Instructions to use xy1e22/text-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use xy1e22/text-lora with PEFT:
from peft import PeftModel from transformers import AutoModelForSeq2SeqLM base_model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base") model = PeftModel.from_pretrained(base_model, "xy1e22/text-lora") - Transformers
How to use xy1e22/text-lora with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("xy1e22/text-lora", dtype="auto") - Notebooks
- Google Colab
- Kaggle
upload text LoRA adapter
Browse files- adapter_model.safetensors +1 -1
- tokenizer.json +1 -6
adapter_model.safetensors
CHANGED
|
@@ -1,3 +1,3 @@
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
-
oid sha256:
|
| 3 |
size 200781496
|
|
|
|
| 1 |
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:1ec52eba6d45e4a641e4d08c74c9c269478115f4b581b37b9a5e0cef8fdc0ea6
|
| 3 |
size 200781496
|
tokenizer.json
CHANGED
|
@@ -1,11 +1,6 @@
|
|
| 1 |
{
|
| 2 |
"version": "1.0",
|
| 3 |
-
"truncation":
|
| 4 |
-
"direction": "Right",
|
| 5 |
-
"max_length": 64,
|
| 6 |
-
"strategy": "LongestFirst",
|
| 7 |
-
"stride": 0
|
| 8 |
-
},
|
| 9 |
"padding": null,
|
| 10 |
"added_tokens": [
|
| 11 |
{
|
|
|
|
| 1 |
{
|
| 2 |
"version": "1.0",
|
| 3 |
+
"truncation": null,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 4 |
"padding": null,
|
| 5 |
"added_tokens": [
|
| 6 |
{
|