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Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Instruct-tune LLaMA on consumer hardware
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
Scripts for fine-tuning Meta Llama with composable FSDP & PEFT methods to cover single/multi-node GPUs. Supports default & custom datasets for applications such as summarization and Q&A. Supporting…
Natural Language Processing Tutorial for Deep Learning Researchers
Companion webpage to the book "Mathematics For Machine Learning"
PRML algorithms implemented in Python
A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API
LAVIS - A One-stop Library for Language-Vision Intelligence
强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/
This repository showcases various advanced techniques for Retrieval-Augmented Generation (RAG) systems. RAG systems combine information retrieval with generative models to provide accurate and cont…
tensorflow2中文教程，持续更新(当前版本:tensorflow2.0)，tag: tensorflow 2.0 tutorials
MiniCPM3-4B: An edge-side LLM that surpasses GPT-3.5-Turbo.
Python code for "Probabilistic Machine learning" book by Kevin Murphy
Notebooks using the Hugging Face libraries 🤗
LLM Finetuning with peft
仅需Python基础，从0构建大语言模型；从0逐步构建GLM4\Llama3\RWKV6， 深入理解大模型原理
Synthetic data generators for tabular and time-series data
Tigramite is a python package for causal inference with a focus on time series data. The Tigramite documentation is at
Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning
Code for paper "Which Training Methods for GANs do actually Converge? (ICML 2018)"
real Transformer TeraFLOPS on various GPUs
A series of Python Jupyter notebooks that help you better understand "The Elements of Statistical Learning" book
Python library for Multi-Armed Bandits
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations
Build, evaluate, understand, and fix LLM-based apps
Approximating Wasserstein distances with PyTorch
Jupyter notebooks for summarizing and reproducing the textbook "The Elements of Statistical Learning" 2/E by Hastie, Tibshirani, and Friedman