# 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.

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# 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

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# 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

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# 3. Training Method

Recommended:

- QLoRA or LoRA for efficiency
- 7B–13B model for best balance
- 3–5 epochs per phase
- curriculum learning (strict order)

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# 4. Evaluation

The model must pass:

- syntax tests
- reversibility tests
- dictionary consistency tests
- context ordering tests
- Soulfile™ encoding tests

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# 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.

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This plan ensures the model becomes a fully Glyphic‑native reasoning engine.