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 skill specializes for the
vision tower and projector; read that skill first
if the LoRA fundamentals (rank, alpha, target
modules) aren't already familiar.
Input: an image+text dataset and a VLM base
model already picked from the model catalog.
Output format: a validated adapter config —
which components are frozen, LoRA target modules,
and a min_pixels/max_pixels budget — that
llm-finetuning-training-engineer consumes
directly when it generates a runnable script.
Quick Reference
| Situation | Default |
|---|---|
| Adapting behavior on familiar images | Frozen tower+projector, LoRA r=8–16, α=16–32 |
| Visual domain shift | Unfreeze last-6 ViT layers, vision LR 5–10x lower |
| Doesn't fit in bf16 at target rank | QLoRA — frozen vision tower only |
fast_inference=True | finetune_vision_layers=False |