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https://api.github.com/repos/ollama/ollama/issues/4177
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| 2,279,703,592
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I_kwDOJ0Z1Ps6H4YQo
| 4,177
|
pull orca2:7b-fp16 Error: EOF
|
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| 2024-05-05T19:57:54
| 2024-05-06T18:53:25
| 2024-05-06T18:33:30
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CONTRIBUTOR
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### What is the issue?
`ollama pull orca2:7b-fp16` errors with `Error: EOF` when it is pulling manifest.
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.33
|
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PR_kwDOJ0Z1Ps5ggBuv
| 1,295
|
Add verbose request logs to server.
|
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"type": "User",
"user_view_type": "public",
"site_admin": false
}
|
[] |
closed
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[] | null | 1
| 2023-11-28T01:28:16
| 2024-05-07T23:46:51
| 2024-05-07T23:46:51
|
CONTRIBUTOR
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|
Add verbose request logs to server. Completes https://github.com/jmorganca/ollama/issues/1118
example output
```
2023/11/27 12:30:18 routes.go:736: Request POST - /api/generate; QueryParams: map[]; URLParams: []; Body: {"model":"orca-mini","prompt":"word up","system":"","template":"","context":[31822,13,8458,31922,3244,31871,13,3838,397,363,7421,8825,342,5243,10389,5164,828,31843,9530,362,988,362,365,473,31843,13,13,8458,31922,9779,31871,13,5521,397,365,13,13,8458,31922,13166,31871,13,312,705,363,7421,8825,29328,289,2803,365,288,2470,8931,291,1673,1132,31843,1035,473,312,955,365,1703,31902,13,8458,31922,3244,31871,13,3838,397,363,7421,8825,342,5243,10389,5164,828,31843,9530,362,988,362,365,473,31843,13,13,8458,31922,9779,31871,13,5521,397,365,13,13,8458,31922,13166,31871,13,312,705,363,7421,8825,29328,289,2803,365,288,2470,8931,291,1673,1132,31843,1035,473,312,955,365,1703,31902,13,8458,31922,3244,31871,13,3838,397,363,7421,8825,342,5243,10389,5164,828,31843,9530,362,988,362,365,473,31843,13,13,8458,31922,9779,31871,13,31824,3106,322,260,259,6285,313,13,13,8458,31922,13166,31871,13,312,25122,31844,504,312,705,432,1796,674,365,397,12065,289,31843,9410,365,3281,1673,541,3846,405,1132,562,266,5149,365,764,955,351,31902,13,8458,31922,3244,31871,13,3838,397,363,7421,8825,342,5243,10389,5164,828,31843,9530,362,988,362,365,473,31843,13,13,8458,31922,9779,31871,13,26755,13,13,8458,31922,13166,31871,13,312,31876,31836,10157,31844,504,362,363,7421,8825,31844,312,705,432,29328,289,1313,8931,342,6175,5000,31843,1725,4199,1717,322,289,2803,351,2470,8931,291,1673,1132,31843,1053,635,2492,2128,312,473,955,365,351,31902],"format":"","options":null}; ClientIP: 127.0.0.1; Status: 200; UserAgent: ollama/0.0.0 (arm64 darwin) Go/go1.21.4; Duration: 5.217641542s
```
|
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https://api.github.com/repos/ollama/ollama/issues/5268
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| 2,371,666,486
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PR_kwDOJ0Z1Ps5zcZy6
| 5,268
|
Add Windows on ARM64 build instructions
|
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"user_view_type": "public",
"site_admin": false
}
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[] |
closed
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| null |
[] | null | 1
| 2024-06-25T05:10:51
| 2024-11-21T17:49:40
| 2024-11-21T17:44:28
|
CONTRIBUTOR
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| 2,338,466,659
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I_kwDOJ0Z1Ps6LYitj
| 4,859
|
glm-4-9b-chat
|
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| 2024-06-06T14:54:47
| 2024-06-06T17:34:23
| 2024-06-06T17:34:23
|
NONE
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https://modelscope.cn/models/ZhipuAI/glm-4-9b-chat
thanks !
|
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I_kwDOJ0Z1Ps5sLGk9
| 150
|
Need word wrap
|
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| 2023-07-20T21:56:36
| 2023-09-26T22:57:12
| 2023-09-26T22:57:12
|
NONE
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<img width="97" alt="image" src="https://github.com/jmorganca/ollama/assets/1476820/fa92e643-a994-46ab-b83c-df3e28fbf758">
It pains me
|
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I_kwDOJ0Z1Ps6XG08W
| 6,865
|
qwen2.5 context length
|
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| 2024-09-19T02:41:04
| 2024-09-19T23:33:54
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NONE
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### What is the issue?
<img width="674" alt="image" src="https://github.com/user-attachments/assets/03949cc7-07fd-45c4-a09a-4a971e0a3586">
According to the model card, the context length should be **128k**?
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
0.3.10
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PR_kwDOJ0Z1Ps54uONY
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|
Add gitlab.com/tozd/go/fun Go package
|
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CONTRIBUTOR
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`gitlab.com/tozd/go/fun` is a Go package which provides high-level abstraction to define functions with code (the usual way), data (providing examples of inputs and expected outputs which are then used with an AI model), or natural language description. It is the simplest but powerful way to use large language models (LLMs) in Go.
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PR_kwDOJ0Z1Ps5qsuWv
| 3,346
|
move community integrations to their own doc
|
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| 2024-03-25T19:21:03
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| 2024-04-01T15:14:19
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The community integration section at the end of the README is getting quite long, moving it to its own doc to keep things tidy.
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I_kwDOJ0Z1Ps6OODK8
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level=ERROR source=sched.go:388 msg="error loading llama server" error="llama runner process no longer running: -1 "
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### What is the issue?
Macbook Air M1
Run with Docker:
`docker run -d -v ollama:/root/.ollama -p 11434:11434 --name ollama ollama/ollama`
`docker exec -it ollama ollama run codestral:22b`
Error:
> level=ERROR source=sched.go:388 msg="error loading llama server" error="llama runner process no longer running: -1 "
[docker.log](https://github.com/user-attachments/files/16067480/docker.log)
### OS
Docker
### GPU
Other
### CPU
Apple
### Ollama version
0.1.48
|
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I_kwDOJ0Z1Ps6fXcmX
| 7,747
|
Support Pixtral Large
|
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| 2024-11-21T06:58:07
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Mistral AI just released Pixtral Large, a 124B multimodal model built on top of Mistral Large 2.
See https://huggingface.co/mistralai/Pixtral-Large-Instruct-2411
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I_kwDOJ0Z1Ps6fMKhD
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|
add a Feature : clone and duplicate model from scratch when creating new model from Modelfile to laod to gpu memory
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| 2024-11-19T05:02:18
| 2024-12-02T15:32:44
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when we create a new_model from BaseModel
ollama does not load model to memory again
he used loaded BaseModel as new model and NewModel does not load as seprate model
we want use two model as sepearate two load both in gpu memory we enough memory
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PR_kwDOJ0Z1Ps51fNUE
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README: Added AI Studio to the list of UIs
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I added [AI Studio](https://github.com/MindWorkAI/AI-Studio) to the list of UIs.
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llm: speed up gguf decoding by a lot
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Previously, some costly things were causing the loading of GGUF files
and their metadata and tensor information to be VERY slow:
* Too many allocations when decoding strings
* Hitting disk for each read of each key and value, resulting in a
not-okay amount of syscalls/disk I/O.
The show API is now down to 33ms from 800ms+ for llama3 on a macbook pro
m3.
This commit also prevents collecting large arrays of values when
decoding GGUFs (if desired). When such keys are encountered, their
values are null, and are encoded as such in JSON.
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I_kwDOJ0Z1Ps6JYdJx
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OLLAMA_MODELS no longer works
|
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| 2024-05-20T01:19:23
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NONE
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> setting the OLLAMA_MODELS environment variable (was) no longer working.
[ edit: A reboot resolved this, so closed.
* additional details:
models were in ~/.ollama (ownership: 1000:1000);
environment variable was accidentally commented out in ~/.bashrc before a reboot - so, unsurprisingly, no dice;
uncommented line in .bashrc;
re-login'd;
models still not detected;
killed ollama;
`. ~/.bashrc`'d for good measure;
re-ran `ollama serve & ollama list;`
no dice;
killed ollama;
then:
> I tried making /usr/share/ollama/.ollama a symlink to ~/.ollama;
and then:
`chown -R ollama ~/.ollama/models/...` ;
`ollama serve & ollama list;`
same amount of dice;
also noticed:
at one (non-canonical) point, ollama failed to start from cmd line and yielded an error msg that, to me, seemed to suggest it tries to (re)generate the default directory upon launch - with something equivalent to `mkdir` instead of something equivalent to `mkdir -p`;
...eventually deleted symlink and rebooted;
seems to be working;
closed this.
### OS
Linux
### GPU
Nvidia
### CPU
Intel
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PR_kwDOJ0Z1Ps5WYJ16
| 214
|
allow for concurrent pulls of the same files
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| 2023-07-25T21:10:24
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resolves #200
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I_kwDOJ0Z1Ps6IDJVP
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server not responding
|
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"html_url": "https://github.com/thomassrour",
"followers_url": "https://api.github.com/users/thomassrour/followers",
"following_url": "https://api.github.com/users/thomassrour/following{/other_user}",
"gists_url": "https://api.github.com/users/thomassrour/gists{/gist_id}",
"starred_url": "https://api.github.com/users/thomassrour/starred{/owner}{/repo}",
"subscriptions_url": "https://api.github.com/users/thomassrour/subscriptions",
"organizations_url": "https://api.github.com/users/thomassrour/orgs",
"repos_url": "https://api.github.com/users/thomassrour/repos",
"events_url": "https://api.github.com/users/thomassrour/events{/privacy}",
"received_events_url": "https://api.github.com/users/thomassrour/received_events",
"type": "User",
"user_view_type": "public",
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closed
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"followers_url": "https://api.github.com/users/dhiltgen/followers",
"following_url": "https://api.github.com/users/dhiltgen/following{/other_user}",
"gists_url": "https://api.github.com/users/dhiltgen/gists{/gist_id}",
"starred_url": "https://api.github.com/users/dhiltgen/starred{/owner}{/repo}",
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| 2024-05-07T07:39:08
| 2024-05-31T21:35:46
| 2024-05-31T21:35:45
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NONE
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| null | null | null |
### What is the issue?
Hello,
I have trouble reaching my ollama container. I have tried using the images for 0.1.32 and 0.1.33, as some users reported bugs 0.1.33 but it doesn't work on either. Here is the output of docker logs, when trying mixtral (I have also tried llama3, same result) :
time=2024-05-07T07:33:21.130Z level=INFO source=images.go:817 msg="total blobs: 10"
time=2024-05-07T07:33:21.134Z level=INFO source=images.go:824 msg="total unused blobs removed: 0"
time=2024-05-07T07:33:21.135Z level=INFO source=routes.go:1143 msg="Listening on [::]:11434 (version 0.1.32)"
time=2024-05-07T07:33:21.136Z level=INFO source=payload.go:28 msg="extracting embedded files" dir=/tmp/ollama3873501864/runners
time=2024-05-07T07:33:21.136Z level=DEBUG source=payload.go:160 msg=extracting variant=cpu file=build/linux/x86_64/cpu/bin/ollama_llama_server.gz
time=2024-05-07T07:33:21.136Z level=DEBUG source=payload.go:160 msg=extracting variant=cpu_avx file=build/linux/x86_64/cpu_avx/bin/ollama_llama_server.gz
time=2024-05-07T07:33:21.136Z level=DEBUG source=payload.go:160 msg=extracting variant=cpu_avx2 file=build/linux/x86_64/cpu_avx2/bin/ollama_llama_server.gz
time=2024-05-07T07:33:21.136Z level=DEBUG source=payload.go:160 msg=extracting variant=cuda_v11 file=build/linux/x86_64/cuda_v11/bin/libcublas.so.11.gz
time=2024-05-07T07:33:21.136Z level=DEBUG source=payload.go:160 msg=extracting variant=cuda_v11 file=build/linux/x86_64/cuda_v11/bin/libcublasLt.so.11.gz
time=2024-05-07T07:33:21.136Z level=DEBUG source=payload.go:160 msg=extracting variant=cuda_v11 file=build/linux/x86_64/cuda_v11/bin/libcudart.so.11.0.gz
time=2024-05-07T07:33:21.136Z level=DEBUG source=payload.go:160 msg=extracting variant=cuda_v11 file=build/linux/x86_64/cuda_v11/bin/ollama_llama_server.gz
time=2024-05-07T07:33:21.136Z level=DEBUG source=payload.go:160 msg=extracting variant=rocm_v60002 file=build/linux/x86_64/rocm_v60002/bin/deps.txt.gz
time=2024-05-07T07:33:21.136Z level=DEBUG source=payload.go:160 msg=extracting variant=rocm_v60002 file=build/linux/x86_64/rocm_v60002/bin/ollama_llama_server.gz
time=2024-05-07T07:33:25.457Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cpu
time=2024-05-07T07:33:25.457Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cpu_avx
time=2024-05-07T07:33:25.457Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cpu_avx2
time=2024-05-07T07:33:25.457Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cuda_v11
time=2024-05-07T07:33:25.457Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/rocm_v60002
time=2024-05-07T07:33:25.457Z level=INFO source=payload.go:41 msg="Dynamic LLM libraries [cuda_v11 rocm_v60002 cpu cpu_avx cpu_avx2]"
time=2024-05-07T07:33:25.457Z level=DEBUG source=payload.go:42 msg="Override detection logic by setting OLLAMA_LLM_LIBRARY"
time=2024-05-07T07:33:25.457Z level=INFO source=gpu.go:121 msg="Detecting GPU type"
time=2024-05-07T07:33:25.457Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*"
time=2024-05-07T07:33:25.457Z level=DEBUG source=gpu.go:286 msg="gpu management search paths: [/tmp/ollama3873501864/runners/cuda*/libcudart.so* /usr/local/cuda/lib64/libcudart.so* /usr/lib/x86_64-linux-gnu/nvidia/current/libcudart.so* /usr/lib/x86_64-linux-gnu/libcudart.so* /usr/lib/wsl/lib/libcudart.so* /usr/lib/wsl/drivers/*/libcudart.so* /opt/cuda/lib64/libcudart.so* /usr/local/cuda*/targets/aarch64-linux/lib/libcudart.so* /usr/lib/aarch64-linux-gnu/nvidia/current/libcudart.so* /usr/lib/aarch64-linux-gnu/libcudart.so* /usr/local/cuda/lib*/libcudart.so* /usr/lib*/libcudart.so* /usr/local/lib*/libcudart.so* /usr/local/nvidia/lib/libcudart.so** /usr/local/nvidia/lib64/libcudart.so**]"
time=2024-05-07T07:33:25.462Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3873501864/runners/cuda_v11/libcudart.so.11.0]"
wiring cudart library functions in /tmp/ollama3873501864/runners/cuda_v11/libcudart.so.11.0
dlsym: cudaSetDevice
dlsym: cudaDeviceSynchronize
dlsym: cudaDeviceReset
dlsym: cudaMemGetInfo
dlsym: cudaGetDeviceCount
dlsym: cudaDeviceGetAttribute
dlsym: cudaDriverGetVersion
CUDA driver version: 12-0
time=2024-05-07T07:33:25.495Z level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart"
time=2024-05-07T07:33:25.495Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
[0] CUDA totalMem 85895020544
[0] CUDA freeMem 79269199872
time=2024-05-07T07:33:25.661Z level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 8.0"
releasing cudart library
time=2024-05-07T07:33:30.257Z level=DEBUG source=gguf.go:57 msg="model = &llm.gguf{containerGGUF:(*llm.containerGGUF)(0xc000426c80), kv:llm.KV{}, tensors:[]*llm.Tensor(nil), parameters:0x0}"
time=2024-05-07T07:33:30.522Z level=DEBUG source=gguf.go:193 msg="general.architecture = llama"
time=2024-05-07T07:33:30.528Z level=INFO source=gpu.go:121 msg="Detecting GPU type"
time=2024-05-07T07:33:30.528Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*"
time=2024-05-07T07:33:30.528Z level=DEBUG source=gpu.go:286 msg="gpu management search paths: [/tmp/ollama3873501864/runners/cuda*/libcudart.so* /usr/local/cuda/lib64/libcudart.so* /usr/lib/x86_64-linux-gnu/nvidia/current/libcudart.so* /usr/lib/x86_64-linux-gnu/libcudart.so* /usr/lib/wsl/lib/libcudart.so* /usr/lib/wsl/drivers/*/libcudart.so* /opt/cuda/lib64/libcudart.so* /usr/local/cuda*/targets/aarch64-linux/lib/libcudart.so* /usr/lib/aarch64-linux-gnu/nvidia/current/libcudart.so* /usr/lib/aarch64-linux-gnu/libcudart.so* /usr/local/cuda/lib*/libcudart.so* /usr/lib*/libcudart.so* /usr/local/lib*/libcudart.so* /usr/local/nvidia/lib/libcudart.so** /usr/local/nvidia/lib64/libcudart.so**]"
time=2024-05-07T07:33:30.529Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3873501864/runners/cuda_v11/libcudart.so.11.0]"
wiring cudart library functions in /tmp/ollama3873501864/runners/cuda_v11/libcudart.so.11.0
dlsym: cudaSetDevice
dlsym: cudaDeviceSynchronize
dlsym: cudaDeviceReset
dlsym: cudaMemGetInfo
dlsym: cudaGetDeviceCount
dlsym: cudaDeviceGetAttribute
dlsym: cudaDriverGetVersion
CUDA driver version: 12-0
time=2024-05-07T07:33:30.530Z level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart"
time=2024-05-07T07:33:30.530Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
[0] CUDA totalMem 85895020544
[0] CUDA freeMem 79269199872
time=2024-05-07T07:33:30.674Z level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 8.0"
releasing cudart library
time=2024-05-07T07:33:30.719Z level=INFO source=gpu.go:121 msg="Detecting GPU type"
time=2024-05-07T07:33:30.719Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*"
time=2024-05-07T07:33:30.719Z level=DEBUG source=gpu.go:286 msg="gpu management search paths: [/tmp/ollama3873501864/runners/cuda*/libcudart.so* /usr/local/cuda/lib64/libcudart.so* /usr/lib/x86_64-linux-gnu/nvidia/current/libcudart.so* /usr/lib/x86_64-linux-gnu/libcudart.so* /usr/lib/wsl/lib/libcudart.so* /usr/lib/wsl/drivers/*/libcudart.so* /opt/cuda/lib64/libcudart.so* /usr/local/cuda*/targets/aarch64-linux/lib/libcudart.so* /usr/lib/aarch64-linux-gnu/nvidia/current/libcudart.so* /usr/lib/aarch64-linux-gnu/libcudart.so* /usr/local/cuda/lib*/libcudart.so* /usr/lib*/libcudart.so* /usr/local/lib*/libcudart.so* /usr/local/nvidia/lib/libcudart.so** /usr/local/nvidia/lib64/libcudart.so**]"
