Search Results for "benchmark" - Page 8
Sort By:
Showing 728 open source projects for "benchmark"
View related business solutions-
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
Veeam Data Platform v13.1Try Veeam Data Platform today. Experience the unified platform that's secure by design, portable by default, and proven to recover clean, fast, and anywhere.
-
1
powerMAX
powerMAX is a CPU and GPU burn-in test
powerMAX is a CPU and GPU burn-in tool designed to push your hardware to its absolute thermal and power limits. It helps users uncover stability issues, cooling weaknesses, and power delivery problems by applying maximum, sustained stress to both the processor and graphics card. The utility supports dedicated CPU tests—SSE or AVX—and a demanding GPU 3D rendering test, with the option to run both simultaneously for full-system power load evaluation. Because it does not generate scores or... -
2
BRL-CAD
Open Source Solid Modeling CAD
BRL-CAD is a powerful cross-platform constructive solid geometry solid modeling system that includes an interactive geometry editor, ray-tracing for rendering & geometric analyses, network distributed framebuffer support, image & signal-processing tools. -
3
PetoronAI-Drug-Discovery
PetoronAI Drug Discovery Experiments
# PetoronAI Drug Discovery Benchmark https://github.com/01alekseev/PetoronAI PetoronAI was evaluated on the NCI-ALMANAC development dataset. • 2,225,137 experimental records • 602 experimentally measured drug pairs • Blind pair-level benchmark • 20×10 cross-validation • 99.5% validation coverage Validation: Correlation = 0.617327 MAE = 3.971799 Null model MAE = 4.656203 Sign accuracy = 64.69% Permutation p = 0.000100 Top HSA hypotheses: • Dactinomycin + Vinblastine sulfate (9.096) • Cabazitaxel + Vinblastine sulfate (8.224) • Mitoxantrone + Vinblastine sulfate (7.254) • Cabazitaxel + Mitoxantrone (7.128) • Dactinomycin + Daunorubicin HCl (6.965) Generated laboratory validation protocols (8×8 dose matrix, HSA, Bliss, Loewe, ZIP). ... -
4
CRAB
CRAB: Cross-environment Agent Benchmark for Multimodal Language Model
CRAB (Composable and Reusable Autonomous Bots) is a framework for building modular, reusable AI agents that can perform complex tasks in various domains. It focuses on creating AI-driven workflows that can be composed of multiple autonomous agents working together. -
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
5
AI File Sorter
Local AI file organization with categorization and rename suggestions
AI File Sorter is a cross-platform desktop application that uses AI (local LLMs run on your computer) to organize files and suggest meaningful file names based on real content, not just filenames or extensions. The app can analyze images locally and propose descriptive rename suggestions (for example, IMG_2048.jpg → clouds_over_lake.jpg). It can also analyze document text to improve categorization and renaming. Supported formats include PDF, DOCX, XLSX, PPTX, ODT, ODS, ODP, and common... -
6
CodeGeeX
CodeGeeX: An Open Multilingual Code Generation Model (KDD 2023)
...Developed with MindSpore and later made PyTorch-compatible, it is capable of multilingual code generation, cross-lingual code translation, code completion, summarization, and explanation. It has been benchmarked on HumanEval-X, a multilingual program synthesis benchmark introduced alongside the model, and achieves state-of-the-art performance compared to other open models like InCoder and CodeGen. CodeGeeX also powers IDE plugins for VS Code and JetBrains, offering features like code completion, translation, debugging, and annotation. The model supports Ascend 910 and NVIDIA GPUs, with optimizations like quantization and FasterTransformer acceleration for faster inference. -
7
Prime number ( primenumbers )
Benchmark for 50 000 000 prime numbers as single and multicore
Simple source files and compiled JAR Java programs, for benchmark 50 000 000 cycle finding prime numbers. On Intel(R) Core(TM) i5-8600K CPU, Windows 10 20H2, i have 39 second on single core and 7,6 second on multi core. (PS: C++ multicore 6 second). Added C files for gcc compiler in Windows 10 and for Xcode C command line project in MacOS ( tested on Mac mini M2 with single core 16 to 25 sec and multicore 2,3 to 5 second by compiler -O switch). -
8
CPQ Requirements Assessment Matrix
