| # Model Card for V2 Models | |
| ## Model Description | |
| This repository contains multiple models trained using the GPT-2 architecture for generating creative stories, superhero names, and abilities. The models are designed to assist in generating narrative content based on user prompts. | |
| ## Model Variants | |
| - **Story Model**: Generates stories based on prompts. | |
| - **Name Model**: Generates superhero names based on story context. | |
| - **Abilities Model**: Generates superhero abilities based on story context. | |
| - **Midjourney Model**: Generates mid-journey prompts for storytelling. | |
| ## Training Data | |
| The models were trained on a custom dataset stored in `batch_ds_v2.txt`, which includes various story prompts, superhero names, and abilities. The dataset was preprocessed to extract relevant parts for training. | |
| ## Training Procedure | |
| - **Framework**: PyTorch with Hugging Face Transformers | |
| - **Model**: GPT-2 | |
| - **Training Arguments**: | |
| - Learning Rate: 1e-4 | |
| - Number of Epochs: 15 | |
| - Max Steps: 5000 | |
| - Batch Size: Auto-detected | |
| - Gradient Clipping: 1.0 | |
| - Logging Steps: 1 | |
| ## Evaluation | |
| The models were evaluated based on their ability to generate coherent and contextually relevant text. Specific metrics were not provided, but qualitative assessments were made during development. | |
| ## Inference | |
| To use the models for inference, you can send a POST request to the `/generate/<model_path>` endpoint of the Flask application. The input should be a JSON object containing the `input_text` key. | |
| ### Example Request | |
| ``` | |
| json | |
| { | |
| "input_text": "[Ivan Ivanov, Lead Software Engineer, Superhero for Justice, Writing code, fixing issues, solving problems, Masculine, Long Hair, Adult]<endoftext>" | |
| } | |
| ``` | |
| ### Example Response | |
| The response will contain the generated text based on the input prompt. | |
| ## Limitations | |
| - The models may generate biased or nonsensical outputs based on the training data. | |
| - They may not always understand complex prompts or context, leading to irrelevant or inaccurate responses. | |
| - The models are sensitive to input phrasing; slight changes in the prompt can yield different results. | |
| ## License | |
| This model is released under the MIT License. Please refer to the LICENSE file for more details. | |
| ## Citation | |
| If you use this model in your research or applications, please cite it as follows: |