Upload README.md

README.md

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 ---
 license: apache-2.0
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+
+# ColonR1
+
+<p align="center">
+    <img src="./assets/ColonR1.jpg"/> <br />
+    <em> 
+    Figure 1: Details of our colonoscopy-specific reasoning model, ColonR1.
+    </em>
+</p>
+
+
+📖 [Paper](https://arxiv.org/abs/2512.03667) | 🏠 [Home](https://github.com/ai4colonoscopy/Colon-X) 
+
+
+# Quick start
+
+Below is a code snippet to help you quickly try out our ColonR1 model using Hugging Face Transformers. For convenience, we manually combined some configuration and code files. Please note that this is a quick code, we recommend you using a source code to explore more.
+
+- Before running the snippet, you need to install the following minimum dependencies.
+
+	```shell
+	conda create -n quickstart python=3.10
+	conda activate quickstart
+	pip install torch transformers accelerate pillow
+	```
+
+- Then you can use `python ColonR1/quickstart.py` to run it, as shown in the following code.
+
+	```python
+	import torch
+	from transformers import AutoProcessor, Qwen2_5_VLForConditionalGeneration
+	from PIL import Image
+	import warnings
+	import os
+
+	warnings.filterwarnings('ignore')
+	device = "cuda" if torch.cuda.is_available() else "cpu"
+
+	MODEL_PATH = "ai4colonoscopy/ColonR1"
+	IMAGE_PATH = "assets/example.jpg"
+	Question = "Does the image contain a polyp? Answer me with Yes or No."
+
+	print(f"[Info] Loading model from {MODEL_PATH}...")
+	model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
+		MODEL_PATH,
+		torch_dtype=torch.bfloat16,
+		attn_implementation="flash_attention_2",
+		device_map="auto"
+	)
+	model.eval()
+
+	processor = AutoProcessor.from_pretrained(MODEL_PATH)
+
+	if not os.path.exists(IMAGE_PATH):
+		raise FileNotFoundError(f"Image not found at {IMAGE_PATH}. Please provide a valid image path.")
+
+	image = Image.open(IMAGE_PATH).convert("RGB")
+
+	TASK_SUFFIX = (
+		"Your task: 1. First, Think through the question step by step, enclose your reasoning process "
+		"in <think>...</think> tags. 2. Then provide the correct answer inside <answer>...</answer> tags. "
+		"3. No extra information or text outside of these tags."
+	)
+
+	final_question = f"{Question}\n{TASK_SUFFIX}"
+
+	messages = [
+		{
+			"role": "user",
+			"content": [
+				{"type": "image", "image": IMAGE_PATH},
+				{"type": "text", "text": final_question},
+			],
+		}
+	]
+
+	print("[Info] Processing inputs...")
+	text_prompt = processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+
+	inputs = processor(
+		text=[text_prompt],
+		images=[image],
+		padding=True,
+		return_tensors="pt",
+	).to(device)
+
+
+	print("[Info] Generating response...")
+	with torch.no_grad():
+		generated_ids = model.generate(
+			**inputs,
+			max_new_tokens=1024,
+			do_sample=False
+		)
+
+	generated_ids_trimmed = generated_ids[:, inputs.input_ids.shape[1]:]
+	output_text = processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True)[0]
+
+	print(output_text)
+	```
+    
+# License
+This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses.
+The content of this project itself is licensed under the Apache license 2.0.