# 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.



**Features:**

- Load balancing between a cluster of different VLLM backends.
- OpenAI-compatible API.
- Streaming support via websockets (and SSE for the OpenAI endpoint).
- Descriptive landing page.
- Logging and insights.
- Tokens and authentication with a priority system.
- Moderation system using OpenAI's moderation API.



## Install VLLM

The VLLM backend and local-llm-server don't need to be on the same machine.

1. Create a venv.
2. Open `requirements.txt` and find the line that defines VLLM (it looks something like `vllm==x.x.x`) and copy it.
3. Install that version of VLLM using `pip install vllm==x.x.x`
4. Clone the repo: `git clone https://git.evulid.cc/cyberes/local-llm-server.git`
5. Download your model.
6. Create a user to run the VLLM server.
   ```shell
   sudo adduser vllm --system
   ```

   Also, make sure the user has access to the necessary files like the models and the venv.

7. Copy the systemd service file from `other/vllm/vllm.service` to `/etc/systemd/system/` and edit the paths to point to your install location. Then activate the server.



## Install

1. Create a user to run the server:
    ```shell
    sudo adduser server --system
    ```

2. `mkdir /srv/server`

3. `git clone https://git.evulid.cc/cyberes/local-llm-server.git /srv/server/local-llm-server`

4. `sudo apt install redis`

5. `python3 -m venv venv`

6. `./venv/bin/pip install -r requirements.txt`

7. `chown -R server:nogroup /srv/server`

8. Create the logs location:
    ```shell
    sudo mkdir /var/log/localllm
    sudo chown -R server:adm /var/log/localllm/

9. Install nginx:
   ```shell
   sudo apt install nginx
   ```

10. An example nginx site is provided at `other/nginx-site.conf`. Copy this to `/etc/nginx/default`.
11. Copy the example config from `config/config.yml.sample` to `config/config.yml`. Modify the config (it's well commented).
12. Set up your MySQL server with a database and user according to what you configured in `config.yml`.
13. Install the two systemd services in `other/` and activate them.



## Creating Tokens

You'll have to execute SQL queries to add tokens. phpMyAdmin makes this easy.



**Fields:**

- `token`: The authentication token. If it starts with `SYSTEM__`, it's reserved for internal usage.
- `type`: The token type. For your reference only, not used by the system (need to confirm this, though).
- `priority`: The priority of the token. Higher priority tokens are bumped up in the queue according to their priority.
- `simultaneous_ip`: How many requests from an IP are allowed to be in the queue.
- `openai_moderation_enabled`: enable moderation for this token. `1` means enabled, `0` is disabled.
- `uses`: How many times this token has been used. Set it to `0` and don't touch it.
- `max_uses`: How many times this token is allowed to be used. Set to `NULL` to disable restriction and allow infinite uses.
- `expire`: When the token expires and will no longer be allowed. A Unix timestamp.
- `disabled`: Set the token to be disabled.



## Updating VLLM

This project is linked to a specific VLLM version due to a dependency on the parameters. When updating, make sure the parameters in the `SamplingParams` object in [llm_server/llm/vllm/vllm_backend.py](https://git.evulid.cc/cyberes/local-llm-server/src/branch/master/llm_server/llm/vllm/vllm_backend.py) match up with those in VLLM's [vllm/sampling_params.py](https://github.com/vllm-project/vllm/blob/93348d9458af7517bb8c114611d438a1b4a2c3be/vllm/sampling_params.py).

Additionally, make sure our VLLM API server at [other/vllm/vllm_api_server.py](https://git.evulid.cc/cyberes/local-llm-server/src/branch/master/other/vllm/vllm_api_server.py) matches [vllm/entrypoints/api_server.py](https://github.com/vllm-project/vllm/blob/93348d9458af7517bb8c114611d438a1b4a2c3be/vllm/entrypoints/api_server.py).

Then, update the VLLM version in `requirements.txt`.



## To Do

- [ ] Support the Oobabooga Text Generation WebUI as a backend
- [ ] Make the moderation apply to the non-OpenAI endpoints as well
- [ ] Make sure stats work when starting from an empty database
- [ ] Make sure we're correctly canceling requests when the client cancels. The blocking endpoints can't detect when a client cancels generation.
- [ ] Add test to verify the OpenAI endpoint works as expected