Filco306

open-llms

📋 A list of open LLMs available for commercial use.

gpt4all

GPT4All: Run Local LLMs on Any Device. Open-source and available for commercial use.

omw-data

This packages up data for the Open Multilingual Wordnet

AutoGPT

AutoGPT is the vision of accessible AI for everyone, to use and to build on. Our mission is to provide the tools, so that you can focus on what matters.

ChatGDB

Harness the power of ChatGPT inside the GDB or LLDB debugger!

langchain

🦜🔗 Build context-aware reasoning applications

hetionet

Hetionet: an integrative network of disease

CodeGeeX

CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)

cursor

The AI Code Editor

RWKV-LM

RWKV is an RNN with transformer-level LLM performance. It can be directly trained like a GPT (parallelizable). So it's combining the best of RNN and transformer - great performance, fast inference, saves VRAM, fast training, "infinite" ctx_len, and free sentence embedding.

OMW

The Open Multilingual Wordnet

PromptORE

PromptORE – A Novel Approach Towards Fully Unsupervised Relation Extraction

Chatbot-Long-Short-Term-Memory

GPT-3 Chatbot with Long and Short Term Memory and advanced logic built in javascript with openai API - short and long memory, KYC, embeddings, openai, database, flexible, gpt-3.5-turbo, react

openie6

OpenIE6 system

hyp_metric

Hyperbolic Vision Transformers: Combining Improvements in Metric Learning | Official repository

tmu

Implements the Tsetlin Machine, Coalesced Tsetlin Machine, Convolutional Tsetlin Machine, Regression Tsetlin Machine, and Weighted Tsetlin Machine, with support for continuous features, drop clause, Type III Feedback, focused negative sampling, multi-task classifier, autoencoder, literal budget, and one-vs-one multi-class classifier. TMU is written in Python with wrappers for C and CUDA-based clause evaluation and updating.

DAGNN

A graph neural network tailored to directed acyclic graphs that outperforms conventional GNNs by leveraging the partial order as strong inductive bias besides other suitable architectural features.

dagma

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

DAG-GNN

HypHC

Hyperbolic Hierarchical Clustering.

Open-World-TaxoKG-CoLearning

Data and code of AKBC'22 paper "Open-World Taxonomy and Knowledge Graph Co-Learning".

Prompt-Engineering-Guide

🐙 Guides, papers, lecture, notebooks and resources for prompt engineering

Differentiable-Frechet-Mean

[ICML 2020] Differentiating through the Fréchet Mean (https://arxiv.org/abs/2003.00335).

PoincareKMeans

K-Means algorithm in the Poincare Disk Model

htsprophet

Hierarchical Time Series Forecasting using Prophet

AdaptiveXGBoostClassifier

Implementation of the Adaptive XGBoost classifier for evolving data streams

Awesome-Hyperbolic-Representation-and-Deep-Learning

Paper list about hyperbolic embedding, hyperbolic models,hyperbolic applications

HyperA

Started as a Team Project for CS690D at UMass Amherst, now turning into pytorch implementation of hyperbolic neural networks using Poincare Ball model. [Final report](https://github.com/dhruvdcoder/HyperA/tree/master/report)

phrase-bert-topic-model

rebel

REBEL is a seq2seq model that simplifies Relation Extraction (EMNLP 2021).