codellama VS refact

The github [0] hasn't been fully updated, but it links to a paper [1] that describes how the smaller code llama models were trained. It would be a good guess that this model is similar.

[0] https://github.com/facebookresearch/codellama

This k8s application deploys an instance of Voyager along with a Fabric Minecraft server with required fabric mods. It assumes you have a local deployment of a Large Language Model (LLM) with 4K-8K token context length with a compatible OpenAI API, including embeddings support.

I have been playing with code Llama (the 7B python one). It does pretty well, but I don't understand what the parameters in the code mean and how I should modify them to work best on my hardware. I'm looking at the code in: https://github.com/facebookresearch/codellama/blob/main/llama/generation.py.

Should I use HuggingFace https://huggingface.co/codellama/CodeLlama-34b-hf or grab the model from Facebook https://github.com/facebookresearch/codellama?

such rights.

https://github.com/facebookresearch/codellama/blob/main/LICE...

https://github.com/facebookresearch/llama/blob/main/LICENSE

Bringing AI to the coding community: Code Llama is designed to support software engineers across sectors – including research, industry, and open-source projects. You can checkout the Github repo here.

GIthub https://github.com/facebookresearch/codellama

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.