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

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.