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A Data Streaming Library for Efficient Neural Network Training
The AI-native database built for LLM applications, providing incredibly fast hybrid search of dense vector, sparse vector, tensor (multi-vector), and full-text
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.
A modular graph-based Retrieval-Augmented Generation (RAG) system
LlamaIndex is a data framework for your LLM applications
Open-source RAG Framework for building GenAI Second Brains 🧠Build productivity assistant (RAG) ⚡️🤖 Chat with your docs (PDF, CSV, ...) & apps using Langchain, GPT 3.5 / 4 turbo, Private, Anthropic…
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 yo…
Implementation of "BitNet: Scaling 1-bit Transformers for Large Language Models" in pytorch
Fast and memory-efficient exact attention
You like pytorch? You like micrograd? You love tinygrad! ❤️
Tensors and Dynamic neural networks in Python with strong GPU acceleration
An Open Source Machine Learning Framework for Everyone
Hackable and optimized Transformers building blocks, supporting a composable construction.
Drift Detection for your PyTorch Models
Always know what to expect from your data.
Optimized primitives for collective multi-GPU communication
Collective communications library with various primitives for multi-machine training.
Official repository for "Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing". Your efficient and high-quality synthetic data generation pipeline!
[ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.
Official repository of Evolutionary Optimization of Model Merging Recipes
Tools for merging pretrained large language models.
Dataframes powered by a multithreaded, vectorized query engine, written in Rust
DuckDB is an analytical in-process SQL database management system
Pytorch implementation of the PEER block from the paper, Mixture of A Million Experts, by Xu Owen He at Deepmind
DSPy: The framework for programming—not prompting—foundation models