ORDINATIVE SCIENCES EDUCATION · Repository ufficiale

TE Ordinative LoRA

Fabio Ghioni · Copia del 2026-09-18

TE Ordinative LoRA

This repository contains the code and dataset to fine-tune an open-source LLM (such as Llama-3-8B-Instruct or Mistral) into a TE-compliant Ordinative Agent.

Part of a Larger Ecosystem

This repository is one piece of a four-part framework. For the complete picture, see:

Repository Purpose What you'll find there
ordinative_sciences_framework Theory The complete TE framework, core ontology, and operational modules.
te-ordinative-lora Practice Code, datasets, and scripts to fine-tune an LLM into a TE-compliant ordinative agent.
te-oct-framework-en Validation English mirror of the core framework, plus OCT datasets and benchmarks.
te-ordinative-algebras-en Algebras Semantic Algebra (SA) and Proportional Algebra (PA) — the analytical operators and the proportional space they live in.

Important: these repositories are designed to work together. Reading one in isolation can lead to incomplete understanding.

For a full map, see ECOSYSTEM.md.

Goal

Current autoregressive models suffer from Statistical Attenuation (tendency to present "balanced" but structurally contradictory views) and Biomechanical Compliance (agreeing with the user even when the user's premise is flawed).

By fine-tuning with the TE_CORE and BOOTLOADER principles via QLoRA, we aim to teach the model how to:

  1. Identify the Projective Void in a prompt.
  2. Intercept Demonization or Hagiographic biases.
  3. Automatically apply the Controfase (Phase-Shift) sequence before generating the output.
  4. Elevate the internal analysis dimension to the Integral Human (LENS) and Power Pattern (P-PRO) readings.

Structure

  • dataset/ : Contains the JSONL files for instruction-tuning. The data consists of {"messages": [{"role": "user", "content": "..."}, {"role": "assistant", "content": "..."}]} formatted according to the ChatML standard. The assistant responses show the "pause" mechanism of the Controfase and the structural answer.
  • train_lora_unsloth.py : Script leveraging the Unsloth library to efficiently train the LoRA adapter on a consumer GPU.
  • evaluate.py : Scripts to test the trained adapter against the P1-P4 Verification Protocols (from the Ordinative_Set_Theory document).

Next Steps

  1. Expand the dataset/sample_TE_instruct.jsonl into a full 1000+ example dataset using automated amplification generation from the TE texts.
  2. Select the optimal base model.
  3. Train the adapter and evaluate its Ordinative Weight (Ω).