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
- 🔗 GitHub Repository: github.com/reprompts/lightchat
- 💼 LinkedIn Group: LightChat Dev Group
- 📰 Dev.to Profile: @repromptsquest
- 🐦 Twitter: @repromptsquest
🔧 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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