This skill should be used when users want to train or fine-tune language models using TRL (Transformer Reinforcement Learning) on Hugging Face Jobs…
Cloud-based language model training with TRL on Hugging Face Jobs, supporting SFT, DPO, GRPO, and reward modeling with automatic Hub persistence.
Covers four training methods (SFT for instruction tuning, DPO for preference alignment, GRPO for online RL, reward modeling for RLHF) with production-ready example scripts and cost estimation tools
Submit training jobs via hf_jobs() MCP tool with inline UV scripts (PEP 723 format); no local GPU required, results automatically saved to Hugging Face Hub
Includes real-time monitoring via Trackio, dataset validation before training, GGUF conversion for local deployment (Ollama, llama.cpp), and comprehensive hardware selection guidance (t4-small to a100-large)
Critical prerequisites: paid HF account, HF_TOKEN in job secrets, dataset format validation, timeout set to 1-2+ hours (default 30 min insufficient), and push_to_hub=True configuration to prevent data loss in ephemeral environment
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.don't have the plugin yet? install it then click "run inline in claude" again.