ml-training

Name: ml-training
Availability: InStock
Author: neuron-one

Automate end-to-end HuggingFace model fine-tuning workflows with sft, dpo, or grpo.

25|10|Updated Apr 1, 2026

Search Tags:#gpu #datasets #huggingface #finetuning #ml-training #training-monitoring

Ready to run

npx skills add https://github.com/neuron-one/GODMODE --skill ml-training-neuron-one

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: ml-training
Source: https://github.com/neuron-one/GODMODE/tree/main/skills/ai-training/ml-training
Command: npx skills add https://github.com/neuron-one/GODMODE --skill ml-training-neuron-one

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates end-to-end training workflows for fine-tuning language models on custom datasets using HuggingFace, reducing setup time and tedious experimentation.

Core Features & Use Cases

Fine-tune LLMs with transfer learning, sft, dpo, or grpo on your domain data.
Create and preprocess datasets, manage experiments, and evaluate model performance.
Monitor training runs, select appropriate GPUs, and estimate training costs for production workloads.

Quick Start

Provide dataset, select a base model, and start a training run with HuggingFace using default settings.

Frequently Asked Questions about ml-training

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I automate fine-tuning HuggingFace models on custom datasets?▼

Automating HuggingFace fine-tuning on custom datasets involves orchestrating end-to-end workflows for dataset preparation, experiment tracking, and training run execution. This process supports transfer learning, sft, dpo, and grpo across domain-specific tasks.

Can I use SFT, DPO, and GRPO for HuggingFace language model training?▼

Yes, HuggingFace language model training supports Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), and Group Relative Policy Optimization (GRPO). These methods allow you to adapt small to large models using domain-specific data and GPU-enabled environments.

What's the best way to estimate GPU costs for fine-tuning large language models?▼

Estimating GPU costs for fine-tuning large language models requires selecting appropriate hardware and calculating workload duration. By evaluating model size and dataset volume, you can project production training costs and monitor runs to optimize resource allocation.

Do I need a GPU-enabled environment to run HuggingFace training workflows?▼

Yes, a GPU-enabled environment is required to run HuggingFace training workflows for large language models. Hardware selection is integrated into the workflow to ensure sufficient compute capacity for processing domain-specific datasets and executing fine-tuning.

Does HuggingFace fine-tuning support dataset preparation and experiment tracking?▼

Yes, HuggingFace fine-tuning workflows support dataset preparation and experiment tracking. You can create and preprocess datasets, manage experimental configurations, evaluate model performance, and monitor training runs to ensure deployment readiness.