VTuber Tag Generation Model
A fine-tuned LoRA model based on Gemma 3 1B for generating VTuber-related tags and completions.
Model Details
Model Description
This model is fine-tuned to generate VTuber-related tags and tag completions. It takes a partial prompt of tags and generates additional relevant tags in the style commonly used for VTuber content tagging.
- Developed by: kayfahaarukku
- Model type: Text Generation (Tag Completion)
- Language(s) (NLP): English (Tags)
- License: Gemma
- Finetuned from model: google/gemma-3-1b-it
Uses
Direct Use
This model is intended for generating VTuber-related tags and tag completions. Users can input partial tag lists and the model will suggest additional relevant tags.
Out-of-Scope Use
This model is specifically trained for VTuber tag generation and may not perform well on general text generation tasks or other domain-specific tagging systems.
Bias, Risks, and Limitations
The model is trained on VTuber-related content and may reflect biases present in the training data. It may generate NSFW content as part of its tag suggestions.
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.
[More Information Needed]
Training Details
Training Data
The model was trained on a dataset of approximately 450,000 VTuber-related tag examples.
Training Procedure
The model was fine-tuned using PEFT (Parameter Efficient Fine-Tuning) with LoRA adapters for 8 epochs.
Training Hyperparameters
- Training regime: [More Information Needed]
- Epochs: 8
- Base Model: google/gemma-3-1b-it
- Fine-tuning Method: PEFT (LoRA)
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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Environmental Impact
Carbon emissions can be estimated using the
Machine Learning Impact calculator presented in
Lacoste et al. (2019).
- Hardware Type: [More Information Needed]
- Hours used: [More Information Needed]
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Technical Specifications [optional]
Model Architecture and Objective
Based on Gemma 3 1B architecture with LoRA adapters for parameter-efficient fine-tuning.
Compute Infrastructure
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Hardware
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Software
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Citation [optional]
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APA:
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Glossary [optional]
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Model Card Authors [optional]
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Model Card Contact
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Framework versions
