📰 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
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 |
| PenguinVL-2B | Qwen3-1.7B | tencent/Penguin-VL-2B |
| PenguinVL-Encoder | Qwen3-0.6B | 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:
@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}
}