VLM Collators, Dataset Format, and Pitfalls

Full detail backing the summary in SKILL.md. Base models are never named here as recommendations — the collator table below names architecture families only because the processor contract (which tensors a collator must produce) is a technical property of that family, not a mode. Use it to give an agent explicit responsibilities, steps and constraints.

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Last verified: 2026-07-13

VLM Collators, Dataset Format, and Pitfalls

Full detail backing the summary in SKILL.md. Base models are never named here as recommendations — the collator table below names architecture families only because the processor contract (which tensors a collator must produce) is a technical property of that family, not a model choice. For which actual model to fine-tune at a given size class, see finetuning-method-selection's references/model-catalog.md.

Per-Architecture Collator Table

Collators are not interchangeable across VLM architecture families — each family's processor expects a different tensor contract, and using the wrong collator produces either a hard error or (worse) silently wrong tensors that train without learning. Each row below describes an architecture family's processor contract, not a model recommendation.

Two practical consequences:

• Picking a collator is an architecture-family decision, made once per base model, not a free parameter to tune.
• A collator built for one family will often run against another family's data without erroring — the shapes are superficially compatible — which is exactly how a mismatched collator becomes a silent-failure run instead of a crash.

Dataset Format: Messages with Image Placeholders

VLM SFT datasets are typically a messages list per example, with an explicit image placeholder token in the content that the processor later expands to the architecture's actual vision-token span:

example = {
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "image"},
                {"type": "text", "text": "What does this chart show?"},
            ],
        },
        {
            "role": "assistant",
            "content": [
                {"type": "text", "text": "Quarterly revenue trending upward."},
            ],
        },
    ],
    "images": [<PIL.Image or path>],
}

The count of {"type": "image"} placeholder entries in messages must equal the count of entries in images, in order, for every single example — this 1:1 mapping is exactly the first silent killer from SKILL.md. A dataset-level assert on this count, run over every example before training starts, catches the mismatch at data-prep time instead of after a wasted training run.

Pre-Training Validation Checklist

Run this checklist against one collated batch before launching a full training run. All three checks are cheap (seconds, one batch) relative to the cost of discovering a silent failure after hours of training:

1. Decode one collated batch back to text. Pull a batch from the dataloader, decode the input_ids with the tokenizer, and read it. Confirm the image placeholder tokens appear where expected and the surrounding text matches the source example — this catches template or collator bugs that reshuffle content.
2. Count image tokens per example. Compare the number of vision tokens the processor actually inserted against the expected count for that image's resolution under the configured min_pixels/max_pixels budget (the second silent killer from SKILL.md). A count that doesn't match the expected budget means the resolution budget isn't being applied the way it's configured.
3. Verify the loss mask covers assistant turns only. Inspect the labels tensor (or token_type_ids for Gemma-3-family collators) and confirm masked (-100) positions cover the system/user turns and image tokens, with only assistant-turn text contributing to the loss. A loss mask that leaks onto image tokens or user turns trains the model to predict input it should only be conditioning on.

If any of the three checks fails, fix the collator or dataset before starting the full run — none of these are the kind of thing a training curve reveals on its own.

Advanced Pattern: Two-Stage Projector-Alignment Recipe

The consensus recipe in SKILL.md freezes the projector. When adapting to a base model or dataset far enough from the projector's original alignment that the frozen-projector recipe underperforms, a two-stage LLaVA-style alignment recipe is the advanced fallback:

1. Stage 1 — projector-only alignment. Freeze both the vision tower and the LLM. Train only the projector (no LoRA involved yet) on a broad, simple image-caption-style dataset. The goal is purely to re-align the projector's output space with the current LLM's embedding space — this stage does not teach the target task.
2. Stage 2 — task LoRA on top. With the realigned projector now frozen again, apply the standard consensus recipe from SKILL.md (LoRA on the LLM only, all-linear, r=8–16, α=16–32) using the actual task dataset.

This two-stage recipe is an escalation path, not a default — reach for it only when the single-stage frozen-projector recipe measurably underperforms, since it roughly doubles the number of training runs required. Most VLM SFT tasks in this plugin's scope stay on the single-stage consensus recipe.

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