Hugging Face LLM Trainer
AI & MLby huggingface
2/3 audits passApache-2.0
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure.
Hugging Face LLM Trainer, published by Hugging Face, teaches an agent to fine-tune language and vision models with TRL or Unsloth on Hugging Face Jobs cloud GPUs. It covers the TRL methods SFT, DPO, GRPO and reward modeling, and when to prefer Unsloth for memory-limited, larger or vision-language training. Key directives include submitting jobs through the hf_jobs MCP tool with the script passed inline, always including Trackio for monitoring, and reporting the job ID and monitoring link after submission. It walks through a prerequisites checklist, UV scripts in PEP 723 format, hardware selection, timeouts, saving results to the Hub so they are not lost, cost estimation, choosing a base model from benchmarks, validating dataset format before training, GGUF conversion for local use, and common failure modes such as out-of-memory errors and misformatted datasets.
Use it when you want to train or fine-tune a model without a local GPU. It assumes a Hugging Face account and token and spends real compute, so it is not a local inference or evaluation guide.
Its example training, inspection and conversion scripts run in a coding environment such as Claude Code. It is published under Apache-2.0 and importable into BusinessMCP as instructions only.
What you can do with it
- Run supervised fine-tuning on a cloud GPU via Hugging Face Jobs
- Train with DPO on preference data after validating its format
- Estimate cost and pick hardware before a training run
- Convert a trained model to GGUF for local use
Run it on your business data
Imported into BusinessMCP, Hugging Face LLM Trainer becomes a playbook your AI business analyst applies to your connected tools and MCP servers.
Use Hugging Face LLM Trainer in BusinessMCPInstall it in a coding agent
One command adds Hugging Face LLM Trainer to your project.
npx skills add https://github.com/huggingface/skills --skill huggingface-llm-trainerHow we vetted it
- Source
- huggingface/skills at 80f9fa5
- Licence
- Apache-2.0
- Security audits (skills.sh)
- Gen Agent Trust Hub: Pass · Socket: Warn · Snyk: Pass
- Bundled scripts
- Yes — read them before installing
- Reviewed by hand
- includes commands that use sudo.
Checked 2026-09-25 against its skills.sh listing. How we vet skills
Related skills
All skillsHugging Face Datasets
Use this skill for Hugging Face Dataset Viewer API workflows that fetch subset/split metadata, paginate rows, search text, apply filters, download parquet URLs, and read size or statistics.
Claude API
Reference for the Claude API / Anthropic SDK — model ids, pricing, params, streaming, tool use, MCP, agents, caching, token counting, model migration. TRIGGER — read BEFORE opening the target file;
Jupyter Notebook
Use when the user asks to create, scaffold, or edit Jupyter notebooks (`.ipynb`) for experiments, explorations, or tutorials;
Frequently asked questions
Which training methods does it cover?
SFT, DPO, GRPO and reward modeling with TRL, plus Unsloth for lower memory use and vision-language models.
Is Hugging Face LLM Trainer safe to install?
Our static scan flagged sudo because the bundled GGUF conversion script prints sudo package-install commands for build tools such as cmake if they are missing. It does not require root to train, and the skill passed the skills.sh security auditors with no FAIL.
Where are trained models saved?
The skill stresses pushing results to the Hugging Face Hub, with a required configuration and verification checklist, so a finished job is not lost.
Run Hugging Face LLM Trainer against your whole business
Free plan, no credit card.
Use Hugging Face LLM Trainer in BusinessMCP