55 lines
2.2 KiB
Markdown
55 lines
2.2 KiB
Markdown
# local-llm-server
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_An HTTP API to serve local LLM Models._
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The purpose of this server is to abstract your LLM backend from your frontend API. This enables you to switch your backend while providing a stable frontend clients.
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### Install
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1. `sudo apt install redis`
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2. `python3 -m venv venv`
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3. `source venv/bin/activate`
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4. `pip install -r requirements.txt`
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5. `wget https://git.evulid.cc/attachments/89c87201-58b1-4e28-b8fd-d0b323c810c4 -O /tmp/vllm_gptq-0.1.3-py3-none-any.whl && pip install /tmp/vllm_gptq-0.1.3-py3-none-any.whl && rm /tmp/vllm_gptq-0.1.3-py3-none-any.whl`
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6. `python3 server.py`
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An example systemctl service file is provided in `other/local-llm.service`.
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### Configure
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First, set up your LLM backend. Currently, only [oobabooga/text-generation-webui](https://github.com/oobabooga/text-generation-webui) is supported, but
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eventually [huggingface/text-generation-inference](https://github.com/huggingface/text-generation-inference) will be the default.
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Then, configure this server. The config file is located at `config/config.yml.sample` so copy it to `config/config.yml`.
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1. Set `backend_url` to the base API URL of your backend.
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2. Set `token_limit` to the configured token limit of the backend. This number is shown to clients and on the home page.
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To set up token auth, add rows to the `token_auth` table in the SQLite database.
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`token`: the token/password.
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`type`: the type of token. Currently unused (maybe for a future web interface?) but required.
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`priority`: the lower this value, the higher the priority. Higher priority tokens are bumped up in the queue line.
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`uses`: how many responses this token has generated. Leave empty.
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`max_uses`: how many responses this token is allowed to generate. Leave empty to leave unrestricted.
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`expire`: UNIX timestamp of when this token expires and is not longer valid.
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`disabled`: mark the token as disabled.
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### Use
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### To Do
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- [x] Implement streaming
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- [ ] Bring streaming endpoint up to the level of the blocking endpoint
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- [x] Add VLLM support
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- [ ] Make sure stats work when starting from an empty database
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- [ ] Make sure we're correctly canceling requests when the client cancels
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- [ ] Make sure the OpenAI endpoint works as expected |