Best AI tools for< Log Eval Results >

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.

Stackie.AI is a life logging and tracking application that empowers users to log, track, recall, and reflect on various aspects of their lives. With embedded AI capabilities, users can create personalized diaries, manage health metrics, organize information, and engage in AI-assisted learning. The application offers auto-formatting, categorization, customizable stack structures, and templates for seamless user experience. Stackie.AI aims to enhance self-awareness, productivity, and overall well-being through efficient logging and tracking functionalities.

CalCount is an AI-powered meal logging app that makes it easy to track your food intake and stay on top of your nutrition goals. With CalCount, you can simply describe your meals or take a picture, and the AI will automatically log your food and calculate your calories, macros, and other nutritional information. You can also share your log with anyone else, and they will have real-time access to your data.

MonitUp is an AI-powered time tracking software that helps you track computer activity, gain insights into work habits, and boost productivity. It uses artificial intelligence to generate personalized suggestions to help you increase productivity and work more efficiently. MonitUp also offers a performance appraisal feature for remote employees, allowing you to evaluate and improve their performance based on objective data.

Bibit AI is a real estate marketing AI designed to enhance the efficiency and effectiveness of real estate marketing and sales. It can help create listings, descriptions, and property content, and offers a host of other features. Bibit AI is the world's first AI for Real Estate. We are transforming the real estate industry by boosting efficiency and simplifying tasks like listing creation and content generation.

Neptune is an MLOps stack component for experiment tracking. It allows users to track, compare, and share their models in one place. Neptune is used by scaling ML teams to skip days of debugging disorganized models, avoid long and messy model handovers, and start logging for free.

ProductlyAI is an AI-powered product management tool that helps you automatically log and track product feedback from all your customer conversations. It integrates with your existing tools, such as Gong, Intercom, and Zoom, and uses AI to find, categorize, and summarize feedback in a single place. This makes it easy to identify trends, prioritize features, and make data-driven decisions about your product roadmap.

Resolvd is an AI-powered incident resolution platform that creates a knowledge base of logs, data sources, and apps to autonomously diagnose and resolve incidents. It helps reduce time to response, correlates events across data sources, and provides automated insights for faster issue resolution. With features like simple data querying, automated anomaly detection, and in-workflow integration with existing systems, Resolvd aims to streamline incident response processes and empower engineers with actionable insights.

CursorLens is an open-source dashboard designed to provide insights for AI-assisted coding within the Cursor.sh IDE. It allows users to log AI code generations, track usage, and control AI models, including local ones. Users can run CursorLens locally or utilize the upcoming hosted version for enhanced convenience and efficiency.

Chat Uncensored AI is the latest and most advanced 2024 AI model. It has zero censorship, bias, or restrictions. You don't need to log in, and it's 100% private and super fast. It works in any language and is trusted by over 10,000 users worldwide.

Roboto AI is an advanced platform that allows users to curate, transform, and analyze robotics data at scale. It provides features for data management, actions, events, search capabilities, and SDK integration. The application helps users understand complex machine data through multimodal queries and custom actions, enabling efficient data processing and collaboration within teams.

Vibely is an AI-powered application that seamlessly integrates with Google Calendar to automatically record and log product feedback from Zoom calls. It offers features such as automatic recording of Zoom calls, summarizing team calls, detecting feature requests using AI, providing insights based on past calls, and cost-effective pricing compared to competitors. With Vibely, users can enhance collaboration, sharing, and learning from their calls effortlessly.

Venice is a permissionless AI application that offers an alternative to popular AI apps by prioritizing user privacy and delivering uncensored, unbiased machine intelligence. It utilizes leading open-source AI technology to ensure privacy while providing intelligent responses. Venice aims to empower users with open intelligence and a censorship-free experience.

Journable is a food and fitness journal that uses AI to make tracking your diet and exercise as easy as having a conversation. With Journable, you can set your own health goals, track your progress, and get instant nutritional and exercise insights based on your conversations. Journable's AI understands a wide variety of foods, from homemade meals to restaurant dishes, and can even analyze your workouts to give you a full picture of your calorie intake and burn. Whether you're looking to lose weight, gain muscle, or simply maintain a healthy lifestyle, Journable provides the tools and insights you need to succeed.

