菜单
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.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
决定是否安装前，请先阅读 SKILL.md，以及 SkillsMP 当前展示的配套文件。
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt；运行前请先检查来源。
npx skills add https://github.com/chrisvoncsefalvay/funsloth --skill funsloth-train命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地？可下载 SkillsMP 当前能够提供的文件。
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.
基于 SOC 职业分类
正在显示 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. |
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