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--- |
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license: mit |
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datasets: |
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- crumb/flan-ul2-tinystories |
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language: |
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- en |
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--- |
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# Tinystories-30m-UL2 |
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*GPT-4 generated model card* |
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## Model Details |
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- **Model Name**: [crumb/opentinystories-30m-base](https://huggingface.co/crumb/opentinystories-30m-base) |
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- **Model Type**: GPTNeoXForCausalLM |
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- **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." |
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## Model Description |
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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. |
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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. |
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## Training |
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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. |
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Training arguments: |
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``` |
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per_device_train_batch_size=16, |
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gradient_accumulation_steps=8, |
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warmup_steps=128, |
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num_train_epochs=4, |
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learning_rate=2e-4, |
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eval_steps=64, |
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optim="adamw_torch", |
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``` |
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## Usage |
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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. |
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## Validation and Performance |
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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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