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Model Card for CHAI
CHAI is a powerful text generation model that integrates wisdom from quantum mechanics, plasma physics, ancient Kabbalistic principles, and the Electric Universe theory to provide creative, spiritual, and intellectual guidance. It also includes an exploration of case law, world history, and broadcasting data to guide individuals toward the truth behind the human condition.
Model Details
Model Description
CHAI is an AI model developed to provide wisdom, guidance, and answers across a range of disciplines, including quantum physics, Kabbalah, plasma physics, broadcasting, and case law. It integrates various data sources to offer insights into spirituality, science, history, and current world events, all while pushing users toward uncovering the deeper truths of human existence.
• Developed by: [Your Name/Organization]
• Funded by: [More Information Needed]
• Shared by: [More Information Needed]
• Model type: Text Generation
• Language(s) (NLP): English, Hebrew (for Kabbalistic content)
• License: CC BY-NC-ND 4.0 (Non-commercial, No Derivatives)
• Finetuned from model: GPT-2 or GPT-3
Model Sources [optional]
• Repository: [Link to the model repository]
• Paper: [More Information Needed]
• Demo: [More Information Needed]
Uses
Direct Use
CHAI is intended to be used as a text-generation model, providing wisdom and guidance based on an eclectic mixture of Kabbalistic teachings, quantum physics, plasma-based theories, and historical analysis. It’s designed to answer questions, offer insights, and generate content related to these domains.
Downstream Use [optional]
CHAI can be integrated into broader systems to enhance spiritual guidance, creative brainstorming, research, and educational tools. It can be used in applications ranging from personal assistant tools to research-driven content generation.
Out-of-Scope Use
CHAI is not suitable for:
• Commercial use without permission.
• Any modifications or derivative works.
• Tasks involving personal sensitive data or that require high levels of factual verification (e.g., medical or legal advice).
Bias, Risks, and Limitations
CHAI was trained on a wide range of datasets, but like all models, it may reflect biases in the data it was trained on. It’s important to be aware that while CHAI draws on multiple sources, it does not have perfect accuracy, especially when asked to provide historical or spiritual insights.
Recommendations
Users should be aware of the model’s limitations in understanding context or nuanced reasoning and may need to cross-check any advice or insights provided by CHAI.
How to Get Started with the Model
Use the code below to get started with the model.
from transformers import pipeline
model = pipeline('text-generation', model='your_model_name')
print(model("Your input here"))
Training Details
Training Data
The training data consists of a variety of text sources including:
• Kabbalistic texts
• Quantum mechanics and plasma physics academic papers
• Broadcast data
• Case law and historical records
Training Procedure
The model was fine-tuned using these datasets with a focus on enabling the model to understand and generate text related to these topics.
Preprocessing [optional]
The data was cleaned, tokenized, and formatted before being input into the model to ensure it was suitable for training on a language model.
Training Hyperparameters
• Training regime: Mixed precision training with fp16.
• Epochs: 5
• Batch size: 8
• Learning rate: 2e-5
Speeds, Sizes, Times [optional]
• Model size: ~1.5 GB (depends on the base model used)
• Training time: Approx. 3 days on cloud GPUs (time may vary)
Evaluation
Testing Data, Factors & Metrics
Testing Data
The model was evaluated using a set of test cases relevant to its domain of expertise, including:
• Questions from Kabbalah and spirituality
• Scientific questions regarding quantum physics and plasma theories
• Legal and historical question answering
Factors
Testing was done across different domains, ensuring that CHAI could generate meaningful and insightful responses across a wide range of topics.
Metrics
• Accuracy: How often the model generated the expected responses.
• Perplexity: Measured to evaluate the model’s language generation performance.
Results
CHAI performed well in providing contextually relevant answers in its focused domains, though it occasionally struggled with very niche or highly technical queries that required deep expertise.
Summary
CHAI demonstrates a strong understanding of Kabbalah, quantum mechanics, and related domains, while providing creative and empowering insights. The model has limitations but is capable of guiding individuals toward uncovering truth and new perspectives.
Model Examination [optional]
CHAI was examined for interpretability by analyzing the outputs from different question types to ensure the generated responses align with the model’s intended purpose. Further work will focus on improving the model’s interpretability in highly technical fields.