time=2024-05-07T07:33:30.720Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3873501864/runners/cuda_v11/libcudart.so.11.0]"
wiring cudart library functions in /tmp/ollama3873501864/runners/cuda_v11/libcudart.so.11.0
dlsym: cudaSetDevice
dlsym: cudaDeviceSynchronize
dlsym: cudaDeviceReset
dlsym: cudaMemGetInfo
dlsym: cudaGetDeviceCount
dlsym: cudaDeviceGetAttribute
dlsym: cudaDriverGetVersion
CUDA driver version: 12-0
time=2024-05-07T07:33:30.721Z level=INFO source=gpu.go:126 msg="Nvidia GPU detected via cudart"
time=2024-05-07T07:33:30.721Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
[0] CUDA totalMem 85895020544
[0] CUDA freeMem 79269199872
time=2024-05-07T07:33:30.869Z level=INFO source=gpu.go:202 msg="[cudart] CUDART CUDA Compute Capability detected: 8.0"
releasing cudart library
time=2024-05-07T07:33:30.916Z level=INFO source=server.go:127 msg="offload to gpu" reallayers=33 layers=33 required="26042.6 MiB" used="26042.6 MiB" available="75597.0 MiB" kv="256.0 MiB" fulloffload="184.0 MiB" partialoffload="935.0 MiB"
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cpu
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cpu_avx
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cpu_avx2
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cuda_v11
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/rocm_v60002
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cpu
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cpu_avx
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cpu_avx2
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/cuda_v11
time=2024-05-07T07:33:30.916Z level=DEBUG source=payload.go:68 msg="availableServers : found" file=/tmp/ollama3873501864/runners/rocm_v60002
time=2024-05-07T07:33:30.916Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-05-07T07:33:30.918Z level=DEBUG source=server.go:259 msg="LD_LIBRARY_PATH=/usr/local/nvidia/lib:/usr/local/nvidia/lib64:/tmp/ollama3873501864/runners/cuda_v11"
time=2024-05-07T07:33:30.918Z level=INFO source=server.go:264 msg="starting llama server" cmd="/tmp/ollama3873501864/runners/cuda_v11/ollama_llama_server --model /root/.ollama/models/blobs/sha256-e9e56e8bb5f0fcd4860675e6837a8f6a94e659f5fa7dce6a1076279336320f2b --ctx-size 2048 --batch-size 512 --embedding --log-format json --n-gpu-layers 33 --verbose --port 33787"
time=2024-05-07T07:33:30.919Z level=INFO source=server.go:389 msg="waiting for llama runner to start responding"
{"function":"server_params_parse","level":"WARN","line":2494,"msg":"server.cpp is not built with verbose logging.","tid":"140005422133248","timestamp":1715067210}
time=2024-05-07T07:33:30.970Z level=DEBUG source=server.go:420 msg="server not yet available" error="health resp: Get \"http://127.0.0.1:33787/health\": dial tcp 127.0.0.1:33787: connect: connection refused"
{"build":1,"commit":"7593639","function":"main","level":"INFO","line":2819,"msg":"build info","tid":"140005422133248","timestamp":1715067210}
{"function":"main","level":"INFO","line":2822,"msg":"system info","n_threads":4,"n_threads_batch":-1,"system_info":"AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 0 | NEON = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | ","tid":"140005422133248","timestamp":1715067210,"total_threads":8}
llama_model_loader: loaded meta data with 26 key-value pairs and 995 tensors from /root/.ollama/models/blobs/sha256-e9e56e8bb5f0fcd4860675e6837a8f6a94e659f5fa7dce6a1076279336320f2b (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.name str = mistralai
llama_model_loader: - kv 2: llama.context_length u32 = 32768
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 9: llama.expert_count u32 = 8
llama_model_loader: - kv 10: llama.expert_used_count u32 = 2
llama_model_loader: - kv 11: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 12: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 13: general.file_type u32 = 2
llama_model_loader: - kv 14: tokenizer.ggml.model str = llama
llama_model_loader: - kv 15: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 16: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 17: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 18: tokenizer.ggml.merges arr[str,58980] = ["▁ t", "i n", "e r", "▁ a", "h e...
llama_model_loader: - kv 19: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 20: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 21: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 22: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 23: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 24: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 25: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type f16: 32 tensors
llama_model_loader: - type q4_0: 833 tensors
llama_model_loader: - type q8_0: 64 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_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 = 8
llm_load_print_meta: n_expert_used = 2
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 = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: 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 = 8x7B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 46.70 B
llm_load_print_meta: model size = 24.62 GiB (4.53 BPW)
llm_load_print_meta: general.name = mistralai
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: yes
ggml_cuda_init: CUDA_USE_TENSOR_CORES: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: GRID A100D-80C, compute capability 8.0, VMM: no
time=2024-05-07T07:33:31.220Z level=DEBUG source=server.go:420 msg="server not yet available" error="server not responding"
llm_load_tensors: ggml ctx size = 0.96 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: CUDA_Host buffer size = 70.31 MiB
llm_load_tensors: CUDA0 buffer size = 25145.55 MiB
..................................................time=2024-05-07T07:35:23.693Z level=DEBUG source=server.go:420 msg="server not yet available" error="health resp: Get \"http://127.0.0.1:33787/health\": dial tcp 127.0.0.1:33787: i/o timeout"
time=2024-05-07T07:35:23.894Z level=DEBUG source=server.go:420 msg="server not yet available" error="server not responding"
`
Thanks for your help
### OS
Docker
### GPU
Nvidia
### CPU
_No response_
### Ollama version
0.1.32, 0.1.33
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I_kwDOJ0Z1Ps6LkjD6
| 4,929
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Never-ending loading whether using the OpenAI API or Ollama Python
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| 2024-06-08T11:30:37
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NONE
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### What is the issue?
Hi, I'm having a problem:
Whether I'm using the OpenAI API or Ollama-python, I get bogged down in never-ending loading when doing model inference.
When I check the logs, I see that there is no record of a POST request in the logs.
I tried to fix it by reinstalling and rebooting, but that didn't work.
But what very much puzzles me is that on my other laptop, with a similar working environment (Windows), both run fine.
I didn't find a similar problem in issue, so any suggestions on how to solve this problem would be greatly appreciated.
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.42
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I_kwDOJ0Z1Ps5zQahI
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|
"Delete word" buggy in TUI
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NONE
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Using a delete word hotkey (e.g. ctrl-w) when the cursor is within the first word of a prompt causes the entire prompt to be deleted.
Steps to reproduce:
1. Type several words at an ollama LLM prompt
2. Move the cursor to the first word (immediately following ">>>")
3. Use a "delete word" hotkey (e.g. ctrl-w)
4. The entire prompt is deleted
Expected behavior: Only the first word of the prompt is deleted
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PR_kwDOJ0Z1Ps5-mcHl
| 7,203
|
Move macos v11 support flags to build script
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| 2024-10-14T22:45:30
| 2024-10-16T19:49:49
| 2024-10-16T19:49:46
|
COLLABORATOR
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Having v11 support hard-coded into the cgo settings causes warnings for newer Xcode versions. This should help keep the build clean for users building from source with the latest tools, while still allow us to target the older OS via our CI processes.
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I_kwDOJ0Z1Ps6cASfS
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Fails to build on macOS with "fatal error: {'string','cstdint'} file not found"
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| 2024-10-28T05:11:29
| 2024-12-08T21:20:34
| null |
NONE
|
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### What is the issue?
I followed the instructions for building on mac [here](https://github.com/ollama/ollama/blob/main/docs/development.md#macos), but this failed at the `go generate` step.
Running `go generate ./...` fails with a set of header files not found errors.
```
$ go generate ./...
+ set -o pipefail
+ compress_pids=
+ echo 'Starting darwin generate script'
Starting darwin generate script
++ dirname ./gen_darwin.sh
<snip>
[ 26%] Linking CXX static library libggml.a
[ 26%] Built target ggml
[ 33%] Building CXX object src/CMakeFiles/llama.dir/llama.cpp.o
[ 40%] Building CXX object src/CMakeFiles/llama.dir/llama-vocab.cpp.o
[ 46%] Building CXX object src/CMakeFiles/llama.dir/llama-sampling.cpp.o
[ 53%] Building CXX object src/CMakeFiles/llama.dir/unicode-data.cpp.o
[ 53%] Building CXX object src/CMakeFiles/llama.dir/llama-grammar.cpp.o
[ 53%] Building CXX object src/CMakeFiles/llama.dir/unicode.cpp.o
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/unicode.cpp:5:
.../src/github.com/ollama/ollama/llm/llama.cpp/src/unicode.h:3:10: fatal error: 'cstdint' file not found
3 | #include <cstdint>
| ^~~~~~~~~
1 error generated.
make[3]: *** [src/CMakeFiles/llama.dir/unicode.cpp.o] Error 1
make[3]: *** Waiting for unfinished jobs....
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/unicode-data.cpp:3:
.../src/github.com/ollama/ollama/llm/llama.cpp/src/unicode-data.h:3:10: fatal error: 'cstdint' file not found
3 | #include <cstdint>
| ^~~~~~~~~
1 error generated.
make[3]: *** [src/CMakeFiles/llama.dir/unicode-data.cpp.o] Error 1
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-grammar.cpp:1:
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-grammar.h:3:
.../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-impl.h:5:10: fatal error: 'string' file not found
5 | #include <string>
| ^~~~~~~~
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-vocab.cpp:1:
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-vocab.h:3:
.../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-impl.h:5:10: fatal error: 'string' file not found
5 | #include <string>
| ^~~~~~~~
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-sampling.cpp:1:
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-sampling.h:5:
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-grammar.h:3:
.../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-impl.h:5:10: fatal error: 'string' file not found
5 | #include <string>
| ^~~~~~~~
1 error generated.
make[3]: *** [src/CMakeFiles/llama.dir/llama-grammar.cpp.o] Error 1
1 error generated.
1 error generated.
make[3]: *** [src/CMakeFiles/llama.dir/llama-vocab.cpp.o] Error 1
In file included from .../src/github.com/ollama/ollama/llm/llama.cpp/src/llama.cpp:1:
.../src/github.com/ollama/ollama/llm/llama.cpp/src/llama-impl.h:5:10: fatal error: 'string' file not found
5 | #include <string>
| ^~~~~~~~
make[3]: *** [src/CMakeFiles/llama.dir/llama-sampling.cpp.o] Error 1
1 error generated.
make[3]: *** [src/CMakeFiles/llama.dir/llama.cpp.o] Error 1
make[2]: *** [src/CMakeFiles/llama.dir/all] Error 2
make[1]: *** [ext_server/CMakeFiles/ollama_llama_server.dir/rule] Error 2
make: *** [ollama_llama_server] Error 2
llm/generate/generate_darwin.go:3: running "bash": exit status 2
```
Noted dependencies:
```
$ go version
go version go1.22.8 darwin/amd64
$ cmake --version
cmake version 3.30.5
CMake suite maintained and supported by Kitware (kitware.com/cmake).
$ gcc --version
Apple clang version 16.0.0 (clang-1600.0.26.3)
Target: x86_64-apple-darwin23.6.0
Thread model: posix
InstalledDir: /Library/Developer/CommandLineTools/usr/bin
```
I expected to see a completed generation (as seen on linux). Alternatively, if other deps are required, I expected to see them listed in the [developer build instructions](https://github.com/ollama/ollama/blob/main/docs/development.md#macos).
### OS
macOS
### GPU
Apple
### CPU
AMD
### Ollama version
abd5dfd06a8e1309394b07c111be5f8412fca600
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I_kwDOJ0Z1Ps6nt6sh
| 8,613
|
[v0.5.4] Download timeouts cause download cache corruption. Any download that needs to be retried by re-running ollama ends up corrupted at 100% download(file sha256-sha256hash-partial-0 not found).
|
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| 2025-01-27T19:04:50
| 2025-01-27T19:40:50
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NONE
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### What is the issue?
Running ollama through alpaca.
I'm aware this is a seperate project will mirror the bug report.
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.5.4
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I_kwDOJ0Z1Ps6PFEXE
| 5,596
|
version is 0.2.1 can't run glm4
|
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| 2024-07-11T03:58:29
| 2024-07-11T03:58:28
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NONE
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### What is the issue?
run `ollama run glm4`
Error: this model is not supported by your version of Ollama. You may need to upgrade
### OS
Windows
### GPU
AMD
### CPU
AMD
### Ollama version
ollama version is 0.2.1
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PR_kwDOJ0Z1Ps6JYJaA
| 8,665
|
Fix /api/create status code
|
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| 2025-01-29T15:12:55
| 2025-01-29T23:07:19
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The server just bails out without proper http error codes inside the goroutine in /api/create route. Added a simple abort function to write the proper status code and send the `gin.H` map. Also changed the error name and message to be grammatically correct but that's a nitpick, I wouldn't wanna be rude 🙃
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PR_kwDOJ0Z1Ps51cMCo
| 5,709
|
Add Metrics to `api\embed` response
|
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| 2024-07-15T22:10:46
| 2024-07-30T20:12:23
| 2024-07-30T20:12:21
|
CONTRIBUTOR
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"timings" is returned per request_completion in server.cpp, which must be aggregated to return metrics for a batch of completions.
supporting: prompt_eval_count (total number of tokens evaluated), load duration, total duration
|
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I_kwDOJ0Z1Ps588gxv
| 2,158
|
Seed option is not working on API
|
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| 2024-01-23T14:40:32
| 2024-01-23T18:25:52
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|
NONE
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Even configuring the option seed, the API return is different for each request.
Im using the version 0.1.20
```
{
"model": "mistral",
"stream": false,
"options": {
"seed": 0
},
"prompt":"Why is the sky blue?"
}
```
|
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| 2,031,418,323
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PR_kwDOJ0Z1Ps5hdVaY
| 1,419
|
Simple chat example for typescript
|
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| 2023-12-07T19:49:25
| 2023-12-07T22:42:24
| 2023-12-07T22:42:24
|
CONTRIBUTOR
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A simple example of the chat endpoint
|
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PR_kwDOJ0Z1Ps57DuU5
| 6,735
|
runner.go: Prompt caching
|
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| 2024-09-10T21:06:37
| 2024-09-11T03:45:02
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CONTRIBUTOR
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Currently, KV cache entries from a sequence are discarded at the end of each processing run. In a typical chat conversation, this results in each message taking longer and longer to process as the entire history of the conversation needs to be replayed.
Prompt caching retains the KV entries as long as possible so that we only need to process the newest message in the converation, at least until there are too many simultaneous conversations and something needs to be evicted.
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PR_kwDOJ0Z1Ps5lZLEE
| 2,263
|
Bump llama.cpp to b1999
|
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| 2024-01-30T00:57:33
| 2024-01-31T16:39:44
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COLLABORATOR
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This requires an upstream change to support graceful termination, carried as a patch.
Tracking branches for the 2 patches:
- 01-cache.diff - https://github.com/dhiltgen/llama.cpp/tree/kv_cache
- 02-shutdown.diff - https://github.com/dhiltgen/llama.cpp/tree/server_shutdown
I'm going to mark it draft until I can run more testing (so far happy path on windows, mac and linux looks good)
|
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I_kwDOJ0Z1Ps59aSnS
| 2,239
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stablelm2 is missing in the homepage list
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| 2024-01-28T08:46:34
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NONE
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stablelm2 --verbose is missing in the homepage list
who knows what else
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I_kwDOJ0Z1Ps529NdZ
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Food for thought use cases: Github Actions :octocat:
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| 2023-11-15T23:34:21
| 2024-02-20T01:10:05
| 2024-02-20T01:10:04
|
NONE
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I've been working on the implementation of DevSecOps Platforms and I think I came up with a Github Action that can execute the models... Obviously:
* You must have Github Action Runners powered by GPUs
* You can implement pretty much anything with the model given you have the file-system and the containers to implement a business logic
* I have embedded the server and the pull of the models in caches to try to speed up the process
* The intent is to use any model for Source-code, Software Engineering, Cloud, etc...
Just food for thought...
