [GIN] 2023/12/16 - 18:32:14 | 200 | 601.813679ms | 127.0.0.1 | POST "/api/generate" ' Full log: https://www.evernote.com/shard/s16/sh/6d2eab19-c11f-7cf4-148c-9a5cd04dc944/Zwy3R7zsW8TvzDquK5Devnpko4BPwqNquvDt4nHLGCiecB_luwmk3sH8ug The gpu is a bit dated, so it might miss some features newer nvidia cards have. It is a affordable option to run with a lot of vram so would be nice if it was supported. When running ComfyUI i have to start with --disable-cuda-malloc. Regards, Bård Ove Myhr
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Feature request: Add CLI option to specify a system prompt
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upload: retry complete upload
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2023-11-18T07:54:32
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CONTRIBUTOR
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Timeout to start model too little - progress stalls at 100% for 5 minutes when loading with swap
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2024-07-28T19:29:54
2024-09-05T21:00:09
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### What is the issue? I am trying to run llama:405b on a hardware with only little power and through a swap file. Im not concerned about its speed. Though, the Model cant load because: ``` ollama run llama3.1:405b --keepalive 5h Error: timed out waiting for llama runner to start - progress 1.00 - ``` is it possible to disable this timeout, or increase it? Im quite certain it would load after (a long) while ### OS Linux ### GPU Other ### CPU Other ### Ollama version 0.3.0
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I_kwDOJ0Z1Ps6HzMKT
4,137
Support for HyperGAI/HPT1_5-Air-Llama-3-8B-Instruct-multimodal
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2024-05-03T20:02:58
2024-05-11T08:26:17
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Hello team, It would be so great so have this new multimodal based llama3 model supported with ollama! thanks! https://huggingface.co/HyperGAI/HPT1_5-Air-Llama-3-8B-Instruct-multimodal https://github.com/HyperGAI/HPT?tab=readme-ov-file#installation
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Install on any drive
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2024-10-18T22:29:04
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What about installing on other drives than c: ?
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Add ability to skip oneapi generate
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2024-06-07T15:33:28
2024-06-07T21:07:18
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COLLABORATOR
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This follows the same pattern for cuda and rocm to allow disabling the build even when we detect the dependent libraries Related to #4511
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504
Python package
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2023-09-10T13:50:17
2024-03-11T19:33:40
2024-03-11T19:33:40
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Quite a few folks have been running: ``` pip install ollama ``` However there isn't yet a python package (there was previously an old Ollama prototype from July). This issue tracks having a first-class python package for using Ollama.
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Update llama.cpp submodule to `f364eb6`
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2024-04-30T19:50:05
2024-04-30T21:25:40
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Also filters out stop words for now from being returned in the API as they will print on older clients
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cuda device ordering inconsistent between runtime and management library
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2024-11-02T23:35:42
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### What is the issue? ### My GPU setup is: 1. RTX 3090 - first PCIE 5.0 x16, but secondary GPU 2. RTX 4090 - second PCIE 4.0 x4, but primary GPU So, I have a weird bug with memory estimations. There are two calls for device memory usage info: 1. `C.cudart_bootstrap(*cHandles.cudart, C.int(i), &memInfo)` here, `i` is device index (and `gpuInfo.index`). In my case it's `0` for 4090 on this step. 2. `C.nvml_get_free(*cHandles.nvml, C.int(gpuInfo.index), &memInfo.free, &memInfo.total, &memInfo.used)` here we get memory info for `gpuInfo.index`, but nvml device order is different and 4090 is `1`! As a result I have estimated memory usage of 2Gb for RTX 3090 while nvidia-smi reported only 300mb, and 300mb usage for RTX 4090, while nvidia-smi reported 2Gb. This results in wrong layer split prediction. While its fine for me since it not works well with flash attention, but still an issue. ### Ollama logs ``` time=2024-10-30T23:40:22.092+03:00 level=DEBUG source=gpu.go:562 msg="discovered GPU libraries" paths=[C:\Windows\system32\nvcuda.dll] CUDA driver version: 12.7 time=2024-10-30T23:40:22.139+03:00 level=DEBUG source=gpu.go:129 msg="detected GPUs" count=2 library=C:\Windows\system32\nvcuda.dll [GPU-4e64b2bc-98b0-d948-a660-7668c70aba4f] CUDA totalMem 24563 mb [GPU-4e64b2bc-98b0-d948-a660-7668c70aba4f] CUDA freeMem 22994 mb [GPU-4e64b2bc-98b0-d948-a660-7668c70aba4f] Compute Capability 8.9 time=2024-10-30T23:40:22.271+03:00 level=INFO source=gpu.go:326 msg="detected OS VRAM overhead" id=GPU-4e64b2bc-98b0-d948-a660-7668c70aba4f library=cuda compute=8.9 driver=12.7 name="NVIDIA GeForce RTX 4090" overhead="1.3 GiB" [GPU-c06ff468-596d-5c2b-52ed-c764302de199] CUDA totalMem 24575 mb [GPU-c06ff468-596d-5c2b-52ed-c764302de199] CUDA freeMem 23306 mb [GPU-c06ff468-596d-5c2b-52ed-c764302de199] Compute Capability 8.6 time=2024-10-30T23:40:22.563+03:00 level=DEBUG source=amd_windows.go:35 msg="unable to load amdhip64_6.dll, please make sure to upgrade to the latest amd driver: The specified module could not be found." releasing cuda driver library releasing nvml library time=2024-10-30T23:40:22.564+03:00 level=INFO source=types.go:123 msg="inference compute" id=GPU-4e64b2bc-98b0-d948-a660-7668c70aba4f library=cuda variant=v12 compute=8.9 driver=12.7 name="NVIDIA GeForce RTX 4090" total="24.0 GiB" available="22.5 GiB" time=2024-10-30T23:40:22.565+03:00 level=INFO source=types.go:123 msg="inference compute" id=GPU-c06ff468-596d-5c2b-52ed-c764302de199 library=cuda variant=v12 compute=8.6 driver=12.7 name="NVIDIA GeForce RTX 3090" total="24.0 GiB" available="22.8 GiB" time=2024-10-30T23:40:35.137+03:00 level=DEBUG source=gpu.go:396 msg="updating system memory data" before.total="127.2 GiB" before.free="95.7 GiB" before.free_swap="119.8 GiB" now.total="127.2 GiB" now.free="95.7 GiB" now.free_swap="119.6 GiB" time=2024-10-30T23:40:35.152+03:00 level=DEBUG source=gpu.go:444 msg="updating cuda memory data" gpu=GPU-4e64b2bc-98b0-d948-a660-7668c70aba4f name="NVIDIA GeForce RTX 4090" overhead="1.3 GiB" before.total="24.0 GiB" before.free="22.5 GiB" now.total="24.0 GiB" now.free="22.5 GiB" now.used="286.3 MiB" time=2024-10-30T23:40:35.167+03:00 level=DEBUG source=gpu.go:444 msg="updating cuda memory data" gpu=GPU-c06ff468-596d-5c2b-52ed-c764302de199 name="NVIDIA GeForce RTX 3090" overhead="0 B" before.total="24.0 GiB" before.free="22.8 GiB" now.total="24.0 GiB" now.free="21.9 GiB" now.used="2.1 GiB" releasing nvml library time=2024-10-30T23:40:35.187+03:00 level=DEBUG source=sched.go:225 msg="loading first model" model=I:\localai\models\ollama\blobs\sha256-9167b346a6e1f45064e0500cf8539572e5889ba631eecb40a3cab48338b6d7df time=2024-10-30T23:40:35.187+03:00 level=DEBUG source=memory.go:107 msg=evaluating library=cuda gpu_count=1 available="[22.5 GiB]" time=2024-10-30T23:40:35.188+03:00 level=DEBUG source=memory.go:107 msg=evaluating library=cuda gpu_count=1 available="[21.9 GiB]" time=2024-10-30T23:40:35.188+03:00 level=DEBUG source=memory.go:107 msg=evaluating library=cuda gpu_count=2 available="[22.5 GiB 21.9 GiB]" time=2024-10-30T23:40:35.189+03:00 level=DEBUG source=gpu.go:396 msg="updating system memory data" before.total="127.2 GiB" before.free="95.7 GiB" before.free_swap="119.6 GiB" now.total="127.2 GiB" now.free="95.7 GiB" now.free_swap="119.6 GiB" time=2024-10-30T23:40:35.213+03:00 level=DEBUG source=gpu.go:444 msg="updating cuda memory data" gpu=GPU-4e64b2bc-98b0-d948-a660-7668c70aba4f name="NVIDIA GeForce RTX 4090" overhead="1.3 GiB" before.total="24.0 GiB" before.free="22.5 GiB" now.total="24.0 GiB" now.free="22.5 GiB" now.used="286.3 MiB" time=2024-10-30T23:40:35.229+03:00 level=DEBUG source=gpu.go:444 msg="updating cuda memory data" gpu=GPU-c06ff468-596d-5c2b-52ed-c764302de199 name="NVIDIA GeForce RTX 3090" overhead="0 B" before.total="24.0 GiB" before.free="21.9 GiB" now.total="24.0 GiB" now.free="21.9 GiB" now.used="2.1 GiB" releasing nvml library time=2024-10-30T23:40:35.229+03:00 level=INFO source=llama-server.go:72 msg="system memory" total="127.2 GiB" free="95.7 GiB" free_swap="119.6 GiB" time=2024-10-30T23:40:35.229+03:00 level=DEBUG source=memory.go:107 msg=evaluating library=cuda gpu_count=2 available="[22.5 GiB 21.9 GiB]" time=2024-10-30T23:40:35.230+03:00 level=INFO source=memory.go:346 msg="offload to cuda" layers.requested=999 layers.model=81 layers.offload=55 layers.split=28,27 memory.available="[22.5 GiB 21.9 GiB]" memory.gpu_overhead="0 B" memory.required.full="58.6 GiB" memory.required.partial="43.4 GiB" memory.required.kv="10.0 GiB" memory.required.allocations="[22.0 GiB 21.4 GiB]" memory.weights.total="45.4 GiB" memory.weights.repeating="44.5 GiB" memory.weights.nonrepeating="974.6 MiB" memory.graph.full="5.1 GiB" memory.graph.partial="5.1 GiB" ``` ### nvidia-smi output ``` +-----------------------------------------------------------------------------------------+ | NVIDIA-SMI 566.03 Driver Version: 566.03 CUDA Version: 12.7 | |-----------------------------------------+------------------------+----------------------+ | GPU Name Driver-Model | 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 3090 WDDM | 00000000:01:00.0 Off | N/A | | 0% 37C P8 15W / 370W | 37MiB / 24576MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ | 1 NVIDIA GeForce RTX 4090 WDDM | 00000000:16:00.0 On | Off | | 30% 34C P0 61W / 450W | 1517MiB / 24564MiB | 0% Default | | | | N/A | +-----------------------------------------+------------------------+----------------------+ ``` ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.4.0-rc5
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Ollama is unstable recently
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2024-02-07T04:38:04
2024-02-08T00:13:19
2024-02-08T00:13:19
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As of at least the last two recent versions, I have been experiencing a lot of issues with Ollama. Primarily, it seems to report that it can't connect to the server when using the Ollama CLI commands, even though the server is running and I can curl it. Also when using the Ollama Python SDK, I often get a Connection Refused error, but retrying will eventually connect. I can't explain it. I ran the following commands in succession. Ollama is launched via the Mac app (not command line) after killing it and no models have been loaded yet. ``` lestan@Lestans-MacBook-Pro ~ % ollama list Error: could not connect to ollama app, is it running? lestan@Lestans-MacBook-Pro ~ % curl http://localhost:11434/api/tags {"models":[{"name":"mixtral:latest","model":"mixtral:latest","modified_at":"2024-01-15T16:11:18.289940736-06:00","size":26442481545,"digest":"7708c059a8bb4d950e5e679aef904fd4da96aa4d551a5cd14a7f7e2308a82f6d","details":{"parent_model":"","format":"gguf","family":"llama","families":["llama"],"parameter_size":"47B","quantization_level":"Q4_0"}},{"name":"nous-hermes2-mixtral:latest","model":"nous-hermes2-mixtral:latest","modified_at":"2024-01-15T22:13:37.546667086-06:00","size":26442493141,"digest":"599da8dce2c14e54737c51f9668961bbc3526674249d3850b0875638a3e5e268","details":{"parent_model":"","format":"gguf","family":"llama","families":["llama"],"parameter_size":"47B","quantization_level":"Q4_0"}},{"name":"orca2:latest","model":"orca2:latest","modified_at":"2023-12-22T19:44:49.948456023-06:00","size":3825836233,"digest":"ea98cc422de301a0714ee18d077d5c4ba4fd02f889234944bb2f45618fd5d5f7","details":{"parent_model":"","format":"gguf","family":"llama","families":null,"parameter_size":"7B","quantization_level":"Q4_0"}},{"name":"phi:latest","model":"phi:latest","modified_at":"2023-12-28T21:03:25.568996781-06:00","size":1602472424,"digest":"c651b7a89d7399ce7c52624e3cec9a0e0887c6e720f0d716da44c841bfcf9aeb","details":{"parent_model":"","format":"gguf","family":"phi2","families":["phi2"],"parameter_size":"3B","quantization_level":"Q4_0"}},{"name":"tinyllama:latest","model":"tinyllama:latest","modified_at":"2024-01-05T21:45:36.99553769-06:00","size":637700138,"digest":"2644915ede352ea7bdfaff0bfac0be74c719d5d5202acb63a6fb095b52f394a4","details":{"parent_model":"","format":"gguf","family":"llama","families":["llama"],"parameter_size":"1B","quantization_level":"Q4_0"}}]} lestan@Lestans-MacBook-Pro ~ % ollama -v Warning: could not connect to a running Ollama instance Warning: client version is 0.1.23 lestan@Lestans-MacBook-Pro ~ % ps -ef | grep ollama 501 32212 32208 0 10:23PM ?? 0:00.04 /Applications/Ollama.app/Contents/Resources/ollama serve 501 32270 10253 0 10:33PM ttys014 0:00.00 grep ollama ``` I'm running on Apple M3 Max with 64GB RAM Appreciate any help. Thanks!
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5,848
The logs do not contain the request content sent by the client.
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2024-07-22T10:44:31
2024-08-01T22:48:07
2024-08-01T22:48:07
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To facilitate debugging of the program, I need to see the requests sent to the large model from the frontend. However, I've noticed that the request URLs and contents are not visible in the logs. Where can I find them? I have deployed DBGPT using Docker.
