๐Ÿค— Hugging Cast S2E4 - Deploying LLMs on AMD GPUs and Ryzen AI PCs

HuggingFace ยท Intermediate ยท๐Ÿง  Large Language Models ยท1y ago
Hugging Cast is a live show about building AI with open source. In this episode, Felix, Mohit and Jeff demo new experiences to deploy LLMs on AMD, on the cloud using the latest MI300X GPU instances on Azure, and on-device using the new Ryzen AI PCs. ๐Ÿš€ How we collaborate with AMD to make open models go brrr everywhere โ˜๏ธ Deploy LLMs on Azure with the new ND MI300X v5 VMs and TGI ๐Ÿ’ป Unlock on-device inference with optimum-amd on the new Ryzen AI PCs Ryzen AI PC demo notebook: https://huggingface.co/mohitsha/ryzenai-hcast-notebook/blob/main/ryzen_demo_hcast.ipynb
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1 The Future of Natural Language Processing
The Future of Natural Language Processing
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 2 Trends in Model Size & Computational Efficiency in NLP
Trends in Model Size & Computational Efficiency in NLP
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 3 Increasing Data Usage in Natural Language Processing
Increasing Data Usage in Natural Language Processing
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 4 In Domain & Out of Domain Generalization in the Future of NLP
In Domain & Out of Domain Generalization in the Future of NLP
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 5 The Limits of NLU & the Rise of NLG in the Future of NLP
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 6 The Lack of Robustness in the Future of NLP
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 7 Inductive Bias, Common Sense, Continual Learning in The Future of NLP
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 8 Train and use a NLP model in 10 mins!
Train and use a NLP model in 10 mins!
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 9 Automatic text classification in a few lines of code
Automatic text classification in a few lines of code
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 10 Train a Hugging Face Transformers Model with Amazon SageMaker
Train a Hugging Face Transformers Model with Amazon SageMaker
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 11 What is Transfer Learning?
What is Transfer Learning?
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 12 The pipeline function
The pipeline function
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 13 Navigating the Model Hub
Navigating the Model Hub
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 14 Character-based tokenizers
Character-based tokenizers
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 15 Transformer models: Decoders
Transformer models: Decoders
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 16 The Transformer architecture
The Transformer architecture
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 17 Transformer models: Encoder-Decoders
Transformer models: Encoder-Decoders
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 18 Transformer models: Encoders
Transformer models: Encoders
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 19 Keras introduction
Keras introduction
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 20 The push to hub API
The push to hub API
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 21 Subword-based tokenizers
Subword-based tokenizers
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 22 Fine-tuning with TensorFlow
Fine-tuning with TensorFlow
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 23 Learning rate scheduling with TensorFlow
Learning rate scheduling with TensorFlow
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 24 TensorFlow Predictions and metrics
TensorFlow Predictions and metrics
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 25 Tokenizers Overview
Tokenizers Overview
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 26 Word-based tokenizers
Word-based tokenizers
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 27 Welcome to the Hugging Face course
Welcome to the Hugging Face course
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 28 The tokenization pipeline
The tokenization pipeline
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 29 Supercharge your PyTorch training loop with Accelerate
Supercharge your PyTorch training loop with Accelerate
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 30 The Trainer API
The Trainer API
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 31 Batching inputs together (PyTorch)
Batching inputs together (PyTorch)
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 32 Batching inputs together (TensorFlow)
Batching inputs together (TensorFlow)
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 33 Hugging Face Datasets overview (Pytorch)
Hugging Face Datasets overview (Pytorch)
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 34 Hugging Face Datasets overview (Tensorflow)
Hugging Face Datasets overview (Tensorflow)
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 35 What is dynamic padding?
What is dynamic padding?
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 36 What happens inside the pipeline function? (PyTorch)
What happens inside the pipeline function? (PyTorch)
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 37 What happens inside the pipeline function? (TensorFlow)
What happens inside the pipeline function? (TensorFlow)
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 38 Instantiate a Transformers model (PyTorch)
Instantiate a Transformers model (PyTorch)
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 39 Instantiate a Transformers model (TensorFlow)
Instantiate a Transformers model (TensorFlow)
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 40 Preprocessing sentence pairs (PyTorch)
Preprocessing sentence pairs (PyTorch)
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 41 Preprocessing sentence pairs (TensorFlow)
Preprocessing sentence pairs (TensorFlow)
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 42 Write your training loop in PyTorch
Write your training loop in PyTorch
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 43 Managing a repo on the Model Hub
Managing a repo on the Model Hub
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 44 Chapter 1 Live Session with Sylvain
Chapter 1 Live Session with Sylvain
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 45 Chapter 2 Live Session with Lewis
Chapter 2 Live Session with Lewis
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 46 The push to hub API
The push to hub API
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 47 Chapter 2 Live Session with Sylvain
Chapter 2 Live Session with Sylvain
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 48 Chapter 3 live sessions with Lewis (PyTorch)
Chapter 3 live sessions with Lewis (PyTorch)
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 49 Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 1 Talks: JAX, Flax & Transformers ๐Ÿค—
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 50 Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
Day 2 Talks: JAX, Flax & Transformers ๐Ÿค—
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 51 Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
Day 3 Talks JAX, Flax, Transformers ๐Ÿค—
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 52 Chapter 4 live sessions with Omar
Chapter 4 live sessions with Omar
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 53 Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
Deploy a Hugging Face Transformers Model from S3 to Amazon SageMaker
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 54 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
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 55 Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
Run a Batch Transform Job using Hugging Face Transformers and Amazon SageMaker
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 56 [Webinar] How to add machine learning capabilities with just a few lines of code
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 57 Hugging Face + Zapier Demo Video
Hugging Face + Zapier Demo Video
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 58 Hugging Face + Google Sheets Demo
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 59 Hugging Face Infinity Launch - 09/28
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 60 Introducing AutoNLP (Trailer)
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5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Next Up
5 Levels of AI Agents - From Simple LLM Calls to Multi-Agent Systems
Dave Ebbelaar (LLM Eng)