Instructions to use OpenGVLab/InternViT-300M-448px with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternViT-300M-448px with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="OpenGVLab/InternViT-300M-448px", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenGVLab/InternViT-300M-448px", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -81,6 +81,13 @@ If you find this project useful in your research, please consider citing:
|
|
| 81 |
journal={arXiv preprint arXiv:2312.14238},
|
| 82 |
year={2023}
|
| 83 |
}
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 84 |
```
|
| 85 |
|
| 86 |
|
|
|
|
| 81 |
journal={arXiv preprint arXiv:2312.14238},
|
| 82 |
year={2023}
|
| 83 |
}
|
| 84 |
+
@article{chen2024far,
|
| 85 |
+
title={How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites},
|
| 86 |
+
author={Chen, Zhe and Wang, Weiyun and Tian, Hao and Ye, Shenglong and Gao, Zhangwei and Cui, Erfei and Tong, Wenwen and Hu, Kongzhi and Luo, Jiapeng and Ma, Zheng and others},
|
| 87 |
+
journal={arXiv preprint arXiv:2404.16821},
|
| 88 |
+
year={2024}
|
| 89 |
+
}
|
| 90 |
+
|
| 91 |
```
|
| 92 |
|
| 93 |
|