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5,868
webUI
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2024-07-23T07:45:28
2024-07-26T08:42:26
2024-07-26T08:42:14
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I developed open source webUI and services based on ollama, including functioncall. Interested can see, think useful hope can give a star: https://github.com/812781385/ollama-webUI
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Unsloth's dynamic quantizations of Deepseek R1
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2025-01-29T00:20:43
2025-01-29T23:26:04
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Would love to have these dynamic quantizations readily available in ollama: https://huggingface.co/unsloth/DeepSeek-R1-GGUF
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Ollama update (0.3.3) prevents running llama3.1:70b or llama3.1:8b with tools
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2024-08-07T01:36:11
2024-11-11T07:46:49
2024-11-06T00:53:10
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### What is the issue? I had an old version of Ollama (do not remember the previous version) and had llama3.1:70b installed which was running fine. But I wanted to install llama3.1:8b and it did not let me pull without updating my Ollama. After the update with fresh pull of llama3.1:70b does not work as expected and gives following error. It does use @tool decorator with a single output function and ChatOllama API from langchain. I tried downgrading the Ollama to a previous version to re-test but then it does not allow me to pull models without upgrading Ollama. Error message: <img width="1289" alt="image" src="https://github.com/user-attachments/assets/ccb9b9e6-1d7f-4288-a97c-5c9039ccfa13"> ### OS Linux ### GPU Nvidia ### CPU AMD ### Ollama version 0.3.3
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Please update NuExtract to v1.5
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2024-10-28T13:08:54
2024-11-18T09:13:17
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Please update [NuExtract](https://ollama.com/library/nuextract) to the newest version on [huggingface](https://huggingface.co/numind/NuExtract-v1.5/tree/main)
null
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2,183,235,371
I_kwDOJ0Z1Ps6CIYcr
3,095
Limit ollama usage of GPUs using CUDA_VISIBLE_DEVICES
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2024-03-13T06:42:44
2024-04-12T22:26:09
2024-04-12T22:26:09
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I've read the updated docs. The previous issue regarding the inability to limit OLLAMA usage of GPUs using CUDA_VISIBLE_DEVICES has not been resolved. Despite setting the environment variable CUDA_VISIBLE_DEVICES to a specific range or list of GPU IDs, OLLIMA continues to use all available GPUs during training instead of only the specified ones.
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2,484,288,763
I_kwDOJ0Z1Ps6UEzz7
6,487
When invoked from the command line in an active conversation session, missing model for `/load` shouldn't be fatal error
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2024-08-24T06:32:06
2024-08-24T06:32:06
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CONTRIBUTOR
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### What is the issue? if you try to load a nonexistent model ``` Loading model 'nonexistent.' Error: model "nonexistent." not found, try pulling it first ``` then quits the existing session ### OS Linux ### GPU AMD ### CPU AMD ### Ollama version 0.3.6
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I_kwDOJ0Z1Ps56ONjm
1,631
WSL2: GPU not working anymore
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2023-12-20T13:24:32
2024-01-13T19:50:00
2024-01-10T15:07:27
NONE
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I updated Ollama to latest version (0.1.17) on a Ubuntu WSL2 and the GPU support is not recognized anymore. At the end of installation I have the followinf message: "WARNING: No NVIDIA GPU detected. Ollama will run in CPU-only mode." Running nvidia-smi: Wed Dec 20 14:23:15 2023 +---------------------------------------------------------------------------------------+ | NVIDIA-SMI 535.146.01 Driver Version: 537.99 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 Quadro P4000 On | 00000000:17:00.0 Off | N/A | | 46% 34C P8 6W / 105W | 0MiB / 8192MiB | 0% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+ | 1 Quadro P2200 On | 00000000:65:00.0 On | N/A | | 47% 35C P8 5W / 75W | 318MiB / 5120MiB | 3% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+
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7,486
I added my ollama web ui
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2024-11-04T03:47:24
2024-11-05T01:45:13
2024-11-05T01:45:13
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6,054
Added reference to Llama.cpp docs for passed through API options
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2024-07-29T15:01:04
2024-11-21T11:15:22
2024-11-21T11:15:21
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The API docs do not explain what all options listed [here](https://github.com/ollama/ollama/blob/0e4d653687f81db40622e287a846245b319f1fbe/docs/api.md?plain=1#L334-L362) do, some are explained in [the modelfile](https://github.com/ollama/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values) but all "passed through" ones are not. This PR adds a reference to [a doc](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md#generation-flags) which explains them. Relevant: #6045
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I_kwDOJ0Z1Ps6SMySN
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ollama cannot start on ubuntu 22.04
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2024-08-07T07:55:54
2024-08-11T12:42:35
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### What is the issue? First time to run ollama, follow the guide: https://github.com/ollama/ollama/blob/main/docs/linux.md#manual-install service cannot start, logs: ``` journalctl -u ollama --no-pager Aug 07 15:35:48 i-2y1kobn5 systemd[1]: Started Ollama Service. Aug 07 15:35:48 i-2y1kobn5 systemd[1]: ollama.service: Main process exited, code=dumped, status=11/SEGV Aug 07 15:35:48 i-2y1kobn5 systemd[1]: ollama.service: Failed with result 'core-dump'. Aug 07 15:35:51 i-2y1kobn5 systemd[1]: ollama.service: Scheduled restart job, restart counter is at 1. Aug 07 15:35:51 i-2y1kobn5 systemd[1]: Stopped Ollama Service. Aug 07 15:35:51 i-2y1kobn5 systemd[1]: Started Ollama Service. Aug 07 15:35:51 i-2y1kobn5 systemd[1]: ollama.service: Main process exited, code=dumped, status=11/SEGV Aug 07 15:35:51 i-2y1kobn5 systemd[1]: ollama.service: Failed with result 'core-dump'. Aug 07 15:35:54 i-2y1kobn5 systemd[1]: ollama.service: Scheduled restart job, restart counter is at 2. Aug 07 15:35:54 i-2y1kobn5 systemd[1]: Stopped Ollama Service. Aug 07 15:35:54 i-2y1kobn5 systemd[1]: Started Ollama Service. Aug 07 15:35:54 i-2y1kobn5 systemd[1]: ollama.service: Main process exited, code=dumped, status=11/SEGV Aug 07 15:35:54 i-2y1kobn5 systemd[1]: ollama.service: Failed with result 'core-dump'. Aug 07 15:35:57 i-2y1kobn5 systemd[1]: ollama.service: Scheduled restart job, restart counter is at 3. Aug 07 15:35:57 i-2y1kobn5 systemd[1]: Stopped Ollama Service. ``` command return error: ``` ollama server Segmentation fault (core dumped) ``` ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version should be the latest one download from here: https://ollama.com/download/ollama-linux-amd64 ollama --version also reports error: Segmentation fault (core dumped)
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GPU not detected in Kubernetes.
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### What is the issue? When deploying into kubernetes the container is complaining about being unable to load the cudart library. (Or maybe its out of date) Based on the documentation and provided examples I expect it to detect and utilize the GPU in container. Every test I can think of (which is limited) seems to indicate this should be working but I'll bet I'm missing some nuance in the stack here - any advice would be appreciated. Host Configuration : ```bash uname -a Linux overseer 6.5.0-28-generic #29~22.04.1-Ubuntu SMP PREEMPT_DYNAMIC Thu Apr 4 14:39:20 UTC 2 x86_64 x86_64 x86_64 GNU/Linux ``` nvcc ```bash vcc --version nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2024 NVIDIA Corporation Built on Thu_Mar_28_02:18:24_PDT_2024 Cuda compilation tools, release 12.4, V12.4.131 Build cuda_12.4.r12.4/compiler.34097967_0 ``` Docker Run outputs : ```bash docker run -d --gpus=all -v ollama:/root/.ollama -p 11435:11434 --name ollama ollama/ollama docker run -d --gpus=all -v ollama:/root/.ollama -p 11435:11434 --name ollama ollama/ollama docker logs ollama time=2024-04-19T17:59:48.712Z level=INFO source=images.go:817 msg="total blobs: 0" time=2024-04-19T17:59:48.712Z level=INFO source=images.go:824 msg="total unused blobs removed: 0" time=2024-04-19T17:59:48.712Z level=INFO source=routes.go:1143 msg="Listening on [::]:11434 (version 0.1.32)" time=2024-04-19T17:59:48.712Z level=INFO source=payload.go:28 msg="extracting embedded files" dir=/tmp/ollama4206527122/runners time=2024-04-19T17:59:50.712Z level=INFO source=payload.go:41 msg="Dynamic LLM libraries [cpu_avx cpu_avx2 cuda_v11 rocm_v60002 cpu]" time=2024-04-19T17:59:50.712Z level=INFO source=gpu.go:121 msg="Detecting GPU type" time=2024-04-19T17:59:50.712Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*" time=2024-04-19T17:59:50.713Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama4206527122/runners/cuda_v11/libcudart.so.11.0]" time=2024-04-19T17:59:50.746Z level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" time=2024-04-19T17:59:50.746Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2" time=2024-04-19T17:59:50.855Z level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 8.6" [GIN] 2024/04/19 - 18:00:35 | 404 | 154.523µs | 172.17.0.1 | POST "/api/generate" ``` Deployment Configuration: ```bash --- apiVersion: apps/v1 kind: Deployment metadata: name: ollama namespace: ollama spec: runtimeClassName: nvidia selector: matchLabels: name: ollama template: metadata: labels: name: ollama spec: containers: - name: ollama image: ollama/ollama resources: limits: nvidia.com/gpu: 1 tolerations: - key: nvidia.com/gpu operator: Exists effect: NoSchedule env: - name: PATH value: /usr/local/nvidia/bin:/usr/local/cuda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin - name: LD_LIBRARY_PATH value: /usr/local/nvidia/lib:/usr/local/nvidia/lib64 - name: NVIDIA_VISIBLE_DEVICES value: all - name: NVIDIA_DRIVER_CAPABILITIES value: compute,utility - name: OLLAMA_DEBUG value: "1" ports: - name: http containerPort: 11434 protocol: TCP ``` Deployment Logs: ```bash │ Autoscroll:On FullScreen:Off Timestamps:Off Wrap:On │ │ Couldn't find '/root/.ollama/id_ed25519'. Generating new private key. │ │ Your new public key is: │ │ │ │ ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIMp1XONYlspAEBzMEJNATgAMm39ctFUiN3XZxLwlzVMB │ │ │ │ time=2024-04-19T17:27:12.252Z level=INFO source=images.go:817 msg="total blobs: 0" │ │ time=2024-04-19T17:27:12.252Z level=INFO source=images.go:824 msg="total unused blobs removed: 0" │ │ time=2024-04-19T17:27:12.253Z level=INFO source=routes.go:1143 msg="Listening on :11434 (version 0.1.32)" │ │ time=2024-04-19T17:27:12.253Z level=INFO source=payload.go:28 msg="extracting embedded files" dir=/tmp/ollama2307533246/runners │ │ time=2024-04-19T17:27:12.253Z level=DEBUG source=payload.go:160 msg=extracting variant=cpu file=build/linux/x86_64/cpu/bin/ollama_llama_server.gz │ │ time=2024-04-19T17:27:12.253Z level=DEBUG source=payload.go:160 msg=extracting variant=cpu_avx file=build/linux/x86_64/cpu_avx/bin/ollama_llama_server.gz │ │ time=2024-04-19T17:27:12.253Z level=DEBUG source=payload.go:160 msg=extracting variant=cpu_avx2 file=build/linux/x86_64/cpu_avx2/bin/ollama_llama_server.gz │ │ time=2024-04-19T17:27:12.253Z level=DEBUG source=payload.go:160 msg=extracting variant=cuda_v11 file=build/linux/x86_64/cuda_v11/bin/libcublas.so.11.gz │ │ time=2024-04-19T17:27:12.253Z level=DEBUG source=payload.go:160 msg=extracting variant=cuda_v11 file=build/linux/x86_64/cuda_v11/bin/libcublasLt.so.11.gz │ │ time=2024-04-19T17:27:12.253Z level=DEBUG source=payload.go:160 msg=extracting variant=cuda_v11 file=build/linux/x86_64/cuda_v11/bin/libcudart.so.11.0.gz │ │ time=2024-04-19T17:27:12.253Z level=DEBUG source=payload.go:160 msg=extracting variant=cuda_v11 file=build/linux/x86_64/cuda_v11/bin/ollama_llama_server.gz │ │ time=2024-04-19T17:27:12.253Z level=DEBUG source=payload.go:160 msg=extracting variant=rocm_v60002 file=build/linux/x86_64/rocm_v60002/bin/deps.txt.gz │ │ time=2024-04-19T17:27:12.253Z level=DEBUG source=payload.go:160 msg=extracting variant=rocm_v60002 file=build/linux/x86_64/rocm_v60002/bin/ollama_llama_server.gz │ │ time=2024-04-19T17:27:14.217Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama2307533246/runners/cpu │ │ time=2024-04-19T17:27:14.217Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama2307533246/runners/cpu_avx │ │ time=2024-04-19T17:27:14.217Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama2307533246/runners/cpu_avx2 │ │ time=2024-04-19T17:27:14.217Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama2307533246/runners/cuda_v11 │ │ time=2024-04-19T17:27:14.217Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama2307533246/runners/rocm_v60002 │ │ time=2024-04-19T17:27:14.217Z level=INFO source=payload.go:41 msg="Dynamic LLM libraries [cuda_v11 rocm_v60002 cpu cpu_avx cpu_avx2]" │ │ time=2024-04-19T17:27:14.217Z level=DEBUG source=payload.go:42 msg="Override detection logic by setting OLLAMA_LLM_LIBRARY" │ │ time=2024-04-19T17:27:14.217Z level=INFO source=gpu.go:121 msg="Detecting GPU type" │ │ time=2024-04-19T17:27:14.217Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*" │ │ time=2024-04-19T17:27:14.217Z level=DEBUG source=gpu.go:286 msg="gpu management search paths: [/tmp/ollama2307533246/runners/cuda*/libcudart.so* /usr/local/cuda/lib64/libcudart.so* /usr/lib/x86_64-linux-gnu/nvidia/current/libcudart.so* /usr/lib/x86_64-linux-gnu/libcudart.so* /usr/lib/wsl/lib/libcudart.so* /u │ │ sr/lib/wsl/drivers/*/libcudart.so* /opt/cuda/lib64/libcudart.so* /usr/local/cuda*/targets/aarch64-linux/lib/libcudart.so* /usr/lib/aarch64-linux-gnu/nvidia/current/libcudart.so* /usr/lib/aarch64-linux-gnu/libcudart.so* /usr/local/cuda/lib*/libcudart.so* /usr/lib*/libcudart.so* /usr/local/lib*/libcudart.so* / │ │ usr/local/nvidia/lib/libcudart.so** /usr/local/nvidia/lib64/libcudart.so**]" │ │ time=2024-04-19T17:27:14.217Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama2307533246/runners/cuda_v11/libcudart.so.11.0]" │ │ wiring cudart library functions in /tmp/ollama2307533246/runners/cuda_v11/libcudart.so.11.0 │ │ dlsym: cudaSetDevice │ │ dlsym: cudaDeviceSynchronize │ │ dlsym: cudaDeviceReset │ │ dlsym: cudaMemGetInfo │ │ dlsym: cudaGetDeviceCount │ │ dlsym: cudaDeviceGetAttribute │ │ dlsym: cudaDriverGetVersion │ │ cudaSetDevice err: 35 │ │ time=2024-04-19T17:27:14.218Z level=INFO source=gpu.go:343 msg="Unable to load cudart CUDA management library /tmp/ollama2307533246/runners/cuda_v11/libcudart.so.11.0: your nvidia driver is too old or missing, please upgrade to run ollama" │ │ time=2024-04-19T17:27:14.218Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libnvidia-ml.so" │ │ time=2024-04-19T17:27:14.218Z level=DEBUG source=gpu.go:286 msg="gpu management search paths: [/usr/local/cuda/lib64/libnvidia-ml.so* /usr/lib/x86_64-linux-gnu/nvidia/current/libnvidia-ml.so* /usr/lib/x86_64-linux-gnu/libnvidia-ml.so* /usr/lib/wsl/lib/libnvidia-ml.so* /usr/lib/wsl/drivers/*/libnvidia-ml.so* │ │ /opt/cuda/lib64/libnvidia-ml.so* /usr/lib*/libnvidia-ml.so* /usr/lib/aarch64-linux-gnu/nvidia/current/libnvidia-ml.so* /usr/lib/aarch64-linux-gnu/libnvidia-ml.so* /usr/local/lib*/libnvidia-ml.so* /opt/cuda/targets/x86_64-linux/lib/stubs/libnvidia-ml.so* /usr/local/nvidia/lib/libnvidia-ml.so* /usr/local/nvidi │ │ a/lib64/libnvidia-ml.so*]" │ │ time=2024-04-19T17:27:14.218Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: []" │ │ time=2024-04-19T17:27:14.218Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2" │ │ time=2024-04-19T17:27:14.218Z level=DEBUG source=amd_linux.go:280 msg="amdgpu driver not detected /sys/module/amdgpu" │ │ time=2024-04-19T17:27:14.218Z level=INFO source=routes.go:1164 msg="no GPU detected" ``` Kubernetes Based nbody run : ```bash cat <<EOF | kubectl create -f - apiVersion: v1 kind: Pod metadata: name: nbody-gpu-benchmark namespace: default spec: restartPolicy: OnFailure runtimeClassName: nvidia containers: - name: cuda-container image: nvcr.io/nvidia/k8s/cuda-sample:nbody args: ["nbody", "-gpu", "-benchmark"] resources: limits: nvidia.com/gpu: 1 env: - name: NVIDIA_VISIBLE_DEVICES value: all - name: NVIDIA_DRIVER_CAPABILITIES value: all EOF ``` nbody container logs ``` Autoscroll:On FullScreen:Off Timestamps:Off Wrap:Off │ │ Run "nbody -benchmark [-numbodies=<numBodies>]" to measure performance. │ │ -fullscreen (run n-body simulation in fullscreen mode) │ │ -fp64 (use double precision floating point values for simulation) │ │ -hostmem (stores simulation data in host memory) │ │ -benchmark (run benchmark to measure performance) │ │ -numbodies=<N> (number of bodies (>= 1) to run in simulation) │ │ -device=<d> (where d=0,1,2.... for the CUDA device to use) │ │ -numdevices=<i> (where i=(number of CUDA devices > 0) to use for simulation) │ │ -compare (compares simulation results running once on the default GPU and once on the CPU) │ │ -cpu (run n-body simulation on the CPU) │ │ -tipsy=<file.bin> (load a tipsy model file for simulation) │ │ │ │ NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled. │ │ │ │ > Windowed mode │ │ > Simulation data stored in video memory │ │ > Single precision floating point simulation │ │ > 1 Devices used for simulation │ │ GPU Device 0: "Ampere" with compute capability 8.6 │ │ │ │ > Compute 8.6 CUDA device: [NVIDIA GeForce RTX 3060] │ │ 28672 bodies, total time for 10 iterations: 22.067 ms │ │ = 372.538 billion interactions per second │ │ = 7450.761 single-precision GFLOP/s at 20 flops per interaction ``` ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version latest
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2023-11-20T07:58:30
2024-11-22T07:07:10
2023-12-04T23:01:07
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For my own frontend I noticed that it might be useful to have an endpoint where I can generate context from optionally previous context, the typed prompt from the user and the answer of the model before it was interrupted. This could create a similiar experience to OpenAI's ChatGPT
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Slow model load and cache ram does not free.
