refact VS text-generation-inference

With over 1.3k stars on GitHub, more than 40k downloads and installs on both VS Code and JetBrains IDEs, and more than 50 positive reviews, it is worth saying that Refact is part of the best product in the AI coding assistant market.

On vscode i sometimes use continue.dev and refact.ai just for fun and they are great!

Refact was made for this: https://github.com/smallcloudai/refact

You should check-out [refact.ai](https://github.com/smallcloudai/refact). It has both autocomplete and chat. It's in active development, with lots of new features coming soon (context search, fine-tuning for larger models, etc)

I don’t recommend that, since that uses the cloud for the actual inference by default (and they provide no guidance for changing that).

I don’t consider cloud inference to count as getting it working “locally” as requested by the comment above yours.

Refact works nicely and works locally, but the challenge with any new model is making it be supported by the existing software: https://github.com/smallcloudai/refact/

Previously, we used Astro for our refact.ai website and wanted to stay within the Astro ecosystem for the documentation.

Refact LLM can be easily integrated into existing developers workflows with an open-source docker container and VS Code and JetBrains plugins. With Refact's intuitive user interface, developers can utilize the model easily for a variety of coding tasks. Finetune is available in the self-hosting (docker) and Enterprise versions, making suggestions more relevant for your private codebase.

I wanted to write that TGI inference engine is not Open Source anymore, but they have reverted the license back to Apache 2.0 for the new version TGI v2.0: https://github.com/huggingface/text-generation-inference/rel...

Good news!

Setting up a server for multiple users is very different from setting up LLM for yourself. A safe bet would be to just use TGI, which supports continuous batching and is very easy to run via Docker on your server. https://github.com/huggingface/text-generation-inference

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.

A simple microservice would be https://github.com/huggingface/text-generation-inference .

Works flawlessly in Docker on my Windows machine, which is extremely shocking.

I am wondering what is the best / most cost-efficient way to serve llama V2. - llama.cpp (is it production ready or just for playing around?) ? - Triton inference server ? - HF text generation inference ?