This repository serves as a curated list of open-source alternatives and counterparts to ChatGPT and GPT-4, targeting developers, researchers, and enthusiasts looking to explore or replicate advanced large language model capabilities. It aims to provide a centralized resource for discovering and evaluating various open-source LLMs, their architectures, training methodologies, and performance benchmarks.

How It Works

The project categorizes open-source LLMs into "Autonomous Models" (those built from scratch or with significant modifications to existing architectures like T5 or GPT) and "Alpaca Mode" models (primarily fine-tuned versions of Meta's LLaMA). It details the technical approaches, including supervised fine-tuning (SFT), reward modeling (RM), and reinforcement learning from human feedback (RLHF), highlighting specific implementations and their advantages.

Quick Start & Requirements

• Installation and usage vary significantly per listed model. Most projects are hosted on GitHub and utilize Python with libraries like Hugging Face Transformers, PyTorch, or JAX.
• Specific hardware requirements (e.g., GPUs, VRAM) and dependencies (e.g., CUDA versions) are detailed within individual project links.
• Setup time and resource footprint are highly model-dependent, ranging from easily runnable on consumer hardware to requiring substantial distributed training infrastructure.
• Links to official GitHub repositories, Hugging Face model pages, and documentation are provided for each entry.

Highlighted Details

• Comprehensive coverage of models supporting both English and Chinese languages.
• Inclusion of multimodal models capable of processing text, images, and other data types.
• Detailed explanations of training methodologies, including RLHF and instruction tuning.
• Regular updates and inclusion of new models and evaluation benchmarks.

Maintenance & Community

• The project is community-driven, with contributions from various academic institutions and research labs.
• Links to relevant community platforms (e.g., GitHub discussions) are typically available within individual project repositories.

Licensing & Compatibility

• Licenses vary widely, including Apache 2.0, MIT, and more restrictive research-only licenses.
• Users must carefully check the license of each individual model for commercial use or closed-source integration compatibility.

Limitations & Caveats

• This is a curated list, not a single runnable project; users must interact with each linked repository individually.
• Performance claims and capabilities are based on the original project's documentation and may require independent verification.
• The rapid pace of LLM development means some listed models or information may become outdated.