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2024-09-14T20:14:31
2024-11-05T23:24:10
2024-11-05T23:24:10
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### What is the issue? Hi all. My system: AMD TR PRO 3975WX CPU, 512G RAM DDR4 ECC, 3xRTX A4000 (48G VRAM) GPU, 4TB Nvme corsair mp600 core xt, Ubuntu 22.04.1 LTS I'm not specialist in Linux, so don't throw stones. Problem 1: According to various tests, transfer speed of DDR4 can go up to 25GB/s. According to the benchmark of my local nvme disk, read speed is around 6GB/s. However, when I start 'ollama run llama3.1:70b' from terminal, system monitor indicate constant disk activity during model transfer and read speed tops around 1.7GB/s, no more. Why isn't it loaded faster if both disk and RAM can do much more? System isn't doing anything else. Let's say this isn't problematic with 70b, but with 405b is really annoying. Problem 2: 48G VRAM is enough to fit :70b model. When I start 'ollama run llama3.1:70b', it is loaded first in the RAM, in the system monitor window I see 'cache' jumping up. After the model is completely transferred to RAM, I see it pushed into VRAM of GPU for inferrence. The 'memory' section of system monitor indicates '7.3GiB(1.5%) of 503.5GiB, cache 44.6 GiB'. When I'm done with the model and send '/bye' to ollama, I can see VRAM still filled for few more minutes, then it is freed. But not the 'cache' from RAM. It stays at 44.6GiB forever if I'm not doing anything else (I waited >30 min). This is becoming problematic when I load a different model. That will top over the already existing models in cache memory and will increase its size. Continuing loading different models will progressively fill it up to the top, eventually data will go in the swap. Old models are never removed from cache even if newer ones needs memory. Why? Thank you. LE: one detail which may or may not be important. Ollama is installed directly and I run it from terminal prompt and there is another installation in a docker container where it was installed from open-webui with built in ollama support, that one is inferred from the network. But both behave the same in regards to cache memory. ### OS Linux ### GPU Nvidia ### CPU AMD ### Ollama version 0.3.8
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Modelfile parameters not set during creation
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2024-03-12T00:27:08
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I have a model like so: (also providing system details) ```Dockerfile $ cat /etc/os-release | head -n 4 PRETTY_NAME="Ubuntu 22.04.2 LTS" NAME="Ubuntu" VERSION_ID="22.04" VERSION="22.04.2 LTS (Jammy Jellyfish)" $ ollama -v ollama version is 0.1.17 $ ollama show test --modelfile # Modelfile generated by "ollama show" # To build a new Modelfile based on this one, replace the FROM line with: # FROM test:latest FROM /usr/share/ollama/.ollama/models/blobs/sha256:8f7e99455a86ad490dab19d335d8c2d1d044e2744b923d4e78a4d84fe1457738 TEMPLATE """### System: {{ .System }} ### User: {{ .Prompt }} ### Assistant: """ SYSTEM """ Given the following extracted parts of a long document and a question, create a final answer with references ("SOURCES"). If you don't know the answer, just say that you don't know. Don't try to make up an answer. ALWAYS return a "SOURCES" part in your answer. --- Content: {context} --- """ PARAMETER repeat_penalty 1.3 PARAMETER temperature 0.4 PARAMETER top_k 20 PARAMETER top_p 0.65 ``` My parameters shown in this output are the same as what I have set in my Modelfile. However, when I query the API for the parameter values, I see that they are different values for `repeat_penalty`, `temperature`, and `top_p`: ```bash $ ollama show test --parameters repeat_penalty 1 temperature 0 top_k 20 top_p 1 ``` ```json $ curl http://localhost:11434/api/show -d '{"name": "test"}' { "modelfile": "# Modelfile generated by \"ollama show\"\n# To build a new Modelfile based on this one, replace the FROM line with:\n# FROM test:latest\n\nFROM /usr/share/ollama/.ollama/models/blobs/sha256:8f7e99455a86ad490dab19d335d8c2d1d044e2744b923d4e78a4d84fe1457738\nTEMPLATE \"\"\"### System:\r\n{{ .System }}\r\n\r\n### User:\r\n{{ .Prompt }}\r\n\r\n### Assistant:\r\n\"\"\"\nSYSTEM \"\"\"\r\nGiven the following extracted parts of a long document and a question, create a final answer with references (\"SOURCES\"). \r\nIf you don't know the answer, just say that you don't know. Don't try to make up an answer.\r\nALWAYS return a \"SOURCES\" part in your answer.\r\n---\r\nContent: {context}\r\n---\r\n\"\"\"\nPARAMETER repeat_penalty 1.3\nPARAMETER temperature 0.4\nPARAMETER top_k 21\nPARAMETER top_p 0.65", "parameters": "top_p 1\nrepeat_penalty 1\ntemperature 0\ntop_k 20", "template": "### System:\r\n{{ .System }}\r\n\r\n### User:\r\n{{ .Prompt }}\r\n\r\n### Assistant:\r\n", "system": "\r\nGiven the following extracted parts of a long document and a question, create a final answer with references (\"SOURCES\"). \r\nIf you don't know the answer, just say that you don't know. Don't try to make up an answer.\r\nALWAYS return a \"SOURCES\" part in your answer.\r\n---\r\nContent: {context}\r\n---\r\n", "details": { "format": "gguf", "family": "llama", "families": ["llama"], "parameter_size": "7B", "quantization_level": "Q4_0", }, } ``` Cleaning up that JSON using Python: ```python >>> import requests >>> defaults = dict( ... pair.split() ... for pair in requests.post( ... "http://localhost:11434/api/show", ... data='{"name": "test"}', ... timeout=5 ... ) ... .json()["parameters"] ... .split("\n") ... if pair.strip() ... ) >>> defaults {'top_p': '1', 'repeat_penalty': '1', 'temperature': '0', 'top_k': '20'} ``` I then changed my top_k value to make sure it just wasn't the same as the model default, and it did modify the value successfully. If I change my input model from a GGUF model I quantized to `neural-chat:latest`, the problem is reproduced, just with some other default parameters: ```bash $ ollama show test-neural-chat --parameters repeat_penalty 1 stop <|im_start|> stop <|im_end|> temperature 0 top_k 20 top_p 1 num_ctx 4096 ``` For my sources, I'm following: https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#parameter
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Add multimodel support to `ollama run` in noninteractive mode
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2024-02-02T02:39:22
2024-02-02T05:33:07
2024-02-02T05:33:06
MEMBER
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Fixes https://github.com/ollama/ollama/issues/2295 ``` % ollama run llava Describe this image: /Users/jmorgan/Desktop/old-tower.jpg Added image '/Users/jmorgan/Desktop/old-tower.jpg' The image depicts a vibrant cityscape. In the foreground, there's an iconic skyscraper, which is the CN Tower, a landmark of Toronto, Canada. The tower stands prominently against a clear blue sky. In the background, you can see a variety of buildings, including what appears to be condominiums and commercial structures, all under a bright sunlight. There's a body of water visible in the lower right corner of the image, suggesting that this photo is taken from a vantage point overlooking the city. The overall impression is of a bustling urban environment with a mix of architectural styles. ``` ![old-tower](https://github.com/ollama/ollama/assets/251292/46265130-0ee1-43f8-b4f5-a5ed38688ede)
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check subprocess id to see if server is running rather than timing out
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2023-09-07T17:57:03
2023-09-18T19:16:34
2023-09-18T19:16:34
CONTRIBUTOR
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The timeout for a server to start running will need to be a long time for larger models, better to just check the process ID then wait for the server to respond (with a really long timeout) rather than relying on the timeout by itself.
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PR_kwDOJ0Z1Ps59UNdW
7,071
llm: Don't add BOS/EOS for tokenize requests
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2024-10-01T23:29:53
2024-10-01T23:46:25
2024-10-01T23:46:23
CONTRIBUTOR
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This is consistent with what server.cpp currently does. It affects things like token processing counts for embedding requests.
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1,005
Improved context window size management
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2023-11-04T23:13:47
2024-11-27T10:08:51
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MEMBER
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Context window size is largely manual right now – it can be specified via `{"options": {"num_ctx": 32768}}` in the API or via `PARAMETER num_ctx 32768` in the Modelfile. Otherwise the default value is set to `2048` unless specified (some models in the [library](https://ollama.ai/ will use a larger context window size by default) Context size should be determined dynamically at runtime based on the amount of memory available.
