Hugging Face LLM Trainer
huggingface/skills
Trains or fine-tunes language and vision models with TRL or Unsloth on Hugging Face Jobs cloud GPUs, then converts the results to GGUF.
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure.
Install Hugging Face Model Trainer
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-model-trainer -a claude-codeProject install by default; add -g for ~/.claude/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-model-trainer --agent claude-codeProject scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/hugging-face-model-trainer .claude/skills/hugging-face-model-trainer && rm -rf skills-srcUse ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.
Claude Code skills documentation · loads skills from .claude/skills/
Install the "hugging-face-model-trainer" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-model-trainer into .claude/skills/hugging-face-model-trainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-model-trainer", then confirm the skill loads.Claude Code copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$skill-installer install https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-model-trainerType this inside Codex. $skill-installer <name> installs a curated skill from openai/skills. The installer writes to $CODEX_HOME/skills (default ~/.codex/skills). Restart Codex if the skill does not show up.
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-model-trainer -a codexProject install goes to .agents/skills/; add -g for ~/.codex/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-model-trainer --agent codexProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .agents/skills && cp -r skills-src/skills/hugging-face-model-trainer .agents/skills/hugging-face-model-trainer && rm -rf skills-srcUse ~/.agents/skills/ instead of .agents/skills for a personal install.
Codex skills documentation · loads skills from .agents/skills/
Install the "hugging-face-model-trainer" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-model-trainer into .agents/skills/hugging-face-model-trainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-model-trainer", then confirm the skill loads.Codex copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-model-trainer -a cursorProject install goes to .agents/skills/; add -g for ~/.cursor/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-model-trainer --agent cursorProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .cursor/skills && cp -r skills-src/skills/hugging-face-model-trainer .cursor/skills/hugging-face-model-trainer && rm -rf skills-srcUse ~/.cursor/skills/ instead of .cursor/skills for a personal install.
Cursor skills documentation · loads skills from .cursor/skills/, .agents/skills/, .claude/skills/, .codex/skills/
Install the "hugging-face-model-trainer" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-model-trainer into .cursor/skills/hugging-face-model-trainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-model-trainer", then confirm the skill loads.Cursor copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gemini skills install https://github.com/sickn33/agentic-awesome-skills.git --path skills/hugging-face-model-trainer--scope user (default) or --scope workspace; --path is the subfolder of the repo that holds the skill; --consent skips the security confirmation prompt.
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-model-trainer -a gemini-cliProject install goes to .agents/skills/; add -g for ~/.gemini/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-model-trainer --agent gemini-cliProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .gemini/skills && cp -r skills-src/skills/hugging-face-model-trainer .gemini/skills/hugging-face-model-trainer && rm -rf skills-srcUse ~/.gemini/skills/ instead of .gemini/skills for a personal install, then run /skills reload.
Gemini CLI skills documentation · loads skills from .gemini/skills/, .agents/skills/
Install the "hugging-face-model-trainer" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-model-trainer into .gemini/skills/hugging-face-model-trainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-model-trainer", then confirm the skill loads.Gemini CLI copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-model-trainerInstalls for Copilot at project scope by default; add --scope user for a personal install. Preview a skill first with gh skill preview. Needs GitHub CLI 2.90.0 or later (public preview).
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-model-trainer -a github-copilotProject install goes to .agents/skills/; add -g for ~/.copilot/skills/.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .github/skills && cp -r skills-src/skills/hugging-face-model-trainer .github/skills/hugging-face-model-trainer && rm -rf skills-srcUse ~/.copilot/skills/ instead of .github/skills for a personal install. Commit .github/skills so cloud agent and code review can use it.
GitHub Copilot skills documentation · loads skills from .github/skills/, .claude/skills/, .agents/skills/
Install the "hugging-face-model-trainer" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-model-trainer into .github/skills/hugging-face-model-trainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-model-trainer", then confirm the skill loads.GitHub Copilot copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
$ npx skills add sickn33/agentic-awesome-skills --skill hugging-face-model-trainer -a opencodeOpenCode documents no install command of its own. Project install goes to .agents/skills/; add -g for ~/.config/opencode/skills/.
$ gh skill install sickn33/agentic-awesome-skills hugging-face-model-trainer --agent opencodeProject scope by default (.agents/skills/); add --scope user for a personal install.
