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Training manager for Hugging Face Jobs - launch fine-tuning on HF cloud GPUs with optional WandB monitoring
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Based on SOC occupation classification
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.
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-hfjobs |
| description | Training manager for Hugging Face Jobs - launch fine-tuning on HF cloud GPUs with optional WandB monitoring |
Run Unsloth training on Hugging Face Jobs (cloud GPU training).
huggingface-cli whoami (login if needed)funsloth-train| GPU | VRAM | Cost | Best For |
|---|---|---|---|
| A10G | 24GB | ~$1.50/hr | 7-14B LoRA |
| A100 40GB | 40GB | ~$4/hr | 14-34B |
| A100 80GB | 80GB | ~$6/hr | 70B |
| H100 | 80GB | ~$8/hr | Fastest |
See references/HARDWARE_GUIDE.md for model-to-GPU mapping.
HF Jobs requires PEP 723 script format:
# /// script
# requires-python = ">=3.10"
# dependencies = [
# "unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git",
# "torch>=2.0",
# "transformers>=4.45",
# "trl>=0.12",
# "peft>=0.13",
# "datasets>=2.18",
# ]
# ///
Use scripts/train_sft.py as a template.
Add to script:
import wandb
wandb.init(project="funsloth-training")
# Add report_to="wandb" in TrainingArguments
Set: export WANDB_API_KEY="your-key"
Use the cost estimator:
python scripts/estimate_cost.py --tokens {total_tokens} --platform hfjobs
# Create job config
cat > job_config.yaml << 'EOF'
compute:
gpu: {gpu_type}
gpu_count: 1
script: train_hfjobs.py
outputs:
- /outputs/*
EOF
# Submit
huggingface-cli jobs create --config job_config.yaml
huggingface-cli jobs status {job_id}
huggingface-cli jobs logs {job_id} --follow
WandB: https://wandb.ai/{username}/funsloth-training
from huggingface_hub import snapshot_download
snapshot_download(repo_id="{username}/funsloth-job", local_dir="./outputs")
Offer funsloth-upload for Hub upload with model card.
| Error | Resolution |
|---|---|
| No HF Jobs access | Get PRO subscription |
| OOM | Reduce batch size or upgrade GPU |
| Job timeout | Enable checkpointing |
| Script error | Check PEP 723 dependencies |