Unsloth Fine-Tune Template

Linux only — This template is designed for Linux systems with NVIDIA GPU (CUDA), AMD GPU (ROCm), or Vulkan support.

A template for fine-tuning LLMs using Unsloth and converting to GGUF format with llama.cpp.

Prerequisites

• Linux OS
• Python 3.10+
• NVIDIA GPU (CUDA) or AMD GPU (ROCm) or Vulkan-compatible GPU
• cmake
• git

Quick Start

# 1. Setup (clones llama.cpp, builds it, installs dependencies)
bash setup.sh

# 2. Configure scripts (see variables below)

# 3. Run full pipeline
bash run-pipeline.sh

Workflow

scripts/generate-data.sh   → Generate synthetic training data (optional)
scripts/finetune.sh        → Fine-tune model with LoRA adapters
scripts/merge-and-convert.sh → Merge LoRA into base model and convert to GGUF
scripts/run-model.sh       → Run the converted GGUF model
run-pipeline.sh            → Run finetune → merge/convert → run in sequence

Setup

setup.sh will:

1. Create a Python virtual environment and install Python dependencies
2. Clone llama.cpp
3. Build llama.cpp with your selected GPU backend
4. Install llama-cpp-python bindings with matching backend flags

Backend Selection

Choice	Backend	Requirements
1	CUDA (NVIDIA)	NVIDIA drivers, CUDA toolkit
2	ROCm (AMD)	AMD drivers, HIP toolkit
3	Vulkan	Vulkan drivers
4	CPU only	None

Scripts

1. scripts/generate-data.sh

Generates synthetic training data using a GGUF model via llama.cpp. Run this if you need to create or extend a training dataset.

Edit synthetic-data.py:

Variable	Description	Example
GGUF_MODEL_PATH	Path to the GGUF model used for generation	./path/to/model.gguf
INPUT_PARQUET_PATH	Path to existing training data to extend	./data/train.parquet
OUTPUT_PARQUET_PATH	Path to save the combined dataset	./data/output.parquet
NEW_ROWS_COUNT	Number of synthetic records to generate	100

bash scripts/generate-data.sh

2. scripts/finetune.sh

Fine-tunes a model using Unsloth with LoRA adapters. Saves LoRA weights to ./model/.

Edit finetune.py:

Variable	Description	Example
DATA_PATH	Path to training Parquet file	./data/output.parquet
OUTPUT_DIR	Directory to save LoRA adapters	./model
BATCH_SIZE	Per-device batch size	2
GRADIENT_ACCUMULATION_STEPS	Gradient accumulation steps	8
LEARNING_RATE	Training learning rate	2e-4
MAX_LENGTH	Maximum sequence length	4096
TRAIN_EPOCHS	Number of training epochs	1
model_name (line 74)	Base model to fine-tune	"unsloth/Llama-3.2-3B-Instruct"

bash scripts/finetune.sh

3. scripts/merge-and-convert.sh

Merges LoRA adapters into the base model, saves the merged model, then converts to GGUF format using llama.cpp.

Edit merge.py:

Variable	Description	Example
BASE_MODEL_PATH	Path to the base model	"" (empty to load from HuggingFace)
LORA_DIR	Path to LoRA adapters	./model
MERGED_MODEL_PATH	Output directory for merged model	./merged_model

bash scripts/merge-and-convert.sh

4. scripts/run-model.sh

Runs the converted GGUF model using llama.cpp's CLI interface for inference.

Edit run-model.sh:

Variable	Description	Example
Model path	Path to the GGUF file	./merged_model/model.gguf

bash scripts/run-model.sh

Output Structure

./model/                  ← LoRA adapters (from finetune.sh)
./merged_model/           ← Merged HF model + GGUF file (from merge-and-convert.sh)
llama.cpp/                ← llama.cpp repository (created by setup.sh)
scripts/                  ← Individual pipeline step scripts
setup.sh                  ← Setup script (venv + llama.cpp build)
run-pipeline.sh           ← Run full pipeline (finetune → merge/convert → run)

Troubleshooting

llama.cpp build fails

See the official build guide: https://github.com/ggml-org/llama.cpp/blob/master/docs/build.md

Common issues:

• CUDA: Ensure NVIDIA drivers and CUDA toolkit are installed
• ROCm: Ensure AMD drivers and HIP toolkit are installed
• Vulkan: Ensure Vulkan drivers and SDK are installed
• cmake: Install via sudo apt install cmake (Debian/Ubuntu)

Out of memory during training

• Reduce BATCH_SIZE in finetune.py
• Increase GRADIENT_ACCUMULATION_STEPS to compensate
• Reduce MAX_LENGTH to fit shorter sequences
• Set load_in_4bit=True in finetune.py (line 77)

llama-cpp-python install fails

• Ensure llama.cpp is built successfully first
• Try CPU-only install first to verify: pip install llama-cpp-python
• Check llama-cpp-python docs for other backends

Project Structure

├── finetune.py           ← Training script
├── merge.py              ← Merge LoRA into base model
├── synthetic-data.py     ← Generate synthetic training data
├── requirements.txt      ← Python dependencies
├── setup.sh              ← One-time setup
├── run-pipeline.sh       ← Run full pipeline
├── scripts/
│   ├── generate-data.sh
│   ├── finetune.sh
│   ├── merge-and-convert.sh
│   └── run-model.sh
└── README.md