Why does this skill recommend using lists instead of .map() for datasets?

Vision datasets containing PIL images are more stable and reliable when handled as plain Python lists during the conversion to chat formats compared to HuggingFace Dataset objects.

What training speed improvements should I expect?

By utilizing Unsloth's FastVisionModel as outlined in this skill, vision model training can be up to 2x faster compared to standard fine-tuning methods.

How are multi-modal batches handled during training?

The skill implements the UnslothVisionDataCollator, which is specifically designed to handle batches containing mixed image and text data for the SFTTrainer.

Can I train both the vision and language components of the model?

Yes, the skill provides LoRA configurations to enable or disable training for vision encoder layers and language model layers independently using specific flags.

Which vision models can I fine-tune with this skill?

This skill supports major VLMs including Pixtral-12B, Ministral-8B-Vision, and Llama-3.2-11B-Vision using Unsloth optimized 4-bit paths.

Vision Model Fine-Tuning

Name: Vision Model Fine-Tuning
Author: atrawog

byatrawog

Ciencia de Datos y ML

Fine-tunes and optimizes vision-language models like Pixtral and Ministral using Unsloth's FastVisionModel and LoRA.

Acerca de

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.

Características Principales

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

Casos de Uso

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

Acerca de

Características Principales

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

Casos de Uso

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