plimb/gladios-tiny.story-0.1B

<p align="center"> <img src="logo.png" alt="Gladios Tiny Story logo" width="200"/> </p>

<h1 align="center">gladios-tiny.story-0.1B</h1>

<p align="center"> <a href="https://huggingface.co/spaces/rusher-code/gladios-tiny-story-demo/"><img alt="Demo" src="https://img.shields.io/badge/%F0%9F%A4%97%20Demo-Gradio%20%2F%20ZeroGPU-blue"></a> <a href="https://github.com/plimb-ai/gladios-tiny-story"><img alt="GitHub" src="https://img.shields.io/badge/GitHub-code-black"></a> </p>

GGUF

A GGUF version (for llama.cpp, Ollama, LM Studio) is available thanks to mradermacher: mradermacher/gladios-tiny.story-0.1B-GGUF

🧠 Overview

gladios-tiny.story-0.1B is a decoder-only Transformer (GPT-2-style architecture, ~124M / 0.1B parameters) trained from scratch on the full TinyStories dataset (~2.1 million children's stories, ~470M tokens).

The model uses the GPT-2 tokenizer (50,257-token vocabulary) and generates short, coherent stories in English, in the style of the training data.

👉 Try it without installing anything: Gradio demo (ZeroGPU) 👉 Full training code: github.com/plimb-ai/gladios-tiny-story

🏗️ Architecture

🚀 Usage

from transformers import pipeline

gen = pipeline("text-generation", model="plimb/gladios-tiny.story-0.1B")
print(gen(
    "Once upon a time",
    max_new_tokens=200,
    do_sample=True,
    temperature=0.8,
    top_k=50,
)[0]["generated_text"])

Or directly with AutoModelForCausalLM:

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model = AutoModelForCausalLM.from_pretrained("plimb/gladios-tiny.story-0.1B")
tok = AutoTokenizer.from_pretrained("plimb/gladios-tiny.story-0.1B")

ids = tok("Once upon a time", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=200, do_sample=True, temperature=0.8, top_k=50,
                      pad_token_id=tok.eos_token_id)
print(tok.decode(out[0], skip_special_tokens=True))

📚 Training data

• —Dataset: roneneldan/TinyStories (full train split)
• —Tokenizer: GPT-2, with each story separated by the special <|endoftext|> token
• —Trained on the entire dataset (no subsampling)

⚠️ Limitations

• —Only writes short children's stories, in English
• —No general world knowledge, no instruction-following, no conversational ability
• —May hallucinate or lose coherence on prompts far outside the TinyStories style

📄 License

MIT