Huggingface

In this blog post you will learn how to deploy Llama 3 70B to Amazon SageMaker.

In this blog post you will learn how to accelerate Mixtral using Speculative Decoding (Medusa) and Quantization (AWQ).

In this blog post you will learn how to deploy Meta Llama 2 70B on AWS Inferentia2 with Hugging Face Optimum on Amazon SageMaker.

In this blog post you will learn how to fine-tune open LLMs from Hugging Face using Amazon SageMaker.

In this blog post you will learn how to evaluate LLMs using Hugging Face lighteval on Amazon SageMaker.

In this blog post you will learn how to fine tune Google Gemma using Hugging Face Transformers, Datasets and TRL.

In this blog post you will learn how to align LLMs using Hugging Face TRL and RLHF through Direct Preference Optimization (DPO).

In this blog post you will learn how to fine-tune LLMs using Hugging Face TRL, Transformers and Datasets in 2024. We will fine-tune a LLM on a text to SQL dataset.

In this blog post you will learn how to increase the throughput of Llama 13B on Amazon SageMaker using single instance multi-replica endpoints.

In this blog post you will learn how to fine-tune Llama 7B on AWS Trainium using the Hugging Face Optimum Neuron library.

In this blog post you will learn how to use the huggingface_hub library to create, send requests to, pause, and delete Hugging Face Inference Endpoints.

In this blog post you will learn how to deploy Mixtral 8x7B to Amazon SageMaker.

Learn how to evaluate LLMs and RAG pipelines using Langchain and Hugging Face

Learn how to deploy Hugging Face Idefics 9B & 80B to Amazon SageMaker and send requests with images and text to the model.

Learn how to fine-tuned and deploy Mistral 7B with Hugging Face on Amazon SageMaker and leverage technique like Qlora, Flash Attention and response streaming

Benchmark evaluating varying sizes of Llama 2 on a range of Amazon EC2 instance types with different load levels on latency (ms per token), and throughput (tokens per second).

In this example we will show how to fine-tune Falcon 180B using DeepSpeed, Hugging Face Transformers, LoRA with Flash Attention on a multi-GPU machine.

Learn how to fine-tune Falcon 180B with QLoRA and Flash Attention on Amazon SageMaker.

Learn how to deploy Falcon 180B to Amazon SageMaker and how to create a chatbot with streaming inference.

Learn how to quantize Llama 2 7B with GPTQ to use 4x less memory.

Learn how to use Infrastructure as Code with (AWS CDK)] to deploy and manage Llama 2

Learn how to deploy Llama 2 models (7B - 70B) to Amazon SageMaker using the Hugging Face LLM Inference DLC.

This blog post is an extended guide on instruction-tuning Llama 2 from Meta AI

All Resources for LLaMA 2, How to test, train, and deploy it.

Learn how to train LLaMa 2 using QLoRA Hugging Face Transformers on Amazon SageMaker

Learn how to train LLMs using QLoRA on Amazon SageMaker

Learn how to deploy LLMs using Hugging Face Inference Endpoints

Learn how to optimize and deploy BERT on AWS Inferentia2

Learn how to deploy LLMs into VPCs from S3 with Amazon SageMaker using the new Hugging Face LLM Inference DLC.

Learn how to deploy Falcon 40B to Amazon SageMaker using the new Hugging Face LLM Inference DLC.

Learn how to fine-tune Hugging Face Transformers using AWS Trainium.

Learn how to deploy the open-source LLMs, like BLOOM to Amazon SageMaker for inference using the new Hugging Face LLM Inference Container.

Learn how to fine-tune Donut-base a Generative AI model for document-understand/document-parsing using Hugging Face Transformers and Amazon SageMaker.

Learn how to fine-tune LLMs on multi-node setups using Amazon SageMaker and Hugging Face Transformers with PyTorch FSDP

Learn how to quickly set up an AWS Trainium using the Hugging Face Neuron Deep Learning AMI and fine-tune BERT

Learn how to fine-tune BLOOMZ 7B with Amazon SageMaker on a Single GPU using LoRA Hugging Face Transformers.

Learn how to fine-tune Google's FLAN-T5 XXL on a Single GPU using LoRA And Hugging Face Transformers.

