tch-rs VS candle

I wanted to do something like this but then I would miss on proper CUDA acceleration and lose performance compared to using torchlib.

I wrote a forgettable llama implementation for https://github.com/LaurentMazare/tch-rs (pytorch's torchlib rust binding).

I first encountered OCaml's PyTorch bindings because apparently they generate a C wrapper around PyTorch's C++ API, and Rust's PyTorch bindings use OCaml's C wrapper. See: https://github.com/LaurentMazare/tch-rs

You could try looking at the min-GPT example of tch-rs. I'd also strongly suggest watching Karpathy's video to understand what's going on.

If you pefer to see it in code there's a succint gpt implementation here https://github.com/LaurentMazare/tch-rs/blob/main/examples/m...

Rust is fully compatible w/ C bindings, so even Python libraries written in C can be easily set up to work in Rust (and have been). For example, see PyTorch Rust bindings, which actually works faster than in Python because all of the glue code around the C++ API is in Rust instead of Python.

Pytorch

I have looked into this a bit and found some crates which help in loading torchscript models called tch-rs

I intend to grow this into an open-source project because deep inside, this is ideally how I would like ComfyUI to be. There's still a long journey ahead for building all the custom nodes, which is especially challenging given that the majority of code for AI workflows is written in Python. However, with my hands-on experience with Candle and Burn libraries, I may be able to get pretty close!

You are correct. This project is "on the CPU", so it will not utilize your GPU for computation. If you would like to try out a Rust framework that does support GPUs, [Candle](https://github.com/huggingface/candle/tree/main) may be worth exploring

While looking into how to create text embeddings quickly and directly, we discovered a few helpful tools that allowed us to achieve our goal. Consequently, we created an easy-to-use PHP extension that can generate text embeddings. This extension lets you pick any model from Sentence Transformers on HuggingFace. It is built on the CandleML framework, which is written in Rust and is a part of the well-known HuggingFace ecosystem. The PHP extension itself is also crafted in Rust using the php-ext-rs library. This approach ensures the extension runs fast while still being easy to develop.

Candle already exists[1], and it runs pretty well. Can use both CUDA and Metal backends (or just plain-old CPU).

[1] https://github.com/huggingface/candle

Check out Candle! It's a Deep Learning framework for Rust. You can run LLMs in binaries.

Kalosm is a meta-framework for AI written in Rust using candle. Kalosm supports local quantized large language models like Llama, Mistral, Phi-1.5, and Zephyr. It also supports other quantized models like Wuerstchen, Segment Anything, and Whisper. In addition to local models, Kalosm supports remote models like GPT-4 and ada embeddings.

I have been working on a machine learning library called candle-lora for Candle. It implementes a technique called LoRA (low rank adaptation), which allows you to reduce a model's trainable parameter count by wrapping and freezing old layers.

[2] https://github.com/huggingface/candle/issues/313