This Project from Hugging Face is completely Open Source, Check out the repo here: https://
github.com/huggingface/pe
ft
… Consider checking this blog from Hugging Face Team for more detailed overview. https://
huggingface.co/blog/peft 5/5
@sumanth_077
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Hugging Face PEFT Project: Open Source Repository and Blog Overview
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That’s a Wrap: Daily Python Data Science Machine Learning Tutorials
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That's a wrap! Everyday, I share tutorials around Python, Data Science & Machine Learning. You can follow me → @Sumanth_077 Like/RT the first tweet to support my work and help this reach more people.
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Getting Started with PEFT: Simple 3-Step Setup Guide
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Finally Getting started with PEFT is really simple: 1. Import the Necessary Libraries
2. Define the Config with PEFT method
3. Wrapping base model from Hugging Face Transformers by calling `get_peft_model` That's it. You can start training now. Check the below code 4/5 -
Interesting Use Cases of PEFT Parameter Efficient Fine-Tuning
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Some of the interesting usecases of PEFT are: 1. Stable Diffusion Dreambooth training.
2. Finetuning the `bigscience/T0_3B` model which has around 3 Billion Parameters. Checkout them here: https://
github.com/huggingface/pe
ft#use-cases
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PEFT: Fine-tuning Large Models on Low-End Hardware
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But fine-tuning large models on low-end hardware is a real challenge PEFT solves this by fine-tuning a small number of model parameters while freezing most parameters of the pre-trained LLMs. This reduces the computational and storage costs 2/5 https://
github.com/huggingface/pe
ft
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Fine-tuning: Faster Model Adaptation for Specific Tasks
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In simple words fine-tuning is taking a pre-trained machine learning model and adjusting it for a specific task Fine-tuning is much faster than training a model from scratch and also reduces the amount of data, compute required for training and lot of other benefits 1/5
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PEFT: Fine-tune Large Language Models on Low-Resource Hardware
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Large Language models are expensive to fine-tune and it's really difficult to train them on local hardware. Introducing PEFT ("Parameter-Efficient Fine-Tuning") from Hugging Face that lets you fine-tune Billion Parameter Models on Low-Resource Hardware. A Thread
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Kaggle as the Essential Starting Point for Machine Learning
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Kaggle is all you need to get started with ML.
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Three Years of Machine Learning Journey with Andrew Ng
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3 years back started my Machine Learning Journey with AndrewNG on Coursera. And now getting followed by Andrew here on Twitter Thankful to you all for the support and hope I'm doing something useful for the ML Community
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Python Data Science Machine Learning Daily Content Follow
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That's it. Thanks for reading. If you are interested in Python, Data Science and Machine Learning. Follow me @Sumanth_077
, will be sharing daily content Also, leave a like and retweet the first tweet and help this reach more people:)