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

The Consensus Recipe

Freeze the vision tower and the projector. Put LoRA on the LLM only, all-linear (the same attention + MLP target list as text-only SFT — see lora-qlora-recipes), at r=8–16, α=16–32. This is the settled default for adapting a VLM's behavior without disturbing how it sees.

• The vision tower and projector stay frozen by default. They already encode a general visual representation; retraining them is rarely necessary and adds risk without adding capability for most tasks.
• LoRA rank runs lower than the text-only general default (r=8–16 here vs r=16–32 for text-only SFT) because the LLM-only adapter is adapting behavior, not injecting new visual knowledge.
• QLoRA is permitted only with a frozen vision tower. Quantizing the base while also unfreezing and training vision layers is unsupported and unstable — treat this as a hard pairing rule, not a tunable. If the vision tower needs to unfreeze, drop QLoRA and use bf16 LoRA instead.

# freeze tower + projector; LoRA on LLM only
for name, param in model.named_parameters():
    if "vision_tower" in name or "projector" in name:
        param.requires_grad = False

target_modules = [
    "q_proj", "k_proj", "v_proj", "o_proj",
    "gate_proj", "up_proj", "down_proj",
]  # LLM-only, all-linear — r=8-16, alpha=16-32

When to Unfreeze

Unfreezing vision layers is a deliberate escalation, not a default decision — reach for it only when the domain shift is visual, not textual.

• Unfreeze only for visual domain shift. If the task is teaching new behavior on images the tower already understands (charts, everyday photos), the frozen-tower recipe above is sufficient. Unfreeze when the visual domain itself is unfamiliar to the tower — satellite imagery, medical scans, dense technical diagrams — and the frozen-tower recipe plateaus.
• Last-6 ViT layers is the sweet spot. Unfreezing the final six vision-transformer layers (not the whole tower) measured +1.7pt DocVQA at ~1.75x training cost over the frozen baseline. Treat six layers as the ceiling worth paying for; going further spends compute without a matched result.
• Vision LR must run 5–10x lower than the LLM LR when unfrozen. The vision tower's pretrained representation is more fragile than the LLM's adapter; the same LR for both risks overwriting the visual representation faster than the LLM adapter can compensate.
• High LoRA rank on the patch- embedding layer risks NaN. If patch embedding is in the unfrozen set, keep its rank low and watch early-step loss closely — one of the most fragile places to apply LoRA in a VLM.

The Two Silent Killers

Both produce a run that trains without error and without learning: the loss curve looks normal, the model doesn't improve, and neither throws an exception — both need an explicit pre-training check, not just a clean training log.

• Image-tag/count mismatch. Every image placeholder token in the templated text must map 1:1 to a media item actually passed to the collator. A mismatch (one placeholder, zero or two images attached; or an image with no placeholder) doesn't error in most collators —

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