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license: mit |
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--- |
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## Usage |
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### Chat format |
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> **IMPORTANT**: This model is **sensitive** to the chat template used. Ensure you use the correct template: |
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``` |
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<s>system |
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[System message]</s> |
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<s>user |
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[Your question or message]</s> |
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<s>assistant |
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[The model's response]</s> |
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``` |
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### Example Usage with HuggingFace Transformers |
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```python |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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# Determine the device to use (GPU if available, otherwise CPU) |
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu") |
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# Load the model and tokenizer, then move the model to the appropriate device |
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model = AutoModelForCausalLM.from_pretrained("adi2606/MenstrualQA").to(device) |
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tokenizer = AutoTokenizer.from_pretrained("adi2606/MenstrualQA") |
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# Function to generate a response from the chatbot |
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def generate_response(message: str, temperature: float = 0.4, repetition_penalty: float = 1.1) -> str: |
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# Apply the chat template and convert to PyTorch tensors |
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messages = [ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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{"role": "user", "content": message} |
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] |
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input_ids = tokenizer.apply_chat_template( |
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messages, add_generation_prompt=True, return_tensors="pt" |
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).to(device) |
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# Generate the response |
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output = model.generate( |
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input_ids, |
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max_length=512, |
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temperature=temperature, |
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repetition_penalty=repetition_penalty, |
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do_sample=True |
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) |
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# Decode the generated output |
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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return generated_text |
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# Example usage |
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message = "how to stop pain during menstruation?" |
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response = generate_response(message) |
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print(response) |
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``` |
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