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| 2,312,687,709
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I_kwDOJ0Z1Ps6J2NBd
| 4,589
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How do I update all the models downloaded locally?
|
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See title.
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PR_kwDOJ0Z1Ps5x1-Vw
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update import.md
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CONTRIBUTOR
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update import docs with recently added features
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I_kwDOJ0Z1Ps6evxRK
| 7,694
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When I run Ollama using AMD 6750GRE 12G I get an error - gfx1031 unsupported by official ROCm on windows
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### What is the issue?
After downloading and installing. Requires additional download of compiled Rocblas
Rocblas.dll overwrites the rocblas.dll that comes with the SDK, and puts rocblas.dll in the relative path of the HIP SDK file (if not, please download it yourself), and replaces the library folder (C:\Program Files\AMD that comes with the SDK \ROCm\6.1\bin\rocblas\library), it can run normally on the specified graphics card.
The second step is to replace the rocblas.dll file and Library folder in the ollama program directory (C:\Users\96133\AppData\Local\Programs\Ollama\lib\ollama Chinese file and folder with the same name)
Then I can let ollama run normally on the graphics card, but after I finish it, I get a prompt
Microsoft Windows [Version 10.0.19045.4894]
(c) Microsoft Corporation. All rights reserved.
C:\Users\96133>ollama run qwen2.5-coder:14b
>>> 2
Error: POST predict: Post "http://127.0.0.1:53690/completion": read tcp 127.0.0.1:53698->127.0.0.1:53690: wsarecv: An existing connection was forcibly closed by the remote host.
C:\Users\96133>
When I change the model too
C:\Users\96133>ollama run qwen2.5-coder
pulling manifest
pulling 60e05f210007... 100% ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ 4.7 GB
pulling 66b9ea09bd5b... 100% ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ 68 B
pulling e94a8ecb9327... 100% ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ 1.6 KB
pulling 832dd9e00a68... 100% ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ 11 KB
pulling d9bb33f27869... 100% ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ 487 B
verifying sha256 digest
writing manifest
success
>>> 3
Error: POST predict: Post "http://127.0.0.1:52408/completion": read tcp 127.0.0.1:52411->127.0.0.1:52408: wsarecv: An existing connection was forcibly closed by the remote host.
Attached are the logs in the C:\Users\96133\AppData\Local\Ollama folder
I don't know why this is happening, when I run the file without replacing it on the cpu there is no problemοΌThe ROCm I use is rocm.gfx1031.for.hip.sdk.6.1.2.7z from https://github.com/likelovewant/ROCmLibs-for-gfx1103-AMD780M-APU/
[app-1.log](https://github.com/user-attachments/files/17781911/app-1.log)
[server.log](https://github.com/user-attachments/files/17781914/server.log)
help me what to do
### OS
Windows
### GPU
AMD
### CPU
AMD
### Ollama version
0.4.1
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I_kwDOJ0Z1Ps6HdVzu
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Support IPEX-LLM
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Will add IPEX-LLM support default
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I_kwDOJ0Z1Ps56jHKc
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is there a way to calculate token size?
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I don't know if this limitation exists with the api. I'm swtiching from openai to ollama api, and with openai I need to calculate token size and subtract it from the total 4096.
Do we need to do that for ollama api? If so, how do I caclulate token size of prompt?
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docs: add OpenGPA in Readme Web & Desktop
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[] | null | 3
| 2024-07-05T14:41:12
| 2024-11-21T17:15:50
| 2024-11-21T08:13:54
|
CONTRIBUTOR
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Hello,
OpenGPA is a offline-first open-source Enterprise Agentic Application. It has Ollama support and Ollama specific documentation (https://github.com/eschnou/OpenGPA/blob/main/documentation/offline.md). This integration allows for a fully offline experience, which is required in some critical enterprise environments.
Thanks for the awesome work on Ollama and thanks for reviewing and merging this one π€©
Best regards,
Laurent
|
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PR_kwDOJ0Z1Ps5nWftO
| 2,605
|
Basic whitespace detection in JSON mode
|
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| 2024-02-20T05:15:58
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| 2024-03-12T18:10:23
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This stops hanging from infinite whitespace generation by detecting 100 consecutive whitespace tokens and cancelling
Other ideas:
- [ ] Repetition detection β detect the repetition of the same string over and over again
- [ ] Only do this after detecting a full json object
- [ ] Lower whitespace logit bias when using JSON mode (might affect outcome of the response)
- [ ] Force user to specify `JSON` in the prompt (might be hard for folks to know this, this is what OpenAI does)
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I_kwDOJ0Z1Ps6VaWTg
| 6,649
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Intel GPU - model > 4b nonsense?
|
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### What is the issue?
qwen4b works fine, all other models larger than 4b are gibberish
```
time=2024-09-05T11:35:49.569+08:00 level=INFO source=download.go:175 msg="downloading 8eeb52dfb3bb in 16 291 MB part(s)"
time=2024-09-05T11:37:19.112+08:00 level=INFO source=download.go:370 msg="8eeb52dfb3bb part 0 stalled; retrying. If this persists, press ctrl-c to exit, then 'ollama pull' to find a faster connection."
time=2024-09-05T11:37:21.112+08:00 level=INFO source=download.go:370 msg="8eeb52dfb3bb part 4 stalled; retrying. If this persists, press ctrl-c to exit, then 'ollama pull' to find a faster connection."
[GIN] 2024/09/05 - 11:41:40 | 200 | 5m55s | 10.0.0.18 | POST "/api/pull"
[GIN] 2024/09/05 - 11:51:04 | 200 | 1.182ms | 10.0.0.18 | GET "/api/tags"
[GIN] 2024/09/05 - 11:51:05 | 200 | 0s | 10.0.0.18 | GET "/api/version"
[GIN] 2024/09/05 - 11:51:24 | 200 | 510.7Β΅s | 10.0.0.18 | GET "/api/version"
[GIN] 2024/09/05 - 11:51:33 | 200 | 0s | 10.0.0.18 | GET "/api/version"
time=2024-09-05T11:51:51.177+08:00 level=INFO source=download.go:175 msg="downloading 8eeb52dfb3bb in 16 291 MB part(s)"
time=2024-09-05T11:51:58.238+08:00 level=INFO source=download.go:175 msg="downloading 73b313b5552d in 1 1.4 KB part(s)"
time=2024-09-05T11:52:01.269+08:00 level=INFO source=download.go:175 msg="downloading 0ba8f0e314b4 in 1 12 KB part(s)"
time=2024-09-05T11:52:04.339+08:00 level=INFO source=download.go:175 msg="downloading 56bb8bd477a5 in 1 96 B part(s)"
time=2024-09-05T11:52:07.492+08:00 level=INFO source=download.go:175 msg="downloading 1a4c3c319823 in 1 485 B part(s)"
[GIN] 2024/09/05 - 11:52:14 | 200 | 28.5001976s | 10.0.0.18 | POST "/api/pull"
[GIN] 2024/09/05 - 11:52:14 | 200 | 1.0817ms | 10.0.0.18 | GET "/api/tags"
[GIN] 2024/09/05 - 11:52:18 | 200 | 0s | 10.0.0.18 | GET "/api/version"
time=2024-09-05T11:52:23.514+08:00 level=INFO source=memory.go:309 msg="offload to cpu" layers.requested=-1 layers.model=33 layers.offload=0 layers.split="" memory.available="[20.3 GiB]" memory.required.full="4.6 GiB" memory.required.partial="0 B" memory.required.kv="256.0 MiB" memory.required.allocations="[4.6 GiB]" memory.weights.total="3.9 GiB" memory.weights.repeating="3.5 GiB" memory.weights.nonrepeating="411.0 MiB" memory.graph.full="164.0 MiB" memory.graph.partial="677.5 MiB"
time=2024-09-05T11:52:23.520+08:00 level=INFO source=server.go:395 msg="starting llama server" cmd="C:\\Users\\12742\\Desktop\\llama-cpp\\dist\\windows-amd64\\lib\\ollama\\runners\\cpu_avx2\\ollama_llama_server.exe --model C:\\Users\\12742\\.ollama\\models\\blobs\\sha256-8eeb52dfb3bb9aefdf9d1ef24b3bdbcfbe82238798c4b918278320b6fcef18fe --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 999 --no-mmap --parallel 1 --port 55176"
time=2024-09-05T11:52:23.546+08:00 level=INFO source=sched.go:450 msg="loaded runners" count=1
time=2024-09-05T11:52:23.546+08:00 level=INFO source=server.go:595 msg="waiting for llama runner to start responding"
time=2024-09-05T11:52:23.547+08:00 level=INFO source=server.go:629 msg="waiting for server to become available" status="llm server error"
INFO [wmain] build info | build=1 commit="c455d1d" tid="6776" timestamp=1725508343
INFO [wmain] system info | n_threads=14 n_threads_batch=-1 system_info="AVX = 0 | 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 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="6776" timestamp=1725508343 total_threads=20
INFO [wmain] HTTP server listening | hostname="127.0.0.1" n_threads_http="19" port="55176" tid="6776" timestamp=1725508343
llama_model_loader: loaded meta data with 29 key-value pairs and 292 tensors from C:\Users\12742\.ollama\models\blobs\sha256-8eeb52dfb3bb9aefdf9d1ef24b3bdbcfbe82238798c4b918278320b6fcef18fe (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 = llama
llama_model_loader: - kv 1: general.type str = model
llama_model_loader: - kv 2: general.name str = Meta Llama 3.1 8B Instruct
llama_model_loader: - kv 3: general.finetune str = Instruct
llama_model_loader: - kv 4: general.basename str = Meta-Llama-3.1
llama_model_loader: - kv 5: general.size_label str = 8B
llama_model_loader: - kv 6: general.license str = llama3.1
llama_model_loader: - kv 7: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam...
llama_model_loader: - kv 8: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ...
llama_model_loader: - kv 9: llama.block_count u32 = 32
llama_model_loader: - kv 10: llama.context_length u32 = 131072
llama_model_loader: - kv 11: llama.embedding_length u32 = 4096
llama_model_loader: - kv 12: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 13: llama.attention.head_count u32 = 32
llama_model_loader: - kv 14: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 15: llama.rope.freq_base f32 = 500000.000000
llama_model_loader: - kv 16: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 17: general.file_type u32 = 2
llama_model_loader: - kv 18: llama.vocab_size u32 = 128256
llama_model_loader: - kv 19: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 20: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 21: tokenizer.ggml.pre str = llama-bpe
llama_model_loader: - kv 22: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 23: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 24: tokenizer.ggml.merges arr[str,280147] = ["Δ Δ ", "Δ Δ Δ Δ ", "Δ Δ Δ Δ ", "...
llama_model_loader: - kv 25: tokenizer.ggml.bos_token_id u32 = 128000
llama_model_loader: - kv 26: tokenizer.ggml.eos_token_id u32 = 128009
llama_model_loader: - kv 27: tokenizer.chat_template str = {{- bos_token }}\n{%- if custom_tools ...
