agenx-lora-training/training

Cyron Summary LoRA Training

This directory contains scripts and configurations for training a LoRA adapter on the Cyron summary dataset.

Directory Structure

training/
├── configs/          # Training configurations
│   └── llama2-7b-lora.yaml
├── data/            # Prepared datasets (train/test splits)
├── output/          # Trained model outputs
├── scripts/         # Training and inference scripts
│   ├── prepare_dataset.py
│   ├── train.py
│   └── inference.py
└── notebooks/       # Jupyter notebooks for analysis

Prerequisites

• Python 3.10+
• GPU with 40GB+ VRAM (A100 recommended)
• 64GB+ system RAM
• 100GB+ free disk space
• CUDA drivers installed

Server Deployment (Recommended)

Use the deployment script to clone and train on your GPU server:

# Deploy and train in one command
bash deploy-and-train.sh

This will:

1. Clone the repo to /opt/loras/agenx-lora-training
2. Setup Python environment with CUDA support
3. Prepare the dataset
4. Train the LoRA adapter

Manual Setup

1. Prepare Dataset

python scripts/prepare_dataset.py --input ../combined_20k.jsonl --output data

This splits the 20k dataset into train/test sets (95/5).

2. Train Model

python scripts/train.py --config configs/llama2-7b-lora.yaml

Or with custom parameters:

python scripts/train.py --config configs/llama2-7b-lora.yaml --epochs 5 --batch-size 8

Training takes approximately 6-24 hours depending on GPU.

3. Generate Summaries

python scripts/inference.py \
    --model output/llama2-7b-lora \
    --task "Fix parser crash on malformed JSON" \
    --files src/parser.cpp \
    --tests-run --test-count 294 \
    --commit --push

Configuration

Edit configs/llama2-7b-lora.yaml to adjust:

• Base model: Change base_model to use different foundation models
• LoRA rank: Adjust lora_r (higher = more capacity, slower)
• Learning rate: Tune learning_rate
• Epochs: Change num_train_epochs
• Batch size: Adjust per_device_train_batch_size

Dataset Format

The dataset (combined_20k.jsonl) contains 20,000 examples with:

• task: Task description
• files_changed: List of changed files
• tests_run: Boolean
• commit: Boolean
• push: Boolean
• errors_seen: Boolean
• test_count: Number of tests
• output: Expected Cyron summary

Output

Trained model is saved to output/llama2-7b-lora/:

• adapter_model.safetensors - LoRA weights
• config.json - Model configuration
• tokenizer.json - Tokenizer
• peft_config.json - LoRA configuration

Troubleshooting

Out of memory:

• Reduce per_device_train_batch_size
• Enable gradient_checkpointing: true
• Use load_in_4bit: true (already enabled)

Training loss not decreasing:

• Lower learning rate (try 1e-4)
• Increase warmup ratio
• Check dataset quality

Generation too long/short:

• Adjust max_new_tokens in inference script
• Tune temperature and top_p