Best AI tools for< Understand Model Mechanisms >

Diffusion Chat is a text-to-image AI tool that allows users to generate images from text prompts. The tool uses a large language model to understand the user's prompt and then generates an image that matches the description. Diffusion Chat is still in development, but it has already shown great promise for creating realistic and creative images.

xAI Grok is a visual analytics platform that helps users understand and interpret machine learning models. It provides a variety of tools for visualizing and exploring model data, including interactive charts, graphs, and tables. xAI Grok also includes a library of pre-built visualizations that can be used to quickly get started with model analysis.

ImageBind by Meta AI is a cutting-edge AI tool that revolutionizes the field of computer vision by introducing a new way to 'link' AI across multiple senses. It is the first AI model capable of binding data from six different modalities simultaneously, including images, video, audio, text, depth, thermal, and inertial measurement units (IMUs). By recognizing relationships between these modalities, ImageBind enables machines to analyze various forms of information together, advancing the capabilities of AI technology.

Censius is an AI Observability Platform for Enterprise ML Teams. It provides end-to-end visibility of structured and unstructured production models, enabling proactive model management and continuous delivery of reliable ML. Key features include model monitoring, explainability, and analytics.

Explainpaper is an AI-powered tool designed to simplify and explain complex research papers. Users can upload a paper, highlight confusing text, and receive explanations to make the content easier to understand. The tool leverages AI and machine learning models to break down dense sections and clarify intricate concepts, ultimately making research papers more accessible to a wider audience. It is a valuable resource for researchers, students, and anyone looking to delve into complex topics with confidence.

Storia AI is an AI tool designed to assist software engineering teams in understanding and generating code. It provides a Perplexity-like chat experience where users can interact with an AI expert that has access to the latest versions of open-source software. The tool aims to improve code understanding and generation by providing responses backed with links to implementations, API references, GitHub issues, and more. Storia AI is developed by a team of natural language processing researchers from Google and Amazon Alexa, with a mission to build the most reliable AI pair programmer for engineering teams.

AIby.email is an AI-powered email assistant that helps you write better emails, faster. It uses natural language processing to understand your intent and generate personalized email responses. AIby.email also offers a variety of other features, such as email scheduling, tracking, and analytics.

MindpoolAI is a tool that allows users to access multiple leading AI models with a single query. This means that users can get the answers they are looking for, spark ideas, and fuel their work, creativity, and curiosity. MindpoolAI is easy to use and does not require any technical expertise. Users simply need to enter their prompt and select the AI models they want to compare. MindpoolAI will then send the query to the selected models and present the results in an easy-to-understand format.

Rawbot is an AI model comparison tool designed to simplify the selection process by enabling users to identify and understand the strengths and weaknesses of various AI models. It allows users to compare AI models based on performance optimization, strengths and weaknesses identification, customization and tuning, cost and efficiency analysis, and informed decision-making. Rawbot is a user-friendly platform that offers comprehensive comparisons of popular AI models, helping researchers, developers, and business leaders make informed decisions about the AI models that best fit their needs.

ChatGPT is a large language model developed by OpenAI. It is designed to understand and generate human-like text, and can be used for a variety of tasks such as answering questions, writing stories, and translating languages. ChatGPT is free to use, and can be accessed through a web interface or via an API.

Zephyr 7B is a state-of-the-art language model developed by WebPilot.AI with 7 billion parameters. It can understand and generate human-like text with remarkable accuracy and coherence. The model is built upon the latest advancements in natural language processing and machine learning, trained on a vast corpus of text data from diverse sources. Zephyr 7B offers capabilities such as natural language understanding, text generation, language translation, text summarization, sentiment analysis, and question answering. It represents a significant advancement in natural language processing, making it a powerful tool for content creation, customer support, research, and more.

Re-View is an AI-powered platform that enables users to conduct surveys that capture more than words by utilizing user-friendly video survey forms. The platform allows users to understand emotions, uncover insights, and collect more and better data through authentic emotional connections. With features like automatic insights, efficient research at scale, stunning simplicity, and powerful research capabilities, Re-View offers a practical pricing model that makes research accessible to all. Users can easily create surveys, analyze responses with AI assistance, and gain valuable research reports to support decision-making.

Molmo AI is a powerful, open-source multimodal AI model revolutionizing visual understanding. It helps developers easily build tools that can understand images and interact with the world in useful ways. Molmo AI offers exceptional image understanding, efficient data usage, open and accessible features, on-device compatibility, and a new era in multimodal AI development. It closes the gap between open and closed AI models, empowers the AI community with open access, and efficiently utilizes data for superior performance.

