Instructions to use DJF-on-arm/Iamam1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Keras
How to use DJF-on-arm/Iamam1 with Keras:
# Available backend options are: "jax", "torch", "tensorflow". import os os.environ["KERAS_BACKEND"] = "jax" import keras model = keras.saving.load_model("hf://DJF-on-arm/Iamam1") - Notebooks
- Google Colab
- Kaggle
Update versions/BaseModels/whatsThis.md
Browse files
versions/BaseModels/whatsThis.md
CHANGED
|
@@ -5,4 +5,5 @@ But these are IAMAM models trained are **still trained from scratch**!
|
|
| 5 |
Training other IAMAM models!
|
| 6 |
|
| 7 |
# Im going to put something in here at a later date
|
| 8 |
-
No, not the base models folder!, I mean this heading!
|
|
|
|
|
|
| 5 |
Training other IAMAM models!
|
| 6 |
|
| 7 |
# Im going to put something in here at a later date
|
| 8 |
+
No, not the base models folder!, I mean this heading!
|
| 9 |
+
edit: I'm not going to do that
|