$ git clone --depth 1 https://github.com/sickn33/agentic-awesome-skills.git skills-src && mkdir -p .opencode/skills && cp -r skills-src/skills/hugging-face-model-trainer .opencode/skills/hugging-face-model-trainer && rm -rf skills-srcUse ~/.config/opencode/skills/ instead of .opencode/skills for a personal install.
OpenCode skills documentation · loads skills from .opencode/skills/, .claude/skills/, .agents/skills/
Install the "hugging-face-model-trainer" agent skill from https://github.com/sickn33/agentic-awesome-skills/tree/main/skills/hugging-face-model-trainer into .opencode/skills/hugging-face-model-trainer/ in this project. Copy the whole folder (SKILL.md and every file beside it), keep the folder name "hugging-face-model-trainer", then confirm the skill loads.OpenCode copies the folder itself, the same result as the manual copy. Check what it changed before you commit it.
Facts
- Skill name
hugging-face-model-trainer- GitHub stars
- 47k
- Used in
- 1 other repo
- Token cost
- ~1.1k tokens
- SKILL.md length
- 444 words
- Files
- 20 (incl. scripts, references)
- Skills in repo
- 1,493
- Repo updated
- First seen
- Licence
- Apache-2.0
At a glance
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure.
- Tasks that involve Fine-tuning
- SKILL.md covers Detailed Guide, When to Use This Skill, Prerequisites Checklist and Example Training Scripts, plus 1 more section
- Runs Python scripts from its folder; needs HF_TOKEN
- Tasks that involve Model hubs and datasets
What it does
Hugging Face Model Trainer is an agent skill from sickn33/agentic-awesome-skills. 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.
Its SKILL.md is about 1.1k tokens, which your agent loads only when the skill is triggered. The skill folder holds 21 other files, including scripts and reference files (for example `references/detailed-guide.md`, `references/gguf_conversion.md` and `references/hardware_guide.md`).
It sits in AI & LLM Engineering, covering Fine-tuning and Model hubs and datasets. It works with Hugging Face and llama.cpp. The repository describes itself as: AAS Core is the local, agent-first control plane for complete catalog discovery, agent-owned selection, stack validation, and planning, backed by 2,400+ agentic skills. Includes… The licence is Apache-2.0.
When your agent uses it
- Tasks that involve Fine-tuning
- Tasks that involve Model hubs and datasets
Example prompts
- “/hugging-face-model-trainer”
Requirements
- Python 3
What it can do on your machine
Read from SKILL.md and the folder at commit 680176d. It shows what the files ask for, not the result of running them.
Tool permissions
Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.
From allowed-tools in the SKILL.md frontmatter.
Runs code
Ships 7 files in scripts/ (Python, from the files we listed), which the agent can run.
From the folder's file list and the shell code blocks in SKILL.md.
Network
Links to these hosts (documentation or services it may open):
hf.co
From URLs in SKILL.md, links to its own repository left out.
Credentials
Names these keys or tokens, usually read from environment variables:
HF_TOKEN
From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.
Context cost
Hugging Face Model Trainer loads about 1.1k tokens when it runs, and up to ~27k if it reads all its reference files. Until then it costs about 68 tokens; SKILL.md has 444 words of instructions outside code blocks.
Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.
Safety
Auto-check passed
The automated check found no risky patterns in SKILL.md.
Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.
SKILL.md
The full file from sickn33/agentic-awesome-skills at commit 680176d, republished under its Apache-2.0 licence (© sickn33). 444 words, ~1,067 tokens.
Download SKILL.mdSave it as
.claude/skills/hugging-face-model-trainer/SKILL.md (or your agent's skills folder). This skill also uses 19 other files; get the full folder from GitHub.- name
- hugging-face-model-trainer
- description
- 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.
- risk
- critical
- source
- https://github.com/huggingface/skills/tree/main/skills/huggingface-llm-trainer
- source_repo
- huggingface/skills
- source_type
- official
- date_added
- 2026-07-01
- license
- Apache-2.0
- license_source
- https://github.com/huggingface/skills/blob/main/LICENSE
TRL Training on Hugging Face Jobs
Detailed Guide
Read the detailed guide before executing this skill. It retains the complete procedure and reference material. Treat its safety, prerequisites, and validation requirements as mandatory. For focused work, load the relevant sections; for end-to-end work, read the guide completely.
When to Use This Skill
Use this skill when users want to:
- Fine-tune language models on cloud GPUs without local infrastructure
- Train with TRL methods (SFT, DPO, GRPO, etc.)
