From chrisvoncsefalvay-funsloth
Training manager for Hugging Face Jobs - launch fine-tuning on HF cloud GPUs with optional WandB monitoring
npx claudepluginhub joshuarweaver/cascade-ai-ml-engineering --plugin chrisvoncsefalvay-funslothThis skill uses the workspace's default tool permissions.
Run Unsloth training on Hugging Face Jobs (cloud GPU training).
Generates design tokens/docs from CSS/Tailwind/styled-components codebases, audits visual consistency across 10 dimensions, detects AI slop in UI.
Records polished WebM UI demo videos of web apps using Playwright with cursor overlay, natural pacing, and three-phase scripting. Activates for demo, walkthrough, screen recording, or tutorial requests.
Delivers idiomatic Kotlin patterns for null safety, immutability, sealed classes, coroutines, Flows, extensions, DSL builders, and Gradle DSL. Use when writing, reviewing, refactoring, or designing Kotlin code.
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 |