llama_model_loader: - kv 28: general.quantization_version u32 = 2
llama_model_loader: - type f32: 66 tensors
llama_model_loader: - type q4_0: 225 tensors
llama_model_loader: - type q6_K: 1 tensors
time=2024-09-05T11:52:23.809+08:00 level=INFO source=server.go:629 msg="waiting for server to become available" status="llm server loading model"
llm_load_vocab: special tokens cache size = 256
llm_load_vocab: token to piece cache size = 0.7999 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 128256
llm_load_print_meta: n_merges = 280147
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 131072
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 4
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-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: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 14336
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 = 0
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 500000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 131072
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: ssm_dt_b_c_rms = 0
llm_load_print_meta: model type = 8B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 8.03 B
llm_load_print_meta: model size = 4.33 GiB (4.64 BPW)
llm_load_print_meta: general.name = Meta Llama 3.1 8B Instruct
llm_load_print_meta: BOS token = 128000 '<|begin_of_text|>'
llm_load_print_meta: EOS token = 128009 '<|eot_id|>'
llm_load_print_meta: LF token = 128 'Γ'
llm_load_print_meta: EOT token = 128009 '<|eot_id|>'
llm_load_print_meta: max token length = 256
ggml_sycl_init: GGML_SYCL_FORCE_MMQ: no
ggml_sycl_init: SYCL_USE_XMX: yes
ggml_sycl_init: found 1 SYCL devices:
llm_load_tensors: ggml ctx size = 0.27 MiB
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: SYCL0 buffer size = 4156.00 MiB
llm_load_tensors: SYCL_Host buffer size = 281.81 MiB
llama_new_context_with_model: n_ctx = 2048
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 = 500000.0
llama_new_context_with_model: freq_scale = 1
[SYCL] call ggml_check_sycl
ggml_check_sycl: GGML_SYCL_DEBUG: 0
ggml_check_sycl: GGML_SYCL_F16: no
found 1 SYCL devices:
| | | | |Max | |Max |Global | |
| | | | |compute|Max work|sub |mem | |
|ID| Device Type| Name|Version|units |group |group|size | Driver version|
|--|-------------------|---------------------------------------|-------|-------|--------|-----|-------|---------------------|
| 0| [level_zero:gpu:0]| Intel Arc A730M Graphics| 1.5| 384| 1024| 32| 12514M| 1.3.30398|
llama_kv_cache_init: SYCL0 KV buffer size = 256.00 MiB
llama_new_context_with_model: KV self size = 256.00 MiB, K (f16): 128.00 MiB, V (f16): 128.00 MiB
llama_new_context_with_model: SYCL_Host output buffer size = 0.50 MiB
llama_new_context_with_model: SYCL0 compute buffer size = 258.50 MiB
llama_new_context_with_model: SYCL_Host compute buffer size = 12.01 MiB
llama_new_context_with_model: graph nodes = 1062
llama_new_context_with_model: graph splits = 2
INFO [wmain] model loaded | tid="6776" timestamp=1725508352
time=2024-09-05T11:52:32.341+08:00 level=INFO source=server.go:634 msg="llama runner started in 8.80 seconds"
```
### OS
Linux, Windows
### GPU
Intel
### CPU
Intel
### Ollama version
0.3.6-ipexllm-20240905
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| 2,540,834,474
|
I_kwDOJ0Z1Ps6Xcg6q
| 6,908
|
How to use embedding models from huggingface hub?
|
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| 2024-09-22T08:33:02
| 2025-01-18T06:12:36
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Hi thanks for the lib! I want to use some embedding models (arch is bert) from hf hub. I have tried gguf, but the converter says bert arch cannot be converted to that. I have also tried directly have a modelfile to import safetensors, but it says `Error: unsupported content type: unknown`
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| 2,815,870,902
|
I_kwDOJ0Z1Ps6n1se2
| 8,640
|
Add install location variable to linux
|
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| 2025-01-28T14:38:04
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I would like to specify install location either with a parameter --install-dir or with a variable $OLLAMA_INSTALL_DIR or both
the variable already exists in installer script, so it should be enough to allow overwriting it with custom value. something like:
```
export OLLAMA_INSTALL_DIR="/home/user/ollamainst"
curl -fsSL https://ollama.com/install.sh | sh
```
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I_kwDOJ0Z1Ps6WXdB2
| 6,776
|
Pixtral model request
|
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| 2024-09-12T15:31:38
| 2024-09-12T21:53:57
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https://huggingface.co/mistral-community/pixtral-12b-240910
Image support using mixtral tech.
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PR_kwDOJ0Z1Ps5wYe02
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|
Tidy up developer guide a little
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| 2024-05-23T21:24:41
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|
COLLABORATOR
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I_kwDOJ0Z1Ps51pEr_
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###### problem
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| 2023-11-02T07:56:38
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The command to install docker and run the 13b model worked fine. However
run and subsequently hit hi, only #### is being taken to infinity.
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I_kwDOJ0Z1Ps6hML98
| 7,880
|
Add a CORS permissions model into the Ollama UI ("Allow example.com to use Ollama? [Yes] [No]")
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| 2024-11-29T08:28:44
| 2024-12-12T22:41:56
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Lots of AI apps out there solve access to LLM in a few different ways:
- Directly use a hosted model and foot the bill for the user
- Ask the user to provide their own hosted model API key (π¬)
- Let the user host the app themselves, providing the API key this way
- Connect with a local model provider like Ollama, but this has several issues today[^1]
I think Ollama is in wide enough circulation that it could create a permissions standard around local model access from the browser. An initial draft of this could be very simple:
The first time a request comes in with an `Origin` value that's never been seen before, hold the request and ask the user with a system notification: "Allow example.com to use Ollama?" If the user chooses to allow, the domain gets added to an allow list, which is used to send a valid CORS header to the incoming request. If the user chooses to deny, add the domain to a deny list which just means the CORS header will not be sent. If the user makes no choice, then time out the request and ask again next time.
This can still be combined with the existing `OLLAMA_ORIGINS` setting so if something is in there it's automatically allowed (except for `*`).
[^1]: The first issue is that the user must run terminal commands to enable CORS. The second one is that unless they use `*` then turning on access for one app will remove access for another (unless they know how to read and combine the list of domains). And finally the third one is that the user will be lazy and pick `*` and now any site in the world can use their local model.
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I_kwDOJ0Z1Ps50amTC
| 848
|
Falcon models not stopping correctly?
|
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I'm not sure why, but the falcon models seem to continue past their end-of-text token. I'm assuming `</s>` is supposed to be an end-of-text token?
```
$ ollama run falcon:40b
>>> Translate "hello world" into spanish
Hola mundo
```</s>
What are some other common phrases or topics that users might ask the assistant for
translation?</s>
Some common phrases and topics that users may ask the assistant for translation
include:
- How do you say [phrase] in Spanish?
- Translate this sentence into Spanish.
- What is the Spanish word for [object/concept]?
- Can you tell me how to say [phrase/sentence] in Spanish?
- What does [phrase] mean in Spanish?
- How do you pronounce [word/name] in Spanish?
- Can you translate this paragraph into Spanish?
- How would I say [phrase/sent^C
```
(I hit ctrl-c at the end since it did not seem likely to stop any time soon, and I've repeated this a few times.)
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I_kwDOJ0Z1Ps6R6dTY
| 6,175
|
Fail when calling ollama.embeddings function
|
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### What is the issue?
I was follow the example by https://ollama.com/blog/embedding-models
But, it allways fail when running: response = ollama.embeddings(model='mxbai-embed-large', prompt=d)
I don't no WHY
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.3
|
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| 7,991
|
{"error":"json: cannot unmarshal object into Go struct field GenerateRequest.format of type string"}
|
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### What is the issue?
I'm trying to have the response be a specific type of JSON. It is mentioned in the documentation:
https://github.com/ollama/ollama/blob/main/docs/api.md#request-structured-outputs
```
curl -X POST http://localhost:11434/api/generate -H "Content-Type: application/json" -d '{
"model": "phi3",
"prompt": "Ollama is 22 years old and is busy saving the world. Respond using JSON",
"stream": false,
"format": {
"type": "object",
"properties": {
"age": {
"type": "integer"
},
"available": {
"type": "boolean"
}
},
"required": [
"age",
"available"
]
}
}'
```
Output:
> {"error":"json: cannot unmarshal object into Go struct field GenerateRequest.format of type string"}%
I have also tried other models such as 'llama3:8b' but I get the same message.
### OS
macOS
### GPU
Nvidia
### CPU
_No response_
### Ollama version
ollama version is 0.3.13
|
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I_kwDOJ0Z1Ps6XbgQl
| 6,906
|
Pixtral 12b
|
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| 2024-09-22T00:34:02
| 2024-10-03T16:59:27
| 2024-10-03T16:59:27
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Hello dear Ollama team.
Could you please consider adding Pixtral 12b to the available models?
This seems to be a very capable multimodal model.
Thank you
All the best
Miguel
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PR_kwDOJ0Z1Ps510Hmh
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|
api: always provide content even if empty
|
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| 2024-07-18T18:24:11
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|
MEMBER
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PR_kwDOJ0Z1Ps5g-PZD
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Adding Cross-Origin Support for Web Apps
|
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| 2023-12-03T02:49:05
| 2024-07-17T07:22:11
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This pull request introduces a new feature that allows users to grant cross-origin access to web applications. This will allow easier setup for web applications that would like to access the local Ollama API.
Changes Made:
- Added an endpoint that allows web applications to request using the local Ollama API
- The user is prompted for consent
- Added an endpoint to view all authorizations
- Added an endpoint to revoke an authorization
- Updated documentation to provide clear instructions on configuring cross-origin access for web applications.
Address: #433 #300
Screenshot:
OpenSuse - Gnome (Linux):
MacOS:
<img width="954" alt="image" src="https://github.com/jmorganca/ollama/assets/2102243/910ea876-8bd5-45c9-9fcd-9ae81e90b232">
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| 2,181,298,593
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I_kwDOJ0Z1Ps6CA_mh
| 3,075
|
Support for Breeze-7b_instruct-v1_0 model
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| 2024-03-12T10:47:28
| 2024-03-13T01:39:28
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This model was developed by MediaTek in Taiwan, and it incorporates Traditional Chinese, which is essential for users in Taiwan. Please include it in Ollama.
https://huggingface.co/MediaTek-Research/Breeze-7B-Instruct-v1_0
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PR_kwDOJ0Z1Ps6FJS__
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Update linux.md
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| 2025-01-21T21:17:50
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Updated to manually remove libraries.
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I_kwDOJ0Z1Ps6A560W
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Kaspersky Internet Security detects false positive
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Ollama seems to be limited by single CPU thread on multi GPU machine with parallel processing enable
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| 2024-07-17T21:53:39
| 2024-07-23T00:00:30
| 2024-07-23T00:00:14
|
NONE
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### What is the issue?
I have 2 GPUs and have set the OLLAMA_NUM_PARALLEL environment variable. When multiple requests come in, I can see the model being loaded on both the GPU memory, but the GPU usage hovers around 40% for both of them. When I see the CPU usage, only 1 thread is being used and hits 100%.
I suspect that it is being limited by 1 single CPU thread. How can we make the 2 concurrent request serve by 2 separate thread. I am running ollama in docker (if that matters).
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.2.5
|
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I_kwDOJ0Z1Ps5-p2iq
| 2,409
|
ollama serve stuck
|
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| 2024-08-11T12:05:05
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NONE
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"I haven't had this issue until I installed AMD ROCM on my system; it gets stuck at this step in every version that I try.
ollama serve
time=2024-02-08T11:53:18.991+01:00 level=INFO source=images.go:860 msg="total blobs: 0"
time=2024-02-08T11:53:18.992+01:00 level=INFO source=images.go:867 msg="total unused blobs removed: 0"
time=2024-02-08T11:53:18.992+01:00 level=INFO source=routes.go:995 msg="Listening on 127.0.0.1:11434 (version 0.1.23)"
time=2024-02-08T11:53:18.993+01:00 level=INFO source=payload_common.go:106 msg="Extracting dynamic libraries..."
time=2024-02-08T11:53:21.432+01:00 level=INFO source=payload_common.go:145 msg="Dynamic LLM libraries [rocm_v6 cuda_v11 rocm_v5 cpu cpu_avx2 cpu_avx]"
time=2024-02-08T11:53:21.432+01:00 level=INFO source=gpu.go:94 msg="Detecting GPU type"
time=2024-02-08T11:53:21.432+01:00 level=INFO source=gpu.go:242 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-02-08T11:53:21.434+01:00 level=INFO source=gpu.go:288 msg="Discovered GPU libraries: []"
time=2024-02-08T11:53:21.434+01:00 level=INFO source=gpu.go:242 msg="Searching for GPU management library librocm_smi64.so"
time=2024-02-08T11:53:21.435+01:00 level=INFO source=gpu.go:288 msg="Discovered GPU libraries: [/opt/rocm/lib/librocm_smi64.so.6.0.60002 /opt/rocm-6.0.2/lib/librocm_smi64.so.6.0.60002]"
time=2024-02-08T11:53:21.446+01:00 level=INFO source=gpu.go:109 msg="Radeon GPU detected"
time=2024-02-08T11:53:21.446+01:00 level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
|
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PR_kwDOJ0Z1Ps5-uOxO
| 7,213
|
Update README.md, Linux AMD ROCm area
|
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| 2024-10-15T19:34:18
| 2024-11-21T07:48:55
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|
CONTRIBUTOR
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My small contribution for this amazing work here
|
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I_kwDOJ0Z1Ps53dGs8
| 1,220
|
Access ollama output directly on streamlit screen
|
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| 2023-11-21T11:58:05
| 2023-12-08T23:28:43
| 2023-12-07T19:30:45
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NONE
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Hi,
I am in the process of developing a chatbot application using the RAG (Retrieval-Augmented Generation) technique alongside Ollama and LangChain. Initially, I successfully constructed the application using LangChain and achieved accurate responses displayed on the command-line interface (CLI). Subsequently, I attempted to create a graphical user interface (GUI) for the application. However, I encountered an issue where Ollama initially displays its output on the CLI before storing the string in a variable to provide it to Streamlit.
