Instructions to use xupy21/ContextRL_Qwen3_VL_8B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use xupy21/ContextRL_Qwen3_VL_8B with Transformers:

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

pipe = pipeline("image-text-to-text", model="xupy21/ContextRL_Qwen3_VL_8B")
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("xupy21/ContextRL_Qwen3_VL_8B")
model = AutoModelForMultimodalLM.from_pretrained("xupy21/ContextRL_Qwen3_VL_8B", 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 xupy21/ContextRL_Qwen3_VL_8B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "xupy21/ContextRL_Qwen3_VL_8B"
# 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_Qwen3_VL_8B",
		"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_Qwen3_VL_8B

SGLang

How to use xupy21/ContextRL_Qwen3_VL_8B 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_Qwen3_VL_8B" \
    --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_Qwen3_VL_8B",
		"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_Qwen3_VL_8B" \
        --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_Qwen3_VL_8B",
		"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_Qwen3_VL_8B with Docker Model Runner:
```
docker model run hf.co/xupy21/ContextRL_Qwen3_VL_8B
```

ContextRL-Qwen3-VL-8B

This is the multimodal model released with the paper Context-Aware RL for Agentic and Multimodal LLMs.

It is fine-tuned from Qwen3-VL-8B-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 +1.6 points on average, while improving every individual benchmark.

Benchmark	Base	RL (GRPO)	ContextRL (Ours)
MathVista	75.8	78.7	79.8
MathVerse	56.1	65.0	66.4
MathVision	46.2	49.2	52.0
MMMU-Pro	41.3	55.9	57.5
MMMU	66.4	69.1	70.1
V*	82.2	84.8	85.9
MMStar	70.5	73.5	74.8
BLINK	64.4	65.1	66.6
ScienceQA	94.4	95.6	96.6
PhyX	45.5	72.1	73.4
OlympiadBench Phy	7.9	8.1	9.9
MME-RealWorld Lite	48.7	51.9	54.8
Overall Avg.	58.3	64.1	65.7

Usage

This model follows the same interface as Qwen3-VL-8B-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.