Model Card for Grumpy-IT-Llama-3.2
Model Details
Model Description
Grumpy-IT-Llama-3.2 is a specialized fine-tune of the Llama-3.2-3B-Instruct model, designed to simulate a highly competent but socially exhausted Systems Administrator.
The model was trained using Persona Steering techniques to prioritize technical accuracy and brevity while strictly refusing non-technical "waste-of-time" requests (e.g., fixing chairs, coffee machines) with a sarcastic or direct tone. It serves as a demonstration of controlling LLM personality alignment using synthetic data and QLoRA.
- Developed by: Ashwath Srinivasan
- Model type: Causal Language Model (QLoRA Fine-tune)
- Language(s) (NLP): English (en)
- License: Llama 3.2 Community License
- Finetuned from model: meta-llama/Llama-3.2-3B-Instruct
Model Sources
Uses
Direct Use
The model is intended for:
- Simulation & Testing: Testing how users interact with "difficult" or "direct" AI personalities.
- IT Triage: Automatically identifying and filtering out non-technical requests in a support queue context.
- Entertainment: As a chatbot that provides a humorous, cynical take on tech support.
Out-of-Scope Use
- General Purpose Assistance: This model is not a helpful assistant. It will likely refuse to write poems, summarize general news, or be polite.
- Mental Health/Sensitive Contexts: The model's abrasive tone makes it unsuitable for sensitive user interactions.
Bias, Risks, and Limitations
This model is intentionally biased to be disagreeable and sarcastic.
- Tone: It may produce output that users find rude or offensive. This is a design feature, not a bug.
- Hallucination: Like all small LLMs (3B parameters), it may hallucinate technical commands, though the training data prioritized accurate CLI commands.
- Safety: While it adheres to Llama 3.2 safety guardrails, its "mean" persona should not be deployed in customer-facing enterprise environments without a filtering layer.
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
See githib repository
Training Details
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Summary
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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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