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--- |
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library_name: transformers |
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license: apache-2.0 |
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language: |
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- en |
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--- |
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### Example |
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``` |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "jaeyong2/QuerySense-Preview" |
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model = AutoModelForCausalLM.from_pretrained(model_name) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = """ |
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# Role |
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You are an AI that receives Questions and Context from users as input and preprocesses the Questions. |
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# Instruction |
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- If the user's Questions contains enough information to create an answer, use the user's Questions as is. |
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- If the information is insufficient or the Context is insufficient, please rephrase the Questions with the necessary information. |
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- If there is insufficient information to generate an answer and there is no Context, it will automatically fill in the appropriate information. |
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# input |
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- Context : Previous conversations or related Context or related information entered by the user (Optional) |
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- Question : User's Questions (Required) |
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""".strip() |
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content =""" |
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Context : |
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Question : name |
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""".strip() |
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system = {"role":"system", "content":prompt} |
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user = {"role":"user", "content":content} |
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messages = [system, user] |
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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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enable_thinking=False # Switches between thinking and non-thinking modes. Default is True. |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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# conduct text completion |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=32768 |
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) |
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() |
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content = tokenizer.decode(output_ids, skip_special_tokens=True).strip("\n") |
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print("content:", content) |
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``` |
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### result |
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``` |
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content: what is the name of the product? |
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``` |
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## License |
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- Qwen/Qwen3-1.7B : https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE |
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## Acknowledgement |
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This research is supported by **TPU Research Cloud program**. |