Assess CPQ requirements, architecture boundaries and vendor fit in XLS
...Use it to identify capability gaps, systems of record, ownership, priorities and acceptance evidence before CPQ software selection or implementation. It is a planning aid, not a market benchmark, vendor recommendation, formal RFP or implementation guarantee. Published by Configure to WIN. https://configure.win/resources/cpq-requirements-assessment -
9
TextDistance
Compute distance between sequences
...For main algorithms, text distance try to call known external libraries (fastest first) if available (installed in your system) and possible (this implementation can compare this type of sequences). Install text distance with extras for this feature. Textdistance use benchmark results for algorithm optimization and try to call the fastest external lib first (if possible). TextDistance show benchmarks results table for your system and saves libraries priorities into the libraries.json file in TextDistance's folder. This file will be used by text distance for calling the fastest algorithm implementation. ... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
OpenFace Face Recognition
Face recognition with deep neural networks
...Accuracies from research papers have just begun to surpass human accuracies on some benchmarks. The accuracies of open source face recognition systems lag behind the state-of-the-art. See our accuracy comparisons on the famous LFW benchmark. -
11
NAVAL-SEM
Award-winning open-source offline SEM platform for PLS-SEM & CB-SEM
NAVAL-SEM is an award-winning, free and open-source offline Structural Equation Modeling (SEM) platform supporting PLS-SEM and CB-SEM workflows. Designed for researchers, PhD scholars, professors, and analysts, it provides visual model building, measurement assessment, structural model analysis, and reproducible research workflows without subscriptions or cloud dependency. Features include: • PLS-SEM & CB-SEM • Bootstrapping, mediation, moderation & conditional process analysis • HTMT,... -
12
Bottleneck Checker
Free CPU & GPU bottleneck checker - honest ranges, not fake %
Bottleneck Checker is a free, open-source tool that shows whether a CPU and GPU hold each other back - as an honest range, not a fake single percentage. Most bottleneck calculators spit out one confident-looking number no real benchmark could back up. Hardware does not work that way: the same pairing looks CPU-limited in a competitive shooter and GPU-limited in a ray-traced AAA game, and different again at 1080p versus 4K. So this tool reports a range instead, weights the result by resolution and game type, and models DLSS/FSR upscaling. It covers 300+ CPUs and 140+ GPUs on a calibrated 0-100 gaming scale, with a balance meter, a confidence rating, and a rough FPS estimate. ... -
13
OWL
Optimized Workforce Learning for General Multi-Agent Assistance
Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation. OWL (Optimized Workforce Learning for General Multi-Agent Assistance in Real-World Task Automation) is an advanced framework designed to enhance multi-agent collaboration, improving task automation across various domains. By utilizing dynamic agent interactions, OWL aims to streamline and optimize complex workflows, making AI collaboration more natural, efficient, and adaptable. It is built on... -
14
B2B Pricing Maturity Assessment
Evaluate B2B pricing execution maturity across six dimensions in Excel
...It evaluates twelve statements across six independently assessed pricing execution dimensions and provides current-state maturity levels, optional target states, gaps, improvement priorities and recommended next controls. The workbook does not calculate an overall pricing maturity score, provide an industry benchmark or recommend a software product or vendor. Published by Configure to WIN. -
15
ARC-AGI
The Abstraction and Reasoning Corpus
ARC-AGI is a benchmark dataset and experimental framework designed to evaluate and advance artificial general intelligence by testing systems on abstract reasoning tasks that require human-like problem-solving abilities. It consists of a curated set of tasks where models must infer patterns from input-output examples and apply those rules to new unseen cases, without relying on memorization or prior training data. -
16
trimwise
Query-aware prompt compression for high-signal LLM prompts.
