Instructions to use aehrc/cxrmate-rrg24 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aehrc/cxrmate-rrg24 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aehrc/cxrmate-rrg24", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("aehrc/cxrmate-rrg24", trust_remote_code=True) model = AutoModel.from_pretrained("aehrc/cxrmate-rrg24", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Upload tokens.drawio.png
Browse files- .gitattributes +1 -0
- tokens.drawio.png +3 -0
.gitattributes
CHANGED
|
@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
|
|
|
|
|
| 33 |
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 34 |
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 35 |
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
| 36 |
+
tokens.drawio.png filter=lfs diff=lfs merge=lfs -text
|
tokens.drawio.png
ADDED
|
Git LFS Details
|