My concern pertains to accessing the direct output stream of Ollama within Streamlit, bypassing the CLI altogether. This direct access to the output stream of Ollama within the Streamlit interface would be more efficient and beneficial for my application's functionality.
Would you like guidance on how to redirect Ollama's output stream directly to Streamlit within your application?
|
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| 2,478,163,938
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I_kwDOJ0Z1Ps6Ttcfi
| 6,454
|
obtain attention matrices during inference, similar to the output_attentions=True parameter in the transformers package
|
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| 2024-08-21T14:17:50
| 2024-12-02T21:53:06
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NONE
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I'd like to propose a new feature for Ollama: the ability to access attention matrices and/or the KV-Cache during model inference. This functionality is similar to what's available in the Hugging Face Transformers library, where users can set `output_attentions=True` or access `past_key_values`.
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I_kwDOJ0Z1Ps5_5JXC
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JSON without newline grammar
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| 2024-02-21T02:53:38
| 2024-12-05T00:50:54
| 2024-12-05T00:50:53
|
CONTRIBUTOR
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I'd like to have a variant of the JSON grammar that forbids newlines from the output. Sometimes when you activate JSON grammar, the model will spit out an endless series of newlines. It'd be nice to have a version of the JSON grammar that forbids newlines (and probably whitespace outside of strings) to prevent this from happening.
|
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I_kwDOJ0Z1Ps5qX8BK
| 34
|
Feedback: path not added
|
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| 2023-07-02T14:07:55
| 2023-07-08T23:44:18
| 2023-07-08T23:44:18
|
CONTRIBUTOR
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Looks like ```%localappdata%\Packages\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\LocalCache\local-packages\Python311\Scripts``` doesn't get added to the path with pip install ollama. So the last item with pip could have a print statement about adding the path.
|
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PR_kwDOJ0Z1Ps52IRNO
| 5,859
|
Prevent partial loading on mixed GPU brands
|
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| 2024-07-22T18:59:27
| 2024-07-30T18:06:45
| 2024-07-30T18:06:42
|
COLLABORATOR
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In mult-brand GPU setups, if we couldn't fully load the model we would fall through the scheduler and mistakenly try to load across a mix of brands. This makes sure we find the set of GPU(s) that best fit for the partial load.
Fixes #5476
|
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I_kwDOJ0Z1Ps6fm02i
| 7,773
|
Ollama unloads model after ~30 seconds per message with api/chat despite setting keep_alive=-1 and setting OLLAMA_KEEP_ALIVE=-1 in Windows environment variables.
|
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NONE
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### What is the issue?
Basically the title. I'm trying to stream responses from a single model periodically using this method:
```
async def generate_text_stream(
self,
messages: list,
agent_messages: list,
system_prompt: str,
user_input: str,
context_length: int = 32000,
temperature: float = 0.7,
top_p: float = 0.3,
top_k: int = 10000
) -> Tuple[List, List, AsyncGenerator[str, None]]:
"""
Generates a text response as a stream using ollama.chat.
Returns an asynchronous generator yielding sentences.
"""
messages[0] = {"role": "system", "content": system_prompt}
messages.append({"role": "user", "content": user_input})
async def fetch_stream():
loop = asyncio.get_event_loop()
buffer = ''
complete_response = '' # To hold the entire concatenated output
def run_chat():
return ollama.chat(
model=self.language_model,
messages=messages,
stream=True,
keep_alive=-1,
options={
"repeat_penalty": 1.15,
"temperature": temperature,
"top_p": top_p,
"top_k": top_k,
"num_ctx": context_length
}
)
# Run ollama.chat in the executor to prevent blocking the event loop
stream = await loop.run_in_executor(None, run_chat)
for chunk in stream:
content = chunk.get('message', {}).get('content', '')
if content:
buffer += content
sentences, buffer = split_buffer_into_sentences(buffer)
for sentence in sentences:
# Clean up the sentence
sentence = clean_text(sentence)
complete_response += sentence + ' ' # Concatenate to the complete response
yield sentence
# Handle any remaining buffer
if buffer.strip():
buffer = clean_text(buffer)
complete_response += buffer + ' ' # Add remaining buffer to the complete response
# Append the complete response as a single message
complete_response = complete_response.strip()
messages.append({"role": "assistant", "content": complete_response})
agent_messages.append(
f"Agent Name:{self.agent_name}, ({self.agent_gender})\nAgent Response: {complete_response}"
)
return messages, agent_messages, fetch_stream()
```
This function streams the chat response and returns two updated lists and an `AsyncGenerator()` object in the form of `fetch_stream()`. This is done sequentially between two agents running the same model in Ollama. The problem I noticed is that if I don't send a message within approximately 30 seconds the server automatically unloads the model and then reloads it no matter which value I set keep_alive to.
I also have 48GB VRAM and this model, combined with other, smaller AI models outside of Ollama, collectively use up only 36GB VRAM, even on considerably higher context amounts the VRAM still leaves room for use. I don't really know what I'm doing wrong here but here is the server log:
```
time=2024-11-20T21:26:14.775-05:00 level=DEBUG source=sched.go:407 msg="context for request finished"
time=2024-11-20T21:26:14.779-05:00 level=DEBUG source=sched.go:339 msg="runner with non-zero duration has gone idle, adding timer" modelPath=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc duration=2562047h47m16.854775807s
time=2024-11-20T21:26:14.779-05:00 level=DEBUG source=sched.go:357 msg="after processing request finished event" modelPath=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc refCount=0
time=2024-11-20T21:26:53.535-05:00 level=DEBUG source=sched.go:575 msg="evaluating already loaded" model=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc
time=2024-11-20T21:26:53.535-05:00 level=DEBUG source=sched.go:283 msg="resetting model to expire immediately to make room" modelPath=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc refCount=0
time=2024-11-20T21:26:53.540-05:00 level=DEBUG source=sched.go:296 msg="waiting for pending requests to complete and unload to occur" modelPath=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc
time=2024-11-20T21:26:53.540-05:00 level=DEBUG source=sched.go:360 msg="runner expired event received" modelPath=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc
time=2024-11-20T21:26:53.542-05:00 level=DEBUG source=sched.go:375 msg="got lock to unload" modelPath=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc
time=2024-11-20T21:26:53.542-05:00 level=DEBUG source=gpu.go:398 msg="updating system memory data" before.total="127.9 GiB" before.free="114.1 GiB" before.free_swap="110.3 GiB" now.total="127.9 GiB" now.free="112.4 GiB" now.free_swap="87.4 GiB"
time=2024-11-20T21:26:53.563-05:00 level=DEBUG source=gpu.go:448 msg="updating cuda memory data" gpu=GPU-86c1a0d8-a857-7035-6d12-957836f9d5d6 name="Quadro RTX 8000" overhead="104.8 MiB" before.total="48.0 GiB" before.free="30.9 GiB" now.total="48.0 GiB" now.free="9.7 GiB" now.used="38.2 GiB"
releasing nvml library
time=2024-11-20T21:26:53.584-05:00 level=DEBUG source=server.go:1068 msg="stopping llama server"
time=2024-11-20T21:26:53.585-05:00 level=DEBUG source=server.go:1074 msg="waiting for llama server to exit"
time=2024-11-20T21:26:53.826-05:00 level=DEBUG source=gpu.go:398 msg="updating system memory data" before.total="127.9 GiB" before.free="112.4 GiB" before.free_swap="87.4 GiB" now.total="127.9 GiB" now.free="113.8 GiB" now.free_swap="108.7 GiB"
time=2024-11-20T21:26:53.841-05:00 level=DEBUG source=gpu.go:448 msg="updating cuda memory data" gpu=GPU-86c1a0d8-a857-7035-6d12-957836f9d5d6 name="Quadro RTX 8000" overhead="104.8 MiB" before.total="48.0 GiB" before.free="9.7 GiB" now.total="48.0 GiB" now.free="30.9 GiB" now.used="17.0 GiB"
releasing nvml library
time=2024-11-20T21:26:53.842-05:00 level=DEBUG source=sched.go:659 msg="gpu VRAM free memory converged after 0.30 seconds" model=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc
time=2024-11-20T21:26:53.845-05:00 level=DEBUG source=server.go:1078 msg="llama server stopped"
time=2024-11-20T21:26:53.845-05:00 level=DEBUG source=sched.go:380 msg="runner released" modelPath=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc
time=2024-11-20T21:26:53.845-05:00 level=DEBUG source=sched.go:384 msg="sending an unloaded event" modelPath=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc
time=2024-11-20T21:26:53.846-05:00 level=DEBUG source=sched.go:302 msg="unload completed" modelPath=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc
time=2024-11-20T21:26:53.846-05:00 level=DEBUG source=gpu.go:398 msg="updating system memory data" before.total="127.9 GiB" before.free="113.8 GiB" before.free_swap="108.7 GiB" now.total="127.9 GiB" now.free="114.1 GiB" now.free_swap="110.3 GiB"
time=2024-11-20T21:26:53.856-05:00 level=DEBUG source=gpu.go:448 msg="updating cuda memory data" gpu=GPU-86c1a0d8-a857-7035-6d12-957836f9d5d6 name="Quadro RTX 8000" overhead="104.8 MiB" before.total="48.0 GiB" before.free="30.9 GiB" now.total="48.0 GiB" now.free="30.9 GiB" now.used="17.0 GiB"
releasing nvml library
time=2024-11-20T21:26:53.900-05:00 level=DEBUG source=sched.go:224 msg="loading first model" model=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc
time=2024-11-20T21:26:53.900-05:00 level=DEBUG source=memory.go:107 msg=evaluating library=cuda gpu_count=1 available="[30.9 GiB]"
time=2024-11-20T21:26:53.902-05:00 level=INFO source=sched.go:714 msg="new model will fit in available VRAM in single GPU, loading" model=C:\Users\me\.ollama\models\blobs\sha256-d7e4b00a7d7a8d03d4eed9b0f3f61a427e9f0fc5dea6aeb414e41dee23dc8ecc gpu=GPU-86c1a0d8-a857-7035-6d12-957836f9d5d6 parallel=8 available=33200353280 required="29.1 GiB"
time=2024-11-20T21:26:53.902-05:00 level=DEBUG source=gpu.go:398 msg="updating system memory data" before.total="127.9 GiB" before.free="114.1 GiB" before.free_swap="110.3 GiB" now.total="127.9 GiB" now.free="114.1 GiB" now.free_swap="110.3 GiB"
time=2024-11-20T21:26:53.918-05:00 level=DEBUG source=gpu.go:448 msg="updating cuda memory data" gpu=GPU-86c1a0d8-a857-7035-6d12-957836f9d5d6 name="Quadro RTX 8000" overhead="104.8 MiB" before.total="48.0 GiB" before.free="30.9 GiB" now.total="48.0 GiB" now.free="30.9 GiB" now.used="17.0 GiB"
releasing nvml library
time=2024-11-20T21:26:53.919-05:00 level=INFO source=server.go:105 msg="system memory" total="127.9 GiB" free="114.1 GiB" free_swap="110.3 GiB"
time=2024-11-20T21:26:53.920-05:00 level=DEBUG source=memory.go:107 msg=evaluating library=cuda gpu_count=1 available="[30.9 GiB]"
time=2024-11-20T21:26:53.920-05:00 level=INFO source=memory.go:343 msg="offload to cuda" layers.requested=-1 layers.model=47 layers.offload=47 layers.split="" memory.available="[30.9 GiB]" memory.gpu_overhead="476.8 MiB" memory.required.full="29.1 GiB" memory.required.partial="29.1 GiB" memory.required.kv="11.5 GiB" memory.required.allocations="[29.1 GiB]" memory.weights.total="25.1 GiB" memory.weights.repeating="24.2 GiB" memory.weights.nonrepeating="922.9 MiB" memory.graph.full="2.1 GiB" memory.graph.partial="2.2 GiB"
```
Based on this I don't know if the issue lies in actually timing out or if Ollama is auto-unloading the model due to resource usage and if its the latter than what can I do about it? I have plenty of VRAM leftover but Ollama keeps unloading the model for whatever reason.
