Update README.md

README.md

@@ -14,4 +14,39 @@ widget:
 
 # 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.
+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!!!