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} | null | null | null | ### What is the issue?
ollama collapses CPU
even when I stop the server the CPU still stuck from 75% to 90%
even when I do have an RTX 3070 and terminal is showind that is using the GPU
there is no error in terminal
I have no verbose since I forcelly shutedown the workstation
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.6 | {
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} | Part of #2589
- Builds only the cpu runner for ARM64 Also, the existing CMake recipe already enables NEON and Armv8.2 extensions when ARM64 is detected.
- I'll create another PR with build instructions. The main trick is that MSY2 has the CLANGARM64 environment that provides gcc aliases to Clang.
Maintainer changes are enabled. Feel free to adjust the changes to your liking. | {
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} | part of #5216
part of #5284
part of #5207 | {
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} | null | null | null | It seems like if you provide a long prompt (I was using one of 1,000ish tokens according to OpenAI tokenizer) with this model, you get an error `Error: Post "http://localhost:11434/api/generate": EOF`. It may or may not relate to the contents of the prompt as well as the length | {
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} | null | null | null | I'd like to be able to set the system prompt from the call to `ollama` in my shell, rather than in the conversation. For example:
```sh
ollama run llama3.1 --system="Your nickname is 'Grass' now"
```
...or...
```sh
ollama run llama3.1 -s "system" "Your nickname is 'Grass' now"
```
With this ability, I could set up aliases in my shell profile so that I can run system-prompt-customized versions of a model with a single command.
```sh
alias grass="ollama run llama3.1 --system=\"Your nickname is 'Grass' now\""
```
It'd be even better if I could also specify a path to a text file with a system prompt (e.g. `ollama run llama3.1 --system-file="~/system_prompts/grass.txt"`, but that wouldn't be necessary.
This is unique from #807, whose fix only works in the conversation. | null | {
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} | Fixes minor typos and grammar in `api/types.go`
I had only reviewed `client.go` in my commit yesterday, sorry I didn't check this one too. Somehow the last PR had Python in the title, but it and this affect Golang. | {
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https://api.github.com/repos/ollama/ollama/issues/8485 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/8485/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/8485/comments | https://api.github.com/repos/ollama/ollama/issues/8485/events | https://github.com/ollama/ollama/issues/8485 | 2,797,657,711 | I_kwDOJ0Z1Ps6mwN5v | 8,485 | [0.5.7] small models are loaded to GPU, but inference is slow and using a lot of CPU | {
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Hello there.
Just upgraded from ollama 0.4.x version to the latest one (0.5.7) and immediately noticed that inference of all models (even small ones, like llama 3.2 3B) become very slow. Like orders of magnitude slow. I can see that during inference CPU is being used intensively, even though the model itself is loaded into VRAM of GPU and there's a lot of VRAM is still free (as per nvtop).
OS Ubuntu 22 LTS
RTX 4090
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.5.7 | {
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https://api.github.com/repos/ollama/ollama/issues/4145 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/4145/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/4145/comments | https://api.github.com/repos/ollama/ollama/issues/4145/events | https://github.com/ollama/ollama/pull/4145 | 2,278,580,561 | PR_kwDOJ0Z1Ps5uhXWg | 4,145 | Fix lint warnings | {
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https://api.github.com/repos/ollama/ollama/issues/290 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/290/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/290/comments | https://api.github.com/repos/ollama/ollama/issues/290/events | https://github.com/ollama/ollama/pull/290 | 1,837,466,485 | PR_kwDOJ0Z1Ps5XPFzC | 290 | implement loading ggml lora adapters through the modelfile | {
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} | LoRA adapters can be added to Ollama models through the Modelfile and automatically applied when the model is loaded:
```
FROM llama2:13b
TEMPLATE {{ .Prompt }}
ADAPTER ./llama2-13b-storywriter-lora.ggml.bin
```
A few caveats:
* LoRA adapters must be GGML. If the adapter isn't GGML, it can be converted with the `convert-lora-to-ggml.py` script in https://github.com/ggerganov/llama.cpp
* Using adapters with quantized weights might not produce good results
* Using adapters disables mmap
* It's possible to apply multiple adapters but
1. Ordering is important
2. There may be unintended side effects since most adapters are not intended to be layered on other adapters
3. Performance may degrade with more adapters | {
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https://api.github.com/repos/ollama/ollama/issues/6651 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/6651/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/6651/comments | https://api.github.com/repos/ollama/ollama/issues/6651/events | https://github.com/ollama/ollama/issues/6651 | 2,507,064,853 | I_kwDOJ0Z1Ps6VbsYV | 6,651 | The speed of using embedded models is much slower compared to xinference | {
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} | null | null | null | I use the BGE-M3 model and send the same request, especially with xinference taking about 10 seconds and ollama taking about 200 seconds.
I'm sure they all use GPUs.
I found that xinference allocates more video memory, while ollama's video memory usage remains basically unchanged. Perhaps this is the reason for the speed difference? | null | {
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```
OLLAMA_HOST=my-test-host:11434 ollama create my-model -f ./Modelfile
``` | {
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There's no way to discover recent updates for existing models.
### How should we solve this?
Add "Last updated" sort choice
### What is the impact of not solving this?
No way to find out recently updated models
### Anything else?
_No response_ | {
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https://api.github.com/repos/ollama/ollama/issues/7820 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/7820/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/7820/comments | https://api.github.com/repos/ollama/ollama/issues/7820/events | https://github.com/ollama/ollama/issues/7820 | 2,687,973,667 | I_kwDOJ0Z1Ps6gNzkj | 7,820 | Instant closure when using shell input with piped output. | {
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} | null | null | null | ### What is the issue?
When running `ollama run [model] | cat` or `ollama run [model] > [file]`, ollama now closes immediately and does not accept any manual input.
`ollama run [model]` still functions as expected.
While `cat | ollama run [model] ...` seems to be the workaround, this requires entering ^d to have the input processed.
Thus closing the input stream and also ollama as a result, expected in this case since input was explicitly closed.
Additionally, thanks to [pull 416](https://github.com/ollama/ollama/pull/416), this removes the ability to queue or follow up with further prompts.
This regression seems specific to 0.4.4.
After downgrading to 0.4.3 or 0.4.2, ollama functions as I expect.
I have not tested older versions and will stick to version 0.4.3 for the time being.
The purpose of the above syntax being to use the ability of the chat functionality for to enter multiple prompts while processing the output by a further script.
Though this will be covered in a following feature request and is only tangentially related to this regression.
Thank you for your time, have a nice rest of your day!
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.4.4 | null | {
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} | null | null | null | It would greatly enhance usability if the Ollama CLI supported tab-autocomplete for model names when using commands like `run`, `show`, `list`, etc. For example:
```bash
# This would autocomplete to something like `llama3.2`, based on the locally available models.
ollama run lla<TAB>
```
Implementing this across all commands where model names are parameters would improve efficiency and user experience.
Thank you! | {
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https://api.github.com/repos/ollama/ollama/issues/2138 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/2138/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/2138/comments | https://api.github.com/repos/ollama/ollama/issues/2138/events | https://github.com/ollama/ollama/pull/2138 | 2,094,374,620 | PR_kwDOJ0Z1Ps5kvv-F | 2,138 | Update README.md - Community Integrations - Obsidian Local GPT plugin | {
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} | Local GPT plugin for Obsidian mainly relies on Ollama provider
Also works with images
<img width="400" src="https://github.com/pfrankov/obsidian-local-gpt/assets/584632/a05d68fa-5419-4386-ac43-82b9513999ad">
<img width="598" alt="Settings" src="https://github.com/pfrankov/obsidian-local-gpt/assets/584632/6ab2d802-13ed-42be-aab1-6a3f689b18a0">
I'd say that Local GPT plugin is enhanced version of [Obsidian Ollama plugin](https://github.com/hinterdupfinger/obsidian-ollama) in every way.
Tried to resolve merge conflicts in https://github.com/jmorganca/ollama/pull/1662 but accidentally closed it. | {
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https://api.github.com/repos/ollama/ollama/issues/5783 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5783/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5783/comments | https://api.github.com/repos/ollama/ollama/issues/5783/events | https://github.com/ollama/ollama/issues/5783 | 2,417,335,104 | I_kwDOJ0Z1Ps6QFZtA | 5,783 | erorr loading models x3 7900 XTX #5708 | {
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Few Days ago I report this error #5708, #5710 suppose to fix the issue but I still have the same error.
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.2.6-rocm | {
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https://api.github.com/repos/ollama/ollama/issues/1528 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/1528/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/1528/comments | https://api.github.com/repos/ollama/ollama/issues/1528/events | https://github.com/ollama/ollama/pull/1528 | 2,042,606,696 | PR_kwDOJ0Z1Ps5iDRhN | 1,528 | Add unit test of API routes | {
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} | [] | closed | false | null | [] | null | 0 | 2023-12-14T22:35:10 | 2023-12-15T00:47:41 | 2023-12-15T00:47:40 | CONTRIBUTOR | {
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} | This change modifies the base server to allow it to be more easily unit tested. It also adds in a simple unit test to "/api/version" to demonstrate how to add unit tests in the future. | {
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https://api.github.com/repos/ollama/ollama/issues/5830 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5830/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5830/comments | https://api.github.com/repos/ollama/ollama/issues/5830/events | https://github.com/ollama/ollama/issues/5830 | 2,421,426,851 | I_kwDOJ0Z1Ps6QVAqj | 5,830 | OpenAI endpoint gives 404 | {
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] | closed | false | null | [] | null | 2 | 2024-07-21T14:20:07 | 2024-07-22T06:18:03 | 2024-07-22T06:18:03 | NONE | {
"total": 0,
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} | null | null | null | ### What is the issue?
- http://localhost:11434/v1/chat/completions (gives 404)
- http://localhost:11434 (shows ollama is running)
Otherwise Ollama is working. I'm not sure what to do.
### OS
Linux
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.2.7 | {
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https://api.github.com/repos/ollama/ollama/issues/2920 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/2920/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/2920/comments | https://api.github.com/repos/ollama/ollama/issues/2920/events | https://github.com/ollama/ollama/issues/2920 | 2,167,396,829 | I_kwDOJ0Z1Ps6BL9nd | 2,920 | ollama call failed with status code 500 llama 2 | {
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"user_view_type": "public",
"site_admin": false
} | [] | closed | false | null | [] | null | 3 | 2024-03-04T17:44:32 | 2024-03-04T17:57:26 | 2024-03-04T17:48:17 | NONE | {
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} | null | null | null | Hi team,
I am trying to run the llama2 model locally ( I was doing it previously for the last couple of weeks without any problems), but now I face the following error when I am trying to -> ollama run llama2
`Error: error loading model /Users/S_Z/.ollama/models/blobs/sha256:8934d96d3f08982e95922b2b7a2c626a1fe873d7c3b06e8e56d7bc0a1`
No ollama models are running for that matter. for context, I have an M1 Max Macbook. Is there any fix to this? | {
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https://api.github.com/repos/ollama/ollama/issues/601 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/601/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/601/comments | https://api.github.com/repos/ollama/ollama/issues/601/events | https://github.com/ollama/ollama/pull/601 | 1,912,778,743 | PR_kwDOJ0Z1Ps5bMTBe | 601 | Update README.md for linux + cleanup | {
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"user_view_type": "public",
"site_admin": false
} | [] | closed | false | null | [] | null | 0 | 2023-09-26T06:30:46 | 2023-09-26T06:44:54 | 2023-09-26T06:44:53 | MEMBER | {
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https://api.github.com/repos/ollama/ollama/issues/5208 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5208/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5208/comments | https://api.github.com/repos/ollama/ollama/issues/5208/events | https://github.com/ollama/ollama/pull/5208 | 2,367,386,322 | PR_kwDOJ0Z1Ps5zOuoI | 5,208 | Support image input for OpenAI chat compatibility | {
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} | Supports passing in base64 encoded image into image_url.
E.g.
```
curl http://localhost:11434/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "llava",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What'\''s in this image?"
},
{
"type": "image_url",
"image_url": {
"url": "'$image'"
}
}
]
}
],
"max_tokens": 300
}' | jq
```
```
{
"id": "chatcmpl-659",
"object": "chat.completion",
"created": 1719016156,
"model": "llava",
"system_fingerprint": "fp_ollama",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": " The image shows a cute cartoon of an animal. It appears to be a dog or similar creature, styled with exaggerated features typical in internet memes. The character has big eyes, a round face, and its arms are raised in the air, as if waving or giving a thumbs-up gesture. There's also some motion blur that gives the impression of movement, suggesting the animal might be jumping or dancing. This kind of image is often used in digital communication to convey emotions or add a playful element to text messages. "
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 1,
"completion_tokens": 112,
"total_tokens": 113
}
}
```
Resolves #3690, #5304 | {
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} | As discussed on discord I implemented the feature. It just passes through the probs from the llamacpp server. Sorry, first time writing Go, might have missed something.
https://discord.com/channels/1128867683291627614/1128867684130508875/1187028494228664340 | {
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} | Relay errors on model load, this is needed to help people troubleshoot the specific problem they are experiencing when running a model. This function is a bottle-neck where many different errors can occur. As seen in #2753, there are many issues when the generic "failed to load model" error being reported.
