dgx-spark-playbooks/nvidia/unsloth/README.md

# Unsloth on DGX Spark

> Optimized fine-tuning with Unsloth

## Table of Contents

- [Overview](#overview)
- [Instructions](#instructions)

---

## Overview

## Basic idea

- **Performance-first**: It claims to speed up training (e.g. 2× faster on single GPU, up to 30× in multi-GPU setups) and reduce memory usage compared to standard methods. :contentReference[oaicite:0]{index=0}  
- **Kernel-level optimizations**: Core compute is built with custom kernels (e.g. with Triton) and hand-optimized math to boost throughput and efficiency. :contentReference[oaicite:1]{index=1}  
- **Quantization & model formats**: Supports dynamic quantization (4-bit, 16-bit) and GGUF formats to reduce footprint, while aiming to retain accuracy. :contentReference[oaicite:2]{index=2}  
- **Broad model support**: Works with many LLMs (LLaMA, Mistral, Qwen, DeepSeek, etc.) and allows training, fine-tuning, exporting to formats like Ollama, vLLM, GGUF, Hugging Face. :contentReference[oaicite:3]{index=3}  
- **Simplified interface**: Provides easy-to-use notebooks and tools so users can fine-tune models with minimal boilerplate. :contentReference[oaicite:4]{index=4}  

## What you'll accomplish

You'll set up Unsloth for optimized fine-tuning of large language models on NVIDIA Spark devices, 
achieving up to 2x faster training speeds with reduced memory usage through efficient 
parameter-efficient fine-tuning methods like LoRA and QLoRA.

## What to know before starting

- Python package management with pip and virtual environments
- Hugging Face Transformers library basics (loading models, tokenizers, datasets)
- GPU fundamentals (CUDA/GPU vs CPU, VRAM constraints, device availability)
- Basic understanding of LLM training concepts (loss functions, checkpoints)
- Familiarity with prompt engineering and base model interaction
- Optional: LoRA/QLoRA parameter-efficient fine-tuning knowledge

## Prerequisites

- NVIDIA Spark device with Blackwell GPU architecture
- `nvidia-smi` shows a summary of GPU information
- CUDA 13.0 installed: `nvcc --version`
- Internet access for downloading models and datasets

##Ancillary files

The python test script can be found [here on GitHub](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}/assets/test_unsloth.py)


## Time & risk

- **Duration**: 30-60 minutes for initial setup and test run
- **Risks**: 
- Triton compiler version mismatches may cause compilation errors
- CUDA toolkit configuration issues may prevent kernel compilation
- Memory constraints on smaller models require batch size adjustments
- **Rollback**: Uninstall packages with `pip uninstall unsloth torch torchvision`

## Instructions

## Step 1. Verify prerequisites

Confirm your NVIDIA Spark device has the required CUDA toolkit and GPU resources available.

```bash
nvcc --version
```
The output should show CUDA 13.0.

```bash
nvidia-smi
```
The output should show a summary of GPU information.

## Step 2. Get the container image
```bash
docker pull nvcr.io/nvidia/pytorch:25.08-py3
```

## Step 3. Launch Docker
```bash
docker run --gpus all --ulimit memlock=-1 -it --ulimit stack=67108864 --entrypoint /usr/bin/bash --rm nvcr.io/nvidia/pytorch:25.08-py3
```

## Step 4. Install dependencies inside Docker

```bash
pip install transformers peft datasets "trl==0.19.1"
pip install --no-deps unsloth unsloth_zoo
```

## Step 5. Build and install bitsandbytes inside Docker
```bash
git clone https://github.com/bitsandbytes-foundation/bitsandbytes.git
cd bitsandbytes
cmake -S . -B build -DCOMPUTE_BACKEND=cuda -DCOMPUTE_CAPABILITY="80;86;87;89;90"
cd build
make -j
cd ..
pip install .
```

## Step 6. Create Python test script

Curl the test script [here](https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}/assets/test_unsloth.py) into the container.

```bash

curl -O https://gitlab.com/nvidia/dgx-spark/temp-external-playbook-assets/dgx-spark-playbook-assets/-/blob/main/${MODEL}/assets/test_unsloth.py

We will use this test script to validate the installation with a simple fine-tuning task.


## Step 7. Run the validation test

Execute the test script to verify Unsloth is working correctly.

```bash
python test_unsloth.py
```

Expected output in the terminal window:
- "Unsloth: Will patch your computer to enable 2x faster free finetuning"
- Training progress bars showing loss decreasing over 60 steps
- Final training metrics showing completion

## Step 8. Next steps

Test with your own model and dataset by updating the `test_unsloth.py` file:

```python
## Replace line 32 with your model choice
model_name = "unsloth/Meta-Llama-3.1-8B-bnb-4bit"

## Load your custom dataset in line 8
dataset = load_dataset("your_dataset_name")

## Adjust training parameter args at line 61
per_device_train_batch_size = 4
max_steps = 1000
```

Visit https://github.com/unslothai/unsloth/wiki
for advanced usage instructions, including:
- [Saving models in GGUF format for vLLM](https://github.com/unslothai/unsloth/wiki#saving-to-gguf)
- [Continued training from checkpoints](https://github.com/unslothai/unsloth/wiki#loading-lora-adapters-for-continued-finetuning)
- [Using custom chat templates](https://github.com/unslothai/unsloth/wiki#chat-templates)
- [Running evaluation loops](https://github.com/unslothai/unsloth/wiki#evaluation-loop---also-fixes-oom-or-crashing)