Best AI tools for< Log Evaluation Results >

FinetuneDB is an AI fine-tuning platform that allows users to easily create and manage datasets to fine-tune LLMs, evaluate outputs, and iterate on production data. It integrates with open-source and proprietary foundation models, and provides a collaborative editor for building datasets. FinetuneDB also offers a variety of features for evaluating model performance, including human and AI feedback, automated evaluations, and model metrics tracking.

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.

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 cut down response time, reduce manual log review efforts, and streamline data querying with automated anomaly detection. Resolvd integrates with various systems like Slack, Jira, and PagerDuty to deliver insights in real-time and supercharge incident response.

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 AI-powered platform that enables users to curate and analyze robotics data at scale. It offers features such as data management, actions to transform data, natural language search, signal search, and support for common data formats. Users can leverage AI capabilities to search and analyze their robotics data efficiently. Roboto AI empowers users to process data, collaborate with teams, and visualize insights from multiple log formats.

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. The platform aims to enhance users' cooking and dining experiences by providing a seamless and organized approach to food management.

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.

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.

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.

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.

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.

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.

Turtle Benchmark is a novel and cheat-proof benchmark test used to evaluate large language models (LLMs). It is based on the Turtle Soup game, focusing on logical reasoning and context understanding abilities. The benchmark does not require background knowledge or model memory, providing all necessary information for judgment from stories under 200 words. The results are objective and unbiased, quantifiable as correct/incorrect/unknown, and impossible to cheat due to using real user-generated questions and dynamic data generation during online gameplay.

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.

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.

Tonic Validate is a framework for the evaluation of LLM outputs, such as Retrieval Augmented Generation (RAG) pipelines. Validate makes it easy to evaluate, track, and monitor your LLM and RAG applications. Validate allows you to evaluate your LLM outputs through the use of our provided metrics which measure everything from answer correctness to LLM hallucination. Additionally, Validate has an optional UI to visualize your evaluation results for easy tracking and monitoring.

LLMBox is a comprehensive library designed for implementing Large Language Models (LLMs) with a focus on a unified training pipeline and comprehensive model evaluation. It serves as a one-stop solution for training and utilizing LLMs, offering flexibility and efficiency in both training and utilization stages. The library supports diverse training strategies, comprehensive datasets, tokenizer vocabulary merging, data construction strategies, parameter efficient fine-tuning, and efficient training methods. For utilization, LLMBox provides comprehensive evaluation on various datasets, in-context learning strategies, chain-of-thought evaluation, evaluation methods, prefix caching for faster inference, support for specific LLM models like vLLM and Flash Attention, and quantization options. The tool is suitable for researchers and developers working with LLMs for natural language processing tasks.

SheetCopilot is an assistant agent that manipulates spreadsheets by following user commands. It leverages Large Language Models (LLMs) to interact with spreadsheets like a human expert, enabling non-expert users to complete tasks on complex software such as Google Sheets and Excel via a language interface. The tool observes spreadsheet states, polishes generated solutions based on external action documents and error feedback, and aims to improve success rate and efficiency. SheetCopilot offers a dataset with diverse task categories and operations, supporting operations like entry & manipulation, management, formatting, charts, and pivot tables. Users can interact with SheetCopilot in Excel or Google Sheets, executing tasks like calculating revenue, creating pivot tables, and plotting charts. The tool's evaluation includes performance comparisons with leading LLMs and VBA-based methods on specific datasets, showcasing its capabilities in controlling various aspects of a spreadsheet.

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.

The Clarifai Python SDK offers a comprehensive set of tools to integrate Clarifai's AI platform to leverage computer vision capabilities like classification , detection ,segementation and natural language capabilities like classification , summarisation , generation , Q&A ,etc into your applications. With just a few lines of code, you can leverage cutting-edge artificial intelligence to unlock valuable insights from visual and textual content.

OpenNARS-for-Applications is an implementation of a Non-Axiomatic Reasoning System, a general-purpose reasoner that adapts under the Assumption of Insufficient Knowledge and Resources. The system combines the logic and conceptual ideas of OpenNARS, event handling and procedure learning capabilities of ANSNA and 20NAR1, and the control model from ALANN. It is written in C, offers improved reasoning performance, and has been compared with Reinforcement Learning and means-end reasoning approaches. The system has been used in real-world applications such as assisting first responders, real-time traffic surveillance, and experiments with autonomous robots. It has been developed with a pragmatic mindset focusing on effective implementation of existing theory.

This repository contains code for training reward models for Deep Reinforcement Learning-based Reward-modulated Hierarchical Fine-tuning (DRL-based RLHF), Iterative Selection Fine-tuning (Rejection sampling fine-tuning), and iterative Decision Policy Optimization (DPO). The reward models are trained using a Bradley-Terry model based on the Gemma and Mistral language models. The resulting reward models achieve state-of-the-art performance on the RewardBench leaderboard for reward models with base models of up to 13B parameters.

The 'create-million-parameter-llm-from-scratch' repository provides a detailed guide on creating a Large Language Model (LLM) with 2.3 million parameters from scratch. The blog replicates the LLaMA approach, incorporating concepts like RMSNorm for pre-normalization, SwiGLU activation function, and Rotary Embeddings. The model is trained on a basic dataset to demonstrate the ease of creating a million-parameter LLM without the need for a high-end GPU.

Generative Fusion Decoding (GFD) is a novel shallow fusion framework that integrates Large Language Models (LLMs) into multi-modal text recognition systems such as automatic speech recognition (ASR) and optical character recognition (OCR). GFD operates across mismatched token spaces of different models by mapping text token space to byte token space, enabling seamless fusion during the decoding process. It simplifies the complexity of aligning different model sample spaces, allows LLMs to correct errors in tandem with the recognition model, increases robustness in long-form speech recognition, and enables fusing recognition models deficient in Chinese text recognition with LLMs extensively trained on Chinese. GFD significantly improves performance in ASR and OCR tasks, offering a unified solution for leveraging existing pre-trained models through step-by-step fusion.

The 'ai-dev-2024-ml-workshop' repository contains materials for the Deploy and Monitor ML Pipelines workshop at the AI_dev 2024 conference in Paris, focusing on deployment designs of machine learning pipelines using open-source applications and free-tier tools. It demonstrates automating data refresh and forecasting using GitHub Actions and Docker, monitoring with MLflow and YData Profiling, and setting up a monitoring dashboard with Quarto doc on GitHub Pages.

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.

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Obsidian wine log assistant with code block outputs

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