lightchat 0.1.2

Lightweight GPT2 training and deployment toolkit

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

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🔧 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

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📋 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?

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⚙️ Installation

pip install lightchat

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

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🚀 Training

lightchat train <model_name> <data.txt> \
  --epochs 3 \
  --batch-size 8 \
  --learning-rate 5e-5

Example Command:
lightchat train newmodel data.txt --epochs 1 --batch-size 8 --learning-rate 5e-5


> **⚠️ Data file path <data.txt>:** Give proper path to the dataset or keep dataset inside the root directory of project where library is installed.

• 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.

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💬 Chatting

lightchat chat <model_name> \
  --max-length 100 \
  --top-k 50 \
  --top-p 0.9 \
  --temperature 1.0


Example Command:
lightchat chat newmodel --max-length 100 --top-k 50 --temperature 0.9

• 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)

Give "exit" as an prompt to the model to exit the conversation and you can load the trained models anytime by following the instructions given below.

Trained models live in models/<model_name>/.

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📂 Model Management

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

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🙌 Contributions

Contributions are welcome! Please see CONTRIBUTING.md.