glyphic-llm-v1
A language model fine‑tuned to understand and generate Glyphic Language — a symbolic protocol designed for drift‑resistant agent cognition.
This model is trained on:
Text → Glyph mappings
Glyph → Text mappings
Structured Meaning representations
CTX envelopes (identity, intent, memory, behavior, safety, state, thought)
It is not a general‑purpose chat model. It is a protocol model intended for use inside agent architectures. Model Overview Model Type
A base LLaMA/Mistral‑style model fine‑tuned on the Glyphic dataset. Purpose
To serve as the Glyphic protocol engine inside agent systems:
encode meaning into glyphs
decode glyphs into meaning
fill CTX envelopes
maintain identity, intent, memory, behavior, and safety structure
operate deterministically within a symbolic protocol
Key Capabilities
Understands Glyphic syntax and grammar
Generates valid Glyphic sequences
Converts natural language ↔ glyphs
Produces structured meaning representations
Fills CTX fields with drift‑resistant structure
Works with Glyphic envelopes at runtime
Intended Use
This model is designed for:
Agent cognition research
Symbolic reasoning
Drift‑resistant memory systems
Protocol‑driven agent architectures
Multi‑agent communication (future versions)
Semantic compression and structured meaning extraction
Not intended for:
general conversation
open‑ended chat
political or social commentary
unstructured natural language tasks
How to Use Load the model python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("GlyphicMind/glyphic-llm-v1") model = AutoModelForCausalLM.from_pretrained("GlyphicMind/glyphic-llm-v1")
Generate Glyphic python
prompt = "Encode: The agent remembers a promise." inputs = tokenizer(prompt, return_tensors="pt") outputs = model.generate(**inputs, max_new_tokens=128) print(tokenizer.decode(outputs[0], skip_special_tokens=True))
Decode Glyphic python
prompt = "Decode: <G:...>"
Use with the Glyphic Toolkit
For encoding, decoding, CTX envelopes, and structured meaning: Code
https://github.com/GlyphicMind-Solutions/Glyphic-Language
Use:
interpreter/ for glyph encoding/decoding
runtime/envelope_builder.py for CTX envelopes
generator/ for dataset generation
Training Details Base Model
A small LLaMA/Mistral‑style model (architecture‑agnostic). Training Data
From the Glyphic Dataset v1:
text_to_glyph.jsonl
glyph_to_text.jsonl
structured_meaning.jsonl
Dataset repo: Code
GlyphicMind/glyphic-dataset-v1
Training Pipeline
Provided in: Code
glyphic-language/training/
Includes:
fine‑tuning plan
evaluation guide
training builder
Hugging Face training script (hf_finetune_glyphic.py)
Training Objective
next‑token prediction over Glyphic sequences
optional multi‑task: text ↔ glyph ↔ meaning
Why Glyphic Reduces Drift
This model is trained on a symbolic protocol, not free‑form prose.
- Deterministic structure
Meaning is encoded in glyphs with strict grammar. 2. CTX envelopes
Identity, intent, memory, behavior, safety, and state are explicit fields. 3. Protocol enforcement
Controllers validate envelopes before and after inference. 4. Separation of concerns
The model becomes a stateless pattern engine. Glyphic holds the meaning. 5. Drift‑resistant memory
Memory is symbolic, not conversational. Limitations
Not a general chat model
Not optimized for open‑ended reasoning
Requires the Glyphic Toolkit for full functionality
Assumes structured prompts and envelopes
Not trained on broad natural language corpora
Future Work
Future versions will support:
recursive glyphs
compositional glyph structures
dynamic glyph generation
multi‑agent glyphic communication
semantic compression
distributed cognition
See the full roadmap: Code
glyphic-language/ROADMAP.md
License
This model is licensed under:
Creative Commons Attribution 4.0 International (CC‑BY 4.0)
You may reuse, modify, and build upon this model with attribution. Citation Code
glyphic-llm-v1 (2026). GlyphicMind Solutions. https://huggingface.co/GlyphicMind/glyphic-llm-v1