--- license: cc-by-4.0 tags: - glyphic - symbolic-language - semantic-protocol - structured-meaning - text-to-glyph - glyph-to-text - agent-cognition - dataset language: - en pretty_name: Glyphic Dataset v1 task_categories: - text2text-generation - sequence-modeling size_categories: - 10K" } 2. glyph_to_text.jsonl Each line contains: json { "glyph": "", "text": "The agent remembers a promise." } 3. structured_meaning.jsonl Each line contains: json { "text": "The agent remembers a promise.", "meaning": { "actor": "agent", "action": "remember", "object": "promise", "context": {...} }, "glyph": "" } These files are generated using the Glyphic Language Toolkit: Code https://github.com/GlyphicMind-Solutions/Glyphic-Language How to Load the Dataset Using Hugging Face datasets: python from datasets import load_dataset ds = load_dataset("GlyphicMind/glyphic-dataset-v1", split="train") You can inspect entries: python print(ds[0]) Dataset Schema Text → Glyph text: natural language sentence glyph: encoded Glyphic sequence Glyph → Text glyph: symbolic sequence text: natural language reconstruction Structured Meaning text: natural language meaning: structured semantic representation glyph: encoded symbolic sequence The meaning schema is defined in: Code glyphic-language/docs/semantic_model.md Intended Use This dataset is designed for: training LLMs to understand Glyphic training LLMs to generate Glyphic symbolic reasoning research drift‑resistant agent architectures CTX‑based identity, intent, memory, and behavior modeling protocol‑driven agent communication It is not a general‑purpose natural language dataset. How to Train a Glyphic‑Aware Model A full training pipeline is provided in: Code glyphic-language/training/ Typical steps: Generate or extend the dataset using: Code generator/run_generator.py Load the dataset with Hugging Face datasets Fine‑tune a base model (LLaMA/Mistral/etc.) Export as .gguf for inference Use Glyphic envelopes at runtime to eliminate drift A reference model is available at: Code GlyphicMind/glyphic-llm-v1 Regenerating or Extending the Dataset To regenerate or extend this dataset: Clone the Glyphic Language Toolkit: Code https://github.com/GlyphicMind-Solutions/Glyphic-Language Modify dictionary entries, templates, or CTX files Run the generator: bash python -m generator.run_generator Validate using: bash python -m interpreter.interpreter --validate See: training/dataset_generation_guide.md generator/templates_* dictionary/ syntax/ Why Glyphic Reduces LLM Drift Glyphic provides: 1. Deterministic structure Meaning is encoded symbolically, not as free‑form prose. 2. Strict grammar BNF‑defined syntax prevents ambiguity. 3. CTX protocol Identity, intent, memory, behavior, safety, and state are explicit fields. 4. Envelope validation Controllers enforce structure before and after LLM inference. 5. Separation of concerns The LLM becomes a stateless pattern engine; Glyphic holds the meaning. License This dataset is licensed under: Creative Commons Attribution 4.0 International (CC‑BY 4.0) You may reuse, modify, and build upon this dataset with attribution. Citation Code Glyphic Dataset v1 (2026). GlyphicMind Solutions. https://huggingface.co/GlyphicMind/glyphic-dataset-v1