This repository contains examples for customers to get started using the Amazon Bedrock Service. This contains examples for all available foundational models

Foundation model benchmarking tool. Run any model on any AWS platform and benchmark for performance across instance type and serving stack options.

✨✨Latest Advances on Multimodal Large Language Models

SageMaker Load Testing using Locust

A modular and comprehensive solution to deploy a Multi-LLM and Multi-RAG powered chatbot (Amazon Bedrock, Anthropic, HuggingFace, OpenAI, Meta, AI21, Cohere, Mistral) using AWS CDK on AWS

Infiniband Verbs Performance Tests

Drop in a screenshot and convert it to clean code (HTML/Tailwind/React/Vue)

Collection of best practices, reference architectures, model training examples and utilities to train large models on AWS.

Repository for training and deploying Generative AI models, including text-text, text-to-image generation and prompt engineering playground using SageMaker Studio.

This is a workshop designed for Amazon Bedrock a foundational model service.

🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

Stable Diffusion AWS Extension User Guide

Use the two different methods (deepspeed and SageMaker model parallelism library) to fine tune llama model on Sagemaker. Then deploy the fine tuned llama on Sagemaker with server side batch.

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

Examples and guides for using the OpenAI API

The repo will show the whole code about DeepSpeed training LLM on SageMaker for multiple nodes.

Demo applications showcasing DJL

ChatGLM-6B: An Open Bilingual Dialogue Language Model | 开源双语对话语言模型

The Generative AI Landscape - A Collection of Awesome Generative AI Applications

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch and FLAX.

A latent text-to-image diffusion model