BitLlama is a high-performance LLM inference engine built in Rust, designed to enable efficient and scalable machine learning model inference. It leverages advanced techniques like 1.58-bit ternary quantization, Test-Time Training (TTT), and the Soul learning system to optimize model performance while maintaining accuracy.
Key Features:
1.58-bit Ternary Quantization: Reduces memory usage and computational overhead without compromising model quality.
Test-Time Training (TTT): Dynamically adjusts models during inference to improve accuracy in real-world scenarios.
Soul Learning System: Enhances adaptability by enabling continuous learning and fine-tuning of models on-the-fly.
MCP Server/Client Architecture: Supports distributed processing for scalable and efficient model deployment.
Model Compatibility: Works with popular architectures like Llama, Mistral, Gemma, Qwen, BitNet, and more.
OpenAI-Compatible API: Offers seamless integration with existing workflows through an API server.
Ideal for developers, researchers, and organizations working in AI and machine learning, BitLlama provides a powerful toolset to deploy efficient, scalable, and adaptable language models. It is particularly beneficial for applications requiring low-resource usage, high performance, or private RAG (Retrieve-and-Generate) setups. Installable via winget, BitLlama empowers users to build and deploy advanced AI solutions with ease and precision.
README
BitLlama
Pure Rust LLM inference engine with Soul learning and hierarchical memory.
> Status: v1.0.0 — Development Complete. This project is fully functional and no longer under active development.
What is BitLlama?
A local LLM inference engine written entirely in Rust. It runs GGUF and safetensors models on your PC, with a unique Soul system that lets the AI learn and remember across conversations.
