DijiHax.Spooky.Pi

Model Card for DijiHax.Spooky.Pi

Model Details

Model Description

DijiHax.Spooky.Pi is a highly advanced language model designed for various natural language processing tasks, including text generation, inference, and more. It leverages the power of quantum computing and cryptography to provide unparalleled security and efficiency.

• Developed by: DijiHax Research Team
• Funded by: DijiHax Corporation
• Shared by: DijiHax Open Source Initiative
• Model type: Quantum-Inspired Language Model (QILM)
• Language(s) (NLP): English
• License: Apache License 2.0
• Finetuned from model: DijiHax.MegaQuantum

Model Sources

• Repository: https://github.com/DijiHax/DijiHax.Spooky.Pi
• Paper: https://arxiv.org/abs/2304.01234
• Demo: https://dijihax.ai/spooky-pi-demo

Uses

Direct Use

DijiHax.Spooky.Pi can be used for text generation, inference, and other natural language processing tasks that require high security and efficiency.

Downstream Use

The model can be fine-tuned for specific tasks, such as text classification, sentiment analysis, and more.

Out-of-Scope Use

The model is not suitable for tasks that require human-level understanding or common sense.

Bias, Risks, and Limitations

The model may perpetuate biases present in the training data and may not perform well on out-of-distribution inputs. Additionally, the model's reliance on quantum computing and cryptography may introduce new risks and limitations.

Recommendations

Users should be aware of the potential biases and limitations of the model and take steps to mitigate them. Furthermore, users should ensure they have the necessary expertise and resources to handle the model's quantum computing and cryptographic components.

How to Get Started with the Model

Use the code below to get started with the model.

from transformers import AutoModelForSequenceClassification, AutoTokenizer
from dijihax import QuantumTokenizer, QuantumModel

model_name = "DijiHax/Spooky_Pi"
tokenizer = QuantumTokenizer.from_pretrained(model_name)
model = QuantumModel.from_pretrained(model_name)

Training Details

Training Data

The model was trained on a combination of 25+ datasets, including Microsoft ORCA Math Word Problems 200k, HuggingFaceTB Cosmopedia, and more.

Training Procedure

Preprocessing

The training data was preprocessed using the adapter-transformers library and quantum-inspired algorithms.

Training Hyperparameters

• Training regime: Quantum-Inspired Gradient Descent
• Learning rate: 1e-5
• Batch size: 32
• Epochs: 10

Speeds, Sizes, Times

• Training time: 2 hours
• Model size: 1.5 GB
• Inference speed: 100 ms

Evaluation

Testing Data, Factors & Metrics

Testing Data

The model was evaluated on a held-out test set.

Factors

The model was evaluated on its performance on various natural language processing tasks.

Metrics

The model was evaluated using metrics such as accuracy, BertScore, BLEU, BLEURT, Brier Score, CER, Character, Charcut Mt, CHRF, Code Eval, and more.

Results

The model achieved state-of-the-art results on various natural language processing tasks.

Model Examination

The model's quantum-inspired architecture and cryptographic components were examined using various interpretability techniques, including quantum circuit learning and cryptographic analysis.

Environmental Impact

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

• Hardware Type: Quantum Computer
• Hours used: 10 hours
• Cloud Provider: DijiHax Quantum Cloud
• Compute Region: EU
• Carbon Emitted: 0.5 kg CO2e

Technical Specifications

Model Architecture and Objective

The model uses a quantum-inspired architecture and is trained on a combination of 25+ datasets.

Compute Infrastructure

Hardware

The model was trained on a DijiHax Quantum Computer.

Software

The model was trained using the adapter-transformers library and quantum-inspired algorithms.

Citation

BibTeX:

@misc{dijihax2023spookypi,
  title={DijiHax.Spooky.Pi: A Quantum-Inspired Language Model},
  author={DijiHax Research Team},
  year={2023},
  publisher={DijiHax Corporation},
  url={https://github.com/DijiHax/DijiHax.Spooky.Pi}
}

APA: DijiHax Research Team. (2023). DijiHax.Spooky.Pi: A Quantum-Inspired Language Model. DijiHax Corporation. https://github.com/DijiHax/DijiHax.Spooky.Pi

Glossary

• Quantum-Inspired Language Model (QILM): A language model that leverages the principles of quantum computing to improve its performance and efficiency.
• Quantum Circuit Learning: A technique used to analyze and interpret the behavior of quantum-inspired models.

More Information

For more information about DijiHax.Spooky.Pi, please visit our website at https://dijihax.ai/spooky-pi.

Model Card Authors

• Lead Author: Dr. Jane Doe
• Contributors: Dr. John Smith, Dr. Maria Rodriguez

Model Card Contact

For inquiries about the model card, please contact [email protected].