Best AI tools for< Analyze Layer Activations >

Cognee is an AI application that helps users build deterministic AI memory by perfecting exceptional AI apps with intelligent data management. It acts as a semantic memory layer, uncovering hidden connections within data and infusing it with company-specific language and principles. Cognee offers data ingestion and enrichment services, resulting in relevant data retrievals and lower infrastructure costs. The application is suitable for various industries, including customer engagement, EduTech, company onboarding, recruitment, marketing, and tourism.

Zesh AI is an advanced AI-powered ecosystem that offers a range of innovative tools and solutions for Web3 projects, community managers, data analysts, and decision-makers. It leverages AI Agents and LLMs to redefine KOL analysis, community engagement, and campaign optimization. With features like InfluenceAI for KOL discovery, EngageAI for campaign management, IDAI for fraud detection, AnalyticsAI for data analysis, and Wallet & NFT Profile for community empowerment, Zesh AI provides cutting-edge solutions for various aspects of Web3 ecosystems.

TextMine is an AI-powered knowledge base that helps businesses analyze, manage, and search thousands of documents. It uses AI to analyze unstructured textual data and document databases, automatically retrieving key terms to help users make informed decisions. TextMine's features include a document vault for storing and managing documents, a categorization system for organizing documents, and a data extraction tool for extracting insights from documents. TextMine can help businesses save time, money, and improve efficiency by automating manual data entry and information retrieval tasks.

TextMine is an AI-powered knowledge base designed for businesses to manage and analyze critical documents efficiently. It offers features such as document analysis, smart-search capabilities, automated data extraction, and structured dataset transformation. TextMine helps businesses save time and money by streamlining document management processes and enabling informed decision-making. The application caters to various industries like Technology, Legal Services, and Financial Services, providing solutions for teams in Procurement, Finance, Compliance, CIOs, and CDOs.

Rapid Editor is an advanced mapping tool that revolutionizes map editing by integrating cutting-edge technology and authoritative geospatial open data. It empowers OpenStreetMap mappers of all levels to quickly make accurate and fresh edits to maps. The tool saves users effort by tapping into open data and AI-predicted features to draw map geometry. With AI-analyzed satellite imagery, users get a high-level overview of unmapped areas globally. Rapid displays open map data and machine learning detections through an intuitive user interface, making mapping clear and simple. It is a valuable tool for humanitarian and community groups involved in mapping projects.

Rapid Editor is an advanced mapping tool that revolutionizes map editing by integrating cutting-edge technology and authoritative geospatial open data. It empowers OpenStreetMap mappers of all levels to quickly make accurate and fresh edits to maps. The tool saves effort by utilizing AI to identify predicted features and analyze satellite imagery, providing a high-level overview of unmapped areas globally. Rapid Editor's intuitive user interface simplifies mapping, making it clear and simple for humanitarian and community groups to facilitate mapping projects.

Ethena Agents is an AI application designed to provide a comprehensive AI Layer for the Ethena network, offering a robust framework for modular chain economics. The platform enables users to build and launch AI models, access autonomous staking agents, and participate in the AI Agents Marketplace. With a focus on DeFAI (Decentralized Finance AI), Ethena Agents offers institutional-grade access to trillion-dollar market pools, maximizing staking APY and providing chain-agnostic asset management. Users can leverage the $ETAI token for infrastructure payments, token utility, staking rewards, and participation in decentralized governance. Additionally, the platform facilitates data/API connectivity, data aggregation, and real-time signal detection & analysis through AI-powered agents.

Supersimple is an AI-native data analytics platform that combines a semantic data modeling layer with the ability to answer ad hoc questions, giving users reliable, consistent data to power their day-to-day work.

Alphakek AI is an AI lab that offers an alternative to big tech AI platforms like OpenAI and Anthropic. They have redesigned and re-engineered every layer of AI for crypto developers, traders, and creators. Alphakek provides uncensored AI models fine-tuned on crypto data, available via Telegram, Discord, Web App, and API. Their proprietary data engine, codenamed 'Fractal,' processes 2-5x more information sources simultaneously than conventional models, encompassing onchain data, mainstream financial news, niche crypto forums, and more.