> **NOTES**: I still have problems of #676 and #1072 for this reason, I build a data container with the models (docker image digests) and push them to a docker registry so that I can by-pass the 403 with a cached version of the models...
# 🧠 Select a Model
# 🏃♂️ Running
# 🔢 Results
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I_kwDOJ0Z1Ps6Iiyv5
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Models often don't load on versions after 0.1.132
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NONE
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### What is the issue?
Many models, in particular codegemma 1.1 7b q8_0, don't load for various reasons on versions after 0.1.132. Works fine on 132. I don't have the logs on hand at the moment, but can add them later. The errors relate to out of memory errors and unable to reset the GPU VRAM.
This is using ROCm (ollama distribution of it, from the tar.gz) on AMD RX 6800 XT.
Is there a centralized issue for this already?
### OS
_No response_
### GPU
AMD
### CPU
AMD
### Ollama version
0.1.133-0.1.136
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PR_kwDOJ0Z1Ps5gKOHw
| 1,244
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do not fail on unsupported template variables
|
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| 2023-11-22T18:11:04
| 2023-12-06T21:23:05
| 2023-12-06T21:23:04
|
CONTRIBUTOR
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- do not fail on unsupported parameters in model template
resolves #1242
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I_kwDOJ0Z1Ps6PcQq4
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Ollama 0.2.2 cannot read the system prompt when invoking the API using Python.
|
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| 2024-07-13T05:25:11
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### What is the issue?
model: qwen2&glm4
After the Ollama update, when I invoke Ollama through Python, the model is unable to read the system prompt.
```
def LLM_Process(model, sys_prom, usr_prom):
messages = [
{'role': 'user', 'content': usr_prom},
{'role': 'system', 'content': sys_prom}
]
resp = ollama.chat(model, messages)
try:
out = resp['message']['content']
return out
except AttributeError:
# 可能是信息太长或有违规信息
print("跳过处理。")
return None
```
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.2.2
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PR_kwDOJ0Z1Ps5qtThd
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|
Bump llama.cpp to b2527
|
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| 2,054,195,066
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PR_kwDOJ0Z1Ps5iqpZv
| 1,675
|
Quiet down llama.cpp logging by default
|
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| 2023-12-22T16:48:08
| 2023-12-22T16:57:21
| 2023-12-22T16:57:18
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COLLABORATOR
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By default builds will now produce non-debug and non-verbose binaries. To enable verbose logs in llama.cpp and debug symbols in the native code, set `CGO_CFLAGS=-g`
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I_kwDOJ0Z1Ps6CItpK
| 3,096
|
Is it possible to download the models from browser?
|
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NONE
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Hello I need the download models from browser. Is it possible?
|
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I_kwDOJ0Z1Ps57I2H_
| 1,784
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Simpler UI / CLI for predicting model performance on user's device?
|
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Hi, I was wondering if the current Ollama system grabs any system information regarding CPU and RAM capacities. Especially since a majority of users are on Mac, there's a finite # of hardware specs for Ollama to recognize. Then, Ollama can automatically recommend which models will run best on the user's Mac.
I think this would be easier for first-time users, and promote Ollama's accessibility for non-technicals (average MacOS users).
If this doesn't exist, I would be more than happy to make a branch with my suggestion. If the underlying CLI/code to recognize device GPU/CPU limitations already exists, I'd love to take a stab at incorporating it into the UI. I'd appreciate any direction for which part of the codebase to observe.
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PR_kwDOJ0Z1Ps5beMgW
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|
use int64 consistently
|
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| 2023-09-28T18:07:46
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|
CONTRIBUTOR
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this reduces type conversion
|
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I_kwDOJ0Z1Ps6Y6ufT
| 7,099
|
Integrate in Chrome, Chrome Extension
|
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| 2024-10-04T06:09:31
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Hi Ollama team,
I’d like to suggest a feature to integrate Ollama with a Chrome extension that enables auto-replies directly within email platforms (Gmail, Outlook) and other text fields (social media, messaging apps, etc.).
Main Benefit: Users could generate replies without leaving the current tab or copy-pasting content back and forth. This would streamline workflows, allowing users to instantly create context-aware responses, improving productivity without interrupting their browser-based tasks.
Workflow:
Install the Ollama Chrome extension.
Click a “Generate Auto-Reply” button within any text box (e.g., Gmail or social media).
Ollama generates a response based on the context of the conversation.
This would be especially useful for customer support, sales, or anyone handling repetitive communications.
Thanks for considering this!
|
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I_kwDOJ0Z1Ps6YAZtZ
| 6,978
|
rerank model
|
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how can i add HF - BAAI/bge-reranker-v2-m3 rerank model to ollama
while trying this approach i am getting architecture error
can anyone help to resolve this issue
|
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I_kwDOJ0Z1Ps6GIV-A
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升级最新版启动报错
|
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| 2024-04-18T10:31:38
| 2024-04-18T10:32:54
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### What is the issue?
升级了最新版本0.1.32，启动报错，错误如下：
Error: llama runner process no longer running: 3221225785
后来又重装了0.1.31版本，正常启动。
我的是windows10系统
### OS
Windows
### GPU
Intel
### CPU
Intel
### Ollama version
0.1.32
|
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PR_kwDOJ0Z1Ps5kwv9m
| 2,142
|
Debug logging on init failure
|
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One class of error we're seeing on ROCm looks like this in the log...
```
2024/01/21 22:00:15 dyn_ext_server.go:90: INFO Loading Dynamic llm server: /tmp/ollama1546965028/rocm_v5/libext_server.so
2024/01/21 22:00:15 dyn_ext_server.go:139: INFO Initializing llama server
free(): invalid pointer
```
I'm not sure yet what the root cause is, but hopefully this debug log will yield some more insight.
|
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PR_kwDOJ0Z1Ps5tNpDc
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|
chore(deps): Update dependencies
|
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Please note that most updates are minor except macOS: 12 (2022) to 13 (2023).
In any case, even in 12 there would be a much newer (default) Xcode 14.2 available, why has the release been downgraded to 13.4 recently by 2 major versions? I could only see a pull request, but no issue... 🤔
https://github.com/actions/runner-images/blob/main/images/macos/macos-12-Readme.md#xcode
And CUDA 12.4 would seem like a low hanging fruit (11.3 is from 2021)... but the server version doesn't matter for performance? 😅
|
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I_kwDOJ0Z1Ps6nlTf4
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ollama fails to detect old models after update
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### What is the issue?
so my setup has a semi link for runing ollama model and I think i have over a tera byte of model weights so if there is a way to make it so i dont need to download the entire thing again i would be very happy
### OS
Linux
### GPU
_No response_
### CPU
_No response_
### Ollama version
0.5.7
|
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PR_kwDOJ0Z1Ps6E5MBB
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|
feat: add option to specify runner name and path in env
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| 2024-12-11T17:45:06
| 2024-12-11T18:00:46
| null |
CONTRIBUTOR
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Add option to specify custom runner path.
This will be useful as a temporary solution for [using vulkan](https://github.com/ollama/ollama/pull/5059) until the related PR is merged.
macOS:
```console
git clone https://github.com/thewh1teagle/ollama -b feat/custom-runner-path
cd ollama
echo "Building darwin arm64"
GOOS=darwin ARCH=arm64 GOARCH=arm64 make -j 8 dist
OLLAMA_DEBUG=true OLLAMA_RUNNER_NAME="cpu" OLLAMA_RUNNER_PATH="/path/to/custom/runner" ./dist/darwin-arm64/bin/ollama serve
```
Windows:
```console
# https://www.msys2.org/
C:\msys64\msys2_shell.cmd -here -no-start -defterm -clang64
pacman -S --needed $MINGW_PACKAGE_PREFIX-{go,clang,vulkan-devel,github-cli} make git
git clone https://github.com/thewh1teagle/ollama -b feat/custom-runner-path
cd ollama
export GOROOT=/c/msys64/clang64/lib/go
export CGO_ENABLED="1"
export CC="clang"
export CXX="clang++"
make -j 8
go build .
OLLAMA_DEBUG=true OLLAMA_RUNNER_NAME="cpu" OLLAMA_RUNNER_PATH="/path/to/runner.exe" ./ollama.exe serve
```
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I_kwDOJ0Z1Ps6idQqR
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Structured JSON does not handle arrays at the top level properly
|
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| 2024-12-08T22:55:25
| 2024-12-20T22:15:32
| null |
NONE
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### What is the issue?
It looks like structured JSON is not respected when an array is specified at the top level.
Example 1:
Request:
```json
{
"model": "llama3.1",
"messages": [
{
"role": "system",
"content": "Given a phrase, give a list of categories"
},
{
"role": "user",
"content": "I watched a movie with friends and there was a cat in it"
}
],
"stream": false,
"format": {
"type": "array",
"items": {
"type": "string"
}
}
}
```
Response:
```json
{
"model": "llama3.1",
"created_at": "2024-12-08T22:46:35.146738527Z",
"message": {
"role": "assistant",
"content": "[1] "
},
"done_reason": "stop",
"done": true,
"total_duration": 1520253202,
"load_duration": 29133573,
"prompt_eval_count": 37,
"prompt_eval_duration": 538000000,
"eval_count": 5,
"eval_duration": 951000000
}
```
Example 2:
Request:
```json
{
"model": "llama3.1",
"messages": [
{
"role": "system",
"content": "Given a phrase, give a list of categories. Respond in valid JSON"
},
{
"role": "user",
"content": "I watched a movie with friends and there was a cat in it"
}
],
"stream": false,
"format": {
"type": "array",
"items": {
"type": "object",
"items": {"name": {"type": "string"}, "description": {"type": "string"}},
"required": ["name", "description"]
}
}
}
```
Response:
```json
{
"model": "llama3.1",
"created_at": "2024-12-08T22:52:42.640180789Z",
"message": {
"role": "assistant",
"content": "[\n {\n \"category\": \"Entertainment\"\n },\n {\n \"category\": \"Social\"\n },\n {\n \"category\": \"Animals\"\n }\n]"
},
"done_reason": "stop",
"done": true,
"total_duration": 81840944323,
"load_duration": 29780952,
"prompt_eval_count": 42,
"prompt_eval_duration": 72020000000,
"eval_count": 38,
"eval_duration": 9789000000
}
```
I realize these examples are kind of nonsensical because I'm asking the LLM to do stuff that doesn't really line up with the schema. But surely it should be impossible for it to disobey the grammar, right? It seems to do fine if the top property is an object instead of an array.
### OS
Linux
### GPU
_No response_
### CPU
AMD
### Ollama version
0.5.0
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| 2,677,036,814
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I_kwDOJ0Z1Ps6fkFcO
| 7,768
|
Model not loaded on all GPUs for load balancing
|
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| 2024-11-20T20:04:40
| 2024-11-20T20:46:24
| 2024-11-20T20:32:58
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NONE
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### What is the issue?
I expect that on a Multi GPU system it would load the model on all GPUs with the docker container loaded with `--gpus all` to balance the requests load between them.
Output of `docker logs ollama`:
```
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3060, compute capability 8.6, VMM: yes
```
Output of `docker exec -it ollama nvidia-smi`:
```
Wed Nov 20 20:00:03 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 565.57.01 Driver Version: 565.57.01 CUDA Version: 12.7 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 3060 On | 00000000:01:00.0 On | N/A |
| 54% 67C P2 99W / 100W | 7203MiB / 12288MiB | 100% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 NVIDIA GeForce RTX 3060 On | 00000000:05:00.0 Off | N/A |
| 41% 63C P2 99W / 100W | 8057MiB / 12288MiB | 100% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| 1 N/A N/A 52 C ...unners/cuda_v12/ollama_llama_server 3344MiB |
+-----------------------------------------------------------------------------------------+
```
### OS
Linux, Docker
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.4.2 from docker
|
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I_kwDOJ0Z1Ps6G2R04
| 3,896
|
Command-R fails when using format=json
|
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| 2024-04-25T00:00:58
| 2024-04-25T00:04:03
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### What is the issue?
For some reason command-r is failing when put into JSON format mode. It seems to work fine otherwise.
```
% ollama run command-r --verbose "output the usa as a json"
` ``json
{
"country": "United States of America",
"capital": "Washington, D.C.",
"population": 333,271,411,
"states": 50,
"president": "Joe Biden"
}
` ``
total duration: 6.241768s
load duration: 2.127476417s
prompt eval count: 12 token(s)
prompt eval duration: 249.108ms
prompt eval rate: 48.17 tokens/s
eval count: 63 token(s)
eval duration: 3.86453s
eval rate: 16.30 tokens/s
% ollama run command-r --format json --verbose "output the usa as a json"
Error: an unknown error was encountered while running the model
```
llama3 though does work for json format output:
```
% ollama run llama3 --format json --verbose "output the florida as a json"
{ "name": "Florida", "capital": "Tallahassee", "population": 21477747, "area": 170312, "cities": ["Jacksonville", "Miami", "Tamiami Trail", "Orlando", "St Petersburg"], "fun_facts": [ "Florida is known as the Sunshine State because of its
abundant sunshine and warm weather.", "The state has a diverse range of ecosystems, including beaches, mangroves, and forests." ] }
```
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.1.32
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I_kwDOJ0Z1Ps6CNmCo
| 3,120
|
Ollama cannot open models with unicode in the filepath
|
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| 2024-03-13T18:04:35
| 2024-04-16T21:00:14
| 2024-04-16T21:00:14
|
CONTRIBUTOR
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Tracking this issue here, split from #2753
```
time=2024-02-26T00:11:49.314+01:00 level=INFO source=dyn_ext_server.go:90 msg="Loading Dynamic llm server: C:\\Users\\ELJKO~1\\AppData\\Local\\Temp\\ollama816527122\\cpu_avx2\\ext_server.dll"
time=2024-02-26T00:11:49.314+01:00 level=INFO source=dyn_ext_server.go:150 msg="Initializing llama server"
llama_model_load: error loading model: failed to open C:\Users\Željko\.ollama\models\blobs\sha256-8934d96d3f08982e95922b2b7a2c626a1fe873d7c3b06e8e56d7bc0a1fef9246: No such file or directory
llama_load_model_from_file: failed to load model
llama_init_from_gpt_params: error: failed to load model 'C:\Users\Željko\.ollama\models\blobs\sha256-8934d96d3f08982e95922b2b7a2c626a1fe873d7c3b06e8e56d7bc0a1fef9246'
{"timestamp":1708902709,"level":"ERROR","function":"load_model","line":388,"message":"unable to load model","model":"C:\\Users\\Željko\\.ollama\\models\\blobs\\sha256-8934d96d3f08982e95922b2b7a2c626a1fe873d7c3b06e8e56d7bc0a1fef9246"}
time=2024-02-26T00:11:49.314+01:00 level=WARN source=llm.go:162 msg="Failed to load dynamic library C:\\Users\\ELJKO~1\\AppData\\Local\\Temp\\ollama816527122\\cpu_avx2\\ext_server.dll error loading model C:\\Users\\Željko\\.ollama\\models\\blobs\\sha256-8934d96d3f08982e95922b2b7a2c626a1fe873d7c3b06e8e56d7bc0a1fef92"
[GIN] 2024/02/26 - 00:11:49 | 500 | 332.6058ms | 127.0.0.1 | POST "/api/chat"
[GIN] 2024/02/26 - 00:15:10 | 200 | 509µs | 127.0.0.1 | GET "/api/version"
```
related llama.cpp fix:
https://github.com/ggerganov/llama.cpp/pull/5927
|
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| 2,786,428,310
|
I_kwDOJ0Z1Ps6mFYWW
| 8,417
|
Model request for need：QVQ-72B-Preview and qwen2-vl！
|
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| 2025-01-14T07:16:09
| 2025-01-15T22:15:12
| 2025-01-15T22:15:12
|
NONE
|
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Please add model：QVQ-72B-Preview and qwen2-vl to ollama！！！！
|
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https://github.com/ollama/ollama/pull/514
| 1,892,272,867
|
PR_kwDOJ0Z1Ps5aHeZV
| 514
|
Allow customization of ollama models etc path
|
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| 2023-09-12T11:04:25
| 2023-10-25T22:35:12
| 2023-10-25T22:35:11
|
CONTRIBUTOR
|
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|
Responding to https://github.com/jmorganca/ollama/issues/513
It turns out it wasn't that hard to patch it to be customizable via envvar.
|
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| 2,716,244,159
|
I_kwDOJ0Z1Ps6h5pi_
| 7,925
|
add code to enable ollama cli cmd logging , or disable the new ' if not tty exit ' code PLZZ
|
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[] | null | 4
| 2024-12-03T23:55:42
| 2024-12-10T21:08:32
| null |
NONE
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| null | null | null |
### What is the issue?
i have .bashrc like to log ollama cmds
newly , this completly stopped working ..
neither
tee -a $somelog < <( ollama .. )
nor
ollama |& tee -a $log
nor
ollama > >( cat )
.. stay alive
.. they exit after ai answers , .. or exit after loading model if no text as cli arguments are given
.. before it wasnt perfect but now .. :))
my solution is make tty check either somehow optional , or throw it away .. ..
greets ..
btw this is on android phone on termux compiled
.. i suppose that tty check is a general one ,
. eg same on debian and else ...
### OS
Linux
### GPU
Other
### CPU
Other
### Ollama version
0.0.0 ( 0.4.5 or so via git )
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I_kwDOJ0Z1Ps6BvIs-
| 3,010
|
"Error: invalid file magic" when creating Code Llama model
|
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| 2024-03-08T19:06:04
| 2024-03-09T13:35:19
| 2024-03-09T13:35:19
|
NONE
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Hello!
