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)
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.
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}
}
