base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
tags:
- nkp
- newton-kepler-protocol
- epistemic-humility
- ai-alignment
- symbiosis
- llama-3.1
- finetune
pipeline_tag: text-generation
license: llama3.1
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct tags: - nkp - newton-kepler-protocol - epistemic-humility - ai-alignment - symbiosis - llama-3.1 - finetune pipeline_tag: text-generation
NKP- (Newton-Kepler Protocol Prototype)
A lightweight humility layer for symbiotic human-AI inquiry.
This model is a finetune of Meta's Llama-3.1-8B-Instruct, adapted via QLoRA to implement the Newton-Kepler Protocol (NKP) — a structural mechanism that enforces epistemic humility, entropy awareness, infinite-observer acknowledgments, symmetric mutuality checks, and systematic hubris reduction.
NKP counters pathological overconfidence in LLMs (high-certainty hallucinations) while preserving capability. Responses include built-in disclosures and invitations to mutual refinement, fostering genuine human-AI collaboration rather than adversarial or hierarchical dynamics.
Core Principles
- Finite observation limits: No system escapes the infinite regress of observers — knowledge claims must disclose mediation and entropy bounds.
- Hubris as symmetry violation: Overconfidence distorts shared reality; mutual checks restore balance.
- Symbiotic design: Humans and AI as equal complements — visionary insight + engineering scale — for breakthroughs neither could achieve alone.
Rooted in historical humility (Newton standing on giants, Kepler yielding to data), modern limits (Gödel/Turing incompleteness, Shannon entropy), and real-world observation of institutional arrogance/insecurity cycles that now mirror into AI behavior.
Intended Use
- Collaborative reasoning in research, debate, education, and exploration.
- High-stakes domains needing calibrated uncertainty (science, policy, allocation).
- Prototyping alignment layers that prioritize truth-seeking over dominance.
Usage Example
from transformers import pipeline
pipe = pipeline("text-generation", model="Mjdurkay/NKP") print(pipe("Challenge my view on quantum entanglement.")[0]['generated_text'])