tabby VS refact

NixOS just got tabbyml[1] which is built on llama-cpp. Working on systemsd services the weekend and updating latest tabbyml release which supports rocm in addition to cuda

[1] https://github.com/TabbyML/tabby

[2] https://github.com/NixOS/nixpkgs/pull/291744

Nice implementation! It should serve as a great reference for a minimal Tabby's backend API. Thank you for sharing it!

Yeah - ultimately, it won't be as performant or feature-rich compared to https://github.com/TabbyML/tabby, but it's still perfect for educational purposes!

Looks interesting! What are the main differences between this and https://github.com/TabbyML/tabby ?

TabbyML | Software Engineer (Rust) | REMOTE

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Project: https://github.com/TabbyML/tabby

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There are a bunch of VSCode extensions that make use of local models. Tabby seems to be the most friendly right now, but I admittedly haven't tried it myself: https://tabbyml.github.io/tabby/

Check out https://github.com/TabbyML/tabby, which is fully self-hostable and comes with niche features. On M1/M2, it offers a convenient single binary deployment, thanks to Rust. You can find the latest release at https://github.com/TabbyML/tabby/releases/tag/latest.

(Disclaimer: I am the author)

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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.

You can use your own GPU to host and fine-tune LLMs with Refact self-hosting server.