# 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:

```bash
# 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

```bash
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

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

Or with custom parameters:

```bash
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

```bash
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`