atuna 0.2.1

A Fine-tuning assistant for large language models

A tasty layer on top of Unsloth for Large Language Model fine-tuning

Atuna: A fine-tuning assistant for large language models with built-in hyperparameter optimization.

Features

• Easy fine-tuning with Unsloth integration for efficient training
• Hyperparameter optimization with Optuna for automated tuning
• Built-in evaluation and monitoring capabilities
• TensorBoard integration for training visualization

Installation

From source (development)

git clone https://github.com/mapa17/atuna.git
cd atuna
uv sync
uv pip install -e .

Using the library

pip install atuna  # When published to PyPI

Quick Start

Basic Fine-tuning

from tuna import Tuna, TunaConfig, TrainingConfig, model_registry

# Configure your model
model = model_registry["unsloth/Qwen3-4B-Instruct-2507"]
config = TunaConfig(
    model_cfg=model,
    dataset_path="./data/training_set.csv",
    max_seq_length=2048,
    precision=16,  # Use 16-bit precision
)

# Create trainer
tuna = Tuna(config=config)

# Configure training
training_config = TrainingConfig(
    num_train_epochs=3,
    batch_size=1,
    learning_rate=5e-5,
    eval_epochs=1.0,
    enable_early_stopping=True,
)

# Train the model
result = tuna.train(config=training_config)
print(f"Training completed: {result.stop_reason}")

Hyperparameter Optimization

from tuna import Tuna, TunaConfig, TrainingConfig, HyperparpamConfig, model_registry

# Setup configuration
model = model_registry["unsloth/Qwen3-4B-Instruct-2507"]
config = TunaConfig(
    model_cfg=model,
    dataset_path="./data/training_set.csv",
    max_seq_length=2048,
    precision=4,  # Use 4-bit quantization for efficiency
)

tuna = Tuna(config=config)

# Configure training
training_config = TrainingConfig(
    num_train_epochs=2,
    eval_epochs=0.25,
    batch_size=1,
    data_sample=0.3,  # Use subset for faster experimentation
)

# Configure hyperparameter search
hyper_config = HyperparpamConfig(
    n_trials=10,
    learning_rate=[1e-5, 5e-5, 7e-5, 1e-4],
    weight_decay=[0.001, 0.01, 0.1],
    peft_r=[16, 32],
    lora_alpha=[32, 50, 64],
    enable_slora=True,
)

# Run optimization
study = tuna.hyperparam_tune(
    study_name="MyOptimization",
    train_config=training_config,
    hyper_config=hyper_config,
)

print(f"Best parameters: {study.best_trial.params}")

Model Evaluation

# Evaluate trained model
responses = tuna.evaluate_prompts([
    "What is machine learning?",
    "Explain fine-tuning in simple terms.",
])

for response in responses:
    print(f"Response: {response}")

CLI Usage

# Show version
tuna --version

# List available models
tuna --list-models

Data Format

Your training data should be a CSV file with request and response columns:

request,response
"What is Python?","Python is a programming language..."
"How does machine learning work?","Machine learning works by..."

Monitoring and Visualization

Optuna Dashboard

# View hyperparameter optimization results
optuna-dashboard sqlite:///./atuna_workspace/optuna_studies.db

TensorBoard

# View training metrics
tensorboard --logdir ./atuna_workspace/logs

Supported Models

• unsloth/Qwen3-4B-Instruct-2507
• unsloth/Qwen3-0.6B-GGUF

More models can be easily added to the model_registry.

Configuration Options

TunaConfig

• model_cfg: Model configuration from registry
• dataset_path: Path to training CSV file
• max_seq_length: Maximum sequence length (default: 2048)
• precision: Training precision - 4, 8, or 16 bit (default: 16)
• peft_r: LoRA rank parameter (default: 32)
• workspace: Working directory (default: "./atuna_workspace")

TrainingConfig

• learning_rate: Learning rate (default: 2e-5)
• batch_size: Batch size (default: 1)
• num_train_epochs: Number of training epochs (default: 1.0)
• eval_epochs: Evaluation frequency in epochs
• enable_early_stopping: Enable early stopping (default: True)
• data_sample: Fraction of data to use (default: 1.0)

HyperparpamConfig

• n_trials: Number of optimization trials
• learning_rate: List of learning rates to try
• peft_r: List of LoRA rank values to try
• lora_alpha: List of LoRA alpha values to try
• weight_decay: List of weight decay values to try

Examples

See the examples/ directory for complete usage examples:

• examples/basic_finetuning.py - Basic fine-tuning workflow
• examples/hyperparameter_search.py - Hyperparameter optimization

Requirements

• Python 3.12+
• CUDA-capable GPU
• See pyproject.toml for full dependency list

Development

# Clone repository
git clone https://github.com/mapa17/atuna.git
cd atuna

# Install development dependencies
uv sync --group dev

# Install pre-commit hooks
pre-commit install

# Run tests
pytest

# Build package
uv build

License

MIT License - see LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.