GitHub

LLaVA: Large Language and Vision Assistant

What is the project about?

LLaVA is a large multimodal model (LMM) that combines a vision encoder and a large language model (LLM) for general-purpose visual and language understanding. It aims to achieve GPT-4 level capabilities in understanding and responding to instructions that involve both images and text. It's an end-to-end trained neural network.

What problem does it solve?

LLaVA addresses the challenge of creating AI systems that can understand and reason about both visual and textual information, similar to how humans do. It moves beyond models that are specialized for single tasks (like image captioning or visual question answering) towards a more general-purpose assistant. Specifically, it aims to improve:

  • Visual Instruction Following: The ability to follow instructions that refer to visual content.
  • Multimodal Reasoning: Combining information from images and text to answer questions or perform tasks.
  • Reducing Hallucination: Making the model more factually grounded and less likely to invent information.
  • Zero-shot Modality Transfer: Applying a model trained on images to video tasks without explicit video training.
  • Efficiency: Achieving strong performance with relatively small datasets and training time compared to other large multimodal models.

What are the features of the project?

  • Visual Instruction Tuning: A key technique used to train LLaVA, involving a dataset of image-text pairs with instructions and corresponding responses.
  • GPT-4-Level Capabilities (Goal): The project aims for performance comparable to GPT-4 on multimodal tasks.
  • Multiple Model Versions: LLaVA has evolved through several versions (LLaVA-1.5, LLaVA-NeXT), each with improved capabilities.
  • Support for Different LLMs: Works with LLMs like Vicuna, LLaMA-2, Llama-3, and Qwen-1.5.
  • High-Resolution Image Input: Can process images at higher resolutions (e.g., 336x336 pixels) than many other models.
  • LoRA Training: Supports efficient training with Low-Rank Adaptation (LoRA), reducing GPU memory requirements.
  • Quantization: Supports 4-bit and 8-bit quantization for reduced memory usage during inference.
  • SGLang Integration: Integration with SGLang for high-throughput serving.
  • Community Contributions: Integrations with popular tools like llama.cpp, Hugging Face Spaces, and AutoGen.
  • Video Understanding (LLaVA-NeXT Video): Demonstrates strong zero-shot video understanding capabilities.
  • Tool Use (LLaVA-Plus): Can learn to use external tools for multimodal agent tasks.
  • Interactive Demo (LLaVA-Interactive): An all-in-one demo for image chat, segmentation, generation, and editing.
  • Efficient Evaluation Pipeline (LMMs-Eval): A streamlined evaluation pipeline for LMMs.

What are the technologies used in the project?

  • Deep Learning Frameworks: Likely PyTorch (based on common practices in similar projects and the installation instructions).
  • Large Language Models (LLMs): Vicuna, LLaMA/LLaMA-2, Llama-3, Qwen-1.5.
  • Vision Encoders: CLIP (Contrastive Language-Image Pre-training) ViT-L/14.
  • Training Techniques: Visual instruction tuning, LoRA (Low-Rank Adaptation), DeepSpeed (for distributed training), Reinforcement Learning from Human Feedback (RLHF).
  • Serving: Gradio (for web UI), SGLang (for efficient serving).
  • Quantization: 4-bit and 8-bit quantization techniques.
  • Hugging Face Transformers: Used for model loading and likely for many other components.

What are the benefits of the project?

  • Improved Multimodal Understanding: Better ability to understand and respond to instructions involving both images and text.
  • State-of-the-Art Performance: Achieves competitive or state-of-the-art results on various multimodal benchmarks.
  • Efficiency: Relatively fast training and inference, with options for reduced memory usage.
  • Open-Source: Code, models, and data are publicly available, fostering research and development.
  • Flexibility: Supports different LLMs and training configurations.
  • Extensibility: The project is actively developed, with new features and improvements regularly released.

What are the use cases of the project?

  • Visual Chatbots: Creating chatbots that can "see" and discuss images.
  • Image Understanding and Question Answering: Answering questions about images, describing their content, and reasoning about them.
  • Multimodal Content Creation: Generating text descriptions of images or creating images based on textual descriptions (with additional tools).
  • Assistive Technology: Helping visually impaired users understand visual content.
  • Education and Research: A platform for exploring and advancing multimodal AI research.
  • Robotics: Enabling robots to interact with the world using both vision and language.
  • Video Analysis: Summarizing, answering questions about, and understanding video content.
  • Multimodal Agents: Building agents that can interact with the world using multiple modalities and tools.
LLaVA screenshot