Hints is a sales AI assistant that helps sales reps to get more hours in a day while keeping CRM data accurate automatically. It works with Salesforce, Hubspot, and Pipedrive. With Hints, sales reps can log and retrieve CRM data on any device with chat and voice, get guidance on their next steps, and reminders of what's missing. Hints can also help sales reps to create complex CRM updates in seconds, find duplicates, suggest actions, automatically create associations, and look up sales data through chat and voice commands. Hints can assist sales reps in building the perfect sales process for their team and provides fast onboarding for new sales reps.

Calorieasy is an AI-powered calorie tracker that simplifies healthy eating. With its advanced AI technology, Calorieasy can instantly recognize and log food items, providing accurate calorie data and nutritional information. The app also offers personalized calorie goals, meal tracking, and progress monitoring to help users achieve their fitness goals.

Recipe Database & Food Journal is an AI-powered platform that offers powerful recipe management, personal restaurant reviews, automatic meal suggestions, and a culinary community for users to craft, savor, and share their culinary adventures. Users can effortlessly manage recipes, log meals, search and retrieve culinary experiences, and receive personalized meal recommendations based on their preferences.

Tidalflow is an AI-powered weight loss coach application that helps users achieve lasting weight loss through personalized workout and diet plans. The app, backed by Google's AI fund, offers features such as daily check-ins, meal logging through photos, progress tracking summaries, and real-time support from an AI coach named Marcus. Tidalflow aims to provide a hyper-personalized experience to users, guiding them towards their weight loss goals with the latest scientific research and data-backed insights.

FitAI is an AI-powered nutrition tracking app that helps users understand their nutritional intake and make healthier choices. The app features a variety of tools to make tracking easy, including a food recognition system, an image logging feature, and an AI trainer. FitAI is designed for health-conscious individuals who want to take control of their nutrition and improve their overall well-being.

FoodIntake is an AI-powered food tracking app that helps users make healthier food choices. It features a large database of branded foods, AI food analysis, and personalized nutrition recommendations. The app is easy to use and provides users with a comprehensive view of their diet, including calorie and nutrient intake, macronutrient distribution, and food processing levels.

Evals provide a framework for evaluating large language models (LLMs) or systems built using LLMs. We offer an existing registry of evals to test different dimensions of OpenAI models and the ability to write your own custom evals for use cases you care about. You can also use your data to build private evals which represent the common LLMs patterns in your workflow without exposing any of that data publicly.

MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). MLflow's current components are: * `MLflow Tracking `_: An API to log parameters, code, and results in machine learning experiments and compare them using an interactive UI. * `MLflow Projects `_: A code packaging format for reproducible runs using Conda and Docker, so you can share your ML code with others. * `MLflow Models `_: A model packaging format and tools that let you easily deploy the same model (from any ML library) to batch and real-time scoring on platforms such as Docker, Apache Spark, Azure ML and AWS SageMaker. * `MLflow Model Registry `_: A centralized model store, set of APIs, and UI, to collaboratively manage the full lifecycle of MLflow Models.

RAGLAB is a modular, research-oriented open-source framework for Retrieval-Augmented Generation (RAG) algorithms. It offers reproductions of 6 existing RAG algorithms and a comprehensive evaluation system with 10 benchmark datasets, enabling fair comparisons between RAG algorithms and easy expansion for efficient development of new algorithms, datasets, and evaluation metrics. The framework supports the entire RAG pipeline, provides advanced algorithm implementations, fair comparison platform, efficient retriever client, versatile generator support, and flexible instruction lab. It also includes features like Interact Mode for quick understanding of algorithms and Evaluation Mode for reproducing paper results and scientific research.

Step-DPO is a method for enhancing long-chain reasoning ability of LLMs with a data construction pipeline creating a high-quality dataset. It significantly improves performance on math and GSM8K tasks with minimal data and training steps. The tool fine-tunes pre-trained models like Qwen2-7B-Instruct with Step-DPO, achieving superior results compared to other models. It provides scripts for training, evaluation, and deployment, along with examples and acknowledgements.

Open-Source Evaluation for LLM Applications. `continuous-eval` is an open-source package created for granular and holistic evaluation of GenAI application pipelines. It offers modularized evaluation, a comprehensive metric library covering various LLM use cases, the ability to leverage user feedback in evaluation, and synthetic dataset generation for testing pipelines. Users can define their own metrics by extending the Metric class. The tool allows running evaluation on a pipeline defined with modules and corresponding metrics. Additionally, it provides synthetic data generation capabilities to create user interaction data for evaluation or training purposes.

