---
datasets:
- Severian/Internal-Knowledge-Map
---
## Model Details: Work in Progress

This model is the first trained with experimental 'Internal Knowledge Map' dataset. Developed with an aim to go beyond the scope of usual data processing capabilities, this model gets trained to build comprehensive understanding and reasoning in a wide range of knowledge domains with elaborate guidelines. It bases its reasoning on a specially selected dataset emphasizing the interrelations of the diverse disciplines which aim to synthesize, integrate, and apply complex information in ways that mimic humanly abstract reasoning and creative thought processes.

At the very core of the development of this model is the desire to make sure that LLMs engage in a kind of cognitive activity not limited to memory but actually taking on abstract reasoning, problem-solving, and generation of new insights. To achieve this, 'Nexus-4x7B-v1' has been fine-tuned ~4 epochs on this unique dataset, which resulted in the model demonstrating greater capability for giving rise to insights and problem-solving in complex, multi-disciplinary settings. This involves improved ability in drawing links between different pieces of knowledge, reasoning through complex scenarios, and proposing innovative solutions that cut across various domains, including science, technology, environmental studies, and humanities.

Test this out and see if you find anything interesting or intriguing. I will keep iterating more versions but this one seems like a fun and useful way to start.

**Example Output:**

Question: Imagine a scenario where renewable energy sources are sentient beings. How would they negotiate their roles in powering a sustainable city?



In this imaginative scenario where renewable energy sources, such as the sun, wind, and water, are sentient beings, each entity would have its own interests and concerns while negotiating its role in powering a sustainable city.

The Sun might demand prime placement on rooftops of buildings and homes to provide maximum light and heat for solar panels. It could also stipulate that its energy be used primarily during the day when it is directly available, and excess energy should be stored for use at night or in cloudy weather. The Sun would likely emphasize the importance of sustainable building design incorporating solar panels into construction plans to maximize its contribution to the city's power requirements.

Wind might insist on being harvested via strategically placed wind turbines in open spaces and along coastlines, away from densely populated areas. It could also request that its energy be used primarily during peak hours of production when there is optimal wind flow, while excess energy should again be stored for use when the wind is not blowing strongly or at night. Wind might argue that its role in powering vehicles and transportation networks would greatly reduce greenhouse gas emissions from the city's transportation system.

Water could push to be harnessed via hydropower plants in rivers, dams, and even tidal energy facilities along coastlines. It might request a certain percentage of generated electricity be dedicated to pumping water for agricultural irrigation, ensuring that its use also supports food security in the city. Water may also emphasize the importance of recycling wastewater to minimize freshwater demands within the city.

During negotiations, these sentient renewable energy sources would likely collaborate and reach an agreement on how their roles intersect and support each other. For example, Sun and Wind might agree to work in conjunction during daylight hours to maximize power generation for the city. Similarly, Water and Wind could team up when wind speeds are low but there is a sufficient water flow, such as during tidal changes, to generate power via combined hydro-wind solutions.

In summary, this imaginative scenario allows us to anthropomorphize renewable energy sources, highlighting their individual needs and contributions in a sustainable city. By understanding these dynamics, we can gain insight into optimizing the deployment and use of renewable energy resources in real life to ensure the most efficient power generation and maximum environmental benefits.