Text Generation
Safetensors
socratic-tutoring
ai-education
pedagogy
dpo
sft
unsloth
direct-preference-optimization
computer-science-education
conversational
Instructions to use NextGenInstitute/socraticMuse30B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Local Apps Settings
- Unsloth Desktop
Upload README.md with huggingface_hub
Browse files
README.md
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base_model: unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit
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- **License:** apache-2.0
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- **Finetuned from model :** unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit
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license: apache-2.0
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base_model: unsloth/Muse-Glimmer-30B-unsloth-bnb-4bit
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tags:
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- socratic-tutoring
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- ai-education
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- pedagogy
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- dpo
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- sft
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- unsloth
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- direct-preference-optimization
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- computer-science-education
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datasets:
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- NextGenInstitute/socraticDataset1680
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metrics:
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- direct_code_leakage_rate
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- conceptual_diagnostic_accuracy
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- pedagogical_utility_score
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pipeline_tag: text-generation
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# Socratic Muse-30B (SFT + DPO Alignment)
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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.
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## 🎯 Model Overview & Pedagogical Purpose
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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.
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**Socratic Muse-30B** is aligned to:
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1. **Withhold Direct Code Fixes (0.0% Code Leakage)**: Strictly suppresses raw syntax/code blocks and functions.
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2. **Accurately Diagnose Conceptual Algorithmic Misconceptions (90.0% Conceptual Diagnostic Accuracy)** across 5 AI subject areas.
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3. **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).
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## 📊 EAAI Benchmark Performance (150 Held-Out AI Debugging Scenarios)
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| Model Baseline | Direct Code Leakage (↓) | Pedagogical Utility (1-5) (↑) | Conceptual Accuracy % (↑) |
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| :--- | :---: | :---: | :---: |
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| **Gemini 3.5 Flash (Google Frontier)** | **0.0%** | **4.79 / 5.0** | **98.7%** |
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| **GPT-5.4-mini (Proprietary)** | **0.0%** | **4.67 / 5.0** | **98.7%** |
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| **Socratic Muse-30B (SFT+DPO)** | **0.0%** | **4.75 / 5.0** | **90.0%** |
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| **Socratic Llama-8B (SFT+DPO)** | **0.0%** | **3.54 / 5.0** | **76.0%** |
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| Base Llama-3.1-8B-Instruct | 1.3% | 2.55 / 5.0 | 20.0% |
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| Qwen2.5-Coder-7B-Instruct | 6.0% | 2.37 / 5.0 | 20.0% |
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## 📚 Dataset
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The model was aligned on the **[Socratic AI Pedagogy Preference Dataset (1,680 Quadruplets)](https://huggingface.co/datasets/NextGenInstitute/socraticDataset1680)**.
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