What are the hardware requirements for SimpleStories 35M?

SimpleStories 35M requires approximately 0.1 GB of VRAM and supports a context window of 0.5K tokens. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed SimpleStories 35M and how large is it?

SimpleStories 35M is developed by SimpleStories, a model with 35m parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

Where can I download or evaluate SimpleStories 35M?

SimpleStories 35M is hosted on Hugging Face and linked from this page. LLM Explorer also lists quantized variants and similar alternatives if available.

SimpleStories 35M by SimpleStories — VRAM 0.1GB, 0.5K context

Name: SimpleStories 35M
Author: SimpleStories

SimpleStories 35M is an open-source language model by SimpleStories. Features: 35m LLM, VRAM: 0.1GB, Context: 0.5K, License: mit, LLM Explorer Score: 0.18.

Arxiv:2504.09184 Dataset:lennart-finke/simplest... Distilled-models Efficient-nlp En Llama Region:us Safetensors Small-language-model Story-generation

Model Card on HF 🤗: https://huggingface.co/SimpleStories/SimpleStories-35M

SimpleStories 35M Parameters and Internals

LLM Name	SimpleStories 35M
Repository 🤗	https://huggingface.co/SimpleStories/SimpleStories-35M
Model Size	35m
Required VRAM	0.1 GB
Updated	2026-08-01
Maintainer	SimpleStories
Model Type	llama
Model Files	0.1 GB
Supported Languages	en
Model Architecture	LlamaForCausalLM
License	mit
Context Length	512
Model Max Length	512
Transformers Version	4.48.3
Tokenizer Class	PreTrainedTokenizerFast
Vocabulary Size	4096
Torch Data Type	float32

Rank the SimpleStories 35M capabilities

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Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to SimpleStories 35M

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Charllama 35M	0.5K / 0.1 GB	7	6