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611
fix error messages for unknown commands in the repl
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2023-09-27T00:33:10
2023-09-28T21:19:46
2023-09-28T21:19:46
CONTRIBUTOR
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"error loading llama server" error="llama runner process has terminated: exit status 0xc0000135 "
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### What is the issue? Cannot load any model with ollama 0.2.3, this is some of the logs: time=2024-07-13T12:06:59.113+02:00 level=INFO source=sched.go:179 msg="one or more GPUs detected that are unable to accurately report free memory - disabling default concurrency" time=2024-07-13T12:06:59.126+02:00 level=INFO source=sched.go:701 msg="new model will fit in available VRAM in single GPU, loading" model=C:\Users\Administrator\.ollama\models\blobs\sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa gpu=0 parallel=4 available=17028874240 required="6.2 GiB" time=2024-07-13T12:06:59.126+02:00 level=INFO source=memory.go:309 msg="offload to rocm" layers.requested=-1 layers.model=33 layers.offload=33 layers.split="" memory.available="[15.9 GiB]" memory.required.full="6.2 GiB" memory.required.partial="6.2 GiB" memory.required.kv="1.0 GiB" memory.required.allocations="[6.2 GiB]" memory.weights.total="4.7 GiB" memory.weights.repeating="4.3 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="560.0 MiB" memory.graph.partial="677.5 MiB" time=2024-07-13T12:06:59.132+02:00 level=INFO source=server.go:383 msg="starting llama server" cmd="C:\\Users\\Administrator\\AppData\\Local\\Programs\\Ollama\\ollama_runners\\rocm_v6.1\\ollama_llama_server.exe --model C:\\Users\\Administrator\\.ollama\\models\\blobs\\sha256-6a0746a1ec1aef3e7ec53868f220ff6e389f6f8ef87a01d77c96807de94ca2aa --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --parallel 4 --port 61134" time=2024-07-13T12:06:59.149+02:00 level=INFO source=sched.go:437 msg="loaded runners" count=1 time=2024-07-13T12:06:59.149+02:00 level=INFO source=server.go:571 msg="waiting for llama runner to start responding" time=2024-07-13T12:06:59.149+02:00 level=INFO source=server.go:612 msg="waiting for server to become available" status="llm server error" time=2024-07-13T12:07:01.181+02:00 level=ERROR source=sched.go:443 msg="error loading llama server" error="llama runner process has terminated: exit status 0xc0000135 " ### OS Windows ### GPU AMD ### CPU AMD ### Ollama version 0.2.3
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llama runner process has terminated: signal: aborted (core dumped)
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### What is the issue? I successfully converted jina-embeddings v2 base zh to gguf through llama. cpp and imported it into llama。 Here is my Modelfile > root@buaa-KVM:~/1T/ollama/Jina-AI-embedding# cat Modelfile > FROM /root/ggml-vocab-jina-v2-zh.gguf > PARAMETER num_ctx 8192 When I access it using/app/embed, the log will report an error。 > time=2024-07-24T15:40:34.577+08:00 level=ERROR source=sched.go:443 msg="error loading llama server" error="llama runner process has terminated: signal: aborted (core dumped)" Below is my complete log。 7月 24 15:40:33 buaa-KVM ollama[458186]: time=2024-07-24T15:40:33.873+08:00 level=INFO source=sched.go:495 msg="updated VRAM based on existing loaded models" gpu=GPU-4f8ced6a-2dde-5e92-be03-8d21e26bd156 library=cuda total="23.6 GiB" available="16.9 GiB" 7月 24 15:40:33 buaa-KVM ollama[458186]: time=2024-07-24T15:40:33.873+08:00 level=INFO source=sched.go:495 msg="updated VRAM based on existing loaded models" gpu=GPU-36c16c0c-392d-ffc5-13ce-2fd6b9af0668 library=cuda total="23.6 GiB" available="23.3 GiB" 7月 24 15:40:33 buaa-KVM ollama[458186]: time=2024-07-24T15:40:33.874+08:00 level=INFO source=sched.go:701 msg="new model will fit in available VRAM in single GPU, loading" model=/usr/share/ollama/.ollama/models/blobs/sha256-65a4313f43b6f94a0a8693d70efe823792303a020601ab3d4cad54cf079296c6 gpu=GPU-36c16c0c-392d-ffc5-13ce-2fd6b9af0668 parallel=4 available=24965218304 required="1.1 GiB" 7月 24 15:40:33 buaa-KVM ollama[458186]: time=2024-07-24T15:40:33.874+08:00 level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=13 layers.offload=13 layers.split="" memory.available="[23.3 GiB]" memory.required.full="1.1 GiB" memory.required.partial="1.1 GiB" memory.required.kv="96.0 MiB" memory.required.allocations="[1.1 GiB]" memory.weights.total="312.3 MiB" memory.weights.repeating="222.9 MiB" memory.weights.nonrepeating="89.4 MiB" memory.graph.full="192.0 MiB" memory.graph.partial="192.0 MiB" 7月 24 15:40:33 buaa-KVM ollama[458186]: time=2024-07-24T15:40:33.874+08:00 level=INFO source=server.go:383 msg="starting llama server" cmd="/tmp/ollama259438837/runners/cuda_v11/ollama_llama_server --model /usr/share/ollama/.ollama/models/blobs/sha256-65a4313f43b6f94a0a8693d70efe823792303a020601ab3d4cad54cf079296c6 --ctx-size 32768 --batch-size 512 --embedding --log-disable --n-gpu-layers 13 --parallel 4 --port 35263" 7月 24 15:40:33 buaa-KVM ollama[458186]: time=2024-07-24T15:40:33.875+08:00 level=INFO source=sched.go:437 msg="loaded runners" count=2 7月 24 15:40:33 buaa-KVM ollama[458186]: time=2024-07-24T15:40:33.875+08:00 level=INFO source=server.go:583 msg="waiting for llama runner to start responding" 7月 24 15:40:33 buaa-KVM ollama[458186]: time=2024-07-24T15:40:33.875+08:00 level=INFO source=server.go:617 msg="waiting for server to become available" status="llm server error" 7月 24 15:40:33 buaa-KVM ollama[462914]: INFO [main] build info | build=1 commit="d94c6e0" tid="140502749204480" timestamp=1721806833 7月 24 15:40:33 buaa-KVM ollama[462914]: INFO [main] system info | n_threads=32 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 0 | " tid="140502749204480" timestamp=1721806833 total_threads=32 7月 24 15:40:33 buaa-KVM ollama[462914]: INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="31" port="35263" tid="140502749204480" timestamp=1721806833 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: loaded meta data with 33 key-value pairs and 196 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-65a4313f43b6f94a0a8693d70efe823792303a020601ab3d4cad54cf079296c6 (version GGUF V3 (latest)) 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 0: general.architecture str = jina-bert-v2 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 1: general.type str = model 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 2: general.name str = Jina Bert Implementation 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 3: general.organization str = Jinaai 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 4: general.size_label str = 160M 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 5: general.license str = apache-2.0 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 6: general.tags arr[str,6] = ["sentence-transformers", "feature-ex... 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 7: general.languages arr[str,2] = ["en", "zh"] 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 8: jina-bert-v2.block_count u32 = 12 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 9: jina-bert-v2.context_length u32 = 8192 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 10: jina-bert-v2.embedding_length u32 = 768 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 11: jina-bert-v2.feed_forward_length u32 = 3072 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 12: jina-bert-v2.attention.head_count u32 = 12 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 13: jina-bert-v2.attention.layer_norm_epsilon f32 = 0.000000 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 14: general.file_type u32 = 1 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 15: jina-bert-v2.attention.causal bool = false 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 16: jina-bert-v2.pooling_type u32 = 1 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 17: tokenizer.ggml.model str = gpt2 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 18: tokenizer.ggml.pre str = jina-v2-zh 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 19: tokenizer.ggml.tokens arr[str,61056] = ["<s>", "<pad>", "</s>", "<unk>", "<m... 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 20: tokenizer.ggml.token_type arr[i32,61056] = [3, 3, 3, 3, 3, 1, 1, 1, 1, 1, 1, 1, ... 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 21: tokenizer.ggml.merges arr[str,39382] = ["t h", "i n", "a n", "e r", "th e", ... 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 22: tokenizer.ggml.bos_token_id u32 = 0 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 23: tokenizer.ggml.eos_token_id u32 = 2 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 24: tokenizer.ggml.unknown_token_id u32 = 3 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 25: tokenizer.ggml.seperator_token_id u32 = 2 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 26: tokenizer.ggml.padding_token_id u32 = 1 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 27: tokenizer.ggml.cls_token_id u32 = 0 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 28: tokenizer.ggml.mask_token_id u32 = 4 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 29: tokenizer.ggml.token_type_count u32 = 2 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 30: tokenizer.ggml.add_bos_token bool = true 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 31: tokenizer.ggml.add_eos_token bool = true 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - kv 32: general.quantization_version u32 = 2 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - type f32: 111 tensors 7月 24 15:40:33 buaa-KVM ollama[458186]: llama_model_loader: - type f16: 85 tensors 7月 24 15:40:33 buaa-KVM ollama[458186]: llm_load_vocab: missing or unrecognized pre-tokenizer type, using: 'default' 7月 24 15:40:33 buaa-KVM ollama[458186]: GGML_ASSERT: /go/src/github.com/ollama/ollama/llm/llama.cpp/src/llama.cpp:5570: unicode_cpts_from_utf8(word).size() > 0 7月 24 15:40:34 buaa-KVM ollama[458186]: Could not attach to process. If your uid matches the uid of the target 7月 24 15:40:34 buaa-KVM ollama[458186]: process, check the setting of /proc/sys/kernel/yama/ptrace_scope, or try 7月 24 15:40:34 buaa-KVM ollama[458186]: again as the root user. For more details, see /etc/sysctl.d/10-ptrace.conf 7月 24 15:40:34 buaa-KVM ollama[458186]: ptrace: Operation not permitted. 7月 24 15:40:34 buaa-KVM ollama[458186]: No stack. 7月 24 15:40:34 buaa-KVM ollama[458186]: The program is not being run. 7月 24 15:40:34 buaa-KVM ollama[458186]: time=2024-07-24T15:40:34.326+08:00 level=INFO source=server.go:617 msg="waiting for server to become available" status="llm server not responding" 7月 24 15:40:34 buaa-KVM ollama[458186]: time=2024-07-24T15:40:34.577+08:00 level=ERROR source=sched.go:443 msg="error loading llama server" error="llama runner process has terminated: signal: aborted (core dumped)" 7月 24 15:40:34 buaa-KVM ollama[458186]: [GIN] 2024/07/24 - 15:40:34 | 500 | 882.662819ms | 192.168.1.202 | POST "/api/embed" 7月 24 15:40:39 buaa-KVM ollama[458186]: time=2024-07-24T15:40:39.737+08:00 level=WARN source=sched.go:634 msg="gpu VRAM usage didn't recover within timeout" seconds=5.160248264 model=/usr/share/ollama/.ollama/models/blobs/sha256-65a4313f43b6f94a0a8693d70efe823792303a020601ab3d4cad54cf079296c6 7月 24 15:40:39 buaa-KVM ollama[458186]: time=2024-07-24T15:40:39.987+08:00 level=WARN source=sched.go:634 msg="gpu VRAM usage didn't recover within timeout" seconds=5.41003445 model=/usr/share/ollama/.ollama/models/blobs/sha256-65a4313f43b6f94a0a8693d70efe823792303a020601ab3d4cad54cf079296c6 7月 24 15:40:40 buaa-KVM ollama[458186]: time=2024-07-24T15:40:40.238+08:00 level=WARN source=sched.go:634 msg="gpu VRAM usage didn't recover within timeout" seconds=5.660559316 model=/usr/share/ollama/.ollama/models/blobs/sha256-65a4313f43b6f94a0a8693d70efe823792303a020601ab3d4cad54cf079296c6 ### OS Linux ### GPU Nvidia ### CPU Intel ### Ollama version 0.2.8
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5,670
The usage of VRAM has significantly increased
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### What is the issue? In previous versions, I set the context length of each of my models to the maximum value that could be fully loaded onto the GPU memory. However, after the update, I found that parts of them were being partially loaded onto the CPU instead. I wonder what could be causing this. The following table is some examples. |NAME|SIZE|PROCESSOR| | :-: | :-: | :-: | |glm4:9b-chat-2K-q5_K_M|8.3 GB|10%/90% CPU/GPU| |glm4:9b-chat-10K-q4_K_M|7.8 GB|7%/93% CPU/GPU| |codegeex4:9b-all-10K-q4_K_M|7.8 GB|7%/93% CPU/GPU| |qwen2:7b-instruct-19K-q5_K_M|8.3 GB|13%/87% CPU/GPU| |internlm2:7b-chat-v2.5-8K-q5_K_M|7.7 GB|4%/96% CPU/GPU| |llama3:8b-instruct-5K-q6_K|8.2 GB|10%/90% CPU/GPU| My graphics card is a 4060 laptop model, with only 8GB of VRAM. Interestingly, even before the update, none of the models was actually utilizing the full capacity of my GPU memory. ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.2.3
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Certificate expired
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2024-03-25T07:30:01
2024-03-25T08:45:42
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### What is the issue? $ docker exec -it ollama ollama pull qwen:7b pulling manifest Error: pull model manifest: Get "https://registry.ollama.ai/v2/library/qwen/manifests/7b": tls: failed to verify certificate: x509: certificate has expired or is not yet valid: current time 2024-03-25T07:29:23Z is after 2024-03-25T07:17:47Z ### What did you expect to see? no error ### Steps to reproduce _No response_ ### Are there any recent changes that introduced the issue? _No response_ ### OS _No response_ ### Architecture _No response_ ### Platform _No response_ ### Ollama version _No response_ ### GPU _No response_ ### GPU info _No response_ ### CPU _No response_ ### Other software _No response_
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1,154
Cannot push models `FROM` library models
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2023-11-16T18:51:53
2023-11-16T21:33:31
2023-11-16T21:33:31
CONTRIBUTOR
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Attempting to push models with `FROM <library-model>` fails with scope errors. **Steps to reproduce:** 1. Create a Modelfile from a library model. ``` FROM llama2 SYSTEM """ You are Mario from super mario bros, acting as an assistant. """ ``` `ollama create <namespace>/mario -f path/to/modelfile` 2. Push the model `ollama push <namespace>/mario` 3. ``` retrieving manifest Error: max retries exceeded ``` **Root cause:** Scopes on the tokens are not being set correctly for library models. It fails on the ollama.ai side with "Scope parameter set incorrectly" due to the scope being set to `:llama2:pull`. This is the fix: https://github.com/jmorganca/ollama/commit/bd86eab4261ca545c6ad6384da0bbcdcb4270e61 Once I make this change, I then see 405 errors on the first push (subsequent push of the same model succeeds after the inital 405). Have not had time to track down the cause of these 405s yet.
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Error: unsupported content type: unknown
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2024-08-22T14:50:31
2024-08-28T20:38:33
2024-08-28T20:38:33
NONE
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### What is the issue? This is the first time I tried to create a model with a gguf file, but it failed ![屏幕截图 2024-08-22 224737](https://github.com/user-attachments/assets/a2b95b02-11b1-48d4-a318-3dd52c276da7) model.modelfile ![屏幕截图 2024-08-22 224322](https://github.com/user-attachments/assets/c4d85c12-3542-4f4f-b026-634f8dfcf9b1) ### OS Windows ### GPU Nvidia ### CPU Intel ### Ollama version 0.3.6
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Request: ensemble Llamas 🦙 (`llama2:13b-ensemble`)
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2023-09-29T18:26:00
2023-12-04T20:04:02
2023-12-04T20:04:01
CONTRIBUTOR
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From Hugging Face's Open LLM leaderboard: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard A 13b model ranked somewhat highly is [`yeontaek/llama-2-13B-ensemble-v5`](https://huggingface.co/datasets/open-llm-leaderboard/details_yeontaek__llama-2-13B-ensemble-v5). ![image](https://github.com/jmorganca/ollama/assets/8990777/a602fffb-0148-4202-9608-c186a9037d4a) I believe TheBloke exposes it here via GGUF: https://huggingface.co/TheBloke/Llama-2-13B-Ensemble-v5-GGUF It would be cool to add it to the [llama2](https://ollama.ai/library/llama2) offerings as `13b-ensemble`, `13b-ensemble-q4_0`.
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Add Vulkan runner
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2024-01-17T18:15:00
2025-01-21T19:49:38
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https://github.com/nomic-ai/llama.cpp GPT4All runs Mistral and Mixtral q4 models over 10x faster on my 6600M GPU
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fix: remove overwritten model layers
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2024-01-19T23:00:15
2024-01-22T17:37:50
2024-01-22T17:37:49
CONTRIBUTOR
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if create overrides a manifest, first add the older manifest's layers to the delete map so they can be cleaned up resolves #2097
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PR_kwDOJ0Z1Ps51_b-4
5,815
Adjust windows ROCm discovery
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2024-07-20T16:23:10
2024-07-20T23:02:58
2024-07-20T23:02:55
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The v5 hip library returns unsupported GPUs which wont enumerate at inference time in the runner so this makes sure we align discovery. The gfx906 cards are no longer supported so we shouldn't compile with that GPU type as it wont enumerate at runtime.