- Run training jobs on Hugging Face Jobs infrastructure
- Convert trained models to GGUF for local deployment (Ollama, LM Studio, llama.cpp)
- Ensure trained models are permanently saved to the Hub
- Use modern workflows with optimized defaults
When to Use Unsloth
Use Unsloth (references/unsloth.md) instead of standard TRL when:
- Limited GPU memory - Unsloth uses ~60% less VRAM
- Speed matters - Unsloth is ~2x faster
- Training large models (>13B) - memory efficiency is critical
- Training Vision-Language Models (VLMs) - Unsloth has
FastVisionModelsupport
See references/unsloth.md for complete Unsloth documentation and scripts/unsloth_sft_example.py for a production-ready training script.
Prerequisites Checklist
Before starting any training job, verify:
✅ Account & Authentication
- Hugging Face Account with Pro, Team, or Enterprise plan (Jobs require paid plan)
- Authenticated login: Check with
hf_whoami() - HF_TOKEN for Hub Push ⚠️ CRITICAL - Training environment is ephemeral, must push to Hub or ALL training results are lost
- Token must have write permissions
- MUST pass
secrets={"HF_TOKEN": "$HF_TOKEN"}in job config to make token available (the$HF_TOKENsyntax references your actual token value)
✅ Dataset Requirements
- Dataset must exist on Hub or be loadable via
datasets.load_dataset() - Format must match training method (SFT: "messages"/text/prompt-completion; DPO: chosen/rejected; GRPO: prompt-only)
- ALWAYS validate unknown datasets before GPU training to prevent format failures (see Dataset Validation section below)
- Size appropriate for hardware (Demo: 50-100 examples on t4-small; Production: 1K-10K+ on a10g-large/a100-large)
Show full SKILL.md (155 more words)Show less
⚠️ Critical Settings
- Timeout must exceed expected training time - Default 30min is TOO SHORT for most training. Minimum recommended: 1-2 hours. Job fails and loses all progress if timeout is exceeded.
- Hub push must be enabled - Config:
push_to_hub=True,hub_model_id="username/model-name"; Job:secrets={"HF_TOKEN": "$HF_TOKEN"}
Example Training Scripts
Production-ready templates with all best practices:
Load these scripts for correctly:
scripts/train_sft_example.py- Complete SFT training with Trackio, LoRA, checkpointsscripts/train_dpo_example.py- DPO training for preference learningscripts/train_grpo_example.py- GRPO training for online RL
These scripts demonstrate proper Hub saving, Trackio integration, checkpoint management, and optimized parameters. Pass their content inline to hf_jobs() or use as templates for custom scripts.
Limitations
- Use this skill only when the task clearly matches its upstream product or API scope.
- Verify commands, API behavior, pricing, quotas, credentials, and deployment effects against current official documentation before making changes.
- Do not treat generated examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.
© sickn33, Apache-2.0. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file
Files
SKILL.md and 19 other files (scripts, references) in skills/hugging-face-model-trainer of sickn33/agentic-awesome-skills.
- SKILL.md
- references/detailed-guide.md
- references/gguf_conversion.md
- references/hardware_guide.md
- references/hub_saving.md
- references/local_training_macos.md
- references/reliability_principles.md
- references/trackio_guide.md
- references/training_methods.md
- references/training_patterns.md
- references/troubleshooting.md
- references/unsloth.md
- scripts/convert_to_gguf.py
- scripts/dataset_inspector.py
- scripts/estimate_cost.py
- scripts/hf_benchmarks.py
- scripts/train_dpo_example.py
- scripts/train_grpo_example.py
- scripts/train_sft_example.py
- … and 1 more
Open the folder on GitHubat commit 680176d
Used in 1 other repository
We found 5 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 1 other GitHub owner. This page covers the copy in sickn33/agentic-awesome-skills, which our catalogue first saw on October 7, 2026.
