Instructions to use OpenGVLab/InternViT-6B-224px with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternViT-6B-224px with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="OpenGVLab/InternViT-6B-224px", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("OpenGVLab/InternViT-6B-224px", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,7 +13,7 @@ datasets:
|
|
| 13 |
|
| 14 |
## What is InternVL?
|
| 15 |
|
| 16 |
-
\[[Paper](https://arxiv.org/abs/2312.14238)\] \[[GitHub](https://github.com/OpenGVLab/InternVL)\]
|
| 17 |
|
| 18 |
InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM.
|
| 19 |
|
|
|
|
| 13 |
|
| 14 |
## What is InternVL?
|
| 15 |
|
| 16 |
+
\[[Paper](https://arxiv.org/abs/2312.14238)\] \[[GitHub](https://github.com/OpenGVLab/InternVL)\] \[[Chat Demo](https://internvl.opengvlab.com/)\]
|
| 17 |
|
| 18 |
InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM.
|
| 19 |
|