LLMs With Hugging Face
The article is a comprehensive guide that delves into several key uses of LLMs, including summarization, sentiment analysis, translation, zero-shot classification, and few-shot learning.
This article is part 1 of 5 Articles.
Download the source code from the link in comment section.
Throughout this journey, we’ll discover how both open-source and proprietary models can be effectively utilized straight out of the box for a wide array of tasks. This is achieved through the integration of Hugging Face models and the art of prompt engineering.
Furthermore, the article offers a deep dive into the APIs provided by Hugging Face, providing readers with a clearer understanding of how to set up and manage LLM pipelines. Essential to this exploration are the learning objectives laid out in the article. These include mastering the use of a variety of existing models for common applications, grasping the fundamentals of prompt engineering, and comprehending the differences between search and sampling in LLM inference. Additionally, the article familiarizes readers with the core Hugging Face abstractions, encompassing datasets, pipelines, tokenizers, and models, thus equipping them with the knowledge to harness the full potential of LLMs in various contexts.
%pip install sacremoses==0.0.53
This code installs a specific version 0.0.53 of the python library sacremoses using pip. SacreMoses is a library that is used for data processing and tokenization for language models and natural language processing tasks. By installing this specific version, the code ensures compatibility with the rest of the code and dependencies in the LLMs with Hugging Face project.
%run ../Includes/Classroom-Setup
This python code imports the necessary modules and libraries for working with Language Model LLM using the Hugging Face framework. It also sets up the classroom environment by defining a class with necessary helper functions, such as downloading datasets and initializing the Hugging Face model. This code makes it easier for learners to set up their environment and get started with working on LLMs using the Hugging Face framework. It also allows for easy access to the solutions for exercises and challenges.
Common LLM Applications
This section aims to provide you with a practical introduction to various applications of Large Language Models (LLMs), illustrating the simplicity and ease of getting started with these advanced tools.
As you navigate through the examples, pay close attention to the datasets, models, APIs, and available options utilized in each case. These examples serve as foundational starting points, offering insights and guidance for when you embark on developing your own applications with LLMs.
from datasets import load_dataset
from transformers import pipeline
Through this code, the load_dataset function from the datasets library is imported, which is used to load various datasets for natural language processing tasks. Then, the pipeline function from the transformers library is imported, which is used to create a standardized workflow for processing text input with pre-trained language models.