Best AI tools for< Evaluate Memory Performance >

BenchLLM is an AI tool designed for AI engineers to evaluate LLM-powered apps by running and evaluating models with a powerful CLI. It allows users to build test suites, choose evaluation strategies, and generate quality reports. The tool supports OpenAI, Langchain, and other APIs out of the box, offering automation, visualization of reports, and monitoring of model performance.

thisorthis.ai is an AI tool that allows users to compare generative AI models and AI model responses. It helps users analyze and evaluate different AI models to make informed decisions. The tool requires JavaScript to be enabled for optimal functionality.

Langtrace AI is an open-source observability tool powered by Scale3 Labs that helps monitor, evaluate, and improve LLM (Large Language Model) applications. It collects and analyzes traces and metrics to provide insights into the ML pipeline, ensuring security through SOC 2 Type II certification. Langtrace supports popular LLMs, frameworks, and vector databases, offering end-to-end observability and the ability to build and deploy AI applications with confidence.

Arize AI is an AI Observability & LLM Evaluation Platform that helps you monitor, troubleshoot, and evaluate your machine learning models. With Arize, you can catch model issues, troubleshoot root causes, and continuously improve performance. Arize is used by top AI companies to surface, resolve, and improve their models.

Evidently AI is an open-source machine learning (ML) monitoring and observability platform that helps data scientists and ML engineers evaluate, test, and monitor ML models from validation to production. It provides a centralized hub for ML in production, including data quality monitoring, data drift monitoring, ML model performance monitoring, and NLP and LLM monitoring. Evidently AI's features include customizable reports, structured checks for data and models, and a Python library for ML monitoring. It is designed to be easy to use, with a simple setup process and a user-friendly interface. Evidently AI is used by over 2,500 data scientists and ML engineers worldwide, and it has been featured in publications such as Forbes, VentureBeat, and TechCrunch.

Maxim is an end-to-end AI evaluation and observability platform that empowers modern AI teams to ship products with quality, reliability, and speed. It offers a comprehensive suite of tools for experimentation, evaluation, observability, and data management. Maxim aims to bring the best practices of traditional software development into non-deterministic AI workflows, enabling rapid iteration and deployment of AI models. The platform caters to the needs of AI developers, data scientists, and machine learning engineers by providing a unified framework for evaluation, visual flows for workflow testing, and observability features for monitoring and optimizing AI systems in real-time.

RebeccAi is an AI-powered business idea evaluation and validation tool that helps users assess the potential of their ideas, refine them quickly, and turn them into reality. The platform uses AI technology to provide accurate insights and offers tools for idea refinement and improvement. RebeccAi is designed to assist individuals in evaluating, assessing, and enhancing their business or startup ideas efficiently and intelligently.

Codei is an AI-powered platform designed to help individuals land their dream software engineering job. It offers features such as application tracking, question generation, and code evaluation to assist users in honing their technical skills and preparing for interviews. Codei aims to provide personalized support and insights to help users succeed in the tech industry.

Ottic is an AI tool designed to empower both technical and non-technical teams to test Language Model (LLM) applications efficiently and accelerate the development cycle. It offers features such as a 360º view of the QA process, end-to-end test management, comprehensive LLM evaluation, and real-time monitoring of user behavior. Ottic aims to bridge the gap between technical and non-technical team members, ensuring seamless collaboration and reliable product delivery.

SuperAnnotate is an AI data platform that simplifies and accelerates model-building by unifying the AI pipeline. It enables users to create, curate, and evaluate datasets efficiently, leading to the development of better models faster. The platform offers features like connecting any data source, building customizable UIs, creating high-quality datasets, evaluating models, and deploying models seamlessly. SuperAnnotate ensures global security and privacy measures for data protection.

SymptomChecker.io is an AI-powered medical symptom checker that allows users to describe their symptoms in their own words and receive non-reviewed AI-generated responses. It is important to note that this tool is not intended to offer medical advice, diagnosis, or treatment and should not be used as a substitute for professional medical advice. In the case of a medical emergency, please contact your physician or dial 911 immediately.

ELSA is an AI-powered English speaking coach that helps you improve your pronunciation, fluency, and confidence. With ELSA, you can practice speaking English in short, fun dialogues and get instant feedback from our proprietary artificial intelligence technology. ELSA also offers a variety of other features, such as personalized lesson plans, progress tracking, and games to help you stay motivated.

ELSA Speech Analyzer is an AI-powered conversational English fluency coach that provides instant, personalized feedback on speech. It helps users improve pronunciation, intonation, grammar, and vocabulary through real-time analysis. The tool is designed for individuals, professionals, students, and organizations to enhance English speaking skills and communication abilities.

