vision-sft
Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM fine-tune that trains without learning.
- Skills
- General
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What it does and how to use it
A skill that teaches Claude Code to do general tasks well. It is packaged knowledge the model uses whenever it is relevant.
- Install the skill (command above).
- In Claude Code, describe what you need - the skill turns on by itself when the context fits.
- No commands to memorize: you talk in plain language and the skill does the rest.
Install (Claude Code)
mkdir -p .claude/skills/wshobson-agents__vision-sft && curl -fsSL https://doutorai.pt/data/lib/skills__wshobson-agents__vision-sft.md -o .claude/skills/wshobson-agents__vision-sft/SKILL.mdPaste this command into the terminal, in your project folder, and the component installs automatically.
Content
---
name: vision-sft
description: Fine-tune vision-language models (VLMs) with supervised learning on image+text data. Use when adapting a VLM to a visual domain or task, configuring frozen-vision-tower LoRA, or debugging a VLM fine-tune that trains without learning.
---
# Vision-Language SFT
This skill assumes `finetuning-method-selection`
already routed here: the data shape is
image+text demonstrations, not preference pairs
or a verifiable reward signal, and the base is a
vision-language model rather than a text-only
one. `lora-qlora-recipes` covers the text-only
LoRA/QLoRA recipe this ski
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