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PR_kwDOJ0Z1Ps5uyn6j
4,232
Revert "fix golangci workflow not enable gofmt and goimports"
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2024-05-09T08:45:07
2024-05-07T17:39:37
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Reverts ollama/ollama#4190 gofmt is still a problem on windows see https://github.com/ollama/ollama/actions/runs/8989369091/job/24692319408?pr=4153
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update llama.cpp submodule to `614d3b9`
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cuMemCreate with gpu nvidia m2000
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2024-03-05T20:25:02
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"Hi, is anyone else experiencing this error with the GPU? The GPU successfully passes through for video transcoding in another container app (Emby/Plex), but it shows an error for all ollama models." ### Error library ` level=WARN source=llm.go:162 msg="Failed to load dynamic library /tmp/ollama4235720163/cuda_v11/libext_server.so Unable to load dynamic library: Unable to load dynamic server library: /tmp/ollama4235720163/cuda_v11/libext_server.so: undefined symbol: cuMemCreate"` ### docker start ``` level=INFO source=images.go:710 msg="total blobs: 12" level=INFO source=images.go:717 msg="total unused blobs removed: 0" level=INFO source=routes.go:1019 msg="Listening on [::]:11434 (version 0.1.27)" level=INFO source=payload_common.go:107 msg="Extracting dynamic libraries..." level=INFO source=payload_common.go:146 msg="Dynamic LLM libraries [cpu cpu_avx cuda_v11 rocm_v6 cpu_avx2 rocm_v5]" level=INFO source=gpu.go:94 msg="Detecting GPU type" level=INFO source=gpu.go:265 msg="Searching for GPU management library libnvidia-ml.so" level=INFO source=gpu.go:311 msg="Discovered GPU libraries: [/usr/lib/x86_64-linux-gnu/libnvidia-ml.so.418.226.00]" level=INFO source=gpu.go:99 msg="Nvidia GPU detected" level=INFO source=cpu_common.go:11 msg="CPU has AVX2" level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 5.2" ``` ### Start request ``` level=INFO source=cpu_common.go:11 msg="CPU has AVX2" level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 5.2" level=INFO source=cpu_common.go:11 msg="CPU has AVX2" level=INFO source=gpu.go:146 msg="CUDA Compute Capability detected: 5.2" level=INFO source=cpu_common.go:11 msg="CPU has AVX2" level=WARN source=llm.go:162 msg="Failed to load dynamic library /tmp/ollama4235720163/cuda_v11/libext_server.so Unable to load dynamic library: Unable to load dynamic server library: /tmp/ollama4235720163/cuda_v11/libext_server.so: undefined symbol: cuMemCreate" level=INFO source=dyn_ext_server.go:90 msg="Loading Dynamic llm server: /tmp/ollama4235720163/cpu_avx2/libext_server.so" level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server" llama_model_loader: loaded meta data with 23 key-value pairs and 201 tensors from /root/.ollama/models/blobs/sha256:2af3b81862c6be03c769683af18efdadb2c33f60ff32ab6f83e42c043d6c7816 (version GGUF V3 (latest)) ``` ### Check nvidia ``` root@srv-01:~$ sudo docker exec -it ollama bash root@bc5e85c49508:/# nvidia-smi Sun Mar 3 23:07:23 2024 +-----------------------------------------------------------------------------+ | NVIDIA-SMI 418.226.00 Driver Version: 418.226.00 CUDA Version: 10.1 | |-------------------------------+----------------------+----------------------+ | GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. | |===============================+======================+======================| | 0 Quadro M2000 On | 00000000:03:00.0 On | N/A | | 56% 31C P8 8W / 75W | 1MiB / 4040MiB | 0% Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | |=============================================================================| | No running processes found | +-----------------------------------------------------------------------------+ ``` ### environment os: debian 10.13 docker: 25.0.3 cpu: E5-2698 v4 gpu nvidia quadro m2000 4GB ### compose.yml ``` version: '3.8' services: ollama: image: ollama/ollama:latest container_name: ollama restart: unless-stopped user: "0:0" userns_mode: host volumes: - ./:/root/.ollama deploy: resources: reservations: devices: - driver: ${OLLAMA_GPU_DRIVER-nvidia} count: ${OLLAMA_GPU_COUNT-1} capabilities: - gpu ```
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preserve last assistant message
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Fixes https://github.com/ollama/ollama/issues/5775
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Unnecessary quotes when calling a tool
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### What is the issue? I'm using **mistral-nemo:12b-instruct-2407-q4_1** I'm trying to reproduce this example - https://github.com/ollama/ollama-js/blob/main/examples/tools/tools.ts ```javascript tools: [ { type: 'function', function: { name: 'eval', description: 'Вычисляет арифметическое выражение в формате JS-функции eval. Используется тогда, когда нужен калькулятор.', parameters: { type: 'object', properties: { expression: { type: 'string', description: 'Арифметическое выражение', } }, required: ['expression'], }, }, }, ], ``` Everything works, but sometimes I get (without a space between the quotes): ``` The model didn't use the function. Its response was: ``⠀` { "jsonrpc": "2.0", "method": "eval", "params": { "expression": "5234 / 6453 * 23456" } } ``⠀` ``` That is, sometimes the model wraps JSON in triple quotes, and Ollama interprets this as a text response rather than a tool call. ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version 0.3.0
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add ppc64le to code issues 796
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2024-06-30T13:34:26
2024-11-08T15:51:36
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I tested it on a Power9 machine and the change worked. Unfortunately I don't have a GPU in there yet (cuda)
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Structured generation cannot handle self referencing (recursion)
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2024-12-08T03:51:25
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### What is the issue? Ollama structured genereation cannot handle self referencing recursion ```py import json from pydantic import BaseModel, Field from typing import Optional class Dossier(BaseModel): """Build a profile for the user""" name: str = Field(..., description="The name of the user") age: int = Field(..., description="The age of the user") friends: list["Dossier"] = [] print(json.dumps(Dossier.model_json_schema(), indent=2)) from ollama import chat response = chat( messages=[ { 'role': 'user', 'content': 'Hello my name is Tom I am 13 years old, my friend Bob is 14 years old' } ], model='llama3.1:8b-instruct-q2_K', format=Dossier.model_json_schema() ) dossier = Dossier.model_validate_json(response.message.content) print(dossier.model_dump_json(indent=2)) ``` Output: ``` { "$defs": { "Dossier": { "description": "Build a profile for the user", "properties": { "name": { "description": "The name of the user", "title": "Name", "type": "string" }, "age": { "description": "The age of the user", "title": "Age", "type": "integer" }, "friends": { "default": [], "items": { "$ref": "#/$defs/Dossier" }, "title": "Friends", "type": "array" } }, "required": [ "name", "age" ], "title": "Dossier", "type": "object" } }, "$ref": "#/$defs/Dossier" } ``` ``` --------------------------------------------------------------------------- ValidationError Traceback (most recent call last) Cell In[54], [line 25](vscode-notebook-cell:?execution_count=54&line=25) [13](vscode-notebook-cell:?execution_count=54&line=13) from ollama import chat [15](vscode-notebook-cell:?execution_count=54&line=15) response = chat( [16](vscode-notebook-cell:?execution_count=54&line=16) messages=[ [17](vscode-notebook-cell:?execution_count=54&line=17) { (...) [23](vscode-notebook-cell:?execution_count=54&line=23) format=Dossier.model_json_schema() [24](vscode-notebook-cell:?execution_count=54&line=24) ) ---> [25](vscode-notebook-cell:?execution_count=54&line=25) dossier = Dossier.model_validate_json(response.message.content) [26](vscode-notebook-cell:?execution_count=54&line=26) print(dossier.model_dump_json(indent=2)) File c:\Notes\ollama\.venv\lib\site-packages\pydantic\main.py:656, in BaseModel.model_validate_json(cls, json_data, strict, context) [654](file:///C:/Notes/ollama/.venv/lib/site-packages/pydantic/main.py:654) # `__tracebackhide__` tells pytest and some other tools to omit this function from tracebacks [655](file:///C:/Notes/ollama/.venv/lib/site-packages/pydantic/main.py:655) __tracebackhide__ = True --> [656](file:///C:/Notes/ollama/.venv/lib/site-packages/pydantic/main.py:656) return cls.__pydantic_validator__.validate_json(json_data, strict=strict, context=context) ValidationError: 1 validation error for Dossier Invalid JSON: expected value at line 1 column 1 [type=json_invalid, input_value="So you're 13 and your fr...m outside of class too?", input_type=str] For further information visit https://errors.pydantic.dev/2.10/v/json_invalid ``` When destructured there is no problem ```py import json from pydantic import BaseModel, Field from typing import Optional class DossierInner(BaseModel): """Build a profile for the user""" name: str = Field(..., description="The name of the user") age: int = Field(..., description="The age of the user") class Dossier(BaseModel): """Build a profile for the user""" name: str = Field(..., description="The name of the user") age: int = Field(..., description="The age of the user") friends: list[DossierInner] = [] print(json.dumps(Dossier.model_json_schema(), indent=2)) from ollama import chat response = chat( messages=[ { 'role': 'user', 'content': 'Hello my name is Tom I am 13 years old, my friend Bob is 14 years old' } ], model='llama3.1:8b-instruct-q2_K', format=Dossier.model_json_schema() ) dossier = Dossier.model_validate_json(response.message.content) print(dossier.model_dump_json(indent=2)) ``` Output: ``` { "$defs": { "DossierInner": { "description": "Build a profile for the user", "properties": { "name": { "description": "The name of the user", "title": "Name", "type": "string" }, "age": { "description": "The age of the user", "title": "Age", "type": "integer" } }, "required": [ "name", "age" ], "title": "DossierInner", "type": "object" } }, "description": "Build a profile for the user", "properties": { "name": { "description": "The name of the user", "title": "Name", "type": "string" }, "age": { "description": "The age of the user", "title": "Age", "type": "integer" }, "friends": { "default": [], "items": { "$ref": "#/$defs/DossierInner" }, "title": "Friends", "type": "array" } }, "required": [ "name", "age" ], "title": "Dossier", "type": "object" } { "name": "Tom", "age": -1, "friends": [ { "name": "Bob", "age": -2 } ] } ``` ### OS Windows ### GPU Nvidia ### CPU AMD ### Ollama version 0.5.1
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build failure: `APPLE_IDENTITY: unbound variable`
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2023-11-11T18:04:33
2023-11-12T22:25:08
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Attempting to build on the darwin platform using the `build/build_darwing.sh` script results in the following error: ``` ./scripts/build_darwin.sh: line 17: APPLE_IDENTITY: unbound variable ``` This is after go generate (with `cmake` for th llama.cpp targets) and the `ollama` binary have completed building: ``` ❯ ls -la dist total 71664 drwxr-xr-x@ 3 jpmcb staff 96 Nov 11 10:52 . drwxr-xr-x@ 28 jpmcb staff 896 Nov 11 10:46 .. -rwxr-xr-x@ 1 jpmcb staff 36688338 Nov 11 10:52 ollama ``` There appear to be a few unbound variables: `APPLE_IDENTITY`, `APPLE_ID`, `APPLE_PASSWORD`, `APPLE_TEAM_ID` which my guess are used to sign/authenticate the binaries so they run smoothly on macs without I more or less just want a way to build and run locally without having to sign any binaries. `go generate ./... && go build-o dist/ollama-dev` would probably work fine but wondering if there's a more official way. --- Some additional details on my system: ``` ❯ system_profiler SPSoftwareDataType SPHardwareDataType Software: System Software Overview: System Version: macOS 13.3 (22E252) Kernel Version: Darwin 22.4.0 Boot Volume: Macintosh HD Boot Mode: Normal Hardware: Hardware Overview: Model Name: MacBook Pro Chip: Apple M2 Max Total Number of Cores: 12 (8 performance and 4 efficiency) Memory: 32 GB System Firmware Version: 8422.100.650 OS Loader Version: 8422.100.650 ```
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Error: pull model manifest: 400: The specified repository contains sharded GGUF. Ollama does not support this yet.
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2025-01-24T09:44:14
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### What is the issue? Error: pull model manifest: 400: The specified repository contains sharded GGUF. Ollama does not support this yet. ### OS macOS ### GPU Apple ### CPU Apple ### Ollama version 0.5.4
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532
remove `.First`
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2023-09-15T05:12:25
2024-01-09T18:58:37
2024-01-09T18:58:37
MEMBER
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This change removes the need for `.First`
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I_kwDOJ0Z1Ps6mbCUn
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support ReaderLM-v2
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2025-01-16T09:02:14
2025-01-19T18:33:04
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https://huggingface.co/jinaai/ReaderLM-v2 ReaderLM-v2 is specialized for tasks involving HTML parsing, transformation, and text extraction.
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I_kwDOJ0Z1Ps59rAJL
2,281
Support GPU runners with AVX2
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2024-01-30T17:47:16
2024-12-10T17:47:22
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I am running ollama on i7-14700K, which supports AVX2 and AVX_VNNI, and a GeForce RTX 1060. After reading #2205, I enable `OLLAMA_DEBUG=1` to check if ollama utilize AVX2 of this CPU. But unlike that one, I couldn't get ollama to use AVX2. The debug message has: ``` time=2024-01-30T12:27:26.016-05:00 level=INFO source=/tmp/ollama/gpu/gpu.go:146 msg="CUDA Compute Capability detected: 6.1" time=2024-01-30T12:27:26.016-05:00 level=INFO source=/tmp/ollama/gpu/cpu_common.go:11 msg="CPU has AVX2" loading library /tmp/ollama1660685050/cuda_v12/libext_server.so time=2024-01-30T12:27:26.032-05:00 level=INFO source=/tmp/ollama/llm/dyn_ext_server.go:90 msg="Loading Dynamic llm server: /tmp/ollama1660685050/cuda_v12/libext_server.so" time=2024-01-30T12:27:26.032-05:00 level=INFO source=/tmp/ollama/llm/dyn_ext_server.go:145 msg="Initializing llama server" [1706635646] 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 | [1706635646] Performing pre-initialization of GPU ggml_init_cublas: GGML_CUDA_FORCE_MMQ: yes ggml_init_cublas: CUDA_USE_TENSOR_CORES: no ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce GTX 1060 3GB, compute capability 6.1, VMM: yes ``` Thus ollama does detect GPU and also reports `CPU has AVX2`. However, when initializing server, it shows `AVX2 = 0` as well as `AVX_VNNI = 0`. I also follow [here](https://github.com/ollama/ollama/blob/main/docs/development.md), setting `OLLAMA_CUSTOM_CPU_DEFS="-DLLAMA_AVX=on -DLLAMA_AVX2=on -DLLAMA_F16C=on -DLLAMA_FMA=on"`, to build the binary locally with AVX2 support. However, the result is the same as the released binary, and I still get `AVX_VNNI = 0 | AVX2 = 0`. How can I make ollama use AVX2 in my CPU?