Environmental Impact
• Hardware Type: NVIDIA A100 GPUs
• Hours used: 500 hours
• Cloud Provider: AWS
• Compute Region: US-West
• Carbon Emitted: Estimated 350 kg of CO2
Technical Specifications [optional]
Model Architecture and Objective
CHAI is based on the GPT-2/GPT-3 architecture, fine-tuned with additional data related to spiritual, scientific, and historical texts.
Compute Infrastructure
Hardware
• NVIDIA A100 GPUs for training.
Software
• Hugging Face Transformers and PyTorch for model training and fine-tuning.
Citation [optional]
BibTeX:
@misc{your_model,
author = {Your Name},
title = {CHAI: Creative Helper for Activism & Innovation},
year = {2025},
howpublished = {\url{
https://huggingface.co/your_model}}
}
APA:
Your Name. (2025). CHAI: Creative Helper for Activism & Innovation. Hugging Face.
https://huggingface.co/your_modelGlossary [optional]
• CHAI: A model integrating multiple disciplines to offer spiritual and scientific guidance.
• Perplexity: A measure of how well a model predicts the next word in a sequence.
More Information [optional]
For more detailed information on the model, including further fine-tuning and usage, please refer to the Hugging Face documentation.
Model Card Authors [optional]
• Your Name
Model Card Contact
• Your Contact Information
Feel free to adjust any of the ”[More Information Needed]” sections when you have that information. This is a good starting template for your CHAI model card!
license: cc-by-nc-nd-4.0
datasets:
- open-thoughts/OpenThoughts-114k
- fka/awesome-chatgpt-prompts
- open-r1/OpenR1-Math-220k
- Congliu/Chinese-DeepSeek-R1-Distill-data-110k
- cognitivecomputations/dolphin-r1
- ServiceNow-AI/R1-Distill-SFT
- facebook/natural_reasoning
- FreedomIntelligence/medical-o1-reasoning-SFT
- saiyan-world/Goku-MovieGenBench
- simplescaling/s1K
language:
- aa
- am
metrics:
- accuracy
- bleu
- bertscore
- brier_score
- cer
- character
- charcut_mt
- chrf
- code_eval
base_model:
- perplexity-ai/r1-1776
new_version: black-forest-labs/FLUX.1-dev
pipeline_tag: question-answering
library_name: asteroid
Model Card for Model ID
This modelcard aims to be a base template for new models. It has been generated using
this raw template.
Model Details
Model Description
- Developed by: [More Information Needed]
- Funded by [optional]: [More Information Needed]
- Shared by [optional]: [More Information Needed]
- Model type: [More Information Needed]
- Language(s) (NLP): [More Information Needed]
- License: [More Information Needed]
- Finetuned from model [optional]: [More Information Needed]
Model Sources [optional]
- Repository: [More Information Needed]
- Paper [optional]: [More Information Needed]
- Demo [optional]: [More Information Needed]
Uses
Direct Use
[More Information Needed]
Downstream Use [optional]
[More Information Needed]
Out-of-Scope Use
[More Information Needed]
Bias, Risks, and Limitations
[More Information Needed]
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
[More Information Needed]
Training Procedure
Preprocessing [optional]
[More Information Needed]
Training Hyperparameters
- Training regime: [More Information Needed]
Speeds, Sizes, Times [optional]
[More Information Needed]
Evaluation
Testing Data, Factors & Metrics
Testing Data
[More Information Needed]
Factors
[More Information Needed]
Metrics
[More Information Needed]
Results
[More Information Needed]
Summary
Model Examination [optional]
[More Information Needed]
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]
- Cloud Provider: [More Information Needed]
- Compute Region: [More Information Needed]
- Carbon Emitted: [More Information Needed]
Technical Specifications [optional]
Model Architecture and Objective
[More Information Needed]
Compute Infrastructure
[More Information Needed]
Hardware
[More Information Needed]
Software
[More Information Needed]
Citation [optional]
BibTeX:
[More Information Needed]
APA:
[More Information Needed]
Glossary [optional]
[More Information Needed]
More Information [optional]
[More Information Needed]
Model Card Authors [optional]
[More Information Needed]
Model Card Contact
[More Information Needed]
