| | --- |
| | 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: |
| |
|
| |  |
| |
|
| | 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} |
| | } |
| | ``` |
