Instructions to use hf-internal-testing/tiny-random-PvtV2Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-PvtV2Model with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-PvtV2Model")# Load model directly from transformers import AutoImageProcessor, AutoModel processor = AutoImageProcessor.from_pretrained("hf-internal-testing/tiny-random-PvtV2Model") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-PvtV2Model") - Notebooks
- Google Colab
- Kaggle
Update tiny models for PvtV2Model
Browse files- config.json +1 -1
- model.safetensors +1 -1
config.json
CHANGED
|
@@ -78,5 +78,5 @@
|
|
| 78 |
2
|
| 79 |
],
|
| 80 |
"torch_dtype": "float32",
|
| 81 |
-
"transformers_version": "4.
|
| 82 |
}
|
|
|
|
| 78 |
2
|
| 79 |
],
|
| 80 |
"torch_dtype": "float32",
|
| 81 |
+
"transformers_version": "4.40.0.dev0"
|
| 82 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 3108048
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a682b27f41d9c8dc29126b3a08558e6749c58a0d0f69b22d9bc518b93118471a
|
| 3 |
size 3108048
|