A programming framework for agentic AI
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Qlib is an AI-oriented Quant investment platform that aims to use AI tech to empower Quant Research, from exploring ideas to implementing productions. Qlib supports diverse ML modeling paradigms, including supervised learning, market dynamics modeling, and RL, and is now equipped with https://github.com/microsoft/RD-Agent to automate R&D process.
- quantitative-finance
- machine-learning
- stock-data
- platform
JARVIS, a system to connect LLMs with ML community. Paper: https://arxiv.org/pdf/2303.17580.pdf
- deep-learning
- platform
- pytorch
Large-scale Self-supervised Pre-training Across Tasks, Languages, and Modalities
- nlp
- pre-trained-model
- unilm
- minilm
The absolute trainer to light up AI agents.
Research and development (R&D) is crucial for the enhancement of industrial productivity, especially in the AI era, where the core aspects of R&D are mainly focused on data and models. We are committed to automating these high-value generic R&D processes through R&D-Agent, which lets AI drive data-driven AI. 🔗https://aka.ms/RD-Agent-Tech-Report
- agent
- ai
- automation
- data-mining
A framework for building, orchestrating and deploying AI agents and multi-agent workflows with support for Python and .NET.
Code for loralib, an implementation of "LoRA: Low-Rank Adaptation of Large Language Models"
- gpt-2
- adaptation
- language-model
- gpt-3
Use Microsoft Edge's online text-to-speech service from Python WITHOUT needing Microsoft Edge
- tts
- speech-synthesis
- text-to-speech
Build high-quality LLM apps - from prototyping, testing to production deployment and monitoring.
MMdnn is a set of tools to help users inter-operate among different deep learning frameworks. E.g. model conversion and visualization. Convert models between Caffe, Keras, MXNet, Tensorflow, CNTK, PyTorch Onnx and CoreML.
- cntk
- visualization
- tensorflow
- model-converter
Sharing Updatable Models (SUM) on Blockchain
- blockchain
- ml
- ai
- economics
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.