---
library_name: transformers
tags:
- philosophy
- conversational
- gpt2
- monk
- wisdom
---

# Monk GPT - Philosophical Wisdom Assistant

## Model Description

**Monk GPT** is a fine-tuned GPT-2 model trained on philosophical conversations about life, death, relationships, career, education, and personal growth. The model responds as a wise monk, offering thoughtful, compassionate, and reflective answers to user questions.

- **Developed by:** Utpalendu Barman
- **Model type:** Causal Language Model (GPT-2)
- **Language:** English
- **Base model:** GPT-2
- **License:** MIT

## Intended Uses

**Direct Use:**  
This model is designed for philosophical Q&A and reflective conversation. It can be used for:
- Personal reflection and journaling
- Educational purposes
- Conversational AI applications
- Meditation and mindfulness tools

**Out-of-Scope Use:**  
The model is not intended for:
- Medical, legal, or professional advice
- Factual information retrieval
- Harmful or manipulative content

## Bias, Risks, and Limitations

- The model reflects the biases present in its training data and base GPT-2 model
- Responses are philosophical and reflective, not factual
- May generate unpredictable or inappropriate content
- Should be used with human oversight

## How to Get Started

```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel

model_name = "utpalendu/monk-gpt"
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)

def ask_monk(question):
    prompt = f"Q: {question} A:"
    inputs = tokenizer.encode(prompt, return_tensors="pt")
    outputs = model.generate(
        inputs,
        max_length=200,
        temperature=0.8,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return response.split("A:")[-1].strip()

print(ask_monk("What is the meaning of life?"))
```