Update README.md

README.md

@@ -7,4 +7,128 @@ library_name: transformers
 tags:
 - multimodal
 - image caption
----
+---
+
+
+
+# CapRL-3B
+
+📖<a href="https://arxiv.org/abs/2509.22647">Paper</a> | 🏠<a href="https://github.com/InternLM/CapRL">Github</a> |🤗<a href="https://huggingface.co/internlm/CapRL-3B">CapRL-3B Model</a> |🤗<a href="https://huggingface.co/yuhangzang/CapRL-InternVL3.5-8B">CapRL-InternVL3.5-8B Model</a> |
+  🤗<a href="https://huggingface.co/datasets/internlm/CapRL-2M">CapRL-2M Dataset</a> 
+  
+  🤗<a href="https://huggingface.co/collections/long-xing1/caprl-68d64ac32ded31596c36e189">CapRL Collection</a> | 🤗<a href="https://huggingface.co/papers/2509.22647">Daily Paper</a>
+
+Based on the same recipe as CapRL-3B, we used InternVL3.5-8B as the policy model and obtained CapRL-InternVL3.5-8B through CapRL. **Its performance significantly surpasses that of Qwen2.5-VL-72B**.
+
+We are working on even stronger base models and upgrading our training recipe — stay tuned!
+
+
+## Introduction
+We are excited to introduce CapRL-3B, a lightweight 3B image captioner that achieves perception capabilities comparable to Qwen2.5-VL-72B.
+
+This is the first study of applying Reinforcement Learning with Verifiable Rewards for the
+open-ended and subjective image captioning task. Unlike traditional Supervised Fine-Tuning, which
+can lead to models memorizing a limited set of annotated captions, our method allows the model to
+explore and generate a broader range of creative and general descriptions.
+CapRL is a new training paradigm featuring a decoupled two-stage pipeline. The initial
+stage uses LVLMs to generate rich and accurate captions. Subsequently, the second stage evaluates
+caption quality by using a vision-only LLM to perform the QA task. We also created a specific QA
+curation pipeline to ensure the quality of the questions and answers used for the second stage.
+
+By employing CapRL training framework, initializing with the Qwen2.5-VL-3B model, and using a carefully 
+filtered 75K QA dataset as the training set, we obtained a highly capable captioner, CapRL-3B.
+
+<p align="center">
+  <img src="./assets/teaser.png"  width="750"/>
+</p>
+<p align="center">
+  <img src="./assets/performance_update.png" width="750"/>
+</p>
+
+## Key Features
+* **Remarkable visual understanding for Chart, Infographics and Document**: CapRL-3B achieves perception accuracy and visual information coverage comparable to Qwen2.5-VL-72B.
+* **Well-organized output**: The outputs of CapRL-3B are relatively well-structured, making them clear and easy to understand.
+* **Detailed description for natural images**: The outputs of CapRL-3B can perfectly cover all valid visual information while containing fewer hallucinations.
+
+## Usage
+If you want to use **CapRL-3B** for captioning, you can directly follow the exact same inference approach as in [Qwen2.5-VL-series](https://github.com/QwenLM/Qwen3-VL/tree/d2240f11656bfe404b9ba56db4e51cd09f522ff1).
+
+We recommend using **vLLM** to speed up inference.
+
+
+
+### Start an OpenAI API Service
+
+Run the command below to start an OpenAI-compatible API service:
+
+```bash
+vllm serve "/PATH/CapRL-3B" \
+    --trust-remote-code \
+    --tensor-parallel-size=1 \
+    --pipeline-parallel-size=1 \
+    --gpu_memory_utilization=0.95 \
+    --served-model-name=caprl \
+    --port 8000 \
+    --host 0.0.0.0
+```
+
+Then you can use the chat API as below: (see [OpenAI API protocol document](https://platform.openai.com/docs/guides/vision/uploading-base-64-encoded-images) for more details):
+```python
+import base64
+from openai import OpenAI
+# Set OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+image_path = "/path/to/local/image.png"
+with open(image_path, "rb") as f:
+    encoded_image = base64.b64encode(f.read())
+encoded_image_text = encoded_image.decode("utf-8")
+base64_qwen = f"data:image;base64,{encoded_image_text}"
+chat_response = client.chat.completions.create(
+    model="caprl",
+    messages=[
+        {"role": "system", "content": "You are a helpful assistant."},
+        {
+            "role": "user",
+            "content": [
+                {
+                    "type": "image_url",
+                    "image_url": {
+                        "url": base64_qwen
+                    },
+                },
+                {"type": "text", "text": "What is the text in the illustrate?"},
+            ],
+        },
+    ],
+    temperature=1.0,
+    max_tokens=max_tokens,
+    top_p=1.0,
+    extra_body={
+        "repetition_penalty": 1.0,
+        },
+)
+print("Chat response:", chat_response)
+```
+
+
+
+## Cases
+<p align="center">
+  <img src="./assets/comparison.png" alt="Main Results on GPT2" width="750"/>
+</p>
+
+<p align="center">
+  <img src="./assets/info_caprl.png" alt="Main Results on GPT2" width="750"/>
+</p>
+
+<p align="center">
+  <img src="./assets/info_caprl2.png" alt="Main Results on GPT2" width="750"/>
+</p>
+<p align="center">
+  <img src="./assets/natural_caprl.png" alt="Main Results on GPT2" width="750"/>
+</p>