EleMo-Alpha-1-LoRA

<div align="center"> <h1>🧸 EleMo-Alpha-1-LoRA</h1> <h3>Pedagogical Fine-Tuning for Early Childhood Education</h3> </div>

Overview

This repository contains the LoRA (Low-Rank Adaptation) weights for EleMo-Alpha-1. These weights represent the pedagogical intelligence specialized in documenting learning stories based on the Margaret Carr framework.

By decoupling this pedagogical logic from the base model, we enable a modular and efficient approach to AI-supported documentation, perfectly suited for local, data-sovereign infrastructures.

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🛠️ Technical Specifications

• Base Model: Mistral-Small-24B-Instruct-2501
• Fine-Tuning Method: PEFT (Parameter-Efficient Fine-Tuning) / LoRA
• Primary Application: Pedagogical observation and documentation of learning stories.

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🚀 Quick Start

You can load this adapter using the peft and transformers libraries.

from peft import PeftModel
from transformers import AutoModelForCausalLM

# 1. Load the base model
base_model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-Small-24B-Instruct-2501")

# 2. Load the EleMo-Alpha-1 Adapter
model = PeftModel.from_pretrained(base_model, "Earlychildhoodeducation/EleMo-Alpha-1-LoRA")

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### ⚙️ How to use the LoRA Adapter

To apply these pedagogical weights, you need to combine this adapter with the base model. 

#### Recommended Workflow (e.g., in LM Studio)
1. **Load Base Model:** Ensure the base model `mistralai/Mistral-Small-24B-Instruct-2501` (GGUF) is loaded in your application.
2. **Apply Adapter:** Navigate to the "Fine-tuning / Adapters" section in your tool.
3. **Load Weights:** Add this LoRA adapter. LM Studio will dynamically merge these weights with the base model to apply the specific EleMo-pedagogy.
4. **Pedagogical Settings:** Configure your system prompt to focus on the Margaret Carr documentation framework to achieve the best results.

> **💡 Requirement:** This adapter is fine-tuned specifically for the `Mistral-Small-24B` architecture. Ensure your base model matches this version for optimal performance.

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*For the easy-to-use version (no configuration required), visit our [Base-Model Repository]