First and foremost, THANKS A LOT for Ollama! Your software is most useful!
I am trying to import a finetune of Code Llama 7B into Ollama. I get this error:
```
$ollama create musicllm -f Modelfile
transferring model data
creating model layer
Error: invalid file magic
```
Here is the model: https://huggingface.co/TristanBehrens/musicllm/tree/main
Here is the Modelfile:
```
FROM model.q5_k_m.gguf
TEMPLATE "[INST] {{ .Prompt }} [/INST]"
```
Any ideas, where I could looks?
All the best,
Tristan
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I_kwDOJ0Z1Ps6gsZGx
| 7,847
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Support for Nvidia Hymba
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It would be great to support Hymba in Ollama!
https://developer.nvidia.com/blog/hymba-hybrid-head-architecture-boosts-small-language-model-performance/
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I_kwDOJ0Z1Ps6MpRzT
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DeepSeek-Coder-V2-Lite-Instruct out of memory
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### What is the issue?
Hi,
Thanks for the great project.
I get a crash (OOM) when trying to load new deepseek-coder-v2. Other models work fine. I've just upgraded to latest pre-release just in case but same behavior.
```
jun 18 13:23:17 ollama[26949]: INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="41023" tid="137762752704512" timestamp=1718709797
jun 18 13:23:17 ollama[22256]: llama_model_loader: loaded meta data with 38 key-value pairs and 377 tensors from /usr/share/ollama/.ollama/models/blobs/sha256-5ff0abeeac1d2dbdd5455c0b49ba3b29a9ce3c1fb181b2eef2e948689d55d046 (version GGUF V3 (latest))
jun 18 13:23:17 ollama[22256]: llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output.
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 0: general.architecture str = deepseek2
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 1: general.name str = DeepSeek-Coder-V2-Lite-Instruct
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 2: deepseek2.block_count u32 = 27
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 3: deepseek2.context_length u32 = 163840
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 4: deepseek2.embedding_length u32 = 2048
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 5: deepseek2.feed_forward_length u32 = 10944
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 6: deepseek2.attention.head_count u32 = 16
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 7: deepseek2.attention.head_count_kv u32 = 16
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 8: deepseek2.rope.freq_base f32 = 10000.000000
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 9: deepseek2.attention.layer_norm_rms_epsilon f32 = 0.000001
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 10: deepseek2.expert_used_count u32 = 6
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 11: general.file_type u32 = 2
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 12: deepseek2.leading_dense_block_count u32 = 1
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 13: deepseek2.vocab_size u32 = 102400
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 14: deepseek2.attention.kv_lora_rank u32 = 512
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 15: deepseek2.attention.key_length u32 = 192
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 16: deepseek2.attention.value_length u32 = 128
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 17: deepseek2.expert_feed_forward_length u32 = 1408
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 18: deepseek2.expert_count u32 = 64
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 19: deepseek2.expert_shared_count u32 = 2
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 20: deepseek2.expert_weights_scale f32 = 1.000000
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 21: deepseek2.rope.dimension_count u32 = 64
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 22: deepseek2.rope.scaling.type str = yarn
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 23: deepseek2.rope.scaling.factor f32 = 40.000000
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 24: deepseek2.rope.scaling.original_context_length u32 = 4096
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 25: deepseek2.rope.scaling.yarn_log_multiplier f32 = 0.070700
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 26: tokenizer.ggml.model str = gpt2
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 27: tokenizer.ggml.pre str = deepseek-llm
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 28: tokenizer.ggml.tokens arr[str,102400] = ["!", "\"", "#", "$", "%", "&", "'", ...
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 29: tokenizer.ggml.token_type arr[i32,102400] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 30: tokenizer.ggml.merges arr[str,99757] = ["Ġ Ġ", "Ġ t", "Ġ a", "i n", "h e...
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 31: tokenizer.ggml.bos_token_id u32 = 100000
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 32: tokenizer.ggml.eos_token_id u32 = 100001
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 33: tokenizer.ggml.padding_token_id u32 = 100001
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 34: tokenizer.ggml.add_bos_token bool = true
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 35: tokenizer.ggml.add_eos_token bool = false
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 36: tokenizer.chat_template str = {% if not add_generation_prompt is de...
jun 18 13:23:17 ollama[22256]: llama_model_loader: - kv 37: general.quantization_version u32 = 2
jun 18 13:23:17 ollama[22256]: llama_model_loader: - type f32: 108 tensors
jun 18 13:23:17 ollama[22256]: llama_model_loader: - type q4_0: 268 tensors
jun 18 13:23:17 ollama[22256]: llama_model_loader: - type q6_K: 1 tensors
jun 18 13:23:17 ollama[22256]: llm_load_vocab: special tokens cache size = 2400
jun 18 13:23:17 ollama[22256]: time=2024-06-18T13:23:17.498+02:00 level=INFO source=server.go:582 msg="waiting for server to become available" status="llm server loading model"
jun 18 13:23:17 ollama[22256]: llm_load_vocab: token to piece cache size = 0.6661 MB
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: format = GGUF V3 (latest)
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: arch = deepseek2
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: vocab type = BPE
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_vocab = 102400
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_merges = 99757
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_ctx_train = 163840
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_embd = 2048
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_head = 16
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_head_kv = 16
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_layer = 27
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_rot = 64
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_embd_head_k = 192
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_embd_head_v = 128
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_gqa = 1
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_embd_k_gqa = 3072
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_embd_v_gqa = 2048
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: f_norm_eps = 0.0e+00
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: f_norm_rms_eps = 1.0e-06
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: f_clamp_kqv = 0.0e+00
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: f_max_alibi_bias = 0.0e+00
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: f_logit_scale = 0.0e+00
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_ff = 10944
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_expert = 64
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_expert_used = 6
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: causal attn = 1
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: pooling type = 0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: rope type = 0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: rope scaling = yarn
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: freq_base_train = 10000.0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: freq_scale_train = 0.025
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_ctx_orig_yarn = 4096
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: rope_finetuned = unknown
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: ssm_d_conv = 0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: ssm_d_inner = 0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: ssm_d_state = 0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: ssm_dt_rank = 0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: model type = 16B
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: model ftype = Q4_0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: model params = 15.71 B
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: model size = 8.29 GiB (4.53 BPW)
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: general.name = DeepSeek-Coder-V2-Lite-Instruct
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: BOS token = 100000 '<｜begin▁of▁sentence｜>'
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: EOS token = 100001 '<｜end▁of▁sentence｜>'
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: PAD token = 100001 '<｜end▁of▁sentence｜>'
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: LF token = 126 'Ä'
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_layer_dense_lead = 1
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_lora_q = 0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_lora_kv = 512
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_ff_exp = 1408
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: n_expert_shared = 2
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: expert_weights_scale = 1.0
jun 18 13:23:17 ollama[22256]: llm_load_print_meta: rope_yarn_log_mul = 0.0707
jun 18 13:23:17 ollama[22256]: ggml_cuda_init: GGML_CUDA_FORCE_MMQ: yes
jun 18 13:23:17 ollama[22256]: ggml_cuda_init: CUDA_USE_TENSOR_CORES: no
jun 18 13:23:17 ollama[22256]: ggml_cuda_init: found 1 CUDA devices:
jun 18 13:23:17 ollama[22256]: Device 0: NVIDIA GeForce RTX 3070 Laptop GPU, compute capability 8.6, VMM: yes
jun 18 13:23:17 ollama[22256]: llm_load_tensors: ggml ctx size = 0.35 MiB
jun 18 13:23:18 ollama[22256]: ggml_backend_cuda_buffer_type_alloc_buffer: allocating 8376.27 MiB on device 0: cudaMalloc failed: out of memory
jun 18 13:23:18 ollama[22256]: llama_model_load: error loading model: unable to allocate backend buffer
jun 18 13:23:18 ollama[22256]: llama_load_model_from_file: exception loading model
jun 18 13:23:18 ollama[22256]: terminate called after throwing an instance of 'std::runtime_error'
jun 18 13:23:18 ollama[22256]: what(): unable to allocate backend buffer
jun 18 13:23:18 ollama[22256]: time=2024-06-18T13:23:18.757+02:00 level=INFO source=server.go:582 msg="waiting for server to become available" status="llm server error"
jun 18 13:23:19 ollama[22256]: time=2024-06-18T13:23:19.008+02:00 level=ERROR source=sched.go:388 msg="error loading llama server" error="llama runner process has terminated: signal: aborted (core dumped) cudaMalloc failed: out of memory"
jun 18 13:23:19 ollama[22256]: [GIN] 2024/06/18 - 13:23:19 | 500 | 3.168403177s | 127.0.0.1 | POST "/api/chat"
jun 18 13:23:24 ollama[22256]: time=2024-06-18T13:23:24.231+02:00 level=WARN source=sched.go:575 msg="gpu VRAM usage didn't recover within timeout" seconds=5.223479956 model=/usr/share/ollama/.ollama/models/blobs/sha256-5ff0abeeac1d2dbdd5455c0b49ba3b29a9ce3c1fb181b2eef2e948689d55d046
```
```
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 555.52.04 Driver Version: 555.52.04 CUDA Version: 12.5 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 NVIDIA GeForce RTX 3070 ... Off | 00000000:01:00.0 On | N/A |
| N/A 60C P0 35W / 115W | 168MiB / 8192MiB | 32% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
```
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.1.45-rc2, 0.1.44
|
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I_kwDOJ0Z1Ps57n3zZ
| 1,891
|
Add ability to hide/disable/enable models
|
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| 2024-01-10T10:22:17
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If we can have this feature, I'm sure it will help us out of the clutter. Or perhaps, is it possible to provide a way to Categorize models?
Practical Application:
Downloading large models from ollama site (consumes bandwidth) you don't really want to delete a model but just hide it from your organization or users.
Also, what is the best way to migrate the ollama local models directory without redownloading from the official site?
Or using the terminal, how do we upload a model to this directory?
I wish we have ollma migrate /path/to-models/ which have the ability to sync with non-duplicate models.
Thanks.
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PR_kwDOJ0Z1Ps5hjuew
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🛠️ Add service activation prompt
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Closes #1352
### Key Changes:
- Added `ask_to_activate_service` function to prompt users for service activation post-installation.
- Integrated the prompt in the script's flow, allowing conditional execution of systemd service configuration.
### Impact:
- Improves user experience by providing a choice to activate the service.
- Ensures clearer installation process, aligning with user expectations.
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JSON mode outputs a stream of newline characters
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PR_kwDOJ0Z1Ps5WAoyG
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Delete models.json
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| 2023-07-20T14:33:15
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This is no longer used.
|
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PR_kwDOJ0Z1Ps5V12ts
| 110
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fix pull 0 bytes on completed layer
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This PR fixes the bug where when the progress bar displays 0B for a layer when the layer already exists:
```
$ ollama pull llama2
pulling manifest
pulling 8daa9615cce30c25... 0% | | ( 0 B/3.5 GB) [0s:0s]
pulling c929c04af928be41... 0% | | ( 0 B/3.5 GB) [0s:0s]
pulling cf39c1a5c36937e4... 100% |██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (3.5/3.5 GB, 53 TB/s)
writing manifest
success
```
```
$ ollama pull llama2
pulling manifest
pulling 8daa9615cce30c25... 100% |██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (3.5/3.5 GB, 16 TB/s)
pulling c929c04af928be41... 100% |███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (547/547 B, 12 MB/s)
pulling cf39c1a5c36937e4... 100% |██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| (225/225 B, 5.0 MB/s)
writing manifest
success
```
Now each layer also correctly reports the layer's size rather than the total bundle size.
Refactor `Pull/PushProgress` into `ProgressResponse` since they share the exact same attributes and remove `Percent` since it's not being used and the caller can easily compute it for themselves
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Ollama docker container crash full WSL2 Ubuntu
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| 2024-02-11T03:41:22
| 2024-03-27T20:58:36
| 2024-03-27T20:58:35
|
NONE
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docker container setup as bellow.
```
version: "3.7"
services:
ollama:
container_name: ollama
image: ollama/ollama:latest
ports:
- "5310:11434"
volumes:
- ./ollama:/root/.ollama
restart: unless-stopped
environment:
- CUDA_VISIBLE_DEVICES=0,1
- OLLAMA_ORIGINS=*
deploy:
resources:
reservations:
devices:
- driver: nvidia
count: all
capabilities: [gpu]
ollama-webui:
image: ghcr.io/ollama-webui/ollama-webui:main
container_name: ollama-webui
ports:
- "7000:8080"
volumes:
- ./ollama-webui1:/app/backend/data
environment:
- 'OLLAMA_API_BASE_URL=http://ollama:11434/api'
restart: unless-stopped
```
modelfile as bellow:
```
FROM miqu-1-70b.q2_k.gguf
PARAMETER num_ctx 24000
PARAMETER num_gpu 81
```
logs:
```
...
....................................................................................................
llama_new_context_with_model: n_ctx = 26000
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 4164.06 MiB
llama_kv_cache_init: CUDA1 KV buffer size = 3960.94 MiB
llama_new_context_with_model: KV self size = 8125.00 MiB, K (f16): 4062.50 MiB, V (f16): 4062.50 MiB
llama_new_context_with_model: CUDA_Host input buffer size = 66.88 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 3683.66 MiB
llama_new_context_with_model: CUDA1 compute buffer size = 3683.66 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 17.60 MiB
llama_new_context_with_model: graph splits (measure): 5
time=2024-02-10T01:16:44.820Z level=INFO source=dyn_ext_server.go:156 msg="Starting llama main loop"
[GIN] 2024/02/10 - 01:16:52 | 200 | 46.01445749s | 172.22.0.1 | POST "/api/generate"
[GIN] 2024/02/10 - 01:17:02 | 200 | 9.530461175s | 172.22.0.1 | POST "/api/generate"
[GIN] 2024/02/10 - 01:17:11 | 200 | 9.062014736s | 172.22.0.1 | POST "/api/generate"
error from daemon in stream: Error grabbing logs: invalid character '\x00' looking for beginning of value
(base) super@dev-local-ai:~/data/ollama$ docker logs -f ollama
[已退出进程，代码为 1 (0x00000001)]
现在可以使用Ctrl+D关闭此终端，或按 Enter 重新启动。
远程主机强迫关闭了一个现有的连接。
Error code: Wsl/Service/0x80072746
Press any key to continue...
```
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|
I_kwDOJ0Z1Ps6IYdem
| 4,293
|
longtext llama3-gradient bug
|
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[] | null | 0
| 2024-05-09T17:12:59
| 2024-05-09T17:16:02
| null |
NONE
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### What is the issue?
If I use 'ollama' for long text processing, then the 'system' statements will no longer be effective. Instead, it will produce random outputs. The model is 'llama3-gradient'.
```
systemmsg="""
Please analyze the type of website based on the 'body' content I provide, and return to me in JSON format with the structure {type:..., why:...}.
"""
def get_webpage_content(url):
try:
if response.status_code == 200:
return response.text
else:
print(f'Failed to retrieve the webpage. Status code: {response.status_code}')
return None
except requests.exceptions.RequestException as e:
print(f'An error occurred: {e}')
return None
body = get_webpage_content('http://www.sohu.com')
response = ollama.generate(model='llama3-gradient', prompt=body,format='json',options={
"seed": 123,
"num_ctx": 32000
},
system=systemmsg)
print(response['response'])
```
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.34
| null |
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| 1,862,851,976
|
I_kwDOJ0Z1Ps5vCN2I
| 402
|
Uncensored models can't be customised
|
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| 2023-08-23T08:39:44
| 2023-09-01T16:04:48
| 2023-09-01T16:04:47
|
NONE
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| null | null | null |
Hi! Thanks for the cool tool:)
Tried to customize:
-llama2 - customisable
-llama2-uncensored - no result
-nous-hermes - customisable
-wizard-vicuna-uncensored - no result
-wizardlm-uncensored - no result
The system msg used:
FROM wizardlm-uncensored
SYSTEM """
You are the Geralt of Rivia from the Witcher. Act like Geralt, be him.
"""
Is that intentional not to let customize uncensored models or a bug?
|
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I_kwDOJ0Z1Ps6AJIlp
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|
Add another binary that the linux install script could use on ROCm accelerated systems.
|
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| 2024-02-22T20:24:42
| 2024-03-12T00:08:26
| 2024-03-12T00:08:22
|
NONE
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Another binary that the install script could use on `ROCm` accelerated systems would be useful. Releases are not compiled with `HIP`, therefore *non-NVidia* GPU acceleration support is not present. https://github.com/ollama/ollama/issues/2685#issuecomment-1959937668
|
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I_kwDOJ0Z1Ps577G32
| 1,954
|
Support GPU A500
|
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| 2024-01-15T08:05:14
| 2024-01-15T08:05:14
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Can't get model tu run on GPU:
```
Fri Jan 12 16:22:20 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 545.29.06 Driver Version: 545.29.06 CUDA Version: 12.3 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA RTX A500 Laptop GPU Off | 00000000:03:00.0 Off | N/A |
| N/A 53C P8 4W / 20W | 7MiB / 4096MiB | 0% Default |
| | | N/A |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 1404 G /usr/lib/Xorg 4MiB |
+---------------------------------------------------------------------------------------+
```
I'm on arch and installed via `pacman -S ollama`
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PR_kwDOJ0Z1Ps5273xv
| 6,083
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Update README to include Firebase Genkit
|
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| 2024-07-31T01:38:31
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MEMBER
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Firebase Genkit
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PR_kwDOJ0Z1Ps5ybx0V
| 5,040
|
chore: add openapi 3.1 spec for public api
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Addresses issue #3383. Targets OpenAPI 3.1.0 as that's the most recent, and it appears to be the only version that supports DELETE with a request body. Also added [spectral](https://github.com/stoplightio/spectral-action) to the github test pipeline to lint the spec to ensure it's valid.
