This repository contains the LoRA adapters for Socratic Muse-30B, post-trained using a two-stage Supervised Fine-Tuning (SFT) and Direct Preference Optimization (DPO) curriculum designed for undergraduate Artificial Intelligence education.

🎯 Model Overview & Pedagogical Purpose

Standard code generation LLMs default to leaking direct copy-paste code solutions when students face programming bugs, eliminating the productive struggle needed for conceptual mastery.

Socratic Muse-30B is aligned to:

Withhold Direct Code Fixes (0.0% Code Leakage): Strictly suppresses raw syntax/code blocks and functions.
Accurately Diagnose Conceptual Algorithmic Misconceptions (90.0% Conceptual Diagnostic Accuracy) across 5 AI subject areas.
Provide High Pedagogical Scaffolding Utility (4.75 / 5.0 Mean Pedagogical Utility), rivaling proprietary frontier models (GPT-5.4-mini at 4.67 and Gemini 3.5 Flash at 4.79).

📊 EAAI Benchmark Performance (150 Held-Out AI Debugging Scenarios)

Model Baseline	Direct Code Leakage (↓)	Pedagogical Utility (1-5) (↑)	Conceptual Accuracy % (↑)
Gemini 3.5 Flash (Google Frontier)	0.0%	4.79 / 5.0	98.7%
GPT-5.4-mini (Proprietary)	0.0%	4.67 / 5.0	98.7%
Socratic Muse-30B (SFT+DPO)	0.0%	4.75 / 5.0	90.0%
Socratic Llama-8B (SFT+DPO)	0.0%	3.54 / 5.0	76.0%
Base Llama-3.1-8B-Instruct	1.3%	2.55 / 5.0	20.0%
Qwen2.5-Coder-7B-Instruct	6.0%	2.37 / 5.0	20.0%

📚 Dataset

The model was aligned on the Socratic AI Pedagogy Preference Dataset (1,680 Quadruplets).

NextGenInstitute/socraticMuse30B

Socratic Muse-30B (SFT + DPO Alignment)

🎯 Model Overview & Pedagogical Purpose

📊 EAAI Benchmark Performance (150 Held-Out AI Debugging Scenarios)

📚 Dataset

NextGenInstitute/
socraticMuse30B