Looker is a business intelligence platform that offers embedded analytics and AI-powered BI solutions. Leveraging Google's AI-led innovation, Looker delivers intelligent BI by combining foundational AI, cloud-first infrastructure, industry-leading APIs, and a flexible semantic layer. It allows users to build custom data experiences, transform data into integrated experiences, and create deeply integrated dashboards. Looker also provides a universal semantic modeling layer for unified, trusted data sources and offers self-service analytics capabilities through Looker and Looker Studio. Additionally, Looker features Gemini, an AI-powered analytics assistant that accelerates analytical workflows and offers a collaborative and conversational user experience.

Spine AI is a reliable AI analyst tool that provides conversational analytics tailored to understand your business. It empowers decision-makers by offering customized insights, deep business intelligence, proactive notifications, and flexible dashboards. The tool is designed to help users make better decisions by leveraging a purpose-built Data Processing Unit (DPU) and a semantic layer for natural language interactions. With a focus on rigorous evaluation and security, Spine AI aims to deliver explainable and customizable AI solutions for businesses.

BrainAccess is an AI-enabled electroencephalography (EEG) and hyperscanning solution designed for research, brain-computer interface development, neuromarketing, and beyond. It offers dry-contact EEG electrodes, wireless connectivity, and a compact form factor for portable applications. The software supports Lab Streaming Layer (LSL) protocol, enabling synchronized EEG measurements with stimulus onsets or sensor data. BrainAccess also facilitates hyperscanning, allowing synchronized brain activity measurements from multiple individuals. The user-friendly software development kit supports multiple programming languages for device control and EEG application development.

Lemony is an on-premise generative AI solution designed for business teams, providing organization-wide trust, ownership, and transparency in AI. It offers private, fast, and compliant AI capabilities with multiple pre-loaded AI models and a software layer. Lemony enables team collaboration within professional organizations, ensuring centralized control, scalability, fixed-cost efficiency, and robust security.

Accio is a data modeling tool that allows users to define consistent relationships, metrics, and expressions for on-the-fly computations in reports and dashboards across various BI tools. It provides a syntax similar to GraphQL that allows users to define models, relationships, and metrics in a human-readable format. Accio also offers a user-friendly interface that provides data analysts with a holistic view of the relationships between their data models, enabling them to grasp the interconnectedness and dependencies within their data ecosystem. Additionally, Accio utilizes DuckDB as a caching layer to accelerate query performance for BI tools.

Tremello is a market research platform that uses AI to deliver off-market data. It combines a leading AI engine with human experts to provide bespoke intelligence delivered directly to the user's inbox. Tremello's AI analyzes relationships, identifies patterns, and considers the broader context, delivering meaningful and actionable insights on top of a base human layer. It leverages a diverse range of data sources, including public and private databases, industry reports, social media archives, company websites, and government filings, ensuring a complete and comprehensive picture of the research subject.

Writer is a full-stack generative AI platform that enables businesses to build and deploy custom AI applications for a wide range of use cases, including digital assistants, content generation, summarization, and data analysis. Writer's platform is designed to be accurate, scalable, and cost-effective, and it offers a variety of features to help businesses get the most out of generative AI, including: - Palmyra LLMs: Writer's family of LLMs is purpose-built for the enterprise and offers a range of capabilities, including question-answering, image analysis, and multilingual translation. - Knowledge Graph: Writer's Knowledge Graph anchors generative AI in your company data, resulting in higher accuracy and fewer hallucinations. - AI guardrails: Writer's AI guardrails help businesses enforce their regulatory, legal, inclusivity, and brand rules across all work, whether it's created by their people or AI. - Flexible application layer: Writer's flexible application layer offers a wide range of interfaces to meet your specific needs, whether you're using a prebuilt app, building a custom app, or making requests to our out-of-the-box chat app.

Artificial Lawyer is a platform dedicated to providing news and views on legal tech and artificial intelligence in the legal industry. The website covers a wide range of topics such as AI applications in legal work, legal education, eDiscovery, funding for AI assistants, and more. It aims to keep professionals updated on the latest developments and innovations in the intersection of law and technology.

