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Model Card for Master Buddy AI v1.0
Master Buddy AI is a fine-tuned conversational language model designed as a computer science and engineering study assistant. It is built to help users understand programming concepts, algorithms, and general technical topics in a structured and helpful manner.
Model Details
Model Description
Master Buddy AI v1.0 is a decoder-only transformer-based language model fine-tuned using:
- Supervised Fine-Tuning (SFT)
- Direct Preference Optimization (DPO)
The goal of this model is to improve response helpfulness, reasoning ability, and instruction-following quality.
- Developed by: Seran Vishwa
- Model type: Causal Language Model (Decoder-only Transformer)
- Language(s): English
- License: Apache-2.0
- Finetuned from model: LLaMA-based architecture or equivalent base model
Model Sources
- Repository: https://huggingface.co/SeranVishwa/master-buddy-v1.0
- Demo: Add deployment link when available
Uses
Direct Use
This model is intended for:
- Programming assistance
- Data structures and algorithms explanations
- Computer science concepts (OS, DBMS, Networks)
- Educational and study support
- General question answering
Downstream Use
The model can be integrated into:
- AI tutoring systems
- Chatbot applications
- Educational platforms
- Developer tools
- Web-based AI assistants
Out-of-Scope Use
This model is not intended for:
- Medical advice
- Legal advice
- Financial decision-making
- Safety-critical systems
- Harmful or malicious use cases
Bias, Risks, and Limitations
- The model may produce incorrect or hallucinated responses
- It reflects biases present in training data
- It should not be used as a sole source of truth
- Outputs should be verified for critical use cases
Recommendations
Users should:
- Verify important technical outputs
- Use the model as a learning assistant, not an authority
- Be aware of possible reasoning errors
How to Get Started
Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("SeranVishwa/master-buddy-v1.0")
tokenizer = AutoTokenizer.from_pretrained("SeranVishwa/master-buddy-v1.0")
prompt = "Explain Dijkstra's algorithm"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=200)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))