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This repository provides tutorials and implementations for various Generative AI Agent techniques, from basic to advanced. It serves as a comprehensive guide for building intelligent, interactive A…
🚀 Power Your World with AI - Explore, Extend, Empower.
A launch point for your personal nvim configuration
The largest collection of PyTorch image encoders / backbones. Including train, eval, inference, export scripts, and pretrained weights -- ResNet, ResNeXT, EfficientNet, NFNet, Vision Transformer (V…
Minimal, clean code for the Byte Pair Encoding (BPE) algorithm commonly used in LLM tokenization.
[ICLR 2024] Sheared LLaMA: Accelerating Language Model Pre-training via Structured Pruning
LLMs build upon Evol Insturct: WizardLM, WizardCoder, WizardMath
Official inference library for Mistral models
Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.
[ICLR 2024] Efficient Streaming Language Models with Attention Sinks
Extend existing LLMs way beyond the original training length with constant memory usage, without retraining
Efficient computing methods developed by Huawei Noah's Ark Lab
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
The simplest, fastest repository for training/finetuning medium-sized GPTs.
An implementation of "Retentive Network: A Successor to Transformer for Large Language Models"
Examples and guides for using the OpenAI API
Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
GPU & Accelerator process monitoring for AMD, Apple, Huawei, Intel, NVIDIA and Qualcomm
Modified MobileNet models for CIFAR100 dataset
Related papers for reinforcement learning, including classic papers and latest papers in top conferences
Implementation of the LLaMA language model based on nanoGPT. Supports flash attention, Int8 and GPTQ 4bit quantization, LoRA and LLaMA-Adapter fine-tuning, pre-training. Apache 2.0-licensed.
Slimmable Networks, AutoSlim, and Beyond, ICLR 2019, and ICCV 2019
A beautiful and useful prompt for your shell
A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in Python using Scikit-Learn, Keras and TensorFlow 2.