What is Funsloth RunPod?

It is a Claude Code Skill designed to simplify and automate the process of running Unsloth-based LLM fine-tuning on RunPod's GPU infrastructure.

Why should I use RunPod for fine-tuning?

RunPod offers cost-effective access to high-performance GPUs like the A100 and H100, often at lower prices than traditional cloud providers or serverless alternatives.

Does this skill handle model checkpoints?

Yes, it provides guidance and workflows for using network volumes to ensure your checkpoints are saved persistently and can be retrieved even after a pod is terminated.

Can I train 70B parameter models with this tool?

Absolutely. The skill includes GPU selection guides that recommend high-VRAM instances like the A100 80GB specifically for large 70B models.

Do I need a RunPod API key to use this skill?

Yes, you must have a valid RunPod API key and the RunPod Python SDK installed to interact with the service.

Funsloth RunPod Training Manager

Name: Funsloth RunPod Training Manager
Author: chrisvoncsefalvay

bychrisvoncsefalvay

•

データサイエンスとML

Automates the lifecycle of LLM fine-tuning workloads on RunPod GPU instances using the Unsloth framework.

概要

Funsloth RunPod is a specialized Claude Code skill designed to streamline the complexities of training large language models on cloud infrastructure. It provides a standardized workflow for selecting cost-effective GPUs, provisioning persistent network volumes, and deploying the official Unsloth Docker environment. By integrating monitoring, checkpoint management, and pod lifecycle commands directly into the development workflow, it allows researchers and developers to focus on model performance rather than infrastructure plumbing.

主な機能

Automated GPU pod provisioning and configuration
Pre-configured Unsloth Docker image deployment
4 GitHub stars
Cost-aware GPU selection and lifecycle control
Real-time SSH and log-based training monitoring
Integrated network volume management for persistent data

ユースケース

Scaling training to 70B parameter models using A100 or H100 GPUs
Fine-tuning 7B-14B models on budget-friendly RTX 3090/4090 instances
Setting up reproducible, isolated training environments for team projects

概要

主な機能

Automated GPU pod provisioning and configuration
Pre-configured Unsloth Docker image deployment
4 GitHub stars
Cost-aware GPU selection and lifecycle control
Real-time SSH and log-based training monitoring
Integrated network volume management for persistent data

ユースケース

Scaling training to 70B parameter models using A100 or H100 GPUs
Fine-tuning 7B-14B models on budget-friendly RTX 3090/4090 instances
Setting up reproducible, isolated training environments for team projects