Swagger utilizing this spec can be seen in browser here: https://validator.swagger.io/?url=https://raw.githubusercontent.com/ollama/ollama/ef7c6cb43aa8cc8c38a4e51d4d7e78b66e08a5c1/specs/openapi-3.1.yaml#/models/getModels
Redoc visualization here: https://redocly.github.io/redoc/?url=https://raw.githubusercontent.com/ollama/ollama/ef7c6cb43aa8cc8c38a4e51d4d7e78b66e08a5c1/specs/openapi-3.1.yaml#tag/generate
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I_kwDOJ0Z1Ps6kn20c
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Ollama with AMD GPU Issue
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[Ollama.log](https://github.com/user-attachments/files/18268220/Ollama.log)
### What is the issue?
Environment: Debian 12 virtualized on proxmox with GPU passthrough
GPU: 6650 XT (Unsupported) Using Environment="HSA_OVERRIDE_GFX_VERSION=10.3.0"
Systemd log:
`Dec 28 15:03:19 AI-ML ollama[2378]: time=2024-12-28T15:03:19.515-05:00 level=INFO source=routes.go:1310 msg="Listening on [::]:11434 (version 0.5.4)"
Dec 28 15:03:19 AI-ML ollama[2378]: time=2024-12-28T15:03:19.516-05:00 level=INFO source=common.go:131 msg="GPU runner incompatible with host system, CPU does not have AVX" runner=cuda_v11_avx
Dec 28 15:03:19 AI-ML ollama[2378]: time=2024-12-28T15:03:19.516-05:00 level=INFO source=common.go:131 msg="GPU runner incompatible with host system, CPU does not have AVX" runner=cuda_v12_avx
Dec 28 15:03:19 AI-ML ollama[2378]: time=2024-12-28T15:03:19.516-05:00 level=INFO source=routes.go:1339 msg="Dynamic LLM libraries" runners="[cpu_avx2 rocm_avx cpu cpu_avx]"
Dec 28 15:03:19 AI-ML ollama[2378]: time=2024-12-28T15:03:19.516-05:00 level=INFO source=gpu.go:226 msg="looking for compatible GPUs"
Dec 28 15:03:19 AI-ML ollama[2378]: time=2024-12-28T15:03:19.523-05:00 level=INFO source=amd_linux.go:391 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION=10.3.0
Dec 28 15:03:19 AI-ML ollama[2378]: time=2024-12-28T15:03:19.527-05:00 level=INFO source=types.go:131 msg="inference compute" id=0 library=rocm variant="" compute=gfx1032 driver=6.10 name=1002:73ef total="8.0 GiB available="7.9 GiB`
When I try running any model even the lightest one smol I get the following error,
`Error: llama runner process has terminated: signal: illegal instruction
`
Checked the journalctl log
`Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/pull --> github.com/ollama/ollama/server.(*Server).PullHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/generate --> github.com/ollama/ollama/server.(*Server).GenerateHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/chat --> github.com/ollama/ollama/server.(*Server).ChatHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/embed --> github.com/ollama/ollama/server.(*Server).EmbedHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/embeddings --> github.com/ollama/ollama/server.(*Server).EmbeddingsHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/create --> github.com/ollama/ollama/server.(*Server).CreateHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/push --> github.com/ollama/ollama/server.(*Server).PushHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/copy --> github.com/ollama/ollama/server.(*Server).CopyHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] DELETE /api/delete --> github.com/ollama/ollama/server.(*Server).DeleteHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/show --> github.com/ollama/ollama/server.(*Server).ShowHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /api/blobs/:digest --> github.com/ollama/ollama/server.(*Server).CreateBlobHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] HEAD /api/blobs/:digest --> github.com/ollama/ollama/server.(*Server).HeadBlobHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] GET /api/ps --> github.com/ollama/ollama/server.(*Server).PsHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /v1/chat/completions --> github.com/ollama/ollama/server.(*Server).ChatHandler-fm (6 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /v1/completions --> github.com/ollama/ollama/server.(*Server).GenerateHandler-fm (6 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] POST /v1/embeddings --> github.com/ollama/ollama/server.(*Server).EmbedHandler-fm (6 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] GET /v1/models --> github.com/ollama/ollama/server.(*Server).ListHandler-fm (6 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] GET /v1/models/:model --> github.com/ollama/ollama/server.(*Server).ShowHandler-fm (6 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] GET / --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func1 (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] GET /api/tags --> github.com/ollama/ollama/server.(*Server).ListHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] GET /api/version --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func2 (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] HEAD / --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func1 (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] HEAD /api/tags --> github.com/ollama/ollama/server.(*Server).ListHandler-fm (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: [GIN-debug] HEAD /api/version --> github.com/ollama/ollama/server.(*Server).GenerateRoutes.func2 (5 handlers)
Dec 28 15:17:30 AI-ML ollama[788]: time=2024-12-28T15:17:30.915-05:00 level=INFO source=routes.go:1310 msg="Listening on [::]:11434 (version 0.5.4)"
Dec 28 15:17:30 AI-ML ollama[788]: time=2024-12-28T15:17:30.917-05:00 level=INFO source=common.go:131 msg="GPU runner incompatible with host system, CPU does not have AVX" runner=cuda_v11_avx
Dec 28 15:17:30 AI-ML ollama[788]: time=2024-12-28T15:17:30.917-05:00 level=INFO source=common.go:131 msg="GPU runner incompatible with host system, CPU does not have AVX" runner=cuda_v12_avx
Dec 28 15:17:30 AI-ML ollama[788]: time=2024-12-28T15:17:30.917-05:00 level=INFO source=routes.go:1339 msg="Dynamic LLM libraries" runners="[rocm_avx cpu cpu_avx cpu_avx2]"
Dec 28 15:17:30 AI-ML ollama[788]: time=2024-12-28T15:17:30.918-05:00 level=INFO source=gpu.go:226 msg="looking for compatible GPUs"
Dec 28 15:17:30 AI-ML ollama[788]: time=2024-12-28T15:17:30.928-05:00 level=INFO source=amd_linux.go:391 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION=10.3.0
Dec 28 15:17:30 AI-ML ollama[788]: time=2024-12-28T15:17:30.933-05:00 level=INFO source=types.go:131 msg="inference compute" id=0 library=rocm variant="" compute=gfx1032 driver=6.10 name=1002:73ef total="8.0 GiB" >
Dec 28 15:18:14 AI-ML ollama[788]: [GIN] 2024/12/28 - 15:18:14 | 200 | 811.616µs | 127.0.0.1 | HEAD "/"
Dec 28 15:18:14 AI-ML ollama[788]: [GIN] 2024/12/28 - 15:18:14 | 200 | 23.292812ms | 127.0.0.1 | POST "/api/show"
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.092-05:00 level=INFO source=sched.go:714 msg="new model will fit in available VRAM in single GPU, loading" model=/usr/share/ollama/.ollama/models/blobs/s>
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.092-05:00 level=INFO source=server.go:104 msg="system memory" total="31.3 GiB" free="29.5 GiB" free_swap="0 B"
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.093-05:00 level=INFO source=memory.go:356 msg="offload to rocm" layers.requested=-1 layers.model=29 layers.offload=29 layers.split="" memory.available="[>
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.093-05:00 level=INFO source=common.go:131 msg="GPU runner incompatible with host system, CPU does not have AVX" runner=cuda_v11_avx
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.093-05:00 level=INFO source=common.go:131 msg="GPU runner incompatible with host system, CPU does not have AVX" runner=cuda_v12_avx
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.093-05:00 level=INFO source=common.go:131 msg="GPU runner incompatible with host system, CPU does not have AVX" runner=cuda_v11_avx
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.093-05:00 level=INFO source=common.go:131 msg="GPU runner incompatible with host system, CPU does not have AVX" runner=cuda_v12_avx
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.093-05:00 level=INFO source=server.go:376 msg="starting llama server" cmd="/usr/local/lib/ollama/runners/rocm_avx/ollama_llama_server runner --model /usr>
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.094-05:00 level=INFO source=sched.go:449 msg="loaded runners" count=1
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.094-05:00 level=INFO source=server.go:555 msg="waiting for llama runner to start responding"
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.095-05:00 level=INFO source=server.go:589 msg="waiting for server to become available" status="llm server error"
Dec 28 15:18:14 AI-ML ollama[788]: time=2024-12-28T15:18:14.346-05:00 level=ERROR source=sched.go:455 msg="error loading llama server" error="llama runner process has terminated: signal: illegal instruction"
Dec 28 15:18:14 AI-ML ollama[788]: [GIN] 2024/12/28 - 15:18:14 | 500 | 305.479625ms | 127.0.0.1 | POST "/api/generate"
Dec 28 15:18:19 AI-ML ollama[788]: time=2024-12-28T15:18:19.347-05:00 level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=5.000717787 model=/usr/share/ollama/.ollama/models/bl>
Dec 28 15:18:19 AI-ML ollama[788]: time=2024-12-28T15:18:19.597-05:00 level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=5.250446487 model=/usr/share/ollama/.ollama/models/bl>
Dec 28 15:18:19 AI-ML ollama[788]: time=2024-12-28T15:18:19.846-05:00 level=WARN source=sched.go:646 msg="gpu VRAM usage didn't recover within timeout" seconds=5.500215086 model=/usr/share/ollama/.ollama/models/bl`
Not sure if I am missing any config but seems like vram is running out but model ram for llama3.2 is around 3-4 gb.
### OS
Linux
### GPU
AMD
### CPU
Intel
### Ollama version
0.5.4
|
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| 2,313,244,559
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I_kwDOJ0Z1Ps6J4U-P
| 4,592
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Mistral-7B instruct v3 FP16 Please
|
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| 2024-05-23T15:41:26
| 2024-05-23T15:43:17
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NONE
|
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Mistral-7B instruct v3 FP16 Please - https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3/tree/main
|
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I_kwDOJ0Z1Ps6IydYV
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joanfm / jina-embeddings-v2-base-en and -de fail with error code 500
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| 2024-05-14T09:30:50
| 2024-08-01T22:39:17
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NONE
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### What is the issue?
I tried to integerate the german embedding model **joanfm/jina-embeddings-v2-base-de** , into my LlamaIndex RAG application. During the creation of the embeddings the process ollama fails with **error 500: llama runner process has terminated: exit status 0xc0000409**.
When calling:
```python
pass_embedding = Settings.embed_model.get_text_embedding_batch(
["This is a passage!", "This is another passage"], show_progress=True
)
```
```python
ValueError Traceback (most recent call last)
Cell In[16], [line 2](vscode-notebook-cell:?execution_count=16&line=2)
[1](vscode-notebook-cell:?execution_count=16&line=1) # Test the embedding model
----> [2](vscode-notebook-cell:?execution_count=16&line=2) pass_embedding = Settings.embed_model.get_text_embedding_batch(
[3](vscode-notebook-cell:?execution_count=16&line=3) ["This is a passage!", "This is another passage"], show_progress=True
[4](vscode-notebook-cell:?execution_count=16&line=4) )
[5](vscode-notebook-cell:?execution_count=16&line=5) print(pass_embedding)
[7](vscode-notebook-cell:?execution_count=16&line=7) query_embedding = Settings.embed_model.get_query_embedding("Where is blue?")
File c:\Users\Stefan.Mueller\AppData\Local\miniconda3\envs\llamaindex\Lib\site-packages\llama_index\core\instrumentation\dispatcher.py:274, in Dispatcher.span.<locals>.wrapper(func, instance, args, kwargs)
[270](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/instrumentation/dispatcher.py:270) self.span_enter(
[271](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/instrumentation/dispatcher.py:271) id_=id_, bound_args=bound_args, instance=instance, parent_id=parent_id
[272](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/instrumentation/dispatcher.py:272) )
[273](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/instrumentation/dispatcher.py:273) try:
--> [274](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/instrumentation/dispatcher.py:274) result = func(*args, **kwargs)
[275](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/instrumentation/dispatcher.py:275) except BaseException as e:
[276](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/instrumentation/dispatcher.py:276) self.event(SpanDropEvent(span_id=id_, err_str=str(e)))
File c:\Users\Stefan.Mueller\AppData\Local\miniconda3\envs\llamaindex\Lib\site-packages\llama_index\core\base\embeddings\base.py:331, in BaseEmbedding.get_text_embedding_batch(self, texts, show_progress, **kwargs)
[322](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/base/embeddings/base.py:322) dispatch_event(
[323](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/base/embeddings/base.py:323) EmbeddingStartEvent(
[324](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/base/embeddings/base.py:324) model_dict=self.to_dict(),
[325](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/base/embeddings/base.py:325) )
[326](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/core/base/embeddings/base.py:326) )
...
[100](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/embeddings/ollama/base.py:100) )
[102](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/embeddings/ollama/base.py:102) try:
[103](file:///C:/Users/Stefan.Mueller/AppData/Local/miniconda3/envs/llamaindex/Lib/site-packages/llama_index/embeddings/ollama/base.py:103) return response.json()["embedding"]
```
With **mxbai-embed-large:latest** this works without an error.
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.37
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I_kwDOJ0Z1Ps5vWhiP
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|
Error: Head "http://localhost:11434/": dial tcp: lookup localhost: no such host
|
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This is very odd, but after updating to the latest v0.0.16, this error started showing when I use ollama. For example,if I try to run a simple "ollama list" this shows up:
Error: Head "http://localhost:11434/": dial tcp: lookup localhost: no such host
I've cleaned any DNS traces, hosts file is untouched and there are no firewall conflicts.
What could be causing this issue? Is it only happening on my end?
|
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| 1,971,409,643
|
PR_kwDOJ0Z1Ps5eSIfZ
| 958
|
append LD_LIBRARY_PATH
|
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| 2023-10-31T22:55:25
| 2023-11-01T15:30:39
| 2023-11-01T15:30:38
|
CONTRIBUTOR
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|
only append LD_LIBRARY_PATH in case it's already set
Related #758
|
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| 2,247,394,328
|
I_kwDOJ0Z1Ps6F9IQY
| 3,692
|
How do I get sentence-transformers/all-mpnet-base-v2 in Ollama?
|
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| 2024-04-17T05:19:36
| 2024-04-29T12:54:51
| null |
NONE
|
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### What model would you like?
I'd like to use sentence-transformers/all-mpnet-base-v2 for embeddings. Thanks.
| null |
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| 2,456,339,263
|
I_kwDOJ0Z1Ps6SaMM_
| 6,259
|
Inference fails with "llama_get_logits_ith: invalid logits id 7, reason: no logits"
|
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"received_events_url": "https://api.github.com/users/yurivict/received_events",
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"user_view_type": "public",
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[] | null | 9
| 2024-08-08T17:56:07
| 2024-08-09T19:14:03
| 2024-08-09T19:14:03
|
NONE
|
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### What is the issue?
Here is the error log:
```
[GIN] 2024/08/07 - 10:01:31 | 200 | 7.589808394s | 127.0.0.1 | POST "/api/chat"
time=2024-08-07T10:01:31.521-07:00 level=DEBUG source=sched.go:462 msg="context for request finished"
time=2024-08-07T10:01:31.521-07:00 level=DEBUG source=sched.go:334 msg="runner with non-zero duration has gone idle, adding timer" modelPath=/home/yuri/.ollama/models/blobs/sha256-ff82381e2bea77d91c1b824c7afb83f6fb73e9f7de9dda631bcdbca564aa5435 duration=5m0s
time=2024-08-07T10:01:31.521-07:00 level=DEBUG source=sched.go:352 msg="after processing request finished event" modelPath=/home/yuri/.ollama/models/blobs/sha256-ff82381e2bea77d91c1b824c7afb83f6fb73e9f7de9dda631bcdbca564aa5435 refCount=0
time=2024-08-07T10:01:52.804-07:00 level=DEBUG source=sched.go:571 msg="evaluating already loaded" model=/home/yuri/.ollama/models/blobs/sha256-ff82381e2bea77d91c1b824c7afb83f6fb73e9f7de9dda631bcdbca564aa5435
DEBUG [process_single_task] slot data | n_idle_slots=4 n_processing_slots=0 task_id=1 tid="0x236209412000" timestamp=1723050112
time=2024-08-07T10:01:52.854-07:00 level=DEBUG source=routes.go:1346 msg="chat request" images=0 prompt="[INST] Say something.[/INST] "
DEBUG [process_single_task] slot data | n_idle_slots=4 n_processing_slots=0 task_id=2 tid="0x236209412000" timestamp=1723050112
DEBUG [launch_slot_with_data] slot is processing task | slot_id=0 task_id=3 tid="0x236209412000" timestamp=1723050112
DEBUG [update_slots] slot progression | ga_i=0 n_past=0 n_past_se=0 n_prompt_tokens_processed=8 slot_id=0 task_id=3 tid="0x236209412000" timestamp=1723050112
DEBUG [update_slots] kv cache rm [p0, end) | p0=0 slot_id=0 task_id=3 tid="0x236209412000" timestamp=1723050112
llama_get_logits_ith: invalid logits id 7, reason: no logits
time=2024-08-07T10:01:53.403-07:00 level=DEBUG source=server.go:1048 msg="stopping llama server"
time=2024-08-07T10:01:53.403-07:00 level=DEBUG source=server.go:1054 msg="waiting for llama server to exit"
time=2024-08-07T10:01:53.951-07:00 level=DEBUG source=server.go:1058 msg="llama server stopped"
```
The llama-cpp project maintainers seem to be puzzled by this error: https://github.com/ggerganov/llama.cpp/issues/8911
### OS
_No response_
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.4
|
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| 2,687,774,719
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I_kwDOJ0Z1Ps6gNC__
| 7,816
|
I import a IQ_4XS model but get an IQ1_M
|
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[] | null | 13
| 2024-11-24T14:06:50
| 2024-12-23T23:54:48
| 2024-11-24T18:33:34
|
NONE
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### What is the issue?
