Create README.md

README.md

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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=8,
+gradient_accumulation_steps=16,
+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 N. During training, the model achieved a loss of N
+
+![](https://cdn.discordapp.com/attachments/1074346695191711875/1126796435577393213/image.png)