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--- |
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language: |
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- en |
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tags: |
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- gpt2 |
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- text-generation |
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- story-generation |
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- creative-writing |
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- TinyStories |
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- ai-story |
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license: mit |
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datasets: |
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- roneneldan/TinyStories |
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metrics: |
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- perplexity |
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model-index: |
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- name: tiny-stories-gpt2 |
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results: [] |
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--- |
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# 🧚♀️ Tiny Stories GPT-2 |
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**Author:** [Fathi7ma](https://huggingface.co/Fathi7ma) |
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**Model type:** Fine-tuned GPT-2 for creative story generation |
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**Dataset:** [roneneldan/TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) |
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--- |
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## 🧠 Model Overview |
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This model is a fine-tuned version of **GPT-2 (small)** trained on the [TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories) dataset — a large collection of short, simple stories for children. |
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The goal of this project is to create a lightweight, fun, and creative **story generator** that can produce short, imaginative stories from user prompts. |
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--- |
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## ✨ Intended Use |
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Use this model for: |
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- Generating **short, creative stories** |
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- Educational or entertainment purposes |
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- Quick writing inspiration or kids’ content |
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Example use: |
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```python |
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from transformers import pipeline |
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generator = pipeline("text-generation", model="Fathi7ma/tiny-stories-gpt2") |
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prompt = "A tiny robot who wanted to fly" |
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print(generator(prompt, max_length=80, num_return_sequences=1)[0]["generated_text"]) |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: linear |
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- num_epochs: 3 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 1.9127 | 1.0 | 2375 | 1.7789 | |
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| 1.7876 | 2.0 | 4750 | 1.7186 | |
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| 1.6976 | 3.0 | 7125 | 1.6995 | |
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### Framework versions |
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- Transformers 4.57.1 |
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- Pytorch 2.8.0+cu126 |
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- Datasets 4.0.0 |
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- Tokenizers 0.22.1 |
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