---
license: cc-by-nc-4.0
tags:
- vision
- image-classification
- vit
- ViTP
- InternVL
- domain-adaptation
- general
language:
- en
library_name: transformers
pipeline_tag: image-feature-extraction
base_model:
- GreatBird/ViTP
---

# ViTP-InternVL-1B-General

ViTP (Visual Instruction Pretraining) vision backbone — **InternVL 1B** variant pretrained on **general** domain visual instruction data. Compatible with `InternVisionModel` from [InternVL](https://github.com/OpenGVLab/InternVL).

## Model Details

- **Architecture**: InternVisionModel (24 layers, 1024 hidden, 16 heads)
- **Image size**: 448×448
- **Patch size**: 14
- **Domain**: General

## Usage

The model repo includes the modeling code. Load with `transformers` (no ViTP repo needed):

```python
from transformers import AutoModel, AutoImageProcessor
import torch

device = "cuda"
model = AutoModel.from_pretrained(
    "BiliSakura/ViTP-InternVL-1B-General",
    trust_remote_code=True,
    torch_dtype=torch.bfloat16,
    device_map=device,
).eval()

processor = AutoImageProcessor.from_pretrained("BiliSakura/ViTP-InternVL-1B-General")
pixel_values = processor(images="image.jpg", return_tensors="pt").pixel_values.to(device, model.dtype)

with torch.no_grad():
    outputs = model(pixel_values=pixel_values)

# Pooled CLS token: (1, 1024)
features = outputs.pooler_output
# Or full sequence: outputs.last_hidden_state
```

## Citation

```bibtex
@article{Li_2025_ViTP,
  title={Visual Instruction Pretraining for Domain-Specific Foundation Models},
  author={Li, Yuxuan and Zhang, Yicheng and Tang, Wenhao and Dai, Yimian and Cheng, Ming-Ming and Li, Xiang and Yang, Jian},
  journal={arXiv},
  year={2025}
}
```