Загрузка видео...

Не удалось загрузить видео

На главную

🪄👩🏾‍💻🏗️ Simplify the process of pretraining and fine-tuning transformer models from scratch. This step-by-step guide breaks down how KerasNLP helps to make building state-of-the-art text processing models easier. ➡️

TensorFlow's profile picture

TensorFlow

383,125 subscribers

16,923 просмотров • 2 лет назад •via X (Twitter)

LIVE

Anya Rossi• Live Now

Private livecam show

Комментарии: 1

Фото профиля Kongfutze
Kongfutze2 лет назад

this is actually super helpful

Похожие видео

📺 New Tutorial: Submitting #AI Models to Learn how to easily upload your models to #Inferium and make them accessible to a wider audience. Our new video guide provides a step-by-step walkthrough of the submission process, covering everything from crafting informative descriptions to uploading your files. Watch now and contribute to the growing AI community! 👇
2:11

📺 New Tutorial: Submitting #AI Models to Learn how to easily upload your models to #Inferium and make them accessible to a wider audience. Our new video guide provides a step-by-step walkthrough of the submission process, covering everything from crafting informative descriptions to uploading your files. Watch now and contribute to the growing AI community! 👇

Inferium

22,121 просмотров • 1 год назад

Fine tuning your game just got even easier. The @bushnellgolf Launch Pro is back with improved technology to help you dial in your data this off season. @rdsbarath breaks down the new models and how it can help your game.
1:03

Fine tuning your game just got even easier. The @bushnellgolf Launch Pro is back with improved technology to help you dial in your data this off season. @rdsbarath breaks down the new models and how it can help your game.

Skratch

157,045 просмотров • 7 месяцев назад

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.
2:52

Small Language Models (SML) are the future of AI. "Small" (SML) instead of "Large" (LLM). These small models are highly specialized models with superhuman abilities on specific tasks. Here are two techniques to build these models: • Spectrum • Model Merging I give you a short introduction in the attached video, but here is a quick summary: Spectrum helps us identify the most relevant layers to solve one specific task. We can ignore everything else and focus on fine-tuning these layers. Using Spectrum, we can fine-tune models in a heartbeat. Model Merging combines multiple models into a unique, much better model than any of the individual input models. You can also combine models specialized in different tasks and get a model with multiple abilities. This is the state of the art of productizing models. It's what Arcee.ai's platform does behind the scenes. Arcee collaborated with me on this post and is sponsoring it. There are three main steps to produce a model for your particular use case: 1. You create a dataset by uploading your data. 2. You train a model. At this step, Arcee uses Spectrum and Model Merging to produce a highly specialized model for your task. 3. You can deploy that model to any environment you want. Three important notes: • Training process is 2x faster and 2x cheaper than regular fine-tuning. • Resultant models are smaller and have higher accuracy. • They create these specialized models from open-source models. Check this site so you can fully appreciate how this works: If you want to fine-tune an open-source model, consider Arcee's platform. This is the state of the art.

Santiago

164,162 просмотров • 2 лет назад

Need help getting started with the #XRP Alliance - Flare campaign? 👀 This detailed video shows how to participate step by step 🔥 XRP Bags💰👨🏽‍🚀 BagMan breaks down the full process. Helpful guide for first-time participants 🙌
12:34

Need help getting started with the #XRP Alliance - Flare campaign? 👀 This detailed video shows how to participate step by step 🔥 XRP Bags💰👨🏽‍🚀 BagMan breaks down the full process. Helpful guide for first-time participants 🙌

D'CENT Wallet

15,555 просмотров • 1 месяц назад

Embeddings (and how to create them) are, perhaps, the most interesting idea behind Large Language Models. I built a simple model to help you understand embeddings from scratch. Here is a step-by-step video explanation:
51:43

Embeddings (and how to create them) are, perhaps, the most interesting idea behind Large Language Models. I built a simple model to help you understand embeddings from scratch. Here is a step-by-step video explanation:

Santiago

44,107 просмотров • 1 год назад

BNB Greenfield is a state-of-the-art decentralized storage solution by BNB Chain. Learn how to use it with this step-by-step tutorial
2:43

BNB Greenfield is a state-of-the-art decentralized storage solution by BNB Chain. Learn how to use it with this step-by-step tutorial

Yerasyl Amanbek

615,155 просмотров • 1 год назад

A step-by-step process for the construction of a duplex residential project. 🏗️🚧🏡 Watch to the end, to learn something new.
2:51

A step-by-step process for the construction of a duplex residential project. 🏗️🚧🏡 Watch to the end, to learn something new.

