Redefining AI Development
Wildnerve.ai
Welcome to Wildnerve.ai, the Technical & Research paper for Wildnerve AI Platform.
The complete concept and behind of Wildnerve and how it works.
To better serve our diverse audience, we have two dedicated platforms.
Our Journey & Research
Started by building simple text based 66 million to 110 million parameters, ‘small language model’ (SLM) using PyTorch Libraries from scratch just to experiment.
This led us to exploring more and eventually discovered a potential way in which the training and inference cost can be lowered via shared inferencing technique to multiple smaller models before being re-combined into highly coherent response, hence also keeping the output quality very high.
In our journey from ideation, conceptualizing, designing the workflow to coding, etc, we invested in our effort to research and validate our idea.
Started on
Whitepaper
Whitepaper introduces a novel large language model (LLM) architecture that demonstrates significant improvements in large language model’s processing tasks, that involves inference fine tuning and predictive tasks. The proposed architecture incorporates a series of chained small Language Models (SLMs) to allow seamless transfer learning in between the connected SLMs and enabling it to work in unison. There will be several innovative techniques, including implementing a ‘main’ routing SLM that is trained on the capabilities of multiple SLMs to be capable of directive and routing service, so that when enquired through the connecting chatbot interface from user prompts, it knows which SLMs in the network that is trained on different but complementary domain specific dataset to process the prompt and respond accordingly.
Attention Mechanism
Cost Efficiency
Smaller Capacity
Performance Caveats
The Platform
How it Works
How its the Solutions going to work & technology behind it.
AppStore
AI AppStore for Developer.
SLM Hub
SLM Hub and Connectivity to Wildnerve Platform.
Business Model
The Business & Revenue model to keep it sustainable & profitable.
AI Model
TLM
Tiny Language Model with ~100 million parameters.
SLM
Small Language Model with ~4 billion parameters.
LLM
Large Language Model with ~trillion parameters.
Architecture
SETH
STDP Enhanced Transformer Hybrid
an AI Architecture—a hybrid combining the strengths of Spiking Neural Network (SNN) and Transformers to deliver a highly efficient, scalable, and adaptable solution for AI development.
Designed & optimized to run on minimal GPU resources while maintaining exceptional performance, ensuring it can be deployed on affordable and accessible hardware.
Blog
Latest From Our blog
Welcome to Wildnerve.ai
IF you're an artificial intelligence enthusiast, you have come to the right place We are a new start up, venturing into interesting domain that will determine how humanity will live in the coming decades. Feel free to browse our site, if you are a cloud hosting...
Sovereign AI
For Nation & Goverment to empower the Development of AI to benefit Country & Industry meanwhile protecting the People from harm.
1. Digital Infrastructure
State-of-the-art data centres equipped with advanced computing capabilities to process and analyze vast amounts of data efficiently. Data localization policies ensure that data generated within national borders is stored and processed locally, enhancing data sovereignty and security.
Workforce Development
A skilled workforce is critical for the advancement of AI technologies. Initiatives must focus on STEM education, encouraging students and professionals to pursue careers in AI. This includes updating all levels of education curricula to include AI and machine learning, offering vocational training programmes and facilitating lifelong learning opportunities.
3. Research, Development & Innovation (RDI)
Governments should provide incentives and allocate funds for AI research, supporting both foundational and applied research as well as commercialization of innovation. This open innovation network will engender collaboration, partnerships and investments and can eventually lead to breakthroughs that propel the nation forward in the global AI landscape.
4. Regulatory & Ethical framework
Developing a comprehensive regulatory and ethical framework involves setting clear guidelines for AI development and deployment, focusing on issues such as privacy, transparency, data protection, cybersecurity and the ethical use of AI. This framework should also include mechanisms for oversight and accountability, ensuring that AI technologies are used responsibly and for the benefit of society.
5. Stimulating AI industry
Creating a conducive environment for the growth of AI-driven businesses and applications, especially across vital sectors such as energy, healthcare, finance, transportation and manufacturing. Government incentives such as tax breaks, grants and streamlined patent processes can encourage innovation and entrepreneurship in AI. Additionally, public sector adoption of AI technologies can serve as a catalyst for growth, setting an example for efficiency and innovation.
6. International Cooperation
International cooperation remains crucial. Engaging in dialogues and partnerships with other nations can help set global standards for AI, facilitate cross-border data flows under agreed-upon norms and address shared challenges such as privacy and cybersecurity threats. Collaborating on international projects can also pool resources and expertise, accelerating progress in AI technologies for mutual benefit.