Instructions to use Chandher/lorabloom with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use Chandher/lorabloom with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("bigscience/bloom-1b7") model = PeftModel.from_pretrained(base_model, "Chandher/lorabloom") - Notebooks
- Google Colab
- Kaggle
Upload model
Browse files- README.md +6 -0
- adapter_model.bin +1 -1
README.md
CHANGED
|
@@ -1,3 +1,9 @@
|
|
| 1 |
---
|
| 2 |
library_name: peft
|
| 3 |
---
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
---
|
| 2 |
library_name: peft
|
| 3 |
---
|
| 4 |
+
## Training procedure
|
| 5 |
+
|
| 6 |
+
### Framework versions
|
| 7 |
+
|
| 8 |
+
|
| 9 |
+
- PEFT 0.4.0.dev0
|
adapter_model.bin
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 6309185
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:42a3185404d543ac23a4f56a011141189ffcc86b2ee0117508629636c55390e7
|
| 3 |
size 6309185
|