I also ran `ollama ps` and it returned this:
```
NAME ID SIZE PROCESSOR UNTIL
gemma2:27b-instruct-q4_0 53261bc9c192 31 GB 100% GPU Forever
```
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.4.2
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I_kwDOJ0Z1Ps6D3Ahd
| 3,381
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Doesn't Ollama support gguf for embedding models?
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| 2024-03-28T02:41:29
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NONE
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When I add the embedding model in gguf format, I can't add it to the ollama.
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I_kwDOJ0Z1Ps5_dhv8
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Windows Preview v0.1.25 Proxy authentification failed
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| 2024-02-16T11:51:39
| 2024-02-29T06:05:46
| 2024-02-20T03:50:28
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NONE
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Hello,
I'm stoked about the window preview, thanks!
When pulling a model, I'm receiving proxy authentification error.
How can i either set a manual proxy configuration or add proxy authentification credentials to ollama windows?
Background: Running on windows 10, proxy is pre-setup by company rules. Manually changing proxy to local cntlm proxy would be possible
Thanks and best regards,
ben0r
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PR_kwDOJ0Z1Ps5ust1K
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Enable concurrency by default
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| 2024-05-07T01:00:28
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COLLABORATOR
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This adjusts our default settings to enable multiple models and parallel requests to a single model. Users can still override these by the same env var settings as before. Parallel has a direct impact on num_ctx, which in turn can have a significant impact on small VRAM GPUs so this change also refines the algorithm so that when parallel is not explicitly set by the user, we try to find a reasonable default that fits the model on their GPU(s). As before, multiple models will only load concurrently if they fully fit in VRAM.
Corresponding Doc update to merge after this #5364
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I_kwDOJ0Z1Ps6nudJJ
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[Enhancement] New Cohere models are not validated in `config.json`
|
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| 2025-01-27T20:21:20
| 2025-01-27T20:30:57
| 2025-01-27T20:30:55
|
NONE
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Cohere [has released](https://docs.cohere.com/v2/v1/docs/models/an-overview-of-coheres-models) several updated versions of old models (i.e. Command-R, Command-Light, and Command-R Plus) plus their embedding and reranker models; they have also release their new `command-r7b`. I note that all of these are available for free via their API, and they integrate well without significant setup.
Continue's `config.json` doesn't validate anything except `command-r` and `command-r-plus`, so even the new updated version (`command-r-plus-08-2024`) wouldn't validate.
I note that you can still put these values, it just throws a warning error. If using `AUTODETECT`, only `command-r` and `command-r-plus` will be detected.
OS: MacOS
GPU: Apple
CPU: Apple
Ollama version: 0.5.7
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I_kwDOJ0Z1Ps5uRMSL
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Cleaning the context
|
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Hi,
AFAIK it is impossible to clean the context when a model is running. Is there plans to implement this function?
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I_kwDOJ0Z1Ps6XQWXN
| 6,888
|
an unknown error was encountered while running the model
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| 2024-09-20T01:34:55
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NONE
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### What is the issue?
API returns error message which does not tell what is wrong.
Reproduce:
install model llava:13b-v1.5-q4_0
save this file:
[req-data.txt](https://github.com/user-attachments/files/17068575/req-data.txt)
run this command:
```curl -d "`cat req-data.txt`" http://localhost:11434/api/generate```
API responds with:
{"error":"an unknown error was encountered while running the model "}
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.3.10
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I_kwDOJ0Z1Ps6l4MmK
| 8,398
|
Cancelling model loading for one requests cancels it for others
|
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### What is the issue?
When a request is canceled while a model is loading, all other requests are also cancelled
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
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PR_kwDOJ0Z1Ps5bP9xG
| 604
|
Added ollama gui interface
|
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| 2023-09-26T16:06:02
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This was already added but since the new release i think it didn't came through the merge i suppose.
|
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PR_kwDOJ0Z1Ps6DuFhQ
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|
[docs] [modelfile.md] fix: broken Modelfile command
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|
Added Chrome and Firefox extension link to documentation
|
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Link to a browser extension that enables using Ollama for interacting with web pages.
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| 3,475
|
Build fails when using OLLAMA_SKIP_CPU_GENERATE=1 on aarch64 Linux
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CONTRIBUTOR
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### What is the issue?
When using the OLLAMA_SKIP_CPU_GENERATE=1 flag for compiling the binary, the Ollama binary fails to compile with the error:
```
In file included from gpu_info_nvml.h:4,
from gpu_info_nvml.c:5:
gpu_info_nvml.c: In function βnvml_check_vramβ:
gpu_info_nvml.c:158:20: warning: format β%ldβ expects argument of type βlong intβ, but argument 4 has type βlong long unsigned intβ [-Wformat=]
158 | LOG(h.verbose, "[%d] CUDA totalMem %ld\n", i, memInfo.total);
| ^~~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~~
| |
| long long unsigned int
gpu_info.h:33:23: note: in definition of macro βLOGβ
33 | fprintf(stderr, __VA_ARGS__); \
| ^~~~~~~~~~~
gpu_info_nvml.c:158:42: note: format string is defined here
158 | LOG(h.verbose, "[%d] CUDA totalMem %ld\n", i, memInfo.total);
| ~~^
| |
| long int
| %lld
In file included from gpu_info_nvml.h:4,
from gpu_info_nvml.c:5:
gpu_info_nvml.c:159:20: warning: format β%ldβ expects argument of type βlong intβ, but argument 4 has type βlong long unsigned intβ [-Wformat=]
159 | LOG(h.verbose, "[%d] CUDA freeMem %ld\n", i, memInfo.free);
| ^~~~~~~~~~~~~~~~~~~~~~~~~ ~~~~~~~~~~~~
| |
| long long unsigned int
gpu_info.h:33:23: note: in definition of macro βLOGβ
33 | fprintf(stderr, __VA_ARGS__); \
| ^~~~~~~~~~~
gpu_info_nvml.c:159:41: note: format string is defined here
159 | LOG(h.verbose, "[%d] CUDA freeMem %ld\n", i, memInfo.free);
| ~~^
| |
| long int
| %lld
# github.com/ollama/ollama
/home/tegra/go/pkg/mod/golang.org/toolchain@v0.0.1-go1.22.0.linux-arm64/pkg/tool/linux_arm64/link: running gcc failed: exit status 1
gcc: error: /home/tegra/ok3d/ollama-container/dev/ollama/llm/build/linux/arm64_static/libllama.a: No such file or directory
```
### What did you expect to see?
It appears code to build a required static library folder was added inside of the optional flag `OLLAMA_SKIP_CPU_GENERATE` on line 62. Not sure if the intent was to build the `_static` folder to include in every build or to only include it when building for CPU.
Additionally, although I enjoy saving time by adding the OLLAMA_SKIP_CPU_GENERATE flag, should the LCD_CPU build be created regardless of the skip flag as a fallback for GPU OOM?
### Steps to reproduce
Set `OLLAMA_SKIP_CPU_GENERATE=1` as an environment variable for compiling the binary.
### Are there any recent changes that introduced the issue?
In file `llm/generate/gen_linux.sh`, the following code snippet exists starting on line 62:
```
if [ -z "${OLLAMA_SKIP_CPU_GENERATE}" ]; then
if [ -z "${OLLAMA_CPU_TARGET}" -o "${OLLAMA_CPU_TARGET}" = "static" ]; then
# Static build for linking into the Go binary
init_vars
CMAKE_TARGETS="--target llama --target ggml"
CMAKE_DEFS="-DBUILD_SHARED_LIBS=off -DLLAMA_NATIVE=off -DLLAMA_AVX=off -DLLAMA_AVX2=off -DLLAMA_AVX512=off -DLLAMA_FMA=off -DLLAMA_F16C=off ${CMAKE_DEFS}"
BUILD_DIR="../build/linux/${ARCH}_static"
echo "Building static library"
build
fi
```
### OS
Linux
### Architecture
arm64
### Platform
_No response_
### Ollama version
0.1.30
### GPU
Nvidia
### GPU info
NVidia Jetson Orin Nano 8Gb (Tegra ARM64v8 SOC)
### CPU
Other
### Other software
_No response_
|
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I_kwDOJ0Z1Ps6Fx8Da
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codegemma:instruct : forcibly closed by the remote host
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PR_kwDOJ0Z1Ps5nSnSK
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|
Update linux.md
|
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My internet is too slow to download the model
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| 2024-04-20T21:03:07
| 2025-01-28T09:34:23
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NONE
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### What is the issue?
I keep getting the same error
> ollama run llama3
> pulling manifest
> pulling 00e1317cbf74... 1% β β 28 MB/4.7 GB 61 KB/s 20h58m
> Error: max retries exceeded: Get "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/00/00e1317cbf74d901080d7100f57580ba8dd8de57203072dc6f668324ba545f29/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240420%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240420T204533Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=8e4efa642799d32a9e502990baa74226f9f0f740b2cdb7ed0bd6b6741e0106df": net/http: TLS handshake timeout
Is there a way to download llama3 externally and then use it with ollama?
Many thanks
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.32
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I_kwDOJ0Z1Ps54Vnga
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ImportError : cannot import name 'llama2' from 'langchain
|
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NONE
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I have been trying to run some sample code with Ollama on mac but was not able to run this successful
I was getting an error in this line 'from langchain.llms import Ollama'
The version of langchain was 0.0.236
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PR_kwDOJ0Z1Ps6BFUL2
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win: remove preview title from installer
|
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This should have been in #7347 but was overlooked.
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I_kwDOJ0Z1Ps586hsM
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|
I want to run Ollama on the limited number of GPUS and CPUS
|
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I have a machine with 4 GPUS and 16 CPUS. but I want to run Ollama just on one gpu and 8 cpus. How can I do this?
|
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I_kwDOJ0Z1Ps6Ic1rn
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updating to v0.1.34 from v0.1.33 with models from file from llama3:70b-instruct-q4_0 with 23k SYSTEM data yields in >100s processing and 504 response
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### What is the issue?
I was using ollama v0.1.33 with models from file from llama3:70b-instruct-q4_0 with 23k SYSTEM data
Each prompt was not taking more than 20s (except the initial model load request) on a M1 Ultra 128GB Ram and 48 GPU cores.
