---
library_name: transformers
tags:
- text-generation
- causal-lm
- conversational
- llama
- dpo
- safetensors
- study-assistant
---

# 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.

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## 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

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## Model Sources

- **Repository:** https://huggingface.co/SeranVishwa/master-buddy-v1.0  
- **Demo:** Add deployment link when available

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## 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

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### Downstream Use

The model can be integrated into:

- AI tutoring systems
- Chatbot applications
- Educational platforms
- Developer tools
- Web-based AI assistants

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### 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

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## 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

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### Recommendations

Users should:

- Verify important technical outputs
- Use the model as a learning assistant, not an authority
- Be aware of possible reasoning errors

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## How to Get Started

### Transformers

```python
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))