Instructions to use xupy21/ContextRL_Qwen2.5_VL_7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xupy21/ContextRL_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="xupy21/ContextRL_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("xupy21/ContextRL_Qwen2.5_VL_7B")
model = AutoModelForMultimodalLM.from_pretrained("xupy21/ContextRL_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?"}
        ]
    },
]
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 xupy21/ContextRL_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 "xupy21/ContextRL_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": "xupy21/ContextRL_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/xupy21/ContextRL_Qwen2.5_VL_7B

SGLang

How to use xupy21/ContextRL_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 "xupy21/ContextRL_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": "xupy21/ContextRL_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 "xupy21/ContextRL_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": "xupy21/ContextRL_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 xupy21/ContextRL_Qwen2.5_VL_7B with Docker Model Runner:
```
docker model run hf.co/xupy21/ContextRL_Qwen2.5_VL_7B
```

ContextRL-Qwen2.5-VL-7B

This is the multimodal model released with the paper Context-Aware RL for Agentic and Multimodal LLMs. It is fine-tuned from Qwen2.5-VL-7B-Instruct using ContextRL, a context-aware reinforcement learning method that augments standard GRPO with an auxiliary context-selection objective to improve fine-grained visual grounding.

Results

Across 12 diverse multimodal benchmarks, ContextRL improves over the standard GRPO baseline by +2.0 points on average, while improving every individual benchmark.

Benchmark	Base	RL (GRPO)	ContextRL (Ours)
MathVista	68.2	72.5	73.6
MathVerse	43.9	45.3	49.1
MathVision	22.8	25.5	26.8
MMMU-Pro	36.6	41.3	42.8
MMMU	50.7	53.3	54.6
V*	70.1	70.7	73.3
MMStar	62.6	64.1	65.1
BLINK	55.3	56.5	58.9
ScienceQA	88.2	91.0	95.4
PhyX	25.4	48.7	50.0
OlympiadBench Phy	1.5	3.1	4.6
MME-RealWorld Lite	38.4	45.1	46.7
Overall Avg.	47.0	51.4	53.4

The +2.0 average gain over GRPO also exceeds the +0.8 of PAPO, a method purpose-built for multimodal perception (see the paper for the full comparison).

Usage

This model follows the same interface as Qwen2.5-VL-7B-Instruct and can be loaded with transformers. Training and evaluation code, data construction pipelines, and detailed configurations are available in the repository: 👉 https://github.com/xupy2003/ContextAwareRL Please refer to the repo's README for environment setup, inference scripts, and reproduction instructions.

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Safetensors

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8B params

Tensor type

BF16

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ContextRL Models

Collection

Models of the paper "Context-Aware RL for Agentic and Multimodal LLMs" • 4 items • Updated 14 days ago