Instructions to use Anwaarma/try1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Anwaarma/try1 with PEFT:
from peft import PeftModel from transformers import AutoModelForSequenceClassification base_model = AutoModelForSequenceClassification.from_pretrained("Anwaarma/edos_taskB_llama3b_merged2_FINAL") model = PeftModel.from_pretrained(base_model, "Anwaarma/try1") - Transformers
How to use Anwaarma/try1 with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Anwaarma/try1", dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload lora/tokenizer.json with huggingface_hub
Browse files- .gitattributes +1 -0
- lora/tokenizer.json +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 |
+
lora/tokenizer.json filter=lfs diff=lfs merge=lfs -text
|
lora/tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c6eeb16665ec244ff3c2ef4dca42e4cfdcbc7162835201919175747a633511cb
|
| 3 |
+
size 17210018
|