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--- |
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license: other |
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license_name: license |
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license_link: https://huggingface.co/Qwen/Qwen1.5-0.5B/blob/main/LICENSE |
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--- |
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# A fine-tuned version of the Qwen/Qwen1.5-0.5B model, the data set used is alpaca_gpt4_data_zh.json |
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· Call example |
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``` |
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import os |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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] |
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device = "cuda" # the device to load the model onto |
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model_path = os.path.dirname(__file__) |
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model = AutoModelForCausalLM.from_pretrained( |
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model_path, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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response = '' |
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if __name__ == '__main__': |
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while True: |
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# prompt = "Give me a short introduction to large language model." |
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prompt = input("input:") |
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messages.append({"role": "user", "content": prompt}) |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(device) |
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generated_ids = model.generate( |
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model_inputs.input_ids, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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messages.append({"role": "system", "content": response}, ) |
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``` |
