Llama Coder is a better and self-hosted Github Copilot replacement for VS Studio Code. Llama Coder uses Ollama and codellama to provide autocomplete that runs on your hardware. Works best with Mac M1/M2/M3 or with RTX 4090.
- 🚀 As good as Copilot
- ⚡️ Fast. Works well on consumer GPUs. RTX 4090 is recommended for best performance.
- 🔐 No telemetry or tracking
- 🔬 Works with any language coding or human one.
The best way: dedicated machine with RTX 4090. Install Ollama on this machine and configure endpoint in extension settings to offload to this machine. Second best way: run on MacBooc M1/M2/M3 with enougth RAM (more == better, but 10gb extra would be enougth). For windows notebooks: it runs good with decent GPU, but dedicated machine with a good GPU is recommended. Perfect if you have a dedicated gaming PC.
Install Ollama on local machine and then launch the extension in VSCode, everything should work as it is.
Install Ollama on dedicated machine and configure endpoint to it in extension settings. Ollama usually uses port 11434 and binds to 127.0.0.1, to change it you should set OLLAMA_HOST to 0.0.0.0.
Currently Llama Coder supports only Codellama. Model is quantized in different ways, but our tests shows that q4 is an optimal way to run network. When selecting model the bigger the model is, it performs better. Always pick the model with the biggest size and the biggest possible quantization for your machine. Default one is codellama:7b-code-q4_K_M and should work everywhere, codellama:34b-code-q4_K_M is the best possible one.
| Name | RAM/VRAM |
|---|---|
| codellama:7b-code-q4_K_M | 5GB |
| codellama:7b-code-q6_K | 6GB |
| codellama:7b-code-fp16 | 14GB |
| codellama:13b-code-q4_K_M | 10GB |
| codellama:13b-code-q6_K | 14GB |
| codellama:34b-code-q4_K_M | 24GB |
| codellama:34b-code-q6_K | 32GB |
MIT