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Ethical Specialized Intelligence (ESI)

Ethos-32B

SustainAI.global's specialized Ethical Specialized Intelligence model — built on the Qwen3-32B-FP8 foundation and carefully fine-tuned to advance human flourishing and ecological regeneration.

Foundation

Qwen3-32B-FP8

Fine-tuning

QLoRA · Unsloth

Access

Private model

Transparency

Radical & public

Overview

A specialist, not a generalist

Unlike general-purpose AGI models, Ethos-32B is deliberately specialized — it focuses deeply on a select group of high-impact domains rather than attempting to be everything to everyone.

This focus enables stronger reasoning, better alignment, and more practical value for the educators, researchers, community organizations, and sustainable businesses we serve.

Core Expertise

Where Ethos-32B goes deep

  • Sustainable economics and green finance

  • Renewable energy systems and project finance

  • Just energy transition and workforce development

  • Ethical AI governance and responsible technology deployment

  • Impact investing and regenerative business models

  • Critical infrastructure, networks, and security — with a sustainability lens

Ethical Foundation

Guided by a strong moral compass

Ethos-32B is explicitly trained to prioritize:

Human dignity

Meaningful job creation over pure automation.

Planetary health

Biodiversity protection and ecosystem regeneration.

Animal welfare

Considerations woven into land-use and energy decisions.

Long-term thinking

Systems-level impacts over short-term gains.

Transparency, humility & intellectual honesty

The model acknowledges its limits and reasons in the open.

Development Approach

Private control. Public transparency.

Ethos-32B remains a private model under SustainAI's direct control. This allows us to maintain strict ethical standards, careful development practices, and alignment with our non-profit mission.

While the model itself is not publicly released, we are committed to radical transparency about its development.

We openly share

  • The model's system prompt and core principles
  • Training methodology (QLoRA fine-tuning with Unsloth)
  • High-level dataset composition and data philosophy
  • Evaluation results and limitations

This balanced approach ensures Ethos remains a trustworthy tool for education, policy research, community organizations, and sustainable businesses.

Purpose

Why Ethos-32B exists

To support SustainAI's mission: building AI systems that create more meaningful jobs than they displace, accelerate the renewable energy transition, and promote genuine human and ecological flourishing.