|
--- |
|
license: apache-2.0 |
|
language: |
|
- ko |
|
- en |
|
tags: |
|
- text-generation |
|
- pytorch |
|
- causal-lm |
|
library_name: transformers |
|
--- |
|
|
|
# gemma3-1b-cpt-final-checkpoint-10000 |
|
|
|
์ด ๋ชจ๋ธ์ ํ์ธํ๋๋ ์ธ์ด ๋ชจ๋ธ ์ฒดํฌํฌ์ธํธ์
๋๋ค. |
|
|
|
## ๋ชจ๋ธ ์ ๋ณด |
|
- **๋ฒ ์ด์ค ๋ชจ๋ธ**: gemma3-1b-cpt-final |
|
- **์ฒดํฌํฌ์ธํธ**: checkpoint-10000 |
|
- **ํ์
**: Causal Language Model |
|
- **๋ผ์ด์ ์ค**: Apache 2.0 |
|
|
|
## ์ฌ์ฉ ๋ฐฉ๋ฒ |
|
|
|
```python |
|
from transformers import AutoModelForCausalLM, AutoTokenizer |
|
import torch |
|
|
|
model_name = "NTIS/gemma3-1b-cpt-final-checkpoint-10000" |
|
tokenizer = AutoTokenizer.from_pretrained(model_name) |
|
model = AutoModelForCausalLM.from_pretrained( |
|
model_name, |
|
torch_dtype=torch.float16, |
|
device_map="auto" |
|
) |
|
|
|
# ํ
์คํธ ์์ฑ |
|
text = "์๋
ํ์ธ์" |
|
inputs = tokenizer(text, return_tensors="pt") |
|
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7) |
|
result = tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
print(result) |
|
``` |
|
|
|
## ์ฃผ์์ฌํญ |
|
- ์ด ๋ชจ๋ธ์ ์ฐ๊ตฌ/์คํ ๋ชฉ์ ์ผ๋ก ์ ๊ณต๋ฉ๋๋ค |
|
- ์์
์ ์ฌ์ฉ ์ ์ ๋ผ์ด์ ์ค๋ฅผ ํ์ธํ์ธ์ |
|
|