# LLaMA Factory

> Install and fine-tune models with LLaMA Factory

## Table of Contents

- [Overview](#overview)
- [Instructions](#instructions)
  - [Step 4. Install LLaMA Factory with dependencies](#step-4-install-llama-factory-with-dependencies)

---

## Overview

## Basic idea
LLaMA Factory is an open-source framework that simplifies the process of training and fine 
tuning large language models. It offers a unified interface for a variety of cutting edge 
methods such as SFT, RLHF, and QLoRA techniques. It also supports a wide range of LLM 
architectures such as LLaMA, Mistral and Qwen. This playbook demonstrates how to fine-tune 
large language models using LLaMA Factory CLI on your NVIDIA Spark device.

## What you'll accomplish

You'll set up LLaMA Factory on NVIDIA Spark with Blackwell architecture to fine-tune large 
language models using LoRA, QLoRA, and full fine-tuning methods. This enables efficient 
model adaptation for specialized domains while leveraging hardware-specific optimizations.

## What to know before starting

- Basic Python knowledge for editing config files and troubleshooting
- Command line usage for running shell commands and managing environments  
- Familiarity with PyTorch and Hugging Face Transformers ecosystem
- GPU environment setup including CUDA/cuDNN installation and VRAM management
- Fine-tuning concepts: understanding tradeoffs between LoRA, QLoRA, and full fine-tuning
- Dataset preparation: formatting text data into JSON structure for instruction tuning
- Resource management: adjusting batch size and memory settings for GPU constraints

## Prerequisites

- NVIDIA Spark device with Blackwell architecture

- CUDA 12.9 or newer version installed: `nvcc --version`

- Docker installed and configured for GPU access: `docker run --gpus all nvidia/cuda:12.9-devel nvidia-smi`

- Git installed: `git --version`

- Python environment with pip: `python --version && pip --version`

- Sufficient storage space (>50GB for models and checkpoints): `df -h`

- Internet connection for downloading models from Hugging Face Hub

## Ancillary files

- Official LLaMA Factory repository: https://github.com/hiyouga/LLaMA-Factory

- NVIDIA PyTorch container: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch

- Example training configuration: `examples/train_lora/llama3_lora_sft.yaml` (from repository)

- Documentation: https://llamafactory.readthedocs.io/en/latest/getting_started/data_preparation.html

## Time & risk

* **Duration:** 30-60 minutes for initial setup, 1-7 hours for training depending on model size and dataset.
* **Risks:** Model downloads require significant bandwidth and storage. Training may consume substantial GPU memory and require parameter tuning for hardware constraints.
* **Rollback:** Remove Docker containers and cloned repositories. Training checkpoints are saved locally and can be deleted to reclaim storage space.
* DGX Spark uses a Unified Memory Architecture (UMA), which enables dynamic memory sharing between the GPU and CPU. With many applications still updating to take advantage of UMA, you may encounter memory issues even when within the memory capacity of DGX Spark. If that happens, manually flush the buffer cache with:
```bash
sudo sh -c 'sync; echo 3 > /proc/sys/vm/drop_caches'
```

## Instructions

## Step 1. Verify system prerequisites

Check that your NVIDIA Spark system has the required components installed and accessible.

```bash
nvcc --version
docker --version
nvidia-smi
python --version
git --version
```

## Step 2. Launch PyTorch container with GPU support

Start the NVIDIA PyTorch container with GPU access and mount your workspace directory.
> **Note:** This NVIDIA PyTorch container supports CUDA 13

```bash
docker run --gpus all --ipc=host --ulimit memlock=-1 -it --ulimit stack=67108864 --rm -v "$PWD":/workspace nvcr.io/nvidia/pytorch:25.09-py3 bash
```

## Step 3. Clone LLaMA Factory repository

Download the LLaMA Factory source code from the official repository.

```bash
git clone --depth 1 https://github.com/hiyouga/LLaMA-Factory.git
cd LLaMA-Factory
```

### Step 4. Install LLaMA Factory with dependencies

Install the package in editable mode with metrics support for training evaluation.

```bash
pip install -e ".[metrics]"
```

## Step 5. Verify Pytorch CUDA support. 

PyTorch is pre-installed with CUDA support.

To verify installation:

```bash
python -c "import torch; print(f'PyTorch: {torch.__version__}, CUDA: {torch.cuda.is_available()}')"
```

## Step 6. Prepare training configuration

Examine the provided LoRA fine-tuning configuration for Llama-3.

```bash
cat examples/train_lora/llama3_lora_sft.yaml
```

## Step 7. Launch fine-tuning training

> **Note:** Login to your hugging face hub to download the model if the model is gated.

Execute the training process using the pre-configured LoRA setup.

```bash
huggingface-cli login # if the model is gated
llamafactory-cli train examples/train_lora/llama3_lora_sft.yaml
```

Example output:
```bash
***** train metrics *****
  epoch                    =        3.0
  total_flos               = 22851591GF
  train_loss               =     0.9113
  train_runtime            = 0:22:21.99
  train_samples_per_second =      2.437
  train_steps_per_second   =      0.306
Figure saved at: saves/llama3-8b/lora/sft/training_loss.png
```

## Step 8. Validate training completion

Verify that training completed successfully and checkpoints were saved.

```bash
ls -la saves/llama3-8b/lora/sft/
```


Expected output should show:
- Final checkpoint directory (`checkpoint-21` or similar)
- Model configuration files (`config.json`, `adapter_config.json`) 
- Training metrics showing decreasing loss values
- Training loss plot saved as PNG file

## Step 9. Test inference with fine-tuned model

Test your fine-tuned model with custom prompts:

```bash
llamafactory-cli chat examples/inference/llama3_lora_sft.yaml
## Type: "Hello, how can you help me today?"
## Expect: Response showing fine-tuned behavior
```

## Step 10. For production deployment, export your model
```bash
llamafactory-cli export examples/merge_lora/llama3_lora_sft.yaml
```

## Step 11. Troubleshooting

| Symptom | Cause | Fix |
|---------|--------|-----|
| CUDA out of memory during training | Batch size too large for GPU VRAM | Reduce `per_device_train_batch_size` or increase `gradient_accumulation_steps` |
| Model download fails or is slow | Network connectivity or Hugging Face Hub issues | Check internet connection, try using `HF_HUB_OFFLINE=1` for cached models |
| Training loss not decreasing | Learning rate too high/low or insufficient data | Adjust `learning_rate` parameter or check dataset quality |

## Step 12. Cleanup and rollback

> **Warning:** This will delete all training progress and checkpoints.

To remove all generated files and free up storage space:

```bash
cd /workspace
rm -rf LLaMA-Factory/
docker system prune -f
```

To rollback Docker container changes:
```bash
exit  # Exit container
docker container prune -f
```