lfm2-1.2b-sermon-instruct-qlora

Author: Delight Aheebwa
Contact: Please contact via Hugging Face or GitHub profile (delight2004)

Model Overview

• Type: Causal Language Model (LM)
• Base Model: LiquidAI/LFM2-1.2B
• Fine-tuning technique: QLoRA with PEFT (LoRA adapters)
• Language(s): English only
• Intended Use: Research, educational, and sermon content generation on Christian and theological topics (especially inspired by John Piper's teachings).
• Tags: Uganda, theology, Christianity
• License: OpenRAIL Non-Commercial Variant

Dataset & Training

• Data source: Transcripts of YouTube sermons by John Piper (excluding "Ask Pastor John" podcast transcripts)
• Filtered dataset size: 165 entries after filtering (~10% set aside for validation)
• Preprocessing: Splitting and curation as detailed in the training notebook
• Training details:
  • Hardware: Google Colab free tier (T4 GPU)
  • epochs: 4
  • batch size: 1 (gradient_accumulation_steps=4)
  • learning rate: 2e-5
  • sequence length: 512
  • quantization: 4-bit (bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.bfloat16)
  • Only LoRA adapter params were trained (~0.05% of total)
  • Full trainer/config code: see Colab notebook above

Evaluation

• No formal evaluation/benchmarking was conducted. Use at your own discretion – feedback and community tests are welcome.

Limitations & Disclaimer

• Not intended for production or commercial use.
• Outputs should not be treated as official theological advice.
• Possible biases and limitations inherited from the dataset/model base – may reflect the original preacher's views.
• Model may hallucinate or generate plausible but incorrect theological claims or references.

Technical

• Architecture: Causal Transformer (1.2B params, LiquidAI flavor)
• Adapter config: PEFT/QLoRA
• Training framework: Hugging Face Transformers, TRL, PEFT, bitsandbytes, PyTorch
• Compute: Google Colab T4 (free tier, single GPU)
• Notebook: john_piper.ipynb

Citation

If you use this model, please cite it or reference its Hugging Face page, and acknowledge John Piper's YouTube sermons as the data source.