Fine-tuning, training en data

TRL

huggingface/trl

A library for post-training transformer models with supervised fine-tuning and preference-optimization methods.

★ 19,5KSterren
⑂ 3,1KForks
109Openstaande problemen
PythonTaal
Apache-2.0Licentie
Q84Redactionele score

Overzicht

TRL builds on the Transformers ecosystem and provides trainers for methods such as supervised fine-tuning (SFT), direct preference optimization (DPO), and group relative policy optimization (GRPO). It supports distributed training integrations and parameter-efficient fine-tuning workflows.

Belangrijkste functies

  • SFT, DPO, GRPO and related trainers
  • Transformers ecosystem integration
  • Distributed training support
  • CLI and Python APIs

Vereisten, installatie en snel aan de slag

Install the Python package with `pip install trl`; follow the docs for optional Accelerate, PEFT, and hardware setup.

Gebruik

Choose a trainer, prepare a compatible dataset and model, configure training arguments, then run the trainer or CLI command.

Modelcompatibiliteit en gebruiksscenario's

Designed for transformer models in the Hugging Face ecosystem; architecture and hardware support depends on the chosen method and dependencies.

Licentie- en risiconotities

The repository declares Apache-2.0. Dataset, base-model, and hosted service terms are separate.

Release en onderhoud

See the official repository for current releases and changes.

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