In order to effectively identify and fix issues we need to relay more information to the client.
This change:
- Re-throws load errors, so that the client can output the problem
This change does not:
- Provide enhanced information to user's about how to resolve their problem. We will have to add these as we see the issues people face, if they can't be fixed on the Ollama side.
Errors will be relayed like this:
```
ollama run bad-model
Error: exception invalid model dimensions
```
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} | - Closes https://github.com/jmorganca/ollama/issues/538
- Upstreams some knowledge from https://github.com/jmorganca/ollama/issues/546
- Simplifies `brew install` to one line | {
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<img width="387" alt="Screenshot 2024-06-22 at 9 01 25 PM" src="https://github.com/ollama/ollama/assets/45348368/bd142627-9426-41af-b451-67dc82c427df">
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https://huggingface.co/apple/DCLM-7B
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} | null | null | null | ### What is the issue?
1. Updated Ollama this morning.
2. Entered `ollama run x/llama3.2-vision` on macbook
3. Got below output:
> pulling manifest
> pulling 652e85aa1e14... 100% ▕████████████████▏ 6.0 GB
> pulling 622429e8d318... 100% ▕████████████████▏ 1.9 GB
> pulling 962e0f69a367... 100% ▕████████████████▏ 163 B
> pulling dc49c86b8ebb... 100% ▕████████████████▏ 30 B
> pulling 6a50468ba2a8... 100% ▕████████████████▏ 498 B
> verifying sha256 digest
> writing manifest
> success
**> Error: llama runner process has terminated: error:Missing required key: clip.has_text_encoder**
Expected: Ollama download without error.
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.3.14 | {
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https://api.github.com/repos/ollama/ollama/issues/7437 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/7437/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/7437/comments | https://api.github.com/repos/ollama/ollama/issues/7437/events | https://github.com/ollama/ollama/pull/7437 | 2,625,863,096 | PR_kwDOJ0Z1Ps6AeYjb | 7,437 | Give unicode test more time to run | {
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} | For some reason, the relative API docs link is broken (api is a particular path in Github).
Replaced the API docs link in README.md with the absolute path.
Fixes issue #802. | {
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} | Changed:
- run ->invoke for updated lib
- updated langchain libraries for non-depreciated
- updated llama2 to llama3 | {
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https://api.github.com/repos/ollama/ollama/issues/3782 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/3782/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/3782/comments | https://api.github.com/repos/ollama/ollama/issues/3782/events | https://github.com/ollama/ollama/pull/3782 | 2,254,683,690 | PR_kwDOJ0Z1Ps5tQTYN | 3,782 | Critical fix from llama.cpp JSON grammar to forbid un-escaped escape characters in JSON strings | {
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} | JSON generation is broken, as models can insert control characters inside strings, which violates JSON. For example, with the current JSON grammar, models could generate:
```
{ "key": "value
broken" }
```
This is incorrect, and if a linebreak is wanted in the middle of the string there, it should be:
```
{ "key": "value\nbroken" }
```
The former breaks at least the nodejs JSON parser, and likely many many others, since it's not compliant JSON.
This PR injects the grammar directly from llama.cpp upstream, which prohibits `\x00` through `\x1f` inside JSON strings, fixing the problem. | {
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How to skip pulling manifest ? because my mechine without internet.
### OS
Linux, Docker
### GPU
_No response_
### CPU
Intel
### Ollama version
newest | {
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Is there any parameter or configuration to allow it.
im new on Ollama, so any answer would be appreciated.
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} | null | null | null | when loading a small model on multiple GPUs, it produces garbage.
the machine has 4 x 3070 (8GB) and an older i5-7400, UBU 22.04, Cuda 11.8
### How to reproduce
starting the server by hand
```bash
ollama serve
```
```bash
ollama run zephyr
>>> why is the sky blue?
acia##############################################################################^C
```
nvidia-smi shows it loads 4 x 1,2 GB
### Workaround
Till we get some flags to control the loading to a discrete GPU
start the server with `CUDA_VISIBLE_DEVICES`
```bash
CUDA_VISIBLE_DEVICES="3" ollama serve # loads the model on GPU No. 4
```
There seems to be a "memory-low-limit-point", under which this error occures.
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} | null | null | null | Hello :wave: , First of all thank you very much for creating and maintaining ollama! It's so simple to use :+1:
Now I wanted to use ollama for creating embeddings, and saw https://huggingface.co/intfloat/e5-mistral-7b-instruct performing very well on the [embeddings benchmark](https://huggingface.co/spaces/mteb/leaderboard). The official ollama model library doesn't contain it yet, so I wanted to create and upload it myself.
But during the quantization step (`docker run --rm -v .:/model:Z ollama/quantize -q q4_0 /model`) I get the error:
> unknown architecture MistralModel
As Mistral is supported by ollama, I'm wondering about this error. The E5 model is based on the Mistral instruct v0.1 one, so I assume it's the same architecture. Right? Is maybe just the `ollama/quantize` image not updated with the support yet? | {
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} | null | null | null | ### What is the issue?
Upon running "ollama run gemma:2b" (though this happens for all tested models: llama3, phi, tinyllama), the loading animation appears and after ~5 minutes (estimate, untimed), the response / result of the command is:
`Error: timed out waiting for llama runner to start - progress 1.00 - `
the server shows this log for this command:
```
2024/06/06 11:21:53 routes.go:1007: INFO server config env="map[OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST: OLLAMA_KEEP_ALIVE: OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:1 OLLAMA_MAX_QUEUE:512 OLLAMA_MAX_VRAM:0 OLLAMA_MODELS: OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:1 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:*] OLLAMA_RUNNERS_DIR: OLLAMA_TMPDIR:]"
time=2024-06-06T11:21:53.848-04:00 level=INFO source=images.go:729 msg="total blobs: 11"
time=2024-06-06T11:21:53.849-04:00 level=INFO source=images.go:736 msg="total unused blobs removed: 0"
time=2024-06-06T11:21:53.849-04:00 level=INFO source=routes.go:1053 msg="Listening on 127.0.0.1:11434 (version 0.1.41)"
time=2024-06-06T11:21:53.850-04:00 level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama3794080172/runners
time=2024-06-06T11:21:58.984-04:00 level=INFO source=payload.go:44 msg="Dynamic LLM libraries [cpu cuda_v11]"
time=2024-06-06T11:21:59.082-04:00 level=INFO source=types.go:71 msg="inference compute" id=GPU-42638932-6929-58db-a006-34d50a6799c1 library=cuda compute=8.7 driver=11.4 name=Orin total="29.9 GiB" available="21.7 GiB"
[GIN] 2024/06/06 - 11:22:14 | 200 | 64.512µs | 127.0.0.1 | HEAD "/"
[GIN] 2024/06/06 - 11:22:14 | 200 | 1.232036ms | 127.0.0.1 | POST "/api/show"
[GIN] 2024/06/06 - 11:22:14 | 200 | 717.058µs | 127.0.0.1 | POST "/api/show"
time=2024-06-06T11:22:16.239-04:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=19 memory.available="21.7 GiB" memory.required.full="2.6 GiB" memory.required.partial="2.6 GiB" memory.required.kv="36.0 MiB" memory.weights.total="1.6 GiB" memory.weights.repeating="1.0 GiB" memory.weights.nonrepeating="531.5 MiB" memory.graph.full="504.2 MiB" memory.graph.partial="918.6 MiB"
time=2024-06-06T11:22:16.239-04:00 level=INFO source=memory.go:133 msg="offload to gpu" layers.requested=-1 layers.real=19 memory.available="21.7 GiB" memory.required.full="2.6 GiB" memory.required.partial="2.6 GiB" memory.required.kv="36.0 MiB" memory.weights.total="1.6 GiB" memory.weights.repeating="1.0 GiB" memory.weights.nonrepeating="531.5 MiB" memory.graph.full="504.2 MiB" memory.graph.partial="918.6 MiB"
time=2024-06-06T11:22:16.240-04:00 level=INFO source=server.go:341 msg="starting llama server" cmd="/tmp/ollama3794080172/runners/cuda_v11/ollama_llama_server --model /home/harper/.ollama/models/blobs/sha256-c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12 --ctx-size 2048 --batch-size 512 --embedding --log-disable --n-gpu-layers 19 --parallel 1 --port 42781"
time=2024-06-06T11:22:16.240-04:00 level=INFO source=sched.go:338 msg="loaded runners" count=1
time=2024-06-06T11:22:16.240-04:00 level=INFO source=server.go:529 msg="waiting for llama runner to start responding"
time=2024-06-06T11:22:16.241-04:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="5921b8f" tid="281473278327040" timestamp=1717687336
INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 0 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 1 | SVE = 0 | ARM_FMA = 1 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 0 | SSSE3 = 0 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="281473278327040" timestamp=1717687336 total_threads=8
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="7" port="42781" tid="281473278327040" timestamp=1717687336
llama_model_loader: loaded meta data with 21 key-value pairs and 164 tensors from /home/harper/.ollama/models/blobs/sha256-c1864a5eb19305c40519da12cc543519e48a0697ecd30e15d5ac228644957d12 (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 = gemma
llama_model_loader: - kv 1: general.name str = gemma-2b-it
llama_model_loader: - kv 2: gemma.context_length u32 = 8192
llama_model_loader: - kv 3: gemma.block_count u32 = 18
llama_model_loader: - kv 4: gemma.embedding_length u32 = 2048
llama_model_loader: - kv 5: gemma.feed_forward_length u32 = 16384
llama_model_loader: - kv 6: gemma.attention.head_count u32 = 8
llama_model_loader: - kv 7: gemma.attention.head_count_kv u32 = 1
llama_model_loader: - kv 8: gemma.attention.key_length u32 = 256
llama_model_loader: - kv 9: gemma.attention.value_length u32 = 256
llama_model_loader: - kv 10: gemma.attention.layer_norm_rms_epsilon f32 = 0.000001
llama_model_loader: - kv 11: tokenizer.ggml.model str = llama
llama_model_loader: - kv 12: tokenizer.ggml.bos_token_id u32 = 2
llama_model_loader: - kv 13: tokenizer.ggml.eos_token_id u32 = 1
llama_model_loader: - kv 14: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 3
llama_model_loader: - kv 16: tokenizer.ggml.tokens arr[str,256128] = ["<pad>", "<eos>", "<bos>", "<unk>", ...
time=2024-06-06T11:22:16.493-04:00 level=INFO source=server.go:567 msg="waiting for server to become available" status="llm server loading model"
llama_model_loader: - kv 17: tokenizer.ggml.scores arr[f32,256128] = [0.000000, 0.000000, 0.000000, 0.0000...
