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Model Details
Model Provider
maveryn
Model Tree
Base
Qwen/Qwen2.5-VL-7B-Instruct
Fine-tuned
this model
Input Modalities
Text
Image
Output Modalities
Text
Supported Functionality
Dedicated Endpoints
Container
| 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.
The canonical output ends with {"answer": ...}. Exact hashes, source
revision, and run provenance are recorded in
trace_training_provenance.json and
.trace_model_revision.json. The repository
does not include optimizer, scheduler, trainer, or FSDP state for continuation.
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
and evaluation documentation.
python
from transformers import AutoProcessor, Qwen2_5_VLForConditionalGenerationmodel_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])
The pinned revision is the checkpoint used by the canonical evaluation; later repository heads may update documentation without changing the model weights.
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.
This checkpoint is released under Apache-2.0, consistent with the upstream base model.