Instructions to use OpenGVLab/InternVL-14B-224px with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use OpenGVLab/InternVL-14B-224px with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="OpenGVLab/InternVL-14B-224px", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("OpenGVLab/InternVL-14B-224px", trust_remote_code=True) model = AutoModel.from_pretrained("OpenGVLab/InternVL-14B-224px", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -20,6 +20,9 @@ InternVL scales up the ViT to _**6B parameters**_ and aligns it with LLM.
|
|
| 20 |
It is _**the largest open-source vision/vision-language foundation model (14B)**_ to date, achieving _**32 state-of-the-art**_ performances on a wide range of tasks such as visual perception, cross-modal retrieval, multimodal dialogue, etc.
|
| 21 |
|
| 22 |
|
|
|
|
|
|
|
|
|
|
| 23 |
## Model Details
|
| 24 |
- **Model Type:** vision-language foundation model
|
| 25 |
- **Model Stats:**
|
|
|
|
| 20 |
It is _**the largest open-source vision/vision-language foundation model (14B)**_ to date, achieving _**32 state-of-the-art**_ performances on a wide range of tasks such as visual perception, cross-modal retrieval, multimodal dialogue, etc.
|
| 21 |
|
| 22 |
|
| 23 |
+

|
| 24 |
+
|
| 25 |
+
|
| 26 |
## Model Details
|
| 27 |
- **Model Type:** vision-language foundation model
|
| 28 |
- **Model Stats:**
|