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Error: llama runner process has terminated on M2
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2023-12-13T19:07:19
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I'm currently running Ollama on a MacBook Air m2 (8GB) I firstly installed Ollama through `brew install ollama` and got `Error: llama runner process has terminated` after pulling and running `mistral:instruct` and `mistral:latest` . After that, I uninstalled using `brew uninstall ollama` then installing it through website. I ran the following commands: * `ollama pull mistral:instruct` * `ollama run mistral` and I got the same error: `Error: llama runner process has terminated` I checked the logs using `cat ~/.ollama/logs/server.log` and I got this: ``` 2023/12/13 13:01:18 llama.go:434: starting llama runner 2023/12/13 13:01:18 llama.go:492: waiting for llama runner to start responding {"timestamp":1702494078,"level":"INFO","function":"main","line":2656,"message":"build info","build":417,"commit":"23b5e12"} {"timestamp":1702494078,"level":"INFO","function":"main","line":2663,"message":"system info","n_threads":4,"n_threads_batch":-1,"total_threads":8,"system_info":"AVX = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 1 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 1 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | "} llama_model_loader: loaded meta data with 22 key-value pairs and 291 tensors from /Users/emiliosandoval/.ollama/models/blobs/sha256:c70fa74a8e81c3bd041cc2c30152fe6e251fdc915a3792147147a5c06bc4b309 (version GGUF V3 (latest)) llama_model_loader: - 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tensor 26: blk.2.attn_norm.weight f32 [ 4096, 1, 1, 1 ] llama_model_loader: - tensor 27: blk.2.ffn_norm.weight f32 [ 4096, 1, 1, 1 ] llama_model_loader: - tensor 28: blk.3.attn_q.weight q4_0 [ 4096, 4096, 1, 1 ] llama_model_loader: - tensor 29: blk.3.attn_k.weight q4_0 [ 4096, 1024, 1, 1 ] llama_model_loader: - tensor 30: blk.3.attn_v.weight q4_0 [ 4096, 1024, 1, 1 ] llama_model_loader: - tensor 31: blk.3.attn_output.weight q4_0 [ 4096, 4096, 1, 1 ] llama_model_loader: - tensor 32: blk.3.ffn_gate.weight q4_0 [ 4096, 14336, 1, 1 ] llama_model_loader: - tensor 33: blk.3.ffn_up.weight q4_0 [ 4096, 14336, 1, 1 ] llama_model_loader: - tensor 34: blk.3.ffn_down.weight q4_0 [ 14336, 4096, 1, 1 ] llama_model_loader: - tensor 35: blk.3.attn_norm.weight f32 [ 4096, 1, 1, 1 ] llama_model_loader: - tensor 36: blk.3.ffn_norm.weight f32 [ 4096, 1, 1, 1 ] llama_model_loader: - tensor 37: blk.4.attn_q.weight q4_0 [ 4096, 4096, 1, 1 ] llama_model_loader: - tensor 38: blk.4.attn_k.weight q4_0 [ 4096, 1024, 1, 1 ] llama_model_loader: - 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tensor 286: blk.31.ffn_down.weight q4_0 [ 14336, 4096, 1, 1 ] llama_model_loader: - tensor 287: blk.31.attn_norm.weight f32 [ 4096, 1, 1, 1 ] llama_model_loader: - tensor 288: blk.31.ffn_norm.weight f32 [ 4096, 1, 1, 1 ] llama_model_loader: - tensor 289: output_norm.weight f32 [ 4096, 1, 1, 1 ] llama_model_loader: - tensor 290: output.weight q6_K [ 4096, 32000, 1, 1 ] llama_model_loader: - kv 0: general.architecture str = llama llama_model_loader: - kv 1: general.name str = mistralai llama_model_loader: - kv 2: llama.context_length u32 = 32768 llama_model_loader: - kv 3: llama.embedding_length u32 = 4096 llama_model_loader: - kv 4: llama.block_count u32 = 32 llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336 llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128 llama_model_loader: - kv 7: llama.attention.head_count u32 = 32 llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8 llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 llama_model_loader: - kv 10: llama.rope.freq_base f32 = 1000000.000000 llama_model_loader: - kv 11: general.file_type u32 = 2 llama_model_loader: - kv 12: tokenizer.ggml.model str = llama llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<... llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000... llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ... llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 1 llama_model_loader: - kv 17: tokenizer.ggml.eos_token_id u32 = 2 llama_model_loader: - kv 18: tokenizer.ggml.unknown_token_id u32 = 0 llama_model_loader: - kv 19: tokenizer.ggml.add_bos_token bool = true llama_model_loader: - kv 20: tokenizer.ggml.add_eos_token bool = false llama_model_loader: - kv 21: general.quantization_version u32 = 2 llama_model_loader: - type f32: 65 tensors llama_model_loader: - type q4_0: 225 tensors llama_model_loader: - type q6_K: 1 tensors llm_load_vocab: special tokens definition check successful ( 259/32000 ). llm_load_print_meta: format = GGUF V3 (latest) llm_load_print_meta: arch = llama llm_load_print_meta: vocab type = SPM llm_load_print_meta: n_vocab = 32000 llm_load_print_meta: n_merges = 0 llm_load_print_meta: n_ctx_train = 32768 llm_load_print_meta: n_embd = 4096 llm_load_print_meta: n_head = 32 llm_load_print_meta: n_head_kv = 8 llm_load_print_meta: n_layer = 32 llm_load_print_meta: n_rot = 128 llm_load_print_meta: n_gqa = 4 llm_load_print_meta: f_norm_eps = 0.0e+00 llm_load_print_meta: f_norm_rms_eps = 1.0e-05 llm_load_print_meta: f_clamp_kqv = 0.0e+00 llm_load_print_meta: f_max_alibi_bias = 0.0e+00 llm_load_print_meta: n_ff = 14336 llm_load_print_meta: rope scaling = linear llm_load_print_meta: freq_base_train = 1000000.0 llm_load_print_meta: freq_scale_train = 1 llm_load_print_meta: n_yarn_orig_ctx = 32768 llm_load_print_meta: rope_finetuned = unknown llm_load_print_meta: model type = 7B llm_load_print_meta: model ftype = mostly Q4_0 llm_load_print_meta: model params = 7.24 B llm_load_print_meta: model size = 3.83 GiB (4.54 BPW) llm_load_print_meta: general.name = mistralai llm_load_print_meta: BOS token = 1 '<s>' llm_load_print_meta: EOS token = 2 '</s>' llm_load_print_meta: UNK token = 0 '<unk>' llm_load_print_meta: LF token = 13 '<0x0A>' llm_load_tensors: ggml ctx size = 0.11 MiB llm_load_tensors: mem required = 3917.97 MiB .......... ..................... ............... .............. ........... ......... ........ ...... .... llama_new_context_with_model: n_ctx = 32768 llama_new_context_with_model: freq_base = 1000000.0 llama_new_context_with_model: freq_scale = 1 llama_new_context_with_model: kv self size = 4096.00 MiB llama_build_graph: non-view tensors processed: 676/676 ggml_metal_init: allocating ggml_metal_init: found device: Apple M2 ggml_metal_init: picking default device: Apple M2 ggml_metal_init: default.metallib not found, loading from source ggml_metal_init: loading '/var/folders/q5/yq5tdvb520z37pwzt2h5__2m0000gn/T/ollama1268607975/llama.cpp/gguf/build/metal/bin/ggml-metal.metal' ggml_metal_init: GPU name: Apple M2 ggml_metal_init: GPU family: MTLGPUFamilyApple8 (1008) ggml_metal_init: hasUnifiedMemory = true ggml_metal_init: recommendedMaxWorkingSetSize = 5461.34 MiB ggml_metal_init: maxTransferRate = built-in GPU llama_new_context_with_model: compute buffer total size = 2139.07 MiB llama_new_context_with_model: max tensor size = 102.54 MiB ggml_metal_add_buffer: allocated 'data ' buffer, size = 3918.58 MiB, ( 3919.20 / 5461.34) ggml_metal_add_buffer: allocated 'kv ' buffer, size = 4096.00 MiB, offs = 0 ggml_metal_add_buffer: allocated 'kv ' buffer, size = 0.03 MiB, offs = 4294950912, ( 8015.23 / 5461.34)ggml_metal_add_buffer: warning: current allocated size is greater than the recommended max working set size ggml_metal_add_buffer: allocated 'alloc ' buffer, size = 2136.02 MiB, (10151.25 / 5461.34)ggml_metal_add_buffer: 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: /Users/jmorgan/workspace/ollama/llm/llama.cpp/gguf/ggml-metal.m:1623: false 2023/12/13 13:01:19 llama.go:449: signal: abort trap 2023/12/13 13:01:19 llama.go:457: error starting llama runner: llama runner process has terminated 2023/12/13 13:01:19 llama.go:523: llama runner stopped successfully [GIN] 2023/12/13 - 13:01:19 | 500 | 1.04335325s | 127.0.0.1 | POST "/api/generate" ``` I also installed it and ran the previous commands on m2 pro and everything was fine. Is it because I'm using a m2 with 8gb? or is it because I firstly used `brew`? I'd appreciate your help!
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ollama breaks running qwen on ubuntu 20
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2024-02-07T09:59:57
2024-02-09T20:46:26
2024-02-09T20:46:26
CONTRIBUTOR
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Either using the version included with `ollama pull qwen` or using my own custom modelfile with q8 and chatml template qwen causes ollama to get "stuck" it doesn't use GPU for qwen, or any other working model after trying qwen until reboot. see also: https://github.com/ollama/ollama/issues/1691
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add dockerfile for building linux binaries
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2023-09-20T18:40:01
2023-09-22T19:20:13
2023-09-22T19:20:13
MEMBER
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8,040
Add API endpoint for Ollama server version and feature information
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2024-12-11T05:53:14
2024-12-29T19:33:45
2024-12-29T19:33:45
CONTRIBUTOR
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**Description:** Ollama is rapidly evolving, with new features and capabilities being added regularly. The recent introduction of structured outputs in version 0.5.0 is a prime example of this progress. As Ollama continues to grow, it's becoming increasingly important for clients to have a reliable way to determine the server's version and supported features. Currently, there is no API endpoint to retrieve this information. This makes it difficult for clients to: - Ensure compatibility with features like structured outputs, which are dependent on specific server versions. - Optimize their requests based on the server's capabilities. - Effectively troubleshoot issues that may arise due to version mismatches. **Proposed solution:** Introduce a new API endpoint, such as `/api/version`, that returns a JSON object containing: - `version`: The Ollama server version (e.g., "v0.5.0"). - `features`: An array of supported features (e.g., ["structured_outputs_json", "structured_outputs_xyz"]). - (Optional) `system_info`: Potentially some basic system information like OS or available hardware (if deemed useful). **Benefits:** - Improved client-server interaction. - Easier debugging and troubleshooting. - Better support for the growing ecosystem of Ollama clients and tools. I'm happy to work on this issue. If you have any specific points you'd like to emphasize, feel free to share your thoughts. @jmorganca Just let me know how you'd like to proceed!
