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
• MongoDB for storing chat history & parameters
• FastAPI + beanie for the API, wrapping calls to llama.cpp

demo.webm

Getting started

Setting up Serge is very easy. TLDR for running it with Alpaca 7B:

Linux

git clone https://github.com/nsarrazin/serge.git && cd serge

cp .env.sample .env

docker compose up -d
docker compose exec api python3 /usr/src/app/utils/download.py tokenizer 7B

Windows

git clone https://github.com/nsarrazin/serge.git
cd serge

copy .env.sample .env

docker compose up -d
docker compose exec api python3 /usr/src/app/utils/download.py tokenizer 7B

Make sure you have docker desktop installed, WSL2 configured and enough free RAM to run models. (see below)

Using serge

(You can pass 7B 13B 30B as an argument to the download.py script to download multiple models.)

Then just go to http://localhost:8008/ and you're good to go!

The API is available at http://localhost:8008/api/

Models

Currently only the 7B, 13B and 30B alpaca models are supported. There's a download script for downloading them inside of the container, described above.

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

What's next

• Front-end to interface with the API
• Pass model parameters when creating a chat
• User profiles & authentication
• Different prompt options
• LangChain integration with a custom LLM
• Support for other llama models, quantization, etc.

And a lot more!