القائمة
Training manager for RunPod GPU instances - configure pods, launch training, monitor progress, retrieve checkpoints
يُحدَّد Prompt الذي يراجع المصدر أولًا بشكل افتراضي. يمكنك التبديل إلى أمر مباشر أو تنزيل نسخة محلية.
اقرأ SKILL.md وأي ملفات مرافقة يعرضها SkillsMP قبل أن تقرر التثبيت.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
يتجاوز الأمر المباشر Prompt المخصّص للمراجعة. افحص المصدر قبل تشغيله.
npx skills add https://github.com/chrisvoncsefalvay/funsloth --skill funsloth-runpodيبقى الأمر في سطر واحد. مرّر أفقيًا لمراجعته كاملًا قبل النسخ.
تفضّل نسخة محلية؟ نزّل الملفات المتاحة حاليًا لدى SkillsMP.
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
استنادا إلى تصنيف SOC المهني
عرض SKILL.md
| 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 |