awesome-rust-llm  by jondot

This repository curates Rust tools, libraries, and frameworks for working with Large Language Models (LLMs), GPT, and AI. It serves as a comprehensive resource for developers and researchers seeking to leverage Rust's performance and safety for AI applications, offering a wide array of models, inference engines, and related utilities.

How It Works

The list categorizes Rust projects based on their functionality within the LLM ecosystem. This includes libraries for running LLM inference (e.g., llm, rust-bert, rllama), frameworks for chaining LLM calls (llm-chain), and tools for specific tasks like web browsing (browser-agent) or code generation (autorust). It also highlights vector databases (pgvecto.rs, qdrant) and memory solutions (indexify, motorhead) crucial for LLM applications.

Quick Start & Requirements

• Installation and usage vary by project; consult individual project READMEs.
• Prerequisites often include Rust toolchain, specific model files (e.g., ggml, LLaMa), and potentially Python for certain components.
• Links to relevant projects and documentation are provided within the list.

Highlighted Details

• rllama: Pure Rust implementation of LLaMa inference, suitable for embedding.
• pgvecto.rs: Postgres extension for vector storage, claiming 20x speed over pgvector.
• tiktoken-rs: Rust implementation of OpenAI's BPE tokenizer, with a Rust core.
• rust-bert: Rust port of Hugging Face's transformers library for local embeddings.

Maintenance & Community

• This is an "awesome list" curated by the community, with PRs welcome.
• No specific maintainer or community channels (like Discord/Slack) are listed.

Licensing & Compatibility

• Licenses vary by project; most appear to be permissive (MIT, Apache 2.0).
• Compatibility for commercial use is generally expected for permissive licenses, but users must verify each project.

Limitations & Caveats

The list is a curated collection, not a single integrated framework. Users must evaluate and integrate individual projects, and some projects may be experimental or have limited documentation.