Exploring Local LLMs with Rust Library: LM.RS - A Versatile, Customizable Solution

Exploring Local LLMs with Rust Library: LM.RS - A Versatile, Customizable Solution

Unlocking the Power of Local Language Models: Harnessing Rust's LMRS Library for Fast, Cost-Effective, and Flexible AI Inference

• 1. Speaker introduces himself as Phil, also known as aone online, from Australia but living in Sweden.
• 2. He works for Ambient, building a game engine of the future.
• 3. Today's topic: LMRS, a Rust library he maintains for local Large Language Models (LLMs).
• 4. LLMs offer an alternative to cloud models by running on your own computer.
• 5. Model size can be used as a rough proxy for intelligence; most popular models are very large.
• 6. Local models have smaller capacity but may solve specific problems better than larger general models.
• 7. GBP4 model size is unknown, only rumors exist about its size.
• 8. Cloud models run on specialized hardware with special configurations while local models use whatever hardware available, including rented hardware.
• 9. The further up the access level for cloud models, the more speed and parallel inference you can achieve but it becomes less accessible.
• 10. Local models have lower latency since they can provide a response immediately after a prompt.
• 11. Cloud models require going back and forth to get probabilities while local models allow direct control of sampling.
• 12. Local models enable trying out innovations before cloud providers offer them, keeping the controller with the user.
• 13. Hardware limitations still exist for running local models effectively.
• 14. Rust has an excellent cross-platform support and build system, making it easy to ship self-contained binaries.
• 15. The Rust ecosystem is strong, allowing for building various applications with LLMs in the same language.
• 16. Local models allow choosing how they generate results by controlling sampling of tokens.
• 17. The innovation in this space moves quickly, and changes can break existing workflows.
• 18. Many open-source models have strange clauses and exceptions for commercial use but Mistral and StableLM offer strong performance with a small size and without licensing issues.

Source: AI Engineer via YouTube

❓ What do you think? What do you think is the most significant implication of using local models, rather than cloud-based models, for your specific application or industry? Feel free to share your thoughts in the comments!