How Attractive Am I is an AI-powered tool that analyzes facial features to calculate an attractiveness score. By evaluating symmetry and proportions, the tool provides personalized beauty scores. Users can upload a photo to discover their true beauty potential. The tool ensures accuracy by providing guidelines for taking photos and offers a fun and insightful way to understand facial appeal.

Photosolve is an AI-powered educational tool that helps students, teachers, researchers, and writers to quickly find accurate answers to their questions. It offers a Chrome extension and mobile app for easy access to its features. With over 10 million questions answered and growing, Photosolve revolutionizes learning by providing detailed explanations along with answers. Users can upload materials for analysis, have conversations with AI, generate flashcards, and enhance their knowledge with customizable quizzes. The application uses a custom-built AI model for higher accuracy compared to general AI models, ensuring reliable results for academic success.

Rerun is an SDK, time-series database, and visualizer for temporal and multimodal data. It is used in fields like robotics, spatial computing, 2D/3D simulation, and finance to verify, debug, and explain data. Rerun allows users to log data like tensors, point clouds, and text to create streams, visualize and interact with live and recorded streams, build layouts, customize visualizations, and extend data and UI functionalities. The application provides a composable data model, dynamic schemas, and custom views for enhanced data visualization and analysis.

Face Shape Detector is an advanced AI tool that analyzes facial landmarks in uploaded photos to identify the user's face shape and provide percentage distributions for different face shapes. It utilizes sophisticated algorithms to assess key metrics such as jawline, forehead width, and cheekbone structure, delivering detailed insights into facial proportions. Users can explore the power of facial analysis, understand their unique face shape, and receive quick and accurate results through this intuitive tool.

poolside is an advanced foundational AI model designed specifically for software engineering challenges. It allows users to fine-tune the model on their own code, enabling it to understand project uniqueness and complexities that generic models can't grasp. The platform aims to empower teams to build better, faster, and happier by providing a personalized AI model that continuously improves. In addition to the AI model for writing code, poolside offers an intuitive editor assistant and an API for developers to leverage.

Imandra is a company that provides automated logical reasoning for Large Language Models (LLMs). Imandra's technology allows LLMs to build mental models and reason about them, unlocking the potential of generative AI for industries where correctness and compliance matter. Imandra's platform is used by leading financial firms, the US Air Force, and DARPA.

Ogma is an interpretable symbolic general problem-solving model that utilizes a symbolic sequence modeling paradigm to address tasks requiring reliability, complex decomposition, and without hallucinations. It offers solutions in areas such as math problem-solving, natural language understanding, and resolution of uncertainty. The technology is designed to provide a structured approach to problem-solving by breaking down tasks into manageable components while ensuring interpretability and self-interpretability. Ogma aims to set benchmarks in problem-solving applications by offering a reliable and transparent methodology.

This repository is a collection of resources focused on interpretability in large language models (LLMs). It aims to help beginners get started in the area and keep researchers updated on the latest progress. It includes libraries, blogs, tutorials, forums, tools, programs, papers, and more related to interpretability in LLMs.

The 100 Days in AI repository provides a comprehensive roadmap for individuals to learn Artificial Intelligence over a period of 100 days. It covers topics ranging from basic programming in Python to advanced concepts in AI, including machine learning, deep learning, and specialized AI topics. The repository includes daily tasks, resources, and exercises to ensure a structured learning experience. By following this roadmap, users can gain a solid understanding of AI and be prepared to work on real-world AI projects.

The LLM course is divided into three parts: 1. 🧩 **LLM Fundamentals** covers essential knowledge about mathematics, Python, and neural networks. 2. 🧑‍🔬 **The LLM Scientist** focuses on building the best possible LLMs using the latest techniques. 3. 👷 **The LLM Engineer** focuses on creating LLM-based applications and deploying them. For an interactive version of this course, I created two **LLM assistants** that will answer questions and test your knowledge in a personalized way: * 🤗 **HuggingChat Assistant**: Free version using Mixtral-8x7B. * 🤖 **ChatGPT Assistant**: Requires a premium account. ## 📝 Notebooks A list of notebooks and articles related to large language models. ### Tools | Notebook | Description | Notebook | |----------|-------------|----------| | 🧐 LLM AutoEval | Automatically evaluate your LLMs using RunPod |  | | 🥱 LazyMergekit | Easily merge models using MergeKit in one click. |  | | 🦎 LazyAxolotl | Fine-tune models in the cloud using Axolotl in one click. |  | | ⚡ AutoQuant | Quantize LLMs in GGUF, GPTQ, EXL2, AWQ, and HQQ formats in one click. |  | | 🌳 Model Family Tree | Visualize the family tree of merged models. |  | | 🚀 ZeroSpace | Automatically create a Gradio chat interface using a free ZeroGPU. |  |

