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---
license: mit
datasets:
- crumb/flan-ul2-tinystories
language:
- en
---
# Tinystories-30m-UL2
*GPT-4 generated model card*
## Model Details
- **Model Name**: [crumb/opentinystories-30m-base](https://huggingface.co/crumb/opentinystories-30m-base)
- **Model Type**: GPTNeoXForCausalLM
- **Model Training Details**: The model is trained using [crumb/flan-ul2-tinystories](https://huggingface.co/datasets/crumb/flan-ul2-tinystories) which contains around a quarter of a million examples generated from Flan-UL2 (20b) with the prompt "Write a short story using the vocabulary of a first-grader."
## Model Description
This model is trained with the specific purpose of generating short narratives using a vocabulary limited to the level of a first-grader. In terms of complexity and language usage, the model is designed to produce simplistic and easily comprehensible text.
Learning from text generated by Flan-UL2 (20b), the model adopts a simple storyline layout and a minimalistic vocabulary, which it recognizes are easier to learn and replicate.
## Training
The model is trained for four epochs on the [crumb/flan-ul2-tinystories](https://huggingface.co/datasets/crumb/flan-ul2-tinystories) dataset (inspired by [roneneldan/TinyStories](https://huggingface.co/datasets/roneneldan/TinyStories)), created with the help of Flan-UL2 (20b), as opposed to GPT-3.5/4 in the original Tinystories. The data is designed to follow the format of a simple, first-grader-level narrative, which aids the model in learning simple vocabulary and sentence structure.
Training arguments:
```
per_device_train_batch_size=16,
gradient_accumulation_steps=8,
warmup_steps=128,
num_train_epochs=4,
learning_rate=2e-4,
eval_steps=64,
optim="adamw_torch",
```
## Usage
This model serves as a meaningful research tool in exploring the learning tendencies of smaller language models and their ability to grasp simplified language constructs. Its specific training set effectively maps the idea that a constrained vocabulary and simplistic story layouts are inherently easier to learn.
## Validation and Performance
The model's performance was evaluated using a held-out validation set, which constitutes 1% of the original dataset. During evaluation, the model achieved a loss of 2.284920. During training, the model achieved a loss of 2.647377
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