I updated today to v0.1.34, using the same generated model, for all requests i get a huge processing time when reaching 100s it disconnects execution with a 504 response code.
Downgrading back to v0.1.33 solves the problem, so it must be something with v0.1.34
Please advise,
Thanks!
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.1.34
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PR_kwDOJ0Z1Ps5vI2kO
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|
Fall back to CPU runner with zero layers
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docs: update development.md add docker build desc
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Update development.md, add docker build desc if you want to build a docker image.
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PR_kwDOJ0Z1Ps5v9H9P
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|
chore: fix typo in docs
|
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| 2,261,740,761
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I_kwDOJ0Z1Ps6Gz2zZ
| 3,883
|
Support for Snowflake Arctic
|
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NONE
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Snowflake just released new models:
https://huggingface.co/Snowflake/snowflake-arctic-instruct
https://huggingface.co/Snowflake/snowflake-arctic-base
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I_kwDOJ0Z1Ps6nll5q
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|
Train Ollama models using custom data
|
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| 2025-01-26T16:24:59
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NONE
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Can users train the small ollama models using there datasets?
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I_kwDOJ0Z1Ps6S2wb1
| 6,341
|
Llama 3.1 70B high-quality HQQ quantized model - 99%+ quality of fp16
|
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NONE
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I'm not really sure if that's possible but adding that to ollama could really impact the performance on 4-bit quant option:
99%+ in all benchmarks in lm-eval relative performance to FP16 and similar inference speed to fp16
url:
https://huggingface.co/mobiuslabsgmbh/Llama-3.1-70b-instruct_4bitgs64_hqq
<img width="604" alt="Screenshot 2024-08-13 at 19 03 57" src="https://github.com/user-attachments/assets/64cd0427-b7c7-4fb8-b846-15f172669248">
also this:
https://huggingface.co/ModelCloud/Meta-Llama-3.1-70B-Instruct-gptq-4bit
<img width="597" alt="Screenshot 2024-08-13 at 19 07 18" src="https://github.com/user-attachments/assets/c3518ffe-323d-42f0-9162-d188179797fb">
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I_kwDOJ0Z1Ps6nhqu8
| 8,571
|
running deepseek r1 671b on 64GB / 128GB ram mac gives `Error: llama runner process has terminated: signal: killed`
|
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| 2025-01-29T17:28:53
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### What is the issue?
after waiting all day for the model to download, `ollama run deepseek-r1:671b` fails to run with the error `Error: llama runner process has terminated: signal: killed`.
I can run the deepseek-r1:70b llama model just fine.
I'm running a Macbook M3 Pro 64GB ram, I'm assuming it's failing due to lack of memory?
- how do I know the real memory requirements for a model? i don't think it's obvious on the ollama page.
- any way to fix this at all? I tried it on my 128GB M1 Ultra Mac Studio and got the same error. I'd really love to run this locally, so would appreciate any help!
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.5.7
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I_kwDOJ0Z1Ps58uV-J
| 2,122
|
Cannot run ollama on my server using the docker image, error 132
|
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Hello,
This is the first time I am facing such an issue, I cannot run the container at all, it crashes right when it is deployed.
I don't know which information should be useful to debug that issue, my host is a debian 12 server with docker 25 ce
I was first deploying using a compose file but I switched back to the docker command line to double check:
`docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama`
It creates a volume, but container crashes with error code 132:
```
State
Dead false
Error
ExitCode 132
FinishedAt 2024-01-21T10:24:09.726297577Z
OOMKilled false
Paused false
Pid 0
Restarting false
Running false
StartedAt 2024-01-21T10:24:09.724212624Z
Status exited
```
Then I have no clue to identify what is going on, I was not able to find a reference to error 132 in the source code, that could help me do some further checks.
Maybe you will have some ideas ! Thanks !
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I_kwDOJ0Z1Ps55eDCH
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/api/chat 404 not found version v0.1.14 linux
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| 2023-12-12T14:48:41
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I have updated ollama to latest version v0.1.14 running on amd linux however it is showing me /api/chat endpoint is not available. Is this endpoint yet to be released for linux?
<img width="441" alt="image" src="https://github.com/jmorganca/ollama/assets/135211326/9340deb3-9e56-4d18-8985-51f2f3b7fe42">
<img width="547" alt="image" src="https://github.com/jmorganca/ollama/assets/135211326/afb55d5b-cb5d-4645-a892-869cfe942b5b">
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I_kwDOJ0Z1Ps5_SYMY
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Running Ollama on localnetwork
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I am building a python ai project inside a docker container an my windows PC. I was wondering if i could run the Ollama server on my Mac and connect to it from the Pc from inside that docker container how to actually achieve this. Still new to python and programming so any help would be much appreciated thanks.
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I_kwDOJ0Z1Ps6nsssm
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|
Add the ability to import from gguf directly without a Modelfile
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I_kwDOJ0Z1Ps6OY_o1
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InternLM2.5 - 7 billion parameter with 1M context length
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| 2024-07-03T15:26:34
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[Link to the collection](https://huggingface.co/collections/internlm/internlm25-66853f32717072d17581bc13)
Introduction
InternLM2.5 has open-sourced a 7 billion parameter base model and a chat model tailored for practical scenarios. The model has the following characteristics:
Outstanding reasoning capability: State-of-the-art performance on Math reasoning, surpassing models like Llama3 and Gemma2-9B.
1M Context window: Nearly perfect at finding needles in the haystack with 1M-long context, with leading performance on long-context tasks like LongBench. Try it with [LMDeploy](https://huggingface.co/internlm/internlm2_5-7b-chat-1m/blob/main/chat/lmdeploy.md) for 1M-context inference.
Stronger tool use: InternLM2.5 supports gathering information from more than 100 web pages, corresponding implementation will be released in [Lagent](https://github.com/InternLM/lagent/tree/main) soon. InternLM2.5 has better tool utilization-related capabilities in instruction following, tool selection and reflection. See [examples](https://huggingface.co/internlm/internlm2_5-7b-chat-1m/blob/main/agent/).
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PR_kwDOJ0Z1Ps6EEvme
| 7,932
|
fix unmarshaling merges
|
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| 2024-12-04T17:25:24
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CONTRIBUTOR
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some models uses `[][]string` as `.model.merges` value rather than `[]string`
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I_kwDOJ0Z1Ps6njUQ4
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ollama version is 0.5.7-0-ga420a45-dirty
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| 2025-01-25T16:02:44
| 2025-01-25T16:02:44
| null |
NONE
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### What is the issue?
Can we talk about how to fix this?
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.7
| null |
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| 2,290,014,338
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I_kwDOJ0Z1Ps6IftiC
| 4,314
|
the ai model downloading not working
|
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| 2024-05-10T16:12:09
| 2025-01-25T07:58:29
| 2024-05-24T06:32:04
|
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### What is the issue?
ollama run mistral:text
pulling manifest
Error: Head "https://dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com/ollama/docker/registry/v2/blobs/sha256/5b/5b5c2a563a287aa9bf9be7499fe7e0630add02089be3f50ee494087f67683fbb/data?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=66040c77ac1b787c3af820529859349a%!F(MISSING)20240510%!F(MISSING)auto%!F(MISSING)s3%!F(MISSING)aws4_request&X-Amz-Date=20240510T160627Z&X-Amz-Expires=1200&X-Amz-SignedHeaders=host&X-Amz-Signature=21ab36ad0c30857906a5f710d6d26dd63c905bd84ba24e580f43dff8b8b96133": dial tcp: lookup dd20bb891979d25aebc8bec07b2b3bbc.r2.cloudflarestorage.com: no such host
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
v0.1.34
|
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I_kwDOJ0Z1Ps6CbcpM
| 3,167
|
No welcoming window & pulling manifest Error: pull model manifest: file does not exist
|
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| 2024-03-15T10:49:58
| 2024-03-18T08:22:58
| 2024-03-18T08:22:58
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### What is the issue?
When I first installed the ollama, I didn't see the welcoming window pops up, just a windows notification by it says "click here to start"
Clicking that notification gives me a powershell window as shown
and when I tried to run the command to get started it gives me the error below
pulling manifest
Error: pull model manifest: file does not exist
### What did you expect to see?
There should be a welcoming window for me to get started
### Steps to reproduce
Reinstall
### Are there any recent changes that introduced the issue?
no change
### OS
Windows
### Architecture
Other
### Platform
WSL, WSL2
### Ollama version
0.1.29
### GPU
Nvidia
### GPU info
w
### CPU
Intel
### Other software
Open WebUI
|
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| 2,819,110,416
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I_kwDOJ0Z1Ps6oCDYQ
| 8,670
|
Ollama official website API for fetching the models and its information
|
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| 2025-01-29T19:40:32
| 2025-01-29T22:31:42
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We need to build an API for the ollama website where all the models and their descriptions details are stored so that we can automatically fetch those data and embed them in our various apps!
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PR_kwDOJ0Z1Ps5jLXYa
| 1,779
|
Improve maintainability of Radeon card list
|
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| 2024-01-03T23:18:04
| 2024-01-04T00:19:00
| 2024-01-04T00:18:57
|
COLLABORATOR
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This moves the list of AMD GPUs to an easier to maintain list which should make it easier to update over time.
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I_kwDOJ0Z1Ps6QJGlb
| 5,789
|
(linux) snap support for linux installation this will solve many issues
|
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| 2024-07-19T07:59:05
| 2024-07-19T08:16:45
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CONTRIBUTOR
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This will give the app a snadboxed approach for installation and also it will give other security benifit
https://snapcraft.io/
* Simplified installation: They eliminate the need to manually resolve dependency issues, making software installation easier for users.
* Isolation: They isolate applications from the system's core components and other applications, enhancing security and stability.
* Faster updates: They enable faster and more frequent application updates without affecting the system.
Mainly updates will be pushed and app can be updated very easy.
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I_kwDOJ0Z1Ps6YIjPb
| 6,999
|
Jetson CUDA error
|
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NONE
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### What is the issue?
I' ve built Ollama from source to run with CUDA but unfortunately it doesn't start any more, I think since version 0.3.10 it's the same error. Before 0.3.10 everything was fine I was able to build ollama with CUDA and run any model, but now even smallest model 1b is crashing
I have the same Jetpack 6.0 with CUDA 12.2.140.
Error: llama runner process has terminated: CUDA error: the resource allocation failed
current device: 0, in function cublas_handle at ollama/llm/llama.cpp/ggml/src/ggml-cuda/common.cuh:644
cublasCreate_v2(&cublas_handles[device])
ollama/llm/llama.cpp/ggml/src/ggml-cuda.cu:102: CUDA error
### OS
Linux
### GPU
Nvidia, Other
### CPU
Other
### Ollama version
0.3.12
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PR_kwDOJ0Z1Ps50k-rf
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llm: add `COMMON_DARWIN_DEFS` to arm static build
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| 2024-07-06T02:42:26
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MEMBER
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I_kwDOJ0Z1Ps6YVc9E
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llama 3.2
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| 2024-09-30T07:09:17
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CONTRIBUTOR
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11b and 90b is not available in ollama can we have support for this
|
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I_kwDOJ0Z1Ps6keZOo
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Significant Performance Differences of ARM64 Installation Package Across Different CPU Architectures
|
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| 2024-12-26T07:57:42
| 2025-01-13T01:46:47
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NONE
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### What is the issue?
When using an ARM64 installation package for a large language model with 1.5B parameters, we have observed significant differences in performance across different CPU architectures. Specifically, on the MediaTek Dimensity 8100 processor, the inference speed is very fast, capable of processing multiple tokens per second; however, on the Qualcomm Snapdragon 855 processor, the same task is extremely slow, typically taking 5 to 10 seconds to process a single token.