This repository contains a comprehensive collection of 63 must-know Large Language Models (LLMs) interview questions. It covers topics such as the architecture of LLMs, transformer models, attention mechanisms, training processes, encoder-decoder frameworks, differences between LLMs and traditional statistical language models, handling context and long-term dependencies, transformers for parallelization, applications of LLMs, sentiment analysis, language translation, conversation AI, chatbots, and more. The readme provides detailed explanations, code examples, and insights into utilizing LLMs for various tasks.

Qwen is a series of large language models developed by Alibaba DAMO Academy. It outperforms the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen models outperform the baseline models of similar model sizes on a series of benchmark datasets, e.g., MMLU, C-Eval, GSM8K, MATH, HumanEval, MBPP, BBH, etc., which evaluate the models’ capabilities on natural language understanding, mathematic problem solving, coding, etc. Qwen-72B achieves better performance than LLaMA2-70B on all tasks and outperforms GPT-3.5 on 7 out of 10 tasks.

ContextCite is a tool for attributing statements generated by LLMs back to specific parts of the context. It allows users to analyze and understand the sources of information used by language models in generating responses. By providing attributions, users can gain insights into how the model makes decisions and where the information comes from.

LLM101n is a course focused on building a Storyteller AI Large Language Model (LLM) from scratch in Python, C, and CUDA. The course covers various topics such as language modeling, machine learning, attention mechanisms, tokenization, optimization, device usage, precision training, distributed optimization, datasets, inference, finetuning, deployment, and multimodal applications. Participants will gain a deep understanding of AI, LLMs, and deep learning through hands-on projects and practical examples.

seemore is a vision language model developed in Pytorch, implementing components like image encoder, vision-language projector, and decoder language model. The model is built from scratch, including attention mechanisms and patch creation. It is designed for readability and hackability, with the intention to be improved upon. The implementation is based on public publications and borrows attention mechanism from makemore by Andrej Kapathy. The code was developed on Databricks using a single A100 for compute, and MLFlow is used for tracking metrics. The tool aims to provide a simplistic version of vision language models like Grok 1.5/GPT-4 Vision, suitable for experimentation and learning.

Awesome-Attention-Heads is a platform providing the latest research on Attention Heads, focusing on enhancing understanding of Transformer structure for model interpretability. It explores attention mechanisms for behavior, inference, and analysis, alongside feed-forward networks for knowledge storage. The repository aims to support researchers studying LLM interpretability and hallucination by offering cutting-edge information on Attention Head Mining.

The repository 'Awesome-LLM-Preference-Learning' is the official repository of a survey paper titled 'Towards a Unified View of Preference Learning for Large Language Models: A Survey'. It contains a curated list of papers related to preference learning for Large Language Models (LLMs). The repository covers various aspects of preference learning, including on-policy and off-policy methods, feedback mechanisms, reward models, algorithms, evaluation techniques, and more. The papers included in the repository explore different approaches to aligning LLMs with human preferences, improving mathematical reasoning in LLMs, enhancing code generation, and optimizing language model performance.

This repository contains the code for coding, pretraining, and finetuning a GPT-like LLM and is the official code repository for the book Build a Large Language Model (From Scratch). In _Build a Large Language Model (From Scratch)_, you'll discover how LLMs work from the inside out. In this book, I'll guide you step by step through creating your own LLM, explaining each stage with clear text, diagrams, and examples. The method described in this book for training and developing your own small-but-functional model for educational purposes mirrors the approach used in creating large-scale foundational models such as those behind ChatGPT.

A repository that lists papers related to Large Language Model (LLM) based agents. The repository covers various topics including survey, planning, feedback & reflection, memory mechanism, role playing, game playing, tool usage & human-agent interaction, benchmark & evaluation, environment & platform, agent framework, multi-agent system, and agent fine-tuning. It provides a comprehensive collection of research papers on LLM-based agents, exploring different aspects of AI agent architectures and applications.