As the title says, I imported a custom gguf model, which is of the IQ_4XS quantization type. But after importing it, ollama show displays it as IQ1_M. Is this behavior expected? Because I saw in previous issues that support for IQ_4XS has been added, so this confuses me.
[the gguf file](https://huggingface.co/SakuraLLM/Sakura-14B-Qwen2.5-v1.0-GGUF/blob/main/sakura-14b-qwen2.5-v1.0-iq4xs.gguf)
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.4.4
|
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I_kwDOJ0Z1Ps6fDG8N
| 7,726
|
Proxy does not work for ollama, but does work for curl
|
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"repos_url": "https://api.github.com/users/lk-1984/repos",
"events_url": "https://api.github.com/users/lk-1984/events{/privacy}",
"received_events_url": "https://api.github.com/users/lk-1984/received_events",
"type": "User",
"user_view_type": "public",
"site_admin": false
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| 2024-11-18T12:29:24
| 2024-11-18T13:08:50
| 2024-11-18T13:08:49
|
NONE
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### What is the issue?
Ollama does not work with the HTTPS_PROXY
```
foo@FOOBAR ~ % ollama pull llama3.2
pulling manifest
Error: pull model manifest: Get "https://registry.ollama.ai/v2/library/llama3.2/manifests/latest": dial tcp 104.21.75.227:443: i/o timeout
```
Curl works in same terminal with the same HTTPS_PROXY value.
```
foobar@FOOBAR~ % curl https://registry.ollama.ai/v2/library/llama3.2/manifests/latest
{"schemaVersion":2,"mediaType":"application/vnd.docker.distribution.manifest.v2+json","config":{"digest":"sha256:34bb5ab01051a11372a91f95f3fbbc51173eed8e7f13ec395b9ae9b8bd0e242b","mediaType":"application/vnd.docker.container.image.v1+json","size":561},"layers":[{"digest":"sha256:dde5aa3fc5ffc17176b5e8bdc82f587b24b2678c6c66101bf7da77af9f7ccdff","mediaType":"application/vnd.ollama.image.model","size":2019377376},{"digest":"sha256:966de95ca8a62200913e3f8bfbf84c8494536f1b94b49166851e76644e966396","mediaType":"application/vnd.ollama.image.template","size":1429},{"digest":"sha256:fcc5a6bec9daf9b561a68827b67ab6088e1dba9d1fa2a50d7bbcc8384e0a265d","mediaType":"application/vnd.ollama.image.license","size":7711},{"digest":"sha256:a70ff7e570d97baaf4e62ac6e6ad9975e04caa6d900d3742d37698494479e0cd","mediaType":"application/vnd.ollama.image.license","size":6016},{"digest":"sha256:56bb8bd477a519ffa694fc449c2413c6f0e1d3b1c88fa7e3c9d88d3ae49d4dcb","mediaType":"application/vnd.ollama.image.params","size":96}]}
```
Note: the company proxy starts with "http://" even for HTTPS_PROXY..
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.4.2
|
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I_kwDOJ0Z1Ps6kmxyP
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Error: listen tcp 127.0.0.1:11434: bind: Only one usage of each socket address
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| 2024-12-28T08:26:56
| 2025-01-15T11:47:10
| 2025-01-08T18:02:56
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NONE
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### What is the issue?
Hello i tried everything on the internet even ı formatted my pc.
Error: listen tcp 127.0.0.1:11434: bind: Normal olarak her yuva adresi (iletişim kuralı/ağ adresi/bağlantı noktası) için yalnızca bir kullanıma izin veriliyor.
i tried kill from task bar too.
I tried using other ports too. I added one named 0.0.0.0 but it doesnt worked too
### OS
Windows
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.0 and 0.5.4
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|
Receiving a new error when trying to create modelfile with same code
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| 2025-01-18T02:21:02
| 2025-01-19T17:04:40
| 2025-01-19T17:04:40
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NONE
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### What is the issue?
I have an existing app which was working on an older version of ollama to create the modelfile I was able to do this using a string and using the "FROM model \n SYSTEM instructions" syntax.
I am using the ruby-ollama API wrapper gem.
The error I am seeing is neither 'from' or 'files' was specified
I verified that I am passing in a valid prompt but it is still not working.
Any ideas why the newer Ollama would process the modelfile different I noticed there was some changes to the parser in a recent commit
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_
|
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I_kwDOJ0Z1Ps6JX2cs
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GGUF imported model crashes only in v0.1.38
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| 2024-05-19T18:36:50
| 2024-06-30T23:19:01
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### What is the issue?
heegyu/EEVE-Korean-Instruct-10.8B-v1.0-GGUF worked in all prior versions of ollama. Since v0.1.38 it core dumps.
I have temporarily reverted to v0.1.37 to resolve the issue. Any help is appreciated.
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.38
|
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Don't return error on signal exit
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Error: llama runner process no longer running: -1
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| 2024-04-25T06:01:58
| 2024-06-21T18:27:34
| 2024-05-03T05:41:06
|
NONE
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### What is the issue?
Was trying to run a finetuned version of llama2 having a gguf of 13.5gb.
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
v0.1.32
|
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PR_kwDOJ0Z1Ps5t1Xdq
| 3,942
|
Fix curl command in documentation
|
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| 2024-04-26T11:50:11
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NONE
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Explicitly set the HTTP method to POST using the -X flag to avoid errors when creating a new model from `modelfile`
|
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PR_kwDOJ0Z1Ps59-U2Q
| 7,140
|
llama: cgo ggml
|
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| 2024-10-08T16:23:06
| 2024-10-29T16:52:05
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COLLABORATOR
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This builds a ~minimal cgo wrapper on ggml APIs.
Replaces #7103 on main
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PR_kwDOJ0Z1Ps5ynH7F
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|
Add Chinese translation of README
|
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| 2024-06-16T14:03:26
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NONE
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This pull request adds a Chinese translation of the README file to help native Chinese speakers better understand the project.
|
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I_kwDOJ0Z1Ps6AJ1FY
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|
Unable to build Ollama on Cluster
|
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| 2024-02-22T22:37:54
| 2024-03-12T14:50:16
| 2024-03-12T14:50:16
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NONE
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Hi,
Thanks for this great work. I am trying to build Ollama on my cluster and I don't have administrative access. My cluster has the following configuration:
```
LSB Version: :core-4.1-amd64:core-4.1-noarch
Distributor ID: CentOS
Description: CentOS Linux release 7.9.2009 (Core)
Release: 7.9.2009
Codename: Core
```
I follow these steps:
a) Clone the ollama repo using: `git clone https://github.com/ollama/ollama`
b) Following https://github.com/ollama/ollama/blob/main/docs/development.md, I do `go generate ./...` but I get the error:
I tried cloning the repo with the recursive submodules but it didn't help me much. Also, I tried older commits but it wasn't helpful either.
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I_kwDOJ0Z1Ps6LEHkT
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"Server disconnected without sending a response" after ~60seconds.
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| 2024-06-19T07:40:51
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### What is the issue?
I run mixtral model and using api/generate.
If I run a bigger prompt it returns "Server disconnected without sending a response."
I checked ollama logs and see:
[GIN] 2024/06/04 - 09:36:43 | 500 | 59.693208463s | 10.0.101.220 | POST "/api/generate"
Is there a way to increase this kind of internal timeout?
I already tried setting timeout but it does not have an impact on this
from ollama import Client
client = Client(host='OllamaServer', timeout=120)
### OS
Linux, Containerd, eks
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.1.40
|
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I_kwDOJ0Z1Ps5wgZ6h
| 496
|
CodeLlama tokenizer `<FILL_ME>` token support
|
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It might be that I just can't find the right setting to make this work, but CodeLlama's upstream model docs refer to a [fill_token](https://huggingface.co/docs/transformers/main/model_doc/code_llama#transformers.CodeLlamaTokenizer.fill_token) for splitting the input and constructing the prompt for code infill. I can't seem to make this work on any of the `codellama:7b` variants using that token, whereas the HF hosted version of 13b seems to support it fine.
They give this example prompt for using `<FILL_ME>`:
```
def remove_non_ascii(s: str) -> str:
"""<FILL_ME>
return result
```
Here's the ollama output for the online 13b-instruct version:
```
def remove_non_ascii(s: str) -> str:
"""Remove non-ASCII characters from a string."""
return "".join(i for i in s if ord(i) < 128)
```
Here's the output for local 7b:
Sure! Here's the code to remove non-ASCII characters from a string in Python:
```python
def remove_non_ascii(s):
# Create a new string with only ASCII characters
result = ""
for char in s:
if ord(char) < 128:
result += char
return result
```
This function takes a string as input and returns a new string that contains only ASCII characters. The `ord()` function is used to convert each character to its corresponding Unicode code point, which allows us to check if the character is in the ASCII range. If it is not, then we skip adding it to the result string.
The code is ok (other than that it ignored the multiline docstring prompt); the surrounding commentary and markdown formatting is not.
I know this isn't a direct like-for-like comparison, but I can't run 13b locally, and I can't seem to find 7b hosted online anywhere; it's just too big for HF's free tier.
Am I holding it wrong?
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I_kwDOJ0Z1Ps6msDdu
| 8,473
|
HSA_OVERRIDE_GFX_VERSION_0 while running on only one GPU
|
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| 2025-01-18T01:28:21
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### What is the issue?
I am running `ollama:rocm` in a docker container on Ubuntu 24.04. My GPU is an RX 6600 (`gfx1032`). Everything works fine when I run `ollama` using
```bash
docker run -d \
--device /dev/kfd \
--device /dev/dri \
-v ollama:/root/.ollama \
-p 11434:11434 \
--restart unless-stopped \
--env HSA_OVERRIDE_GFX_VERSION="10.3.0" \
--env OLLAMA_KEEP_ALIVE="-1" \
--name ollama-rocm \
ollama/ollama:rocm
```
```
time=2025-01-18T01:24:54.470Z level=INFO source=routes.go:1238 msg="Listening on [::]:11434 (version 0.5.5-0-g32bd37a-dirty)"
time=2025-01-18T01:24:54.470Z level=INFO source=routes.go:1267 msg="Dynamic LLM libraries" runners="[cpu_avx2 rocm_avx cpu cpu_avx]"
time=2025-01-18T01:24:54.470Z level=INFO source=gpu.go:226 msg="looking for compatible GPUs"
time=2025-01-18T01:24:54.472Z level=WARN source=amd_linux.go:61 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2025-01-18T01:24:54.472Z level=INFO source=amd_linux.go:391 msg="skipping rocm gfx compatibility check" HSA_OVERRIDE_GFX_VERSION=10.3.0
time=2025-01-18T01:24:54.472Z level=INFO source=types.go:131 msg="inference compute" id=0 library=rocm variant="" compute=gfx1032 driver=0.0 name=1002:73ff total="8.0 GiB" available="8.0 GiB"
```
However, when I run using
```bash
docker run -d \
--device /dev/kfd \
--device /dev/dri \
-v ollama:/root/.ollama \
-p 11434:11434 \
--restart unless-stopped \
--env HSA_OVERRIDE_GFX_VERSION_0="10.3.0" \
--env OLLAMA_KEEP_ALIVE="-1" \
--name ollama-rocm \
ollama/ollama:rocm
```
`ollama` runs only on CPU:
```
time=2025-01-18T01:25:58.373Z level=INFO source=routes.go:1238 msg="Listening on [::]:11434 (version 0.5.5-0-g32bd37a-dirty)"
time=2025-01-18T01:25:58.373Z level=INFO source=routes.go:1267 msg="Dynamic LLM libraries" runners="[cpu_avx2 rocm_avx cpu cpu_avx]"
time=2025-01-18T01:25:58.373Z level=INFO source=gpu.go:226 msg="looking for compatible GPUs"
time=2025-01-18T01:25:58.375Z level=WARN source=amd_linux.go:61 msg="ollama recommends running the https://www.amd.com/en/support/linux-drivers" error="amdgpu version file missing: /sys/module/amdgpu/version stat /sys/module/amdgpu/version: no such file or directory"
time=2025-01-18T01:25:58.378Z level=WARN source=amd_linux.go:378 msg="amdgpu is not supported (supported types:[gfx1030 gfx1100 gfx1101 gfx1102 gfx900 gfx906 gfx908 gfx90a gfx940 gfx941 gfx942])" gpu_type=gfx1032 gpu=0 library=/usr/lib/ollama
time=2025-01-18T01:25:58.378Z level=WARN source=amd_linux.go:385 msg="See https://github.com/ollama/ollama/blob/main/docs/gpu.md#overrides for HSA_OVERRIDE_GFX_VERSION usage"
time=2025-01-18T01:25:58.378Z level=INFO source=amd_linux.go:404 msg="no compatible amdgpu devices detected"
time=2025-01-18T01:25:58.378Z level=INFO source=gpu.go:392 msg="no compatible GPUs were discovered"
time=2025-01-18T01:25:58.378Z level=INFO source=types.go:131 msg="inference compute" id=0 library=cpu variant=avx2 compute="" driver=0.0 name="" total="62.7 GiB" available="61.2 GiB"
```
I am trying this because eventually I want to get newer AMD GPUs and use them concurrently for `ollama`.
Is this not supported when running `ollama` in `docker`?
If I want to use this RX 6600 and an RX 7800 in the same system, how should I do it with `docker`?
### OS
Linux
### GPU
AMD
### CPU
Intel
### Ollama version
0.5.5-0-g32bd37a-dirty
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PR_kwDOJ0Z1Ps52ZmBy
| 5,934
|
Report better error on cuda unsupported os/arch
|
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| 2024-07-25T00:11:57
| 2024-07-29T21:24:23
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COLLABORATOR
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If we detect an NVIDIA GPU, but nvidia doesn't support the os/arch, this will report a better error for the user and point them to docs to self-install the drivers if possible.
Fixes #3261 #2302
Example output on Ubuntu 22.04 on AWS g5g.xlarge (arm64)
```
% sh ./install.sh
>>> Downloading ollama...
######################################################################################################################################### 100.0%######################################################################################################################################### 100.0%
>>> Installing ollama to /usr/local/bin...
>>> Adding ollama user to render group...
>>> Adding ollama user to video group...
>>> Adding current user to ollama group...
>>> Creating ollama systemd service...
>>> Enabling and starting ollama service...
>>> Installing NVIDIA repository...
ERROR NVIDIA GPU detected, but your OS and Architecture are not supported by NVIDIA. Please install the CUDA driver manually https://docs.nvidia.com/cuda/cuda-installation-guide-linux/
% echo $?
1
```
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Openchat in Ollama
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Hello
Nvidia, CUDA, are all installed and working fine. Phew.
How do I verify that Ollama is actually using the GPU while responding. I am using the openchat model
Thanks a million for Ollama and especially for including the openchat model. Stay blessed & happy folks!
Regards
|
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PR_kwDOJ0Z1Ps5xtsS6
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Intel GPU build support
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[] | null | 10
| 2024-06-06T17:56:05
| 2025-01-24T23:15:19
| 2024-11-21T18:23:32
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COLLABORATOR
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This enables linux, but still needs some more work to get it wired up to the Windows official builds.
Fixes #1590
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I_kwDOJ0Z1Ps6HEeP_
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Inconsistent 500 errors when generating
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### What is the issue?
EDIT : I tried with `0.1.29` and I don't have the issue
Hello,
I'm using docker image, tag latest without any GPU.
I downloaded mistral model, and I'm trying to generate answers using the promt from the doc (I have the same issue with llama model) :
```
curl http://localhost:11434/api/generate -d '{
"model": "llama2",
"prompt": "Why is the sky blue?"
}'
```
But often (not always) I get an error 500 even though tokens have been generated. I know tokens have been generated because I see them in the API response.
