thaitea/laya-vision-smolvlm-256m

Laya Vision (SmolVLM-256M)

This model makes calibrated, typed decisions about an image plus optional text. It answers choice, score and noul (yes/no probability) questions in one forward pass, with no text generation.

It adds image input to Laya by replacing Laya's ModernBERT encoder with SmolVLM-256M-Instruct. Laya's predict(state, questions) API, proper-scoring-rule training and temperature calibration are unchanged.

• Code: github.com/r33drichards/laya-vision
• Demo: thaitea/laya-vision-demo
• Status: experimental. This is an independent research fork, not affiliated with Convai Innovations, the authors of Laya.

Usage

git clone https://github.com/r33drichards/laya-vision && pip install -e ./laya-vision torchvision

import laya
from PIL import Image

agent = laya.load_vlm("thaitea/laya-vision-smolvlm-256m")
result = agent.predict(
    {"image": Image.open("photo.jpg"), "note": "customer says it arrived broken"},
    {
        "damaged":  {"type": "noul",   "instructions": "Does the item in the photo look damaged?"},
        "category": {"type": "choice", "instructions": "What kind of item is this?",
                     "criteria": ["electronics", "clothing", "furniture", "food", "other"]},
    },
)
result["answers"]["damaged"]["noul"]        # calibrated P(true)
result["answers"]["category"]["choice"]     # top option; see ["probabilities"], ["confidence"]

Results

Scores are on the full validation splits. "Calibrated" uses the per-type temperatures stored in vlm_agent_config.json, which are applied automatically.

Dataset	Question type	Chance	Accuracy	ECE raw	ECE calibrated
A-OKVQA (n=1,138)	4-way choice	25%	63.1%	0.266	0.094
ScienceQA, image subset (n=2,097)	2–5-way choice	~36%	89.0%	0.080	0.034
VQAv2 yes/no (n=5,000)*	noul	50%	73.2%	0.085	0.042
All (n=8,235)			75.9%	0.108	0.035

* The VQAv2 train/val split is a re-split of the official VQAv2 validation set by image (the only official split with answers), so these numbers are not comparable to published VQAv2 results.

• Latency: about 71 ms for one image question on an NVIDIA L4 (bf16). The image is encoded once per predict call and shared by every question.
• Option order: across 4 rotations of the A-OKVQA option order, accuracy varies by 1.2 points (63.1 / 61.9 / 62.1 / 61.9). Averaging over orders (n_permutations=4) doesn't help.

Training

• Backbone: SmolVLM-256M-Instruct. The vision tower is frozen, and the language model and decision head are trained, about 150M parameters.
• How options are read: the question and state come first and the options are listed last. Each option is scored from the hidden state at the end of its own line.
• Loss: RLCD \u2014 a policy gradient on strictly proper scoring rules (log + 0.5 \u00d7 spherical, minus the ranked probability score on ordinal questions), with no cross-entropy term. Eight noisy copies of the logits are scored per step and exploration noise decays from 1.0 to 0.3. The option order is shuffled at random.
• Data:
  • A-OKVQA: 17k questions, choice
  • ScienceQA image subset: 6k questions, choice
  • VQAv2 yes/no: 50k questions, noul
  • Datasets were sampled equally — about 72k samples drawn from each — so the smaller sets repeat more: ScienceQA made 12.2 passes, A-OKVQA 4.3 and VQAv2 1.5. No per-dataset cap was applied.
• Schedule: 6,783 steps at batch 32 on one A100 (27.8 minutes training plus 6.8 evaluating). Head LR 1.4e-4, backbone LR 2.8e-5, 3% warmup, then cosine decay. The checkpoint with the best mean validation accuracy was kept — step 5,650, epoch 2.5; the third epoch made validation accuracy worse.
• Calibration: per-type temperatures fitted on the last 300 training records of each dataset, which were held out of training: choice 3.15, noul 1.56.
• training_metrics.json has every evaluation from the run.

Limitations

• score questions are untrained. There was no ordinal image data, so their outputs are meaningless.
• A-OKVQA overfits. Training accuracy reaches 94.7% at the kept checkpoint against 63.1% on validation, and raw confidence is too high there, so rely on the calibrated outputs.
• ScienceQA repeats most. At 12.2 passes its training accuracy reaches 99.3%, so treat its validation number as the most optimistic of the three.
• Narrow domain. The model was trained on everyday photos (COCO) and science diagrams. Expect to fine-tune on your own data for other domains.
• One image per state. Each image is resized to a single 512 px tile.
• Text-only states work but are unevaluated. This model is not a drop-in replacement for Laya's text checkpoint.
• The action field is not trained. Every answer carries result["answers"][qid]["action"]["act_probability"] from the act/escalate head. That head enters the loss only as 0.0 * act.sum(), so it receives exactly zero gradient and reports its random initialisation. The value still varies with the input — the confidence features feeding it are real, the weights reading them are not — so it looks like a calibrated gate and is not one. Ignore it; do not gate on it.

License

The weights are released under CC BY-NC-SA 4.0, because they were trained partly on ScienceQA, which uses that license. The base model, SmolVLM, is Apache 2.0. A-OKVQA is Apache 2.0. VQAv2 annotations are CC BY 4.0, and its COCO images carry Flickr terms. The code is Apache 2.0.