llama_model_loader: - kv 18: tokenizer.ggml.token_type arr[i32,256128] = [3, 3, 3, 2, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 19: general.quantization_version u32 = 2
llama_model_loader: - kv 20: general.file_type u32 = 2
llama_model_loader: - type f32: 37 tensors
llama_model_loader: - type q4_0: 126 tensors
llama_model_loader: - type q8_0: 1 tensors
llm_load_vocab: special tokens cache size = 388
llm_load_vocab: token to piece cache size = 3.2028 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = gemma
llm_load_print_meta: vocab type = SPM
llm_load_print_meta: n_vocab = 256128
llm_load_print_meta: n_merges = 0
llm_load_print_meta: n_ctx_train = 8192
llm_load_print_meta: n_embd = 2048
llm_load_print_meta: n_head = 8
llm_load_print_meta: n_head_kv = 1
llm_load_print_meta: n_layer = 18
llm_load_print_meta: n_rot = 256
llm_load_print_meta: n_embd_head_k = 256
llm_load_print_meta: n_embd_head_v = 256
llm_load_print_meta: n_gqa = 8
llm_load_print_meta: n_embd_k_gqa = 256
llm_load_print_meta: n_embd_v_gqa = 256
llm_load_print_meta: f_norm_eps = 0.0e+00
llm_load_print_meta: f_norm_rms_eps = 1.0e-06
llm_load_print_meta: f_clamp_kqv = 0.0e+00
llm_load_print_meta: f_max_alibi_bias = 0.0e+00
llm_load_print_meta: f_logit_scale = 0.0e+00
llm_load_print_meta: n_ff = 16384
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 1
llm_load_print_meta: pooling type = 0
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_yarn_orig_ctx = 8192
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 = 2B
llm_load_print_meta: model ftype = Q4_0
llm_load_print_meta: model params = 2.51 B
llm_load_print_meta: model size = 1.56 GiB (5.34 BPW)
llm_load_print_meta: general.name = gemma-2b-it
llm_load_print_meta: BOS token = 2 '<bos>'
llm_load_print_meta: EOS token = 1 '<eos>'
llm_load_print_meta: UNK token = 3 '<unk>'
llm_load_print_meta: PAD token = 0 '<pad>'
llm_load_print_meta: LF token = 227 '<0x0A>'
llm_load_print_meta: EOT token = 107 '<end_of_turn>'
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: Orin, compute capability 8.7, VMM: yes
llm_load_tensors: ggml ctx size = 0.17 MiB
llm_load_tensors: offloading 18 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 19/19 layers to GPU
llm_load_tensors: CPU buffer size = 531.52 MiB
llm_load_tensors: CUDA0 buffer size = 1594.93 MiB
llama_new_context_with_model: n_ctx = 2048
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 36.00 MiB
llama_new_context_with_model: KV self size = 36.00 MiB, K (f16): 18.00 MiB, V (f16): 18.00 MiB
llama_new_context_with_model: CUDA_Host output buffer size = 0.98 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 504.25 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 8.01 MiB
llama_new_context_with_model: graph nodes = 601
llama_new_context_with_model: graph splits = 2
time=2024-06-06T11:29:16.920-04:00 level=ERROR source=sched.go:344 msg="error loading llama server" error="timed out waiting for llama runner to start - progress 1.00 - "
[GIN] 2024/06/06 - 11:29:16 | 500 | 7m2s | 127.0.0.1 | POST "/api/chat"
time=2024-06-06T11:29:22.037-04:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=5.117139389
time=2024-06-06T11:29:22.288-04:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=5.367497274
time=2024-06-06T11:29:22.537-04:00 level=WARN source=sched.go:512 msg="gpu VRAM usage didn't recover within timeout" seconds=5.616874999
```
If it helps, this is running on a [Jetson AGX Orin with 32GB of memory](https://www.seeedstudio.com/AGX-Orin-32GB-H01-Kit-p-5569.html)
### OS
Linux
### GPU
Nvidia
### CPU
Other: 8-core NVIDIA Arm® Cortex A78AE v8.2 64-bit CPU 2MB L2 + 4MB L3
### Ollama version
0.1.41 | {
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https://api.github.com/repos/ollama/ollama/issues/7141 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/7141/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/7141/comments | https://api.github.com/repos/ollama/ollama/issues/7141/events | https://github.com/ollama/ollama/pull/7141 | 2,573,860,762 | PR_kwDOJ0Z1Ps59-4qQ | 7,141 | Fix build leakages | {
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} | [] | closed | false | null | [] | null | 0 | 2024-10-08T18:00:51 | 2024-10-08T20:05:03 | 2024-10-08T20:05:00 | COLLABORATOR | {
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"merged_at": "2024-10-08T20:05:00"
} | The recent change to applying patches leaves the submodule dirty based on "new commits" being present. This ensures we clean up so the tree no longer reports dirty after a `go generate ./...` run.
The Makefile was being a bit too aggressive in cleaning things up and would result in deleting the placeholder files which someone might accidentally commit. | {
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https://api.github.com/repos/ollama/ollama/issues/6269 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/6269/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/6269/comments | https://api.github.com/repos/ollama/ollama/issues/6269/events | https://github.com/ollama/ollama/issues/6269 | 2,456,908,603 | I_kwDOJ0Z1Ps6ScXM7 | 6,269 | Please add LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct model | {
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} | null | null | null | * huggingface: [LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct](https://huggingface.co/LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct) | {
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https://api.github.com/repos/ollama/ollama/issues/3931 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/3931/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/3931/comments | https://api.github.com/repos/ollama/ollama/issues/3931/events | https://github.com/ollama/ollama/issues/3931 | 2,264,958,659 | I_kwDOJ0Z1Ps6HAIbD | 3,931 | Digest mismatch, file must be downloaded again | {
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} | null | null | null | ### What is the issue?
I tried to pull nomic-embed-text, but got an error below.
I also tried to pull another models, but got the same error.
$ ollama pull nomic-embed-text
pulling manifest
pulling 970aa74c0a90... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████▏ 274 MB
pulling c71d239df917... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████▏ 11 KB
pulling ce4a164fc046... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████▏ 17 B
pulling 31df23ea7daa... 100% ▕███████████████████████████████████████████████████████████████████████████████████████████▏ 420 B
verifying sha256 digest
Error: digest mismatch, file must be downloaded again: want sha256:970aa74c0a90ef7482477cf803618e776e173c007bf957f635f1015bfcfef0e6, got sha256:bea7e365d4085c35b0cfc78f9285682c6c7df7a15ac58f1905542659871024fd
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.32 | {
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} | Fixes #4791
Fixes #3799
Fixes #4388 | {
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} | This change adds some help in the REPL for using the keyboard shortcut commands. | {
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https://api.github.com/repos/ollama/ollama/issues/4200 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/4200/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/4200/comments | https://api.github.com/repos/ollama/ollama/issues/4200/events | https://github.com/ollama/ollama/issues/4200 | 2,280,915,245 | I_kwDOJ0Z1Ps6H9AEt | 4,200 | http://localhost:11434/api endpoint giving 404 error | {
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} | null | null | null | ### What is the issue?
I am using latest version of windows . As per readme file when I am hitting http://localhost:11434 i am getting "ollama is running" but "http://localhost:11434/api" is giving error 404
### OS
Windows
### GPU
AMD
### CPU
Intel
### Ollama version
0.1.33 | {
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https://api.github.com/repos/ollama/ollama/issues/3554 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/3554/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/3554/comments | https://api.github.com/repos/ollama/ollama/issues/3554/events | https://github.com/ollama/ollama/issues/3554 | 2,233,075,542 | I_kwDOJ0Z1Ps6FGgdW | 3,554 | Potential problems with the `llm/ext_server/server.cpp` not accepting `--ubatch-size ` option | {
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} | null | null | null | ### What is the issue?
Not sure what to list this issue under (it's a potential bug I think).
Recently `llama.cpp` has added an option called `--ubatch-size ` and appears to have changed the default value (and possibly meaning of) the old `--batch-size ` option:
https://github.com/ggerganov/llama.cpp/pull/6017
The PR says:
> By default `n_batch` is 4096, `n_ubatch` is 512
but I think it is actually 2048 and 512 now.
This better explains the difference between the options:
https://github.com/ggerganov/llama.cpp/discussions/6328
There are several potential problems and implications of this change for Ollama:
- It looks like `llm/ext_server/server.cpp` is using some old code that handles the `n_batch` in the old way, but this in turn seems to be importing headers, etc from a newer version of `lamma.cpp` (b2581), who's `examples/server.cpp` code does appear to have the new `n_batch` / `n_ubatch` logic in it.
- It looks like `api/types.go` is setting the `n_batch` value back down to 512, when the new default is supposed to be 2048.
- Since Ollama is using the `--embedding` option there are some potential problems mentioned here: https://github.com/ggerganov/llama.cpp/issues/6263
- Not being able to set the `n_ubatch` value is causing regressions for people using multiple GPUs (https://github.com/ggerganov/llama.cpp/issues/6075 & https://github.com/ggerganov/llama.cpp/issues/6476).
---
https://github.com/ollama/ollama/blob/main/llm/ext_server/server.cpp:
```
printf(" --pooling {none,mean,cls}\n");
printf(" pooling type for embeddings, use model default if unspecified\n");
printf(" -b N, --batch-size N batch size for prompt processing (default: %d)\n", params.n_batch);
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
```
vs:
https://github.com/ggerganov/llama.cpp/blob/37e7854c104301c5b5323ccc40e07699f3a62c3e/examples/server/server.cpp:
```
printf(" --pooling {none,mean,cls} pooling type for embeddings, use model default if unspecified\n");
printf(" -dt N, --defrag-thold N\n");
printf(" KV cache defragmentation threshold (default: %.1f, < 0 - disabled)\n", params.defrag_thold);
printf(" -b N, --batch-size N logical maximum batch size (default: %d)\n", params.n_batch);
printf(" -ub N, --ubatch-size N physical maximum batch size (default: %d)\n", params.n_ubatch);
printf(" --memory-f32 use f32 instead of f16 for memory key+value (default: disabled)\n");
```
---
```
- `-b N`, `--batch-size N`: Set the batch size for prompt processing. Default: `2048`
- `-ub N`, `--ubatch-size N`: Physical maximum batch size. Default: `512`
``` | null | {
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https://api.github.com/repos/ollama/ollama/issues/4742 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/4742/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/4742/comments | https://api.github.com/repos/ollama/ollama/issues/4742/events | https://github.com/ollama/ollama/issues/4742 | 2,327,001,333 | I_kwDOJ0Z1Ps6Kszj1 | 4,742 | VRAM allocation error when loading different models with different OLLAMA_VRAM_MAX configurations | {
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} | null | null | null | ### What is the issue?
I have two amd 7900xtx 24g gpu. When using ollama, I encounter different memory allocation errors and exit errors.
1. No OLLAMA_VRAM_MAX configuration
The large model deepseek-llm:67b-chat can be loaded correctly
But if you call something bigger, such as qwen:72b and command-r-plus, the display memory allocation will report an error and exit.
2. Use OLLAMA_VRAM_MAX configuration
Models larger than 67b-q4 load correctly, such as qwen:72b and command-r-plus.
However, smaller models cannot be loaded correctly, and memory allocation errors will be reported and exited, such as deepseek-llm:67b-chat.
There might be some errors in the vram configuration.
### OS
Linux
### GPU
AMD
### CPU
AMD
### Ollama version
0.1.39 | {
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https://api.github.com/repos/ollama/ollama/issues/8526 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/8526/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/8526/comments | https://api.github.com/repos/ollama/ollama/issues/8526/events | https://github.com/ollama/ollama/issues/8526 | 2,803,177,332 | I_kwDOJ0Z1Ps6nFRd0 | 8,526 | how to get English output | {
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] | open | false | null | [] | null | 1 | 2025-01-22T01:51:56 | 2025-01-22T04:17:26 | null | NONE | {
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} | null | null | null | ### What is the issue?
M:\AI\ollama>ollama run deepseek-r1:7b
>>> list philosophers
<think>
</think>
Here is a list of some of the most influential and notable philosophers throughout history, organized by era and
region:
### Ancient Philosophy (c. 600–321 BCE)
- **Thales of Miletus** (c. 624–548 BCE):被认为是第一个哲学家，提出“万物源于水”的学说。
- **Anaximander of Miletus** (c. 570–495 BCE): 提出“无限”概念，并认为万物起源于自然。
- **Anaximenes of Mileti** (c. 510–441 BCE): 认为万物来源于某种原始物质，如“air”（空气）。
### OS
Microsoft Windows [Version 10.0.22635.4800]
### GPU
NVIDIA GeForce GTX 1660 SUPER
### CPU
Processor AMD Ryzen 7 3700X 8-Core Processor, 3600 Mhz, 8 Core(s), 16 Logical Processor(s)
### Ollama version
ollama version is 0.5.7 | null | {
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} | Thanks to @hallh for #663. This change cherry-picks that PR, relays all our defaults, and does some re-organizing of the code to make it easier to read. | {
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https://api.github.com/repos/ollama/ollama/issues/8113 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/8113/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/8113/comments | https://api.github.com/repos/ollama/ollama/issues/8113/events | https://github.com/ollama/ollama/pull/8113 | 2,741,618,542 | PR_kwDOJ0Z1Ps6FULcl | 8,113 | llama: add qwen2vl support | {
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https://api.github.com/repos/ollama/ollama/issues/685 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/685/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/685/comments | https://api.github.com/repos/ollama/ollama/issues/685/events | https://github.com/ollama/ollama/issues/685 | 1,923,133,063 | I_kwDOJ0Z1Ps5yoK6H | 685 | Question: where are all the `Modelfile`s? | {
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} | null | null | null | https://ollama.ai/library has a lot of models. I would like to add a new model, and want to make sure it uses the GPU. So I am looking to refer to `Modelfile`s for models featured on https://ollama.ai/library.
Where is the source `Modelfile`s for the current "built in" models? | {
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https://api.github.com/repos/ollama/ollama/issues/2246 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/2246/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/2246/comments | https://api.github.com/repos/ollama/ollama/issues/2246/events | https://github.com/ollama/ollama/pull/2246 | 2,104,401,475 | PR_kwDOJ0Z1Ps5lRIEy | 2,246 | Don't disable GPUs on arm without AVX | {
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} | AVX is an x86 feature, so ARM should be excluded from the check.
Related to #1979 | {
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I try few small but are really bad. | {
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% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
100 8354 0 8354 0 0 16163 0 --:--:-- --:--:-- --:--:-- 16189
>>> Downloading ollama...