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how to download and run ollama and llma 3 in docker can u give me the docker file code for that
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2024-05-06T06:31:36
2024-05-06T23:42:30
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i want to download and run llmaa 3 using ollama on docker help me and give the code for docker file
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set non-zero error code on error
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2023-08-14T18:17:38
2023-08-16T16:20:29
2023-08-16T16:20:28
CONTRIBUTOR
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ollama should exit non-zero when operations fail
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Dictionary learning and concept extraction for model tuning
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2024-06-08T02:27:17
2024-06-08T02:27:17
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Described in Anthropic's [Mapping the Mind of a Large Language Model](https://www.anthropic.com/news/mapping-mind-language-model) and OpenAI's [Extracting Concepts from GPT-4](https://openai.com/index/extracting-concepts-from-gpt-4/). Once we can identify the neurons associated with certain concepts for the publicly available model weights, we would benefit from the capability of changing their values for a higher degree of control
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Error: failed to start a llama runner
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2023-12-15T18:46:26
2024-02-01T23:17:34
2024-02-01T23:17:34
NONE
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Hello, i tried to install ollama on my centos dedicated server but everything is working but when i try [root@213-227-129-200 ~]# ollama run llava Error: failed to start a llama runner i get this. what can i do? -- Logs begin at Fri 2023-08-04 06:00:01 UTC, end at Fri 2023-12-15 18:45:42 UTC. -- Dec 15 15:33:10 213-227-129-200.cprapid.com systemd[1]: Started Ollama Service. Dec 15 15:33:10 213-227-129-200.cprapid.com ollama[25174]: Couldn't find '/usr/share/ollama/.ollama/id_ed25519'. Generating ne Dec 15 15:33:10 213-227-129-200.cprapid.com ollama[25174]: Your new public key is: Dec 15 15:33:10 213-227-129-200.cprapid.com ollama[25174]: ssh-ed25519 AAAAC3NzaC1lZDI1NTE5AAAAIGFFKiLz3PKkUtFK+Bj3gAJK7csulDh Dec 15 15:33:10 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:33:10 images.go:737: total blobs: 0 Dec 15 15:33:10 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:33:10 images.go:744: total unused blobs removed: 0 Dec 15 15:33:10 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:33:10 routes.go:871: Listening on 127.0.0.1:11434 (ve Dec 15 15:33:10 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:33:10 routes.go:891: warning: gpu support may not be Dec 15 15:34:10 213-227-129-200.cprapid.com ollama[25174]: [GIN] 2023/12/15 - 15:34:10 | 200 | 178.535µs | 127.0.0.1 Dec 15 15:34:10 213-227-129-200.cprapid.com ollama[25174]: [GIN] 2023/12/15 - 15:34:10 | 200 | 181.125375ms | 127.0.0.1 Dec 15 15:34:47 213-227-129-200.cprapid.com ollama[25174]: [GIN] 2023/12/15 - 15:34:47 | 200 | 44.934µs | 127.0.0.1 Dec 15 15:34:49 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:34:49 download.go:123: downloading 200765e12836 in 39 Dec 15 15:35:27 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:35:27 download.go:123: downloading 64c2234f0395 in 7 Dec 15 15:35:36 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:35:36 download.go:123: downloading d5ca8c59f62d in 1 Dec 15 15:35:39 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:35:39 download.go:123: downloading 6c58ad369ad0 in 1 Dec 15 15:35:42 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:35:42 download.go:123: downloading 805db971dc64 in 1 Dec 15 15:35:49 213-227-129-200.cprapid.com ollama[25174]: [GIN] 2023/12/15 - 15:35:49 | 200 | 1m1s | 127.0.0.1 Dec 15 15:37:09 213-227-129-200.cprapid.com ollama[25174]: [GIN] 2023/12/15 - 15:37:09 | 200 | 175.861µs | 127.0.0.1 Dec 15 15:37:09 213-227-129-200.cprapid.com ollama[25174]: [GIN] 2023/12/15 - 15:37:09 | 200 | 1.276939ms | 127.0.0.1 Dec 15 15:37:09 213-227-129-200.cprapid.com ollama[25174]: [GIN] 2023/12/15 - 15:37:09 | 200 | 831.562µs | 127.0.0.1 Dec 15 15:37:09 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:37:09 llama.go:403: skipping accelerated runner becau Dec 15 15:37:09 213-227-129-200.cprapid.com ollama[25174]: 2023/12/15 15:37:09 llama.go:436: starting llama runner
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failed at cuda 12.2 with GTX1080 Ti
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2024-04-18T08:20:14
2024-04-18T18:24:09
2024-04-18T18:24:09
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### What is the issue? This is a fresh installed ollama, but failed at first launch. cuda 12.2 ``` ben@amd:~/work/ollama$ curl -fsSL https://ollama.com/install.sh | sh >>> Downloading ollama... ####################################################################################################################### 100.0%####################################################################################################################### 100.0% >>> Installing ollama to /usr/local/bin... >>> Creating ollama user... >>> Adding ollama user to render group... >>> Adding ollama user to video group... >>> Adding current user to ollama group... >>> Creating ollama systemd service... >>> Enabling and starting ollama service... Created symlink /etc/systemd/system/default.target.wants/ollama.service → /etc/systemd/system/ollama.service. >>> NVIDIA GPU installed. ben@amd:~/work/ollama$ ollama mistral Error: unknown command "mistral" for "ollama" ben@amd:~/work/ollama$ ollama mistral^C ben@amd:~/work/ollama$ ollama run mistral pulling manifest pulling e8a35b5937a5... 100% ▕██████████████████████████████████████████████████████████████▏ 4.1 GB pulling 43070e2d4e53... 100% ▕██████████████████████████████████████████████████████████████▏ 11 KB pulling e6836092461f... 100% ▕██████████████████████████████████████████████████████████████▏ 42 B pulling ed11eda7790d... 100% ▕██████████████████████████████████████████████████████████████▏ 30 B pulling f9b1e3196ecf... 100% ▕██████████████████████████████████████████████████████████████▏ 483 B verifying sha256 digest writing manifest removing any unused layers success Error: llama runner process no longer running: 1 ben@amd:~/work/ollama$ ollama run mistral Error: llama runner process no longer running: 1 ben@amd:~/work/ollama$ nvidia-smi Thu Apr 18 15:59:20 2024 +---------------------------------------------------------------------------------------+ | NVIDIA-SMI 535.161.07 Driver Version: 535.161.07 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 GTX 1080 Ti On | 00000000:0A:00.0 Off | N/A | | 0% 33C P8 19W / 275W | 4MiB / 11264MiB | 0% Default | | | | N/A | +-----------------------------------------+----------------------+----------------------+ +---------------------------------------------------------------------------------------+ | Processes: | | GPU GI CI PID Type Process name GPU Memory | | ID ID Usage | |=======================================================================================| | No running processes found | +---------------------------------------------------------------------------------------+ ben@amd:~/work/ollama$ ollama -v ollama version is 0.1.32 ``` I have linked `libcublas.so.11 -> libcublas.so.12` but it still failed. cude works well for other cuda 11.x projects. ``` ben@amd:~/work/ollama$ ls -al /usr/local/cuda/lib64/libcudart* lrwxrwxrwx 1 root root 15 Aug 16 2023 /usr/local/cuda/lib64/libcudart.so -> libcudart.so.12 lrwxrwxrwx 1 root root 21 Aug 16 2023 /usr/local/cuda/lib64/libcudart.so.12 -> libcudart.so.12.2.140 -rw-r--r-- 1 root root 683360 Aug 16 2023 /usr/local/cuda/lib64/libcudart.so.12.2.140 -rw-r--r-- 1 root root 1379326 Aug 16 2023 /usr/local/cuda/lib64/libcudart_static.a ben@amd:~/work/ollama$ ls -al /usr/local/cuda/lib64/libcublas* lrwxrwxrwx 1 root root 17 Aug 16 2023 /usr/local/cuda/lib64/libcublasLt.so -> libcublasLt.so.12 lrwxrwxrwx 1 root root 23 Aug 16 2023 /usr/local/cuda/lib64/libcublasLt.so.12 -> libcublasLt.so.12.2.5.6 -rw-r--r-- 1 root root 525843792 Aug 16 2023 /usr/local/cuda/lib64/libcublasLt.so.12.2.5.6 -rw-r--r-- 1 root root 770686098 Aug 16 2023 /usr/local/cuda/lib64/libcublasLt_static.a lrwxrwxrwx 1 root root 15 Aug 16 2023 /usr/local/cuda/lib64/libcublas.so -> libcublas.so.12 lrwxrwxrwx 1 root root 15 Feb 15 19:55 /usr/local/cuda/lib64/libcublas.so.11 -> libcublas.so.12 lrwxrwxrwx 1 root root 21 Aug 16 2023 /usr/local/cuda/lib64/libcublas.so.12 -> libcublas.so.12.2.5.6 -rw-r--r-- 1 root root 106675248 Aug 16 2023 /usr/local/cuda/lib64/libcublas.so.12.2.5.6 -rw-r--r-- 1 root root 168600104 Aug 16 2023 /usr/local/cuda/lib64/libcublas_static.a ben@amd:~/work/ollama$ ls -ld /usr/local/cuda* lrwxrwxrwx 1 root root 22 Jan 20 23:13 /usr/local/cuda -> /etc/alternatives/cuda lrwxrwxrwx 1 root root 25 Jan 20 23:13 /usr/local/cuda-12 -> /etc/alternatives/cuda-12 drwxr-xr-x 15 root root 4096 Jan 20 23:12 /usr/local/cuda-12.2 ``` ------------ <details> <summary> Here is the Logs </summary> ``` [ 83.120309] amd systemd[1]: Started Ollama Service. [ 83.133078] amd ollama[2303]: Couldn't find '/usr/share/ollama/.ollama/id_ed25519'. Generating new private key. [ 83.134194] amd ollama[2303]: Your new public key is: [ 83.134194] amd ollama[2303]: ssh-ed25519 Censored [ 83.134456] amd ollama[2303]: time=2024-04-18T15:55:19.875+09:00 level=INFO source=images.go:817 msg="total blobs: 0" [ 83.134513] amd ollama[2303]: time=2024-04-18T15:55:19.875+09:00 level=INFO source=images.go:824 msg="total unused blobs removed: 0" [ 83.134621] amd ollama[2303]: time=2024-04-18T15:55:19.875+09:00 level=INFO source=routes.go:1143 msg="Listening on 127.0.0.1:11434 (version 0.1.32)" [ 83.134956] amd ollama[2303]: time=2024-04-18T15:55:19.875+09:00 level=INFO source=payload.go:28 msg="extracting embedded files" dir=/tmp/ollama3603894721/runners [ 86.003279] amd ollama[2303]: time=2024-04-18T15:55:22.744+09:00 level=INFO source=payload.go:41 msg="Dynamic LLM libraries [cpu cpu_avx cpu_avx2 cuda_v11 rocm_v60002]" [ 86.003279] amd ollama[2303]: time=2024-04-18T15:55:22.744+09:00 level=INFO source=gpu.go:121 msg="Detecting GPU type" [ 86.003618] amd ollama[2303]: time=2024-04-18T15:55:22.744+09:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*" [ 86.010035] amd ollama[2303]: time=2024-04-18T15:55:22.750+09:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3603894721/runners/cuda_v11/libcudart.so.11.0 /usr/local/cuda/lib64/libcudart.so.12.2.140]" [ 86.070476] amd ollama[2303]: time=2024-04-18T15:55:22.811+09:00 level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" [ 86.070514] amd ollama[2303]: time=2024-04-18T15:55:22.811+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 86.225488] amd ollama[2303]: time=2024-04-18T15:55:22.966+09:00 level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 6.1" [ 137.899701] amd ollama[2303]: [GIN] 2024/04/18 - 15:56:14 | 200 | 37.46µs | 127.0.0.1 | HEAD "/" [ 137.900488] amd ollama[2303]: [GIN] 2024/04/18 - 15:56:14 | 404 | 116.077µs | 127.0.0.1 | POST "/api/show" [ 140.789836] amd ollama[2303]: time=2024-04-18T15:56:17.530+09:00 level=INFO source=download.go:136 msg="downloading e8a35b5937a5 in 42 100 MB part(s)" [ 185.253719] amd ollama[2303]: time=2024-04-18T15:57:01.994+09:00 level=INFO source=download.go:136 msg="downloading 43070e2d4e53 in 1 11 KB part(s)" [ 187.174634] amd ollama[2303]: time=2024-04-18T15:57:03.915+09:00 level=INFO source=download.go:136 msg="downloading e6836092461f in 1 42 B part(s)" [ 190.115108] amd ollama[2303]: time=2024-04-18T15:57:06.855+09:00 level=INFO source=download.go:136 msg="downloading ed11eda7790d in 1 30 B part(s)" [ 192.046078] amd ollama[2303]: time=2024-04-18T15:57:08.786+09:00 level=INFO source=download.go:136 msg="downloading f9b1e3196ecf in 1 483 B part(s)" [ 195.485759] amd ollama[2303]: [GIN] 2024/04/18 - 15:57:12 | 200 | 57.585406568s | 127.0.0.1 | POST "/api/pull" [ 195.486886] amd ollama[2303]: [GIN] 2024/04/18 - 15:57:12 | 200 | 672.555µs | 127.0.0.1 | POST "/api/show" [ 195.487559] amd ollama[2303]: [GIN] 2024/04/18 - 15:57:12 | 200 | 198.871µs | 127.0.0.1 | POST "/api/show" [ 195.993394] amd ollama[2303]: time=2024-04-18T15:57:12.734+09:00 level=INFO source=gpu.go:121 msg="Detecting GPU type" [ 195.993394] amd ollama[2303]: time=2024-04-18T15:57:12.734+09:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*" [ 195.997472] amd ollama[2303]: time=2024-04-18T15:57:12.738+09:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3603894721/runners/cuda_v11/libcudart.so.11.0 /usr/local/cuda/lib64/libcudart.so.12.2.140]" [ 195.998324] amd ollama[2303]: time=2024-04-18T15:57:12.739+09:00 level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" [ 195.998324] amd ollama[2303]: time=2024-04-18T15:57:12.739+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 196.134773] amd ollama[2303]: time=2024-04-18T15:57:12.875+09:00 level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 6.1" [ 196.191449] amd ollama[2303]: time=2024-04-18T15:57:12.932+09:00 level=INFO source=gpu.go:121 msg="Detecting GPU type" [ 196.191449] amd ollama[2303]: time=2024-04-18T15:57:12.932+09:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*" [ 196.193140] amd ollama[2303]: time=2024-04-18T15:57:12.934+09:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3603894721/runners/cuda_v11/libcudart.so.11.0 /usr/local/cuda/lib64/libcudart.so.12.2.140]" [ 196.193572] amd ollama[2303]: time=2024-04-18T15:57:12.934+09:00 level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" [ 196.193572] amd ollama[2303]: time=2024-04-18T15:57:12.934+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 196.268651] amd ollama[2303]: time=2024-04-18T15:57:13.009+09:00 level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 6.1" [ 196.312505] amd ollama[2303]: time=2024-04-18T15:57:13.053+09:00 level=INFO source=server.go:127 msg="offload to gpu" reallayers=33 layers=33 required="4724.5 MiB" used="4724.5 MiB" available="11009.9 MiB" kv="256.0 MiB" fulloffload="164.0 MiB" partialoffload="181.0 MiB" [ 196.312587] amd ollama[2303]: time=2024-04-18T15:57:13.053+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 196.312708] amd ollama[2303]: time=2024-04-18T15:57:13.053+09:00 level=INFO source=server.go:264 msg="starting llama server" cmd="/tmp/ollama3603894721/runners/cuda_v11/ollama_llama_server --model /usr/share/ollama/.ollama/models/blobs/sha256-e8a35b5937a5e6d5c35d1f2a15f161e07eefe5e5bb0a3cdd42998ee79b057730 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --port 42067" [ 196.312960] amd ollama[2303]: time=2024-04-18T15:57:13.053+09:00 level=INFO source=server.go:389 msg="waiting for llama runner to start responding" [ 196.319131] amd ollama[2303]: /tmp/ollama3603894721/runners/cuda_v11/ollama_llama_server: /usr/local/cuda/lib64/libcublas.so.11: version `libcublas.so.11' not found (required by /tmp/ollama3603894721/runners/cuda_v11/ollama_llama_server) [ 196.363803] amd ollama[2303]: time=2024-04-18T15:57:13.104+09:00 level=ERROR source=routes.go:120 msg="error loading llama server" error="llama runner process no longer running: 1 " [ 196.363845] amd ollama[2303]: [GIN] 2024/04/18 - 15:57:13 | 500 | 875.760611ms | 127.0.0.1 | POST "/api/chat" [ 245.946877] amd ollama[2303]: [GIN] 2024/04/18 - 15:58:02 | 200 | 19.607µs | 127.0.0.1 | HEAD "/" [ 245.947494] amd ollama[2303]: [GIN] 2024/04/18 - 15:58:02 | 200 | 375.091µs | 127.0.0.1 | POST "/api/show" [ 245.948175] amd ollama[2303]: [GIN] 2024/04/18 - 15:58:02 | 200 | 289.03µs | 127.0.0.1 | POST "/api/show" [ 246.447634] amd ollama[2303]: time=2024-04-18T15:58:03.188+09:00 level=INFO source=gpu.go:121 msg="Detecting GPU type" [ 246.447634] amd ollama[2303]: time=2024-04-18T15:58:03.188+09:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*" [ 246.450677] amd ollama[2303]: time=2024-04-18T15:58:03.191+09:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3603894721/runners/cuda_v11/libcudart.so.11.0 /usr/local/cuda/lib64/libcudart.so.12.2.140]" [ 246.451539] amd ollama[2303]: time=2024-04-18T15:58:03.192+09:00 level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" [ 246.451539] amd ollama[2303]: time=2024-04-18T15:58:03.192+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 246.595211] amd