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warvan-ml-model

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Model Card for Warvan-ML-Model

This model is a machine learning model developed for general-purpose AI tasks, including natural language processing and image recognition. It has been optimized for high efficiency and accuracy.

Model Details

Model Description

The Warvan-ML-Model is designed for high-performance AI tasks. It leverages deep learning techniques and has been trained on diverse datasets to ensure robustness.

  • Developed by: Warvan
  • Funded by: Self-funded
  • Shared by: Warvan
  • Model type: Deep Learning Model (Transformer-based for NLP, CNN-based for vision tasks)
  • Language(s) (NLP): English, Indonesian
  • License: MIT License
  • Finetuned from model: Custom-trained architecture based on OpenAI GPT and Vision Transformer

Model Sources

Uses

Direct Use

This model can be used for:

  • Text generation
  • Sentiment analysis
  • Image classification
  • Object detection

Downstream Use

The model can be fine-tuned for:

  • Chatbots and virtual assistants
  • Personalized recommendation systems
  • Autonomous navigation
  • Healthcare diagnostics

Out-of-Scope Use

This model should not be used for:

  • Generating misleading or harmful content
  • Biased decision-making without human oversight
  • Unauthorized surveillance

Bias, Risks, and Limitations

Bias

The model may exhibit biases present in the training data, especially in sentiment analysis and language generation.

Risks

  • Potential misclassification in image recognition
  • Hallucinations in NLP tasks
  • Ethical concerns in decision-making

Limitations

  • Requires a high-end GPU for real-time inference
  • Limited support for low-resource languages

Recommendations

Users should validate model outputs before deployment and use additional fairness measures where applicable.

How to Get Started with the Model

Use the following code to load and run the model:

from transformers import AutoModel, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("warvan/warvan-ml-model")
model = AutoModel.from_pretrained("warvan/warvan-ml-model")

def generate_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs)
    return tokenizer.decode(outputs[0])

print(generate_text("Hello, how are you?"))

Training Details

Training Data

  • Text data sourced from open-domain datasets
  • Image data collected from public repositories

Training Procedure

Preprocessing

  • Tokenization for text
  • Normalization and augmentation for images

Training Hyperparameters

  • Batch Size: 64
  • Learning Rate: 3e-5
  • Optimizer: AdamW
  • Training Steps: 500,000

Speeds, Sizes, Times

  • Model Size: 2.3GB
  • Training Time: 20 days on 8x A100 GPUs
  • Inference Speed: ~30ms per token generation

Evaluation

Testing Data, Factors & Metrics

Testing Data

  • Standard NLP and vision benchmarks (GLUE, ImageNet, COCO)

Factors

  • Performance across different demographics
  • Variation in accuracy based on data distribution

Metrics

  • NLP: BLEU, ROUGE, Perplexity
  • Vision: Top-1 and Top-5 accuracy

Results

  • NLP Perplexity: 15.4
  • Image Classification Accuracy: 92.5% (Top-1)

Model Examination

The model was evaluated using Explainable AI techniques, and attention heatmaps were analyzed for bias detection.

Environmental Impact

  • Hardware Type: NVIDIA A100 GPUs
  • Hours used: 480 GPU hours
  • Cloud Provider: AWS
  • Compute Region: US-West
  • Carbon Emitted: ~200 kg CO2eq

Technical Specifications

Model Architecture and Objective

  • Transformer-based for NLP
  • CNN-based for vision tasks

Compute Infrastructure

Hardware

  • 8x NVIDIA A100 GPUs
  • 256GB RAM

Software

  • PyTorch 2.0
  • TensorFlow 2.8
  • Hugging Face Transformers 4.28

Citation

BibTeX:

@article{warvan2025,
  title={Warvan-ML-Model: A Versatile AI Model for NLP and Computer Vision},
  author={Warvan},
  year={2025},
  journal={Journal of AI Research}
}

APA: Warvan. (2025). Warvan-ML-Model: A Versatile AI Model for NLP and Computer Vision. Journal of AI Research.

Glossary

  • Transformer: A deep learning architecture used in NLP models.
  • CNN (Convolutional Neural Network): A neural network for image processing.
  • Perplexity: A metric to measure the fluency of language models.

More Information

For further inquiries, please visit our GitHub Repository.

Model Card Authors

  • Warvan

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