𝐍𝐞𝐱𝐭𝐆𝐞𝐧 𝐂𝐨𝐧𝐬𝐭𝐫𝐮𝐜𝐭𝐢𝐨𝐧 𝐋𝐭𝐝

243,913 просмотров • 7 месяцев назад

Portfolio 🎉 Check out the Repo through this link: - I'm telling my journey of building my Portfolio step by step. If you want to know how to make a website step by step, then check out the Repo. How is it?
0:23

Portfolio 🎉 Check out the Repo through this link: - I'm telling my journey of building my Portfolio step by step. If you want to know how to make a website step by step, then check out the Repo. How is it?

Ali Reza

37,292 просмотров • 3 лет назад

How to Make an Image Transparent (Step by Step Guide) ( pro_graphic7 )
0:34

How to Make an Image Transparent (Step by Step Guide) ( pro_graphic7 )

Mutia

29,927 просмотров • 10 месяцев назад

How to perform ablution 🚰 This is a step by step guide and take note of the mistakes . 🎙️: Sheikh Abdullah Gadon Kaya 1/3
2:20

How to perform ablution 🚰 This is a step by step guide and take note of the mistakes . 🎙️: Sheikh Abdullah Gadon Kaya 1/3

فَـ

42,080 просмотров • 2 лет назад

Announcing How Transformer LLMs Work, created with Jay Alammar and Maarten Grootendorst, co-authors of the beautifully illustrated book, “Hands-On Large Language Models.” This course offers a deep dive into the inner workings of the transformer architecture that powers large language models (LLMs). The transformer architecture revolutionized generative AI; in fact, the "GPT" in ChatGPT stands for "Generative Pre-Trained Transformer." Originally introduced in the Google Brain team's groundbreaking 2017 paper "Attention Is All You Need," by Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, Anthropic and DeepSeek. In this course, you’ll learn in detail how LLMs process text. You'll also work through code examples that illustrate that transformer's individual components. In details, you’ll learn: - How the representation of language has evolved, from Bag-of-Words to Word2Vec embeddings to the transformer architecture that captures a word's meanings taking into account the context of other words in the input. - How inputs are broken down into tokens before they are sent to the language model. - The details of a transformer's main stages: Tokenization and embedding, the stack of transformer blocks, and the language model head. - The inner workings of the transformer block, including attention, which calculates relevance scores, and the feedforward layer, which incorporates stored information learned in training. - How cached calculations make transformers faster. - Some of the most recent ideas in the latest models such as Mixture-of-Experts (MoE) which uses multiple sub-models and a router on each layer to improve the quality of LLMs. By the end of this course, you’ll have a deep understanding of how LLMs actually process text and be able to read through papers describing the latest models and understand the details. Gaining this intuition will improve your approach to building LLM applications. Please sign up here:
3:36

Announcing How Transformer LLMs Work, created with Jay Alammar and Maarten Grootendorst, co-authors of the beautifully illustrated book, “Hands-On Large Language Models.” This course offers a deep dive into the inner workings of the transformer architecture that powers large language models (LLMs). The transformer architecture revolutionized generative AI; in fact, the "GPT" in ChatGPT stands for "Generative Pre-Trained Transformer." Originally introduced in the Google Brain team's groundbreaking 2017 paper "Attention Is All You Need," by Vaswani and others, transformers were a highly scalable model for machine translation tasks. Variants of this architecture now power today’s LLMs such as those from OpenAI, Google, Meta, Cohere, Anthropic and DeepSeek. In this course, you’ll learn in detail how LLMs process text. You'll also work through code examples that illustrate that transformer's individual components. In details, you’ll learn: - How the representation of language has evolved, from Bag-of-Words to Word2Vec embeddings to the transformer architecture that captures a word's meanings taking into account the context of other words in the input. - How inputs are broken down into tokens before they are sent to the language model. - The details of a transformer's main stages: Tokenization and embedding, the stack of transformer blocks, and the language model head. - The inner workings of the transformer block, including attention, which calculates relevance scores, and the feedforward layer, which incorporates stored information learned in training. - How cached calculations make transformers faster. - Some of the most recent ideas in the latest models such as Mixture-of-Experts (MoE) which uses multiple sub-models and a router on each layer to improve the quality of LLMs. By the end of this course, you’ll have a deep understanding of how LLMs actually process text and be able to read through papers describing the latest models and understand the details. Gaining this intuition will improve your approach to building LLM applications. Please sign up here:

Andrew Ng

253,812 просмотров • 1 год назад

Detailed step-by-step guide on how to claim your MFTs and receive the tokens from our new contract
1:19

Detailed step-by-step guide on how to claim your MFTs and receive the tokens from our new contract

Memecoin

12,437 просмотров • 3 месяцев назад

🧠 Janelia researchers have mapped out the step-by-step process of cognitive map formation in the brain’s hippocampus during learning and identified the type of mathematical model that best reproduces this learning process ➡️
1:05

🧠 Janelia researchers have mapped out the step-by-step process of cognitive map formation in the brain’s hippocampus during learning and identified the type of mathematical model that best reproduces this learning process ➡️

HHMI | Janelia

23,497 просмотров • 1 год назад

NIRVANNA THE BAND THE SHOW THE MOVIE stars Matt Johnson and Jay McCarrol give a step-by-step guide on how to make a movie.
1:55

NIRVANNA THE BAND THE SHOW THE MOVIE stars Matt Johnson and Jay McCarrol give a step-by-step guide on how to make a movie.

