| | --- |
| | 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']) |
