huggingface-llm-trainer

1# TRL Training on Hugging Face Jobs
2 
3## Overview
4 
5Train 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.
6 
7**TRL provides multiple training methods:**
8- **SFT** (Supervised Fine-Tuning) - Standard instruction tuning
9- **DPO** (Direct Preference Optimization) - Alignment from preference data
10- **GRPO** (Group Relative Policy Optimization) - Online RL training
11- **Reward Modeling** - Train reward models for RLHF
12 
13**For detailed TRL method documentation:**
14```python
15hf_doc_search("your query", product="trl")
16hf_doc_fetch("https://huggingface.co/docs/trl/sft_trainer")  # SFT
17hf_doc_fetch("https://huggingface.co/docs/trl/dpo_trainer")  # DPO
18# etc.
19```
20 
21**See also:** `references/training_methods.md` for method overviews and selection guidance
22 
23## When to Use This Skill
24 
25Use this skill when users want to:
26- Fine-tune language models on cloud GPUs without local infrastructure
27- Train with TRL methods (SFT, DPO, GRPO, etc.)
28- Run training jobs on Hugging Face Jobs infrastructure
29- Convert trained models to GGUF for local deployment (Ollama, LM Studio, llama.cpp)
30- Ensure trained models are permanently saved to the Hub
31- Use modern workflows with optimized defaults
32 
33### When to Use Unsloth
34 
35Use **Unsloth** (`references/unsloth.md`) instead of standard TRL when:
36…