Update README.md

README.md

@@ -7,6 +7,11 @@ library_name: transformers
 tags:
 - multimodal
 - image caption
+- captioning
+datasets:
+- internlm/CapRL-2M
+base_model:
+- OpenGVLab/InternVL3_5-8B
 ---
 
 
@@ -20,21 +25,17 @@ tags:
 
 ## 📢 News
 We are working on even stronger base models and upgrading our training recipe — stay tuned!
-- 🔥 [10/15/2025] The total downloads of the CapRL-related model and dataset reached 6,000 within just 20 days!
-- 🚀 [10/15/2025] We are excited to announce the release of **CapRL-InternVL3.5-8B**, whose image captioning capability outperforms Qwen2.5-VL-72B!
-- 🚀 [10/15/2025] We release QA curation code.
-- 🚀 [09/25/2025] We release **CapRL** repository, model, evaluation code and dataset.
-  
-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.
-
-
-CapRL-3B-GGUF is static quants version, and CapRL-3B-i1-GGUF is weighted/imatrix quants version. Thanks for their contribution!
+- 🔥 [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 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, a lightweight 3B image captioner that achieves perception capabilities comparable to Qwen2.5-VL-72B.
+Based on the same recipe as [CapRL-3B](https://huggingface.co/internlm/CapRL-3B), we used [InternVL3.5-8B](https://huggingface.co/OpenGVLab/InternVL3_5-8B) as the policy model and obtained **[CapRL-InternVL3.5-8B](https://huggingface.co/yuhangzang/CapRL-InternVL3.5-8B)** through CapRL.
 
-This is the first study of applying Reinforcement Learning with Verifiable Rewards for the
+CapRL 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.
@@ -43,8 +44,8 @@ stage uses LVLMs to generate rich and accurate captions. Subsequently, the secon
 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.
+By employing the CapRL training framework, initializing with the [InternVL3.5-8B](https://huggingface.co/OpenGVLab/InternVL3_5-8B) model, and using a carefully 
+filtered 75K QA dataset as the training set, we obtained a highly capable captioner, CapRL-InternVL3.5-8B.
 
 <p align="center">
   <img src="./assets/teaser.png"  width="750"/>
@@ -59,12 +60,11 @@ filtered 75K QA dataset as the training set, we obtained a highly capable captio
 * **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).
+If you want to use **CapRL-InternVL3.5-8B** for captioning, you can directly follow the exact same inference approach as in [InternVL-3.5-series](https://huggingface.co/collections/internlm/internvl35-68ab285d4a1f0871ddcb75b2).
 
 We recommend using **vLLM** to speed up inference.
 
 
-
 ### Start an OpenAI API Service
 
 Run the command below to start an OpenAI-compatible API service: