DirectEd-Curriculum-Bot-LoRA

Model Card for Model ID

This model is a fine-tuned version of google/gemma-2b. It has been trained using TRL.

Model Details

Model Description

This model is a fine-tuned language model designed for chatbot interactions. It was trained on a dataset of ~669 lines of curated text, including conversational prompts, responses, and domain-specific knowledge. The goal of the model is to generate coherent, contextually relevant, and user-friendly responses for chatbot use cases.

Developed by: Ian Karanja

Finetuned from model: google/gemma-2b

Training data size: ~669 lines of text

Model type: Causal Language Model

Intended use: Chatbot interactions in learning assistant

• Developed by: Ian Karanja
• Funded by [optional]: [More Information Needed]
• Shared by [optional]: [More Information Needed]
• Model type: LoRA Adapter for Causal Language Model (Gemma-2B base)
• Language(s) (NLP): English
• License: Google Gemma-2B
• Finetuned from model [optional]: https://huggingface.co/google/gemma-2b

Model Sources [optional]

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• Repository: https://huggingface.co/ianxkaranja/DirectEd-Curriculum-Bot-LoRA
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• Demo [optional]: [More Information Needed]

Uses

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Direct Use

This LoRA adapter is intended to support educational chatbots for the DirectEd e-learning curriculum. It specializes in:

Web design & development

MERN stack (TypeScript + React + MongoDB + Node.js)

Service Design & Product Management basics

Generative AI & LLMOps (Prompt Engineering, RAG, LoRA fine-tuning)

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Downstream Use [optional]

Can be integrated into tutoring platforms, e-learning assistants, or LangChain-powered educational bots.

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Out-of-Scope Use

Not designed for:

General chit-chat outside of educational domains

Medical, legal, or sensitive advice

Toxic or harmful content generation

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Bias, Risks, and Limitations

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Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

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Training Procedure

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Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Compute Infrastructure

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Hardware

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Software

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Citation [optional]

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BibTeX:

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APA:

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Glossary [optional]

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Model Card Authors [optional]

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Framework versions

• PEFT 0.17.0