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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}
}
``` |