TempCompass is a benchmark designed to evaluate the temporal perception ability of Video LLMs. It encompasses a diverse set of temporal aspects and task formats to comprehensively assess the capability of Video LLMs in understanding videos. The benchmark includes conflicting videos to prevent models from relying on single-frame bias and language priors. Users can clone the repository, install required packages, prepare data, run inference using examples like Video-LLaVA and Gemini, and evaluate the performance of their models across different tasks such as Multi-Choice QA, Yes/No QA, Caption Matching, and Caption Generation.

This repository provides an LLM-driven audio codec model, LLM-Codec, for building multi-modal LLMs (text and audio modalities). The model enables frozen LLMs to achieve multiple audio tasks in a few-shot style without parameter updates. It compresses the audio modality into a well-trained LLMs token space, treating audio representation as a 'foreign language' that LLMs can learn with minimal examples. The proposed approach supports tasks like speech emotion classification, audio classification, text-to-speech generation, speech enhancement, etc., demonstrating feasibility and effectiveness in simple scenarios. The LLM-Codec model is open-sourced to facilitate research on few-shot audio task learning and multi-modal LLMs.

FastFit is a Python package designed for fast and accurate few-shot classification, especially for scenarios with many semantically similar classes. It utilizes a novel approach integrating batch contrastive learning and token-level similarity score, significantly improving multi-class classification performance in speed and accuracy across various datasets. FastFit provides a convenient command-line tool for training text classification models with customizable parameters. It offers a 3-20x improvement in training speed, completing training in just a few seconds. Users can also train models with Python scripts and perform inference using pretrained models for text classification tasks.

This repository contains code for the paper 'Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models'. It focuses on fine-tuning CLIP in an unsupervised manner to enhance its robustness against visual adversarial attacks. By replacing the vision encoder of large vision-language models with the fine-tuned CLIP models, it achieves state-of-the-art adversarial robustness on various vision-language tasks. The repository provides adversarially fine-tuned ViT-L/14 CLIP models and offers insights into zero-shot classification settings and clean accuracy improvements.

Uncheatable Eval is a tool designed to assess the language modeling capabilities of LLMs on real-time, newly generated data from the internet. It aims to provide a reliable evaluation method that is immune to data leaks and cannot be gamed. The tool supports the evaluation of Hugging Face AutoModelForCausalLM models and RWKV models by calculating the sum of negative log probabilities on new texts from various sources such as recent papers on arXiv, new projects on GitHub, news articles, and more. Uncheatable Eval ensures that the evaluation data is not included in the training sets of publicly released models, thus offering a fair assessment of the models' performance.

This repository contains the code that Defog uses for the evaluation of generated SQL. It's based off the schema from the Spider, but with a new set of hand-selected questions and queries grouped by query category. The testing procedure involves generating a SQL query, running both the 'gold' query and the generated query on their respective database to obtain dataframes with the results, comparing the dataframes using an 'exact' and a 'subset' match, logging these alongside other metrics of interest, and aggregating the results for reporting. The repository provides comprehensive instructions for installing dependencies, starting a Postgres instance, importing data into Postgres, importing data into Snowflake, using private data, implementing a query generator, and running the test with different runners.

LLM-jp-eval is a tool designed to automatically evaluate Japanese large language models across multiple datasets. It provides functionalities such as converting existing Japanese evaluation data to text generation task evaluation datasets, executing evaluations of large language models across multiple datasets, and generating instruction data (jaster) in the format of evaluation data prompts. Users can manage the evaluation settings through a config file and use Hydra to load them. The tool supports saving evaluation results and logs using wandb. Users can add new evaluation datasets by following specific steps and guidelines provided in the tool's documentation. It is important to note that using jaster for instruction tuning can lead to artificially high evaluation scores, so caution is advised when interpreting the results.

DevQualityEval is an evaluation benchmark and framework designed to compare and improve the quality of code generation of Language Model Models (LLMs). It provides developers with a standardized benchmark to enhance real-world usage in software development and offers users metrics and comparisons to assess the usefulness of LLMs for their tasks. The tool evaluates LLMs' performance in solving software development tasks and measures the quality of their results through a point-based system. Users can run specific tasks, such as test generation, across different programming languages to evaluate LLMs' language understanding and code generation capabilities.

Nejumi Leaderboard 3 is a comprehensive evaluation platform for large language models, assessing general language capabilities and alignment aspects. The evaluation framework includes metrics for language processing, translation, summarization, information extraction, reasoning, mathematical reasoning, entity extraction, knowledge/question answering, English, semantic analysis, syntactic analysis, alignment, ethics/moral, toxicity, bias, truthfulness, and robustness. The repository provides an implementation guide for environment setup, dataset preparation, configuration, model configurations, and chat template creation. Users can run evaluation processes using specified configuration files and log results to the Weights & Biases project.

