--- title: CapRL-3B canonical_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/CapRL-3B" md_url: "https://www.modelscope.cn/models/Shanghai_AI_Laboratory/CapRL-3B.md" repository: Shanghai_AI_Laboratory/CapRL-3B last_updated: 2026-04-16 license: apache-2.0 pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - qwen2_5_vl architectures: - Qwen2_5_VLForConditionalGeneration parameters: 3.8B tensor_type: - BF16 library_name: - safetensors - pytorch frameworks: - pytorch language: - en inference_backends: - "deploy_task vlm/text/emb" - "lmdeploy 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 235 stars: 0 tags: - multimodal - "image caption" - captioning --- # CapRL-3B > CapRL-3B - Shanghai_AI_Laboratory 在 ModelScope 开源的模型。CapRL 📖 Paper | 🏠 Github | 🤗 CapRL Collection | 🤗 Daily Paper Shanghai_AI_Laboratory/CapRL-3B 是 ModelScope 魔搭社区上的 3.8B 参数image-text-to-text模型，采用 apache-2.0 许可，可用 deploy_task vlm/text/emb、lmdeploy 0.9.1、sglang 0.5.2 部署。 - **Repository**: Shanghai_AI_Laboratory/CapRL-3B - **License**: apache-2.0 - **Tasks**: image-text-to-text - **Parameters**: 3.8B - **Inference backends**: deploy_task vlm/text/emb, lmdeploy 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Tags**: multimodal, image caption, captioning - **Downloads**: 235 - **Stars**: 0 - **Last updated**: 2026-04-16 Source: https://www.modelscope.cn/models/Shanghai_AI_Laboratory/CapRL-3B --- # CapRL 📖Paper | 🏠Github | 🤗CapRL Collection | 🤗Daily Paper ### 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-QA-75K Dataset](https://huggingface.co/datasets/internlm/CapRL-QA-75K) \| [📊 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) 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-2B outperforms both CapRL-Qwen2.5VL-3B and Qwen2.5VL-72B in captioning tasks**. 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! ## CapRL-3B Now you can try out CapRL-3B with your own images🎨!    ➡️    [🌈CapRL Space](https://huggingface.co/spaces/yuhangzang/caprl) 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-3B](https://huggingface.co/internlm/CapRL-3B)|3B|Speed, Efficiency| |🤗[CapRL-InternVL3.5-8B](https://huggingface.co/yuhangzang/CapRL-InternVL3.5-8B)|8B|High Performance, Advanced Captioning Ability| ## 📢 News We are working on even stronger base models and upgrading our training recipe — stay tuned! - 🔥 [04/16/2026] We have released the **[CapRL-QA-75K](https://huggingface.co/datasets/internlm/CapRL-QA-75K)** training dataset! - 🔥 [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 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).

## 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