LLaMA-O1 provides an open framework for training, inference, and evaluation of large reasoning models, specifically targeting the development of open-source Large Language Models (LLMs) with enhanced reasoning capabilities. It is designed for researchers and developers working with PyTorch and HuggingFace ecosystems.

How It Works

The framework leverages PyTorch and HuggingFace libraries for model implementation and training. It focuses on curated datasets for pretraining and supervised fine-tuning, with a roadmap including Reinforcement Learning from Human Feedback (RLHF) and inference-time reasoning enhancements. The approach emphasizes structured reasoning through datasets like OpenLongCoT.

Quick Start & Requirements

• Install: Primarily through HuggingFace model downloads and PyTorch.
• Prerequisites: PyTorch, HuggingFace libraries. GPU acceleration is recommended for training and efficient inference.
• Demo: An online CPU-only demo is available: https://huggingface.co/spaces/SimpleBerry/LLaMA-O1-Supervised-1129-Demo
• Models: Available on HuggingFace: https://huggingface.co/SimpleBerry/LLaMA-O1-Supervised-1129, https://huggingface.co/SimpleBerry/LLaMA-O1-Base-1127
• Datasets: https://huggingface.co/datasets/SimpleBerry/OpenLongCoT-SFT, https://huggingface.co/datasets/SimpleBerry/OpenLongCoT-Pretrain-1202

Highlighted Details

• Open-source models and datasets for large reasoning models.
• Includes supervised and base pre-trained models.
• Roadmap includes RLHF and inference-time reasoning enhancements.
• GGUF quantized versions are available for broader compatibility.

Maintenance & Community

The project is hosted on GitHub: https://github.com/SimpleBerry/LLaMA-O1. Related research papers are linked for further context.

Licensing & Compatibility

The README does not explicitly state the license. Compatibility for commercial use or closed-source linking is not specified.

Limitations & Caveats

The framework is actively under development, with some features like RLHF and inference-time reasoning enhancements still in progress. The online demo is CPU-only, suggesting limited performance for interactive use without dedicated hardware.