Joao, Basavaraja #opencvkickstarter4stablediffusion #aiartcontest #aiart #midjourney #dalle2 #stablediffusion #computervision #ai #deeplearning #machinelearning #artificialintelligence
MACHINE LEARNING
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OpenCV Kickstarter for Stable Diffusion AI Art Contest
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Suzana, Ad K, Amar, Alex #opencvkickstarter4stablediffusion #aiartcontest #aiart #midjourney #dalle2 #stablediffusion #computervision #ai #deeplearning #machinelearning #artificialintelligence
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Top 10 Contest Winners Announced Part 2 Coming Soon
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Top 10 Winning entries
Eray, Ahmed, aicarvan, Raghavan Part 2 of the contest is COMING SOON! Watch this space tomorrow. #opencvkickstarter4stablediffusion #aiartcontest #aiart #midjourney #dalle2 #stablediffusion #computervision #ai #deeplearning #machinelearning -
Meta Launches Toolformer: An Autonomous Model Using External Tools
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Meta introduces Toolformer, a language model that has independently learned to use external tools https://actuia.com/actualite/meta-presente-toolformer-un-modele-de-langage-ayant-appris-par-lui-meme-a-utiliser-des-outils-externes/
… #AI #artificialintelligence
@Meta -
Hugging Face PEFT Project: Open Source Repository and Blog Overview
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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 -
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
