LLaMA2-Accessory is an open-source toolkit for developing, finetuning, and deploying large language models (LLMs) and multimodal LLMs (MLLMs). It extends the LLaMA-Adapter project with advanced features, including the SPHINX MLLM, which supports diverse training tasks, data domains, and visual embeddings, aiming to provide a comprehensive solution for LLM practitioners.

How It Works

The toolkit supports parameter-efficient finetuning methods like Zero-init Attention and Bias-norm Tuning, alongside distributed training strategies such as Fully Sharded Data Parallel (FSDP) and optimizations like Flash Attention 2 and QLoRA. It integrates various visual encoders (CLIP, Q-Former, ImageBind, DINOv2) and supports a wide range of LLMs including LLaMA, LLaMA2, CodeLlama, InternLM, Falcon, and Mixtral-8x7B. This modular design allows for flexible customization and efficient scaling of LLM development.

Quick Start & Requirements

• Installation: Refer to the Environment Setup documentation.
• Prerequisites: Python, PyTorch, Hugging Face libraries, and potentially CUDA for GPU acceleration. Specific model requirements may vary.
• Documentation: Comprehensive guides for model pretraining, finetuning, and inference are available.

Highlighted Details

• SPHINX-MoE achieves state-of-the-art performance on MMVP (49.33%) and AesBench.
• Supports finetuning on a wide array of datasets for both single-modal and multi-modal tasks.
• Includes efficient quantization with OmniQuant for reduced model size and faster inference.
• Offers demos for various LLM applications, including chatbots and multimodal interactions.

Maintenance & Community

• Active development with recent updates in early 2024, including releases for Large-DiT-T2I and SPHINX-Tiny.
• The project is associated with the General Vision Group at Shanghai AI Lab, with hiring announcements for researchers.
• Community engagement is encouraged via WeChat.

Licensing & Compatibility

• The LLaMA 2 models are licensed under the LLAMA 2 Community License.
• The toolkit itself appears to be open-source, but specific licensing for all components and datasets should be verified. Compatibility with commercial or closed-source applications depends on the underlying model licenses.

Limitations & Caveats

• The project is heavily reliant on the LLaMA 2 Community License, which may have restrictions on commercial use.
• While extensive, the breadth of supported models and datasets means specific configurations might require careful setup and dependency management.