######################################################################## 100.0%##O=# # Warning: Failed to open the file /tmp/tmp.AO1TPHxNpB/ollama: No such file or
Warning: directory
0.0%curl: (23) Failure writing output to destination
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https://api.github.com/repos/ollama/ollama/issues/6461 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/6461/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/6461/comments | https://api.github.com/repos/ollama/ollama/issues/6461/events | https://github.com/ollama/ollama/issues/6461 | 2,480,507,753 | I_kwDOJ0Z1Ps6T2Ytp | 6,461 | "/clear" command is not clearing history | {
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} | null | null | null | ### What is the issue?
`Ctrl` + `l` is clearing history
but `/clear` command is not clearing history
https://github.com/user-attachments/assets/511ee922-9252-41d4-8b5f-ac324a75aaf1
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.3.6 | {
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https://api.github.com/repos/ollama/ollama/issues/3473 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/3473/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/3473/comments | https://api.github.com/repos/ollama/ollama/issues/3473/events | https://github.com/ollama/ollama/pull/3473 | 2,222,435,172 | PR_kwDOJ0Z1Ps5rimQ_ | 3,473 | Add BrainSoup to compatible clients list | {
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"merged_at": "2024-05-06T20:42:16"
} | Hi there,
BrainSoup is a native multi-LLM client for Windows with advanced features such as local document indexing, RAG, multi-modality, multi-agent automation, code interpreter, sandboxed file system and the ability for agents to interact with the local system via customizable events and tools. More information can be found [here](https://www.nurgo-software.com/products/brainsoup).
The latest version is know compatible with Ollama, making BrainSoup an excellent choice for users looking to leverage Ollama's LLMs to their full potential.
I kindly ask for your review of this PR. Thanks for creating such an awesome platform! | {
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How to fix the template in https://ollama.com/library/glm4 to make the ollama-tools mechanism work? | null | {
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https://api.github.com/repos/ollama/ollama/issues/4823 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/4823/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/4823/comments | https://api.github.com/repos/ollama/ollama/issues/4823/events | https://github.com/ollama/ollama/issues/4823 | 2,334,769,831 | I_kwDOJ0Z1Ps6LKcKn | 4,823 | I encountered this error when converting the Tongyi-Finance-14B-Chat-Int4-AWQ model | {
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`(.venv) [root@bastion ollama]# python llm/llama.cpp/convert-hf-to-gguf.py ./model --outtype f16 --outfile converted.bin
INFO:hf-to-gguf:Loading model: model
INFO:gguf.gguf_writer:gguf: This GGUF file is for Little Endian only
INFO:hf-to-gguf:Set model parameters
INFO:hf-to-gguf:Set model tokenizer
Traceback (most recent call last):
File "/root/model/ollama/llm/llama.cpp/convert-hf-to-gguf.py", line 2865, in <module>
main()
File "/root/model/ollama/llm/llama.cpp/convert-hf-to-gguf.py", line 2850, in main
model_instance.set_vocab()
File "/root/model/ollama/llm/llama.cpp/convert-hf-to-gguf.py", line 1592, in set_vocab
self._set_vocab_qwen()
File "/root/model/ollama/llm/llama.cpp/convert-hf-to-gguf.py", line 530, in _set_vocab_qwen
assert len(merged) == 2
AssertionError
`
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.41 | null | {
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https://api.github.com/repos/ollama/ollama/issues/7973 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/7973/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/7973/comments | https://api.github.com/repos/ollama/ollama/issues/7973/events | https://github.com/ollama/ollama/pull/7973 | 2,723,621,546 | PR_kwDOJ0Z1Ps6EW47n | 7,973 | Document that `--format` now supports passing JSON Schemas | {
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} | JSON Schema support was added in #7900.
--------
I removed `e.g.` because I don't believe it supports anything else, right? Let me know if that's wrong. | null | {
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https://api.github.com/repos/ollama/ollama/issues/5466 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5466/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5466/comments | https://api.github.com/repos/ollama/ollama/issues/5466/events | https://github.com/ollama/ollama/pull/5466 | 2,389,318,735 | PR_kwDOJ0Z1Ps50XSIm | 5,466 | Fix clip model loading with unicode paths | {
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} | On windows, if the model dir contained unicode characters clip models would fail to load. This fixes the file name handling in clip.cpp to support utf16 on windows.
Fixes #5329 #4365 | {
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https://api.github.com/repos/ollama/ollama/issues/3581 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/3581/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/3581/comments | https://api.github.com/repos/ollama/ollama/issues/3581/events | https://github.com/ollama/ollama/issues/3581 | 2,236,296,576 | I_kwDOJ0Z1Ps6FSy2A | 3,581 | MacOS Ollama not binding to 0.0.0.0 | {
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} | null | null | null | ### What is the issue?
So when set the OLLAMA_HOST to 0.0.0.0, I cannot access Ollama via the IP, but I can still access it via localhost.
### What did you expect to see?
I expect it to be available under <myIP>:11434
### Steps to reproduce
_No response_
### Are there any recent changes that introduced the issue?
_No response_
### OS
macOS
### Architecture
arm64
### Platform
_No response_
### Ollama version
0.1.31
### GPU
Apple
### GPU info
_No response_
### CPU
Apple
### Other software
_No response_ | {
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https://api.github.com/repos/ollama/ollama/issues/2049 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/2049/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/2049/comments | https://api.github.com/repos/ollama/ollama/issues/2049/events | https://github.com/ollama/ollama/issues/2049 | 2,088,341,295 | I_kwDOJ0Z1Ps58eY8v | 2,049 | Embedding API could return empty embedding while using completion API from LiteLLM | {
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} | null | null | null | To reproduce:
Launch a LiteLLM service:
```bash
litellm --model ollama/openhermes2.5-mistral --drop_params
```
Call the service `/completion` API continuously first, meanwhile you call embedding API via Langchain, and hopefully during the very gap (very short) between each `/completion` call you get empty embedding from Langchain.
To call the `/completion` API:
```python
import os
os.environ['OPENAI_API_KEY'] = 'any'
os.environ['OPENAI_API_BASE'] = 'http://0.0.0.0:8000'
from contextlib import contextmanager
from langchain.llms import OpenAI
import tiktoken
def print_center(banner: str):
print(banner.center(50, "="))
class LLM:
"""
A class for running a Language Model Chain.
"""
def __init__(self, prompt: str, temperature=0, gpt_4=False):
"""
Initializes the LLM class.
Args:
prompt (PromptTemplate): The prompt template to use.
temperature (int): The temperature to use for the model.
gpt_4 (bool): Whether to use GPT-4 or Text-Davinci-003.
Side Effects:
Sets the class attributes.
"""
self.prompt = prompt
self.prompt_size = self.number_of_tokens(prompt)
self.temperature = temperature
self.gpt_4 = gpt_4
self.model_name = "gpt-4" if self.gpt_4 else "text-davinci-003"
self.max_tokens = 4097 * 2 if self.gpt_4 else 4097
self.show_init_config()
def show_init_config(self):
print_center("init params")
print(f"Model: {self.model_name}")
print(f"Max Tokens: {self.max_tokens}")
print(f"Prompt Size: {self.prompt_size}")
print(f"Temperature: {self.temperature}")
print_center("init config")
print(self.prompt)
def run(self, query):
"""
Runs the Language Model Chain.
Args:
code (str): The code to use for the chain.
**kwargs (dict): Additional keyword arguments.
Returns:
str: The generated text.
"""
llm = OpenAI(
temperature=self.temperature,
max_tokens=-1,
model_name=self.model_name,
disallowed_special=(), # to suppress error when special tokens within the input text (encode special tokens as normal text)
)
# chain = LLMChain(llm=llm, prompt=self.prompt)
chunk_list = []
print_center("query")
print(query)
print_center("response")
_input = "\n".join([self.prompt, query])
for chunk in llm.stream(input=_input):
print(chunk, end="", flush=True)
chunk_list.append(chunk)
print()
result = "".join(chunk_list)
return result
def number_of_tokens(self, text):
"""
Counts the number of tokens in a given text.
Args:
text (str): The text to count tokens for.
Returns:
int: The number of tokens in the text.
"""
encoding = tiktoken.encoding_for_model("gpt-4")
return len(encoding.encode(text, disallowed_special=()))
@contextmanager
def llm_context(prompt: str, temperature=0, gpt_4=False):
model = LLM(prompt, temperature=temperature, gpt_4=gpt_4)
try:
yield model
finally:
del model
if __name__ == "__main__":
while True:
with llm_context("You are a helpful assistant.") as model:
model.run("Write me a 100 words news.")
```
To call the embedding API:
```python
from langchain.embeddings import OllamaEmbeddings
ollama_emb = OllamaEmbeddings(
model="openhermes2.5-mistral:latest",
)
while True:
embed = ollama_emb.embed_query("Hello world")
print("empty embedding?", embed is None)
``` | null | {
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https://api.github.com/repos/ollama/ollama/issues/946 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/946/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/946/comments | https://api.github.com/repos/ollama/ollama/issues/946/events | https://github.com/ollama/ollama/issues/946 | 1,967,119,694 | I_kwDOJ0Z1Ps51P91O | 946 | ollama show --modelfile gives incorrect FROM when multiple tags of base model are downloaded. | {
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] | closed | false | null | [] | null | 6 | 2023-10-29T19:13:09 | 2023-12-04T18:32:40 | 2023-12-04T18:32:40 | CONTRIBUTOR | {
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```
% ollama list
NAME ID SIZE MODIFIED
[...]
codellama:13b 9f438cb9cd58 7.4 GB 27 hours ago
codellama:13b-16k e86141f13814 7.4 GB 45 hours ago
codellama:34b-16k be95e5b84e7f 16 GB 41 seconds ago
codellama:34b-instruct-q3_K_M f534f618ea64 16 GB 27 hours ago
[...]
```
Note the file sizes; it's clear that ollama isn't confused about which data file to use for which.
However, if I `ollama show --modelfile`, the FROM just lists the base model name, not the tag.
```
% ollama show codellama:13b-16k --modelfile
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this one, replace the FROM line with:
# FROM codellama:13b-16k
FROM library/codellama
TEMPLATE """[INST] <<SYS>>{{ .System }}<</SYS>>
{{ .Prompt }} [/INST]
"""
PARAMETER num_ctx 16384
PARAMETER rope_frequency_base 1e+06
PARAMETER stop "[INST]"
PARAMETER stop "[/INST]"
PARAMETER stop "<<SYS>>"
PARAMETER stop "<</SYS>>"
% ollama show codellama:34b-16k --modelfile
# Modelfile generated by "ollama show"
# To build a new Modelfile based on this one, replace the FROM line with:
# FROM codellama:34b-16k
FROM library/codellama
TEMPLATE """[INST] <<SYS>>{{ .System }}<</SYS>>
{{ .Prompt }} [/INST]
"""
PARAMETER num_ctx 16384
PARAMETER num_gqa 8
PARAMETER rope_frequency_base 1e+06
PARAMETER stop "[INST]"
PARAMETER stop "[/INST]"
PARAMETER stop "<<SYS>>"
PARAMETER stop "<</SYS>>"
```
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} | null | null | null | ### What is the issue?
## desc
I implemented the deployment following the official Docker GPU container tutorial. And successfully got the graphics card information using nvidia-smi in the Docker container.
I'm using a nvidia discrete graphics card 3090
```info
# ollama ps
NAME ID SIZE PROCESSOR UNTIL
# ollama ps
NAME ID SIZE PROCESSOR UNTIL
qwen2:7b e0d4e1163c58 4.7 GB 100% CPU 4 minutes from now
# nvidia-smi
Thu Jun 20 09:36:11 2024
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 525.147.05 Driver Version: 525.147.05 CUDA Version: 12.0 |
|-------------------------------+----------------------+----------------------+
| 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 ... Off | 00000000:02:00.0 On | N/A |
| 0% 38C P8 16W / 350W | 114MiB / 24576MiB | 0% Default |
| | | N/A |
+-------------------------------+----------------------+----------------------+
+-----------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=============================================================================|
+-----------------------------------------------------------------------------+
# ollama -v
ollama version is 0.1.44
# ollama -v
ollama version is 0.1.44
# ollama ps
NAME ID SIZE PROCESSOR UNTIL
qwen2:7b e0d4e1163c58 4.7 GB 100% CPU 37 seconds from now
#
```
### OS
Linux, Docker
### GPU
Nvidia
### CPU
Intel
### Ollama version
ollama version is 0.1.44 | {
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I have completed the first version of the Ollama library for Dart, making it possible to integrate Ollama into Flutter applications. I thought it would be nice to mention it in the readme file.
