---
license: apache-2.0
language:
- en
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- multimodal
- image caption
- captioning
datasets:
- internlm/CapRL-2M
base_model:
- OpenGVLab/InternVL3_5-8B
---
# CapRL-InternVL3.5-8B
πPaper | π Github |π€CapRL-3B Model |π€CapRL-InternVL3.5-8B Model |
π€CapRL-2M Dataset
π€CapRL Collection | π€Daily Paper ο½π€CapRL-3B-GGUF ο½π€CapRL-3B-i1-GGUF
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|
|-|-|-|
|π€[CapRL-3B](https://huggingface.co/internlm/CapRL-3B)|3B|Speed, Efficiency|
|π€[CapRL-InternVL3.5-8B](https://huggingface.co/yuhangzang/CapRL-InternVL3.5-8B)|8B|High Performance, Advanced Captioning Ability|
Now you can try out CapRL-3B with your own imagesπ¨! β‘οΈ [πCapRL Space](https://huggingface.co/spaces/yuhangzang/caprl)
## π’ News
We are working on even stronger base models and upgrading our training recipe β stay tuned!
- π₯ [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!
- π [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!
- π [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.
- π [10/15/2025] We release [QA curation code](https://github.com/InternLM/CapRL).
- π [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
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.
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.
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 [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.
## 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-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:
```bash
vllm serve "/PATH/CapRL-InternVL3.5-8B" \
--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
