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huggingface/trl

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TLDR

Python library for improving trained AI language models through post-training techniques like fine-tuning and preference alignment, with built-in support for scaling and efficient training on modest hardware.

Mindmap

mindmap
  root((TRL))
    What it does
      Fine-tune models
      Align to preferences
      Train reward models
      Scale efficiently
    Training methods
      Supervised Fine-Tuning
      Direct Preference Optimization
      Group Relative Policy Optimization
      Reward Training
    Tech stack
      Python
      Hugging Face Transformers
      PEFT and LoRA
      Multi-GPU support
    Use cases
      Improve model quality
      Reduce hardware costs
      Custom model training
      Production deployment

Things people build with this

USE CASE 1

Fine-tune a language model on your own data to make it better at a specific task without retraining from scratch.

USE CASE 2

Align a model's responses to match human preferences using DPO or GRPO without building a full reinforcement learning pipeline.

USE CASE 3

Train a reward model that scores response quality, then use it to improve another model's outputs.

USE CASE 4

Run large model training on a single GPU or small cluster using LoRA to reduce memory and compute costs.

Tech stack

PythonHugging Face TransformersPEFTLoRAQLoRAPyTorch

Getting it running

Difficulty · moderate Time to first run · 30min

Requires PyTorch and Hugging Face dependencies; GPU recommended but not strictly required for basic examples.

Use freely for any purpose, including commercial use, as long as you include the original copyright notice and license text.

In plain English

TRL (Transformers Reinforcement Learning) is a Python library for taking already-trained AI language models and improving them further using techniques developed after the initial training phase, a process called post-training. It is built on top of the Hugging Face Transformers ecosystem and supports multiple model types. The library provides ready-to-use trainer classes for different post-training approaches. Supervised Fine-Tuning (SFT) continues training a model on new example data. Direct Preference Optimization (DPO) and Group Relative Policy Optimization (GRPO) are methods that align a model's outputs more closely with human preferences, without the complexity of traditional reinforcement learning setups. There is also a RewardTrainer for training separate models that score how good a response is. Training can scale from a single graphics card to large multi-machine clusters. Integration with PEFT (Parameter-Efficient Fine-Tuning) tools like LoRA and QLoRA allows training of large models on more modest hardware by only updating a small fraction of the model's parameters. A command-line interface makes it possible to start fine-tuning runs without writing any code. The library is released under the Apache 2.0 license.

Copy-paste prompts

Prompt 1
Show me how to use TRL's SFTTrainer to fine-tune a Hugging Face model on my custom dataset with LoRA.
Prompt 2
I want to align my language model to human preferences using DPO. Walk me through the TRL setup and what data I need.
Prompt 3
How do I scale TRL training across multiple GPUs and machines? Show me a working example.
Prompt 4
Can you help me train a reward model with TRL's RewardTrainer and then use it to improve another model?
Prompt 5
I have limited GPU memory. Show me how to use QLoRA with TRL to fine-tune a large model efficiently.
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.