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] | closed | false | null | [] | null | 5 | 2024-08-12T03:54:30 | 2024-09-02T23:49:18 | 2024-09-02T23:49:18 | NONE | {
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} | null | null | null | **Why ?**
So i just found out that Qwen2 have tools support. However, ollama current do not have tools support for qwen2 models. And there is a section of [Qwen2 github talking about ollama on tools use](https://github.com/QwenLM/Qwen2?tab=readme-ov-file#-run-locally
). And this seems prombles of ollama in older versions. I think this should be resolvable with version 0.3.x
**Request**
- I would like to request tools support for Qwen2 models
- If possible I would like to know how the process of adding tools support for a model (like ollama team can add tools to Qwen2 directly or participation of Qwen2 team is required)
Thanks for reading | {
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https://api.github.com/repos/ollama/ollama/issues/5870 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5870/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5870/comments | https://api.github.com/repos/ollama/ollama/issues/5870/events | https://github.com/ollama/ollama/issues/5870 | 2,424,725,170 | I_kwDOJ0Z1Ps6Qhl6y | 5,870 | The embeddings api interface is not working properly. | {
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} | null | null | null | ### What is the issue?
I use the bge-m3 model in graphrag with the following parameters
```
embeddings:
## parallelization: override the global parallelization settings for embeddings
async_mode: asyncio
llm:
api_key:
type: openai_embedding # or azure_openai_embedding
model: chatfire/bge-m3:q8_0
api_base: http://localhost:11434/api
```
The following error is returned
```
17:11:30,126 httpx INFO HTTP Request: POST http://localhost:11434/api/embeddings "HTTP/1.1 200 OK"
17:11:30,129 datashaper.workflow.workflow ERROR Error executing verb "text_embed" in create_final_entities: 'NoneType' object is not iterable
Traceback (most recent call last):
File "E:\Langchain-Chatchat\glut\lib\site-packages\datashaper\workflow\workflow.py", line 415, in _execute_verb
result = await result
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\text_embed.py", line 105, in text_embed
return await _text_embed_in_memory(
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\text_embed.py", line 130, in _text_embed_in_memory
result = await strategy_exec(texts, callbacks, cache, strategy_args)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\strategies\openai.py", line 61, in run
embeddings = await _execute(llm, text_batches, ticker, semaphore)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\strategies\openai.py", line 105, in _execute
results = await asyncio.gather(*futures)
File "E:\Langchain-Chatchat\glut\lib\asyncio\tasks.py", line 304, in __wakeup
future.result()
File "E:\Langchain-Chatchat\glut\lib\asyncio\tasks.py", line 232, in __step
result = coro.send(None)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\index\verbs\text\embed\strategies\openai.py", line 99, in embed
chunk_embeddings = await llm(chunk)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\caching_llm.py", line 104, in __call__
result = await self._delegate(input, **kwargs)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\rate_limiting_llm.py", line 177, in __call__
result, start = await execute_with_retry()
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\rate_limiting_llm.py", line 159, in execute_with_retry
async for attempt in retryer:
File "E:\Langchain-Chatchat\glut\lib\site-packages\tenacity\_asyncio.py", line 71, in __anext__
do = self.iter(retry_state=self._retry_state)
File "E:\Langchain-Chatchat\glut\lib\site-packages\tenacity\__init__.py", line 314, in iter
return fut.result()
File "E:\Langchain-Chatchat\glut\lib\concurrent\futures\_base.py", line 451, in result
return self.__get_result()
File "E:\Langchain-Chatchat\glut\lib\concurrent\futures\_base.py", line 403, in __get_result
raise self._exception
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\rate_limiting_llm.py", line 165, in execute_with_retry
return await do_attempt(), start
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\rate_limiting_llm.py", line 147, in do_attempt
return await self._delegate(input, **kwargs)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\base_llm.py", line 49, in __call__
return await self._invoke(input, **kwargs)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\base\base_llm.py", line 53, in _invoke
output = await self._execute_llm(input, **kwargs)
File "E:\Langchain-Chatchat\glut\lib\site-packages\graphrag\llm\openai\openai_embeddings_llm.py", line 36, in _execute_llm
embedding = await self.client.embeddings.create(
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\resources\embeddings.py", line 215, in create
return await self._post(
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_base_client.py", line 1826, in post
return await self.request(cast_to, opts, stream=stream, stream_cls=stream_cls)
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_base_client.py", line 1519, in request
return await self._request(
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_base_client.py", line 1622, in _request
return await self._process_response(
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_base_client.py", line 1714, in _process_response
return await api_response.parse()
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\_response.py", line 419, in parse
parsed = self._options.post_parser(parsed)
File "E:\Langchain-Chatchat\glut\lib\site-packages\openai\resources\embeddings.py", line 203, in parser
for embedding in obj.data:
TypeError: 'NoneType' object is not iterable
17:11:30,131 graphrag.index.reporting.file_workflow_callbacks INFO Error executing verb "text_embed" in create_final_entities: 'NoneType' object is not iterable details=None
17:11:30,142 graphrag.index.run ERROR error running workflow create_final_entities
```
### OS
Windows
### GPU
Nvidia
### CPU
AMD
### Ollama version
0.2.8 | {
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thanks 谢谢
ollama0.1.43
error format not yet support!
错误格式尚不支持！
错误格式尚不支持！
搞了多次 均报错 | null | {
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} | null | null | null | ### What is the issue?
[root@localhost ~]# cat /etc/redhat-release
AlmaLinux release 9.3 (Shamrock Pampas Cat)
[root@localhost ~]# ollama -v
Warning: could not connect to a running Ollama instance
Warning: client version is 0.1.30
[root@localhost ~]# ./ollama -v
Segmentation fault (core dumped)
[root@localhost ~]#
### What did you expect to see?
[root@localhost ~]# ./ollama -v
Segmentation fault (core dumped)
### Steps to reproduce
[root@localhost ~]# ./ollama -v
Segmentation fault (core dumped)
### Are there any recent changes that introduced the issue?
[root@localhost ~]# ./ollama -v
Segmentation fault (core dumped)
### OS
Linux
### Architecture
amd64
### Platform
_No response_
### Ollama version
0.1.31
### GPU
Nvidia
### GPU info
[root@localhost ~]# nvidia-smi
Tue Apr 9 09:31:38 2024
+-----------------------------------------------------------------------------------------+
| NVIDIA-SMI 550.54.15 Driver Version: 550.54.15 CUDA Version: 12.4 |
|-----------------------------------------+------------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+========================+======================|
| 0 Tesla T4 Off | 00000000:06:00.0 Off | 0 |
| N/A 34C P0 27W / 70W | 0MiB / 15360MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
| 1 Tesla T4 Off | 00000000:86:00.0 Off | 0 |
| N/A 34C P0 26W / 70W | 0MiB / 15360MiB | 0% Default |
| | | N/A |
+-----------------------------------------+------------------------+----------------------+
+-----------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=========================================================================================|
| No running processes found |
+-----------------------------------------------------------------------------------------+
### CPU
Intel
### Other software
AlmaLinux release 9.3 (Shamrock Pampas Cat) | {
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https://api.github.com/repos/ollama/ollama/issues/5712 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5712/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5712/comments | https://api.github.com/repos/ollama/ollama/issues/5712/events | https://github.com/ollama/ollama/pull/5712 | 2,409,800,448 | PR_kwDOJ0Z1Ps51cb6s | 5,712 | Add Windows arm64 support to official builds | {
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} | Wire up CI and build rigging to generate a unified Windows installer with x64 and arm64 payloads. At install time, the correct binaries will be installed for the platform.
I was unable to find a combination of hand-picked msvc redist DLLs manually that yielded a working setup on a pristine Windows 11 install, but running the vc_redist installer works reliably, so for arm64, we run the nested installer conditionally. If it is already installed, that step will be skipped.
Fixes #2589
Note: I've tested most of the CI steps in the PR, but signing isn't yet verified and might require minor fixes on the first release after this merges.
Resulting build artifacts: (Note: current OllamaSetup.exe with only x64 binaries is 273MB)
```
% ls -lh dist/
total 932M
-rw-r--r-- 1 daniel 197609 12K Jul 17 09:24 ollama_welcome.ps1
-rwxr-xr-x 1 daniel 197609 291M Jul 17 09:27 OllamaSetup.exe*
-rw-r--r-- 1 daniel 197609 649M Jul 17 09:27 ollama-windows-amd64.zip
-rw-r--r-- 1 daniel 197609 20M Jul 19 15:41 ollama-windows-arm64.zip
drwxr-xr-x 1 daniel 197609 0 Jul 17 09:24 windows-amd64/
-rwxr-xr-x 1 daniel 197609 5.9M Jul 17 09:24 windows-amd64-app.exe*
drwxr-xr-x 1 daniel 197609 0 Jul 16 15:53 windows-arm64/
-rwxr-xr-x 1 daniel 197609 5.5M Jul 16 16:12 windows-arm64-app.exe*
% du -sh dist/windows-a*64
2.1G dist/windows-amd64
37M dist/windows-arm64
```
On a Snapdragon X 12-core laptop:
```
> ollama run --verbose llama3 why is the sky blue
...
total duration: 23.6819409s
load duration: 4.738127s
prompt eval count: 16 token(s)
prompt eval duration: 430.297ms
prompt eval rate: 37.18 tokens/s
eval count: 348 token(s)
eval duration: 18.513796s
eval rate: 18.80 tokens/s
```
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https://api.github.com/repos/ollama/ollama/issues/2120 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/2120/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/2120/comments | https://api.github.com/repos/ollama/ollama/issues/2120/events | https://github.com/ollama/ollama/issues/2120 | 2,092,519,133 | I_kwDOJ0Z1Ps58uU7d | 2,120 | How to install libnvidia-ml.so? | {
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} | null | null | null | Hi guys! I have been using ollama with ollama webui this month.However,it output
```
WARNING:
You should always run with libnvidia-ml.so that is installed with your
NVIDIA Display Driver. By default it's installed in /usr/lib and /usr/lib64.
libnvidia-ml.so in GDK package is a stub library that is attached only for
build purposes (e.g. machine that you build your application doesn't have
to have Display Driver installed).
```
And whenever I want to run any model(which is capable to load it and the speed is about 5 tokens/s) it will always run into cuda memory error.
My system:
RAM:16GB
GPU:3060ti 8GB
SYSTEM:archlinux
Kernel:6.7.0-arch3-1
Nvidia GPU Driver:nvidia-dkms 545.29.06-1
I have also installed following package which is related to nvidia:
```
lib32-nvidia-utils 545.29.06-1
libnvidia-container 1.14.3-1
libnvidia-container-tools 1.14.3-1
libva-nvidia-driver-git 0.0.11.r1.gea6d862-1
nvidia-container-toolkit 1.14.3-9
nvidia-docker-compose 0.1.6-1
nvidia-utils 545.29.06-1
``` | {
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https://api.github.com/repos/ollama/ollama/issues/1033 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/1033/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/1033/comments | https://api.github.com/repos/ollama/ollama/issues/1033/events | https://github.com/ollama/ollama/issues/1033 | 1,981,678,318 | I_kwDOJ0Z1Ps52HgLu | 1,033 | Are these system specs good enough for any models? | {
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} | null | null | null | Just a question, I have an old laptop that i turned into a server with Ubuntu LTS. It has an AMD E1-6015 APU and 8gb of ram. I would like to know if that's enough to run any of these models, thank you! | {
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https://api.github.com/repos/ollama/ollama/issues/5907 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5907/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5907/comments | https://api.github.com/repos/ollama/ollama/issues/5907/events | https://github.com/ollama/ollama/issues/5907 | 2,427,317,383 | I_kwDOJ0Z1Ps6QreyH | 5,907 | Support token embeddings for `v1/embeddings` | {
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"total": 0,
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} | null | null | null | ### What is the issue?
When encoding with `tiktoken`, the interface is no longer compatible
```
tiktoken.get_encoding("cl100k_base").encode(text)
```
```json
{
"input": [30624,99849,64479,51392,31809,29207,233,45829],
"model": "nomic-embed-text"
}
```
```
{
"error": {
"message": "invalid input type",
"type": "api_error",
"param": null,
"code": null
}
}
```
I hope it can be compatible, thank you
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
ollama version is 0.2.7 | null | {
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https://api.github.com/repos/ollama/ollama/issues/5107 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5107/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5107/comments | https://api.github.com/repos/ollama/ollama/issues/5107/events | https://github.com/ollama/ollama/issues/5107 | 2,358,707,183 | I_kwDOJ0Z1Ps6MlwPv | 5,107 | ollama 模型授权 | {
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} | null | null | null | ### What is the issue?
我想问一下ollama怎么自定义设置KEY_传参， 每个模型定义不同的key，2.ollama是否支持模型授权，加密模型之类的，
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
最新的 | {
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https://api.github.com/repos/ollama/ollama/issues/4621 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/4621/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/4621/comments | https://api.github.com/repos/ollama/ollama/issues/4621/events | https://github.com/ollama/ollama/issues/4621 | 2,316,267,571 | I_kwDOJ0Z1Ps6KD3Az | 4,621 | phi3-medium-128k wrong number of tensors | {
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} | null | null | null | ### What is the issue?
