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Model: benchang1110/Taiwan-tinyllama-v1.1-base
Source: Original Platform

README.md

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+---
+base_model:
+- TinyLlama/TinyLlama_v1.1
+datasets:
+- benchang1110/Taiwan-pretrain-9B
+- benchang1110/Taiwan-book-1B
+language:
+- zh
+library_name: transformers
+license: apache-2.0
+---
+
+# Model Card for Model ID
+
+![image](image.png)
+This is a continue-pretrained version of [Tinyllama-v1.1](TinyLlama/TinyLlama_v1.1) tailored for traditional Chinese. The continue-pretraining dataset contains over 10B tokens. Using bfloat16, the VRAM required during inference is only around 3GB!!! 
+
+# Usage
+**This is a causal language model not a chat model !** It is not designed to generate human-like responses. It is designed to generate text based on previous text.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig
+import torch
+from transformers import TextStreamer
+
+def generate_response(input):
+    '''
+    simple test for the model
+    '''
+    # tokenzize the input
+    tokenized_input = tokenizer.encode_plus(input, return_tensors='pt').to(device)
+    print(tokenized_input['input_ids'])
+    # generate the response
+    _ = model.generate(
+        input_ids=tokenized_input['input_ids'], 
+        attention_mask=tokenized_input['attention_mask'],
+        pad_token_id=tokenizer.pad_token_id,
+        do_sample=True,
+        repetition_penalty=1.0,
+        max_length=2048,
+        streamer=streamer,
+    )
+
+
+if __name__ == '__main__':
+    device = 'cuda' if torch.cuda.is_available() else 'cpu'
+    model = AutoModelForCausalLM.from_pretrained("benchang1110/Taiwan-tinyllama-v1.1-base",attn_implementation="flash_attention_2",device_map=device,torch_dtype=torch.bfloat16)
+    tokenizer = AutoTokenizer.from_pretrained("benchang1110/Taiwan-tinyllama-v1.1-base",use_fast=True)
+    streamer = TextStreamer(tokenizer)
+    while(True):
+        text = input("input a simple prompt:")
+        generate_response(text)
+```
+
+### Training Procedure
+
+The following training hyperparameters are used:
+
+| Data size    | Global Batch Size | Learning Rate | Epochs | Max Length | Weight Decay | 
+|--------------|-------------------|---------------|--------|------------|--------------|
+| 10B        | 32               | 5e-5          | 1      | 2048       | 1e-4            |
+
+![loss](loss.png)
+### Compute Infrastructure
+1xA100(80GB), took approximately 200 GPU hours.