القائمة
Training manager for Hugging Face Jobs - launch fine-tuning on HF cloud GPUs with optional WandB monitoring
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
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 |