Instructions to use brildev7/gemma-7b-it-summarization-ko-sft-qlora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use brildev7/gemma-7b-it-summarization-ko-sft-qlora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-7b-it") model = PeftModel.from_pretrained(base_model, "brildev7/gemma-7b-it-summarization-ko-sft-qlora") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -13,10 +13,9 @@ model-index:
|
|
| 13 |
results: []
|
| 14 |
---
|
| 15 |
|
| 16 |
-
#
|
| 17 |
-
|
| 18 |
-
<!-- Provide a quick summary of what the model is/does. -->
|
| 19 |
|
|
|
|
| 20 |
|
| 21 |
|
| 22 |
## Model Details
|
|
@@ -32,6 +31,14 @@ model-index:
|
|
| 32 |
### Dataset
|
| 33 |
- https://huggingface.co/datasets/brildev7/new_summary_by_gpt4
|
| 34 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 35 |
### Inference Examples
|
| 36 |
```
|
| 37 |
import torch
|
|
|
|
| 13 |
results: []
|
| 14 |
---
|
| 15 |
|
| 16 |
+
# gemma-7b-it-summarization-sft-qlora
|
|
|
|
|
|
|
| 17 |
|
| 18 |
+
This model is a fine-tuned version of [google/gemma-7b-it](https://huggingface.co/google/gemma-7b-it) on the generator dataset.
|
| 19 |
|
| 20 |
|
| 21 |
## Model Details
|
|
|
|
| 31 |
### Dataset
|
| 32 |
- https://huggingface.co/datasets/brildev7/new_summary_by_gpt4
|
| 33 |
|
| 34 |
+
### Framework versions
|
| 35 |
+
|
| 36 |
+
- PEFT 0.8.2
|
| 37 |
+
- Transformers 4.38.0
|
| 38 |
+
- Pytorch 2.2.1+cu121
|
| 39 |
+
- Datasets 2.17.0
|
| 40 |
+
- Tokenizers 0.15.2
|
| 41 |
+
|
| 42 |
### Inference Examples
|
| 43 |
```
|
| 44 |
import torch
|