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# Model Card for Model
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MTG Llama is a QLoRA fine-tuned Llama 3 model on the [MTG Eval](https://huggingface.co/datasets/jakeboggs/MTG-Eval) dataset.
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## Model Details
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- **Developed by:** Jake Boggs
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- **Model type:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **License:** [More Information Needed]
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- **Finetuned from model :** NousResearch/Meta-Llama-3-8B-Instruct
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### Model Sources [optional]
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<!-- Provide the basic links for the model. -->
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- **Repository:** [More Information Needed]
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- **Paper [optional]:** [More Information Needed]
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- **Demo [optional]:** [More Information Needed]
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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[More Information Needed]
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### Downstream Use [optional]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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[More Information Needed]
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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[More Information Needed]
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### Recommendations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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[More Information Needed]
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## Training Details
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### Training Data
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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[More Information Needed]
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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[More Information Needed]
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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##
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- jakeboggs/MTG-Eval
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# Model Card for MTG-Llama: Fine-Tuned Model for Magic: The Gathering
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## Model Details
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**Model Name:** MTG-Llama
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**Version:** 1.0
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**Base Model:** Llama 3 8B Instruct
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**Fine-Tuning Dataset:** MTG-Eval
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**Author:** Jake Boggs
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## Model Description
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MTG-Llama is a fine-tuned version of Llama 3 8B Instruct, tailored specifically for understanding and generating responses related to Magic: The Gathering (MTG). The model has been fine-tuned using a custom dataset, MTG-Eval, which includes question-answer pairs covering card descriptions, rules questions, and card interactions.
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## Intended Use
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MTG-Llama is designed to assist users with:
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- Generating deck construction ideas.
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- Answering in-game rules questions.
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- Understanding card interactions and abilities.
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## Training Data
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The fine-tuning dataset, MTG-Eval, consists of 80,032 question-answer pairs generated synthetically. The dataset is categorized into:
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- **Card Descriptions:** 26,702 examples
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- **Rules Questions:** 27,104 examples
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- **Card Interactions:** 26,226 examples
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The data was sourced from the MTGJSON project and the Commander Spellbook combo database, reformatted into natural language question-answer pairs using ChatGPT 3.5.
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## Training Procedure
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The model was fine-tuned using QLoRA with the following hyperparameters:
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- **r:** 64
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- **alpha:** 32
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- **Steps:** 75
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## Acknowledgments
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Thanks to the team at Commander Spellbook for generously sharing their dataset, without which this research would not be possible. All generated data is unofficial Fan Content permitted under the Fan Content Policy. Not approved/endorsed by Wizards. Portions of the materials used are property of Wizards of the Coast. ©Wizards of the Coast LLC.
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## Resources
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- **Dataset:** [MTG-Eval on HuggingFace](https://huggingface.co/datasets/jakeboggs/MTG-Eval)
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- **Training Code:** [GitHub Repository](https://github.com/JakeBoggs/Large-Language-Models-for-Magic-the-Gathering)
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