Day 3 Talks JAX, Flax, Transformers ๐Ÿค—

HuggingFace ยท Advanced ยท๐Ÿง  Large Language Models ยท4y ago
Day 3 Talks JAX, Flax, Transformers ๐Ÿค— 0:00:00 Lucas Beyer (Google Brain): Vision Transformer 0:30:03 Ben Wang (EleutherAI): Multihost Training in Mesh Transformer JAX 0:56:28 Iurii Kemaev, Soลˆa Mokrรก, Junhyuk Oh (DeepMind): DeepMind JAX Ecosystem 1:24:55 Siddhartha Kamalakara, Joanna Yoo & Joรฃo G M Araรบjo (Cohere): Training large scale language models Find more information about the speakers and the talks here https://github.com/huggingface/transformers/blob/master/examples/research_projects/jax-projects/README.md#friday-july-2nd
Watch on YouTube โ†— (saves to browser)
Sign in to unlock AI tutor explanation ยท โšก30

Playlist

Uploads from HuggingFace ยท HuggingFace ยท 45 of 60

1 The Future of Natural Language Processing
The Future of Natural Language Processing
HuggingFace
2 Trends in Model Size & Computational Efficiency in NLP
Trends in Model Size & Computational Efficiency in NLP
HuggingFace
3 Increasing Data Usage in Natural Language Processing
Increasing Data Usage in Natural Language Processing
HuggingFace
4 In Domain & Out of Domain Generalization in the Future of NLP
In Domain & Out of Domain Generalization in the Future of NLP
HuggingFace
5 The Limits of NLU & the Rise of NLG in the Future of NLP
The Limits of NLU & the Rise of NLG in the Future of NLP
HuggingFace
6 The Lack of Robustness in the Future of NLP
The Lack of Robustness in the Future of NLP
HuggingFace
7 Inductive Bias, Common Sense, Continual Learning in The Future of NLP
Inductive Bias, Common Sense, Continual Learning in The Future of NLP
HuggingFace
8 Train a Hugging Face Transformers Model with Amazon SageMaker
Train a Hugging Face Transformers Model with Amazon SageMaker
HuggingFace
9 What is Transfer Learning?
What is Transfer Learning?
HuggingFace
10 The pipeline function
The pipeline function
HuggingFace
11 Navigating the Model Hub
Navigating the Model Hub
HuggingFace
12 Transformer models: Decoders
Transformer models: Decoders
HuggingFace
13 The Transformer architecture
The Transformer architecture
HuggingFace
14 Transformer models: Encoder-Decoders
Transformer models: Encoder-Decoders
HuggingFace
15 Transformer models: Encoders
Transformer models: Encoders
HuggingFace
16 Keras introduction
Keras introduction
HuggingFace
17 The push to hub API
The push to hub API
HuggingFace
18 Fine-tuning with TensorFlow
Fine-tuning with TensorFlow
HuggingFace
19 Learning rate scheduling with TensorFlow
Learning rate scheduling with TensorFlow
HuggingFace
20 TensorFlow Predictions and metrics
TensorFlow Predictions and metrics
HuggingFace
21 Welcome to the Hugging Face course
Welcome to the Hugging Face course
HuggingFace
22 The tokenization pipeline
The tokenization pipeline
HuggingFace
23 Supercharge your PyTorch training loop with Accelerate
Supercharge your PyTorch training loop with Accelerate
HuggingFace
24 The Trainer API
The Trainer API
HuggingFace
25 Batching inputs together (PyTorch)
Batching inputs together (PyTorch)
HuggingFace
26 Batching inputs together (TensorFlow)
Batching inputs together (TensorFlow)
HuggingFace
27 Hugging Face Datasets overview (Pytorch)
Hugging Face Datasets overview (Pytorch)
HuggingFace
28 Hugging Face Datasets overview (Tensorflow)
Hugging Face Datasets overview (Tensorflow)
HuggingFace
29 What is dynamic padding?
What is dynamic padding?
HuggingFace
30 What happens inside the pipeline function? (PyTorch)
What happens inside the pipeline function? (PyTorch)
HuggingFace
31 What happens inside the pipeline function? (TensorFlow)
What happens inside the pipeline function? (TensorFlow)
HuggingFace
32 Instantiate a Transformers model (PyTorch)
Instantiate a Transformers model (PyTorch)
HuggingFace
33 Instantiate a Transformers model (TensorFlow)
Instantiate a Transformers model (TensorFlow)
HuggingFace
34 Preprocessing sentence pairs (PyTorch)
Preprocessing sentence pairs (PyTorch)
HuggingFace
35 Preprocessing sentence pairs (TensorFlow)
Preprocessing sentence pairs (TensorFlow)
HuggingFace
36 Write your training loop in PyTorch
Write your training loop in PyTorch
HuggingFace
37 Managing a repo on the Model Hub
Managing a repo on the Model Hub
HuggingFace
38 Chapter 1 Live Session with Sylvain
Chapter 1 Live Session with Sylvain
HuggingFace
39 Chapter 2 Live Session with Lewis
Chapter 2 Live Session with Lewis
HuggingFace
40 The push to hub API
The push to hub API
HuggingFace
41 Chapter 2 Live Session with Sylvain
Chapter 2 Live Session with Sylvain
HuggingFace
42 Chapter 3 live sessions with Lewis (PyTorch)
Chapter 3 live sessions with Lewis (PyTorch)
HuggingFace
43 Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
HuggingFace
44 Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
HuggingFace
โ–ถ Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
HuggingFace
46 Chapter 4 live sessions with Omar
Chapter 4 live sessions with Omar
HuggingFace
47 Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
HuggingFace
48 Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
Deploy a Hugging Face Transformers Model from the Model Hub to Amazon SageMaker
HuggingFace
49 Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
HuggingFace
50 [Webinar] How to add machine learning capabilities with just a few lines of code
[Webinar] How to add machine learning capabilities with just a few lines of code
HuggingFace
51 Hugging Face + Zapier Demo Video
Hugging Face + Zapier Demo Video
HuggingFace
52 Hugging Face + Google Sheets Demo
Hugging Face + Google Sheets Demo
HuggingFace
53 Hugging Face Infinity Launch - 09/28
Hugging Face Infinity Launch - 09/28
HuggingFace
54 Build and Deploy a Machine Learning App in 2 Minutes
Build and Deploy a Machine Learning App in 2 Minutes
HuggingFace
55 Hugging Face Infinity - GPU Walkthrough
Hugging Face Infinity - GPU Walkthrough
HuggingFace
56 Otto - ๐Ÿค— Infinity Case Study
Otto - ๐Ÿค— Infinity Case Study
HuggingFace
57 Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it
Workshop: Getting started with Amazon Sagemaker Train a Hugging Face Transformers and deploy it
HuggingFace
58 Workshop: Going Production: Deploying, Scaling & Monitoring Hugging Face Transformer models
Workshop: Going Production: Deploying, Scaling & Monitoring Hugging Face Transformer models
HuggingFace
59 ๐Ÿค— Tasks: Causal Language Modeling
๐Ÿค— Tasks: Causal Language Modeling
HuggingFace
60 ๐Ÿค— Tasks: Masked Language Modeling
๐Ÿค— Tasks: Masked Language Modeling
HuggingFace

