Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities

Qwen2.5 is the large language model series developed by Qwen team, Alibaba Cloud.

ColBERT: state-of-the-art neural search (SIGIR'20, TACL'21, NeurIPS'21, NAACL'22, CIKM'22, ACL'23, EMNLP'23)

Evolutionary Scale Modeling (esm): Pretrained language models for proteins

Python bindings for llama.cpp

LLM inference in C/C++

🪢 Open source LLM engineering platform: LLM Observability, metrics, evals, prompt management, playground, datasets. Integrates with OpenTelemetry, Langchain, OpenAI SDK, LiteLLM, and more. 🍊YC W23

iOS support agent for automation

UI Automation Framework for Games and Apps

A simple example implementation of the VoiceRAG pattern to power interactive voice generative AI experiences using RAG with Azure AI Search and Azure OpenAI's gpt-4o-realtime-preview model.

The LLM Evaluation Framework

Educational framework exploring ergonomic, lightweight multi-agent orchestration. Managed by OpenAI Solution team.

ReLM is a Regular Expression engine for Language Models

A programming framework for agentic AI 🤖 PyPi: autogen-agentchat Discord: https://aka.ms/autogen-discord Office Hour: https://aka.ms/autogen-officehour

Image classification project from Udacity's AWS Machine Learning Engineering Course

newly created cd0387 from nd009t-c3repo

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows" on Object Detection and Instance Segmentation.

This is an official implementation for "Swin Transformer: Hierarchical Vision Transformer using Shifted Windows".

[CVPR 2021] Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

[ICCV2021] Official PyTorch implementation of Segmenter: Transformer for Semantic Segmentation

Focus on prompting and generating

An open source implementation of CLIP.

CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image

Similar image search

This project focuses on using the AWS open-source AutoML library, AutoGluon, to predict bike sharing demand using the Kaggle Bike Sharing demand dataset.