Penguin-VL-2B

📰 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. 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

• 🧠 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.

• 🎥 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.

• 🏗 Unified Architecture
  The model consists of:

  1. LLM-initialized vision encoder
  2. Lightweight MLP projector
  3. Qwen3 language backbone

• 📊 Compact but Strong
  At 2B scale, Penguin-VL achieves competitive performance across image, document, OCR, math, and video benchmarks while remaining deployment-friendly.

🧪 Quick Start — Transformers Inference

import torch
from transformers import AutoModelForCausalLM, AutoProcessor

model_name = "tencent/Penguin-VL-2B"

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)

🌎 Model Zoo

🚀 Main Results

Chart / OCR / Document Understanding

General Knowledge / Multi-Image / Math Reasoning

Video Understanding

Bold indicates the best score 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:

@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}
}