hugging-face-model-trainer

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 method
category	Document Processing
source	antigravity
tags	["python","api","mcp","claude","ai","llm","gpt","workflow","template","document"]
url	https://github.com/sickn33/antigravity-awesome-skills/tree/main/skills/hugging-face-model-trainer

TRL Training on Hugging Face Jobs

Overview

Train 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.

TRL provides multiple training methods:

• SFT (Supervised Fine-Tuning) - Standard instruction tuning
• DPO (Direct Preference Optimization) - Alignment from preference data
• GRPO (Group Relative Policy Optimization) - Online RL training
• Reward Modeling - Train reward models for RLHF

For detailed TRL method documentation:

hf_doc_search("your query", product="trl")
hf_doc_fetch("https://huggingface.co/docs/trl/sft_trainer")  # SFT
hf_doc_fetch("https://huggingface.co/docs/trl/dpo_trainer")  # DPO
# etc.

See also: references/training_methods.md for method overviews and selection guidance

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 FastVisionModel support

See references/unsloth.md for complete Unsloth documentation and scripts/unsloth_sft_example.py for a production-ready training script.

Key Directives

When assisting with training jobs:

1. ALWAYS use hf_jobs() MCP tool - Submit jobs using hf_jobs("uv", {...}), NOT bash trl-jobs commands. The script parameter accepts Python code directly. Do NOT save to local files unless the user explicitly requests it. Pass the script content as a string to hf_jobs(). If user asks to "train a model", "fine-tune", or similar requests, you MUST create the training script AND submit the job immediately using hf_jobs().

2. Always include Trackio - Every training script should include Trackio for real-time monitoring. Use example scripts in scripts/ as templates.

3. Provide job details after submission - After submitting, provide job ID, monitoring URL, estimated time, and note that the user can request status checks later.

4. Use example scripts as templates - Reference scripts/train_sft_example.py, scripts/train_dpo_example.py, etc. as starting points.

Local Script Execution

Repository scripts use PEP 723 inline dependencies. Run them with uv run:

uv run scripts/estimate_cost.py --help
uv run scripts/dataset_inspector.py --help

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_TOKEN syntax 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)

⚠️ 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"}

Asynchronous Job Guidelines

⚠️ IMPORTANT: Training jobs run asynchronously and can take hours

Action Required

When user requests training:

1. Create the training script with Trackio included (use scripts/train_sft_example.py as template)
2. Submit immediately using hf_jobs() MCP tool with script content inline - don't save to file unless user requests
3. Report submission with job ID, monitoring URL, and estimated time
4. Wait for user to request status checks - don't poll automatically

Ground Rules

• **Job