Instructions to use Qianxingyu/ActiveCTReasoner-7B-RL with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Qianxingyu/ActiveCTReasoner-7B-RL with Transformers:

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

pipe = pipeline("image-text-to-text", model="Qianxingyu/ActiveCTReasoner-7B-RL")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Qianxingyu/ActiveCTReasoner-7B-RL")
model = AutoModelForMultimodalLM.from_pretrained("Qianxingyu/ActiveCTReasoner-7B-RL", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Qianxingyu/ActiveCTReasoner-7B-RL with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Qianxingyu/ActiveCTReasoner-7B-RL"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Qianxingyu/ActiveCTReasoner-7B-RL",
		"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/Qianxingyu/ActiveCTReasoner-7B-RL

SGLang

How to use Qianxingyu/ActiveCTReasoner-7B-RL 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 "Qianxingyu/ActiveCTReasoner-7B-RL" \
    --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": "Qianxingyu/ActiveCTReasoner-7B-RL",
		"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 "Qianxingyu/ActiveCTReasoner-7B-RL" \
        --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": "Qianxingyu/ActiveCTReasoner-7B-RL",
		"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 Qianxingyu/ActiveCTReasoner-7B-RL with Docker Model Runner:
```
docker model run hf.co/Qianxingyu/ActiveCTReasoner-7B-RL
```

ActiveCTReasoner-7B-RL

GRPO-trained agent, initialised from ActiveCTReasoner-7B-SFT.

Paper: ACTOR: Agentic CT Reasoning with Radiologist-Aligned Actions (EMNLP 2026)
Code: https://github.com/qianxingyucode/ACTOR
Benchmark: Qianxingyu/ACTOR-Bench

This model needs a tool environment

It is not a single-turn VQA model. It emits <tool_call> and expects <tool_response> with a fresh rendered slice, looping until it emits <answer>. Serving the weights and showing one image will score far below the table above, because that is a different task. The environment — the five radiologist-aligned tools, the window presets, the bounding-box overlay and the prior-scan alignment — ships in the code repository as actor/env/simulator.py.

vllm serve Qianxingyu/ActiveCTReasoner-7B-RL --port 8100 \
    --limit-mm-per-prompt image=35 --max-model-len 32768 --trust-remote-code
python -m benchmark.eval.eval_benchmark

Keep the image cap at or above the evaluator's --max-context-images (default 30): a DualSource episode runs up to 18 tool rounds and every observation adds a slice, so a lower cap makes the server reject precisely the longest episodes. 35 is what we evaluated with.

Not for clinical use

Research artefact only, evaluated on CT-RATE validation scans. It has not been validated for diagnosis and must not inform patient care. It can miss findings and can state measurements confidently that are wrong.

Licence

CC BY-NC-SA 4.0, non-commercial. The weights derive from training on CT-RATE-derived observations, so the data licence propagates here even though the ACTOR code itself is Apache-2.0.

CT-RATE is gated, and its Terms and Conditions section 5 state:

Redistribution of the dataset or any portion thereof is not allowed.

That is why no CT volumes or rendered slices are published with these weights, and why the evaluation data lives in Qianxingyu/ACTOR-Bench as text plus a render manifest that you replay locally against your own CT-RATE download.

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for Qianxingyu/ActiveCTReasoner-7B-RL

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Finetuned

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