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---
library_name: transformers
license: apache-2.0
datasets:
- benchang1110/pretrainedtw
- HuggingFaceTB/cosmopedia-100k
language:
- zh
widget:
- text: '在很久以前，這座島上'
example_title: Example1
---
# Model Card for Model ID
This is a continue-pretrained version of [Tinyllama](TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) tailored for traditional Chinese. The continue-pretraining dataset contains roughly 2B tokens.
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
def generate_response(input):
'''
simple test for the model
'''
# tokenzize the input
tokenized_input = tokenizer.encode_plus(input, return_tensors='pt').to(device)
# generate the response
outputs = model.generate(
input_ids=tokenized_input['input_ids'],
attention_mask=tokenized_input['attention_mask'],
pad_token_id=tokenizer.pad_token_id,
do_sample=False,
repetition_penalty=1.3,
max_length=500
)
# decode the response
return tokenizer.decode(outputs[0], skip_special_tokens=True)
if __name__ == '__main__':
device = 'cuda' if torch.cuda.is_available() else 'cpu'
model = AutoModelForCausalLM.from_pretrained("benchang1110/Taiwan-tinyllama-v1.0-base",device_map=device,torch_dtype=torch.bfloat16)
tokenizer = AutoTokenizer.from_pretrained("benchang1110/Taiwan-tinyllama-v1.0-base")
while(True):
text = input("input a simple prompt:")
print('System:', generate_response(text))
```
Using bfloat16, the VRAM required is around 3GB!!! |