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fine-tuning-with-trl

Fine-tune LLMs with TRL using SFT, DPO, PPO, GRPO, and reward model training.

5|2|Updated Aug 10, 2026
Search Tags:#fine-tuning#HuggingFace#TRL#RLHF#GRPO#dpo#SFT
One-click install
npx skills add https://github.com/MedTiLab/Auto-meta-analysis --skill fine-tuning-with-trl-medtilab
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: fine-tuning-with-trl
Source: https://github.com/MedTiLab/Auto-meta-analysis/tree/main/skills/post-training/trl-fine-tuning
Command: npx skills add https://github.com/MedTiLab/Auto-meta-analysis --skill fine-tuning-with-trl-medtilab

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires trl, transformers, datasets, peft, accelerate, torch, and includes references (resource) components.

What problem does it solve? Aligning a base language model with human preferences requires orchestrating multiple post-training stages—supervised fine-tuning, reward modeling, and reinforcement learning—which is complex to configure correctly. This Skill provides ready-to-use workflows and configurations for the full RLHF pipeline using HuggingFace TRL. ## Core Features & Use Cases - Supervised Fine-Tuning (SFT): Train base models on instruction datasets with chat templates, packing, LoRA, and multi-GPU support via SFTTrainer. - Preference Alignment (DPO): Align models with chosen/rejected preference pairs using 10+ loss variants (sigmoid, IPO, hinge, robust DPO) without needing a reward model. - Online RL (PPO/GRPO): Optimize policies with reward models or custom reward functions, including memory-efficient GRPO training. - Reward Model Training: Build reward models with Bradley-Terry loss for RLHF pipelines and completion scoring. - Use Case: You have a Qwen2.5 base model and a dataset of human preference pairs. Use this Skill to run SFT for instruction following, then apply DPO to align outputs with preferences, and evaluate the aligned model. ## Quick Start Fine-tune Qwen/Qwen2.5-0.5B on my instruction dataset using TRL's SFTTrainer, then align it with my preference data using DPO.

Frequently Asked Questions about fine-tuning-with-trl

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I fine-tune an LLM with TRL SFTTrainer?▼

Load a model with AutoModelForCausalLM, prepare a prompt-completion or conversational dataset, configure SFTConfig with batch size and learning rate, then pass everything to SFTTrainer and call train(). LoRA can be added via peft_config for memory efficiency.

What is the difference between DPO, PPO, and GRPO in TRL?▼

DPO aligns models directly from preference pairs without a reward model. PPO uses a trained reward model for maximum control but needs about 40GB VRAM for 7B models. GRPO is memory-efficient online RL using group-relative rewards, needing about 24GB.

How to train a reward model for RLHF with TRL?▼

Load a model with AutoModelForSequenceClassification and num_labels=1, prepare a dataset with prompt, chosen, and rejected fields, then train with RewardTrainer using RewardConfig. The Bradley-Terry loss teaches the model to score chosen responses above rejected ones.

Does TRL DPO training work with limited GPU memory?▼

Yes, reduce per_device_train_batch_size to 1, lower max_length, increase gradient_accumulation_steps, and enable gradient checkpointing. Using LoRA or QLoRA adapters further reduces VRAM requirements for DPO on 7B models to around 24GB.

Why is my DPO training producing poor alignment quality?▼

Poor alignment usually comes from the beta parameter. Higher beta (0.5) keeps the model conservative and close to the reference, while lower beta (0.01) applies more aggressive alignment. Also verify your preference dataset has clearly better chosen responses.

When should I use Axolotl or Unsloth instead of TRL?▼

Use Axolotl for YAML-based training configuration, Unsloth for fast LoRA training, or plain HuggingFace Trainer for basic fine-tuning without RL. TRL is the right choice when you need RLHF, preference alignment, or reward model training.

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