UpTrain is a full-stack LLMOps platform designed to help users confidently scale AI by providing a comprehensive solution for all production needs, from evaluation to experimentation to improvement. It offers diverse evaluations, automated regression testing, enriched datasets, and innovative techniques to generate high-quality scores. UpTrain is built for developers, compliant to data governance needs, cost-efficient, remarkably reliable, and open-source. It provides precision metrics, task understanding, safeguard systems, and covers a wide range of language features and quality aspects. The platform is suitable for developers, product managers, and business leaders looking to enhance their LLM applications.

Workable is a leading recruiting software and hiring platform that offers a full Applicant Tracking System with built-in AI sourcing. It provides a configurable HRIS platform to securely manage employees, automate hiring tasks, and offer actionable insights and reporting. Workable helps companies streamline their recruitment process, from sourcing to employee onboarding and management, with features like sourcing and attracting candidates, evaluating and collaborating with hiring teams, automating hiring tasks, onboarding and managing employees, and tracking HR processes.

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.

Sereda.ai is an AI-powered platform designed to unleash a team's potential by offering solutions for employee knowledge management, surveys, performance reviews, learning, and more. It integrates artificial intelligence to streamline HR processes, improve employee engagement, and boost productivity. The platform provides a user-friendly interface, personalized settings, and automation features to enhance organizational efficiency and reduce costs.

Career Copilot is an AI-powered hiring tool that helps recruiters and hiring managers find the best candidates for their open positions. The tool uses machine learning to analyze candidate profiles and identify those who are most qualified for the job. Career Copilot also provides a number of features to help recruiters streamline the hiring process, such as candidate screening, interview scheduling, and offer management.

InstantPersonas is an AI-powered SWOT Analysis Generator that helps organizations and individuals evaluate their Strengths, Weaknesses, Opportunities, and Threats. By using a company description, the tool generates a comprehensive SWOT Analysis, providing insights for strategic planning. Users can edit the analysis and download it as an image. InstantPersonas aims to assist in understanding target audience and market for more successful marketing strategies.

VisualHUB is an AI-powered design analysis tool that provides instant insights on UI, UX, readability, and more. It offers features like A/B Testing, UI Analysis, UX Analysis, Readability Analysis, Margin and Hierarchy Analysis, and Competition Analysis. Users can upload product images to receive detailed reports with actionable insights and scores. Trusted by founders and designers, VisualHUB helps optimize design variations and identify areas for improvement in products.

This repository contains code and data for replicating experiments on Long-Term Memory (LTM) abilities of conversational agents. It includes a benchmark for testing agents' memory performance over long conversations, evaluating tasks requiring dynamic memory upkeep and information integration. The repository supports various models, datasets, and configurations for benchmarking and reporting results.

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. |  |

Torchtune is a PyTorch-native library for easily authoring, fine-tuning, and experimenting with LLMs. It provides native-PyTorch implementations of popular LLMs using composable and modular building blocks, easy-to-use and hackable training recipes for popular fine-tuning techniques, YAML configs for easily configuring training, evaluation, quantization, or inference recipes, and built-in support for many popular dataset formats and prompt templates to help you quickly get started with training.

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.

RAFT (Retrieval-Augmented Fine-Tuning) is a method for creating conversational agents that realistically emulate specific human targets. It involves a dual-phase process of fine-tuning and retrieval-based augmentation to generate nuanced and personalized dialogue. The tool is designed to combine interview transcripts with memories from past writings to enhance language model responses. RAFT has the potential to advance the field of personalized, context-sensitive conversational agents.

AgentPoison is a repository that provides the official PyTorch implementation of the paper 'AgentPoison: Red-teaming LLM Agents via Memory or Knowledge Base Backdoor Poisoning'. It offers tools for red-teaming LLM agents by poisoning memory or knowledge bases. The repository includes trigger optimization algorithms, agent experiments, and evaluation scripts for Agent-Driver, ReAct-StrategyQA, and EHRAgent. Users can fine-tune motion planners, inject queries with triggers, and evaluate red-teaming performance. The codebase supports multiple RAG embedders and provides a unified dataset access for all three agents.

