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- Edit this `README.md` markdown file to author your organization card 🔥
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ # Rustformers - Large Language Models in Rust
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+ Rustformers is a developer-centric initiative aiming to bring the robustness and efficiency of Rust to the world of large language models. At the core of this initiative is llm (Large Language Models), an ecosystem of Rust libraries specifically designed for handling and interacting with large language models.
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+ ## About `llm`
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+ [![Latest version](https://img.shields.io/crates/v/llm.svg)](https://crates.io/crates/llm)
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+ `llm` is a set of Rust libraries that provide an interface for working with large language models. It is built on top of the fast, efficient [GGML](./crates/ggml) library for machine learning. `llm` includes support for various models such as BLOOM, GPT-2, GPT-J, GPT-NeoX, LLaMA, and MPT. The `llm` crate serves as a primary entry point for developers and also provides a CLI (Command Line Interface) for end users to interact with these models.
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+ You can include `llm` in your Rust projects by listing it as a dependency in your `Cargo.toml` file. The package requires Rust v1.65.0 or above and a modern C toolchain for building. Detailed instructions on how to use `llm` in a Rust project and via the `llm` CLI are provided in the documentation.
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+ ## Contact Us
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+ [![Discord](https://img.shields.io/discord/1085885067601137734)](https://discord.gg/YB9WaXYAWU)
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+ For any queries or support related to `llm`, feel free to raise an [issue](https://github.com/rustformers/llm/issues/new) on our GitHub repository or start a [discussion](https://github.com/rustformers/llm/discussions/new). We are also available on [Discord](https://discord.gg/YB9WaXYAWU), where you can chat with us directly. We encourage contributions and look forward to your involvement in enhancing `llm`!
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+ ## Further Reading
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+ For more information, please refer to the [documentation](https://docs.rs/llm). The documentation provides an in-depth guide to using `llm` in your Rust projects, getting models, using the `llm` CLI, and answers to frequently asked questions.
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