Hugging Face LLM Trainer Agent Skill

Train or fine-tune language and vision models using TRL or Unsloth with Hugging Face Jobs infrastructure.

Context cost
26.9k tokensestimated from the bundle, loaded when it triggers
Bundle
19 files · 186.6 kB8 scripts among them — read before you run
Licence
Complete terms in LICENSE.txtthe author’s own terms
Last change
—no release on file
Servers it uses
Noneruns standalone

What it does

Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Covers SFT, DPO, GRPO and reward modeling training methods, plus GGUF conversion for local deployment. Includes guidance on the TRL Jobs package, UV scripts with PEP 723 format, dataset preparation and validation, hardware selection, cost estimation, Trackio monitoring, Hub authentication, model selection/leaderboards and model persistence.

What this skill changes about the agent has not been set out here as a list. Its SKILL.md, shown below, says it in the author’s own words.

fine-tuninghuggingfacellmtraining

The skill itself

A skill is instructions the model reads, so you can see what you are getting: below is the start of its SKILL.md. The front matter loads with every session, the body loads when the skill triggers, and the whole file comes with the install.

SKILL.md28.8 kB · preview
--- name: huggingface-llm-trainer license: Complete terms in LICENSE.txt ---
7# TRL Training on Hugging Face Jobs
8
9## Overview
10
11Train language models using TRL (Transformer Reinforcement Learning) on fully managed Hugging Face infrastructure. No local GPU setup required—models train on cloud GPUs and results are automatically saved to the Hugging Face Hub.
12
13**TRL provides multiple training methods:**
14- **SFT** (Supervised Fine-Tuning) - Standard instruction tuning
15- **DPO** (Direct Preference Optimization) - Alignment from preference data
16- **GRPO** (Group Relative Policy Optimization) - Online RL training
17- **Reward Modeling** - Train reward models for RLHF
18
19**For detailed TRL method documentation:**
20```python
21hf_doc_search("your query", product="trl")
22hf_doc_fetch("https://huggingface.co/docs/trl/sft_trainer") # SFT
23hf_doc_fetch("https://huggingface.co/docs/trl/dpo_trainer") # DPO
24# etc.
25```
This is a preview of SKILL.md. Download or install the skill to get the whole thing.
In the file
SKILL.md3,596 words
Files19
LicenceComplete terms in LICENSE.txt
Why you can read it

Nothing in a skill executes. The client loads the text and the model follows it, so a skill can be audited the way a runbook is — by reading it.

What it costs in context

Skills are not billed by the call. They are paid for in context: every token the instructions occupy is a token your code, your diff and your conversation cannot use. Here is what this one takes and when it takes it.

≈180
always loaded
The name and description, so the model knows the skill exists and when to reach for it.
26,720
on trigger
The instruction body and 18 supporting files, read only when the skill fires.
13.5%
of a 200k window
Ten skills this size would not fit in the window together.
050k100k150k200k context window

26.9k tokens, estimated from the bundle at four bytes to the token, held for the rest of the session once it triggers. Heavy. Teams tend to install this one per project rather than globally, and load it only when the job comes up.

Servers bill, skills cost

A server charges by the month. A skill charges once per session, in context, and then keeps charging it for as long as the session lives.

Before and after

The same question, put to the same model twice: once as it comes, and once with these instructions loaded.

No worked example has been published for this skill yet.

The procedure it runs

The procedure has not been set out here step by step. It is in the body of its SKILL.md: the start of it is shown above, and the whole file comes with the install.

Prose, not code

These steps are written for a model to follow, not executed by a runtime. It can still be told to skip one, and it will say so when it does.

Servers it uses

None. This skill calls no MCP servers at all.

Everything it needs is in the instructions, so it works in a project with nothing connected — the model reads the file and changes how it works with what it can already reach.

Its SKILL.md does not declare which tools it asks the agent for, so what it touches is whatever the instructions themselves ask for — read them before you install it.

What it asks for
Writes filesnot declared
Network accessnot declared

This SKILL.md carries no allowed-tools line, so neither is declared. A skill grants no permissions of its own — it can only ask for tools your client already has.

What it will not do

Every skill is narrow, and the useful ones say where they stop. These are the jobs this one is the wrong tool for.

What this skill is not for has not been published here. Nothing is implied by that: it is a section the author has not filled in.

What is in the bundle

19 files, 186.6 kB on disk. Mostly text — the instructions the model reads — with 8 scripts in it that your client would run only if the instructions tell it to.

