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PPL Quantization Tool (PPQ) is a powerful offline neural network quantization tool.

TradeMaster is an open-source platform for quantitative trading empowered by reinforcement learning 🔥 ⚡ 🌈

整理开源的中文大语言模型，以规模较小、可私有化部署、训练成本较低的模型为主，包括底座模型，垂直领域微调及应用，数据集与教程等。

The official GitHub page for the survey paper "A Survey of Large Language Models".

Awesome-LLM: a curated list of Large Language Model

本项目旨在分享大模型相关技术原理以及实战经验（大模型工程化、大模型应用落地）

LLMs interview notes and answers:该仓库主要记录大模型（LLMs）算法工程师相关的面试题和参考答案

中国大模型

FinGPT: Open-Source Financial Large Language Models! Revolutionize 🔥 We release the trained model on HuggingFace.

Awesome-LLM-Tabular: a curated list of Large Language Model applied to Tabular Data

A list of awesome papers and resources of recommender system on large language model (LLM).

An up-to-date list of works on Multi-Task Learning

基于ChatGLM-6B、ChatGLM2-6B、ChatGLM3-6B模型，进行下游具体任务微调，涉及Freeze、Lora、P-tuning、全参微调等

使用peft库，对chatGLM-6B/chatGLM2-6B实现4bit的QLoRA高效微调，并做lora model和base model的merge及4bit的量化（quantize）。

🤗 PEFT: State-of-the-art Parameter-Efficient Fine-Tuning.

Python 开源项目之「自学编程之路」，保姆级教程：AI实验室、宝藏视频、数据结构、学习指南、机器学习实战、深度学习实战、网络爬虫、大厂面经、程序人生、资源分享。

This is the code for "Capsule Networks: An Improvement to Convolutional Networks" by Siraj Raval on Youtube

You like pytorch? You like micrograd? You love tinygrad! ❤️

A novel algorithm to detect non-wear time from raw accelerometer data using convolutional neural networks.

A meta-learning attempt on the ARC Dataset

Benchmarking continual learning techniques for Human Activity Recognition data. We offer interesting insights on how the performance techniques vary with a domain other than images.

Evaluate three types of task shifting with popular continual learning algorithms.

Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).

Time series classification with deep learning in streaming

Implementation of Vision Transformer, a simple way to achieve SOTA in vision classification with only a single transformer encoder, in Pytorch

PyTorch implementation of "Transformer Transducer: A Streamable Speech Recognition Model with Transformer Encoders and RNN-T Loss" (ICASSP 2020)