Notus is a collection of fine-tuned LLMs using SFT, DPO, SFT+DPO, and/or any other RLHF techniques, while always keeping a data-first approach

Generate conversational, tool-calling, structured-output, and preference datasets — easily and at scale

MCP server for human-in-the-loop surveys, A/B preference tests, ratings, and rankings. Get real human feedback inside Claude Code, Claude Desktop, Cursor, Windsurf, and any MCP client — powered by Datapoint AI.

Curated tools, papers, datasets, and practices for LLM training data engineering.

Rapidata provides human feedback at scale — crowd-sourced labeling, model evaluation, ranking, and preference data (RLHF-style) collected from real people worldwide. Teams use the Rapidata API and Python SDK to run online RLHF, refine model behavior, and evaluate model outputs (image, video, audio, and text) with pairwise A/B comparisons, ranking…

Capybara Arena — deployed platform collecting pairwise human preferences on AI-generated CAD models (Elo rating, blob storage, CI).

RLHF preference data curation pipeline: HH-RLHF + UltraFeedback + OASST1 → quality filter → MinHash dedup → DPO-ready JSONL

This repository contains all artifacts produced during my bachelor's thesis on data modeling for collective decision-making.

A forkable example of the human-in-the-loop model-improvement loop: AI generates, humans judge via the Terac MCP, you improve the model. Built as an SVG illustration arena.

Pairwise rating CLI for AI responses — per-axis scoring (helpfulness/harmlessness/accuracy/instruction-following), JSONL in/out, inter-rater Cohen's kappa