  • SKILL.md28.8 kB
  • references/gguf_conversion.md9.9 kB
  • references/hardware_guide.md6.8 kB
  • references/hub_saving.md8.5 kB
  • references/local_training_macos.md8.3 kB
  • references/reliability_principles.md10.9 kB
  • references/trackio_guide.md6.4 kB
  • references/training_methods.md5.0 kB
  • references/training_patterns.md6.1 kB
  • references/troubleshooting.md8.9 kB
  • references/unsloth.md8.0 kB
  • scripts/convert_to_gguf.py12.6 kB
  • scripts/dataset_inspector.py15.7 kB
  • scripts/estimate_cost.py4.9 kB
  • scripts/hf_benchmarks.py20.1 kB
  • scripts/train_dpo_example.py3.1 kB
  • scripts/train_grpo_example.py2.4 kB
  • scripts/train_sft_example.py3.3 kB
  • scripts/unsloth_sft_example.py16.9 kB
What is not in it

A skill installs nothing and depends on nothing: it is a folder your client reads. This one carries 8 scripts beside the text, so the bundle is 19 files you can review in full before installing. The licence the author declares covers the templates and examples as well as the instructions.

Install

Installing copies the bundle into your project. Nothing runs at install time — the files sit on disk until the model reads them.

# Hugging Face LLM Trainer · 26.9k tokens when loaded · macOS, Linux mkdir -p .claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/hugging-face-llm-trainer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/hugging-face-llm-trainer" ] && { [ ! -e .claude/skills/hugging-face-llm-trainer ] || { mv .claude/skills/hugging-face-llm-trainer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/hugging-face-llm-trainer" .claude/skills/ && echo "Installed .claude/skills/hugging-face-llm-trainer"
On Windows, or any OS with Node 20+
npx mcprush@latest skill add huggingface/hugging-face-llm-trainer

Windows PowerShell says 'running scripts is disabled on this system'? Run Set-ExecutionPolicy -Scope CurrentUser RemoteSigned once, or type npx.cmd instead of npx (codex.cmd, gemini.cmd, grok.cmd likewise). PowerShell 7 needs neither. Writes to .claude/skills/hugging-face-llm-trainer/ in the current project. For every project instead, run mkdir -p ~/.claude/skills && t=$(mktemp -d) && curl -fsSL -o "$t/bundle.tar.gz" https://mcprush.com/api/skills/hugging-face-llm-trainer/bundle.tar.gz && tar -xzf "$t/bundle.tar.gz" -C "$t" && [ -d "$t/hugging-face-llm-trainer" ] && { [ ! -e ~/.claude/skills/hugging-face-llm-trainer ] || { mv ~/.claude/skills/hugging-face-llm-trainer "$t/.previous" && echo "Previous copy moved to $t/.previous"; }; } && mv "$t/hugging-face-llm-trainer" ~/.claude/skills/ && echo "Installed ~/.claude/skills/hugging-face-llm-trainer" (macOS, Linux) — or add --global to the npx line. The curl line replaces .claude/skills/hugging-face-llm-trainer/ only after the download succeeds, so files left there by another skill of the same name, or by an older release, do not mix in. A copy already there is moved aside, not deleted, and the line prints where. It appears as huggingface-llm-trainer in Gemini CLI, Codex, Copilot and Grok (its own name); Claude Code lists it by the folder name, hugging-face-llm-trainer.

Which clients pick it up on their own

A skill is a folder of text. Most of these read a skills folder and pick it up without being told; the rest take the same folder another way — an upload in the app, a parameter on an API call — and then choose it by its description just the same.

Claude Code.claude/skills/
Claude DesktopCustomize › Skills
ChatGPTSkills › Create › Upload
Cursor.cursor/skills/
VS Code.github/skills/
Codex CLI.agents/skills/
Gemini CLI.gemini/skills/
Grok Build.grok/skills/
Microsoft CopilotSkills › Upload a skill
PerplexityComputer › Skills › Upload a skill
DeepSeek Harness.dsh/skills/
Zed.agents/skills/
Devin Desktop.devin/skills/
Windsurf (legacy).windsurf/skills/
OpenAI Agents SDKInstructions or sandbox Skills
HTTP / APIa request parameter
This release
Versionnot versioned
Publishedno release date on file
PriceFree
Referencehuggingface/hugging-face-llm-trainer

Versions

Its author publishes no version number, so there is nothing here to pin to: what you install is the folder as it stands today. Instructions change more often than APIs do — a skill can be rewritten entirely without anything it depends on moving.

Pinning

Nothing to pin to: this skill carries no version number of its own. What you install is what the folder holds on the day you install it.

Reviews

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