Instructions to use Kimhi/AWARES-Qwen2.5-VL-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kimhi/AWARES-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="Kimhi/AWARES-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, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("Kimhi/AWARES-Qwen2.5-VL-7B")
model = AutoModelForImageTextToText.from_pretrained("Kimhi/AWARES-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

vLLM

How to use Kimhi/AWARES-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 "Kimhi/AWARES-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": "Kimhi/AWARES-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/Kimhi/AWARES-Qwen2.5-VL-7B

SGLang

How to use Kimhi/AWARES-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 "Kimhi/AWARES-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": "Kimhi/AWARES-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 "Kimhi/AWARES-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": "Kimhi/AWARES-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 Kimhi/AWARES-Qwen2.5-VL-7B with Docker Model Runner:
```
docker model run hf.co/Kimhi/AWARES-Qwen2.5-VL-7B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

AWARES-Qwen2.5-VL-7B

Paper | Code

AWARES (Adaptive Resolution with Active REqueStingS) is a fine-tuned Qwen2.5-VL-7B-Instruct model trained with GRPO (Group Relative Policy Optimization) to intelligently request high-resolution image crops when needed to answer visual questions.

How It Works

Given a low-resolution image and a question, the model decides whether it needs more visual detail. If so, it emits a GET_CROPS tool call specifying which region(s) to zoom into. The high-res crops are then provided back, and the model produces its final answer.

Crop regions — the model can request any of 9 predefined crop indices:

CROPS_MAP = {
    '0': 'top-left', '1': 'top-right', '2': 'bottom-left', '3': 'bottom-right',
    '4': 'center', '5': 'top', '6': 'bottom', '7': 'left', '8': 'right',
}

Quick Start

from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor

model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
    "Kimhi/AWARES-Qwen2.5-VL-7B", torch_dtype="auto", device_map="auto"
)
processor = AutoProcessor.from_pretrained("Kimhi/AWARES-Qwen2.5-VL-7B")

For full usage examples (multi-turn inference with GET_CROPS, crop extraction, etc.) and evaluation scripts, see the AwaRes repository.

Evaluation

We provide a custom lmms-eval model type (qwen2_5_vl_awares) that handles the full AWARES multi-turn pipeline automatically — including low-res input, GET_CROPS parsing, crop extraction, and second-turn generation with KV-cache reuse.

See the evaluation instructions in the AwaRes repository for setup and benchmarking details.

Training Details

Base model: Qwen2.5-VL-7B-Instruct
Training method: GRPO (Group Relative Policy Optimization) with LoRA, then merged
Reward signals: Text similarity, crop cost penalty, LLM-as-a-judge
Framework: Custom TRL fork + DeepSpeed ZeRO-2

Citation

@article{shabtay2026awares,
  title={Look Where It Matters: High-Resolution Crops Retrieval for Efficient VLMs},
  author={Shabtay, Nimrod and Kimhi, Moshe and Spector, Artem and Haray, Sivan and Rivlin, Ehud and Baskin, Chaim and Giryes, Raja and Schwartz, Eli},
  journal={arXiv preprint arXiv:2603.16932},
  year={2026}
}