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Lightweight GPT2 training and deployment toolkit

Project description

LightChat

LightChat is a lightweight GPT-2–based toolkit built on top of DistilGPT2. It enables anyone to train, deploy, and interact with a custom chatbot on low‑end devices using simple CLI commands.

🌐 Links & Community


🔧 Features

  • Train your own language model on plain text files
  • Chat interactively with your fine‑tuned model
  • List & delete saved models
  • Supports top‑k and top‑p (nucleus) sampling

📋 Dataset Preparation

  • Provide a plain text file (.txt) with one sentence per line.
  • Aim for at least 1,000–10,000 lines for reasonable results on CPU.
  • Clean, focused content yields better chat relevance.

Example (data.txt):

Hello, how can I help you today?
I love reading sci‑fi novels.
What's the weather like?

⚙️ Installation

pip install lightchat

⚠️ CPU install note: Transformers and PyTorch may take several minutes to compile on CPU.


🚀 Training

lightchat train <model_name> <data.txt> \
  --epochs 3 \
  --batch-size 8 \
  --learning-rate 5e-5
  • model_name: directory under models/ to save to
  • epochs: full passes over your data
  • batch-size: number of samples per step
  • learning-rate: step size for optimizer

⚠️ CPU training note: Training on CPU is slow. More epochs/bigger batch sizes = longer time but better fit.


💬 Chatting

lightchat chat <model_name> \
  --max-length 100 \
  --top-k 50 \
  --top-p 0.9 \
  --temperature 1.0
  • max-length: max generated tokens per reply
  • top-k: sample from top k tokens
  • top-p: sample from top cumulative probability p
  • temperature: randomness control (higher = more creative)

Trained models live in models/<model_name>/.


📂 Model Management

  • List saved models: lightchat list-models
  • Delete a model: lightchat delete-model <model_name>
  • Or manually remove models/<model_name>/ directory.

🙌 Contributions

Contributions are welcome! Please see CONTRIBUTING.md.

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