training/README.md

Training Pipeline

Fine-tune Nemotron Nano 12B v2 VL on AWS service documentation so the model has built-in knowledge of new/recent AWS services and features.

Overview

• Base model: nvidia/NVIDIA-Nemotron-Nano-12B-v2-VL-BF16
• Method: QLoRA (4-bit quantization + LoRA adapters)
• Data: AWS documentation converted to instruction-response pairs
• Target: Push fine-tuned model to HF Hub for deployment

Directory Structure

training/
├── prepare_data.py      # Convert raw docs → training JSONL
├── train.py             # QLoRA fine-tuning script
├── push_to_hub.py       # Merge adapter + push to HF Hub
├── requirements.txt     # Training dependencies
└── data/
    ├── raw/             # Put AWS documentation markdown files here
    └── train.jsonl      # Generated training data (output of prepare_data.py)

Steps

1. Gather AWS documentation

Add markdown files to training/data/raw/. One file per service or feature:

training/data/raw/
├── amazon-q.md
├── s3-express-one-zone.md
├── aurora-serverless-v2.md
├── bedrock.md
└── ...

2. Generate training data

python training/prepare_data.py

This reads the raw docs and creates training/data/train.jsonl with instruction-response pairs.

3. Train on AWS (EC2 with GPU)

pip install -r training/requirements.txt
python training/train.py

Recommended: g5.2xlarge (A10G, 24GB VRAM) or p3.2xlarge (V100, 16GB).

4. Push to Hugging Face Hub

python training/push_to_hub.py

5. Update app.py

Change MODEL_ID in app.py to point at your fine-tuned model.