I'm getting the following error when testing the new 128k versions of phi3-medium:
```sh
$ ollama run phi3:14b-medium-128k-instruct-q4_0
Error: llama runner process has terminated: signal: abort trap error:done_getting_tensors: wrong number of tensors; expected 245, got 243
```
```sh
$ ollama -v
ollama version is 0.1.38
```
### OS
macOS
### GPU
Apple
### CPU
Apple
### Ollama version
0.1.38 | {
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https://api.github.com/repos/ollama/ollama/issues/4226 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/4226/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/4226/comments | https://api.github.com/repos/ollama/ollama/issues/4226/events | https://github.com/ollama/ollama/issues/4226 | 2,282,922,640 | I_kwDOJ0Z1Ps6IEqKQ | 4,226 | run llama3-70B-q8_0 error | {
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"total": 0,
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} | null | null | null | ### What is the issue?
api call error
message:
{"error":{"message":"timed out waiting for llama runner to start:CUDA error:uncorrectable ECC error encountered\
n current device:0,in function ggml cuda_compute_forward at /go/src/github.com/ollama/ollama/11m/1lama.cpp/ggml
-cuda.cu:2300\n err\nGGML_ASSERT:/go/src/github.com/ollama/ollama/11m/1lama.cpp/ggml-cuda.cu:60:!\"CUDA error\"
""type":"api_error","param"null,"code":null}}
### OS
Linux, Docker
### GPU
Nvidia
### CPU
Intel
### Ollama version
_No response_ | {
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https://api.github.com/repos/ollama/ollama/issues/5750 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5750/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5750/comments | https://api.github.com/repos/ollama/ollama/issues/5750/events | https://github.com/ollama/ollama/pull/5750 | 2,414,156,448 | PR_kwDOJ0Z1Ps51q_iz | 5,750 | stub response | {
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https://api.github.com/repos/ollama/ollama/issues/4183 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/4183/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/4183/comments | https://api.github.com/repos/ollama/ollama/issues/4183/events | https://github.com/ollama/ollama/issues/4183 | 2,279,709,927 | I_kwDOJ0Z1Ps6H4Zzn | 4,183 | pull orca2:7b-fp16 Error: EOF | {
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`ollama pull orca2:7b-fp16` results in `Error: EOF`
### OS
Linux
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.1.33 | {
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https://api.github.com/repos/ollama/ollama/issues/3397 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/3397/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/3397/comments | https://api.github.com/repos/ollama/ollama/issues/3397/events | https://github.com/ollama/ollama/pull/3397 | 2,214,232,629 | PR_kwDOJ0Z1Ps5rG7Q3 | 3,397 | Parallel requests | {
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} | Stage: PoC
Related issue: #358
- loaded.mu.{Lock,Unlock}() is not implemented correctly in this change
- sparams.n_parallel is hardcoded to 4
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https://api.github.com/repos/ollama/ollama/issues/1158 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/1158/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/1158/comments | https://api.github.com/repos/ollama/ollama/issues/1158/events | https://github.com/ollama/ollama/issues/1158 | 1,997,987,411 | I_kwDOJ0Z1Ps53Ft5T | 1,158 | max retries exceeded: unexpected EOF | {
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} | null | null | null | Hi there,
I am not sure if this is related to your file service, but I am getting this connection drops out very often.
Maybe there is a way to throttle requests? | {
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https://api.github.com/repos/ollama/ollama/issues/216 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/216/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/216/comments | https://api.github.com/repos/ollama/ollama/issues/216/events | https://github.com/ollama/ollama/issues/216 | 1,822,180,868 | I_kwDOJ0Z1Ps5snEYE | 216 | Something might still be wrong with K-Quant | {
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} | null | null | null | When I run a 30B model (in this case upstage-llama-30b-instruct-2048.ggmlv3.q5_K_M.bin) the debug output in ollama talks about a 13B model size:
when running the same model with llama.cpp it outputs the correct size:
I tested with a 13B model and the output seems correct. Both models seem to work (as in generating output). | {
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https://api.github.com/repos/ollama/ollama/issues/4880 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/4880/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/4880/comments | https://api.github.com/repos/ollama/ollama/issues/4880/events | https://github.com/ollama/ollama/issues/4880 | 2,339,156,469 | I_kwDOJ0Z1Ps6LbLH1 | 4,880 | Extend ollama show command | {
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} | null | null | null | In reference to #3570 | {
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} | null | null | null | ### What is the issue?
Scanning linux binary with `grype` yields the following report
Upgrading to 1.22.7+ for building would fix this issue:
https://github.com/ollama/ollama/blob/3085c47bea508e638c0b9f15a2f4c00afd83b66d/Dockerfile#L1
https://github.com/ollama/ollama/blob/3085c47bea508e638c0b9f15a2f4c00afd83b66d/llama/Dockerfile#L2
https://github.com/ollama/ollama/blob/3085c47bea508e638c0b9f15a2f4c00afd83b66d/go.mod#L3
### OS
Linux, macOS, Docker
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.14 | {
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} | multiple templates may appear in a model if a model is created from another model that 1) has an autodetected template and 2) defines a custom template
this fixes the bug by not detecting chat template when inheriting from another model | {
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} | null | null | null | ### What is the issue?
when upload profile pic , a page to move the pic into o round place .. the canvas doesnt fit into the display , of andro chrome canary
### OS
_No response_
### GPU
_No response_
### CPU
_No response_
### Ollama version
_No response_ | null | {
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} | null | null | null | ### What is the issue?
I am running the ollama on intel xeon 32 processors (CPU only) previously which high token generation count using version 0.1.38
However, once I migrate to the latest ollama version 0.1.41, I found that the inference speed for even a model like phi3 on pure CPU slow to a halt.
I retest the version and reproducing the slowness start with 0.1.39.
Unable to provide log details as there is no error just pure slowness.
Didn't change any model nor configuration.
Revert back to ver 0.1.38, the performance turn high speed again.
Using centos 8 linux
Xeon gold processor 32 core
### OS
Linux
### GPU
Other
### CPU
Intel
### Ollama version
0.1.39 | {
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https://api.github.com/repos/ollama/ollama/issues/6115 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/6115/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/6115/comments | https://api.github.com/repos/ollama/ollama/issues/6115/events | https://github.com/ollama/ollama/pull/6115 | 2,441,859,247 | PR_kwDOJ0Z1Ps53Fqs2 | 6,115 | Fix context in /api/generate grows too much (#5980). | {
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} | [] | closed | false | null | [] | null | 6 | 2024-08-01T08:47:37 | 2024-08-01T22:14:00 | 2024-08-01T22:13:59 | CONTRIBUTOR | {
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"merged_at": "2024-08-01T22:13:59"
} | This PR fixes [Context in /api/generate response grows too big. #5980
](https://github.com/ollama/ollama/issues/5980) | {
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https://api.github.com/repos/ollama/ollama/issues/6094 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/6094/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/6094/comments | https://api.github.com/repos/ollama/ollama/issues/6094/events | https://github.com/ollama/ollama/issues/6094 | 2,439,598,593 | I_kwDOJ0Z1Ps6RaVIB | 6,094 | "embedding generation failed: do embedding request: Post \"http://127.0.0.1:33967/embedding\": EOF" | {
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] | closed | false | null | [] | null | 19 | 2024-07-31T09:39:08 | 2025-01-10T08:14:20 | 2024-09-02T23:36:51 | NONE | {
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} | null | null | null | ### What is the issue?
2024/07/31 09:18:15 routes.go:1099: INFO server config env="map[CUDA_VISIBLE_DEVICES: GPU_DEVICE_ORDINAL: HIP_VISIBLE_DEVICES: HSA_OVERRIDE_GFX_VERSION: OLLAMA_DEBUG:false OLLAMA_FLASH_ATTENTION:false OLLAMA_HOST:http://0.0.0.0:11434 OLLAMA_INTEL_GPU:false OLLAMA_KEEP_ALIVE:2562047h47m16.854775807s OLLAMA_LLM_LIBRARY: OLLAMA_MAX_LOADED_MODELS:0 OLLAMA_MAX_QUEUE:512 OLLAMA_MODELS:/root/.ollama/models OLLAMA_NOHISTORY:false OLLAMA_NOPRUNE:false OLLAMA_NUM_PARALLEL:0 OLLAMA_ORIGINS:[http://localhost https://localhost http://localhost:* https://localhost:* http://127.0.0.1 https://127.0.0.1 http://127.0.0.1:* https://127.0.0.1:* http://0.0.0.0 https://0.0.0.0 http://0.0.0.0:* https://0.0.0.0:* app://* file://* tauri://*] OLLAMA_RUNNERS_DIR: OLLAMA_SCHED_SPREAD:false OLLAMA_TMPDIR: ROCR_VISIBLE_DEVICES:]"
time=2024-07-31T09:18:16.095Z level=INFO source=images.go:786 msg="total blobs: 2"
time=2024-07-31T09:18:16.095Z level=INFO source=images.go:793 msg="total unused blobs removed: 0"
time=2024-07-31T09:18:16.095Z level=INFO source=routes.go:1146 msg="Listening on [::]:11434 (version 0.3.1)"
time=2024-07-31T09:18:16.095Z level=INFO source=payload.go:30 msg="extracting embedded files" dir=/tmp/ollama37639419/runners
time=2024-07-31T09:18:18.739Z level=INFO source=payload.go:44 msg="Dynamic LLM libraries [rocm_v60102 cpu cpu_avx cpu_avx2 cuda_v11]"
time=2024-07-31T09:18:18.739Z level=INFO source=gpu.go:205 msg="looking for compatible GPUs"
time=2024-07-31T09:18:18.808Z level=INFO source=types.go:105 msg="inference compute" id=GPU-31fa3c8c-f42e-bade-72ec-f936eb48ac45 library=cuda compute=8.6 driver=12.2 name="NVIDIA GeForce RTX 3090 Ti" total="23.7 GiB" available="17.2 GiB"
time=2024-07-31T09:20:14.214Z level=INFO source=sched.go:701 msg="new model will fit in available VRAM in single GPU, loading" model=/root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527 gpu=GPU-31fa3c8c-f42e-bade-72ec-f936eb48ac45 parallel=4 available=18469158912 required="737.9 MiB"
time=2024-07-31T09:20:14.214Z level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=13 layers.offload=13 layers.split="" memory.available="[17.2 GiB]" memory.required.full="737.9 MiB" memory.required.partial="737.9 MiB" memory.required.kv="24.0 MiB" memory.required.allocations="[737.9 MiB]" memory.weights.total="186.5 MiB" memory.weights.repeating="155.5 MiB" memory.weights.nonrepeating="30.9 MiB" memory.graph.full="48.0 MiB" memory.graph.partial="48.0 MiB"
time=2024-07-31T09:20:14.214Z level=INFO source=server.go:384 msg="starting llama server" cmd="/tmp/ollama37639419/runners/cuda_v11/ollama_llama_server --model /root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527 --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 13 --parallel 4 --port 44985"
time=2024-07-31T09:20:14.214Z level=INFO source=sched.go:437 msg="loaded runners" count=1
time=2024-07-31T09:20:14.214Z level=INFO source=server.go:584 msg="waiting for llama runner to start responding"
time=2024-07-31T09:20:14.214Z level=INFO source=server.go:618 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="6eeaeba" tid="127422522179584" timestamp=1722417614
INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="127422522179584" timestamp=1722417614 total_threads=16
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="44985" tid="127422522179584" timestamp=1722417614
llama_model_loader: loaded meta data with 22 key-value pairs and 197 tensors from /root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527 (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 = bert
llama_model_loader: - kv 1: general.name str = Dmeta-embedding-zh
llama_model_loader: - kv 2: bert.block_count u32 = 12
llama_model_loader: - kv 3: bert.context_length u32 = 1024
llama_model_loader: - kv 4: bert.embedding_length u32 = 768
llama_model_loader: - kv 5: bert.feed_forward_length u32 = 3072
llama_model_loader: - kv 6: bert.attention.head_count u32 = 12
llama_model_loader: - kv 7: bert.attention.layer_norm_epsilon f32 = 0.000000
llama_model_loader: - kv 8: general.file_type u32 = 1
llama_model_loader: - kv 9: bert.attention.causal bool = false
llama_model_loader: - kv 10: bert.pooling_type u32 = 2
llama_model_loader: - kv 11: tokenizer.ggml.token_type_count u32 = 2
llama_model_loader: - kv 12: tokenizer.ggml.model str = bert
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,21128] = ["[PAD]", "[unused1]", "[unused2]", "...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,21128] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 100
llama_model_loader: - kv 16: tokenizer.ggml.seperator_token_id u32 = 102
llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 18: tokenizer.ggml.cls_token_id u32 = 101
llama_model_loader: - kv 19: tokenizer.ggml.mask_token_id u32 = 103
llama_model_loader: - kv 20: tokenizer.ggml.bos_token_id u32 = 0
llama_model_loader: - kv 21:
tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - type f32: 123 tensors
llama_model_loader: - type f16: 74 tensors
llm_load_vocab: special tokens cache size = 5
llm_load_vocab: token to piece cache size = 0.0769 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = bert
llm_load_print_meta: vocab type = WPM
llm_load_print_meta: n_vocab = 21128
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 1024
llm_load_print_meta: n_embd = 768
llm_load_print_meta: n_layer = 12
llm_load_print_meta: n_head = 12
llm_load_print_meta: n_head_kv = 12
llm_load_print_meta: n_rot = 64
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 64
llm_load_print_meta: n_embd_head_v = 64
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 768
llm_load_print_meta: n_embd_v_gqa = 768
llm_load_print_meta: f_norm_eps = 1.0e-12
llm_load_print_meta: f_norm_rms_eps = 0.0e+00
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 = 3072
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 0
llm_load_print_meta: pooling type = 2
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 1024
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 = 109M
