Instructions to use hf-internal-testing/tiny-random-UnivNetModel with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hf-internal-testing/tiny-random-UnivNetModel with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="hf-internal-testing/tiny-random-UnivNetModel")# Load model directly from transformers import AutoFeatureExtractor, AutoModel extractor = AutoFeatureExtractor.from_pretrained("hf-internal-testing/tiny-random-UnivNetModel") model = AutoModel.from_pretrained("hf-internal-testing/tiny-random-UnivNetModel") - Notebooks
- Google Colab
- Kaggle
Update tiny models for UnivNetModel
#121
by hf-transformers-bot - opened
- config.json +1 -1
- model.safetensors +1 -1
config.json
CHANGED
|
@@ -43,5 +43,5 @@
|
|
| 43 |
4
|
| 44 |
],
|
| 45 |
"torch_dtype": "float32",
|
| 46 |
-
"transformers_version": "4.
|
| 47 |
}
|
|
|
|
| 43 |
4
|
| 44 |
],
|
| 45 |
"torch_dtype": "float32",
|
| 46 |
+
"transformers_version": "4.40.0.dev0"
|
| 47 |
}
|
model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 535452
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c839be05c8b295252fd5554c21741b3a8af5daad3f3b475af13f8240c1977f9a
|
| 3 |
size 535452
|