Tiny Stories
Collection
30M and 125M GPT-2 models pre-trained and instruction fine tuned on TinyStories dataset.
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GPT-2 125M — TinyStories
A 125M parameter GPT-2 model trained from scratch on the TinyStoriesV2 (cleaned) dataset. Built as a learning project to understand PyTorch and transformer architectures deeply.
Model Details
| Parameter | Value |
|---|---|
| Parameters | ~125M |
| Vocabulary | 50,257 (GPT-2 tiktoken) |
| Context Length | 512 |
| Embedding Dim | 768 |
| Attention Heads | 12 |
| Transformer Layers | 12 |
| Dropout | 0.1 |
| Activation | GELU |
Architecture: Token + positional embeddings → Dropout → 12x Transformer blocks (pre-norm, residual connections) → LayerNorm → Linear output
Training
| Metric | Value |
|---|---|
| Dataset | TinyStoriesV2 (cleaned) |
| Epochs | 2 |
| Batch Size | 32 |
| Learning Rate | 3e-4 |
| Final Train Loss | 1.103 |
| Final Val Loss | 1.06 |
| Hardware | NVIDIA H100 80GB |
Usage
This is a custom PyTorch model (not a transformers-compatible model). You need the source code from the GitHub repository to load it.
Setup
# Clone the repository with the model code
git clone https://github.com/aryandeore/monday_morning_moral.git
cd monday_morning_moral
uv sync
See the GitHub repository for usage examples and the full API reference.
Limitations
- Trained only on TinyStories — generates simple children's stories, not general text
- No instruction tuning — does not follow prompts or answer questions
- Only trained for 2 epochs — could benefit from more training
- English only
Source Code
Full implementation: github.com/aryandeore/monday_morning_moral
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