Cyberes d2651756df | ||
---|---|---|
config | ||
llm_server | ||
other | ||
templates | ||
.gitignore | ||
LICENSE | ||
README.md | ||
VLLM INSTALL.md | ||
requirements.txt | ||
server.py |
README.md
local-llm-server
An HTTP API to serve local LLM Models.
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.
Install
sudo apt install redis
python3 -m venv venv
source venv/bin/activate
pip install -r requirements.txt
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
python3 server.py
An example systemctl service file is provided in other/local-llm.service
.
Configure
First, set up your LLM backend. Currently, only oobabooga/text-generation-webui is supported, but eventually huggingface/text-generation-inference will be the default.
Then, configure this server. The config file is located at config/config.yml.sample
so copy it to config/config.yml
.
- Set
backend_url
to the base API URL of your backend. - Set
token_limit
to the configured token limit of the backend. This number is shown to clients and on the home page.
To set up token auth, add rows to the token_auth
table in the SQLite database.
token
: the token/password.
type
: the type of token. Currently unused (maybe for a future web interface?) but required.
priority
: the lower this value, the higher the priority. Higher priority tokens are bumped up in the queue line.
uses
: how many responses this token has generated. Leave empty.
max_uses
: how many responses this token is allowed to generate. Leave empty to leave unrestricted.
expire
: UNIX timestamp of when this token expires and is not longer valid.
disabled
: mark the token as disabled.
Use
To Do
- Implement streaming
- Bring streaming endpoint up to the level of the blocking endpoint
- Add VLLM support
- Make sure stats work when starting from an empty database
- Make sure we're correctly canceling requests when the client cancels
- Make sure the OpenAI endpoint works as expected