| | --- |
| | language: |
| | - en |
| | library_name: transformers |
| | license: apache-2.0 |
| | pipeline_tag: image-text-to-text |
| | tags: |
| | - multimodal |
| | - image caption |
| | - captioning |
| | datasets: |
| | - internlm/CapRL-2M |
| | --- |
| | |
| | # CapRL |
| | 📖<a href="https://arxiv.org/abs/2509.22647">Paper</a> | 🏠<a href="https://github.com/InternLM/CapRL">Github</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> |
| |
|
| | ### CapRL Series Model & Dataset |
| | | Series | Models & Resources | |
| | | :--- | :--- | |
| | | **CapRL 2.0 Series** | [🤗 CapRL-Qwen3VL-2B](https://huggingface.co/internlm/CapRL-Qwen3VL-2B) \| [🤗 CapRL-Qwen3VL-4B](https://huggingface.co/internlm/CapRL-Qwen3VL-4B) \| [📦 CapRL-Qwen3VL-2B-GGUF](https://huggingface.co/internlm/CapRL-Qwen3VL-2B-GGUF) \| [📦 CapRL-Qwen3VL-4B-GGUF](https://huggingface.co/internlm/CapRL-Qwen3VL-4B-GGUF) \| [🌈CapRL-Qwen3VL-4B Space](https://huggingface.co/spaces/yuhangzang/CapRL-Qwen3VL-4B) |
| | | **CapRL 1.0 Series** | [🤗 CapRL-Qwen2.5VL-3B](https://huggingface.co/internlm/CapRL-3B) \| [🤗 CapRL-InternVL3.5-8B](https://huggingface.co/yuhangzang/CapRL-InternVL3.5-8B) \| [📊 CapRL-2M Dataset](https://huggingface.co/datasets/internlm/CapRL-2M) \| [📦 CapRL-3B-GGUF](https://huggingface.co/mradermacher/CapRL-3B-GGUF) \| [📦 CapRL-3B-i1-GGUF](https://huggingface.co/mradermacher/CapRL-3B-i1-GGUF) \| [🌈CapRL-Qwen2.5VL-3B Space](https://huggingface.co/spaces/yuhangzang/caprl) |
| |
|
| | ### CapRL-Qwen3VL-4B |
| | We are excited to release the **CapRL 2.0 series**: **CapRL-Qwen3VL-2B** and **CapRL-Qwen3VL-4B**. These models feature fewer parameters while delivering even more powerful captioning performance. |
| | Notably, **CapRL-Qwen3VL-4B significantly outperforms both CapRL-Qwen2.5VL-3B and Qwen2.5VL-72B in captioning tasks**, establishing itself as the top-performing model for captioning within the CapRL series. |
| | This leap in efficiency is driven by our upgraded training recipe, which includes a more rigorous QA data filter and a significantly more diverse image dataset. We welcome everyone to try them out! |
| |
|
| |
|
| | When selecting between the available CapRL models, it's essential to consider the trade-off between performance and computational cost. |
| | This guide will help you choose the most suitable model for your specific needs: |
| | |Model|Parameters|Strength| |
| | |-|-|-| |
| | |🤗[CapRL-Qwen3VL-2B](https://huggingface.co/internlm/CapRL-Qwen3VL-2B)|2B|Speed, Efficiency| |
| | |🤗[CapRL-Qwen3VL-4B](https://huggingface.co/internlm/CapRL-Qwen3VL-4B)|4B|High Performance, Advanced Captioning Ability| |
| |
|
| | Now you can try out CapRL with your own images🎨! ➡️ [🌈CapRL-Qwen2.5VL-3B Space](https://huggingface.co/spaces/yuhangzang/caprl) and [🌈CapRL-Qwen3VL-4B Space](https://huggingface.co/spaces/yuhangzang/CapRL-Qwen3VL-4B). |
| |
|
| | ## 📢 News |
| | We are working on even stronger base models and upgrading our training recipe — stay tuned! |
| | - 🔥 [12/24/2025] We are excited to release the CapRL 2.0 series: **[CapRL-Qwen3VL-2B](https://huggingface.co/internlm/CapRL-Qwen3VL-2B)** and **[CapRL-Qwen3VL-4B](https://huggingface.co/internlm/CapRL-Qwen3VL-4B)**! |
| | - 🔥 [12/24/2025] The total downloads of the CapRL-related [models and dataset](https://huggingface.co/collections/long-xing1/caprl-68d64ac32ded31596c36e189) reached 17,000! |
| | - 🔥 [10/15/2025] The total downloads of the CapRL-related [models and dataset](https://huggingface.co/collections/long-xing1/caprl-68d64ac32ded31596c36e189) reached 6,000 within just 20 days! |
| | - 🚀 [10/15/2025] We are excited to announce the release of **[CapRL-InternVL3.5-8B](https://huggingface.co/internlm/CapRL-InternVL3.5-8B)**, whose image captioning capability outperforms Qwen2.5-VL-72B! |
| | - 🚀 [10/15/2025] Thanks [mradermacher](https://huggingface.co/mradermacher) for the valuable contribution! [CapRL-3B-GGUF](https://huggingface.co/mradermacher/CapRL-3B-GGUF) is the static quants version, and [CapRL-3B-i1-GGUF](https://huggingface.co/mradermacher/CapRL-3B-i1-GGUF) is weighted/imatrix quants version. |
| | - 🚀 [10/15/2025] We release [QA curation code](https://github.com/InternLM/CapRL). |
| | - 🚀 [09/25/2025] We release **CapRL** repository, [CapRL-3B model](https://huggingface.co/internlm/CapRL-3B), [evaluation code](https://github.com/InternLM/CapRL) and [dataset](https://huggingface.co/datasets/internlm/CapRL-2M). |
| |
|
| | ## Introduction of CapRL |
| | We are excited to introduce [CapRL-3B](https://huggingface.co/internlm/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 the 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](https://huggingface.co/internlm/CapRL-3B). |
| |
|
| | <p align="center"> |
| | <img src="./assets/teaser.png" width="750"/> |
| | </p> |
| | <p align="center"> |
| | <img src="./assets/performance_caprl2_0.png" width="750"/> |
| | </p> |
| |
|
| | ## Key Features |
| | * **Remarkable visual understanding for Chart, Infographics and Document**: [CapRL-3B](https://huggingface.co/internlm/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](https://huggingface.co/internlm/CapRL-3B) can perfectly cover all valid visual information while containing fewer hallucinations. |
| |
|
| | ## Usage |
| | If you want to use **[CapRL-3B](https://huggingface.co/internlm/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" width="750"/> |
| | </p> |
| |
|
| | <p align="center"> |
| | <img src="./assets/info_caprl.png" width="750"/> |
| | </p> |
| |
|
| | <p align="center"> |
| | <img src="./assets/info_caprl2.png" width="750"/> |
| | </p> |
| | <p align="center"> |
| | <img src="./assets/natural_caprl.png" width="750"/> |
| | </p> |