Weights & Biases (W&B) is a platform that helps users build better machine learning models faster by tracking and visualizing all components of the machine learning pipeline, from datasets to production models. It offers tools for tracking, debugging, evaluating, and monitoring machine learning applications. W&B provides integrations with popular frameworks like PyTorch, TensorFlow/Keras, Hugging Face Transformers, PyTorch Lightning, XGBoost, and Sci-Kit Learn. Users can easily log metrics, visualize performance, and compare experiments using W&B. The platform also supports hosting options in the cloud or on private infrastructure, making it versatile for various deployment needs.

DeepEval is a simple-to-use, open-source LLM evaluation framework specialized for unit testing LLM outputs. It incorporates various metrics such as G-Eval, hallucination, answer relevancy, RAGAS, etc., and runs locally on your machine for evaluation. It provides a wide range of ready-to-use evaluation metrics, allows for creating custom metrics, integrates with any CI/CD environment, and enables benchmarking LLMs on popular benchmarks. DeepEval is designed for evaluating RAG and fine-tuning applications, helping users optimize hyperparameters, prevent prompt drifting, and transition from OpenAI to hosting their own Llama2 with confidence.

This repository, Online RLHF, focuses on aligning large language models (LLMs) through online iterative Reinforcement Learning from Human Feedback (RLHF). It aims to bridge the gap in existing open-source RLHF projects by providing a detailed recipe for online iterative RLHF. The workflow presented here has shown to outperform offline counterparts in recent LLM literature, achieving comparable or better results than LLaMA3-8B-instruct using only open-source data. The repository includes model releases for SFT, Reward model, and RLHF model, along with installation instructions for both inference and training environments. Users can follow step-by-step guidance for supervised fine-tuning, reward modeling, data generation, data annotation, and training, ultimately enabling iterative training to run automatically.

This repository contains scripts and tools for evaluating a chat app that uses the RAG architecture. It provides parameters to assess the quality and style of answers generated by the chat app, including system prompt, search parameters, and GPT model parameters. The tools facilitate running evaluations, with examples of evaluations on a sample chat app. The repo also offers guidance on cost estimation, setting up the project, deploying a GPT-4 model, generating ground truth data, running evaluations, and measuring the app's ability to say 'I don't know'. Users can customize evaluations, view results, and compare runs using provided tools.

This project explores the capability of Large Language Models (LLMs) to perform regression tasks using in-context examples. It compares the performance of LLMs like GPT-4 and Claude 3 Opus with traditional supervised methods such as Linear Regression and Gradient Boosting. The project provides preprints and results demonstrating the strong performance of LLMs in regression tasks. It includes datasets, models used, and experiments on adaptation and contamination. The code and data for the experiments are available for interaction and analysis.

TokenFormer is a fully attention-based neural network architecture that leverages tokenized model parameters to enhance architectural flexibility. It aims to maximize the flexibility of neural networks by unifying token-token and token-parameter interactions through the attention mechanism. The architecture allows for incremental model scaling and has shown promising results in language modeling and visual modeling tasks. The codebase is clean, concise, easily readable, state-of-the-art, and relies on minimal dependencies.

I'm designed to help You analyze any logs like Linux system logs, Windows logs, any security logs, access logs, error logs, etc. Please do not share information that You would like to keep private. The author does not collect or process any personal data.

A digital gym log for tracking workouts and offering exercise tips.

Formal, technical expert in creating realistic, fictional IT logs. Contact: [email protected]

Drop your Support Log Here, Allowing it to automatically generate concise summaries reporting to the tech team.

Azeroth Data Sage: A detailed Warcraft Log analysis with direct API access. Give the Sage link to a log, ask a question, and the Data Sage will provide!

Obsidian wine log assistant with code block outputs

I help you with all your sysadmin tasks, from setting up your server to scaling your already exsisting one. I can help you with understanding the long list of log files and give you solutions to the problems.

数据和日志分析专家，使用中文回答

Nutritional assistant for diet tracking and advice. Any language available. Type: "Start" to start a new diet tracking session.

Practical diet tracking assistant for health and wellness.

Creates bash scripts with automated boilerplate

Guides in structuring CPD reflections into formal documents.

Generiert realistische Berichtsheft-Einträge.