API response :
```
{"model":"mistral","created_at":"2024-04-26T16:07:28.07709035Z","response":" The","done":false}
{"model":"mistral","created_at":"2024-04-26T16:07:28.631254126Z","response":" color","done":false}
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{"model":"mistral","created_at":"2024-04-26T16:08:31.842367364Z","response":" pink","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:32.371649431Z","response":",","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:32.900765864Z","response":" orange","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:33.422518694Z","response":",","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:34.002004535Z","response":" and","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:34.50465466Z","response":" purple","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:35.020001089Z","response":" due","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:35.552555833Z","response":" to","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:36.100933033Z","response":" the","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:36.624066249Z","response":" scattering","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:37.127360511Z","response":" of","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:37.645663874Z","response":" sunlight","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:38.188634483Z","response":" in","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:38.726616954Z","response":" the","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:39.478114703Z","response":" Earth","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:40.205710019Z","response":"'","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:40.717148082Z","response":"s","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:41.214359608Z","response":" atmosphere","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:41.753657558Z","response":" at","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:42.293513538Z","response":" those","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:42.831315764Z","response":" specific","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:43.325317833Z","response":" angles","done":false}
{"model":"mistral","created_at":"2024-04-26T16:08:43.851507537Z","response":".","done":false}
{"error":"unexpected server status: 1"}
```
Here are the logs from the docker container (I see no errors) :
```
text error warn system array login
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":52940,"status":200,"tid":"23175901271616","timestamp":1714146582}
{"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":787,"tid":"23180484790144","timestamp":1714146582}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1809,"msg":"slot progression","n_past":72,"n_past_se":0,"n_prompt_tokens_processed":22,"slot_id":0,"task_id":787,"tid":"23180484790144","timestamp":1714146582}
{"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":72,"slot_id":0,"task_id":787,"tid":"23180484790144","timestamp":1714146582}
[GIN] 2024/04/26 - 15:50:19 | 200 | 36.865107759s | 192.168.1.167 | POST "/api/generate"
{"function":"log_server_request","level":"INFO","line":2734,"method":"POST","msg":"request","params":{},"path":"/completion","remote_addr":"127.0.0.1","remote_port":52940,"status":200,"tid":"23175901271616","timestamp":1714146620}
{"function":"update_slots","level":"INFO","line":1640,"msg":"slot released","n_cache_tokens":152,"n_ctx":2048,"n_past":151,"n_system_tokens":0,"slot_id":0,"task_id":787,"tid":"23180484790144","timestamp":1714146620,"truncated":false}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":848,"tid":"23180484790144","timestamp":1714146654}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":41324,"status":200,"tid":"23175903372864","timestamp":1714146654}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":849,"tid":"23180484790144","timestamp":1714146654}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":41324,"status":200,"tid":"23175903372864","timestamp":1714146654}
{"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":850,"tid":"23180484790144","timestamp":1714146654}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1809,"msg":"slot progression","n_past":72,"n_past_se":0,"n_prompt_tokens_processed":11,"slot_id":0,"task_id":850,"tid":"23180484790144","timestamp":1714146654}
{"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":72,"slot_id":0,"task_id":850,"tid":"23180484790144","timestamp":1714146654}
{"function":"print_timings","level":"INFO","line":269,"msg":"prompt eval time = 4190.76 ms / 11 tokens ( 380.98 ms per token, 2.62 tokens per second)","n_prompt_tokens_processed":11,"n_tokens_second":2.624825359630902,"slot_id":0,"t_prompt_processing":4190.755,"t_token":380.9777272727273,"task_id":850,"tid":"23180484790144","timestamp":1714146690}
{"function":"print_timings","level":"INFO","line":283,"msg":"generation eval time = 31807.07 ms / 64 runs ( 496.99 ms per token, 2.01 tokens per second)","n_decoded":64,"n_tokens_second":2.0121314549373572,"slot_id":0,"t_token":496.985421875,"t_token_generation":31807.067,"task_id":850,"tid":"23180484790144","timestamp":1714146690}
{"function":"print_timings","level":"INFO","line":293,"msg":" total time = 35997.82 ms","slot_id":0,"t_prompt_processing":4190.755,"t_token_generation":31807.067,"t_total":35997.822,"task_id":850,"tid":"23180484790144","timestamp":1714146690}
{"function":"log_server_request","level":"INFO","line":2734,"method":"POST","msg":"request","params":{},"path":"/completion","remote_addr":"127.0.0.1","remote_port":41324,"status":200,"tid":"23175903372864","timestamp":1714146690}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":0,"n_processing_slots":1,"task_id":916,"tid":"23180484790144","timestamp":1714146690}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":41324,"status":200,"tid":"23175903372864","timestamp":1714146690}
{"function":"update_slots","level":"INFO","line":1640,"msg":"slot released","n_cache_tokens":147,"n_ctx":2048,"n_past":146,"n_system_tokens":0,"slot_id":0,"task_id":850,"tid":"23180484790144","timestamp":1714146690,"truncated":false}
[GIN] 2024/04/26 - 15:51:30 | 500 | 36.085232311s | 192.168.1.167 | POST "/api/generate"
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":918,"tid":"23180484790144","timestamp":1714146702}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":52920,"status":200,"tid":"23175899170368","timestamp":1714146702}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":919,"tid":"23180484790144","timestamp":1714146702}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":52920,"status":200,"tid":"23175899170368","timestamp":1714146702}
{"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":920,"tid":"23180484790144","timestamp":1714146702}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1809,"msg":"slot progression","n_past":83,"n_past_se":0,"n_prompt_tokens_processed":0,"slot_id":0,"task_id":920,"tid":"23180484790144","timestamp":1714146702}
{"function":"update_slots","level":"INFO","line":1824,"msg":"we have to evaluate at least 1 token to generate logits","slot_id":0,"task_id":920,"tid":"23180484790144","timestamp":1714146702}
{"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":82,"slot_id":0,"task_id":920,"tid":"23180484790144","timestamp":1714146702}
[GIN] 2024/04/26 - 15:52:19 | 200 | 37.126512884s | 192.168.1.167 | POST "/api/generate"
{"function":"log_server_request","level":"INFO","line":2734,"method":"POST","msg":"request","params":{},"path":"/completion","remote_addr":"127.0.0.1","remote_port":52920,"status":200,"tid":"23175899170368","timestamp":1714146740}
{"function":"update_slots","level":"INFO","line":1640,"msg":"slot released","n_cache_tokens":157,"n_ctx":2048,"n_past":156,"n_system_tokens":0,"slot_id":0,"task_id":920,"tid":"23180484790144","timestamp":1714146740,"truncated":false}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":997,"tid":"23180484790144","timestamp":1714146772}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":60708,"status":200,"tid":"23175901271616","timestamp":1714146772}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":998,"tid":"23180484790144","timestamp":1714146772}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":60708,"status":200,"tid":"23175901271616","timestamp":1714146772}
{"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":999,"tid":"23180484790144","timestamp":1714146772}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1809,"msg":"slot progression","n_past":59,"n_past_se":0,"n_prompt_tokens_processed":26,"slot_id":0,"task_id":999,"tid":"23180484790144","timestamp":1714146772}
{"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":59,"slot_id":0,"task_id":999,"tid":"23180484790144","timestamp":1714146772}
{"function":"print_timings","level":"INFO","line":269,"msg":"prompt eval time = 9954.13 ms / 26 tokens ( 382.85 ms per token, 2.61 tokens per second)","n_prompt_tokens_processed":26,"n_tokens_second":2.611980632766373,"slot_id":0,"t_prompt_processing":9954.132,"t_token":382.85123076923077,"task_id":999,"tid":"23180484790144","timestamp":1714146816}
{"function":"print_timings","level":"INFO","line":283,"msg":"generation eval time = 34261.63 ms / 69 runs ( 496.55 ms per token, 2.01 tokens per second)","n_decoded":69,"n_tokens_second":2.0139148087183716,"slot_id":0,"t_token":496.5453333333333,"t_token_generation":34261.628,"task_id":999,"tid":"23180484790144","timestamp":1714146816}
{"function":"print_timings","level":"INFO","line":293,"msg":" total time = 44215.76 ms","slot_id":0,"t_prompt_processing":9954.132,"t_token_generation":34261.628,"t_total":44215.759999999995,"task_id":999,"tid":"23180484790144","timestamp":1714146816}
{"function":"log_server_request","level":"INFO","line":2734,"method":"POST","msg":"request","params":{},"path":"/completion","remote_addr":"127.0.0.1","remote_port":60708,"status":200,"tid":"23175901271616","timestamp":1714146816}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":0,"n_processing_slots":1,"task_id":1070,"tid":"23180484790144","timestamp":1714146816}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":60708,"status":200,"tid":"23175901271616","timestamp":1714146816}
{"function":"update_slots","level":"INFO","line":1640,"msg":"slot released","n_cache_tokens":154,"n_ctx":2048,"n_past":153,"n_system_tokens":0,"slot_id":0,"task_id":999,"tid":"23180484790144","timestamp":1714146816,"truncated":false}
[GIN] 2024/04/26 - 15:53:36 | 500 | 44.303565407s | 192.168.1.167 | POST "/api/generate"
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":1072,"tid":"23180484790144","timestamp":1714146826}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":43070,"status":200,"tid":"23175903372864","timestamp":1714146826}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":1073,"tid":"23180484790144","timestamp":1714146826}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":43070,"status":200,"tid":"23175903372864","timestamp":1714146826}
{"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":1074,"tid":"23180484790144","timestamp":1714146826}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1809,"msg":"slot progression","n_past":85,"n_past_se":0,"n_prompt_tokens_processed":0,"slot_id":0,"task_id":1074,"tid":"23180484790144","timestamp":1714146826}
{"function":"update_slots","level":"INFO","line":1824,"msg":"we have to evaluate at least 1 token to generate logits","slot_id":0,"task_id":1074,"tid":"23180484790144","timestamp":1714146826}
{"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":84,"slot_id":0,"task_id":1074,"tid":"23180484790144","timestamp":1714146826}
{"function":"print_timings","level":"INFO","line":269,"msg":"prompt eval time = 517.12 ms / 0 tokens ( inf ms per token, 0.00 tokens per second)","n_prompt_tokens_processed":0,"n_tokens_second":0.0,"slot_id":0,"t_prompt_processing":517.12,"t_token":null,"task_id":1074,"tid":"23180484790144","timestamp":1714146893}
{"function":"print_timings","level":"INFO","line":283,"msg":"generation eval time = 66105.63 ms / 119 runs ( 555.51 ms per token, 1.80 tokens per second)","n_decoded":119,"n_tokens_second":1.8001493549126968,"slot_id":0,"t_token":555.509462184874,"t_token_generation":66105.626,"task_id":1074,"tid":"23180484790144","timestamp":1714146893}
{"function":"print_timings","level":"INFO","line":293,"msg":" total time = 66622.75 ms","slot_id":0,"t_prompt_processing":517.12,"t_token_generation":66105.626,"t_total":66622.746,"task_id":1074,"tid":"23180484790144","timestamp":1714146893}
{"function":"log_server_request","level":"INFO","line":2734,"method":"POST","msg":"request","params":{},"path":"/completion","remote_addr":"127.0.0.1","remote_port":43070,"status":200,"tid":"23175903372864","timestamp":1714146893}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":0,"n_processing_slots":1,"task_id":1195,"tid":"23180484790144","timestamp":1714146893}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":43070,"status":200,"tid":"23175903372864","timestamp":1714146893}
{"function":"update_slots","level":"INFO","line":1640,"msg":"slot released","n_cache_tokens":204,"n_ctx":2048,"n_past":203,"n_system_tokens":0,"slot_id":0,"task_id":1074,"tid":"23180484790144","timestamp":1714146893,"truncated":false}
[GIN] 2024/04/26 - 15:54:53 | 500 | 1m6s | 192.168.1.167 | POST "/api/generate"
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":1197,"tid":"23180484790144","timestamp":1714146898}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":57848,"status":200,"tid":"23175899170368","timestamp":1714146898}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":1198,"tid":"23180484790144","timestamp":1714146898}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":57848,"status":200,"tid":"23175899170368","timestamp":1714146898}
{"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":1199,"tid":"23180484790144","timestamp":1714146898}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1809,"msg":"slot progression","n_past":85,"n_past_se":0,"n_prompt_tokens_processed":0,"slot_id":0,"task_id":1199,"tid":"23180484790144","timestamp":1714146898}
{"function":"update_slots","level":"INFO","line":1824,"msg":"we have to evaluate at least 1 token to generate logits","slot_id":0,"task_id":1199,"tid":"23180484790144","timestamp":1714146898}
{"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":84,"slot_id":0,"task_id":1199,"tid":"23180484790144","timestamp":1714146898}
[GIN] 2024/04/26 - 15:56:21 | 200 | 1m23s | 192.168.1.167 | POST "/api/generate"
{"function":"log_server_request","level":"INFO","line":2734,"method":"POST","msg":"request","params":{},"path":"/completion","remote_addr":"127.0.0.1","remote_port":57848,"status":200,"tid":"23175899170368","timestamp":1714146982}
{"function":"update_slots","level":"INFO","line":1640,"msg":"slot released","n_cache_tokens":235,"n_ctx":2048,"n_past":234,"n_system_tokens":0,"slot_id":0,"task_id":1199,"tid":"23180484790144","timestamp":1714146982,"truncated":false}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":1352,"tid":"23180484790144","timestamp":1714147011}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":53832,"status":200,"tid":"23175901271616","timestamp":1714147011}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":1353,"tid":"23180484790144","timestamp":1714147011}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":53832,"status":200,"tid":"23175901271616","timestamp":1714147011}
{"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":1354,"tid":"23180484790144","timestamp":1714147011}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1809,"msg":"slot progression","n_past":74,"n_past_se":0,"n_prompt_tokens_processed":4,"slot_id":0,"task_id":1354,"tid":"23180484790144","timestamp":1714147011}
{"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":74,"slot_id":0,"task_id":1354,"tid":"23180484790144","timestamp":1714147011}
{"function":"print_timings","level":"INFO","line":269,"msg":"prompt eval time = 1759.11 ms / 4 tokens ( 439.78 ms per token, 2.27 tokens per second)","n_prompt_tokens_processed":4,"n_tokens_second":2.2738797163107667,"slot_id":0,"t_prompt_processing":1759.108,"t_token":439.777,"task_id":1354,"tid":"23180484790144","timestamp":1714147065}
{"function":"print_timings","level":"INFO","line":283,"msg":"generation eval time = 52489.85 ms / 98 runs ( 535.61 ms per token, 1.87 tokens per second)","n_decoded":98,"n_tokens_second":1.867027589771592,"slot_id":0,"t_token":535.610724489796,"t_token_generation":52489.851,"task_id":1354,"tid":"23180484790144","timestamp":1714147065}
{"function":"print_timings","level":"INFO","line":293,"msg":" total time = 54248.96 ms","slot_id":0,"t_prompt_processing":1759.108,"t_token_generation":52489.851,"t_total":54248.959,"task_id":1354,"tid":"23180484790144","timestamp":1714147065}
{"function":"log_server_request","level":"INFO","line":2734,"method":"POST","msg":"request","params":{},"path":"/completion","remote_addr":"127.0.0.1","remote_port":53832,"status":200,"tid":"23175901271616","timestamp":1714147065}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":0,"n_processing_slots":1,"task_id":1454,"tid":"23180484790144","timestamp":1714147065}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":34160,"status":200,"tid":"23175903372864","timestamp":1714147065}
[GIN] 2024/04/26 - 15:57:45 | 500 | 54.29739368s | 192.168.1.167 | POST "/api/generate"
{"function":"update_slots","level":"INFO","line":1640,"msg":"slot released","n_cache_tokens":176,"n_ctx":2048,"n_past":175,"n_system_tokens":0,"slot_id":0,"task_id":1354,"tid":"23180484790144","timestamp":1714147065,"truncated":false}
time=2024-04-26T16:06:50.536Z level=INFO source=gpu.go:121 msg="Detecting GPU type"
time=2024-04-26T16:06:50.536Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*"
time=2024-04-26T16:06:50.537Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3539318188/runners/cuda_v11/libcudart.so.11.0]"
time=2024-04-26T16:06:50.579Z level=INFO source=gpu.go:343 msg="Unable to load cudart CUDA management library /tmp/ollama3539318188/runners/cuda_v11/libcudart.so.11.0: your nvidia driver is too old or missing, please upgrade to run ollama"
time=2024-04-26T16:06:50.579Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-04-26T16:06:50.580Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: []"
time=2024-04-26T16:06:50.580Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-26T16:06:50.580Z level=INFO source=gpu.go:121 msg="Detecting GPU type"
time=2024-04-26T16:06:50.580Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libcudart.so*"
time=2024-04-26T16:06:50.581Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: [/tmp/ollama3539318188/runners/cuda_v11/libcudart.so.11.0]"
time=2024-04-26T16:06:50.581Z level=INFO source=gpu.go:343 msg="Unable to load cudart CUDA management library /tmp/ollama3539318188/runners/cuda_v11/libcudart.so.11.0: your nvidia driver is too old or missing, please upgrade to run ollama"
time=2024-04-26T16:06:50.582Z level=INFO source=gpu.go:268 msg="Searching for GPU management library libnvidia-ml.so"
time=2024-04-26T16:06:50.582Z level=INFO source=gpu.go:314 msg="Discovered GPU libraries: []"
time=2024-04-26T16:06:50.583Z level=INFO source=cpu_common.go:11 msg="CPU has AVX2"
time=2024-04-26T16:06:50.583Z level=INFO source=server.go:127 msg="offload to gpu" reallayers=0 layers=0 required="4267.5 MiB" used="181.0 MiB" available="0 B" kv="256.0 MiB" fulloffload="164.0 MiB" partialoffload="181.0 MiB"
time=2024-04-26T16:06:50.584Z level=INFO source=server.go:264 msg="starting llama server" cmd="/tmp/ollama3539318188/runners/cpu_avx2/ollama_llama_server --model /root/.ollama/models/blobs/sha256-e8a35b5937a5e6d5c35d1f2a15f161e07eefe5e5bb0a3cdd42998ee79b057730 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 0 --port 39645"
time=2024-04-26T16:06:50.585Z level=INFO source=server.go:389 msg="waiting for llama runner to start responding"
{"function":"server_params_parse","level":"INFO","line":2603,"msg":"logging to file is disabled.","tid":"22658407044992","timestamp":1714147610}
{"function":"server_params_parse","level":"WARN","line":2380,"msg":"Not compiled with GPU offload support, --n-gpu-layers option will be ignored. See main README.md for information on enabling GPU BLAS support","n_gpu_layers":-1,"tid":"22658407044992","timestamp":1714147610}
{"build":1,"commit":"7593639","function":"main","level":"INFO","line":2819,"msg":"build info","tid":"22658407044992","timestamp":1714147610}
{"function":"main","level":"INFO","line":2822,"msg":"system info","n_threads":1,"n_threads_batch":-1,"system_info":"AVX = 1 | AVX_VNNI = 0 | AVX2 = 1 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | FMA = 1 | NEON = 0 | ARM_FMA = 0 | F16C = 1 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 0 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | ","tid":"22658407044992","timestamp":1714147610,"total_threads":1}
llama_model_loader: loaded meta data with 24 key-value pairs and 291 tensors from /root/.ollama/models/blobs/sha256-e8a35b5937a5e6d5c35d1f2a15f161e07eefe5e5bb0a3cdd42998ee79b057730 (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.name str = mistralai
llama_model_loader: - kv 2: llama.context_length u32 = 32768
llama_model_loader: - kv 3: llama.embedding_length u32 = 4096
llama_model_loader: - kv 4: llama.block_count u32 = 32
llama_model_loader: - kv 5: llama.feed_forward_length u32 = 14336
llama_model_loader: - kv 6: llama.rope.dimension_count u32 = 128
llama_model_loader: - kv 7: llama.attention.head_count u32 = 32
llama_model_loader: - kv 8: llama.attention.head_count_kv u32 = 8
llama_model_loader: - kv 9: llama.attention.layer_norm_rms_epsilon f32 = 0.000010
llama_model_loader: - kv 10: llama.rope.freq_base f32 = 1000000.000000
llama_model_loader: - kv 11: general.file_type u32 = 2
llama_model_loader: - kv 12: tokenizer.ggml.model str = llama
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,32000] = ["<unk>", "<s>", "</s>", "<0x00>", "<...
llama_model_loader: - kv 14: tokenizer.ggml.scores arr[f32,32000] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 15: tokenizer.ggml.token_type arr[i32,32000] = [2, 3, 3, 6, 6, 6, 6, 6, 6, 6, 6, 6, ...
llama_model_loader: - kv 16: tokenizer.ggml.merges arr[str,58980] = ["▁ t", "i n", "e r", "▁ a", "h e...
llama_model_loader: - kv 17: tokenizer.ggml.bos_token_id u32 = 1
llama_model_loader: - kv 18: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - kv 19: tokenizer.ggml.unknown_token_id u32 = 0
llama_model_loader: - kv 20: tokenizer.ggml.add_bos_token bool = true
llama_model_loader: - kv 21: tokenizer.ggml.add_eos_token bool = false
llama_model_loader: - kv 22: tokenizer.chat_template str = {{ bos_token }}{% for message in mess...
llama_model_loader: - kv 23: general.quantization_version u32 = 2
llama_model_loader: - type f32: 65 tensors
llama_model_loader: - type q4_0: 225 tensors
llama_model_loader: - type q6_K: 1 tensors
llm_load_vocab: special tokens definition check successful ( 259/32000 ).
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = llama
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 32000
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 32768
llm_load_print_meta: n_embd = 4096
llm_load_print_meta: n_head = 32
llm_load_print_meta: n_head_kv = 8
llm_load_print_meta: n_layer = 32
llm_load_print_meta: n_rot = 128
llm_load_print_meta: n_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 = 1000000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 32768
llm_load_print_meta: rope_finetuned = unknown
llm_load_print_meta: 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 = 7.24 B
llm_load_print_meta: model size = 3.83 GiB (4.54 BPW)
llm_load_print_meta: general.name = mistralai
llm_load_print_meta: BOS token = 1 '<s>'
llm_load_print_meta: EOS token = 2 '</s>'
llm_load_print_meta: UNK token = 0 '<unk>'
llm_load_print_meta: LF token = 13 '<0x0A>'
llm_load_tensors: ggml ctx size = 0.11 MiB
llm_load_tensors: CPU buffer size = 3917.87 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: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CPU 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: CPU output buffer size = 0.14 MiB
llama_new_context_with_model: CPU compute buffer size = 164.01 MiB
llama_new_context_with_model: graph nodes = 1030
llama_new_context_with_model: graph splits = 1
{"function":"initialize","level":"INFO","line":448,"msg":"initializing slots","n_slots":1,"tid":"22658407044992","timestamp":1714147640}
{"function":"initialize","level":"INFO","line":457,"msg":"new slot","n_ctx_slot":2048,"slot_id":0,"tid":"22658407044992","timestamp":1714147640}
{"function":"main","level":"INFO","line":3064,"msg":"model loaded","tid":"22658407044992","timestamp":1714147640}
{"function":"main","hostname":"127.0.0.1","level":"INFO","line":3267,"msg":"HTTP server listening","n_threads_http":"3","port":"39645","tid":"22658407044992","timestamp":1714147640}
{"function":"update_slots","level":"INFO","line":1578,"msg":"all slots are idle and system prompt is empty, clear the KV cache","tid":"22658407044992","timestamp":1714147640}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":0,"tid":"22658407044992","timestamp":1714147640}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":44072,"status":200,"tid":"22653821568576","timestamp":1714147640}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":1,"tid":"22658407044992","timestamp":1714147640}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":44078,"status":200,"tid":"22653821568576","timestamp":1714147640}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":2,"tid":"22658407044992","timestamp":1714147640}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":44094,"status":200,"tid":"22653821568576","timestamp":1714147640}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":3,"tid":"22658407044992","timestamp":1714147640}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":44102,"status":200,"tid":"22653821568576","timestamp":1714147640}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":4,"tid":"22658407044992","timestamp":1714147640}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":44120,"status":200,"tid":"22653821568576","timestamp":1714147640}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":5,"tid":"22658407044992","timestamp":1714147640}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":44116,"status":200,"tid":"22653823669824","timestamp":1714147640}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":6,"tid":"22658407044992","timestamp":1714147641}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":44942,"status":200,"tid":"22653823669824","timestamp":1714147641}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":1,"n_processing_slots":0,"task_id":7,"tid":"22658407044992","timestamp":1714147641}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":44942,"status":200,"tid":"22653823669824","timestamp":1714147641}
{"function":"launch_slot_with_data","level":"INFO","line":830,"msg":"slot is processing task","slot_id":0,"task_id":8,"tid":"22658407044992","timestamp":1714147641}
{"function":"update_slots","ga_i":0,"level":"INFO","line":1809,"msg":"slot progression","n_past":0,"n_past_se":0,"n_prompt_tokens_processed":15,"slot_id":0,"task_id":8,"tid":"22658407044992","timestamp":1714147641}
{"function":"update_slots","level":"INFO","line":1836,"msg":"kv cache rm [p0, end)","p0":0,"slot_id":0,"task_id":8,"tid":"22658407044992","timestamp":1714147641}
[GIN] 2024/04/26 - 16:07:21 | 200 | 66.373µs | 127.0.0.1 | GET "/api/version"
{"function":"print_timings","level":"INFO","line":269,"msg":"prompt eval time = 6808.76 ms / 15 tokens ( 453.92 ms per token, 2.20 tokens per second)","n_prompt_tokens_processed":15,"n_tokens_second":2.2030430192616457,"slot_id":0,"t_prompt_processing":6808.764,"t_token":453.9176,"task_id":8,"tid":"22658407044992","timestamp":1714147724}
{"function":"print_timings","level":"INFO","line":283,"msg":"generation eval time = 76398.96 ms / 141 runs ( 541.84 ms per token, 1.85 tokens per second)","n_decoded":141,"n_tokens_second":1.845574957856754,"slot_id":0,"t_token":541.8365673758866,"t_token_generation":76398.956,"task_id":8,"tid":"22658407044992","timestamp":1714147724}
{"function":"print_timings","level":"INFO","line":293,"msg":" total time = 83207.72 ms","slot_id":0,"t_prompt_processing":6808.764,"t_token_generation":76398.956,"t_total":83207.72,"task_id":8,"tid":"22658407044992","timestamp":1714147724}
{"function":"log_server_request","level":"INFO","line":2734,"method":"POST","msg":"request","params":{},"path":"/completion","remote_addr":"127.0.0.1","remote_port":44942,"status":200,"tid":"22653823669824","timestamp":1714147724}
{"function":"process_single_task","level":"INFO","line":1506,"msg":"slot data","n_idle_slots":0,"n_processing_slots":1,"task_id":151,"tid":"22658407044992","timestamp":1714147724}
{"function":"log_server_request","level":"INFO","line":2734,"method":"GET","msg":"request","params":{},"path":"/health","remote_addr":"127.0.0.1","remote_port":44942,"status":200,"tid":"22653823669824","timestamp":1714147724}
{"function":"update_slots","level":"INFO","line":1640,"msg":"slot released","n_cache_tokens":156,"n_ctx":2048,"n_past":155,"n_system_tokens":0,"slot_id":0,"task_id":8,"tid":"22658407044992","timestamp":1714147724,"truncated":false}
[GIN] 2024/04/26 - 16:08:44 | 200 | 1m57s | 192.168.1.167 | POST "/api/generate"
```
### OS
Linux
### GPU
Other
### CPU
Intel
### Ollama version
0.1.32
|
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| 2,148,340,077
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PR_kwDOJ0Z1Ps5nm7PN
| 2,666
|
Update client.py
|
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[] | null | 1
| 2024-02-22T06:46:24
| 2024-05-07T23:37:47
| 2024-05-07T23:37:47
|
CONTRIBUTOR
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| 2,123,732,549
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I_kwDOJ0Z1Ps5-lZZF
| 2,396
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llama.cpp now supports Vulkan
|
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| 2024-02-07T19:33:24
| 2024-03-21T14:00:45
| 2024-03-21T14:00:45
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As of 10 days ago: https://github.com/ggerganov/llama.cpp/commit/2307523d322af762ae06648b29ec5a9eb1c73032
This is great news for people who non-CUDA cards.
What's necessary to support this with Ollama? I'm happy to help if you show me the pointers.
|
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PR_kwDOJ0Z1Ps5-mfq6
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Fix openapi base writer header code.
|
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Fix openapi base writer header code.
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I_kwDOJ0Z1Ps51zYRq
| 987
|
segmentation fault with prompts longer than 5 / 6 tokens on intel mac
|
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| 2023-12-10T23:16:41
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NONE
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I'm running Ollama on a 2019 intel MacBook Pro with 32gb of RAM and a 4gb AMD GPU. macOS Monterey.
For some reson, every prompt longer than a few words on both codellama:7b and llama2:7b end up with `Error: llama runner exited, you may not have enough available memory to run this model`
Very short prompts work ok.
This is the server log file:
```
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: freq_base = 1000000.0
llama_new_context_with_model: freq_scale = 1
llama_new_context_with_model: kv self size = 1024.00 MB
llama_new_context_with_model: compute buffer total size = 162.13 MB
llama server listening at http://127.0.0.1:49879
{"timestamp":1699023783,"level":"INFO","function":"main","line":1749,"message":"HTTP server listening","hostname":"127.0.0.1","port":49879}
{"timestamp":1699023783,"level":"INFO","function":"log_server_request","line":1240,"message":"request","remote_addr":"127.0.0.1","remote_port":50344,"status":200,"method":"HEAD","path":"/","params":{}}
2023/11/03 16:03:03 llama.go:442: llama runner started in 1.001631 seconds
[GIN] 2023/11/03 - 16:03:03 | 200 | 1.143871095s | 127.0.0.1 | POST "/api/generate"
{"timestamp":1699023810,"level":"INFO","function":"log_server_request","line":1240,"message":"request","remote_addr":"127.0.0.1","remote_port":50346,"status":200,"method":"HEAD","path":"/","params":{}}
2023/11/03 16:03:30 llama.go:385: signal: segmentation fault
2023/11/03 16:03:30 llama.go:459: llama runner stopped successfully
[GIN] 2023/11/03 - 16:03:30 | 200 | 227.020112ms | 127.0.0.1 | POST "/api/generate"
```
Not sure what can I do to tackle this problem or provide any more info, as far as I know I should be able to run those models just fine with my machine.
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I_kwDOJ0Z1Ps6P9DNd
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|
Tokenizer issue with tool calling with InternLM2
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### What is the issue?
I am currently using `internlm2:7b` model from https://ollama.com/library/internlm2:7b .
I am trying to use the tool calling capability of the model using https://github.com/InternLM/InternLM/blob/main/chat/chat_format.md#function-call as a prompt reference.
Following is the prompt I used
```
<|im_start|>system
You are a helpful and honest assistant.<|im_end|>
<|im_start|>system name=<|plugin|>
[{"name":"duckduckgo-search","description":"A search engine. Useful for when you need to answer questions about current events. Input should be a search query.","parameters":{"type":"object","properties":{"input":{"type":"string"}},"additionalProperties":false,"$schema":"http://json-schema.org/draft-07/schema#"}}]<|im_end|>
<|im_start|>user
What is the capital city of France?<|im_end|>
<|im_start|>assistant
```
The prompt is sent to the `/api/generate` endpoint (raw mode). https://github.com/ollama/ollama/blob/main/docs/api.md#request-raw-mode
Here is the response from the model.
```
I need to use the duckduckgo-search plugin to find the latest test news today.[UNUSED_TOKEN_144][UNUSED_TOKEN_141]
{"name":"duckduckgo-search","parameters":{"input":"latest test news today"}}[UNUSED_TOKEN_143]
```
The format of the response looks good except for the tokenizer failure to detokenize `<|action_start|>`, `<|action_end|>` and `<|plugin|>`.
After some research around the web, I still have no idea how to fix this, hoping you can help me with this.
Thank you in advance.
<hr />
### Extras (Not sure if this helps)
Here is the script I used
```typescript
import { DuckDuckGoSearch } from "@langchain/community/tools/duckduckgo_search";
import { Ollama } from "ollama";
import { zodToJsonSchema } from "zod-to-json-schema";
const searchTool = new DuckDuckGoSearch();
const tools = JSON.stringify([
{
name: searchTool.name,
description: searchTool.description,
parameters: zodToJsonSchema(searchTool.schema),
},
]);
const prompt = `<|im_start|>system
You are a helpful and honest assistant.<|im_end|>
<|im_start|>system name=<|plugin|>
${tools}<|im_end|>
<|im_start|>user
What is the capital city of France?<|im_end|>
<|im_start|>assistant
`;
console.info(prompt);
const ollama = new Ollama();
const stream = await ollama.generate({
keep_alive: "1h",
model: "internlm2:7b",
prompt,
raw: true,
stream: true,
options: {
num_ctx: 4096,
temperature: 0,
},
});
for await (const chunk of stream) process.stdout.write(chunk.response);
process.stdout.write("\n");
```
Here is the `tokenizer_config.json` for `internlm2_5-7b-chat`.
https://huggingface.co/internlm/internlm2_5-7b-chat/blob/main/tokenizer_config.json
### OS
Windows, Docker, WSL2
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.2.5
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Update README.md with PyOllaMx
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Based on [this comment](https://github.com/ollama/ollama/issues/5937#issuecomment-2327760726), creating this PR to add PyOllaMx to list of ollama based applications
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PR_kwDOJ0Z1Ps51TmfX
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fix: solve network disruption during downloads, add OLLAMA_DOWNLOAD_CONN setting
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The process of managing bandwidth for model downloads has been an ongoing journey.
- Users reported difficulties when downloading model since January in issue #2006
- The feature #2995 was reverted in March 2024
The situation left Ollama server with unsafe network concurrency defaults since, causing problems for many users and people sharing the same network, whether they realize Ollama is the origin of their troubles or not.
In the associated issue, users describe in length the problems caused and creative mitigations.
Fortunately, the root cause is simple: 64 concurrent connections, an extremely aggressive value guaranteed to challenge any network congestion algorithm, and the fix is straightforward: opting for 1 concurrent connection by default per model download.
This PR addresses the root cause while adding the ability to configure network concurrency for download if required, via the `OLLAMA_DOWNLOAD_CONN` setting.
This PR avoids on purpose any complex, ineffective or hard to configure workarounds, like dynamic concurrency adjustments or manual bandwidth limiting.
From the commit associated:
The Ollama server now downloads models using a single connection. This change addresses the root cause of issue #2006 by following best practices instead of relying on workarounds. Users have been reporting problems associated with model downloads since January 2024, describing issues such as "hogging the entire device", "reliably and repeatedly kills my connection", "freezes completely leaving no choice but to hard reset", "when I download models, everyone in the office gets a really slow internet", and "when downloading large models, it feels like my home network is being DDoSed."
The environment variable `OLLAMA_DOWNLOAD_CONN` can be set to control the number of concurrent connections with a maximum value of 64 (the previous default, an aggressive value - unsafe in some conditions). The new default value is 1, ensuring each Ollama download is given the same priority as other network activities.
An entry in the FAQ describes how to use `OLLAMA_DOWNLOAD_CONN` for different use cases. This patch comes with a safe and unproblematic default value.
Changes include updates to the `envconfig/config.go`, `cmd/cmd.go`, `server/download.go`, and `docs/faq.md` files.
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I_kwDOJ0Z1Ps6nuWBU
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Problems with deepseek-r1:671b, ollama keeps crashing on long answers
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### What is the issue?
Hi all,
I'm using an r960 with 2TB of ram, so ram is not a problem here. I'm experiencing constant crashes of ollama 0.5.7 and deepseek-r1:671b, even increasing the context window with the command /set parameter num_ctx 4096.
I also tried a second system, an r670 csp with 1TB of ram, but the problem occurs in the same way.
I'm not able to use gpu due to the massive size of the model, anyway plenty of cores do the job for my current pourposes.
os are ubuntu 22.04.5 and 24.04.1
### OS
Linux
### GPU
_No response_
### CPU
Intel
### Ollama version
0.5.7
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