Learn how to deploy Google's FLAN-UL 20B on Amazon SageMaker for inference.

Learn how to get started with Pytorch 2.0 and Hugging Face Transformers and reduce your training time up to 2x.

Learn how to deploy ControlNet Stable Diffusion Pipeline on Hugging Face Inference Endpoints to generate controlled images.

Learn how to fine-tune Google's FLAN-T5 XXL on Amazon SageMaker using DeepSpeed and Hugging Face Transformers.

Learn how to fine-tune Google's FLAN-T5 XXL using DeepSpeed & Hugging Face Transformers.

Learn how to deploy Google's FLAN-T5 XXL on Amazon SageMaker for inference.

Learn how to leverage Hugging Face Transformers to easily fine-tune your models.

Learn how to get started with Hugging Face Transformers and TPUs using PyTorch, fine-tune a BERT model for Text Classification using the newest Google Cloud TPUs.

Learn how to fine-tune Google's FLAN-T5 for chat & dialogue summarization using Hugging Face Transformers.

Learn how to deploy OpenAI Whisper for speech recognition and transcription using Hugging Face Inference Endpoints.

Learn how to deploy Stable Diffusion 2.0 Inpainting on Hugging Face Inference Endpoints to manipulate images.

Learn how to deploy Stable Diffusion 2.0 on Hugging Face Inference Endpoints to generate images based from text.

Learn how to fine-tune LiLt (Language independent Layout Transformer) for document-understand/document-parsing using Hugging Face Transformers.

Learn how to deploy a multiple models on to a GPU with Hugging Face multi-model inference endpoints.

Learn how to deploy a Hugging Face Gradio Application using Hugging Face Transformers to AWS Lambda for serverless workloads.

Learn how to optimize Stable Diffusion for GPU inference with a 1-line of code using Hugging Face Diffusers and DeepSpeed.

Learn how to deploy Stable Diffusion to Amazon SageMaker to generate images.

Learn how to deploy T5 11B on a single GPU using Hugging Face Inference Endpoints.

Learn how to use SetFit to create a text-classification model with only a `8` labeled samples per class, or `32` samples in total. You will also learn how to improve your model by using hyperparamter tuning.

Learn how to fine-tune LayoutLM for document-understand using Keras & Hugging Face Transformers.

Learn how to deploy LayoutLM for document-understand using Hugging Face Inference Endpoints.

Learn how to fine-tune LayoutLM for document-understand using Hugging Face Transformers. LayoutLM is a document image understanding and information extraction transformers.

Welcome to this tutorial on how to create a custom inference handler for Hugging Face Inference Endpoints.

Learn how to optimize GPT-J for GPU inference with a 1-line of code using Hugging Face Transformers and DeepSpeed.

Learn how to fine-tune Donut-base for document-understand/document-parsing using Hugging Face Transformers. Donut is a new document-understanding model achieving state-of-art performance and can be used for commercial applications.

Learn how to Sentence Transformers model with TensorFlow and Keras for creating document embeddings

Learn how to pre-traing BERT from scratch using Hugging Face Transformers and Habana Gaudi.

Learn how to optimize BERT for GPU inference with a 1-line of code using Hugging Face Transformers and DeepSpeed.

Learn how to optimize Sentence Transformers using Hugging Face Optimum. You will learn how dynamically quantize and optimize a Sentence Transformer for ONNX Runtime.

Learn how to migrate your training jobs to a Habana Gaudi-based DL1 instance on AWS using EC2 Remote Runner.

Learn how to optimize Vision Transformer (ViT) using Hugging Face Optimum. You will learn how dynamically quantize a ViT model for ONNX Runtime.

Learn how to optimize Hugging Face Transformers models for NVIDIA GPUs using Optimum. You will learn how to optimize a DistilBERT for ONNX Runtime

Learn how to learn how to fine-tune XLM-RoBERTa for multi-lingual multi-class text-classification using a Habana Gaudi-based DL1 instance.

Learn how to optimize Hugging Face Transformers models using Optimum. The session will show you how to dynamically quantize and optimize a DistilBERT model using Hugging Face Optimum and ONNX Runtime. Hugging Face Optimum is an extension of 🤗 Transformers, providing a set of performance optimization tools enabling maximum efficiency to train and run models on targeted hardware.

