Overview
LLaVA-NeXT is an open-source large-scale multimodal model and toolkit from the LLaVA team. It aims to unify training and inference across images, multi-image, video, and 3D tasks, and provides training scripts, evaluation tools and multiple model variants suitable for research and engineering.
Key features
- Interleaved multimodal training format supporting multi-image and video inference.
- Multiple model variants and reproduction scripts, including training, evaluation and benchmarking tools (lmms-eval).
- Regularly released checkpoints and evaluation results, with demos and blog posts documenting updates.
Use cases
- Multimodal benchmarks, model comparisons, and academic reproductions.
- Video understanding, image question answering, image editing and multi-image scene understanding.
- Research baselines and engineering prototypes.
Technical details
- Implemented in PyTorch with support for large-scale training, quantization and inference optimizations.
- Employs scalable architectures and training strategies, including critic models and DPO/RLHF training methods.
- Provides comprehensive docs, demos (including Hugging Face Spaces) and dataset links for reproducibility and evaluation.