--- datasets: - knkarthick/dialogsum language: - en metrics: - accuracy pipeline_tag: text-generation library_name: "torch, transformers" --- Certainly! Here's a short README for using the pre-trained `distilgpt2` model for chatting: --- # DistilGPT-2 Chatbot This project demonstrates how to use the pre-trained `distilgpt2` model from Hugging Face for creating a simple chatbot. It includes code for loading the model, generating responses, and running an interactive conversation loop. ## Prerequisites Ensure you have the following libraries installed: ```bash pip install transformers torch ``` ## Usage 1. **Load the Pre-trained Model and Tokenizer** ```python from transformers import GPT2LMHeadModel, GPT2Tokenizer model_name = "distilgpt2" model = GPT2LMHeadModel.from_pretrained(model_name) tokenizer = GPT2Tokenizer.from_pretrained(model_name) ``` 2. **Generate a Response** Use the following function to generate a response based on user input: ```python def generate_response(prompt, max_length=100): input_ids = tokenizer.encode(prompt, return_tensors='pt') output = model.generate( input_ids, max_length=max_length, pad_token_id=tokenizer.eos_token_id, no_repeat_ngram_size=2, num_return_sequences=1, temperature=0.7, top_p=0.9, top_k=50 ) response = tokenizer.decode(output[0], skip_special_tokens=True) return response ``` 3. **Interactive Conversation Loop** Run the following code to start a chat session: ```python while True: user_input = input("You: ") prompt = f" {user_input}" response = generate_response(prompt) print(f"AI: {response}") if user_input.lower() in ["exit", "quit"]: break ``` ## Configuration - **Temperature**: Controls randomness. Lower values are more deterministic. - **Top-p and top-k**: Limit word selection for balanced diversity and coherence. - **Max_length**: Limits the length of the response.