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# Hugging Face v2 Models README & Model Card |
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## Overview |
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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. |
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## Model Variants |
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- **Story Model**: Generates stories based on prompts. |
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- **Name Model**: Generates superhero names based on story context. |
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- **Abilities Model**: Generates superhero abilities based on story context. |
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- **Midjourney Model**: Generates mid-journey prompts for storytelling. |
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## Training Data |
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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. |
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## Training Procedure |
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- **Framework**: PyTorch with Hugging Face Transformers |
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- **Model**: GPT-2 |
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- **Training Arguments**: |
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- Learning Rate: 1e-4 |
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- Number of Epochs: 15 |
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- Max Steps: 5000 |
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- Batch Size: Auto-detected |
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- Gradient Clipping: 1.0 |
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- Logging Steps: 1 |
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## Evaluation |
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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. |
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## Inference |
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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. |
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### Example Request |
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``` |
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{ |
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"input_text": "[Ivan Ivanov, Lead Software Engineer, Superhero for Justice, Writing code, fixing issues, solving problems, Masculine, Long Hair, Adult]<|endoftext|>" |
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} |
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``` |
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## Usage |
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### Loading a Model |
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You can load a model and its tokenizer as follows: |
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``` |
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from transformers import GPT2LMHeadModel, GPT2Tokenizer |
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model_name = "v2/story/small" # Change to your desired model path |
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model = GPT2LMHeadModel.from_pretrained(model_name) |
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tokenizer = GPT2Tokenizer.from_pretrained(model_name) |
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``` |
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### Generating Text |
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To generate text using the loaded model, use the following code: |
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``` |
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input_text = "Once upon a time" |
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input_ids = tokenizer.encode(input_text, return_tensors="pt") |
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output = model.generate(input_ids, max_length=50, do_sample=True) |
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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print(generated_text) |
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``` |
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## Limitations |
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- The models may generate biased or nonsensical outputs based on the training data. |
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- They may not always understand complex prompts or context, leading to irrelevant or inaccurate responses. |
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- The models are sensitive to input phrasing; slight changes in the prompt can yield different results. |