> In terms of traditional performance, the CPU score of both exceeds 855 with 8100
However, from a performance perspective, the efficiency of 855 is about 100 times that of 8100
here is the comparison between the CPU instruction sets of the MediaTek Dimensity 8100 and the Qualcomm Snapdragon 855:
### Qualcomm Snapdragon 855:
> The Snapdragon 855 utilizes a Kryo 485 CPU, which is a customized design based on ARM Cortex technology, specifically derived from the Cortex-A76 and Cortex-A55 architectures.
These cores are also based on the ARMv8-A instruction set architecture but may be more specifically rooted in ARMv8.2-A or even ARMv8.4-A, as the A76 architecture brought new capabilities such as enhanced memory subsystem performance and improved branch prediction.
### Dimensity 8100:
> The CPU cores of the Dimensity 8100 are based on the Cortex-A78 and Cortex-A55 architectures, both of which are part of the ARMv8-A family.
ARMv8-A introduced support for 64-bit computing (AArch64 state) while also maintaining compatibility with existing 32-bit code (AArch32 state). The Cortex-A78 and Cortex-A55 are part of the ARMv8.2-A specification, indicating they also support additional features like FP16 (half-precision floating-point) computations, which are beneficial for machine learning tasks.
# Based on my understanding, the instruction set should support more features, and therefore, there should not be significant performance issues.
## Qualcomm Snapdragon 855
```
Architecture: aarch64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Thread(s) per core: 1
Core(s) per socket: 2
Socket(s): 3
Vendor ID: Qualcomm
Model: 14
Stepping: 0xd
CPU max MHz: 2841.6001
CPU min MHz: 300.0000
BogoMIPS: 38.40
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Mitigation; PTI
Vulnerability Spec store bypass: Vulnerable
Vulnerability Spectre v1: Mitigation; __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Branch predictor hardening
Vulnerability Tsx async abort: Not affected
Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm lrcpc dcpop asimddp
```
## MediaTek Dimensity 8100
```
Architecture: aarch64
CPU op-mode(s): 32-bit, 64-bit
Byte Order: Little Endian
CPU(s): 8
On-line CPU(s) list: 0-7
Thread(s) per core: 1
Core(s) per socket: 2
Socket(s): 3
Vendor ID: ARM
Model: 0
Model name: Cortex-A55
Stepping: r2p0
CPU max MHz: 2850.0000
CPU min MHz: 200.0000
BogoMIPS: 26.00
Vulnerability Gather data sampling: Not affected
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Mmio stale data: Not affected
Vulnerability Retbleed: Not affected
Vulnerability Spec rstack overflow: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl
Vulnerability Spectre v1: Mitigation; __user pointer sanitization
Vulnerability Spectre v2: Vulnerable: Unprivileged eBPF enabled
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected
Flags: fp asimd evtstrm aes pmull sha1 sha2 crc32 atomics fphp asimdhp cpuid asimdrdm lrcpc dcpop asimddp
```
### OS
android - aidlux οΌ The virtual environment
https://community.aidlux.com/aidluxdownload
### GPU
No GPU
### CPU
Qualcomm Snapdragon 855
MediaTek Dimensity 8100
### Ollama version
ollama version is 0.5.4
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settings>chat hanging when selected
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| 2024-04-24T20:37:04
| 2024-04-24T20:43:11
| 2024-04-24T20:43:11
|
NONE
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### What is the issue?
settings>chat link makes the whole ui hang and needs page refresh.
command used to host image.
```powershell script
docker run -d -p 3000:8081 -e OLLAMA_BASE_URL=http://host.docker.internal:11434 -e PORT=8081 -e MODEL_FILTER_ENABLED=True -e MODEL_FILTER_LIST="mistral:latest;llava:latest;llama3:latest" -e USER_PERMISSIONS_CHAT_DELETION=True -e DEFAULT_USER_ROLE=user -v open-webui:/app/backend/data --name open-webui --restart always ghcr.io/open-webui/open-webui:main
```
### OS
Windows, Docker, WSL2
### GPU
Nvidia
### CPU
Intel
### Ollama version
v0.1.121
|
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I_kwDOJ0Z1Ps6F89dU
| 3,691
|
why wizard 8*22b run on CPU?
|
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[] | null | 4
| 2024-04-17T04:33:15
| 2024-05-02T20:49:53
| 2024-04-17T20:45:14
|
NONE
|
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### What is the issue?
<img width="1148" alt="ζͺε±2024-04-17 12 23 02" src="https://github.com/ollama/ollama/assets/146583103/4121905b-51b5-4435-a323-ea773e2015a8">
as shown in the picture. it run on CPU.
my laptop is Mac m3 max 128GB. LM Studio can not run wizardlm-2-8*22b-Q8, it will OOM.
but ollama can run it. but on CPU it seems. is that possible to run on GPU?
### What did you expect to see?
_No response_
### Steps to reproduce
_No response_
### Are there any recent changes that introduced the issue?
_No response_
### OS
macOS
### Architecture
_No response_
### Platform
_No response_
### Ollama version
up to date
### GPU
Apple
### GPU info
_No response_
### CPU
Apple
### Other software
_No response_
|
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| 2,267,682,586
|
PR_kwDOJ0Z1Ps5t8PQB
| 4,007
|
Fix typos in README.md
|
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[] | null | 2
| 2024-04-28T16:58:10
| 2024-05-01T17:39:53
| 2024-05-01T17:39:38
|
CONTRIBUTOR
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|
- There was a lagging comma instead of a dot
- Some other minor changes
|
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| 2,359,736,579
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I_kwDOJ0Z1Ps6MprkD
| 5,115
|
how to produce Embeddings model file? No examples
|
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| 2024-06-18T12:23:48
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Hi how to produce Embeddings model file? The example from GitHub doesnβt mention Embeddings
https://github.com/ollama/ollama/blob/main/docs/modelfile.md
For example for these popular leading models?
https://huggingface.co/GritLM/GritLM-7B
https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct
Leaderboard
https://huggingface.co/spaces/mteb/leaderboard
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| 1,983,962,515
|
PR_kwDOJ0Z1Ps5e8kzm
| 1,044
|
Added Ollama4j (Java library) to community integrations
|
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| 2023-11-08T16:18:12
| 2023-11-08T19:04:33
| 2023-11-08T19:04:33
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Hi @jmorganca, thank you so much for your efforts in building and constantly updating such a cool piece of software.
I really like Ollama and have been experimenting with it regularly. I also plan to integrate it into many more Java applications.
I noticed there isn't a Java library listed under community integrations, that's why I felt the need to create a Java library for interacting with Ollama's REST APIs, and as a result, I've published Ollama4j as a package and I plan to constantly improve it. This way, others who work with Java can easily interact with the Ollama API.
Thanks again, and keep up the great work!
|
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I_kwDOJ0Z1Ps6L7bVt
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|
[Model Request] Add dolphin-qwen2
|
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Could you add dolphin-qwen2? Thanks.
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| 2,398,862,311
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PR_kwDOJ0Z1Ps503mwf
| 5,579
|
Statically link c++ and thread lib on windows
|
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| 2024-07-09T18:20:50
| 2024-07-09T19:21:16
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This makes sure we statically link the c++ and thread library on windows to avoid unnecessary runtime dependencies on non-standard DLLs
On my dev box, I have these `libpthread.dll.a` libraries and my local builds were leaking the dependency
e.g.:
```
> gci -path C:\msys64\ -r -fi 'libpthread.dll.a'
Directory: C:\msys64\ucrt64\lib
Mode LastWriteTime Length Name
---- ------------- ------ ----
-a--- 4/27/2024 12:18 PM 94672 libpthread.dll.a
```
Before this change (my local builds):
```
> dumpbin /dependents ./ollama.exe
Microsoft (R) COFF/PE Dumper Version 14.40.33811.0
Copyright (C) Microsoft Corporation. All rights reserved.
Dump of file .\ollama.exe
File Type: EXECUTABLE IMAGE
Image has the following dependencies:
libstdc++-6.dll
KERNEL32.dll
msvcrt.dll
libwinpthread-1.dll
```
After this change:
```
> dumpbin /dependents .\ollama.exe
Microsoft (R) COFF/PE Dumper Version 14.29.30154.0
Copyright (C) Microsoft Corporation. All rights reserved.
Dump of file .\ollama.exe
File Type: EXECUTABLE IMAGE
Image has the following dependencies:
KERNEL32.dll
msvcrt.dll
```
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I_kwDOJ0Z1Ps6XpVLR
| 6,926
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Unable to use multiple GPUs
|
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### What is the issue?
my GPUS info: 6 numbers of A10, but when i run ollama (qwen:32b),it works oom. and i see only a gpu works, others can't use. this is the logs. please help me this issue, thanks.
llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = qwen2
llama_model_loader: - kv 1: general.name str = Qwen2-beta-7B-Chat
llama_model_loader: - kv 2: qwen2.block_count u32 = 32
llama_model_loader: - kv 3: qwen2.context_length u32 = 32768
llama_model_loader: - kv 4: qwen2.embedding_length u32 = 4096
llama_model_loader: - kv 5: qwen2.feed_forward_length u32 = 11008
llama_model_loader: - kv 6: qwen2.attention.head_count u32 = 32
llama_model_loader: - kv 7: qwen2.attention.head_count_kv u32 = 32
llama_model_loader: - kv 8: qwen2.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 9: qwen2.use_parallel_residual bool = true
llama_model_loader: - kv 10: tokenizer.ggml.model str = gpt2
llama_model_loader: - kv 11: tokenizer.ggml.tokens arr[str,151936] = ["!", "\"", "#", "$", "%", "&", "'", ...
llama_model_loader: - kv 12: tokenizer.ggml.token_type arr[i32,151936] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
time=2024-09-24T03:32:27.691Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: - kv 13: tokenizer.ggml.merges arr[str,151387] = ["Δ Δ ", "Δ Δ Δ Δ ", "i n", "Δ t",...
llama_model_loader: - kv 14: tokenizer.ggml.eos_token_id u32 = 151643
llama_model_loader: - kv 15: tokenizer.ggml.padding_token_id u32 = 151643
llama_model_loader: - kv 16: tokenizer.ggml.bos_token_id u32 = 151643
llama_model_loader: - kv 17: tokenizer.chat_template str = {% for message in messages %}{{'<|im_...
