README.md

---
license: apache-2.0
language:
- en
metrics:
- accuracy
base_model:
- Qwen/Qwen3-0.6B
library_name: transformers
tags:
- multi-modal
- large-language-model
- vision-language-model
- vision-encoder
---

<p align="center">
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6258a6455ea3a0a9b6de3f22/mIMYeUFquGSbm89lT61TG.png" width="160" />
</p>


<h2 align="center">Vision Encoder of Penguin-VL</h2>
<h4 align="center">
Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders
</h4>

<h4 align="center">
  <b>Project Page:</b> <a href="https://penguin-vl.github.io">penguin-vl.github.io</a> | 
  <b>GitHub:</b> <a href="https://github.com/tencent-ailab/Penguin-VL">tencent-ailab/Penguin-VL</a> | 
  <b>arXiv:</b> <a href="https://arxiv.org/abs/2603.06569">2603.06569</a>
  <br><br>
  <a href="https://penguin-vl.github.io"><img src="https://img.shields.io/badge/Project-Page-green?logo=github" alt="Project Page"></a>
  <a href="https://github.com/tencent-ailab/Penguin-VL"><img src="https://img.shields.io/badge/GitHub-Repo-blue?logo=github" alt="GitHub Badge"></a>
  <a href="https://huggingface.co/spaces/tencent/Penguin-VL"><img src="https://img.shields.io/badge/HuggingFace-Spaces-yellow?logo=huggingface" alt="Hugging Face Spaces"></a>
  <a href="https://arxiv.org/abs/2603.06569"><img src="https://img.shields.io/badge/arXiv-2603.06569-b31b1b.svg?logo=arxiv" alt="arXiv"></a>
</h4>

---

## 📰 News

* **2026.03** — PenguinVL-Encoder now available for general use.
* **2026.03** — Released PenguinVL-2B, PenguinVL-8B.

---

## 🌟 Model Overview

PenguinVL is a compact Vision-Language Model, designed to explore the efficiency limits of small-scale VLMs.

Unlike most existing VLMs that rely on contrastive-pretrained vision encoders (e.g., CLIP/SigLIP), Penguin-VL initializes its vision encoder directly from a **text-only LLM**. This design avoids the objective mismatch between contrastive learning and autoregressive language modeling, enabling tighter alignment between visual representations and the language backbone.

### Key Characteristics

- 🧠 **LLM-based Vision Encoder**  
  The vision encoder is adapted from a pretrained text LLM (Qwen3-0.6B), modified with bidirectional attention and 2D-RoPE for spatial modeling.  
  This provides strong semantic priors and native compatibility with the downstream LLM.

---

## 🧪 Quick Start — Transformers Inference

```python
import torch
from transformers import AutoModel, AutoImageProcessor
from transformers.image_utils import load_image

model_name = "tencent/Penguin-Encoder"
image_path = "your_img.jpg"
images = load_image(image_path)

model = AutoModel.from_pretrained(
    model_name,
    trust_remote_code=True,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    attn_implementation="flash_attention_2",
)
processor = AutoImageProcessor.from_pretrained(model_name, trust_remote_code=True)

inputs = processor(images=images, merge_size=1)
inputs = {k: torch.tensor(v).cuda() for k, v in inputs.items()}
if "pixel_values" in inputs:
    inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
image_features = model(**inputs)
```

## 🌎 Model Zoo
| Model                | Base Model   | HF Link                                                      |
| -------------------- | ------------ | ------------------------------------------------------------ |
| PenguinVL-8B         | Qwen3-8B     | [tencent/Penguin-VL-8B](https://huggingface.co/tencent/Penguin-VL-8B) |
| PenguinVL-2B         | Qwen3-1.7B   | [tencent/Penguin-VL-2B](https://huggingface.co/tencent/Penguin-VL-2B) |
| PenguinVL-Encoder    | Qwen3-0.6B   | [tencent/Penguin-Encoder](https://huggingface.co/tencent/Penguin-Encoder) |

## 🚀 Main Results
Ablation Study:

![image](https://cdn-uploads.huggingface.co/production/uploads/626938b16f8f86ad21deb989/JOSRpV_qEbTqdbYwH-hJr.png)

Main Results can see the ablation section in our paper.

## Citation

If you find Penguin-VL useful for your research and applications, please cite using this BibTeX:
```bibtex
@article{Penguin-VL,
  title={Penguin-VL: Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders},
  author={Boqiang Zhang and Lei Ke and Ruihan Yang and Qi Gao and Tianyuan Qu and Rossell Chen and Dong Yu and Leoweiliang},
  journal={arXiv preprint arXiv:2603.06569},
  year={2026}
}
```