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A curated GitHub list of top AI models and large language models, organized into AI model hosting platforms and top LLMs sections. It includes hosting platforms such as Hugging Face and GitHub Models, and lists models from providers including Meta, Google, Mistral AI and DeepSeek. Contributions are made through pull requests.
Awesome Core ML Models is a GitHub repository that hosts the largest collection of Core ML models for iOS 11 and newer versions. It allows users to contribute new models to the list via GitHub.
Repository of machine-checkable security models for Clawdbot, written primarily in TLA+ and checked with TLC. It functions as a security regression suite in which green models should pass and deliberately buggy red models should fail with a counterexample trace. Some checks are conformance-style, validating that the formal model matches Clawdbot's implementation constants.
A collaboration platform where the machine learning community shares models, datasets and applications. It hosts public models, datasets and Spaces, and offers an open source stack including libraries such as Transformers, Datasets and Accelerate. It also provides paid Compute and Enterprise solutions, with an Inference Providers API giving access to models from multiple AI providers.
Models.dev is an open-source database that lists AI model specifications, pricing and features. It covers models from multiple providers with details such as context length, modalities and open or closed availability.
The Hundred-Page Language Models Book by Andriy Burkov is a concise hands-on guide covering machine learning basics through advanced transformer architectures and large language models. It explains AI fundamentals, text representation, count-based models, recurrent neural networks, and transformers, with mathematical foundations and Python code examples. The book targets ML practitioners and engineers building text-based applications.
Wiki LLM List is a Wikipedia article that catalogs large language models, organized chronologically by release year from 2018 to 2026. The entry includes a structured list of these AI models, supporting references, and links to related content.
An NBER working paper presenting an approach for automatically generating and testing social scientific hypotheses in silico, using large language models with structural causal models. The approach is demonstrated in a negotiation, a bail hearing, a job interview, and an auction.
Awesome Turkish Language Models is a Hugging Face Space that hosts a curated list of Turkish AI models, datasets, and papers. It also functions as an application that fetches Markdown content from user-provided URLs and converts it to formatted HTML for reading.
brexhq/prompt-engineering is a GitHub repository containing Brex's Prompt Engineering Guide. It provides tips and tricks for working with Large Language Models such as OpenAI's GPT-4, with content covering the definition of large language models, an incomplete history of language models, and the concept of prompts.
Cybertron is a package in pure Go built upon spaGO that gives Go developers an interface for using NLP technologies with pre-trained Transformer models from the HuggingFace models repository. It is primarily aimed at running inference, with the possibility of adding fine-tuning in the future, and supports models including BERT, ELECTRA, BART, PEGASUS and MarianMT.
Explore Clinical & Biomedical Language Models provides machine-learning models, research, training resources, or evaluation tools.
Materials for the Hugging Face Diffusion Models Course, a free course on diffusion models. It covers theory, generating images and audio with Diffusers, training and fine-tuning models, and conditional generation. Prerequisites include Python skills and basics in deep learning and PyTorch.
ChainForge is an open-source visual programming environment for battle-testing prompts to large language models. It supports querying multiple models at once, comparing response quality across prompts, models and model settings, and setting evaluation metrics to visualize results. Built on ReactFlow and Flask, it can be installed locally with pip or tried on the web, where features are limited.
LifeArchitect.ai hosts a Models Table of large language model data, including parameters, tokens trained, and benchmark scores such as MMLU, MMLU-Pro, GPQA and HLE. The page also presents rankings, a data dictionary and visualization snapshots of frontier models.
LLM Explorer is a directory of open-source language models and AI agents. It lists 150 AI agents and 64,811 open-source large and small language models, grouped into categories with benchmarks and analytics.
LM Council presents source-dated benchmark snapshots for AI models. It covers GPT, Claude, Gemini and other frontier models across benchmarks including Humanity's Last Exam, FrontierMath, GPQA and SWE-bench, with a comparison tool selecting two models. Each snapshot links its source and checked date, and evaluation settings and metrics vary.
Official repository to use and implement LLaMA 3.1 models, Meta's state-of-the-art large language models.
ML.NET is an open source, cross-platform machine learning framework for .NET. It lets developers build, train, deploy and consume custom models in .NET applications, with data loading from files and databases, data transformations and many ML algorithms. Models can be trained for scenarios such as classification, forecasting and anomaly detection, and TensorFlow and ONNX models can be consumed within the framework.
Models.dev is a GitHub repository or organization with source code, releases, documentation, or project resources.
NVIDIA NIM is a catalog of models offered as inference microservices for deployment on the user's GPU infrastructure. The listings include downloadable models and free or partner endpoints, organized by use cases such as image generation, speech-to-text and drug discovery.
pgmpy is a Python toolkit for causal and probabilistic reasoning using graphical models. It implements data structures for models such as DAGs, PDAGs, MAGs, PAGs, Bayesian Networks, Dynamic Bayesian Networks and Structural Equation Models, with algorithms for tasks including causal discovery, causal identification, inference, model validation, parameter estimation and simulations. Algorithms follow a unified composable API and are scikit-learn compatible when possible. It is available on PyPI and conda.
PricePerToken is a free comparison site for LLM API pricing. It presents per-token costs for models from providers including OpenAI, Anthropic, Google and Mistral, alongside model listings with context windows and benchmark figures. Pages cover individual models such as Amazon Nova Lite 1.0 and Nova Micro 1.0, whose pricing and context windows are stated.
pyhsmm is a Python library for approximate unsupervised inference in Bayesian Hidden Markov Models and explicit-duration Hidden semi-Markov Models, focusing on the HDP-HMM and HDP-HSMM with weak-limit approximations. Extensions include autoregressive models, switching linear dynamical systems and factorial models. The package is stated as no longer maintained and was last tested with Python 3.7.
