stanfordnlp/pyreft

Health v2 · maintenance only

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1587 stars · 134 forks observed · 2026-09-29

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

pyreft is Stanford NLP's Python library for Representation Finetuning (ReFT), which adapts frozen language models by learning task-specific interventions on hidden representations. It works with any pretrained HuggingFace model and serves as a drop-in, more parameter-efficient alternative to LoRA and other PEFT methods.

Use cases

• finetune an LLM with fewer trainable parameters than LoRA
• train representation interventions on a frozen HuggingFace model
• adapt a language model for commonsense or arithmetic reasoning tasks
• instruction-tune a model with parameter-efficient interventions
• share trained ReFT adapters to the HuggingFace Hub
• study how hidden representations encode task-relevant semantics

When to choose

• you want maximum parameter efficiency (15x-65x fewer than LoRA) when adapting LLMs
• you already use HuggingFace transformers and pyvene
• you're researching representation-based interventions or interpretability-driven finetuning

When to avoid

• you need a battle-tested, widely-supported PEFT method with broad ecosystem tooling
• you're finetuning non-transformer or non-HuggingFace models
• you want simple full finetuning without learning a new intervention paradigm

Facets

library · maturity active

machine-learning llm-training nlp large-language-models machine-learning python representation-finetuning reft peft interpretability parameter-efficient-finetuning huggingface interventions natural-language-processing research

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