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--- |
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license: mit |
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datasets: |
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- lennart-finke/SimpleStories |
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language: |
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- en |
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tags: |
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- small-language-model |
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- story-generation |
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- text-generation |
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- efficient-nlp |
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- distilled-models |
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--- |
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# SimpleStories Model Family |
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The SimpleStories models are a tiny model family created for interpretability research, trained on the [SimpleStories dataset](https://huggingface.co/datasets/SimpleStories/SimpleStories). This is the second iteration of the model family. |
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**Paper:** https://arxiv.org/abs/2504.09184 |
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**Training code:** https://github.com/simple-stories/simple_stories_train |
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**Traning checkpoints:** https://wandb.ai/finke/simplestories-v2 |
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## Usage |
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```python |
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import torch |
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from transformers import AutoTokenizer, LlamaForCausalLM |
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MODEL_SIZE = "5M" |
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model_path = "SimpleStories/SimpleStories-V2-{}".format(MODEL_SIZE) |
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tokenizer = AutoTokenizer.from_pretrained(model_path) |
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model = LlamaForCausalLM.from_pretrained(model_path) |
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model.to("cuda") |
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model.eval() |
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prompt = "The curious cat looked at the" |
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inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False) |
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input_ids = inputs.input_ids.to("cuda") |
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eos_token_id = 1 |
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with torch.no_grad(): |
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output_ids = model.generate( |
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input_ids=input_ids, |
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max_new_tokens=400, |
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temperature=0.7, |
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do_sample=True, |
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eos_token_id=eos_token_id |
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) |
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output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) |
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print(f"\nGenerated text:\n{output_text}") |
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``` |
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## Model Variants |
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| Model Name | n_params | n_layers | d_model | n_heads | n_ctx | d_vocab | |
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|------------|----------|----------|---------|---------|-------|---------| |
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| SimpleStories-35M | 35 million | 12 | 512 | 8 | 512 | 4019 | |
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| SimpleStories-30M | 30 million | 10 | 512 | 8 | 512 | 4019 | |
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| SimpleStories-11M | 11 million | 6 | 384 | 6 | 512 | 4019 | |
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| SimpleStories-5M | 5 million | 6 | 256 | 4 | 512 | 4019 | |
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| SimpleStories-1.25M | 1.25 million | 4 | 128 | 4 | 512 | 4019 | |
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## Dataset |
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The SimpleStories dataset is a collection of short stories generated by state-of-the-art language models. It features: |
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- Story annotation with high-level concepts: theme, topic, style, etc. |
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- Higher semantic and syntactic diversity through seeded story generation |
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- Generated by 2024 models |
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- Several NLP-metrics pre-computed to aid filtering |
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- ASCII-only guarantee for the English dataset |
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## Key improvements from previous version |
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- Improved evaluation scores due to the increased training epochs |
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- Pruning and optimization of the tokenizer resulting in vocabulary size from 4096 to 4019 |
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- Model training checkpoints are stored periodically in wandb for further research |
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