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funsloth-train

@chrisvoncsefalvay/funsloth
4
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Generate Unsloth training notebooks and scripts. Use when the user wants to create a training notebook, configure fine-tuning parameters, or set up SFT/DPO/GRPO training.

Install Skill

1Download skill
2Enable skills in Claude

Open claude.ai/settings/capabilities and find the "Skills" section

3Upload to Claude

Click "Upload skill" and select the downloaded ZIP file

Note: Please verify skill by going through its instructions before using it.

SKILL.md

name funsloth-train
description Generate Unsloth training notebooks and scripts. Use when the user wants to create a training notebook, configure fine-tuning parameters, or set up SFT/DPO/GRPO training.

Unsloth Training Notebook Generator

Generate training notebooks for fine-tuning with Unsloth.

Quick Start

Copy and customize the template notebook:

notebooks/sft_template.ipynb

Or use a training script directly:

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:

  1. Sensible defaults - Production-ready notebook with recommended settings
  2. Guide me - Walk through each option with explanations
  3. 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

  1. Model family: Llama, Qwen, Gemma, Phi, Mistral, DeepSeek?
  2. Model size: Based on VRAM (see HARDWARE_GUIDE.md)
  3. Training technique: SFT, DPO, GRPO, ORPO, KTO?
  4. Quantization: 4-bit (recommended), 8-bit, 16-bit?
  5. LoRA rank: 8, 16, 32, 64?
  6. Sequence length: 512, 1024, 2048, 4096?
  7. Batch size: 1, 2, 4, 8?
  8. Learning rate: 1e-5, 5e-5, 2e-4, 5e-4?
  9. 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:

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

After Generation

Ask where to run training:

  1. Hugging Face Jobs - Cloud GPUs (funsloth-hfjobs)
  2. RunPod - Flexible GPU rentals (funsloth-runpod)
  3. Local - Your own GPU (funsloth-local)

Context to Pass

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