README.md

---
library_name: transformers
license: apache-2.0
---
# 🧠 JAT-GPT: Just Another Tiny GPT

Welcome to **JAT-GPT**, the world's most underwhelming large language model — clocking in at a mighty **17.9 million parameters** (yes, million, not billion — stop laughing). 

## 📦 Model Details

- **Model type**: GPT2-based decoder-only transformer
- **Architecture**: GPT-2
- **Library**: Hugging Face 🤗 Transformers
- **Parameters**: 17.9 million (size isn't everything... right?)
- **Training Objective**: Learn to predict the next word — and sometimes even the *right* one!
- **Pretrained on**: A secret* dataset (*"secret" means the dataset was just some text I could find lying around)
- **Training Purpose**: Solely educational. Also for flexing on friends who haven’t trained a language model from scratch.

## 🚀 Capabilities

- Can generate small sentences
  - "Please lower your expectations."
- Can hallucinate confidently, but in a very short and polite way.
- Can generate random words after few tokens.

## 🙅 Limitations

- Not very smart.
- Only Pretrained.
- Understands context... if it fits within few tokens.
- Cannot replace ChatGPT. (But look how cute it is!)

## 🤷 Why Train This?

> "Because I could." – :-)

- To understand the internals of language modeling.
- To cry less when training real models later.
- To appreciate just how powerful modern LLMs are by comparison.

## 🛠️ Usage

```python
from transformers import GPT2Tokenizer, GPT2LMHeadModel

tokenizer = GPT2Tokenizer.from_pretrained("itsme-nishanth/JAT-GPT")
model = GPT2LMHeadModel.from_pretrained("itsme-nishanth/JAT-GPT")

input_ids = tokenizer.encode("Once upon a time", return_tensors="pt")
output = model.generate(input_ids, max_length=20, do_sample=True)
print(tokenizer.decode(output[0]))
```