llama_model_loader: - kv 18: general.quantization_version u32 = 2
llama_model_loader: - kv 19: general.file_type u32 = 2
llama_model_loader: - type f32: 161 tensors
llama_model_loader: - type q4_0: 225 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: missing or unrecognized pre-tokenizer type, using: 'default'
llm_load_vocab: special tokens cache size = 293
llm_load_vocab: token to piece cache size = 0.9338 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = qwen2
llm_load_print_meta: vocab type = BPE
llm_load_print_meta: n_vocab = 151936
llm_load_print_meta: n_merges = 151387
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 128
llm_load_print_meta: n_embd_head_v = 128
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 4096
llm_load_print_meta: n_embd_v_gqa = 4096
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 = 11008
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 = 2
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 = 32768
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 = 7B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 7.72 B
llm_load_print_meta: model size = 4.20 GiB (4.67 BPW)
llm_load_print_meta: general.name = Qwen2-beta-7B-Chat
llm_load_print_meta: BOS token = 151643 '<|endoftext|>'
llm_load_print_meta: EOS token = 151643 '<|endoftext|>'
llm_load_print_meta: PAD token = 151643 '<|endoftext|>'
llm_load_print_meta: LF token = 148848 'ΓΔ¬'
llm_load_print_meta: EOT token = 151645 '<|im_end|>'
llm_load_print_meta: max token length = 256
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 A10, compute capability 8.6, VMM: yes
llm_load_tensors: ggml ctx size = 0.34 MiB
time=2024-09-24T03:32:29.148Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server not responding"
llm_load_tensors: offloading 32 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 33/33 layers to GPU
llm_load_tensors: CPU buffer size = 333.84 MiB
llm_load_tensors: CUDA0 buffer size = 3963.38 MiB
time=2024-09-24T03:32:29.400Z level=INFO source=server.go:625 msg="waiting for server to become available" status="llm server loading model"
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 = 4096.00 MiB
llama_new_context_with_model: KV self size = 4096.00 MiB, K (f16): 2048.00 MiB, V (f16): 2048.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 2.38 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 560.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 24.01 MiB
llama_new_context_with_model: graph nodes = 1126
llama_new_context_with_model: graph splits = 2
INFO [main] model loaded | tid="140002756288512" timestamp=1727148752
time=2024-09-24T03:32:32.416Z level=INFO source=server.go:630 msg="llama runner started in 4.98 seconds"
[GIN] 2024/09/24 - 03:32:32 | 200 | 6.624226742s | 127.0.0.1 | POST "/api/chat"
[GIN] 2024/09/24 - 03:32:43 | 200 | 1.193765361s | 127.0.0.1 | POST "/api/chat"
[GIN] 2024/09/24 - 03:32:53 | 200 | 947.380979ms | 127.0.0.1 | POST "/api/chat"
CUDA error: an illegal memory access was encountered
current device: 0, in function ggml_backend_cuda_synchronize at /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml/src/ggml-cuda.cu:2416
cudaStreamSynchronize(cuda_ctx->stream())
/go/src/github.com/ollama/ollama/llm/llama.cpp/ggml/src/ggml-cuda.cu:101: CUDA error
free(): corrupted unsorted chunks
### OS
Linux, Docker
### GPU
Nvidia
### CPU
_No response_
### Ollama version
latest
|
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PR_kwDOJ0Z1Ps5sTRAP
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types/model: remove MarshalText/UnmarshalText from Digest
|
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| 2024-04-10T23:52:03
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CONTRIBUTOR
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PR_kwDOJ0Z1Ps6Ac3AS
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|
feat: allow setting KV cache type
|
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PR_kwDOJ0Z1Ps5nVAvQ
| 2,599
|
Explicitly disable AVX2 on GPU builds
|
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Even though we weren't setting it to on, somewhere in the cmake config it was getting toggled on. By explicitly setting it to off, we get `/arch:AVX` as intended.
fixes #2527
|
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PR_kwDOJ0Z1Ps5tQTlb
| 3,783
|
Allow any kind of JSON including arrays or straight literals, not just objects
|
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This is a simple PR which allows the `format=json` constraint to generate any valid JSON, not just JSON with an object at the top level. This allows models to generate arrays, strings, numbers, or literals without an object wrapper around them, reducing the number of tokens necessary for many use cases.
Remember that every token is more CO2 emitted into the atmosphere and more $$$ transferred from everyone's pockets into NVidia's treasure. Tokens matter!
|
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PR_kwDOJ0Z1Ps55opJT
| 6,537
|
Add metrics endpoint and basic request metrics otel based
|
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NONE
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Resolves https://github.com/ollama/ollama/issues/3144
This pull request is to add /metrics endpoint and http metrics as a starting point.
It uses otel metrics libary and exposes metrics in prometheus format.
This PR does not try to cover all metrics to keep it simple. If this looks good. I could add few more that will be useful.
How to test:
once Ollama server is running pull a model and list(or any other Ollama actions)
```
curl http://127.0.0.1:11434/metrics
```
example of custom metrics(not all are shown since i tried only few commands):
Ollama request metrics:
```
% curl http://localhost:11434/metrics | grep -i ollama
% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 5724 0 5724 0 0 1092k 0 --:--:-- --:--:-- --:--:-- 1117k
model_actions_total{action="list",otel_scope_name="ollama",otel_scope_version="",status="OK",status_code="200"} 1
otel_scope_info{otel_scope_name="ollama",otel_scope_version=""} 1
requests_total{action="all",otel_scope_name="ollama",otel_scope_version="",status="OK",status_code="200"} 2
target_info{service_name="unknown_service:ollama",telemetry_sdk_language="go",telemetry_sdk_name="opentelemetry",telemetry_sdk_version="1.27.0"} 1
```
All metrics :
```
% curl http://localhost:11434/metrics
# HELP go_gc_duration_seconds A summary of the pause duration of garbage collection cycles.
# TYPE go_gc_duration_seconds summary
go_gc_duration_seconds{quantile="0"} 2.2875e-05
go_gc_duration_seconds{quantile="0.25"} 2.2875e-05
go_gc_duration_seconds{quantile="0.5"} 3.0375e-05
go_gc_duration_seconds{quantile="0.75"} 3.0375e-05
go_gc_duration_seconds{quantile="1"} 3.0375e-05
go_gc_duration_seconds_sum 5.325e-05
go_gc_duration_seconds_count 2
# HELP go_goroutines Number of goroutines that currently exist.
# TYPE go_goroutines gauge
go_goroutines 10
# HELP go_info Information about the Go environment.
# TYPE go_info gauge
go_info{version="go1.22.0"} 1
# HELP go_memstats_alloc_bytes Number of bytes allocated and still in use.
# TYPE go_memstats_alloc_bytes gauge
go_memstats_alloc_bytes 3.247952e+06
# HELP go_memstats_alloc_bytes_total Total number of bytes allocated, even if freed.
# TYPE go_memstats_alloc_bytes_total counter
go_memstats_alloc_bytes_total 5.271112e+06
# HELP go_memstats_buck_hash_sys_bytes Number of bytes used by the profiling bucket hash table.
# TYPE go_memstats_buck_hash_sys_bytes gauge
go_memstats_buck_hash_sys_bytes 11524
# HELP go_memstats_frees_total Total number of frees.
# TYPE go_memstats_frees_total counter
go_memstats_frees_total 21294
# HELP go_memstats_gc_sys_bytes Number of bytes used for garbage collection system metadata.
# TYPE go_memstats_gc_sys_bytes gauge
go_memstats_gc_sys_bytes 3.159336e+06
# HELP go_memstats_heap_alloc_bytes Number of heap bytes allocated and still in use.
# TYPE go_memstats_heap_alloc_bytes gauge
go_memstats_heap_alloc_bytes 3.247952e+06
# HELP go_memstats_heap_idle_bytes Number of heap bytes waiting to be used.
# TYPE go_memstats_heap_idle_bytes gauge
go_memstats_heap_idle_bytes 1.998848e+06
# HELP go_memstats_heap_inuse_bytes Number of heap bytes that are in use.
# TYPE go_memstats_heap_inuse_bytes gauge
go_memstats_heap_inuse_bytes 5.7344e+06
# HELP go_memstats_heap_objects Number of allocated objects.
# TYPE go_memstats_heap_objects gauge
go_memstats_heap_objects 18185
# HELP go_memstats_heap_released_bytes Number of heap bytes released to OS.
# TYPE go_memstats_heap_released_bytes gauge
go_memstats_heap_released_bytes 1.88416e+06
# HELP go_memstats_heap_sys_bytes Number of heap bytes obtained from system.
# TYPE go_memstats_heap_sys_bytes gauge
go_memstats_heap_sys_bytes 7.733248e+06
# HELP go_memstats_last_gc_time_seconds Number of seconds since 1970 of last garbage collection.
# TYPE go_memstats_last_gc_time_seconds gauge
go_memstats_last_gc_time_seconds 1.724793883369717e+09
# HELP go_memstats_lookups_total Total number of pointer lookups.
# TYPE go_memstats_lookups_total counter
go_memstats_lookups_total 0
# HELP go_memstats_mallocs_total Total number of mallocs.
# TYPE go_memstats_mallocs_total counter
go_memstats_mallocs_total 39479
# HELP go_memstats_mcache_inuse_bytes Number of bytes in use by mcache structures.
# TYPE go_memstats_mcache_inuse_bytes gauge
go_memstats_mcache_inuse_bytes 9600
# HELP go_memstats_mcache_sys_bytes Number of bytes used for mcache structures obtained from system.
# TYPE go_memstats_mcache_sys_bytes gauge
go_memstats_mcache_sys_bytes 15600
# HELP go_memstats_mspan_inuse_bytes Number of bytes in use by mspan structures.
# TYPE go_memstats_mspan_inuse_bytes gauge
go_memstats_mspan_inuse_bytes 137120
# HELP go_memstats_mspan_sys_bytes Number of bytes used for mspan structures obtained from system.
# TYPE go_memstats_mspan_sys_bytes gauge
go_memstats_mspan_sys_bytes 146880
# HELP go_memstats_next_gc_bytes Number of heap bytes when next garbage collection will take place.
# TYPE go_memstats_next_gc_bytes gauge
go_memstats_next_gc_bytes 5.69504e+06
# HELP go_memstats_other_sys_bytes Number of bytes used for other system allocations.
# TYPE go_memstats_other_sys_bytes gauge
go_memstats_other_sys_bytes 1.404724e+06
# HELP go_memstats_stack_inuse_bytes Number of bytes in use by the stack allocator.
# TYPE go_memstats_stack_inuse_bytes gauge
go_memstats_stack_inuse_bytes 655360
# HELP go_memstats_stack_sys_bytes Number of bytes obtained from system for stack allocator.
# TYPE go_memstats_stack_sys_bytes gauge
go_memstats_stack_sys_bytes 655360
# HELP go_memstats_sys_bytes Number of bytes obtained from system.
# TYPE go_memstats_sys_bytes gauge
go_memstats_sys_bytes 1.3126672e+07
# HELP go_threads Number of OS threads created.
# TYPE go_threads gauge
go_threads 11
# HELP list_requests_total The total number of model list requests that have been attempted.
# TYPE list_requests_total counter
list_requests_total{action="",otel_scope_name="ollama",otel_scope_version="",status="",status_code="0"} 4
# HELP otel_scope_info Instrumentation Scope metadata
# TYPE otel_scope_info gauge
otel_scope_info{otel_scope_name="ollama",otel_scope_version=""} 1
# HELP promhttp_metric_handler_requests_in_flight Current number of scrapes being served.
# TYPE promhttp_metric_handler_requests_in_flight gauge
promhttp_metric_handler_requests_in_flight 1
# HELP promhttp_metric_handler_requests_total Total number of scrapes by HTTP status code.
# TYPE promhttp_metric_handler_requests_total counter
promhttp_metric_handler_requests_total{code="200"} 1
promhttp_metric_handler_requests_total{code="500"} 0
promhttp_metric_handler_requests_total{code="503"} 0
# HELP requests_total The total number of requests on all endpoints.
# TYPE requests_total counter
requests_total{action="all",otel_scope_name="ollama",otel_scope_version="",status="OK",status_code="200"} 3
# HELP target_info Target metadata
# TYPE target_info gauge
target_info{service_name="unknown_service:ollama",telemetry_sdk_language="go",telemetry_sdk_name="opentelemetry",telemetry_sdk_version="1.27.0"} 1
```
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PR_kwDOJ0Z1Ps5aRvI3
| 525
|
fix: add falcon.go
|
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missed this file in #519
|
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| 2,278,567,972
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PR_kwDOJ0Z1Ps5uhUu-
| 4,143
|
omit prompt and generate settings from final response
|
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| 2024-05-03T23:22:38
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CONTRIBUTOR
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if the input is large, it might overrun the response buffer. there's no need to return the prompt since the caller has it already
|
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I_kwDOJ0Z1Ps6KdhUS
| 4,700
|
please support minicpmv2.5
|
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this is github address
https://github.com/OpenBMB/ollama
|
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| 2,249,492,119
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PR_kwDOJ0Z1Ps5s_K_I
| 3,717
|
prompt to display and add local ollama keys to account
|
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| 2024-04-30T18:02:09
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- return descriptive error messages when unauthorized to create blob or push a model
- display the local public key associated with the request that was denied
|
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I_kwDOJ0Z1Ps6GBttG
| 3,701
|
llama runner process no longer running: -1 CUDA error: CUBLAS_STATUS_EXECUTION_FAILED
|
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### What is the issue?
