pixas

langchain

🦜🔗 Build context-aware reasoning applications

gpt4free

The official gpt4free repository | various collection of powerful language models

annotated_deep_learning_paper_implementations

🧑‍🏫 60 Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, sophia, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠

spleeter

Deezer source separation library including pretrained models.

LLaVA

[NeurIPS'23 Oral] Visual Instruction Tuning (LLaVA) built towards GPT-4V level capabilities and beyond.

numpy-100

100 numpy exercises (with solutions)

chatGPTBox

Integrating ChatGPT into your browser deeply, everything you need is here

TinyLlama

The TinyLlama project is an open endeavor to pretrain a 1.1B Llama model on 3 trillion tokens.

automl

Google Brain AutoML

GPU-Puzzles

Solve puzzles. Learn CUDA.

lora-scripts

LoRA & Dreambooth training scripts & GUI use kohya-ss's trainer, for diffusion model.

OpenAgents

[COLM 2024] OpenAgents: An Open Platform for Language Agents in the Wild

Otter

🦦 Otter, a multi-modal model based on OpenFlamingo (open-sourced version of DeepMind's Flamingo), trained on MIMIC-IT and showcasing improved instruction-following and in-context learning ability.

InternLM-XComposer

InternLM-XComposer-2.5: A Versatile Large Vision Language Model Supporting Long-Contextual Input and Output

Chain-of-ThoughtsPapers

A trend starts from "Chain of Thought Prompting Elicits Reasoning in Large Language Models".

TransCoder

Public release of the TransCoder research project https://arxiv.org/pdf/2006.03511.pdf

AgentTuning

AgentTuning: Enabling Generalized Agent Abilities for LLMs

ring

Simple and flexible programming language for applications development

MING

明医 (MING)：中文医疗问诊大模型

DiffuSeq

[ICLR'23] DiffuSeq: Sequence to Sequence Text Generation with Diffusion Models

ScienceQA

Data and code for NeurIPS 2022 Paper "Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering".

pony-tutorial

:horse: Tutorial for the Pony programming language

MMMU

This repo contains evaluation code for the paper "MMMU: A Massive Multi-discipline Multimodal Understanding and Reasoning Benchmark for Expert AGI"

LRV-Instruction

[ICLR'24] Mitigating Hallucination in Large Multi-Modal Models via Robust Instruction Tuning

DS-1000

[ICML 2023] Data and code release for the paper "DS-1000: A Natural and Reliable Benchmark for Data Science Code Generation".

RLTF

Accepted by Transactions on Machine Learning Research (TMLR)

Tnn

[ICLR 2023] Official implementation of Transnormer in our ICLR 2023 paper - Toeplitz Neural Network for Sequence Modeling

LibriSQA

CMLMC

Code for the ICLR'22 paper "Improving Non-Autoregressive Translation Models Without Distillation"

Covid-19-Outbreak-Prediction

In this study, we leverage the fusion of edge computing, artificial intelligence (AI) methods, and facilities provided by B5G to build a heterogeneous set of AI techniques for COVID-19 outbreak prediction. Advancement in the areas of AI, edge computing, the Internet of Things (IoT), and fast communication networks provided by beyond 5G (B5G) networks has opened doors for new possibilities by fusing these technologies and techniques. In a pandemic outbreak, such as COVID-19, the need for rapid analysis, decision making, and prediction of future trends becomes paramount. On a global map, the distributed processing and analysis of data at the source is now possible and much more efficient. With the features provided by B5G, such as low latency, larger area coverage, higher data rate, and realtime communication, building new intelligent and efficient frameworks is becoming easier. In this study, our aim is to achieve higher accuracy in prediction by fusing multiple AI methods and leveraging the B5G communication architecture. We propose a distributed architecture for training AI methods on edge devices, with the results of edge-trained models then propagated to a central cloud AI method, which then combines all the received edge-trained models into a global and final prediction model. The experimental results of five countries (United States, India, Italy, Bangladesh, and Saudi Arabia) show that the proposed distributed AI on edges can predict COVID-19 outbreak better than that of each individual AI method in terms of correlation coefficient scores.