[🏠 Project Page](https://youtu-tip.com/#llm) • [📃 License](LICENSE.txt) • [💻 Code](https://github.com/TencentCloudADP/youtu-vl) • [📑 Technical Report](https://arxiv.org/abs/2601.19798) • [📊 Benchmarks](#benchmarks) • [🚀 Getting Started](#quickstart)
[🏠 Project Page](https://youtu-tip.com/#llm) • [📃 License](LICENSE.txt) • [💻 Code](https://github.com/TencentCloudADP/youtu-vl) • [📑 Technical Report](https://arxiv.org/abs/2601.19798) • [📊 Benchmarks](#benchmarks) • [🚀 Getting Started](#quickstart)
## 🤗 Model Download | Model Name | Description | Download | | ----------- | ----------- |----------- | Youtu-VL-4B-Instruct | Visual language model of Youtu-LLM | 🤗 [Model](https://huggingface.co/tencent/Youtu-VL-4B-Instruct)| | Youtu-VL-4B-Instruct-GGUF | Visual language model of Youtu-LLM, in GGUF format | 🤗 [Model](https://huggingface.co/tencent/Youtu-VL-4B-Instruct-GGUF)| ## 🧠 Model Architecture Highlights - **Vision–Language Unified Autoregressive Supervision (VLUAS)**: Youtu-VL is built on the VLUAS paradigm to mitigate the text-dominant optimization bias in conventional VLMs, where visual signals are treated as passive conditions and fine-grained details are often dropped. Rather than using vision features only as inputs, Youtu-VL expands the text lexicon into a unified multimodal vocabulary through a learned visual codebook, turning visual signals into autoregressive supervision targets. Jointly reconstructing visual tokens and text explicitly preserves dense visual information while strengthening multimodal semantic understanding. - **Vision-Centric Prediction with a Standard Architecture (no task-specific modules)**: Youtu-VL treats image and text tokens with equivalent autoregressive status, empowering it to perform vision-centric tasks for both dense vision prediction (e.g., segmentation, depth) and text-based prediction (e.g., grounding, detection) within a standard VLM architecture, eliminating the need for task-specific additions. This design yields a versitile general-purpose VLM, allowing a single model to flexibly accommodate a wide range of vision-centric and vsion-language requirements.
## 🏆 Model Performance ### Vision-Centric Tasks
### General Multimodal Tasks
## 🚀 Quickstart
### Using Transformers to Chat
Ensure your Python environment has the `transformers` library installed and that the version meets the requirements.
```bash
pip install "transformers>=4.56.0,<=4.57.1" torch accelerate pillow torchvision git+https://github.com/lucasb-eyer/pydensecrf.git opencv-python-headless
```
The snippet below shows how to interact with the chat model using `transformers`:
```python
from transformers import AutoProcessor, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"tencent/Youtu-VL-4B-Instruct", attn_implementation="flash_attention_2", torch_dtype="auto", device_map="cuda", trust_remote_code=True
).eval()
processor = AutoProcessor.from_pretrained(
"tencent/Youtu-VL-4B-Instruct", use_fast=True, trust_remote_code=True
)
img_path = "./assets/logo.png"
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": img_path},
{"type": "text", "text": "Describe the image"},
],
}
]
inputs = processor.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_dict=True,
return_tensors="pt"
).to(model.device)
generated_ids = model.generate(
**inputs,
temperature=0.1,
top_p=0.001,
repetition_penalty=1.05,
do_sample=True,
max_new_tokens=32768,
img_input=img_path,
)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
outputs = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
generated_text = outputs[0]
print(f"Youtu-VL output: {generated_text}")
```
### Demo for VL and CV tasks
A simple demo for quick start, including VL and CV tasks: [jupyter notebook](https://github.com/TencentCloudADP/youtu-vl/blob/main/demo/demo.ipynb)
The core part of this demo is three lines below:
```python
model_path = "tencent/Youtu-VL-4B-Instruct"
youtu_vl = YoutuVL(model_path)
response = youtu_vl(prompt, img_path, seg_mode=seg_mode)
```
### Qualitative Results
* **Task: Grounding**
> **Prompt:** Please provide the bounding box coordinate of the region this sentence describes: a black and white cat sitting on the edge of the bathtub
>
> 
* **Task: Object Detection**
> **Prompt:** Detect all objects in the provided image.
>
> 
* **Task: Referring Segmentation**
> **Prompt:** Can you segment "hotdog on left" in this image?
>
> 
For more examples, please refer to paper and Jupyter notebooks.
## 🎉 Citation
If you find our work useful in your research, please consider citing our paper:
```
@article{youtu-vl,
title={Youtu-VL: Unleashing Visual Potential via Unified Vision-Language Supervision},
author={Tencent Youtu Lab},
year={2026},
eprint={2601.19798},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2601.19798},
}
@article{youtu-llm,
title={Youtu-LLM: Unlocking the Native Agentic Potential for Lightweight Large Language Models},
author={Tencent Youtu Lab},
year={2025},
eprint={2512.24618},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2512.24618},
}
```