raccoote/angrybirds-levelcreator-ollama

Model Card for Model ID

A fine-tuned version of Llama-3.1-8B, designed to generate Angry Birds levels based on simple text descriptions. The model is trained using the Unsloth library and is optimized to produce game-level designs that can be directly imported into the official Angry Birds game.

Model Details

Model Description

This model can be used to generate new levels for the Angry Birds game using simple text inputs. Users can describe elements like "a tall tower made of wood with a pig on top" or "multiple structures with TNT boxes and glass blocks," and the model will create a level design matching the description.

• Developed by: Dimitra Pazouli
• Model type: Text Generation
• Language(s) (NLP): English
• License: Apache license 2.0
• Finetuned from: Meta-Llama-3.1-8B using the Unsloth library

Model Sources [optional]

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Uses

Direct Use

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Downstream Use [optional]

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Out-of-Scope Use

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Bias, Risks, and Limitations

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Recommendations

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

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Training Details

Training Data

The model was fine-tuned using a diverse dataset that includes:

• Existing Angry Birds levels, descriptions, and user-generated content to capture the typical structure, patterns, and elements of the game.
• Additional levels created by us to introduce new variations and elements not found in the original dataset.
• Data augmentation techniques were employed, such as creating variations of the same level with different bird types (e.g., red birds and then yellow birds), to enhance the diversity of the generated outputs.

https://huggingface.co/datasets/raccoote/angry-birds-levels

Training Procedure

Preprocessing [optional]

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Training Hyperparameters

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Speeds, Sizes, Times [optional]

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Evaluation

Testing Data, Factors & Metrics

Testing Data

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Factors

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Metrics

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Results

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Summary

Model Examination [optional]

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