Vision Model Fine-Tuning

About

This skill provides domain-specific guidance for fine-tuning Vision-Language Models (VLMs) such as Pixtral, Ministral VL, and Llama 3.2 Vision. It streamlines the implementation of Unsloth's FastVisionModel for 2x faster training, covering critical aspects like vision-specific LoRA configurations, multi-modal dataset preparation using PIL, and specialized SFTTrainer setups. Whether you are building OCR systems or advanced visual reasoning models, this skill ensures Claude Code follows best practices for vision model optimization and inference.

Key Features

• Implementation patterns for UnslothVisionDataCollator
• Vision-specific LoRA configuration for encoder and attention modules
• Specialized FastVisionModel loading with Unsloth 4-bit optimizations
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• Multi-modal dataset preparation using optimized list formats
• Precision settings and sequence length management for VLM training

Use Cases

• Optimizing vision-language model performance for resource-efficient training
• Developing high-precision OCR models for specialized notation like LaTeX
• Fine-tuning VLMs for domain-specific image classification or description tasks