README.md

metadata

language: en
license: apache-2.0
datasets: daily_dialog
pipeline_tag: text-generation
library_name: transformers
tags:
  - gpt2
  - conversational
  - chatbot
  - nlp
base_model: gpt2

GPT-2 Personal Assistant

Model repo: hmnshudhmn24/gpt2-personal-assistant A lightweight conversational assistant based on GPT-2, fine-tuned on the DailyDialog dataset for chat and casual Q&A.

Model details

• Base model: gpt2
• Task: Conversational text generation / Chatbot
• Dataset used for demo: daily_dialog (small subset used in training script for quick demo)
• Language: English
• License: Apache-2.0

How to use (inference)

from transformers import pipeline

generator = pipeline("text-generation", model="hmnshudhmn24/gpt2-personal-assistant")
prompt = "User: Hello\nAssistant: Hi! How can I help you?\nUser: What's the weather like today?\nAssistant:"
print(generator(prompt, max_length=100, num_return_sequences=1)[0]["generated_text"])

Train locally (quick demo)

Run:

python train_chatbot.py

This script fine-tunes gpt2 on a subset of the DailyDialog dataset and saves the model to ./gpt2-personal-assistant folder.

Files in this repo

• config.json, tokenizer_config.json, special_tokens_map.json — model/tokenizer configs
• train_chatbot.py — training script (demo)
• inference.py — simple inference example
• utils.py — helper to build conversation prompts
• example_conversations.txt — small sample dialogues
• requirements.txt — Python dependencies

Notes & limitations

• GPT-2 is a general-purpose LM; it can generate incorrect or unsafe outputs. Do not rely on it for critical advice.
• For production, use larger datasets, more epochs, and safety filtering.
• If uploading to Hugging Face, include pytorch_model.bin (weights) after training.

License

Apache-2.0