---
license: other
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
language:
- en
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
datasets:
- maveryn/trace
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- multimodal
- visual-reasoning
- reinforcement-learning
- grpo
- rlvr
- trace
---

# TRACE Qwen2.5-VL 3B

TRACE Qwen2.5-VL 3B is a GRPO checkpoint derived from
[`Qwen/Qwen2.5-VL-3B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct)
and trained on 64,000 grounded visual-reasoning examples spanning 1,000 tasks
from
[`maveryn/trace`](https://huggingface.co/datasets/maveryn/trace). This
repository contains the merged step-500 inference checkpoint.

[Paper](https://arxiv.org/abs/2607.19790) ·
[Project page](https://maveryn.github.io/trace/) ·
[GitHub](https://github.com/maveryn/trace) ·
[Collection](https://huggingface.co/collections/maveryn/trace-6a604291b4be4ed6399b9f24) ·
[Training configuration](https://github.com/maveryn/trace/blob/rlvr/rlvr/configs/trace-qwen2.5-vl-3b.yaml) ·
[Evaluation suite](https://github.com/maveryn/trace/tree/rlvr/rlvr/evaluation/trace_eval) ·
[Run artifacts](https://huggingface.co/datasets/maveryn/trace-eval-runs)

## Training and provenance

| Field | Value |
| --- | --- |
| Base model | `Qwen/Qwen2.5-VL-3B-Instruct@66285546d2b821cf421d4f5eb2576359d3770cd3` |
| Dataset | `maveryn/trace@4e5b54361360296a855542b40cfd8b7f81b355fe` |
| Method | GRPO, 500 steps, global/rollout batch 128, 8 responses per prompt |
| Optimization | AdamW, learning rate `1e-6`, constant schedule, KL disabled |
| Reward | `0.95 × exact JSON answer + 0.05 × valid JSON format`; annotation reward `0` |
| Reference run | 8 NVIDIA H100 80GB GPUs |
| Released artifact | Merged step-500 inference weights |

The released training profile reads `prompt_answer`, scores `answer_gt`, and
does not use the advisory `trace_supervision_mode` column. The consumed fields,
embedded image bytes, and row order in the current dataset release were
verified identical to the original training input in the public
[equivalence receipt](https://github.com/maveryn/trace/blob/rlvr/rlvr/dataset_equivalence.v1.json).

The canonical output ends with `{"answer": ...}`. Exact hashes, source
revision, and run provenance are recorded in
[`trace_training_provenance.json`](trace_training_provenance.json) and
[`.trace_model_revision.json`](.trace_model_revision.json). The repository
does not include optimizer, scheduler, trainer, or FSDP state for continuation.

## Evaluation

`trace_eval_v1` evaluates 24 external benchmarks and 32,805 examples per model
and decoding seed. Scores below are the unweighted macro mean of the 24
benchmark percentages, reported as mean ± sample standard deviation across
seeds 42, 43, and 44.

| Model | Overall score |
| --- | ---: |
| Qwen2.5-VL-3B-Instruct | 39.34 ± 0.63 |
| TRACE Qwen2.5-VL 3B | **42.85 ± 0.39** |
| Paired improvement | **+3.51 ± 0.25** |

Per-benchmark scores, model revisions, aggregation, and benchmark provenance
are available in the public
[`results.json`](https://github.com/maveryn/trace/blob/rlvr/rlvr/evaluation/trace_eval/results.json)
and [evaluation documentation](https://github.com/maveryn/trace/blob/rlvr/rlvr/evaluation/trace_eval/README.md).

## Usage

```python
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration

model_id = "maveryn/trace-qwen2.5-vl-3b"
revision = "2ec2374d5c219e6b12e26bda93d3b3adeb1e30c5"
image_url = "https://raw.githubusercontent.com/maveryn/trace/main/docs/assets/paper-domain-montage/trace-paper-domain-montage.png"

processor = AutoProcessor.from_pretrained(model_id, revision=revision)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    model_id,
    revision=revision,
    dtype="auto",
    device_map="auto",
)

messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": image_url},
            {"type": "text", "text": "How many visual domains are shown with example images? Respond with only a JSON object using the key \"answer\"."},
        ],
    }
]
inputs = processor.apply_chat_template(
    messages,
    tokenize=True,
    add_generation_prompt=True,
    return_dict=True,
    return_tensors="pt",
).to(model.device)
output_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids = [
    output[len(prompt) :] for prompt, output in zip(inputs.input_ids, output_ids)
]
print(processor.batch_decode(generated_ids, skip_special_tokens=True)[0])
```

### Verified reference answer

For the published montage used above, the expected output is:

```json
{"answer": 11}
```

The value is backed by the committed
[montage manifest](https://github.com/maveryn/trace/blob/main/docs/gallery/paper-domain-montage.v1.json)
(`layout.panel_count`). This is reference ground truth, not a claim about a
particular decoding run; publish qualitative model outputs only with their
recorded inference settings.

The pinned revision is the checkpoint used by the canonical evaluation; later
repository heads may update documentation without changing the model weights.

## Intended use and limitations

This checkpoint is intended for research on multimodal reasoning and
verifiable-reward post-training. It can produce incorrect answers,
unsupported reasoning, or unreliable grounding. It was trained on synthetic
tasks and has not been validated for safety-critical, medical, legal, or
autonomous decision-making uses. Results depend on the recorded prompts,
decoding, parsers, and scorers.

## License

This checkpoint is subject to the upstream
[Qwen Research License](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE).