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.