メニュー
Training manager for RunPod GPU instances - configure pods, launch training, monitor progress, retrieve checkpoints
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
Training manager for local GPU training - validate CUDA, manage GPU selection, monitor progress, handle 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.
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-runpod |
| description | Training manager for RunPod GPU instances - configure pods, launch training, monitor progress, retrieve checkpoints |
Run Unsloth training on RunPod GPU instances.
echo $RUNPOD_API_KEY (get at runpod.io/console/user/settings)pip install runpodfunsloth-train| GPU | VRAM | Cost | Best For |
|---|---|---|---|
| RTX 3090 | 24GB | ~$0.35/hr | Budget 7-14B |
| RTX 4090 | 24GB | ~$0.55/hr | Fast 7-14B |
| A100 40GB | 40GB | ~$1.50/hr | 14-34B |
| A100 80GB | 80GB | ~$2.00/hr | 70B |
| H100 | 80GB | ~$3.50/hr | Fastest |
RunPod typically has better prices than HF Jobs.
import runpod
volume = runpod.create_network_volume(name="funsloth-training", size_gb=50, region="US")
Allows: resume training, download checkpoints, share between pods.
Use the official Unsloth Docker image for a pre-configured environment:
import runpod
pod = runpod.create_pod(
name="funsloth-training",
image_name="unsloth/unsloth", # Official image, supports all GPUs incl. Blackwell
gpu_type_id="{gpu_type}",
volume_in_gb=50,
network_volume_id="{volume_id}",
env={
"HF_TOKEN": "{token}",
"WANDB_API_KEY": "{key}",
"JUPYTER_PASSWORD": "unsloth",
},
ports="8888/http,22/tcp",
)
The Unsloth image includes Jupyter Lab (port 8888) and example notebooks in /workspace/unsloth-notebooks/.
# SSH into pod
ssh root@{pod_ip}
# Upload script
scp train.py root@{pod_ip}:/workspace/
# Run training (use tmux for persistence)
tmux new -s training
cd /workspace && python train.py
# Ctrl+B, D to detach
# SSH monitoring
tail -f /workspace/training.log
nvidia-smi -l 1
# Dashboard
https://runpod.io/console/pods/{pod_id}
# Save to network volume
cp -r /workspace/outputs /runpod-volume/
# Download via SCP
scp -r root@{pod_ip}:/workspace/outputs ./
# Or push to HF Hub from pod
runpod.stop_pod(pod_id) # Can resume later
runpod.terminate_pod(pod_id) # Deletes pod, keeps volume
Offer funsloth-upload for Hub upload with model card.
save_steps| Error | Resolution |
|---|---|
| Pod creation failed | Try different GPU type or region |
| SSH refused | Wait 1-2 min, check IP |
| Out of disk | Increase volume or clean up |
| Volume not mounting | Check same region as pod |