README.md

metadata

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).

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Architecture

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

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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.

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Intended Use

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

This is not a language model.

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Limitations

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

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Weights

File: vil-encoder-v1.2.pt Checkpoint contains:

• vision_encoder
• temporal_head
• embed_dim
• canon_size
• gvlp_version = 1.2

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Relationship to VIL

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

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Author

Matthew Blake Ward (Nine1Eight)
Tulsa, Oklahoma, USA