القائمة
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.
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
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