NEON

48,810 просмотров • 4 месяцев назад

Simplify Your VKYC Process with Our Step-by-Step Guide! Want to complete your VKYC hassle-free? Watch our latest video to see how easy it is! #DigitalBanking #VKYC #BankingSimplified #StepByStepGuide #EasyBanking #NSDLPaymentsBank #SecureBanking #KYC
0:47

Simplify Your VKYC Process with Our Step-by-Step Guide! Want to complete your VKYC hassle-free? Watch our latest video to see how easy it is! #DigitalBanking #VKYC #BankingSimplified #StepByStepGuide #EasyBanking #NSDLPaymentsBank #SecureBanking #KYC

NSDL Payments Bank

11,679 просмотров • 1 год назад

New short course on Pretraining LLMs! Developed with Upstage and taught by their CEO Sung Kim and CSO Lucy Park. While prompting or fine-tuning existing models works well for many general language tasks, pretraining is valuable for specialized domains or languages with limited representation in current models. This course walks you through the LLM pretraining pipeline: 1. Data preparation: Learn to source, clean, and prepare training data using HuggingFace. 2. Model architecture: Configure transformer networks, including modifying existing models. 3. Training: Set up and run training using open-source libraries. 4. Evaluation: Benchmark performance using popular evaluation strategies. As an example use case, you'll also compare the output of a base model with its fine-tuned and further pretrained variants, to see the impact of pretraining on a model's ability to write Python. The course also explores an innovative technique called depth up-scaling, which Upstage used to train their Solar model family, reducing pretraining compute costs by up to 70%. This technique works by first duplicating layers of a smaller pretrained model to form a larger model, and then further pretraining the result. Sign up here!
3:54

New short course on Pretraining LLMs! Developed with Upstage and taught by their CEO Sung Kim and CSO Lucy Park. While prompting or fine-tuning existing models works well for many general language tasks, pretraining is valuable for specialized domains or languages with limited representation in current models. This course walks you through the LLM pretraining pipeline: 1. Data preparation: Learn to source, clean, and prepare training data using HuggingFace. 2. Model architecture: Configure transformer networks, including modifying existing models. 3. Training: Set up and run training using open-source libraries. 4. Evaluation: Benchmark performance using popular evaluation strategies. As an example use case, you'll also compare the output of a base model with its fine-tuned and further pretrained variants, to see the impact of pretraining on a model's ability to write Python. The course also explores an innovative technique called depth up-scaling, which Upstage used to train their Solar model family, reducing pretraining compute costs by up to 70%. This technique works by first duplicating layers of a smaller pretrained model to form a larger model, and then further pretraining the result. Sign up here!

Andrew Ng

85,657 просмотров • 1 год назад

F*ck it... Step by step guide on how to make $10k/month in 2024 with the best skill on earth *Full Guide*
26:58

F*ck it... Step by step guide on how to make $10k/month in 2024 with the best skill on earth *Full Guide*

Luke Alexander

322,894 просмотров • 2 лет назад

🚀Introducing Code Arena: the next generation of live coding evals for frontier AI models. Built to test how models plan, scaffold, debug, and build real web apps step-by-step. Try Claude, GPT-5, GLM-4.6 and Gemini in Code Arena today!
0:56

🚀Introducing Code Arena: the next generation of live coding evals for frontier AI models. Built to test how models plan, scaffold, debug, and build real web apps step-by-step. Try Claude, GPT-5, GLM-4.6 and Gemini in Code Arena today!

Arena.ai

327,995 просмотров • 7 месяцев назад

Step-by-step drilling process 🧠 Drilling down to the water aquifer!!!
0:27

Step-by-step drilling process 🧠 Drilling down to the water aquifer!!!

Architecture Presentation

419,624 просмотров • 12 дней назад

How do you make the PERFECT nachos? Chef Andrew Gruel (Chef Andrew Gruel) breaks it down step by step for Adam Carolla 👇
1:27

How do you make the PERFECT nachos? Chef Andrew Gruel (Chef Andrew Gruel) breaks it down step by step for Adam Carolla 👇

The Adam Carolla Show

14,651 просмотров • 1 год назад