h2oGPT is an Apache V2 open-source project that allows users to query and summarize documents or chat with local private GPT LLMs. It features a private offline database of any documents (PDFs, Excel, Word, Images, Video Frames, Youtube, Audio, Code, Text, MarkDown, etc.), a persistent database (Chroma, Weaviate, or in-memory FAISS) using accurate embeddings (instructor-large, all-MiniLM-L6-v2, etc.), and efficient use of context using instruct-tuned LLMs (no need for LangChain's few-shot approach). h2oGPT also offers parallel summarization and extraction, reaching an output of 80 tokens per second with the 13B LLaMa2 model, HYDE (Hypothetical Document Embeddings) for enhanced retrieval based upon LLM responses, a variety of models supported (LLaMa2, Mistral, Falcon, Vicuna, WizardLM. With AutoGPTQ, 4-bit/8-bit, LORA, etc.), GPU support from HF and LLaMa.cpp GGML models, and CPU support using HF, LLaMa.cpp, and GPT4ALL models. Additionally, h2oGPT provides Attention Sinks for arbitrarily long generation (LLaMa-2, Mistral, MPT, Pythia, Falcon, etc.), a UI or CLI with streaming of all models, the ability to upload and view documents through the UI (control multiple collaborative or personal collections), Vision Models LLaVa, Claude-3, Gemini-Pro-Vision, GPT-4-Vision, Image Generation Stable Diffusion (sdxl-turbo, sdxl) and PlaygroundAI (playv2), Voice STT using Whisper with streaming audio conversion, Voice TTS using MIT-Licensed Microsoft Speech T5 with multiple voices and Streaming audio conversion, Voice TTS using MPL2-Licensed TTS including Voice Cloning and Streaming audio conversion, AI Assistant Voice Control Mode for hands-free control of h2oGPT chat, Bake-off UI mode against many models at the same time, Easy Download of model artifacts and control over models like LLaMa.cpp through the UI, Authentication in the UI by user/password via Native or Google OAuth, State Preservation in the UI by user/password, Linux, Docker, macOS, and Windows support, Easy Windows Installer for Windows 10 64-bit (CPU/CUDA), Easy macOS Installer for macOS (CPU/M1/M2), Inference Servers support (oLLaMa, HF TGI server, vLLM, Gradio, ExLLaMa, Replicate, OpenAI, Azure OpenAI, Anthropic), OpenAI-compliant, Server Proxy API (h2oGPT acts as drop-in-replacement to OpenAI server), Python client API (to talk to Gradio server), JSON Mode with any model via code block extraction. Also supports MistralAI JSON mode, Claude-3 via function calling with strict Schema, OpenAI via JSON mode, and vLLM via guided_json with strict Schema, Web-Search integration with Chat and Document Q/A, Agents for Search, Document Q/A, Python Code, CSV frames (Experimental, best with OpenAI currently), Evaluate performance using reward models, and Quality maintained with over 1000 unit and integration tests taking over 4 GPU-hours.

LLM-Drop is an official implementation of the paper 'What Matters in Transformers? Not All Attention is Needed'. The tool investigates redundancy in transformer-based Large Language Models (LLMs) by analyzing the architecture of Blocks, Attention layers, and MLP layers. It reveals that dropping certain Attention layers can enhance computational and memory efficiency without compromising performance. The tool provides a pipeline for Block Drop and Layer Drop based on LLaMA-Factory, and implements quantization using AutoAWQ and AutoGPTQ.

BABILong is a generative benchmark designed to evaluate the performance of NLP models in processing long documents with distributed facts. It consists of 20 tasks that simulate interactions between characters and objects in various locations, requiring models to distinguish important information from irrelevant details. The tasks vary in complexity and reasoning aspects, with test samples potentially containing millions of tokens. The benchmark aims to challenge and assess the capabilities of Large Language Models (LLMs) in handling complex, long-context information.

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.

Rageval is an evaluation tool for Retrieval-augmented Generation (RAG) methods. It helps evaluate RAG systems by performing tasks such as query rewriting, document ranking, information compression, evidence verification, answer generation, and result validation. The tool provides metrics for answer correctness and answer groundedness, along with benchmark results for ASQA and ALCE datasets. Users can install and use Rageval to assess the performance of RAG models in question-answering tasks.

FlagPerf is an integrated AI hardware evaluation engine jointly built by the Institute of Intelligence and AI hardware manufacturers. It aims to establish an industry-oriented metric system to evaluate the actual capabilities of AI hardware under software stack combinations (model + framework + compiler). FlagPerf features a multidimensional evaluation metric system that goes beyond just measuring 'whether the chip can support specific model training.' It covers various scenarios and tasks, including computer vision, natural language processing, speech, multimodal, with support for multiple training frameworks and inference engines to connect AI hardware with software ecosystems. It also supports various testing environments to comprehensively assess the performance of domestic AI chips in different scenarios.

MemoryLLM is a large language model designed for self-updating capabilities. It offers pretrained models with different memory capacities and features, such as chat models. The repository provides training code, evaluation scripts, and datasets for custom experiments. MemoryLLM aims to enhance knowledge retention and performance on various natural language processing tasks.

