Huggingface LLM Trainer
Train or fine-tune language and vision models using TRL (Transformer Reinforcement...
Install
npx skills add huggingface/skills --skill huggingface-llm-trainer -g -a claude-code -y- Repository
- huggingface/skills11.2k
- Category
- Data and ML
- Updated
- 5d ago
- License
- Complete terms in LICENSE.txt
- Context cost
- Mediumabout 3,000 tokens
- Runs scripts
- Yes, review before use
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. Use for tasks involving cloud GPU training, GGUF conversion, or when users mention training on Hugging Face Jobs without local GPU setup.
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
FastVisionModelsupport
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:
