localpilot VS llm-ls

> I'd have expected that the main lever the product has in being better than others is having a custom model that understands code edits much more than others.

True, but this is not something this particular product would solve. There are already models specifically trained to work on code. What's appealing to me is the flexibility of being able to choose which one to use, rather than my workflow being tied to a specific product or company.

> the IDE integration seems to be the "easy bit"

I admittedly haven't researched this much, but this is not currently the case. There is no generic API for LLMs that IDEs can plug into, so all plugins must target a specific model. We ultimately need an equivalent of an LSP server for LLMs, and while such a project exists[1], it looks to be in its infancy, as expected.

[1]: https://github.com/huggingface/llm-ls

Okay, I actually got local co-pilot set up. You will need these 4 things.

1) CodeLlama 13B or another FIM model https://huggingface.co/codellama/CodeLlama-13b-hf. You want "Fill in Middle" models because you're looking at context on both sides of your cursor.

2) HuggingFace llm-ls https://github.com/huggingface/llm-ls A large language mode Language Server (is this making sense yet)

3) HuggingFace inference framework. https://github.com/huggingface/text-generation-inference At least when I tested you couldn't use something like llama.cpp or exllama with the llm-ls, so you need to break out the heavy duty badboy HuggingFace inference server. Just config and run. Now config and run llm-ls.

4) Okay, I mean you need an editor. I just tried nvim, and this was a few weeks ago, so there may be better support. My expereicen was that is was full honest to god copilot. The CodeLlama models are known to be quite good for its size. The FIM part is great. Boilerplace works so much easier with the surrounding context. I'd like to see more models released that can work this way.