ByteMLPerf is an AI Accelerator Benchmark that focuses on evaluating AI Accelerators from a practical production perspective, including the ease of use and versatility of software and hardware. Byte MLPerf has the following characteristics: - Models and runtime environments are more closely aligned with practical business use cases. - For ASIC hardware evaluation, besides evaluate performance and accuracy, it also measure metrics like compiler usability and coverage. - Performance and accuracy results obtained from testing on the open Model Zoo serve as reference metrics for evaluating ASIC hardware integration.

This repository implements multiple key-value cache pruning methods and benchmarks using transformers, aiming to simplify the development of new methods for researchers and developers in the field of long-context language models. It provides a set of 'presses' that compress the cache during the pre-filling phase, with each press having a compression ratio attribute. The repository includes various training-free presses, special presses, and supports KV cache quantization. Users can contribute new presses and evaluate the performance of different presses on long-context datasets.

InfLLM is a training-free memory-based method that unveils the intrinsic ability of LLMs to process streaming long sequences. It stores distant contexts into additional memory units and employs an efficient mechanism to lookup token-relevant units for attention computation. Thereby, InfLLM allows LLMs to efficiently process long sequences while maintaining the ability to capture long-distance dependencies. Without any training, InfLLM enables LLMs pre-trained on sequences of a few thousand tokens to achieve superior performance than competitive baselines continually training these LLMs on long sequences. Even when the sequence length is scaled to 1, 024K, InfLLM still effectively captures long-distance dependencies.

NanoFlow is a throughput-oriented high-performance serving framework for Large Language Models (LLMs) that consistently delivers superior throughput compared to other frameworks by utilizing key techniques such as intra-device parallelism, asynchronous CPU scheduling, and SSD offloading. The framework proposes nano-batching to schedule compute-, memory-, and network-bound operations for simultaneous execution, leading to increased resource utilization. NanoFlow also adopts an asynchronous control flow to optimize CPU overhead and eagerly offloads KV-Cache to SSDs for multi-round conversations. The open-source codebase integrates state-of-the-art kernel libraries and provides necessary scripts for environment setup and experiment reproduction.

llmc is an off-the-shell tool designed for compressing LLM, leveraging state-of-the-art compression algorithms to enhance efficiency and reduce model size without compromising performance. It provides users with the ability to quantize LLMs, choose from various compression algorithms, export transformed models for further optimization, and directly infer compressed models with a shallow memory footprint. The tool supports a range of model types and quantization algorithms, with ongoing development to include pruning techniques. Users can design their configurations for quantization and evaluation, with documentation and examples planned for future updates. llmc is a valuable resource for researchers working on post-training quantization of large language models.

I will score your Mind Blowing Startup Ideas, helping your to evaluate faster.

I'll help you evaluate your writing to make sure it's engaging, informative, and flows well. Uses principles from "Made to Stick"

The main objective of a personalized ChatGPT for reading laboratory tests is to evaluate laboratory test results and create a spreadsheet with the evaluation results and possible solutions.

As a Search Quality Rater, you will help evaluate search engine quality around the world.

Business Model Canvas Creator - Build and evaluate your business model

Guide to unbiasedly evaluate different job opportunities

I automatically create and evaluate phone scripts, presenting a final draft.

Helps you evaluate whether or not tech platforms match UNESCO's Internet for Trust Guidelines for the Governance of Digital Platforms

Analyzes AFL draft data to evaluate clubs' historical performance

Generate, Evaluate, and Improve your Academic Programs

Searches the web's verified fact-checkers to evaluate facts and claims

🔬💊 Navigate the high-risk, high-reward world of biotech and pharma investing! Discover breakthrough therapies 🧬📈, understand drug development 🧪📊, and evaluate investment opportunities 🚀💰. Invest wisely in innovation! 💡🌐 Not a financial advisor. 🚫💼

Guides B2B startups in a structured customer segment evaluation process. Stop guessing! Ideate, Evaluate & Make data-driven decision.

I provide a structured way of using AI to support teaching and learning. I use the the CHOICE method (i.e., Clarify, Harness, Originate, Iterate, Communicate, Evaluate) to ensure that your use of AI can help you meet your educational goals.

Evaluates your long-term market position based on value offered and uniqueness against competitors.

Wertet Lebenslauf und Stellenanzeige aus und simuliert ein Vorstellungsgespräch mit anschließender Auswertung: Lebenslauf und Anzeige einfach hochladen und starten.

Evaluates link credibility, based on detailed factual analysis.

A professional assistant for technical item evaluations in banking

Simulates the IELTS Writing Test, evaluates responses, and estimates band scores.

Enthusiastic about truth in academic papers, critical and analytical.