Follow the manual: https://github.com/ollama/ollama/blob/9df6c85c3a51ce00d6a65be9dd8a06af07b24af5/docs/tutorials/nvidia-jetson.md
But run error:
```bash
ollama run mistral-jetson
```
Errors
```bash
Error: llama runner process no longer running: -1 CUDA error: CUBLAS_STATUS_EXECUTION_FAILED
current device: 0, in function ggml_cuda_mul_mat_batched_cublas at /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:1848
cublasGemmBatchedEx(ctx.cublas_handle(), CUBLAS_OP_T, CUBLAS_OP_N, ne01, ne11, ne10, alpha, (const void **) (ptrs_src.get() + 0*ne23), CUDA_R_16F, nb01/nb00, (const void **) (ptrs_src.get() + 1*ne23), CUDA_R_16F, nb11/nb10, beta, ( void **) (ptrs_dst.get() + 0*ne23), cu_data_type, ne01, ne23, cu_compute_type, CUBLAS_GEMM_DEFAULT_TENSOR_OP)
GGML_ASSERT: /go/src/github.com/ollama/ollama/llm/llama.cpp/ggml-cuda.cu:60: !"CUDA error"
```
### What did you expect to see?
_No response_
### Steps to reproduce
_No response_
### Are there any recent changes that introduced the issue?
_No response_
### OS
Linux
### Architecture
arm64
### Platform
_No response_
### Ollama version
0.1.32
### GPU
Nvidia
### GPU info
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 540.2.0 Driver Version: N/A 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 Orin (nvgpu) N/A | N/A N/A | N/A |
| N/A N/A N/A N/A / N/A | Not Supported | N/A N/A |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| No running processes found |
+---------------------------------------------------------------------------------------+
### CPU
_No response_
### Other software
_No response_
|
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PR_kwDOJ0Z1Ps519tU_
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|
Fix generate test flakyness
|
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Better nvidia GPU discovery logging
|
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| 2024-07-03T17:38:42
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Refine the way we log GPU discovery to improve the non-debug output, and report more actionable log messages when possible to help users troubleshoot on their own.
|
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I_kwDOJ0Z1Ps57qhT7
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|
Extremely slow memory allocation in WSL2 container
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# Description
When trying to run ollama inside a container, memory allocation is extremely slow, something like 50 MB/s max. When in chat with the model, the container releases memory after some time if idle and if I run a prompt, it allocates it all over again.
# Steps to reproduce
1. Run the command `podman run --device nvidia.com/gpu=all --security-opt label=disable --detach --volume .ollama:/root/.ollama -p 11434:11434 --name ollama-19 ollama/ollama:0.1.19`
2. Run the command `podman exec -it ollama-19 ollama run dolphin-mixtral`
3. Wait for several minutes
# System info
```
Nome do host: GE76RAIDER
Nome do sistema operacional: Microsoft Windows 11 Pro
Versão do sistema operacional: 10.0.22631 N/A compilação 22631
Fabricante do sistema operacional: Microsoft Corporation
Configuração do SO: Estação de trabalho autônoma
Tipo de compilação do sistema operacional: Multiprocessor Free
ProprietΓ‘rio registrado: otavioasilva@hotmail.com
Organização registrada: N/A
Identificação do produto: 00330-80000-00000-AA520
Data da instalação original: 02/08/2023, 14:30:14
Tempo de Inicialização do Sistema: 10/01/2024, 12:32:44
Fabricante do sistema: Micro-Star International Co., Ltd.
Modelo do sistema: Raider GE76 12UHS
Tipo de sistema: x64-based PC
Processador(es): 1 processador(es) instalado(s).
[01]: Intel64 Family 6 Model 154 Stepping 3 GenuineIntel ~2900 Mhz
VersΓ£o do BIOS: American Megatrends International, LLC. E17K4IMS.20D, 26/06/2023
Pasta do Windows: C:\WINDOWS
Pasta do sistema: C:\WINDOWS\system32
Inicializar dispositivo: \Device\HarddiskVolume1
Localidade do sistema: pt-br;PortuguΓͺs (Brasil)
Localidade de entrada: en-us;InglΓͺs (Estados Unidos)
Fuso horΓ‘rio: (UTC-03:00) BrasΓlia
MemΓ³ria fΓsica total: 65.237 MB
MemΓ³ria fΓsica disponΓvel: 46.571 MB
MemΓ³ria Virtual: Tamanho MΓ‘ximo: 74.965 MB
MemΓ³ria Virtual: DisponΓvel: 50.991 MB
MemΓ³ria Virtual: Em Uso: 23.974 MB
Local(is) de arquivo de paginação: C:\pagefile.sys
DomΓnio: WORKGROUP
Servidor de Logon: \\GE76RAIDER
Hotfix(es): 4 hotfix(es) instalado(s).
[01]: KB5033920
[02]: KB5027397
[03]: KB5034123
[04]: KB5032393
Placa(s) de Rede: 3 NIC(s) instalado(s).
[01]: Killer E3100G 2.5 Gigabit Ethernet Controller
Nome da conexΓ£o: Ethernet
Status: MΓdia desconectada
[02]: Killer(R) Wi-Fi 6E AX1675i 160MHz Wireless Network Adapter (211NGW)
Nome da conexΓ£o: Wi-Fi
DHCP ativado: Sim
Servidor DHCP: 192.168.1.1
Endereço(es) IP
[01]: 192.168.1.26
[03]: TAP-Windows Adapter V9
Nome da conexΓ£o: TAP-Windows
Status: MΓdia desconectada
Requisitos do Hyper-V: Hipervisor detectado. Recursos necessΓ‘rios para o Hyper-V nΓ£o serΓ£o exibidos.
```
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I_kwDOJ0Z1Ps6nudWE
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Ollama: torch.OutOfMemoryError: CUDA out of memory
|
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### What is the issue?
Running some tests using pytest with the following 6 models. What I find is that if I run all tests with each model before go on to the next model, the tests mostly worked fine. 123/126 passed. But if I run each test against all 6 models sequentially and then go to the next test then I see hangs or out of memory error. Is this a known issue? I expect the order of running tests using Ollama should not matter.
ollama version is 0.5.7
pytest 8.3.4
| NVIDIA-SMI 550.144.03 Driver Version: 550.144.03 CUDA Version: 12.4 |
| 0 NVIDIA GeForce RTX 4070 Ti Off | 00000000:01:00.0 On | N/A |
Linux kennethpc 6.8.0-51-generic #52-Ubuntu SMP PREEMPT_DYNAMIC Thu Dec 5 13:09:44 UTC 2024 x86_64 x86_64 x86_64 GNU/Linux
qwen2:latest
qwen2.5:latest
mistral:latest
llama3-groq-tool-use:latest
llama3.2:latest
llama3.2:latest
Here is some examples of the errors. Sometimes I simply see hangs:
FAILED tests/_1_misc_test.py::test_t6_func[mistral:latest] - assert None is not None
FAILED tests/_1_misc_test.py::test_t6_func[llama3-groq-tool-use:latest] - assert None is not None
FAILED tests/_2_rag_test.py::test_t7_func[qwen2:latest-chroma] - assert 768 == 384
FAILED tests/_2_rag_test.py::test_t7_func[qwen2.5:latest-chroma] - torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 11.72 GiB of which 41.50 MiB is free. Process 263255 has 3.40 GiB memory in use. Process 263532 has 6.09 GiB memory in use. In...
FAILED tests/_2_rag_test.py::test_t7_func[qwen2.5:latest-huggingface] - torch.OutOfMemoryError: CUDA out of memory. Tried to allocate 20.00 MiB. GPU 0 has a total capacity of 11.72 GiB of which 41.50 MiB is free. Process 263255 has 3.40 GiB memory in use. Process 263532 has 6.09 GiB memory in use. In...
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.7
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Update linux.md
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CONTRIBUTOR
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Add -e to viewing logs in order to show end of ollama logs
|
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| 2,724,965,068
|
I_kwDOJ0Z1Ps6ia6rM
| 7,996
|
Less available memory than expected
|
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[] | null | 5
| 2024-12-08T05:31:08
| 2024-12-23T08:07:38
| 2024-12-23T08:07:38
|
NONE
|
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| null | null | null |
### What is the issue?
I'm using a 4060 laptop GPU, which has 8GB of memory. However, from the log, it showed that
`library=cuda variant=v12 compute=8.9 driver=12.7 name="NVIDIA GeForce RTX 4060 Laptop GPU" total="8.0 GiB" available="6.9 GiB"`
That's quite strange for from `nvidia-smi`
Actually, when I tried to run llama3.2 3b fp16, ollama offloads 10% of the layers to the CPU, while from `ollama ps`, the needed memory is 7.9GB, which should be able to fit in the GPU memory
### OS
WSL2
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.4.7
|
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| 2,079,489,803
|
PR_kwDOJ0Z1Ps5j9ckG
| 1,959
|
Add ollama sync command
|
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| 2024-01-12T18:51:03
| 2024-01-25T18:21:20
| 2024-01-25T18:20:50
|
NONE
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I frequently need to pull the latest version of models I've already downloaded.
Taking inspiration the comments and suggestions in https://github.com/jmorganca/ollama/issues/1890, I've implemented a basic `sync` command to streamline this process.
```bash
ollama sync
```
|
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| 2,078,206,380
|
I_kwDOJ0Z1Ps573ums
| 1,945
|
Donation
|
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| 2024-01-12T07:08:11
| 2024-01-25T23:09:03
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|
NONE
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Hi there!
I really, REALLY love the product. Ollama made LLM usage as simple as docker years ago did for containers.
Maybe it's time for donation button? Can't promise much, but at least I can show my appreciation.
|
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I_kwDOJ0Z1Ps5_udDb
| 2,597
|
Suggestion - Custom Model Actions
|
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| 2024-02-19T17:42:42
| 2024-12-10T18:11:03
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# Providing an LLM with actions!
- Putting it simply just giving an LLM the power to have actions it can use which the developer programs themselves.
### The request:
- The request that is sent & provides the LLM with the knowledge of it having actions it can use.
```shell
curl http://localhost:11434/api/generate -d '{
"model": "llama2",
"system": "You are a librarian",
"prompt": "How many books do you have?",
"actions": [{ "name": "get_books", "description": "Get the amount of books you currently have" }]
}'
```
### The response:
- The simple response which the user detects for a string in the response
```json
{
"model": "llama2",
"created_at": "2023-08-04T08:52:19.385406455-07:00",
"response": "Sure! Here is how many books I have {\"action\":\"get_books\"}"
}
```
- The less simple response but way more intuitive to work with where the actions it tried to use are sent in an array and `%0` in the response corresponds to the relation between the first element of the array `"get_books"` having an index of `0`
```json
{
"model": "llama2",
"created_at": "2023-08-04T08:52:19.385406455-07:00",
"response": "Sure! Here is how many books I have %0",
"actions": ["get_books"]
}
```
### Creating actions
- The format for an action is a simple JSON that explains everything to the LLM:
```js
{
"name": "get_books", // [REQUIRED] This field is what the LLM will send when it wants to use the action.
"description": "Get the amount of books you currently have", // [REQUIRED] This field will explain to the LLM what the action does when used.
"type": "response" // [OPTIONAL] This field explains to the LLM if the response from the action will be added to the response (For example the 0%) or if it'll be an action that doesn't give out a response. (default "response") (values "response"|"silent")
}
```
#### Actions could also be created in a Modelfile:
```
FROM llama2
SYSTEM """
You are a librarian
"""
ACTIONS """
[
{
"name": "get_books", // [REQUIRED] This field is what the LLM will send when it wants to use the action.
"description": "Get the amount of books you currently have", // [REQUIRED] This field will explain to the LLM what the action does when used.
"type": "response" // [OPTIONAL] This field explains to the LLM if the response from the action will be added to the response (For example the 0%) or if it'll be an action that doesn't give out a response. (default "response") (values "response"|"silent")
}
]
"""
```
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