Compare with similar skills
Hugging Face Model Trainer next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.
| Skill | Stars | Used in | Tokens | Auto-check | Licence | Repo updated |
|---|---|---|---|---|---|---|
| Hugging Face Model Trainer this skillsickn33/agentic-awesome-skills | 47k | 1 repos | ~1.1k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face LLM Trainerhuggingface/skills | 11k | 1 repos | ~7.2k | Automated safety check: Pass | Apache-2.0 | |
| Huggingface LLM Trainerwaybarrios/opencode-power-pack | 533 | — | ~3k | Automated safety check: Pass | Apache-2.0 | |
| Hugging Face Model Trainerhenryalouf/ruflow | 157 | — | ~6.9k | Automated safety check: Pass | MIT | |
| Qwen Mtp GgufR6410418/Jackrong-llm-finetuning-guide | 1.7k | — | ~1.7k | Automated safety check: Pass | MIT | |
| Dataset Transformationawslabs/agent-plugins | 915 | 1 repos | ~3.5k | Automated safety check: Pass | Apache-2.0 |
Similar skills
- Official
Huggingface LLM Trainer
waybarrios/opencode-power-pack
Train or fine-tune language models with TRL or Unsloth on Hugging Face Jobs, including SFT, DPO, GRPO, reward models, and GGUF conversion.
Hugging Face Model Trainer
henryalouf/ruflow
Train or fine-tune TRL language models on Hugging Face Jobs, including SFT, DPO, GRPO, and GGUF export.
Qwen Mtp Gguf
R6410418/Jackrong-llm-finetuning-guide
Complete agent-ready workflow for Qwen-family MTP or nextn GGUF conversion and release.
- Official
Dataset Transformation
awslabs/agent-plugins
Generates code that transforms datasets between ML schemas for model training or evaluation.
- Official
Hugging Face Local Models
huggingface/skills
Finds llama.cpp-compatible GGUF models on the Hugging Face Hub, picks a quantization for your hardware and launches them with llama-cli or llama-server.
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Related
Works with
Categories
Questions about Hugging Face Model Trainer
What does Hugging Face Model Trainer do?
Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure. Hugging Face Model Trainer is an agent skill from sickn33/agentic-awesome-skills. Train or fine-tune language and vision models using TRL (Transformer Reinforcement Learning) or Unsloth with Hugging Face Jobs infrastructure.
When should I use Hugging Face Model Trainer?
Hugging Face Model Trainer fits situations like: tasks that involve Fine-tuning; tasks that involve Model hubs and datasets.
How do I install Hugging Face Model Trainer in Claude Code?
Run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-model-trainer -a claude-code`. Or copy the skill folder (skills/hugging-face-model-trainer in sickn33/agentic-awesome-skills) into .claude/skills/hugging-face-model-trainer in your project. Claude Code loads it when a task matches its description.
How do I install Hugging Face Model Trainer in Codex?
Run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-model-trainer -a codex`. Or copy the skill folder (skills/hugging-face-model-trainer in sickn33/agentic-awesome-skills) into .agents/skills/hugging-face-model-trainer in your project. Codex loads it when a task matches its description.
Can I use Hugging Face Model Trainer in Cursor, Gemini CLI or GitHub Copilot?
Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add sickn33/agentic-awesome-skills --skill hugging-face-model-trainer -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/hugging-face-model-trainer, .gemini/skills/hugging-face-model-trainer, .github/skills/hugging-face-model-trainer and .opencode/skills/hugging-face-model-trainer in your project.
What does Hugging Face Model Trainer need to run?
Going by SKILL.md and its folder, Hugging Face Model Trainer needs Python for the scripts in its folder and credentials named HF_TOKEN. Our summary lists: Python 3.
Does Hugging Face Model Trainer access the network?
SKILL.md names 1 domain. As links in the text: hf.co. This is read from the text; nothing was executed.
Is Hugging Face Model Trainer safe to install?
Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.
What licence does Hugging Face Model Trainer use?
Hugging Face Model Trainer is published under the Apache-2.0 licence (declared in SKILL.md). It allows redistribution, so the full SKILL.md is shown on this page.
How many tokens does Hugging Face Model Trainer use?
About 1.1k tokens (SKILL.md is roughly 4.3k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 26k tokens, read only when the agent opens those files.
What are the alternatives to Hugging Face Model Trainer?
Skills that share tags, products or a category with Hugging Face Model Trainer: Hugging Face LLM Trainer (huggingface/skills, 11k stars), Huggingface LLM Trainer (waybarrios/opencode-power-pack, 533 stars), Hugging Face Model Trainer (henryalouf/ruflow, 157 stars) and Qwen Mtp Gguf (R6410418/Jackrong-llm-finetuning-guide, 1.7k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.
Who maintains Hugging Face Model Trainer?
sickn33 (a GitHub user) maintains it in sickn33/agentic-awesome-skills, which has 47,379 GitHub stars. The repository holds 1,493 skills in this directory. The repository was last updated on October 9, 2026.
Source: sickn33/agentic-awesome-skills on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.