Prompt Tuning is an alternative to model fine-tuning that adapts a large language model (LLM) for specific tasks by updating only a small set of additional parameters called soft prompts, while …

This conceptual guide provides a brief overview of the soft prompt methods included in 🤗 PEFT: prompt tuning, prefix tuning, P-tuning, and multitask prompt tuning. Only train and store a …

In this paper, we reframe the prompt learning framework from a generative perspective and propose a simple yet efficient method for the Domain Generalization (DG) task, namely Soft …

Learn prompt engineering techniques with a practical, real-world project to get better results from large language models. This tutorial covers zero-shot and few-shot prompting, delimiters, …

Dive into the world of advanced prompt engineering by mastering soft prompts and continuous embeddings. Learn how these techniques enable more nuanced, context-aware interactions …

Sep 29, 2023 · In the notebook, we will train two different models, but starting from the same pretrained model. This will allow us to see one of the most distinctive features of this fine …

In this notebook we are introducing how to apply prompt tuning with the PEFT library to a pre-trained model. For a complete list of models compatible with PEFT refer to their …

May 10, 2024 · In this blog, we delved into the intriguing concept of soft prompts and their application in enhancing the performance of large language models like GPT-2. By creating a …

This repository contains an reimplementation of Soft Prompt tuning, a parameter-efficient fine-tuning method for LLM. The methods included are listed below. We currently only provide …

In this page, we will cover autocompletion, syntax highlighting, key bindings, and so on. The following snippet is the most simple example, it uses the prompt() function to ask the user for …