---
license: mit
tags:
- vision
- encoder
- multimodal
- self-supervised
- video
- execution
- symbolic
library_name: pytorch
pipeline_tag: feature-extraction
datasets:
- Nine1Eight/vil-canonical-glyph-system
---

# VIL Encoder v1.2 (GVL-P)

**VIL Encoder v1.2** is a glyphmatic vision encoder trained using  
**GVL-P (Glyphmatic Video-Language Pretraining) v1.2**.

This model learns **temporal execution structure** from canonical glyph
sequences derived from text, code, binaries, and other data.

> ⚠️ This model does **not tokenize language**.  
> All inputs are compiled into a **canonical glyph IR (base-111)**.

---

## Architecture

- **Vision Encoder:** GlyphVisionEncoder
- **Temporal Head:** TemporalGlyphTransformer
- **Embedding Dimension:** 768
- **Canon Size:** 111
- **Deterministic:** Yes

---

## Training (GVL-P v1.2)

Training is **fully self-supervised**:

1. Arbitrary input (text, code, binary)
2. Deterministic compilation → glyph indices
3. Sliding temporal windows
4. Next-step temporal consistency objective

No labels, captions, or annotations were used.

---

## Intended Use

- Execution-aware embeddings
- Vision–language research
- Glyph-based reasoning systems
- Multimodal IR experiments

This is **not** a language model.

---

## Limitations

- Requires canonical glyph compilation
- No text generation
- No decoding or execution

---

## Weights

File:
vil-encoder-v1.2.pt
Checkpoint contains:
- `vision_encoder`
- `temporal_head`
- `embed_dim`
- `canon_size`
- `gvlp_version = 1.2`

---

## Relationship to VIL

Canonical dataset:
https://huggingface.co/datasets/Nine1Eight/vil-canonical-glyph-system

---

## Author

Matthew Blake Ward (Nine1Eight)  
Tulsa, Oklahoma, USA