README.md

---
language:
  - en
license: mit
library_name: transformers
pipeline_tag: text-generation
datasets:
  - roneneldan/TinyStories
tags:
  - custom_code
  - educational
---

# Tiny GPT

Tiny GPT is an educational decoder-only Transformer trained from scratch on
the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories)
dataset. The implementation is intentionally small and readable.

## Model details

- Architecture: decoder-only causal language model
- Context length: 512 tokens
- Vocabulary size: 10,000
- Hidden size: 256
- Transformer layers: 6
- Attention heads: 8

Source code: https://github.com/alainbrown/tiny-gpt

## Usage

This repository contains custom Transformers code. Review it before enabling
`trust_remote_code`.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

repo_id = "alainbrown/tiny-gpt"
tokenizer = AutoTokenizer.from_pretrained(repo_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(repo_id, trust_remote_code=True)

inputs = tokenizer("Once upon a time", return_tensors="pt")
logits = model(**inputs).logits
```

## Intended use

This model is intended for education and experimentation. It is not intended
for production, factual question answering, or safety-critical applications.

## Limitations

The model is small, trained on synthetic children's stories, and has not been
comprehensively evaluated. It may produce incoherent, repetitive, incorrect,
or inappropriate text. English is the only supported language.

## Training

The training pipeline is available in the linked GitHub repository. This model
repository excludes optimizer and progress state and contains inference files
only.