Unsloth Training Notebook Generator

Generate training notebooks for fine-tuning with Unsloth.

Quick Start

Copy and customize the template notebook:

code

notebooks/sft_template.ipynb

Or use a training script directly:

bash

python scripts/train_sft.py  # Supervised fine-tuning
python scripts/train_dpo.py  # Direct preference optimization
python scripts/train_grpo.py # Group relative policy optimization

Configuration Modes

Ask the user which mode they prefer:

•Sensible defaults - Production-ready notebook with recommended settings
•Guide me - Walk through each option with explanations
•Leave it empty - Notebook with ipywidgets for runtime configuration

Mode 1: Sensible Defaults

Use these production-ready defaults:

Parameter	Default	Reasoning
Model	`unsloth/llama-3.1-8b-unsloth-bnb-4bit`	Good balance
Max seq length	2048	Covers most use cases
Load in 4-bit	True	70% VRAM reduction
LoRA rank	16	Good trade-off
Batch size	2	Works on 8GB+ VRAM
Gradient accumulation	4	Effective batch of 8
Learning rate	2e-4	Unsloth recommended
Epochs	1	Often sufficient

Mode 2: Guide Me

Ask questions in order. See MODEL_SELECTION.md for model options and TRAINING_METHODS.md for technique details.

Key Questions

•Model family: Llama, Qwen, Gemma, Phi, Mistral, DeepSeek?
•Model size: Based on VRAM (see HARDWARE_GUIDE.md)
•Training technique: SFT, DPO, GRPO, ORPO, KTO?
•Quantization: 4-bit (recommended), 8-bit, 16-bit?
•LoRA rank: 8, 16, 32, 64?
•Sequence length: 512, 1024, 2048, 4096?
•Batch size: 1, 2, 4, 8?
•Learning rate: 1e-5, 5e-5, 2e-4, 5e-4?
•Training duration: 1 epoch, 3 epochs, or specific steps?

Mode 3: ipywidgets

Generate a notebook with interactive configuration widgets. Users select options at runtime.

Notebook Structure

Generate notebooks with these sections:

•Title and Overview - What the notebook does
•Installation - Install Unsloth
•Imports and GPU Check - Verify environment
•Configuration - All tunable parameters
•Load Model - FastLanguageModel.from_pretrained()
•Apply LoRA - FastLanguageModel.get_peft_model()
•Load Dataset - Format-appropriate loading
•Training - SFTTrainer/DPOTrainer/GRPOTrainer
•Save Model - LoRA adapter + merged model
•Test Inference - Quick verification

After Generation

Ask where to run training:

•Hugging Face Jobs - Cloud GPUs (funsloth-hfjobs)
•RunPod - Flexible GPU rentals (funsloth-runpod)
•Local - Your own GPU (funsloth-local)

Context to Pass

yaml

notebook_path: "./training_notebook.ipynb"
model_name: "unsloth/llama-3.1-8b-unsloth-bnb-4bit"
dataset_name: "mlabonne/FineTome-100k"
technique: "SFT"
lora_rank: 16
max_seq_length: 2048
batch_size: 2
learning_rate: 2e-4
num_epochs: 1

Bundled Resources

•notebooks/sft_template.ipynb - Ready-to-use SFT template
•scripts/train_sft.py - SFT script template
•scripts/train_dpo.py - DPO script template
•scripts/train_grpo.py - GRPO script template
•references/MODEL_SELECTION.md - Model recommendations
•references/HARDWARE_GUIDE.md - VRAM requirements
•references/TRAINING_METHODS.md - SFT vs DPO vs GRPO