Instructions to use maveryn/trace-qwen2.5-vl-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maveryn/trace-qwen2.5-vl-7b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="maveryn/trace-qwen2.5-vl-7b")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("maveryn/trace-qwen2.5-vl-7b")
model = AutoModelForMultimodalLM.from_pretrained("maveryn/trace-qwen2.5-vl-7b", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use maveryn/trace-qwen2.5-vl-7b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maveryn/trace-qwen2.5-vl-7b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maveryn/trace-qwen2.5-vl-7b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/maveryn/trace-qwen2.5-vl-7b

SGLang

How to use maveryn/trace-qwen2.5-vl-7b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "maveryn/trace-qwen2.5-vl-7b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maveryn/trace-qwen2.5-vl-7b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "maveryn/trace-qwen2.5-vl-7b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maveryn/trace-qwen2.5-vl-7b",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use maveryn/trace-qwen2.5-vl-7b with Docker Model Runner:
```
docker model run hf.co/maveryn/trace-qwen2.5-vl-7b
```

TRACE Qwen2.5-VL 7B

TRACE Qwen2.5-VL 7B is a GRPO checkpoint derived from Qwen/Qwen2.5-VL-7B-Instruct and trained on 64,000 grounded visual-reasoning examples spanning 1,000 tasks from maveryn/trace. This repository contains the merged step-500 inference checkpoint.

Paper · Project page · GitHub · Collection · Training configuration · Evaluation suite · Run artifacts

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.

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.

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 and evaluation documentation.

Usage

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])

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.