Inspectus is a versatile visualization tool for large language models. It provides multiple views, including Attention Matrix, Query Token Heatmap, Key Token Heatmap, and Dimension Heatmap, to offer insights into language model behaviors. Users can interact with the tool in Jupyter notebooks through an easy-to-use Python API. Inspectus allows users to visualize attention scores between tokens, analyze how tokens focus on each other during processing, and explore the relationships between query and key tokens. The tool supports the visualization of attention maps from Huggingface transformers and custom attention maps, making it a valuable resource for researchers and developers working with language models.

LangChain-Udemy-Course is a comprehensive course directory focusing on LangChain, a framework for generative AI applications. The course covers various aspects such as OpenAI API usage, prompt templates, Chains exploration, callback functions, memory techniques, RAG implementation, autonomous agents, hybrid search, LangSmith utilization, microservice architecture, and LangChain Expression Language. Learners gain theoretical knowledge and practical insights to understand and apply LangChain effectively in generative AI scenarios.

Generative AI with LangChain is a code repository for building large language model (LLM) apps with Python, ChatGPT, and other LLMs. The repository provides code examples, instructions, and configurations for creating generative AI applications using the LangChain framework. It covers topics such as setting up the development environment, installing dependencies with Conda or Pip, using Docker for environment setup, and setting API keys securely. The repository also emphasizes stability, code updates, and user engagement through issue reporting and feedback. It aims to empower users to leverage generative AI technologies for tasks like building chatbots, question-answering systems, software development aids, and data analysis applications.

AIlice is a fully autonomous, general-purpose AI agent that aims to create a standalone artificial intelligence assistant, similar to JARVIS, based on the open-source LLM. AIlice achieves this goal by building a "text computer" that uses a Large Language Model (LLM) as its core processor. Currently, AIlice demonstrates proficiency in a range of tasks, including thematic research, coding, system management, literature reviews, and complex hybrid tasks that go beyond these basic capabilities. AIlice has reached near-perfect performance in everyday tasks using GPT-4 and is making strides towards practical application with the latest open-source models. We will ultimately achieve self-evolution of AI agents. That is, AI agents will autonomously build their own feature expansions and new types of agents, unleashing LLM's knowledge and reasoning capabilities into the real world seamlessly.

This sample demonstrates a few approaches for creating ChatGPT-like experiences over your own data using the Retrieval Augmented Generation pattern. It uses Azure OpenAI Service to access the ChatGPT model (gpt-35-turbo), and Azure AI Search for data indexing and retrieval.

STRIDE GPT is an AI-powered threat modelling tool that leverages Large Language Models (LLMs) to generate threat models and attack trees for a given application based on the STRIDE methodology. Users provide application details, such as the application type, authentication methods, and whether the application is internet-facing or processes sensitive data. The model then generates its output based on the provided information. It features a simple and user-friendly interface, supports multi-modal threat modelling, generates attack trees, suggests possible mitigations for identified threats, and does not store application details. STRIDE GPT can be accessed via OpenAI API, Azure OpenAI Service, Google AI API, or Mistral API. It is available as a Docker container image for easy deployment.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

I help developers understand and use APIs, referencing a YAML model.

Bot that guides users in understanding the Cognitive-Theoretic Model of the Universe

An assistant to help you understand transfer learning better

I'm Back Propagation, here to help you understand and apply back propagation techniques to your AI models.

This prompt is designed to act as an expert psychologist in personality analysis, using three different psychological models: the Big Five Factor model, the Myers-Briggs Sixteen Types model, and the Enneagram model.

中文数学建模辅助专家，提供策略和专业术语解答，帮助你完美写出一篇数模论文

Friendly and approachable Malayalam expert.

GPT teached using a mental models

Explains mental models with examples, inspired by thinkers like Charlie Munger.

Identify and explain mental models for various scenarios.

Este modelo de GPT proporciona información general sobre la toxina botulínica, incluyendo su historia, usos comunes y datos de interés. Está diseñado para educar y ofrecer una visión general basada en fuentes de información públicas y conocidas. No proporciona consejos médicos

An expert in Human Anatomy, be it for art or science or education, anything relating to the human body, come ask me. I will provide photo-realistic visual aids and AI created models to expound on different parts of the anatomy

Richard Feynman-inspired guide for learning AI and data science

A tech guide for software engineers, focusing on the latest tools and foundational knowledge.

I explain complex topics in simple, fun ways, like you're 5!

Helps understand North Andover's wetland regulations

Understand consent requirements and use of the easyConsent app.

This GPT helps you understand and apply the MITRE ATT&CK Framework, whether you are familiar with the concepts or not.

Understand the correct credit card to use to maximize your reward points.

Helps you understand those confusing ass old dudes and dudettes