Vision Model Fine-Tuning FAQs

Question 1

Can I fine-tune only the vision encoder or only the language model?

Accepted Answer

Yes, the skill provides specific LoRA flag combinations to independently enable or disable fine-tuning for vision layers, language layers, attention modules, and MLP modules.

Question 2

What are the critical SFTConfig settings for vision training?

Accepted Answer

You must set remove_unused_columns=False to preserve images, dataset_text_field='' to use message formats, and skip_prepare_dataset=True to prevent incorrect processing of vision data.

Question 3

Does this skill support 4-bit quantization?

Accepted Answer

Yes, the implementation patterns include instructions for loading models in 4-bit using bitsandbytes (bnb) to significantly reduce VRAM requirements during fine-tuning.

Question 4

Which vision-language models are supported by this skill?

Accepted Answer

This skill supports leading vision-language models (VLMs) including Pixtral-12B, Ministral-8B-Vision, and Llama-3.2-11B-Vision using Unsloth's optimized weights.

Question 5

Why does this skill recommend list comprehension over .map() for datasets?

Accepted Answer

Vision datasets containing PIL images work more reliably as plain Python lists than HuggingFace Dataset objects when being processed by specialized vision data collators.

Vision Model Fine-Tuning

Key Features

Use Cases

Vision Model Fine-Tuning

Key Features

Use Cases