---
license: apache-2.0
language:
- en
metrics:
- accuracy
base_model:
- Qwen/Qwen3-8B
library_name: transformers
tags:
- multi-modal
- large-language-model
- vision-language-model
- vision-encoder
---
Penguin-VL
Exploring the Efficiency Limits of VLM with LLM-based Vision Encoders
---
## ๐ฐ News
* **2026.03** โ PenguinVL-Encoder now available for general use.
* **2026.03** โ Released PenguinVL-2B, PenguinVL-8B.
---
## ๐ Model Overview
PenguinVL is a compact Vision-Language Model designed to explore the efficiency limits of small-scale VLMs. Rather than being only an instruction-tuned model, PenguinVL is built from the ground up through **LLM-based vision encoder construction, multimodal pretraining, and subsequent instruction tuning**.
Unlike most existing VLMs that rely on contrastive-pretrained vision encoders (e.g., CLIP/SigLIP), PenguinVL 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
- ๐ง **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.
- ๐ฅ **Efficient Video Understanding**
A Temporal Redundancy-Aware (TRA) token compression strategy dynamically allocates token budgets across frames, enabling long-video reasoning within a limited context window.
- ๐ Unified Architecture
The model consists of:
1. LLM-initialized vision encoder
2. Lightweight MLP projector
3. Qwen3 language backbone
- ๐ Compact but Strong
At 8B scale, Penguin-VL achieves competitive performance across image, document, OCR, math, and video benchmarks while remaining deployment-friendly.
---
## ๐งช Quick Start โ Transformers Inference
```python
import torch
from transformers import AutoModelForCausalLM, AutoProcessor
model_name = "tencent/Penguin-VL-8B"
model = AutoModelForCausalLM.from_pretrained(
model_name,
trust_remote_code=True,
device_map="auto",
torch_dtype=torch.bfloat16,
)
processor = AutoProcessor.from_pretrained(model_name, trust_remote_code=True)
# Example: Image + Text
inputs = processor(
conversation=[
{"role": "system", "content": "You are a helpful assistant."},
{
"role": "user",
"content": [
{"type": "image", "image": {"image_path": "assets/example.jpg"}},
{"type": "text", "text": "Describe this image."}
],
},
],
return_tensors="pt",
)
inputs = {k: v.cuda() if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
if "pixel_values" in inputs:
inputs["pixel_values"] = inputs["pixel_values"].to(torch.bfloat16)
output_ids = model.generate(**inputs, max_new_tokens=128)
response = processor.decode(output_ids[0], skip_special_tokens=True)
print(response)
```
## ๐ 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) |
## ๐ Main Results
### Chart / OCR / Document Understanding
| Benchmark | **Penguin-VL 8B** | Qwen3-VL 8B | InternVL3.5 8B | OpenAI GPT-5 nano |
|---|---:|---:|---:|---:|
| InfoVQA | **86.8** | 83.1 | 79.1 | 49.2 |
| ChartQA | **90.5** | 89.6 | 86.7 | 48.6 |
| DocVQA | **96.2** | 96.1 | 92.3 | 78.3 |
| CharXiv (DQ / RQ) | 75.7 / 40.0 | **83.0 / 46.4** | 72.2 / 44.4 | 64.4 / 31.7 |
| OCRBench | 852 | **896** | 840 | 701 |
### General Knowledge / Multi-Image / Math Reasoning
| Benchmark | **Penguin-VL 8B** | Qwen3-VL 8B | InternVL3.5 8B | OpenAI GPT-5 nano |
|---|---:|---:|---:|---:|
| AI2D | **86.1** | 85.7 | 84.0 | 65.7 |
| RealWorldQA | **75.8** | 71.5 | 67.5 | 60.7 |
| V-star | **90.2** | 90.1 | 70.7 | 63.4 |
| MMMU-Pro | 40.2 | **55.9** | 39.7 | 36.5 |
| BLINK | 58.2 | **69.1** | 59.5 | 42.2 |
| MathVista | **77.4** | 77.2 | 74.2 | 40.9 |
| MathVerse | 50.8 | **62.1** | 55.8 | 27.0 |
| LogicVista | 53.8 | 55.3 | **57.3** | 40.5 |
### Video Understanding
| Benchmark | **Penguin-VL 8B** | Qwen3-VL 8B | InternVL3.5 8B | OpenAI GPT-5 nano |
|---|---:|---:|---:|---:|
| MVBench | 71.7 | 68.7 | **72.1** | 52.9 |
| LongVideoBench | **67.0** | 62.6 | 62.1 | 38.1 |
| VideoMME | 66.2 | **71.4** | 66.0 | 49.4 |
| Egochema | 67.0 | **70.2** | 61.0 | 34.8 |
| MMVU | 53.9 | **58.7** | 51.5 | 51.0 |
| CharadesSTA | **61.4** | 56.0 | 32.8 | 5.0 |
| NextQA | **85.4** | 82.3 | 81.3 | 59.3 |
| ActivityNetQA | **65.2** | 63.7 | 60.1 | โ |
| Perception Test | **78.0** | 72.7 | 72.7 | โ |
> **Bold** indicates the best result among compared models.
> More details can see 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}
}
```
