LoRA is low-rank decomposition method to reduce the number of trainable parameters which speeds up finetuning large models and uses less memory. In PEFT, using LoRA is as easy as setting up a LoraConfig and wrapping it with get_peft_model () to create a trainable PeftModel.

Prepare a model for training with a PEFT method such as LoRA by wrapping the base model and PEFT configuration with get_peft_model. For the bigscience/mt0-large model, you're only training 0.19% of the parameters!

LoRA is a similar strategy to Adapter layers but it aims to further reduce the number of trainable parameters. It takes a more mathematically rigorous approach. LoRA works by modifying how the updatable parameters are trained and updated in the neural network. ‍

2023年7月31日 · Two key PEFT methods are LoRA and Prompt Tuning. LoRA reduces trainable parameters by introducing rank decomposition matrices, while Prompt Tuning adds trainable soft prompts to the input...

2025年2月12日 · PEFT (Parameter-Efficient Fine-Tuning) is like giving your AI model a performance boost by only adjusting the most important parameters, rather than retraining the entire thing. Think of it as overclocking your model without needing to …

2024年9月27日 · PEFT LoraConfig makes the LoRA technique highly customizable and efficient. By understanding each parameter and its role, you can fine-tune large models effectively, even on limited hardware.

2024年5月24日 · PEFT techniques, such as Low-Rank Adaptation (LoRA) and soft prompts, offer a promising solution to reduce the computational burden and cost associated with fine-tuning large models.

2025年3月21日 · With PEFT, you can achieve similar performance by tweaking only a small fraction of the model, making it much more practical for real-world applications. ... LoRA (Low-Rank Adaptation) LoRA (Low-Rank Adaptation) reduces the number of trainable parameters by decomposing weight updates into low-rank matrices. Instead of updating the entire weight ...

2023年8月21日 · PEFT, or Parameter-Efficient Fine-Tuning (PEFT), is a library for efficiently adapting pre-trained language models (PLMs) to various downstream applications without fine-tuning all the model’s parameters.

2024年2月11日 · Lightweight RoBERTa Sequence Classification Fine-Tuning with LORA using the Hugging Face PEFT library. Fine-tuning large language models (LLMs) like RoBERTa can produce remarkable results when...