Introduction guide about ONNX and Transformers. Learn how to convert transformers like BERT to ONNX and what you can do with it.

Learn how to setup a Deep Learning Environment for Hugging Face Transformers with Habana Gaudi on AWS using the DL1 instance type.

Learn how to do post-training static quantization on Hugging Face Transformers model with `optimum` to achieve up to 3x latency improvements.

Learn how to do advanced PII detection and anonymization with Hugging Face Transformers and Amazon SageMaker.

Learn about the different existing Amazon SageMaker Inference options and and how to use them.

Learn how to deploy a Transformer model like BERT to Amazon SageMaker Serverless using the Python SageMaker SDK.

Learn how to accelerate Sentence Transformers inference inference using Hugging Face Transformers and AWS Inferentia.

Learn how to leverage AWS Spot Instances when training Hugging Face Transformers with Amazon SageMaker to save up to 90% training cost.

Learn how to accelerate BERT and Transformers inference using Hugging Face Transformers and AWS Inferentia.

Learn how to use a custom Inference script for creating document embeddings with Hugging Face's Transformers, Amazon SageMaker, and Sentence Transformers.

Learn how to apply autoscaling to Hugging Face Transformers and Amazon SageMaker using Terraform.

Learn how to deploy multiple Hugging Face Transformers for inference with Amazon SageMaker and Multi-Container Endpoints.

Learn how to deploy an Asynchronous Inference model with Hugging Face Transformers and Amazon SageMaker, with autoscaling to zero.

Learn how to deploy BERT/DistilBERT with Hugging Face Transformers using Amazon SageMaker and Terraform module.

Learn how to run apply task-specific knowledge distillation for BERT and text-classification using Hugging Face Transformers & Amazon SageMaker including Hyperparameter search.

Learn how to run large-scale distributed training using multilingual BERT on over 1 million data points with Hugging Face Transformers & Amazon SageMaker

Learn how to fine-tune a a Hugging Face Transformer for Financial Text Summarization using vanilla `Keras`, `Tensorflow` , `Transformers`, `Datasets` & Amazon SageMaker.

Learn how to deploy EleutherAIs GPT-J 6B for inference using Hugging Face Transformers and Amazon SageMaker.

Learn how to fine-tune a Vision Transformer for Image Classification Example using vanilla `Keras`, `Transformers`, `Datasets`.

In October and November, we held a workshop series on “Enterprise-Scale NLP with Hugging Face & Amazon SageMaker”. This workshop series consisted out of 3 parts and covers: Getting Started, Going Production & MLOps.

Learn how to fine-tune a non-English BERT using Hugging Face Transformers and Keras/TF, Transformers, datasets.

Learn how to Compile and fine-tune a Multi-Class Classification Transformers with `Trainer` and `emotion` dataset using Amazon SageMaker Training Compiler.

Learn how to automatically save your model weights, logs, and artifacts to the Hugging Face Hub using Amazon SageMaker and how to deploy the model afterwards for inference.

Learn how to build an End-to-End MLOps Pipeline for Hugging Face Transformers from training to production using Amazon SageMaker.

Learn how to add auto-scaling to your Hugging Face Transformers SageMaker Endpoints.

🌸 BigScience released their first modeling paper introducing T0 which outperforms GPT-3 on many zero-shot tasks while being 16x smaller! Deploy BigScience the 3 Billion version (T0_3B) to Amazon SageMaker with a few lines of code to run a scalable production workload!

Deploy Hugging Face Transformers to Amazon SageMaker and create an API for the Endpoint using AWS Lambda, API Gateway and AWS CDK.

The latest developments in NLP show that you can overcome this limitation by providing a few examples at inference time with a large language model - a technique known as Few-Shot Learning. In this blog post, we'll explain what Few-Shot Learning is, and explore how a large language model called GPT-Neo.

Learn how to train distributed models for summarization using Hugging Face Transformers and Amazon SageMaker and upload them afterwards to huggingface.co.

Fine-tune non-English, German GPT-2 model with Huggingface on German recipes. Using their Trainer class and Pipeline objects.

Build a non-English (German) BERT multi-class text classification model with HuggingFace and Simple Transformers.