Agent skill · practicalswan
huggingface-llm-trainer
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. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
What it needs
About 10k tokens when loaded.
What this skill does
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: See also: references/trainingmethods.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/unslothsftexample.py for a production-ready training script. Key Directives When assisting with training jobs: 1. ALWAYS use hfjobs() MCP tool - Submit jobs using hfjobs("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 hfjobs(). If user asks to "train a model", "fine-tune", or similar requests, you MUST create the training script AND submit the job immediately using hfjobs(). …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills practicalswan/huggingface-llm-trainer