القائمة
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.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
Training manager for local GPU training - validate CUDA, manage GPU selection, monitor progress, handle checkpoints
Training manager for RunPod GPU instances - configure pods, launch training, monitor progress, retrieve checkpoints
Validate datasets for Unsloth fine-tuning. Use when the user wants to check a dataset, analyze tokens, calculate Chinchilla optimality, or prepare data for training.
Training manager for Hugging Face Jobs - launch fine-tuning on HF cloud GPUs with optional WandB monitoring
Generate comprehensive model cards and upload fine-tuned models to Hugging Face Hub with professional documentation
| 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. |
Generate training notebooks for fine-tuning with Unsloth.
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
Ask the user which mode they prefer:
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 |
Ask questions in order. See MODEL_SELECTION.md for model options and TRAINING_METHODS.md for technique details.
Generate a notebook with interactive configuration widgets. Users select options at runtime.
Generate notebooks with these sections:
Ask where to run training:
funsloth-hfjobs)funsloth-runpod)funsloth-local)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