Learn how to design, develop, deploy and iterate on production-grade ML applications.
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Updated
Sep 28, 2023 - Jupyter Notebook
Learn how to design, develop, deploy and iterate on production-grade ML applications.
12 Lessons, Get Started Building with Generative AI 🔗 https://microsoft.github.io/generative-ai-for-beginners/
Self-hosted AI coding assistant
the AI-native open-source embedding database
📋 A list of open LLMs available for commercial use.
The official GitHub page for the survey paper "A Survey of Large Language Models".
Open Source Neural Machine Translation and (Large) Language Models in PyTorch
Guided Text Generation
Modern columnar data format for ML and LLMs implemented in Rust. Convert from parquet in 2 lines of code for 100x faster random access, vector index, and data versioning. Compatible with Pandas, DuckDB, Polars, Pyarrow, with more integrations coming..
An easy-to-use LLMs quantization package with user-friendly apis, based on GPTQ algorithm.
Learn how to design, develop, deploy and iterate on production-grade ML applications.
Data Science Roadmap from A to Z
An open-source ChatGPT app with a voice
Open-source tools for prompt testing and experimentation, with support for both LLMs (e.g. OpenAI, LLaMA) and vector databases (e.g. Chroma, Weaviate, LanceDB).
A distributed system for embedding-based vector retrieval
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