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Cataloged from tencent/Penguin-VL-2B
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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.
🧠 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:
📊 Compact but Strong
At 2B scale, Penguin-VL achieves competitive performance across image, document, OCR, math, and video benchmarks while remaining deployment-friendly.
Bold indicates the best score among compared models. More details can see our paper.
If you find Penguin-VL useful for your research and applications, please cite using this BibTeX:
import torchfrom 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 + Textinputs = 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)@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}}