llm_load_print_meta: model ftype = F16
llm_load_print_meta: model params = 102.07 M
llm_load_print_meta: model size = 194.92 MiB (16.02 BPW)
llm_load_print_meta: general.name = Dmeta-embedding-zh
llm_load_print_meta: BOS token = 0 '[PAD]'
llm_load_print_meta: EOS token = 2 '[unused2]'
llm_load_print_meta: UNK token = 100 '[UNK]'
llm_load_print_meta: SEP token = 102 '[SEP]'
llm_load_print_meta: PAD token = 0 '[PAD]'
llm_load_print_meta: CLS token = 101 '[CLS]'
llm_load_print_meta: MASK token = 103 '[MASK]'
llm_load_print_meta: LF token = 0 '[PAD]'
llm_load_print_meta: max token length = 48
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090 Ti, compute capability 8.6, VMM: yes
llm_load_tensors: ggml ctx size = 0.16 MiB
llm_load_tensors: offloading 12 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 13/13 layers to GPU
llm_load_tensors: CPU buffer size = 32.46 MiB
llm_load_tensors: CUDA0 buffer size = 162.46 MiB
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 288.00 MiB
llama_new_context_with_model: KV self size = 288.00 MiB, K (f16): 144.00 MiB, V (f16): 144.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.00 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 19.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 4.00 MiB
llama_new_context_with_model: graph nodes = 429
llama_new_context_with_model: graph splits = 2
time=2024-07-31T09:20:14.465Z level=INFO source=server.go:618 msg="waiting for server to become available" status="llm server loading model"
INFO [main] model loaded | tid="127422522179584" timestamp=1722417614
time=2024-07-31T09:20:14.966Z level=INFO source=server.go:623 msg="llama runner started in 0.75 seconds"
[GIN] 2024/07/31 - 09:20:15 | 200 | 862.184786ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:20:15 | 200 | 91.260258ms | 10.234.218.0 | POST "/api/embeddings"
time=2024-07-31T09:20:15.383Z level=INFO source=routes.go:426 msg="embedding generation failed: do embedding request: Post \"http://127.0.0.1:44985/embedding\": EOF"
[GIN] 2024/07/31 - 09:20:15 | 500 | 140.114654ms | 10.234.218.0 | POST "/api/embeddings"
time=2024-07-31T09:23:45.923Z level=WARN source=server.go:503 msg="llama runner process no longer running" sys=139 string="signal: segmentation fault (core dumped)"
time=2024-07-31T09:23:50.993Z level=WARN source=sched.go:634 msg="gpu VRAM usage didn't recover within timeout" seconds=5.069197565 model=/root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527
time=2024-07-31T09:23:51.075Z level=INFO source=sched.go:701 msg="new model will fit in available VRAM in single GPU, loading" model=/root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527 gpu=GPU-31fa3c8c-f42e-bade-72ec-f936eb48ac45 parallel=4 available=18469158912 required="737.9 MiB"
time=2024-07-31T09:23:51.075Z level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=13 layers.offload=13 layers.split="" memory.available="[17.2 GiB]" memory.required.full="737.9 MiB" memory.required.partial="737.9 MiB" memory.required.kv="24.0 MiB" memory.required.allocations="[737.9 MiB]" memory.weights.total="186.5 MiB" memory.weights.repeating="155.5 MiB" memory.weights.nonrepeating="30.9 MiB" memory.graph.full="48.0 MiB" memory.graph.partial="48.0 MiB"
time=2024-07-31T09:23:51.075Z level=INFO source=server.go:384 msg="starting llama server" cmd="/tmp/ollama37639419/runners/cuda_v11/ollama_llama_server --model /root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527 --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 13 --parallel 4 --port 42155"
time=2024-07-31T09:23:51.075Z level=INFO source=sched.go:437 msg="loaded runners" count=1
time=2024-07-31T09:23:51.075Z level=INFO source=server.go:584 msg="waiting for llama runner to start responding"
time=2024-07-31T09:23:51.076Z level=INFO source=server.go:618 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="6eeaeba" tid="131709034942464" timestamp=1722417831
INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="131709034942464" timestamp=1722417831 total_threads=16
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="42155" tid="131709034942464" timestamp=1722417831
llama_model_loader: loaded meta data with 22 key-value pairs and 197 tensors from /root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527 (version GGUF V3 (latest))
llama_model_loader: D
umping metadata keys/values. Note: KV overrides do not apply in this output.
llama_model_loader: - kv 0: general.architecture str = bert
llama_model_loader: - kv 1: general.name str = Dmeta-embedding-zh
llama_model_loader: - kv 2: bert.block_count u32 = 12
llama_model_loader: - kv 3: bert.context_length u32 = 1024
llama_model_loader: - kv 4: bert.embedding_length u32 = 768
llama_model_loader: - kv 5: bert.feed_forward_length u32 = 3072
llama_model_loader: - kv 6: bert.attention.head_count u32 = 12
llama_model_loader: - kv 7: bert.attention.layer_norm_epsilon f32 = 0.000000
llama_model_loader: - kv 8: general.file_type u32 = 1
llama_model_loader: - kv 9: bert.attention.causal bool = false
llama_model_loader: - kv 10: bert.pooling_type u32 = 2
llama_model_loader: - kv 11: tokenizer.ggml.token_type_count u32 = 2
llama_model_loader: - kv 12: tokenizer.ggml.model str = bert
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,21128] = ["[PAD]", "[unused1]", "[unused2]", "...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,21128] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 100
llama_model_loader: - kv 16: tokenizer.ggml.seperator_token_id u32 = 102
llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 18: tokenizer.ggml.cls_token_id u32 = 101
llama_model_loader: - kv 19: tokenizer.ggml.mask_token_id u32 = 103
llama_model_loader: - kv 20: tokenizer.ggml.bos_token_id u32 = 0
llama_model_loader: - kv 21: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - type f32: 123 tensors
llama_model_loader: - type f16: 74 tensors
llm_load_vocab: special tokens cache size = 5
llm_load_vocab: token to piece cache size = 0.0769 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = bert
llm_load_print_meta: vocab type = WPM
llm_load_print_meta: n_vocab = 21128
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 1024
llm_load_print_meta: n_embd = 768
llm_load_print_meta: n_layer = 12
llm_load_print_meta: n_head = 12
llm_load_print_meta: n_head_kv = 12
llm_load_print_meta: n_rot = 64
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 64
llm_load_print_meta: n_embd_head_v = 64
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 768
llm_load_print_meta: n_embd_v_gqa = 768
llm_load_print_meta: f_norm_eps = 1.0e-12
llm_load_print_meta: f_norm_rms_eps = 0.0e+00
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 = 3072
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 0
llm_load_print_meta: pooling type = 2
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 1024
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 = 109M
llm_load_print_meta: model ftype = F16
llm_load_print_meta: model params = 102.07 M
llm_load_print_meta: model size = 194.92 MiB (16.02 BPW)
llm_load_print_meta: general.name = Dmeta-embedding-zh
llm_load_print_meta: BOS token = 0 '[PAD]'
llm_load_print_meta: EOS token = 2 '[unused2]'
llm_load_print_meta: UNK token = 100 '[UNK]'
llm_load_print_meta: SEP token = 102 '[SEP]'
llm_load_print_meta: PAD token = 0 '[PAD]'
llm_load_print_meta: CLS token = 101 '[CLS]'
llm_load_print_meta: MASK token = 103 '[MASK]'
llm_load_print_meta: LF token = 0 '[PAD]'
llm_load_print_meta: max token length = 48
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090 Ti, compute capability 8.6, VMM: yes
llm_load_tensors: ggml ctx size = 0.16 MiB
llm_load_tensors: offloading 12 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 13/13 layers to GPU
llm_load_tensors: CPU buffer size = 32.46 MiB
llm_load_tensors: CUDA0 buffer size = 162.46 MiB
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 288.00 MiB
llama_new_context_with_model: KV self size = 288.00 MiB, K (f16): 144.00 MiB, V (f16): 144.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.00 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 19.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 4.00 MiB
llama_new_context_with_model: graph nodes = 429
llama_new_context_with_model: graph splits = 2
time=2024-07-31T09:23:51.243Z level=WARN source=sched.go:634 msg="gpu VRAM usage didn't recover within timeout" seconds=5.319657234 model=/root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527
time=2024-07-31T09:23:51.327Z level=INFO source=server.go:618 msg="waiting for server to become available" status="llm server loading model"
INFO [main] model loaded | tid="131709034942464" timestamp=1722417831
time=2024-07-31T09:23:51.829Z level=INFO source=server.go:623 msg="llama runner started in 0.75 seconds"
[GIN] 2024/07/31 - 09:23:51 | 200 | 5.954027368s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:51 | 200 | 5.997875851s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:51 | 200 | 6.001301156s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:51 | 200 | 6.05401596s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 6.093406397s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 6.093515843s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 141.106871ms | 10.234.218.0 | POST "/api/embeddings"
INFO [update_slots] input truncated | n_ctx=2048 n_erase=1989 n_keep=0 n_left=2048 n_shift=1024 tid="131709034942464" timestamp=1722417832
[GIN] 2024/07/31 - 09:23:52 | 200 | 156.396038ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 159.160468ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 155.371305ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 150.237024ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 161.78585ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 158.374292ms | 10.234.218.0 | POST "/api/embeddings"
INFO [update_slots] input truncated | n_ctx=2048 n_erase=1517 n_keep=0 n_left=2048 n_shift=1024 tid="131709034942464" timestamp=1722417832
[GIN] 2024/07/31 - 09:23:52 | 200 | 144.427285ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 192.549717ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 131.371235ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 185.844931ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 151.950066ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 141.888776ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 171.173954ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 130.251712ms | 10.234.218.0 | POST "/api/embeddings"
INFO [update_slots] input truncated | n_ctx=2048 n_erase=1709 n_keep=0 n_left=2048 n_shift=1024 tid="131709034942464" timestamp=1722417832
[GIN] 2024/07/31 - 09:23:52 | 200 | 140.112505ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 171.12123ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 227.184409ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 264.346952ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 189.302007ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 183.643992ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 165.703255ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 200 | 229.741451ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:52 | 500 | 303.282026ms | 10.234.218.0 | POST "/api/embeddings"
time=2024-07-31T09:23:52.825Z level=INFO source=routes.go:426 msg="embedding generation failed: do embedding request: Post \"http://127.0.0.1:42155/embedding\": EOF"
time=2024-07-31T09:23:57.889Z level=WARN source=sched.go:634 msg="gpu VRAM usage didn't recover within timeout" seconds=5.063724982 model=/root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527
time=2024-07-31T09:23:57.975Z level=INFO source=sched.go:701 msg="new model will fit in available VRAM in single GPU, loading" model=/root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527 gpu=GPU-31fa3c8c-f42e-bade-72ec-f936eb48ac45 parallel=4 available=18469158912 required="737.9 MiB"
time=2024-07-31T09:23:57.975Z level=INFO source=memory.go:309 msg="offload to cuda" layers.requested=-1 layers.model=13 layers.offload=13 layers.split="" memory.available="[17.2 GiB]" memory.required.full="737.9 MiB" memory.required.partial="737.9 MiB" memory.required.kv="24.0 MiB" memory.required.allocations="[737.9 MiB]" memory.weights.total="186.5 MiB" memory.weights.repeating="155.5 MiB" memory.weights.nonrepeating="30.9 MiB" memory.graph.full="48.0 MiB" memory.graph.partial="48.0 MiB"
time=2024-07-31T09:23:57.975Z level=INFO source=server.go:384 msg="starting llama server" cmd="/tmp/ollama37639419/runners/cuda_v11/ollama_llama_server --model /root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527 --ctx-size 8192 --batch-size 512 --embedding --log-disable --n-gpu-layers 1
3 --parallel 4 --port 33967"
time=2024-07-31T09:23:57.976Z level=INFO source=sched.go:437 msg="loaded runners" count=1
time=2024-07-31T09:23:57.976Z level=INFO source=server.go:584 msg="waiting for llama runner to start responding"
time=2024-07-31T09:23:57.976Z level=INFO source=server.go:618 msg="waiting for server to become available" status="llm server error"
INFO [main] build info | build=1 commit="6eeaeba" tid="125558191894528" timestamp=1722417837
INFO [main] system info | n_threads=8 n_threads_batch=-1 system_info="AVX = 1 | AVX_VNNI = 0 | AVX2 = 0 | AVX512 = 0 | AVX512_VBMI = 0 | AVX512_VNNI = 0 | AVX512_BF16 = 0 | FMA = 0 | NEON = 0 | SVE = 0 | ARM_FMA = 0 | F16C = 0 | FP16_VA = 0 | WASM_SIMD = 0 | BLAS = 1 | SSE3 = 1 | SSSE3 = 1 | VSX = 0 | MATMUL_INT8 = 0 | LLAMAFILE = 1 | " tid="125558191894528" timestamp=1722417837 total_threads=16
INFO [main] HTTP server listening | hostname="127.0.0.1" n_threads_http="15" port="33967" tid="125558191894528" timestamp=1722417837
llama_model_loader: loaded meta data with 22 key-value pairs and 197 tensors from /root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527 (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 = bert
llama_model_loader: - kv 1: general.name str = Dmeta-embedding-zh
llama_model_loader: - kv 2: bert.block_count u32 = 12
llama_model_loader: - kv 3: bert.context_length u32 = 1024
llama_model_loader: - kv 4: bert.embedding_length u32 = 768
llama_model_loader: - kv 5: bert.feed_forward_length u32 = 3072
llama_model_loader: - kv 6: bert.attention.head_count u32 = 12
llama_model_loader: - kv 7: bert.attention.layer_norm
_epsilon f32 = 0.000000
llama_model_loader: - kv 8: general.file_type u32 = 1
llama_model_loader: - kv 9: bert.attention.causal bool = false
llama_model_loader: - kv 10: bert.pooling_type u32 = 2
llama_model_loader: - kv 11: tokenizer.ggml.token_type_count u32 = 2
llama_model_loader: - kv 12: tokenizer.ggml.model str = bert
llama_model_loader: - kv 13: tokenizer.ggml.tokens arr[str,21128] = ["[PAD]", "[unused1]", "[unused2]", "...
