Eti Zymatica

Publish UFO Python framework implementation

49a119f verified 3 months ago

3.55 kB

license: other
tags:
  - ufo-compression
  - model-quantization
  - tokenizer-compression
  - rust
  - cpp
  - go
  - patent-evidence
language:
  - en
pipeline_tag: translation
title: UFO Compression Python

UFO Hyper-Compression & Self-Reconstruction Framework Kit (Python Edition)

This repository houses the core algorithmic framework for the UFO (Ultra-Frequency-Optimized) 7-Level Compression & Self-Reconstruction Pipeline.

This codebase is published privately to establish legal ownership and empirical utility evidence for USPTO provisional patent filings. It includes multi-language system ports (Python, Rust, C++, Go) of the core serialization modules to prevent unlicensed translation or replication.

1. Intellectual Property & Patent Claims

This framework implements the following proprietary claims under USPTO provisional applications:

Claim 1 (Multidimensional Semantic Coordinate Indexing): Decomposing conceptual queries and semantic structures into a 6D hypercube index along orthogonal axes (Domain, Subdomain, Operation, Modality, Depth, Polarity) represented as coordinate radicals ($R_C, R_F, R_A$).
Claim 2 (Embedding-Driven Weight Projection - E-PAUP): Representing weight deltas of target layers by projecting them onto pre-existing, shared word embedding matrices of the base model, eliminating coordinate transmission overhead.
Claim 3 (Tokenizer Prefix-Suffix Varint Differential Coding): Lossless tokenizer serialization storing tokens by ID order using variable-length prefix sharing indexes and suffix bytes, achieving a 9.37x stand-alone reduction and 3200x reference-mode reduction.
Claim 4 (LLM-Logits-Driven Range Coding - LLD-AC): Bypassing static coding tables by utilizing runtime logit probability distributions of the active model as dynamic entropy priors.

2. Tokenizer 7-Level Compression Paradigm

The tokenizer framework compresses original metadata files (~23 MB) down to either a stand-alone 2.4 MB capsule or a 28-byte base-oracle reference capsule using a 7-Level descent:

Level 1 (Raw Baseline): Loading raw tokenizer.json, merges.txt, vocab.json (23 MB).
Level 2 (Structured Extraction): Isolating vocab mappings and merge tuples (15 MB).
Level 3 (Byte/ID Delta Packing): Varint packing of contiguous IDs; merges represented as vocabulary index pairs (7 MB).
Level 4 (Prefix-Suffix Differential Compression): Prefix character length extraction + suffix arrays (4 MB).
Level 5 (Base Oracle Reference): Zero-delta alignment vs Qwen/Qwen3.5-0.8B.
Level 6 (Deflate Entropy Coding): Zlib Level 9 hyper-deflate (2.4 MB absolute, 28 bytes reference).
Level 7 (XOR-FEC Chirp Packetization): Packetization into 28 × 255-byte packets (27 data + 1 FEC parity).

3. Multi-Language System Implementations

To ensure broad patent coverage and prevent unauthorized ports, the core algorithms have been transpiled and verified in:

python/: Production-ready Python modules.
rust/: Systems-level Rust implementation (tokenizer_coder.rs) for native speed.
cpp/: Low-level C++ port (tokenizer_coder.cpp / .hpp) for embedded systems and edge microcontrollers.
go/: Go port (tokenizer_coder.go) for high-concurrency server wrappers.