---
license: mit
datasets:
- lennart-finke/SimpleStories
language:
- en
tags:
- small-language-model
- story-generation
- text-generation
- efficient-nlp
- distilled-models
---

# SimpleStories Model Family
The SimpleStories models are a tiny model family created for interpretability research, trained on the [SimpleStories dataset](https://huggingface.co/datasets/lennart-finke/SimpleStories).

## Usage

```bash
pip install simple_stories_train
```

```python
from transformers import AutoTokenizer
import torch

from simple_stories_train.models.llama import Llama
from simple_stories_train.models.model_configs import MODEL_CONFIGS

# Select the model size you want to use
model_size = "35M"  # Options: "35M", "30M", "11M", "5M", "1.25M"

# Load model configuration
model_config = MODEL_CONFIGS[model_size]

# Load appropriate model
model_path = f"SimpleStories/SimpleStories-{model_size}"
model = Llama.from_pretrained(model_path, model_config)
device = torch.device("cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu")
model.to(device)
model.eval()

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)

# Define your prompt
prompt = "The curious cat looked at the"

inputs = tokenizer(prompt, return_tensors="pt")
input_ids = inputs.input_ids.to(device)

# Generate text
with torch.no_grad():
    output_ids = model.generate(
        idx=input_ids,
        max_new_tokens=50,
        temperature=0.0,
        top_k=40,
        eos_token_id=tokenizer.eos_token_id
    )

# Decode output
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
print(f"Generated text:\n{output_text}")

```

## Model Variants

| Model Name | n_params | n_layers | d_model | n_heads | n_ctx | d_vocab |
|------------|----------|----------|---------|---------|-------|---------|
| SimpleStories-35M | 35 million | 12 | 512 | 8 | 512 | 4096 |
| SimpleStories-30M | 30 million | 10 | 512 | 8 | 512 | 4096 |
| SimpleStories-11M | 11 million | 6 | 384 | 6 | 512 | 4096 |
| SimpleStories-5M | 5 million | 6 | 256 | 4 | 512 | 4096 |
| SimpleStories-1.25M | 1.25 million | 4 | 128 | 4 | 512 | 4096 |

## Performance Comparison
Model-evaluated generation quality metrics:
<p align="center">
  <img width="80%" src="figures/simplestories_comparison.png">
</p>


## Tokenizer

We use a custom WordPiece tokenizer with a small vocabulary size of 4096. We conducted morphological analysis and coverage gain analysis on the dataset
to build a small tokenizer without compromising on the quality of generation.

## Dataset

The SimpleStories dataset is a collection of short stories generated by state-of-the-art language models. It features:

- Story annotation with high-level concepts: theme, topic, style, etc.
- Higher semantic and syntactic diversity through seeded story generation
- Generated by 2024 models
- Several NLP-metrics pre-computed to aid filtering
- ASCII-only guarantee for the English dataset

Read the dataset paper on [arXiv](https://arxiv.org/abs/2504.09184]).