Popular GitHub Repositories for Gaussian Graphical Model

? Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.

Dify is an open-source LLM app development platform. Dify's intuitive interface combines AI workflow, RAG pipeline, agent capabilities, model management, observability features and more, letting you quickly go from prototype to production.

21 Lessons, Get Started Building with Generative AI ? https://microsoft.github.io/generative-ai-for-beginners/

Implement a ChatGPT-like LLM in PyTorch from scratch, step by step

A high-throughput and memory-efficient inference and serving engine for LLMs

Unified Efficient Fine-Tuning of 100+ LLMs & VLMs (ACL 2024)

1 min voice data can also be used to train a good TTS model! (few shot voice cloning)

Port of OpenAI's Whisper model in C/C++

?? - a deep learning toolkit for Text-to-Speech, battle-tested in research and production

Making large AI models cheaper, faster and more accessible

DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.

A generative speech model for daily dialogue.

OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.

Instant voice cloning by MIT and MyShell. Audio foundation model.

Easily train a good VC model with voice data <= 10 mins!

Enhanced ChatGPT Clone: Features Agents, DeepSeek, Anthropic, AWS, OpenAI, Assistants API, Azure, Groq, o1, GPT-4o, Mistral, OpenRouter, Vertex AI, Gemini, Artifacts, AI model switching, message search, Code Interpreter, langchain, DALL-E-3, OpenAPI Actions, Functions, Secure Multi-User Auth, Presets, open-source for self-hosting. Active project.

Pretrain, finetune ANY AI model of ANY size on multiple GPUs, TPUs with zero code changes.

Deezer source separation library including pretrained models.

Interactive deep learning book with multi-framework code, math, and discussions. Adopted at 500 universities from 70 countries including Stanford, MIT, Harvard, and Cambridge.

A game theoretic approach to explain the output of any machine learning model.