metadata
license: apache-2.0
base_model: unsloth/Meta-Llama-3.1-8B-Instruct-bnb-4bit
tags:
- socratic-tutoring
- ai-education
- pedagogy
- dpo
- sft
- unsloth
- direct-preference-optimization
- computer-science-education
datasets:
- NextGenInstitute/socraticDataset1680
metrics:
- direct_code_leakage_rate
- conceptual_diagnostic_accuracy
- pedagogical_utility_score
pipeline_tag: text-generation
Socratic Llama-8B (SFT + DPO Alignment)
This repository contains the LoRA adapters for Socratic Llama-8B, 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 Llama-8B is aligned to:
- Withhold Direct Code Fixes (0.0% Code Leakage): Strictly suppresses raw syntax/code blocks and functions.
- Accurately Diagnose Conceptual Algorithmic Misconceptions (76.0% Conceptual Diagnostic Accuracy) across 5 AI subject areas.
- Provide High Pedagogical Scaffolding Utility (3.54 / 5.0 Mean Pedagogical Utility).
📊 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).