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Model: maveryn/trace-qwen2.5-vl-7b
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- en
+base_model:
+- Qwen/Qwen2.5-VL-7B-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 7B
+
+TRACE Qwen2.5-VL 7B is a GRPO checkpoint derived from
+[`Qwen/Qwen2.5-VL-7B-Instruct`](https://huggingface.co/Qwen/Qwen2.5-VL-7B-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-7b.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-7B-Instruct@cc594898137f460bfe9f0759e9844b3ce807cfb5` |
+| 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-7B-Instruct | 47.93 ± 0.30 |
+| TRACE Qwen2.5-VL 7B | **51.99 ± 0.17** |
+| VERO Qwen2.5-VL 7B | 52.22 ± 0.07 |
+| Game-RL Qwen2.5-VL 7B | 48.05 ± 0.45 |
+| Sphinx Qwen2.5-VL 7B | 49.34 ± 0.15 |
+| PCGRPO Qwen2.5-VL 7B | 48.80 ± 0.28 |
+| Paired improvement | **+4.06 ± 0.41** |
+
+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-7b"
+revision = "4d0f1ae8ee25022058090dbdbff61957ece7331d"
+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 released under
+[Apache-2.0](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct/blob/main/LICENSE),
+consistent with the upstream base model.