llama_model_loader: - kv 14: tokenizer.ggml.token_type arr[i32,21128] = [3, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ...
llama_model_loader: - kv 15: tokenizer.ggml.unknown_token_id u32 = 100
llama_model_loader: - kv 16: tokenizer.ggml.seperator_token_id u32 = 102
llama_model_loader: - kv 17: tokenizer.ggml.padding_token_id u32 = 0
llama_model_loader: - kv 18: tokenizer.ggml.cls_token_id u32 = 101
llama_model_loader: - kv 19: tokenizer.ggml.mask_token_id u32 = 103
llama_model_loader: - kv 20: tokenizer.ggml.bos_token_id u32 = 0
llama_model_loader: - kv 21: tokenizer.ggml.eos_token_id u32 = 2
llama_model_loader: - type f32: 123 tensors
llama_model_loader: - type f16: 74 tensors
llm_load_vocab: special tokens cache size = 5
llm_load_vocab: token to piece cache size = 0.0769 MB
llm_load_print_meta: format = GGUF V3 (latest)
llm_load_print_meta: arch = bert
llm_load_print_meta: vocab type = WPM
llm_load_print_meta: n_vocab = 21128
llm_load_print_meta: n_merges = 0
llm_load_print_meta: vocab_only = 0
llm_load_print_meta: n_ctx_train = 1024
llm_load_print_meta: n_embd = 768
llm_load_print_meta: n_layer = 12
llm_load_print_meta: n_head = 12
llm_load_print_meta: n_head_kv = 12
llm_load_print_meta: n_rot = 64
llm_load_print_meta: n_swa = 0
llm_load_print_meta: n_embd_head_k = 64
llm_load_print_meta: n_embd_head_v = 64
llm_load_print_meta: n_gqa = 1
llm_load_print_meta: n_embd_k_gqa = 768
llm_load_print_meta: n_embd_v_gqa = 768
llm_load_print_meta: f_norm_eps = 1.0e-12
llm_load_print_meta: f_norm_rms_eps = 0.0e+00
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 = 3072
llm_load_print_meta: n_expert = 0
llm_load_print_meta: n_expert_used = 0
llm_load_print_meta: causal attn = 0
llm_load_print_meta: pooling type = 2
llm_load_print_meta: rope type = 2
llm_load_print_meta: rope scaling = linear
llm_load_print_meta: freq_base_train = 10000.0
llm_load_print_meta: freq_scale_train = 1
llm_load_print_meta: n_ctx_orig_yarn = 1024
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 = 109M
llm_load_print_meta: model ftype = F16
llm_load_print_meta: model params = 102.07 M
llm_load_print_meta: model size = 194.92 MiB (16.02 BPW)
llm_load_print_meta: general.name = Dmeta-embedding-zh
llm_load_print_meta: BOS token = 0 '[PAD]'
llm_load_print_meta: EOS token = 2 '[unused2]'
llm_load_print_meta: UNK token = 100 '[UNK]'
llm_load_print_meta: SEP token = 102 '[SEP]'
llm_load_print_meta: PAD token = 0 '[PAD]'
llm_load_print_meta: CLS token = 101 '[CLS]'
llm_load_print_meta: MASK token = 103 '[MASK]'
llm_load_print_meta: LF token = 0 '[PAD]'
llm_load_print_meta: max token length = 48
ggml_cuda_init: GGML_CUDA_FORCE_MMQ: no
ggml_cuda_init: GGML_CUDA_FORCE_CUBLAS: no
ggml_cuda_init: found 1 CUDA devices:
Device 0: NVIDIA GeForce RTX 3090 Ti, compute capability 8.6, VMM: yes
llm_load_tensors: ggml ctx size = 0.16 MiB
llm_load_tensors: offloading 12 repeating layers to GPU
llm_load_tensors: offloading non-repeating layers to GPU
llm_load_tensors: offloaded 13/13 layers to GPU
llm_load_tensors: CPU buffer size = 32.46 MiB
llm_load_tensors: CUDA0 buffer size = 162.46 MiB
llama_new_context_with_model: n_ctx = 8192
llama_new_context_with_model: n_batch = 512
llama_new_context_with_model: n_ubatch = 512
llama_new_context_with_model: flash_attn = 0
llama_new_context_with_model: freq_base = 10000.0
llama_new_context_with_model: freq_scale = 1
llama_kv_cache_init: CUDA0 KV buffer size = 288.00 MiB
llama_new_context_with_model: KV self size = 288.00 MiB, K (f16): 144.00 MiB, V (f16): 144.00 MiB
llama_new_context_with_model: CPU output buffer size = 0.00 MiB
llama_new_context_with_model: CUDA0 compute buffer size = 19.00 MiB
llama_new_context_with_model: CUDA_Host compute buffer size = 4.00 MiB
llama_new_context_with_model: graph nodes = 429
llama_new_context_with_model: graph splits = 2
time=2024-07-31T09:23:58.139Z level=WARN source=sched.go:634 msg="gpu VRAM usage didn't recover within timeout" seconds=5.312995606 model=/root/.ollama/models/blobs/sha256-9b18b416fe232d5a834e15ce0d6cc353d7f6366423b8a7ef236db9ecee320527
time=2024-07-31T09:23:58.226Z level=INFO source=server.go:618 msg="waiting for server to become available" status="llm server loading model"
INFO [main] model loaded | tid="125558191894528" timestamp=1722417838
time=2024-07-31T09:23:58.729Z level=INFO source=server.go:623 msg="llama runner started in 0.75 seconds"
[GIN] 2024/07/31 - 09:23:58 | 200 | 6.175518609s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:58 | 200 | 6.173129645s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/
31 - 09:23:58 | 200 | 6.181901759s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:58 | 200 | 6.217999442s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:58 | 200 | 6.128390115s | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:58 | 200 | 139.275881ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:58 | 200 | 141.805964ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:58 | 200 | 147.553231ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 147.626781ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 90.649859ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 134.183906ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 100.703301ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 76.093064ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 139.579148ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 195.963998ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 184.951077ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 204.863879ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 93.607337ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 92.691741ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 122.460956ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:23:59 | 200 | 164.876363ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:26:50 | 200 | 93.430143ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:26:50 | 200 | 51.56662ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:26:
50 | 200 | 139.845262ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:26:50 | 200 | 48.229681ms | 10.234.218.0 | POST "/api/embeddings"
INFO [update_slots] input truncated | n_ctx=2048 n_erase=1522 n_keep=0 n_left=2048 n_shift=1024 tid="125558191894528" timestamp=1722418010
[GIN] 2024/07/31 - 09:26:50 | 200 | 103.527766ms | 10.234.218.0 | POST "/api/embeddings"
[GIN] 2024/07/31 - 09:26:50 | 500 | 138.709641ms | 10.234.218.0 | POST "/api/embeddings"
time=2024-07-31T09:26:50.849Z level=INFO source=routes.go:426 msg="embedding generation failed: do embedding request: Post \"http://127.0.0.1:33967/embedding\": EOF"
[GIN] 2024/07/31 - 09:37:35 | 200 | 19.4µs | 127.0.0.1 | GET "/api/version"
### OS
Docker
### GPU
Nvidia
### CPU
Intel
### Ollama version
0.3.1 | {
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https://api.github.com/repos/ollama/ollama/issues/6743 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/6743/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/6743/comments | https://api.github.com/repos/ollama/ollama/issues/6743/events | https://github.com/ollama/ollama/pull/6743 | 2,518,666,601 | PR_kwDOJ0Z1Ps57GpZP | 6,743 | Fixed no redirect URL scenario when downloading blobs | {
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https://api.github.com/repos/ollama/ollama/issues/1312 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/1312/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/1312/comments | https://api.github.com/repos/ollama/ollama/issues/1312/events | https://github.com/ollama/ollama/issues/1312 | 2,016,449,283 | I_kwDOJ0Z1Ps54MJMD | 1,312 | trouble with deepseek-coder | {
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} | null | null | null | I'm having trouble getting this model to run on mac m1 16gb ram:
ollama run deepseek-coder:6.7b-base-q8_0
but this model work without any troubles:
ollama run neural-chat:7b-v3.1-q8_0
it has more weights and bigger file size. | {
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https://api.github.com/repos/ollama/ollama/issues/2529 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/2529/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/2529/comments | https://api.github.com/repos/ollama/ollama/issues/2529/events | https://github.com/ollama/ollama/issues/2529 | 2,137,653,184 | I_kwDOJ0Z1Ps5_af_A | 2,529 | Ollama Windows is much slower at inference than Ollama on WSL2 | {
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} | null | null | null | CPU: AMD 5500U with Radion internal GPU. Ollama runs on CPU mode on both WSL2 and Windows. Attached are the logs from Windows, and Linux.
[server.log](https://github.com/ollama/ollama/files/14303692/server.log)
[ollama-log-linux.log](https://github.com/ollama/ollama/files/14303696/ollama-log-linux.log) | {
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https://api.github.com/repos/ollama/ollama/issues/3068 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/3068/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/3068/comments | https://api.github.com/repos/ollama/ollama/issues/3068/events | https://github.com/ollama/ollama/pull/3068 | 2,180,371,586 | PR_kwDOJ0Z1Ps5pT6Cw | 3,068 | Use stdin for term discovery on windows | {
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} | When you feed input to the cmd via a pipe it no longer reports a warning
Before:
```
> echo "what is the captial of australia" | .\ollama.exe run phi
failed to get console mode for stdin: The handle is invalid.
The capital of Australia is Canberra. It's located in the Australian Capital Territory, about 120 kilometers
northwest of Sydney and 130 kilometers southwest of Melbourne.
```
After fix:
```
> echo "what is the captial of australia" | .\ollama.exe run phi
The capital city of Australia is Canberra. It is located in the Australian Capital Territory (ACT) and is home to important government buildings, such as Parliament House and the National Museum of Australia
```
Fixes #2698 | {
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https://api.github.com/repos/ollama/ollama/issues/5146 | https://api.github.com/repos/ollama/ollama | https://api.github.com/repos/ollama/ollama/issues/5146/labels{/name} | https://api.github.com/repos/ollama/ollama/issues/5146/comments | https://api.github.com/repos/ollama/ollama/issues/5146/events | https://github.com/ollama/ollama/pull/5146 | 2,362,711,736 | PR_kwDOJ0Z1Ps5y-1ig | 5,146 | Put back temporary intel GPU env var | {
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} | Until we merge #4876 lets keep the opt-in env var to avoid confusion in the binary releases if we discover an Intel GPU but don't actually have the runner built in.
This reverts commit 755b4e4fc291366595ed7bfb37c2a91ff5834df8. | {
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} | null | null | null | 麻烦把官网的模型进行分类整理下吧，模型太多了以后也会越来越多，官网的模型列表感觉会很乱 | null | {
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