Serge - LLaMA made easy 🦙
A chat interface based on llama.cpp for running Alpaca models. Entirely self-hosted, no API keys needed. Fits on 4GB of RAM and runs on the CPU.
- SvelteKit frontend
- Redis for storing chat history & parameters
- FastAPI + langchain for the API, wrapping calls to llama.cpp using the python bindings
demo.webm
Getting started
Setting up Serge is very easy. Starting it up can be done in a single command:
docker run -d -v weights:/usr/src/app/weights -v datadb:/data/db/ -p 8008:8008 ghcr.io/nsarrazin/serge:latest
Then just go to http://localhost:8008/ and you're good to go!
The API documentation can be found at http://localhost:8008/api/docs
Windows
Make sure you have docker desktop installed, WSL2 configured and enough free RAM to run models. (see below)
Kubernetes & docker compose
Setting up Serge on Kubernetes or docker compose can be found in the wiki: https://github.com/nsarrazin/serge/wiki/Integrating-Serge-in-your-orchestration#kubernetes-example
Models
Currently the following models are supported:
- GPT4-Alpaca-LoRA-30B
- Alpaca-LoRA-65B
- OpenAssistant-30B
- GPT4All-13B
- Stable-Vicuna-13B
- Guanaco-7B
- Guanaco-13B
- Guanaco-33B
- Guanaco-65B
If you have existing weights from another project you can add them to the serge_weights volume using docker cp.
⚠️ A note on memory usage
LLaMA will just crash if you don't have enough available memory for your model.
- 7B requires about 4.5GB of free RAM
- 13B requires about 12GB free
- 30B requires about 20GB free
Support
Feel free to join the discord if you need help with the setup: https://discord.gg/62Hc6FEYQH
Contributing
Serge is always open for contributions! If you catch a bug or have a feature idea, feel free to open an issue or a PR.
If you want to run Serge in development mode (with hot-module reloading for svelte & autoreload for FastAPI) you can do so like this:
git clone https://github.com/nsarrazin/serge.git
DOCKER_BUILDKIT=1 docker compose -f docker-compose.dev.yml up -d --build
You can test the production image with
DOCKER_BUILDKIT=1 docker compose up -d --build
What's next
- Front-end to interface with the API
- Pass model parameters when creating a chat
- Manager for model files
- Support for other models
- LangChain integration
- User profiles & authentication
And a lot more!