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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.
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SOC 職業分類に基づく
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
Training manager for Hugging Face Jobs - launch fine-tuning on HF cloud GPUs with optional WandB monitoring
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 comprehensive model cards and upload fine-tuned models to Hugging Face Hub with professional documentation
| name | funsloth-check |
| description | 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. |
Validate datasets before fine-tuning with Unsloth.
For automated validation, use the script:
python scripts/validate_dataset.py --dataset "dataset-id" --model llama-3.1-8b --lora-rank 16
Ask for: HF dataset ID (e.g., mlabonne/FineTome-100k) or local path (e.g., ./data.jsonl)
Auto-detect format from structure. See DATA_FORMATS.md for details.
| Format | Detection | Key Fields |
|---|---|---|
| Raw | text only | text |
| Alpaca | instruction + output | instruction, output |
| ShareGPT | conversations array | from, value |
| ChatML | messages array | role, content |
Check required fields exist. Report issues with fix suggestions.
Display 2-3 examples for visual verification.
Report statistics: total tokens, min/max/mean/median sequence length.
Flag concerns:
Ask for target model and LoRA rank, then calculate:
| Chinchilla Fraction | Interpretation |
|---|---|
| < 0.5x | Dataset may be too small |
| 0.5x - 2.0x | Good range |
| > 2.0x | Large dataset, may take longer |
Based on analysis, suggest:
standardize_sharegpt() for ShareGPT dataOffer to upload local datasets to Hub.
Pass context to funsloth-train:
dataset_id: "mlabonne/FineTome-100k"
format_type: "sharegpt"
total_tokens: 15000000
target_model: "llama-3.1-8b"
use_lora: true
lora_rank: 16
chinchilla_fraction: 1.2