Instructions to use qwopqwop/EEVE-ALMA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use qwopqwop/EEVE-ALMA with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("yanolja/EEVE-Korean-Instruct-10.8B-v1.0") model = PeftModel.from_pretrained(base_model, "qwopqwop/EEVE-ALMA") - Notebooks
- Google Colab
- Kaggle
Update README.md
Browse files
README.md
CHANGED
|
@@ -33,7 +33,8 @@ tokenizer = AutoTokenizer.from_pretrained(model_path, padding_side='left')
|
|
| 33 |
|
| 34 |
en_text = 'Hi.'
|
| 35 |
ko_text = '์๋
ํ์ธ์.'
|
| 36 |
-
|
|
|
|
| 37 |
ko_prompt = f"Translate this from Korean to English:\nKorean: {ko_text}\nEnglish:"
|
| 38 |
|
| 39 |
input_ids = tokenizer(en_prompt, return_tensors="pt", padding=True, max_length=256, truncation=True).input_ids.cuda()
|
|
|
|
| 33 |
|
| 34 |
en_text = 'Hi.'
|
| 35 |
ko_text = '์๋
ํ์ธ์.'
|
| 36 |
+
|
| 37 |
+
en_prompt = f"Translate this from English to Korean:\nEnglish: {en_text}\nKorean:"
|
| 38 |
ko_prompt = f"Translate this from Korean to English:\nKorean: {ko_text}\nEnglish:"
|
| 39 |
|
| 40 |
input_ids = tokenizer(en_prompt, return_tensors="pt", padding=True, max_length=256, truncation=True).input_ids.cuda()
|