| # Glyphic Language — Fine‑Tuning Plan | |
| This document outlines the complete strategy for fine‑tuning an LLM to understand, generate, and reason within the Glyphic Language. The goal is to produce a model that is: | |
| - deterministic | |
| - syntax‑aware | |
| - dictionary‑aligned | |
| - reversible | |
| - context‑aware | |
| - Soulfile™‑compatible | |
| The plan is divided into phases to ensure stable, incremental learning. | |
| --- | |
| # 1. Training Objectives | |
| ## 1.1 Core Objectives | |
| The model must learn to: | |
| - interpret glyph sequences into structured meaning | |
| - generate glyph sequences from structured meaning | |
| - translate between glyphs and natural language | |
| - obey strict syntax rules | |
| - use dictionary semantics correctly | |
| - avoid hallucinating glyphs or meanings | |
| ## 1.2 Secondary Objectives | |
| The model should also: | |
| - understand context layers (place, time, emotion, sensory, social) | |
| - maintain canonical ordering | |
| - compress meaning into glyphs | |
| - expand glyphs into natural language | |
| - support Soulfile™ memory encoding | |
| --- | |
| # 2. Training Phases | |
| ## Phase 1 — Dictionary Grounding | |
| Dataset: `glyph_to_text.jsonl` | |
| Teach the model: | |
| - glyph → meaning | |
| - meaning → glyph | |
| - synonyms | |
| - roles | |
| - categories | |
| ## Phase 2 — Structured Meaning | |
| Dataset: `structured_meaning.jsonl` | |
| Teach the model: | |
| - how to interpret full scenes | |
| - how to output structured meaning dicts | |
| - how to understand context layers | |
| ## Phase 3 — Text ↔ Glyph Translation | |
| Dataset: `text_to_glyph.jsonl` | |
| Teach the model: | |
| - natural language → glyph sequences | |
| - glyph sequences → natural language | |
| - canonical ordering | |
| ## Phase 4 — Syntax Enforcement | |
| Synthetic dataset: | |
| - valid vs invalid sequences | |
| - ordering violations | |
| - context violations | |
| - role violations | |
| ## Phase 5 — Scene Construction | |
| Synthetic dataset: | |
| - multi‑glyph scenes | |
| - symbolic scenes | |
| - emotional/sensory/social context | |
| ## Phase 6 — Soulfile™ Integration | |
| Teach the model: | |
| - how glyphs map to Soulfile™ memory entries | |
| - how to compress/expand meaning | |
| - how to maintain continuity | |
| --- | |
| # 3. Training Method | |
| Recommended: | |
| - QLoRA or LoRA for efficiency | |
| - 7B–13B model for best balance | |
| - 3–5 epochs per phase | |
| - curriculum learning (strict order) | |
| --- | |
| # 4. Evaluation | |
| The model must pass: | |
| - syntax tests | |
| - reversibility tests | |
| - dictionary consistency tests | |
| - context ordering tests | |
| - Soulfile™ encoding tests | |
| --- | |
| # 5. Deployment | |
| The fine‑tuned model is loaded by: | |
| - life.py or brainbot.py (agent brain) | |
| - controllers | |
| - Soulfile™ systems | |
| - glyph interpreter | |
| The model must never bypass the interpreter; it must work *with* it. | |
| --- | |
| This plan ensures the model becomes a fully Glyphic‑native reasoning engine. | |