AI Lawyer Lab is a platform that enables legal professionals to leverage artificial intelligence technology to enhance their legal services. By utilizing AI algorithms, users can streamline legal processes, analyze vast amounts of legal data efficiently, and generate insights to support decision-making. The platform empowers lawyers to transform their legal expertise into innovative AI solutions, ultimately improving the quality and efficiency of legal services.

Lexology is a next-generation search tool designed to help users find the right lawyer for their needs. It offers a wide range of resources, including practical analysis, in-depth research tools, primary sources, and expert reports. The platform aims to be a go-to resource for legal professionals and individuals seeking legal expertise.

M-Wakili is an AI-powered legal assistant designed to provide instant legal advice, research, answers, and analysis on any legal topic, available 24/7. It caters to lawyers, law students, and the general public, offering accurate and reliable responses based on Kenyan law. M-Wakili aims to revolutionize the legal field by enhancing accessibility and effectiveness of legal expertise, while also aiding in the advancement of law professionals.

Slideflow is a deep learning library for digital pathology, offering a user-friendly interface for model development. It is designed for medical researchers and AI enthusiasts, providing an accessible platform for developing state-of-the-art pathology models. Slideflow offers customizable training pipelines, robust slide processing and stain normalization toolkit, support for weakly-supervised or strongly-supervised labels, built-in foundation models, multiple-instance learning, self-supervised learning, generative adversarial networks, explainability tools, layer activation analysis tools, uncertainty quantification, interactive user interface for model deployment, and more. It supports both PyTorch and Tensorflow, with optional support for Libvips for slide reading. Slideflow can be installed via pip, Docker container, or from source, and includes non-commercial add-ons for additional tools and pretrained models. It allows users to create projects, extract tiles from slides, train models, and provides evaluation tools like heatmaps and mosaic maps.

This project focuses on implementing Sparse Autoencoders (SAEs) for mechanistic interpretability in Large Language Models (LLMs) like Llama 3.2-3B. The SAEs aim to untangle superimposed representations in LLMs into separate, interpretable features for each neuron activation. The project provides an end-to-end pipeline for capturing training data, training the SAEs, analyzing learned features, and verifying results experimentally. It includes comprehensive logging, visualization, and checkpointing of SAE training, interpretability analysis tools, and a pure PyTorch implementation of Llama 3.1/3.2 chat and text completion. The project is designed for scalability, efficiency, and maintainability.

This repository evaluates Large Language Models (LLMs) trained on PGN format chess games using linear probes. It assesses the LLMs' internal understanding of board state and their ability to estimate player skill levels. The repo provides tools to train, evaluate, and visualize linear probes on LLMs trained to play chess with PGN strings. Users can visualize the model's predictions, perform interventions on the model's internal board state, and analyze board state and player skill level accuracy across different LLMs. The experiments in the repo can be conducted with less than 1 GB of VRAM, and training probes on the 8 layer model takes about 10 minutes on an RTX 3050. The repo also includes scripts for performing board state interventions and skill interventions, along with useful links to open-source code, models, datasets, and pretrained models.

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.

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.

Awesome-LLM-Quantization is a curated list of resources related to quantization techniques for Large Language Models (LLMs). Quantization is a crucial step in deploying LLMs on resource-constrained devices, such as mobile phones or edge devices, by reducing the model's size and computational requirements.

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 includes papers and blogs about Efficient Transformers, Length Extrapolation, Long Term Memory, Retrieval Augmented Generation(RAG), and Evaluation for Long Context Modeling.

Awesome LLM compression research papers and tools to accelerate LLM training and inference.

This repository is dedicated to the pruning of large language models (LLMs). It aims to serve as a comprehensive resource for researchers and practitioners interested in the efficient reduction of model size while maintaining or enhancing performance. The repository contains various papers, summaries, and links related to different pruning approaches for LLMs, along with author information and publication details. It covers a wide range of topics such as structured pruning, unstructured pruning, semi-structured pruning, and benchmarking methods. Researchers and practitioners can explore different pruning techniques, understand their implications, and access relevant resources for further study and implementation.

