model-benchmarking

Rapid Evaluation Framework for climate data

Open source code for AIOpsServing

All-in-one AI workbench program for Vision AI model inference, evaluation, benchmarking, optimization & dataset management

A reproducible, leak-free machine learning benchmarking lab for regression model comparison, cross-validation, diagnostics, experiment tracking, and interactive Streamlit-based inference using the California Housing dataset.

Real-time AI model comparison and benchmarking platform with live benchmark data, streaming model responses, and side-by-side evaluation.

This repo contains a study on performance of LLMs on STS(Semantic Textual Similarity) Data.

Predict stock prices using Linear Regression and LSTM models. Includes data preprocessing, visualization, and benchmarking tools for analyzing historical stock data.

Machine Learning Model using Decision Trees on US Voting Dataset

Deep learning benchmarking project in PyTorch comparing MLP, CNN, and ResNet architectures on FashionMNIST with structured training pipelines, data augmentation, and evaluation across accuracy, F1-score, and per-class metrics.

🌿 Analyze, optimize, and track LLM prompt energy usage, cost, and carbon footprint with research-backed recommendations and real-time dashboards.

A Streamlit web app that uses a Groq-powered LLM (Llama 3) to act as an impartial judge for evaluating and comparing two model outputs. Supports custom criteria, presets like creativity and brand tone, and returns structured scores, explanations, and a winner. Built end-to-end with Python, Groq API, and Streamlit.

This project demonstrates a production-grade Evaluation (Evals) Framework used to benchmark multiple Large Language Models (LLMs) against a "Source of Truth" NBA dataset.

Interactive Python CLI for benchmarking and comparing Amazon Bedrock and multi-provider foundation models side-by-side by latency, token usage, and cost, with timestamped Markdown reports.

A complete, self-contained implementation of a modern transformer language model built from scratch using PyTorch.

InfernoBench: open-source AI model installers, local inference benchmarks, and reproducible browser labs. Wan2.1 and Sulphur-2 included.

Side-by-side benchmark of BiLSTM + Attention vs BERT for multi-class emotion classification with real-time inference comparison

Anonymized consumer classification model benchmarking high-dimensional K-Nearest Neighbors (KNN) against Logistic Regression, achieving 96% predictive accuracy.

Causal analysis framework using Double Machine Learning to quantitatively isolate the effect of model size on deep learning performance while controlling for confounders such as dataset size, training time, and hyperparameters.

An integrated Deep Learning suite for sustainable civil engineering. Features an end-to-end Multi-Layer Perceptron neural network to predict concrete compressive strength, execute dynamic low-carbon chemical mix optimization, visualize time-series hardening kinetics, and benchmark predictive machine learning algorithms.