Related AI Lessons

โšก
How I Made My Android App Discoverable on 4 LLMs in 24 Hours (llms.txt, IndexNow, JSON-LD, the Bing Cycle)
Make your Android app discoverable on 4 LLMs in 24 hours using llms.txt, IndexNow, JSON-LD, and the Bing Cycle
Dev.to ยท TAMSIV
โšก
What LLMs Can Actually Do for Your Business
Discover how LLMs can revolutionize your business by automating written content generation, improving email management, and enhancing overall productivity
Medium ยท AI
โšก
MiMo-V2.5-Pro: The Long-Context LLM Iโ€™d Actually Test Before Paying More for Claude or GPT
Learn about MiMo-V2.5-Pro, a long-context LLM, and why you should test it before paying for alternatives like Claude or GPT
Medium ยท Programming
โšก
25 Deep Learning Questions Every GenAI Engineer Gets Asked (And How to Answer Them)- Part I
Learn how to answer 25 deep learning questions for GenAI engineers, covering topics like RAG pipelines and multi-agent workflows
Medium ยท Deep Learning

Chapters (4)

Lucas Beyer (Google Brain): Vision Transformer
30:03 Ben Wang (EleutherAI): Multihost Training in Mesh Transformer JAX
56:28 Iurii Kemaev, Soลˆa Mokrรก, Junhyuk Oh (DeepMind): DeepMind JAX Ecosystem
1:24:55 Siddhartha Kamalakara, Joanna Yoo & Joรฃo G M Araรบjo (Cohere): Training large sca
Up next
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)
Watch โ†’