TinyLLaVA Factory is a modular Python framework for building, training, evaluating, and serving small-scale Large Multimodal Models (LMMs). It provides a pluggable architecture with separate components for vision towers (CLIP, SigLIP, DINOv2), language models (Phi, TinyLlama, Gemma, Qwen2, etc.), and connectors (MLP, QFormer, Resampler), all accessible via factory registries. The training pipeline supports two-stage pretraining (alignment) and finetuning with full, LoRA, or QLoRA recipes using DeepSpeed. It includes a comprehensive evaluation suite for standard benchmarks (VQA, GQA, SQA, POPE, MMMU, etc.) and a Gradio-based web demo. The codebase is well-organized and follows clean architectural patterns, though many modules are tightly coupled to the internal framework. It is actively maintained, with recent additions like a visualization tool and support for new models.
The repository implements LLaVA-VLA, a Vision-Language-Action model for robotic manipulation tasks. It extends the LLaVA framework to predict action sequences from visual inputs, proprioceptive state, and language instructions using a diffusion-based action head. The system includes training pipelines for fine-tuning on datasets like CALVIN, inference servers (Flask and Gradio) for real-time robot control, and evaluation tools for benchmarks. The architecture features modular vision encoders, projectors, and resamplers with factory patterns for flexibility, action tokenization for discrete action prediction, and integration with PEFT/DeepSpeed for efficient training.
LLaVA-NeXT is a large multimodal model (LMM) research codebase that extends the LLaVA family to support single-image, multi-image, video, and interleaved tasks. It provides training pipelines for supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning (PPO, GRPO) with custom vision encoders (CLIP, SigLIP, EVA-CLIP, MLCD) and modular projector/resampler components. The repository also includes a full RL training framework (EasyR1/verl) for large-scale GRPO training using Ray, FSDP, and vLLM, as well as evaluation scripts for video and interleaved benchmarks. While primarily a research codebase with extensive training configurations and experimental scripts, it also contains serving infrastructure (Gradio, SGLang, controller-worker) for deployment. The code is well-structured with reusable vision encoder wrappers, data protocols, and RL algorithm implementations, but suffers from hardcoded internal paths, multiple overlapping RL implementations, and legacy training pipelines that increase maintenance burden.