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Published directory entries matching your search.
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.
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.
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.
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.
Explore Clinical & Biomedical Language Models provides machine-learning models, research, training resources, or evaluation tools.
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.
The Dark Risk of Large Language Models WIRED UK provides machine-learning models, research, training resources, or evaluation tools.
Hands-On Large Language Models is an illustrated guide to large language models. It includes over 275 custom figures, working code examples, timelines, and references to key research papers. The book has received positive reviews from Andrew Ng, Nils Reimers, Josh Starmer, Luis Serrano, Leland McInnes, and Prof. DDr. Roman Egger.
A blog article about red-teaming large language models, described as a form of evaluation that elicits model vulnerabilities that might lead to undesirable behaviors. It discusses undesirable behaviors such as revealing personal information, misinformation, bias, hatefulness and toxic content, and compares red-teaming with adversarial attacks. It notes that red-teaming prompts look like regular natural language prompts.
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.
A directory of abstracts and links to academic papers focused on local language models, current as of the end of March 2026. The listing compiles research published between 2017 and 2023, including foundational works such as Attention Is All You Need, BERT, T5, and PaLM, alongside studies on efficient transformer inference, multimodal models, and instruction tuning.
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.
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.
RankedAGI is a website that compares benchmarks across different AI models. It features benchmarks such as SWE Bench Verified, Codeforces, LMSYS and LiveBench, and presents a ranked table of models with size, version, release date, context length, cutoff knowledge, license and organization.
Unsloth Studio is an open-source, no-code web UI for training, running and exporting open models in one local interface. It supports GGUF, MLX and diffusion image/video models on Mac, Windows and Linux, and runs AI models offline without a GPU. A graphical Data Recipes workflow creates synthetic datasets from uploaded files.
A site for browsing AI voice models for RVC. It lists 30,000+ voice models covering characters, singers and creators, allows previewing samples, and supports running compatible voices online with EasyAIVoice.
A web tool for choosing an AI model that fits a given GPU. Users select a GPU or enter VRAM, then check compatibility by VRAM, bandwidth, and context size. Minimum context window, tokens per second, and required features can be set, and optional system RAM enables offloading to extend context windows or run larger models at reduced speed.
Direct is a chat tool for interacting with multiple frontier AI models. It allows prompting several open source and proprietary large language models in one place, for research, coding, and creative tasks.
A links page listing resources related to running local language models. It gathers guides, including a quick start guide for running models locally, model sources such as HuggingFace for current quants, and inferencing frontends including Text Gen WebUI.
MathArena evaluates large language models on uncontaminated math benchmarks. It presents model analysis across competitions such as ArXivLean, BrokenArXiv, ArXivMath, Visual Math, Final-Answer Comps, Proof-Based Comps and Project Euler. A competition is selected to load tables and a leaderboard, and clicking a cell shows the raw model output.
Odysseus is a self-hosted AI workspace for running language models on one's own hardware. It offers chat, autonomous agents, tools and MCP connections, model serving, an email assistant, deep research, model comparison and persistent memory. The project is described as local-first and privacy-first, with no telemetry.