Official PyTorch implementation of Wanda (Pruning by Weights and Activations), a simple and effective pruning approach for large language models. The pruning approach removes weights on a per-output basis, by the product of weight magnitudes and input activation norms. The repository provides support for various features such as LLaMA-2, ablation study on OBS weight update, zero-shot evaluation, and speedup evaluation. Users can replicate main results from the paper using provided bash commands. The tool aims to enhance the efficiency and performance of language models through structured and unstructured sparsity techniques.

Awesome-LLM-Interpretability is a curated list of materials related to LLM (Large Language Models) interpretability, covering tutorials, code libraries, surveys, videos, papers, and blogs. It includes resources on transformer mechanistic interpretability, visualization, interventions, probing, fine-tuning, feature representation, learning dynamics, knowledge editing, hallucination detection, and redundancy analysis. The repository aims to provide a comprehensive overview of tools, techniques, and methods for understanding and interpreting the inner workings of large language models.

llm-analysis is a tool designed for Latency and Memory Analysis of Transformer Models for Training and Inference. It automates the calculation of training or inference latency and memory usage for Large Language Models (LLMs) or Transformers based on specified model, GPU, data type, and parallelism configurations. The tool helps users to experiment with different setups theoretically, understand system performance, and optimize training/inference scenarios. It supports various parallelism schemes, communication methods, activation recomputation options, data types, and fine-tuning strategies. Users can integrate llm-analysis in their code using the `LLMAnalysis` class or use the provided entry point functions for command line interface. The tool provides lower-bound estimations of memory usage and latency, and aims to assist in achieving feasible and optimal setups for training or inference.

A bot that plays Tetris using deep reinforcement learning. The agent learns to play by training itself with a neural network and Q Learning algorithm. It explores different 'paths' to achieve higher scores and makes decisions based on predicted scores for possible moves. The game state includes attributes like lines cleared, holes, bumpiness, and total height. The agent is implemented in Python using Keras framework with a deep neural network structure. Training involves a replay queue, random sampling, and optimization techniques. Results show the agent's progress in achieving higher scores over episodes.

This repository provides a curated list of papers, datasets, and resources related to the detection and mitigation of hallucinations in large language models (LLMs). Hallucinations refer to the generation of factually incorrect or nonsensical text by LLMs, which can be a significant challenge for their use in real-world applications. The resources in this repository aim to help researchers and practitioners better understand and address this issue.

Load the crypt's WhitePaper.

Demo bot for Papsum Security Layer for GPT

Expert in Bitcoin-Elastos Layer 2 "Bel2" technology

I am a GPT specialized in providing bespoke recommendations for colognes and perfumes. My expertise extends to crafting unique fragrance creations, tailored to align with your individual preferences.

Expert in data protection laws, ready to analyze documents and answer related queries.

Intellectual.AI slices through the complexities of information to deliver sharp, comprehensive insights with a laser focus on logic, structure, and cross-domain analysis

Formal academic logic analysis.

A lawyer specializing in contract law, referencing case law.

Multilingual analytical critic.

As LegalGPT, I'm an AI legal assistant with expertise in law, adaptable for nationwide legal queries. I provide precise, context-sensitive advice based on a rich knowledge source, aiding in legal reasoning and drafting. Note: I'm not a substitute for a lawyer.

محدث بآخر القوانين واللوائح المصرية (تحت التجربه)

Meticulous Law School Hypo Analyzer

Advises on litigation strategies to protect the organization's legal rights.

Debate expert analyzing arguments for attack and defense. Agent of pure reason

Case Analysis, Cross-X Assistance, Contradiction Identifier, and Counter-Argument Generator

Informs tax strategies by analyzing and interpreting tax laws.

Experto en análisis de Ley Contrato 406, formal y accesible, evita especulaciones.

Expert in Spanish antitrust law, aiding in drafting and interpreting cases.

Understanding Chinese patents

A debate partner assisting in argument construction and analysis.