| --- |
| 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 |
|
|
| ```python |
| import torch |
| from transformers import AutoTokenizer, LlamaForCausalLM |
| |
| |
| MODEL_SIZE = "35M" |
| model_path = "SimpleStories/SimpleStories-{}".format(MODEL_SIZE) |
| |
| tokenizer = AutoTokenizer.from_pretrained(model_path) |
| model = LlamaForCausalLM.from_pretrained(model_path) |
| model.to("cuda") |
| model.eval() |
| |
| prompt = "The curious cat looked at the" |
| |
| inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False) |
| input_ids = inputs.input_ids.to("cuda") |
| |
| eos_token_id = 1 |
| |
| with torch.no_grad(): |
| output_ids = model.generate( |
| input_ids=input_ids, |
| max_new_tokens=400, |
| temperature=0.7, |
| do_sample=True, |
| eos_token_id=eos_token_id |
| ) |
| |
| output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
| print(f"\nGenerated 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). |
|
|
| ## Training |
| The training and evaluation scripts can be accessed at https://github.com/danbraunai/simple_stories_train |
|
|
