tch-rs VS llm

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

We're open-sourcing a project that lets you build simple automations/agent workflows that use LLMs for different tasks. Kinda like Zapier or IFTTT but focused on using natural language to accomplish your tasks.It's super early but we'd love to start getting feedback to steer it in the right direction. It currently supports OpenAI and local models through llm.

> Tensorflow is a C++ framework that has Python bindings and a Python library, but when the models are served they are running on C++

Sure, and it's only a simple 20 step process that involves building Tensorflow from source. Yeay!

https://medium.com/@hamedmp/exporting-trained-tensorflow-mod...

Let me see what the process for compiling a LLM written in Rust is....

https://github.com/rustformers/llm

    cargo install llm-cli

Floneum is a graph editor for local AI workflows. It uses llm to run large language models locally, egui, and dioxus for the frontend, and wasmtime for the plugin system. If you are interested in the project, consider joining the discord, or building a plugin for Floneum in rust using WASI

Run inference for Large Language Models on CPU, with Rust (https://github.com/rustformers/llm)

On the rustformers github page I see that one of the commands to generate the answer is llm llama infer -m ggml-gpt4all-j-v1.3-groovy.bin -p "Rust is a cool programming language because", my basic idea for now is to change the Tauri app to let it do -p prompt, which receives from my code through the link or through a shared variable (if I don't use the link and start different times your app)

wonnx has done some fantastic work in this regard, so that's where we plan to start once we get there. In terms of general discussion of alternate backends, see this issue.