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The repository shows how to implement MLOps process for LLM-based backend
A modular graph-based Retrieval-Augmented Generation (RAG) system
Resources for the "SummEval: Re-evaluating Summarization Evaluation" paper
The RAG Experiment Accelerator is a versatile tool designed to expedite and facilitate the process of conducting experiments and evaluations using Azure Cognitive Search and RAG pattern.
A Python Package to Tackle the Curse of Imbalanced Datasets in Machine Learning
A template that shows how to setup MLOps in Azure AI Search using pull approach
The source code of "Deep Exemplar-based Colorization".
Retrieval Augmented Generation (RAG) Chatbot solution in .NET
Always know what to expect from your data.
A guidance language for controlling large language models.
A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour
A framework for few-shot evaluation of language models.
AI-in-a-Box leverages the expertise of Microsoft across the globe to develop and provide AI and ML solutions to the technical community. Our intent is to present a curated collection of solution ac…
A curated list of 🌌 Azure OpenAI, 🦙 Large Language Models (incl. RAG, Agent), and references with memos.
Context aware, pluggable and customizable data protection and de-identification SDK for text and images
Fast and flexible image augmentation library. Paper about the library: https://www.mdpi.com/2078-2489/11/2/125
A clean, modular implementation of the Yolov7 model family, which uses the official pretrained weights, with utilities for training the model on custom (non-COCO) tasks.
Pretrained Pytorch face detection (MTCNN) and facial recognition (InceptionResnet) models
Implementation of Denoising Diffusion Probabilistic Model in Pytorch
🦜🔗 Build context-aware reasoning applications
🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
this is the repo of the Synapse VS Code extension for Microsoft Fabric
Samples and data for Microsoft Fabric Learn content
🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.
Integrate cutting-edge LLM technology quickly and easily into your apps