For locally fine-tuning multimodal models on video datasets using LoRA, the best open-source choices are LLaVA-NeXT (Video), Qwen-VL, and Video-LLaVA. These models sample video frames into sequential tokens, allowing you to easily fine-tune them with video-question or video-captioning datasets using standard parameter-efficient fine-tuning (PEFT) frameworks.
Top Models & Ecosystems
• LLaVA-NeXT-Video: A state-of-the-art framework for video reasoning. It natively supports video temporal understanding, making it highly customizable using LoRA and parameter-efficient methods. You can train it easily using the LLaVA-NeXT GitHub Repository.
• Qwen-VL: An incredibly powerful vision-language model family known for high-quality spatial and temporal reasoning. You can train and adapt custom Qwen models for video inputs using Llama Factory, which is an excellent low-code framework for LoRA tuning.
• Video-LLaVA: Designed specifically to unify visual representations of images and videos. It allows you to freeze the heavy base weights of the underlying LLM and vision encoders while only fine-tuning an adapter matrix (LoRA). This approach drastically reduces VRAM requirements.
Recommended Libraries & Local Setup
To run and fine-tune these models locally, you can utilize the following open-source stacks:
1. Unsloth: Highly optimized for fine-tuning multimodal models, Unsloth provides fast Vision+Text training that significantly cuts down GPU memory consumption.
2. Hugging Face PEFT / TRL: These standard python libraries power almost all local LoRA fine-tuning workflows, letting you supply a JSONL dataset of video paths/frames alongside text prompts.
For a conceptual understanding of how LoRA maps directly into Vision-Language models to allow training on consumer GPUs:
https://www.youtube.com/watch?v=Oj27kALfvr0&t=1388
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