Python library that runs large language model inference layer by layer, so 70B-class models fit on a single 4GB GPU without quantization.
Chinese-language hands-on programming tutorial series on large language models, covering fine-tuning, prompting, editing, watermarking, jailbreaks, and agents.
Implement a ChatGPT-like LLM in PyTorch from scratch, step by step
Code examples for the O'Reilly book Hands-On Large Language Models, covering LLM concepts and practical techniques in Jupyter notebooks.
Code for Machine Learning for Trading, 3rd edition — from data sourcing to live execution.
A collection of examples, tutorials, and apps for building with Liquid AI's open-weight LFM models and the LEAP SDK on laptops, mobile, and edge devices.
An interactive Jupyter notebook course teaching prompt engineering techniques for Anthropic's Claude models.
Jupyter notebooks and code accompanying Ed Donner's 8-week LLM engineering course, from local models through building LLM applications.
Open-source GPU cluster orchestrator and PyTorch-based framework for training LLMs with billions to trillions of parameters.
Reference PyTorch implementation and models for DINOv3
A free, cohort-based course teaching ML engineering: training models, exposing them via APIs, and deploying with Docker, Kubernetes, and AWS Lambda.
Developer hub with training recipes, cookbooks, datasets, and end-to-end examples for building with NVIDIA's open Nemotron models.
Python toolkit for speaker diarization built on PyTorch, with pretrained pipelines and models that can be fine-tuned on your own data.
Implement a reasoning LLM in PyTorch from scratch, step by step
Open-source large language models and tooling for financial tasks such as sentiment analysis, forecasting, and robo-advisory.
This project presents a comparative machine learning framework for heart attack risk prediction using Python. It includes data preprocessing, feature engineering, SMOTE for class balancing, cross validation, and performance evaluation of multiple ML models.
Sample notebooks, code, and apps demonstrating how to build with generative AI models on Google Cloud.
PyTorch code and models for the DINOv2 self-supervised learning method.
A collection of guides and examples for the Gemma open models from Google.
中文翻译的 Hands-On-Large-Language-Models (hands-on-llms),动手学习大模型
Silero Models: pre-trained text-to-speech models made embarrassingly simple
A collection of pre-trained, state-of-the-art models in the ONNX format
A course on aligning smol models.
A collection of scientific methods, processes, algorithms, and systems to build stories & models.
The /llms.txt file, helping language models use your website
This course is designed to guide beginners through the exciting world of Edge AI, covering fundamental concepts, popular models, inference techniques, device-specific applications, model optimization, and the development of intelligent Edge AI agents.
Collection of notebooks about quantitative finance, with interactive python code.
《Pytorch实用教程》(第二版)无论是零基础入门,还是CV、NLP、LLM项目应用,或是进阶工程化部署落地,在这里都有。相信在本书的帮助下,读者将能够轻松掌握 PyTorch 的使用,成为一名优秀的深度学习工程师。
Refusal in Language Models
A collection of various deep learning architectures, models, and tips
A collection of tutorials on state-of-the-art computer vision models and techniques. Explore everything from foundational architectures like ResNet to cutting-edge models like RF-DETR, YOLO11, SAM 3, and Qwen3-VL.
Materials for the Hugging Face Diffusion Models Course
TimeGPT-2.1: production ready pre-trained Time Series Foundation Model for forecasting and anomaly detection. Generative pretrained transformer for time series trained on over 100B data points. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code 🚀.
🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models
A Lighting Pytorch Framework for Recommendation Models, Easy-to-use and Easy-to-extend.
TorchXRayVision: A library of chest X-ray datasets and models. Classifiers, segmentation, and autoencoders.
Foundation Models for Time Series
End-to-end project training and building AI models / LLMs to detect AI-written text
H2O is an Open Source, Distributed, Fast & Scalable Machine Learning Platform: Deep Learning, Gradient Boosting (GBM) & XGBoost, Random Forest, Generalized Linear Modeling (GLM with Elastic Net), K-Means, PCA, Generalized Additive Models (GAM), RuleFit, Support Vector Machine (SVM), Stacked Ensembles, Automatic Machine Learning (AutoML), etc.
Practical course about Large Language Models.
This repository hosts the code behind the online book, Coding for Economists.
This repository introduces PIXIU, an open-source resource featuring the first financial large language models (LLMs), instruction tuning data, and evaluation benchmarks to holistically assess financial LLMs. Our goal is to continually push forward the open-source development of financial artificial intelligence (AI).
Curso de Introducción a Machine Learning con Python
[CVPR 2025 Highlight] Official code and models for Encoder-only Mask Transformer (EoMT).
250+ Fine-tuning & RL Notebooks for text, vision, audio, embedding, TTS models.
This is a Phi Family of SLMs book for getting started with Phi Models. Phi a family of open sourced AI models developed by Microsoft. Phi models are the most capable and cost-effective small language models (SLMs) available, outperforming models of the same size and next size up across a variety of language, reasoning, coding, and math benchmarks
Convert TensorFlow, Keras, Tensorflow.js and Tflite models to ONNX
Interpretable ML package 🔍 for concise, transparent, and accurate predictive modeling (sklearn-compatible).
[ICASSP 2024] 🍵 Matcha-TTS: A fast TTS architecture with conditional flow matching
A collection of notebooks/recipes showcasing usecases of open-source models with Together AI.
SMT: The Surrogate Modeling Toolbox
Cell2Sentence: Teaching Large Language Models the Language of Biology
Python package for Causal Discovery by learning the graphical structure of Bayesian networks. Structure Learning, Parameter Learning, Inferences, Sampling methods.
Systematic ETF relative-strength research, overlapping-sleeve portfolio construction, LEAN implementation, and factor attribution.
[CoRL 2026] EgoWAM: World Action Models Beyond Pixels with In-the-Wild Egocentric Human Data
A practical toolkit for process-level robot evaluation with Process Reward Models (PRMs).
Build a World Model from Scratch — the Vizuara AI lecture series: slides, from-scratch code, and Colab notebooks
Operations Research Lab — continuous optimisation models for Management Engineering: lecture notes, Python/Gurobi code, data and case studies (English version)
Example 📓 Jupyter notebooks that demonstrate how to build, train, and deploy machine learning models using 🧠 Amazon SageMaker.
[ACL 2024] An Easy-to-use Knowledge Editing Framework for LLMs.