LoomAI

Model Card for LoomAI

Model Details

Model Description

LoomAI is a fine-tuned 🤗 transformers model designed to generate stories or the beginning of a story based on a user-provided prompt. This model is a key component of the Loomina project, enabling users to interact with AI to kickstart their creative writing process.

• Developed by: Shrujan
• Funded by: Self-funded
• Shared by: Shrujan
• Model type: Transformer-based language model
• Language(s) (NLP): English

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• Finetuned from model: LLAMA2

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Uses

Direct Use

LoomAI is specifically designed to generate story introductions or full stories based on prompts provided by users. It is ideal for writers seeking inspiration or anyone looking to explore creative storytelling.

Downstream Use

LoomAI can be further fine-tuned for specific genres or styles of storytelling, or integrated into applications that assist in creative writing.

Out-of-Scope Use

The model is not intended for factual content generation or use cases requiring high accuracy in information. It should not be used for generating harmful, misleading, or inappropriate content.

Bias, Risks, and Limitations

The model may generate biased or inappropriate content, as it relies on data that could include such biases. Users should carefully review outputs, especially in sensitive contexts.

Recommendations

• Implement content filters to manage the generation of inappropriate material.
• Encourage user supervision to ensure content aligns with intended use.

<!-- ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data The model was fine-tuned on a dataset curated for storytelling, including a variety of story beginnings, plots, and narrative styles to enhance its ability to generate engaging story introductions. ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> <!-- #### Preprocessing [optional] [More Information Needed] --> <!-- #### Training Hyperparameters - **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> <!-- #### Speeds, Sizes, Times [optional] --> <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> <!-- [More Information Needed] -->

Evaluation

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Testing Data, Factors & Metrics

Testing Data

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Factors

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Results

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

Model Architecture and Objective

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Model Card Authors

Shrujan

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