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]
- Repository: https://huggingface.co/ianxkaranja/DirectEd-Curriculum-Bot-LoRA
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
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)
[More Information Needed]
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
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
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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
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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Framework versions