Trimwise is an extractive Python library for the step between collecting text and assembling a prompt. Give it a document, a maximum size, and optionally the task you care about. It selects useful fragments from across the document, returns them in source order, and guarantees that the measured result stays within your token, word, or character limit. This is especially useful when an agent has several sources but cannot place every source in the context window. Instead of taking the... -
17
PI-Based Image Encoder / Converter
Python code able to convert / compress image to PI (3.14, π) Indexes
...Features high-performance Numba-accelerated search and a signature 'film-grain' aesthetic upon reconstruction. ZIP also include 16 MB file with 16,7 mil numbers of PI Benchmark(Single-Thread): Hardware & Environment Apple Silicon: Apple M2 (Mac mini/MacBook) x86_64 Platform: Intel Core Ultra 5 225F (Arrow Lake, 10 Cores) OS 1: Fedora 43 (GNOME) OS 2: Windows 11 Pro (23H2/24H2) Software: Python 3.14.3 + Numba JIT (latest) Results (Lower is better) Platform / OS CPU Time (Seconds) macOS (Native) Apple M2 52.151311 s (in default setup) Fedora Linux Intel Core Ultra 5 225F 58.536457 s (in default Power Management: Balanced) Windows 11 Intel Core Ultra 5 225F 59.681427 s (important! ... -
18
GLM-4-32B-0414
Open Multilingual Multimodal Chat LMs
GLM-4-32B-0414 is a powerful open-source large language model featuring 32 billion parameters, designed to deliver performance comparable to leading models like OpenAI’s GPT series. It supports multilingual and multimodal chat capabilities with an extensive 32K token context length, making it ideal for dialogue, reasoning, and complex task completion. The model is pre-trained on 15 trillion tokens of high-quality data, including substantial synthetic reasoning datasets, and further enhanced... -
19
ChatGLM2-6B
ChatGLM2-6B: An Open Bilingual Chat LLM
ChatGLM2-6B is the second-gen Chinese-English conversational LLM from ZhipuAI/Tsinghua. It upgrades the base model with GLM’s hybrid pretraining objective, 1.4 TB bilingual data, and preference alignment—delivering big gains on MMLU, CEval, GSM8K, and BBH. The context window extends up to 32K (FlashAttention), and Multi-Query Attention improves speed and memory use. The repo includes Python APIs, CLI & web demos, OpenAI-style/FASTAPI servers, and quantized checkpoints for lightweight local... -
20
MLPerf
Reference implementations of MLPerf™ training benchmarks
...Together, we create the reference implementations, rules, policies, and procedures to benchmark a wide variety of AI workloads. -
21
InternVL
A Pioneering Open-Source Alternative to GPT-4o
InternVL is a large-scale multimodal foundation model designed to integrate computer vision and language understanding within a unified architecture. The project focuses on scaling vision models and aligning them with large language models so that they can perform tasks involving both visual and textual information. InternVL is trained on massive collections of image-text data, enabling it to learn representations that capture both visual patterns and semantic meaning. The model supports a... -
22
Evals
Evals is a framework for evaluating LLMs and LLM systems
The openai/evals repository is a framework and registry for evaluating large language models and systems built with LLMs. It’s designed to let you define “evals” (evaluation tasks) in a structured way and run them against different models or agents, with the ability to score, compare, and analyze results. The framework supports templated YAML eval definitions, solver-based evaluations, custom metrics, and composition of multi-step evaluations. It includes utilities and APIs to plug in... -
23
DeepSeek VL
Towards Real-World Vision-Language Understanding
DeepSeek-VL is DeepSeek’s initial vision-language model that anchors their multimodal stack. It enables understanding and generation across visual and textual modalities—meaning it can process an image + a prompt, answer questions about images, caption, classify, or reason about visuals in context. The model is likely used internally as the visual encoder backbone for agent use cases, to ground perception in downstream tasks (e.g. answering questions about a screenshot). The repository... -
24
DeepSeek LLM
DeepSeek LLM: Let there be answers
The DeepSeek-LLM repository hosts the code, model files, evaluations, and documentation for DeepSeek’s LLM series (notably the 67B Chat variant). Its tagline is “Let there be answers.” The repo includes an “evaluation” folder (with results like math benchmark scores) and code artifacts (e.g. pre-commit config) that support model development and deployment. According to the evaluation files, DeepSeek LLM 67B Chat achieves strong performance on math benchmarks under both chain-of-thought (CoT) and tool-assisted reasoning modes. The model is trained from scratch, reportedly on a vast multilingual + code + reasoning dataset, and competes with other open or open-weight models. ... -
25
Foolbox
Python toolbox to create adversarial examples
Foolbox: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX. Foolbox 3 is built on top of EagerPy and runs natively in PyTorch, TensorFlow, and JAX. Foolbox provides a large collection of state-of-the-art gradient-based and decision-based adversarial attacks. Catch bugs before running your code thanks to extensive type annotations in Foolbox.
