| name | funsloth-hfjobs |
| description | Training manager for Hugging Face Jobs - launch fine-tuning on HF cloud GPUs with optional WandB monitoring |
Hugging Face Jobs Training Manager
Run Unsloth training on Hugging Face Jobs (cloud GPU training).
Prerequisites
- HF Authentication:
huggingface-cli whoami (login if needed)
- HF Jobs Access: Requires PRO subscription or org compute access
- Training notebook/script: From
funsloth-train
Workflow
1. Select Hardware
| 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.
2. Convert Notebook to Script
HF Jobs requires PEP 723 script format:
Use scripts/train_sft.py as a template.
3. Optional: WandB Integration
Add to script:
import wandb
wandb.init(project="funsloth-training")
Set: export WANDB_API_KEY="your-key"
4. Estimate Costs
Use the cost estimator:
python scripts/estimate_cost.py --tokens {total_tokens} --platform hfjobs
5. Launch Job
cat > job_config.yaml << 'EOF'
compute:
gpu: {gpu_type}
gpu_count: 1
script: train_hfjobs.py
outputs:
- /outputs/*
EOF
huggingface-cli jobs create --config job_config.yaml
6. Monitor Progress
huggingface-cli jobs status {job_id}
huggingface-cli jobs logs {job_id} --follow
WandB: https://wandb.ai/{username}/funsloth-training
7. Download Artifacts
from huggingface_hub import snapshot_download
snapshot_download(repo_id="{username}/funsloth-job", local_dir="./outputs")
8. Handoff
Offer funsloth-upload for Hub upload with model card.
Error Handling
| 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 |
Bundled Resources
