Popular GitHub Repositories for Model Compression

? 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

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

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

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.

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

A generative speech model for daily dialogue.

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

Real-ESRGAN aims at developing Practical Algorithms for General Image/Video Restoration.

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

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

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.

Deezer source separation library including pretrained models.

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