llm VS crates.io

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.

Rust has a rich ecosystem of frameworks and libraries that let you read, parse, and manipulate text files, interact with cloud services and databases, and perform any other job that your project's development workflow may require. And because of its strong typing and tight memory management, you are much less likely to write programs that behave unexpectedly in production.

All the code discussed in this article can be accessed through this repository. For integration with existing projects consider using keyword_extraction crate available on crates.io.

So, be sure to double-check your critical libraries and be sure their alternatives exist in the Rust ecosystem. There’s a good chance the crates you need are available in Rust's crates.io repository.

The previous section was very simple, this section is also very simple but introduces us to cargo which is Rust's package manager, as a JS dev my mind goes straight to NPM.

Now, there has to be a place where all these packages come from. Similar to npmjs registry, where all node packages are registered, stored and retrieved, Rust also has something called crates.io where many helpful packages and dependencies are registered.

Before proceeding, let's check https://crates.io/, the official Rust package registry.

The most thrilling aspect has been the joy of writing the backend. It's like every struct, enum, and method in Rust forms this interconnected Multiverse of code , which you can see in crates.io which is best Documentation experience I Ever Had.

5. Crates.io

When using crates from eg. crates.io, and also things like std and core

Vendoring your packages was very tedious to even remotely get to work with Cargo. I spent a very long time getting Cargo to work together with cargo-local-registry. We vendor crates from crates.io and a custom internal registry.