Support for ReFT (Representational Finetuning)

[creatorrr]

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🔖 Feature description

ReFT seems very interesting. Dropping it here for further consideration. @winglian

https://huggingface.co/papers/2404.03592 https://github.com/stanfordnlp/pyreft

Abstract

Parameter-efficient fine-tuning (PEFT) methods seek to adapt large models via updates to a small number of weights. However, much prior interpretability work has shown that representations encode rich semantic information, suggesting that editing representations might be a more powerful alternative. Here, we pursue this hypothesis by developing a family of Representation Finetuning (ReFT) methods. ReFT methods operate on a frozen base model and learn task-specific interventions on hidden representations. We define a strong instance of the ReFT family, Low-rank Linear Subspace ReFT (LoReFT). LoReFT is a drop-in replacement for existing PEFTs and learns interventions that are 10x-50x more parameter-efficient than prior state-of-the-art PEFTs. We showcase LoReFT on eight commonsense reasoning tasks, four arithmetic reasoning tasks, Alpaca-Eval v1.0, and GLUE. In all these evaluations, LoReFT delivers the best balance of efficiency and performance, and almost always outperforms state-of-the-art PEFTs. We release a generic ReFT training library publicly at https://github.com/stanfordnlp/pyreft.

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Acknowledgements

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[creatorrr]

On second thoughts, this probably doesn’t fit axoltl’s scope. To be specific, this feature request pertains to LoReFT but even that is very simple to achieve directly with their library and an axolotl integration is probably overkill. Closing for now.