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
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Updated
Jan 15, 2024 - Python
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.
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.
synthetic-data research and engineering: generation, deterministic curation, validation, provenance, and release tooling using local and openrouter distill models
Human evaluation workbench for scoring LLM responses, collecting pairwise preferences, detecting hallucinations, and analyzing model quality.
Pairwise code evaluation: two responses rated on five dimensions, a 7-point preference, and a check that the preference matches the ratings.
Self-hostable labeling and LLM-evaluation platform: configurable task templates, gold questions, rater reputation, position-bias counterbalancing, and LLM judges that merge with human votes.
Capybara Arena — deployed platform collecting pairwise human preferences on AI-generated CAD models (Elo rating, blob storage, CI).
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 with per-axis scoring (helpfulness/harmlessness/accuracy/instruction-following), JSONL in/out, inter-rater Cohen's kappa
Measuring verbosity bias in UltraFeedback (61K GPT-4-annotated preference pairs): statistically unambiguous, but a small effect. Python + Streamlit.
Rapidata — independent third-party profile of a public API surface, by API Evangelist. 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
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