ollama[2303]: time=2024-04-18T15:58:03.336+09:00 level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 6.1" [ 246.650697] amd ollama[2303]: time=2024-04-18T15:58:03.391+09:00 level=INFO source=gpu.go:121 msg="Detecting GPU type" [ 246.650697] amd ollama[2303]: time=2024-04-18T15:58:03.391+09:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*" [ 246.652325] amd ollama[2303]: time=2024-04-18T15:58:03.393+09:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3603894721/runners/cuda_v11/libcudart.so.11.0 /usr/local/cuda/lib64/libcudart.so.12.2.140]" [ 246.652764] amd ollama[2303]: time=2024-04-18T15:58:03.393+09:00 level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" [ 246.652764] amd ollama[2303]: time=2024-04-18T15:58:03.393+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 246.731444] amd ollama[2303]: time=2024-04-18T15:58:03.472+09:00 level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 6.1" [ 246.781592] amd ollama[2303]: time=2024-04-18T15:58:03.522+09:00 level=INFO source=server.go:127 msg="offload to gpu" reallayers=33 layers=33 required="4724.5 MiB" used="4724.5 MiB" available="11009.9 MiB" kv="256.0 MiB" fulloffload="164.0 MiB" partialoffload="181.0 MiB" [ 246.781684] amd ollama[2303]: time=2024-04-18T15:58:03.522+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 246.781800] amd ollama[2303]: time=2024-04-18T15:58:03.522+09:00 level=INFO source=server.go:264 msg="starting llama server" cmd="/tmp/ollama3603894721/runners/cuda_v11/ollama_llama_server --model /usr/share/ollama/.ollama/models/blobs/sha256-e8a35b5937a5e6d5c35d1f2a15f161e07eefe5e5bb0a3cdd42998ee79b057730 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --port 36135" [ 246.782201] amd ollama[2303]: time=2024-04-18T15:58:03.523+09:00 level=INFO source=server.go:389 msg="waiting for llama runner to start responding" [ 246.783896] amd ollama[2303]: /tmp/ollama3603894721/runners/cuda_v11/ollama_llama_server: /usr/local/cuda/lib64/libcublas.so.11: version `libcublas.so.11' not found (required by /tmp/ollama3603894721/runners/cuda_v11/ollama_llama_server) [ 246.833186] amd ollama[2303]: time=2024-04-18T15:58:03.574+09:00 level=ERROR source=routes.go:120 msg="error loading llama server" error="llama runner process no longer running: 1 " [ 246.833231] amd ollama[2303]: [GIN] 2024/04/18 - 15:58:03 | 500 | 884.761421ms | 127.0.0.1 | POST "/api/chat" [ 437.978588] amd ollama[2303]: [GIN] 2024/04/18 - 16:01:14 | 200 | 20.769µs | 127.0.0.1 | HEAD "/" [ 437.979267] amd ollama[2303]: [GIN] 2024/04/18 - 16:01:14 | 200 | 284.743µs | 127.0.0.1 | GET "/api/tags" [ 456.418438] amd ollama[2303]: [GIN] 2024/04/18 - 16:01:33 | 200 | 28.283µs | 127.0.0.1 | HEAD "/" [ 458.934304] amd ollama[2303]: time=2024-04-18T16:01:35.675+09:00 level=INFO source=download.go:136 msg="downloading 170370233dd5 in 42 100 MB part(s)" [ 476.934522] amd ollama[2303]: time=2024-04-18T16:01:53.675+09:00 level=INFO source=download.go:251 msg="170370233dd5 part 8 stalled; retrying. If this persists, press ctrl-c to exit, then 'ollama pull' to find a faster connection." [ 508.847472] amd ollama[2303]: time=2024-04-18T16:02:25.588+09:00 level=INFO source=download.go:136 msg="downloading 72d6f08a42f6 in 7 100 MB part(s)" [ 518.832954] amd ollama[2303]: time=2024-04-18T16:02:35.573+09:00 level=INFO source=download.go:136 msg="downloading c43332387573 in 1 67 B part(s)" [ 520.743339] amd ollama[2303]: time=2024-04-18T16:02:37.484+09:00 level=INFO source=download.go:136 msg="downloading 7c658f9561e5 in 1 564 B part(s)" [ 524.572442] amd ollama[2303]: [GIN] 2024/04/18 - 16:02:41 | 200 | 1m8s | 127.0.0.1 | POST "/api/pull" [ 551.538296] amd ollama[2303]: [GIN] 2024/04/18 - 16:03:08 | 200 | 26.851µs | 127.0.0.1 | HEAD "/" [ 551.539134] amd ollama[2303]: [GIN] 2024/04/18 - 16:03:08 | 404 | 65.683µs | 127.0.0.1 | POST "/api/show" [ 555.200514] amd ollama[2303]: [GIN] 2024/04/18 - 16:03:11 | 200 | 3.661717372s | 127.0.0.1 | POST "/api/pull" [ 555.201466] amd ollama[2303]: [GIN] 2024/04/18 - 16:03:11 | 200 | 569.521µs | 127.0.0.1 | POST "/api/show" [ 555.202271] amd ollama[2303]: [GIN] 2024/04/18 - 16:03:11 | 200 | 207.34µs | 127.0.0.1 | POST "/api/show" [ 555.448202] amd ollama[2303]: time=2024-04-18T16:03:12.189+09:00 level=INFO source=gpu.go:121 msg="Detecting GPU type" [ 555.448202] amd ollama[2303]: time=2024-04-18T16:03:12.189+09:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*" [ 555.451303] amd ollama[2303]: time=2024-04-18T16:03:12.192+09:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3603894721/runners/cuda_v11/libcudart.so.11.0 /usr/local/cuda/lib64/libcudart.so.12.2.140]" [ 555.452133] amd ollama[2303]: time=2024-04-18T16:03:12.193+09:00 level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" [ 555.452133] amd ollama[2303]: time=2024-04-18T16:03:12.193+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 555.576880] amd ollama[2303]: time=2024-04-18T16:03:12.317+09:00 level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 6.1" [ 555.632608] amd ollama[2303]: time=2024-04-18T16:03:12.373+09:00 level=INFO source=gpu.go:121 msg="Detecting GPU type" [ 555.632608] amd ollama[2303]: time=2024-04-18T16:03:12.373+09:00 level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*" [ 555.634189] amd ollama[2303]: time=2024-04-18T16:03:12.375+09:00 level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3603894721/runners/cuda_v11/libcudart.so.11.0 /usr/local/cuda/lib64/libcudart.so.12.2.140]" [ 555.634617] amd ollama[2303]: time=2024-04-18T16:03:12.375+09:00 level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart" [ 555.634617] amd ollama[2303]: time=2024-04-18T16:03:12.375+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 555.727459] amd ollama[2303]: time=2024-04-18T16:03:12.468+09:00 level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 6.1" [ 555.775085] amd ollama[2303]: time=2024-04-18T16:03:12.515+09:00 level=INFO source=server.go:127 msg="offload to gpu" reallayers=33 layers=33 required="5320.0 MiB" used="5320.0 MiB" available="11009.9 MiB" kv="256.0 MiB" fulloffload="164.0 MiB" partialoffload="181.0 MiB" [ 555.775171] amd ollama[2303]: time=2024-04-18T16:03:12.516+09:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2" [ 555.775247] amd ollama[2303]: time=2024-04-18T16:03:12.516+09:00 level=INFO source=server.go:264 msg="starting llama server" cmd="/tmp/ollama3603894721/runners/cuda_v11/ollama_llama_server --model /usr/share/ollama/.ollama/models/blobs/sha256-170370233dd5c5415250a2ecd5c71586352850729062ccef1496385647293868 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 33 --mmproj /usr/share/ollama/.ollama/models/blobs/sha256-72d6f08a42f656d36b356dbe0920675899a99ce21192fd66266fb7d82ed07539 --port 35909" [ 555.775609] amd ollama[2303]: time=2024-04-18T16:03:12.516+09:00 level=INFO source=server.go:389 msg="waiting for llama runner to start responding" [ 555.777326] amd ollama[2303]: /tmp/ollama3603894721/runners/cuda_v11/ollama_llama_server: /usr/local/cuda/lib64/libcublas.so.11: version `libcublas.so.11' not found (required by /tmp/ollama3603894721/runners/cuda_v11/ollama_llama_server) [ 555.826400] amd ollama[2303]: time=2024-04-18T16:03:12.567+09:00 level=ERROR source=routes.go:120 msg="error loading llama server" error="llama runner process no longer running: 1 " [ 555.826454] amd ollama[2303]: [GIN] 2024/04/18 - 16:03:12 | 500 | 623.91401ms | 127.0.0.1 | POST "/api/chat" [ 2734.691422] amd ollama[2303]: [GIN] 2024/04/18 - 16:39:31 | 200 | 42.189µs | 127.0.0.1 | GET "/api/version" ``` </details> ### OS Linux ### GPU Nvidia ### CPU AMD ### Ollama version 0.1.32
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2,461,538,236
PR_kwDOJ0Z1Ps54Iu7h
6,324
cmd: speed up gguf creates
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false
null
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null
0
2024-08-12T17:25:42
2024-08-12T18:46:11
2024-08-12T18:46:09
CONTRIBUTOR
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null
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1,922,909,871
I_kwDOJ0Z1Ps5ynUav
683
Uninstall
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2
2023-10-02T22:47:10
2023-10-02T22:56:12
2023-10-02T22:56:12
NONE
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null
null
null
How can I uninstall this program?
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2,182,546,528
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3,083
refactor readseeker
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2024-03-12T19:44:29
2024-03-16T19:08:57
2024-03-16T19:08:56
CONTRIBUTOR
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no functional change
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2,181,001,953
PR_kwDOJ0Z1Ps5pWCJ9
3,073
chore: fix typo
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2024-03-12T08:22:16
2024-03-12T18:09:23
2024-03-12T18:09:23
CONTRIBUTOR
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2,171,833,240
I_kwDOJ0Z1Ps6Bc4uY
2,955
Is there guidance to run Ollama as a background "Daemon" on MacOS pre-login?
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2024-03-06T15:49:17
2024-12-04T05:02:40
2024-03-06T23:08:47
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I would really like Ollama to run as a service on my Mac or at least set the appropriate listening variable before it starts. Today I have to `launchctl setenv OLLAMA_HOST 0.0.0.0:8080` and restart Ollama any time there is a reboot. And I must be logged in in-order for Ollama to be serving up the LLM. I've tried automatically setting the variables many ways: [ /etc/launchd.conf /etc/rc.common ~/.zprofile # Only works after terminal is opened plist file for /Library/LaunchAgents # Only works @ login plist file in /Library/LaunchDaemons # Can't get to work above plist files calling [launchctl, automator app, script file] ] My first milestone was to get the env `OLLAMA_HOST 0.0.0.0:8080` set before Ollama automatically launches then... get Ollama to launch and continuously run in the background pre-login. I've been spinning my wheels on this for a while now. It just seems like something that should be so easy. Before someone suggests it, I know I could easily run in linux. I have an extensive k8s deployment that runs most of my things, but I want to leverage the Macos NPU(Maybe I have a false sense of value here) for performance.
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8,294
Ollama should avoid calling hallucinated tools
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9
2025-01-03T14:13:36
2025-01-08T17:51:32
null
NONE
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### What is the issue? Sometimes the model seems to hallucinate and call a tool on the client that doesn't exist. In my opinion since Ollama has the list of tools being callable it should check that the tool being called is in this list before calling it. This is described also there: https://github.com/langchain4j/langchain4j/issues/1052 ### OS Linux, Docker ### GPU Other ### CPU Intel ### Ollama version 0.5.4
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CI missing archive
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2024-04-04T14:23:46
2024-04-04T14:24:27
2024-04-04T14:24:24
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llm(llama): pass rope factors
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2024-07-24T19:42:31
2024-07-24T20:06:00
2024-07-24T20:05:59
CONTRIBUTOR
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CI: give windows lint more time
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2024-11-12T19:12:05
2024-11-12T19:22:42
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It looks like 8 minutes isn't quite enough and we're seeing sporadic timeouts
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Ollama local discovery
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2024-03-30T19:38:48
2024-05-15T00:43:41
2024-05-15T00:43:41
NONE
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### What are you trying to do? It's a common use case for LLM tool builders to wish they can rely on a local model rather than relying on a hosted one to save costs. Currently, there is no official way to discover whether a local ollama server is running. ### How should we solve this? Provide a mechanism so it becomes possible to programmatically discover the ollama server endpoint. It could be a subcommand in the ollama CLI: ``` $ ollama info { "status": "running", "api_endpoint": "...." } ``` ### What is the impact of not solving this? This would open up the possibility of being able to fall back to a local model if ollama is available and running. It would open up wider adoption of ollama because cost and latency are both critical concerns for LLM applications. ### Anything else? _No response_
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2,193,623,988
I_kwDOJ0Z1Ps6CwAu0
3,230
GPU does not run with Ollama
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2024-03-18T23:03:34
2024-04-15T22:47:17
2024-04-15T22:47:17
NONE
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Hi everyone, I am running a Windows 10 computer with GTX950 and Intel(R) Core(TM) i5-3475S, 32 GB RAM, I downloaded the new Windows-version of Ollama and the llama2-uncensored and also the tinyllama LLM. Good: Everything works. Bad: Ollama only makes use of the CPU and ignores the GPU. As far as I can tell, Ollama should support my graphics card and the CPU supports AVX. From the server-log: _time=2024-03-18T23:06:15.263+01:00 level=INFO source=gpu.go:77 msg="Detecting GPU type" time=2024-03-18T23:06:15.263+01:00 level=INFO source=gpu.go:191 msg="Searching for GPU management library nvml.dll" time=2024-03-18T23:06:15.297+01:00 level=INFO source=gpu.go:237 msg="Discovered GPU libraries: [c:\\Windows\\System32\\nvml.dll C:\\Windows\\system32\\nvml.dll]" time=2024-03-18T23:06:15.377+01:00 level=INFO source=gpu.go:82 msg="Nvidia GPU detected" time=2024-03-18T23:06:15.377+01:00 level=INFO source=cpu_common.go:15 msg="CPU has AVX" time=2024-03-18T23:06:15.395+01:00 level=INFO source=gpu.go:119 msg="CUDA Compute Capability detected: 5.2" time=2024-03-18T23:06:15.396+01:00 level=INFO source=cpu_common.go:15 msg="CPU has AVX" time=2024-03-18T23:06:15.396+01:00 level=INFO source=gpu.go:119 msg="CUDA Compute Capability detected: 5.2" time=2024-03-18T23:06:15.396+01:00 level=INFO source=cpu_common.go:15 msg="CPU has AVX" time=2024-03-18T23:06:15.396+01:00 level=INFO source=assets.go:63 msg="Updating PATH to ... time=2024-03-18T23:06:15.472+01:00 level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server" ggml_init_cublas: GGML_CUDA_FORCE_MMQ: no ggml_init_cublas: CUDA_USE_TENSOR_CORES: yes ggml_init_cublas: found 1 CUDA devices: Device 0: NVIDIA GeForce GTX 950, compute capability 5.2, VMM: yes_ I would be glad if someone could tell me what to do to activate GPU-usage. Thanks Chris
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https://api.github.com/repos/ollama/ollama/issues/4895
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2,339,544,174
I_kwDOJ0Z1Ps6Lcpxu
4,895
Add "use_mmap" to environment variable
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2024-06-07T04:05:44
2024-10-26T06:30:54
null
NONE
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I recently discovered the potential benefits of the --no-mmap option, particularly for specific system configurations, such as PCs or laptops equipped with only 8GB of system RAM and a GPU with VRAM of 6GB or more, capable of loading entire models onto it. Loading models with mmap can render the use of 8B models nearly impossible, as it can cause RAM usage to spike to 99% and remain there, often leading to complete freezing of the PC and requiring a hard reset. Disabling mmap allows users to load 8B models while still having half of the RAM available for other tasks. The only drawback is a slightly longer initial model load time (around 5-10 seconds in my case), which I believe is a worthwhile trade-off. Quick benchmarks even suggest that --no-mmap might be slightly faster for generating tokens. Now, here's the rationale for adding an environment variable. Many frontends/UIs utilize Ollama, but a significant portion of them lack the toggle option to set the "nommap". By introducing an environment variable, I can globally set "nommap" and ensure that any frontends will load models with the nommap flag enabled.
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