Where You Can Get Pre-trained Models ?

The ultimate guide to finding and utilizing pre-trained models

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Why reinvent the wheel when you can supercharge your AI projects with pre-trained models? In a world where time is money and data is king, accessing high-quality pre-trained models can be your secret weapon, catapulting your machine learning endeavors from concept to reality in record time. But where can you find these treasure troves of pre-built intelligence? Let’s dive into the best sources for pre-trained models that can give your projects the head start they need.

Table of Content

• Where you can get pre-trained models
• Popular pre-trained model
• NLP
• Computer vision

Where You Can Get Pre-trained Models

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Here, is a list of reporitories where you can get a pre-trained model:

1. Hugging Face — A widely-used library that offers state-of-the-art pre-trained models for machine learning tasks, such as BERT, GPT-2,
   
   RoBERTa, T5, ViT and many more. https://huggingface.co/transformers/
2. Pytorch Hub — A repository for pre-trained models provided by the
   PyTorch team, including models for image classification, object
   detection, and NLP tasks. https://pytorch.org/hub/
3. Torch Image Models (timm) — A repository by Ross Wightman that
   contains a collection of pre-trained image classification models,
   including EfficientNet, ResNet, and many others. https://github.com/rwightman/pytorch-image-models

These repositories and libraries offer a wide range of pre-trained models that
can be used for transfer learning, fine-tuning or as feature extractors for
various machine learning tasks.

Popular Pre-trained Models

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In this section, we are going to explore a variety of transformer based pre-trained models specifically used in the fields of Natural Language
Processing, computer vision and speech processing. These models

are a huge time saver since they come pre-trained on massive amounts of
data. This allows users to tweak them for specific tasks, saving time and
computational power. Each of these models is unique, both in terms of their strengths and the areas
where they are most commonly used. This means they can be used in a wide

variety of different projects. For instance, BERT was trained with a masked
language modeling objective, while GPT was trained with a sequence
modeling objective. There are also differences in each model’s structure.
Our goal in exploring these models is to help you better understand and use
these models in your own work. By the end of it, you should have a good
grasp of how you can take advantage of these powerful tools to improve

your own projects.

NLP

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Here, is a list of some widely used NLP pre-trained models:

1. Bidirectional Encoder Representations from Transformers
   (BERT) — A powerful pre-trained model for various NLP tasks, such as
   sentiment analysis, named entity recognition and question answering.
2. Generative pre-trained Transformer (GPT-2) — A large scale
   language model known for its impressive text generation capabilities.
3. Text-to-Text Transfer Transformer (T5) — A versatile pre-trained
   model designed to handle a wide range of NLP tasks using a unified
   text-to-text format.
4. BART — This model incorporates a bi-directional encoder similar to
   BERT and an autoregressive decoder, establishing itself as an
   
   encoder decoder framework.
5. LLAMA2 — As of the writing of this section in August 2024, it is
   arguably the most popular open-source autoregressive model, holding
   its ground against GPT 3.5 in numerous benchmarks. It features a
   
   range of models with varying parameters, spanning from 2 billion to
   70 billion.
6. Falcon — It is created by Technology Innovation Institute in Abu Dhabi and released under Apache 2.0 license. Falcon 180b is 2.5 times larger
   
   than Llama2 70b. The Falcon 180b surpasses the performance of
   Llama 2 70B and OpenAI’s GPT-3.5 in the MMLU tests and is
   comparable to Google’s PaLM 2-Large in various benchmarks1.

Computer Vision

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Here, is a list of some widely used computer vision pre-trained models:

1. ViT — A transformer-based model that applies the transformer
   architecture to image classification tasks, dividing images into patches
   
   and treating them as sequences.
2. Data-efficient Image Transformer (DeiT) — A variant of the Vision
   Transformer that is specifically designed for data-efficient training,
   
   requiring fewer labeled images for good performance.
3. Swin transformer — A hierarchical transformer model for computer
   vision tasks, using shifted windows to capture local and global
   information efficiently.
4. Stable Diffusion — It is arguably the most popular model for generating
   images from text. This model is grounded on latent diffusion
   principles and incorporates three primary components.

1) An
Autoencoder

2) U-Net

3) CLIP’s Text Encoder

Conclusion

Finally, As we wrap up our exploration of pre-trained models, it’s evident that these powerful tools are reshaping the landscape of machine learning and AI. We have uncovered where you can find these invaluable resources, from trusted repositories to cutting-edge platforms that giving you instant access to models that can accelerate your development process. We have also highlighted some of the most popular pre-trained models that are leading the charge in transforming industries. Whether you are delving into natural language processing to unlock the secrets of human communication or diving into computer vision to give machines the gift of sight, pre-trained models provide a robust foundation upon which you can build innovative solutions. By tapping into these ready-made engines of intelligence, you are not just saving time, you are positioning yourself at the forefront of the AI revolution, ready to tackle complex challenges with the best tools at your disposal.