# v2 Model Card

## Overview

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|>"
}
```

## Usage

### Loading a Model

You can load a model and its tokenizer as follows:

```python
from transformers import GPT2LMHeadModel, GPT2Tokenizer
model_name = "v2/story/small" # Change to your desired model path
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
```


### Generating Text

To generate text using the loaded model, use the following code:

```python
input_text = "Once upon a time"
input_ids = tokenizer.encode(input_text, return_tensors="pt")
output = model.generate(input_ids, max_length=50, do_sample=True)
generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(generated_text)
```


## 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.