| ---
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| license: other
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| tags:
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| - ufo-compression
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| - model-quantization
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| - tokenizer-compression
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| - rust
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| - cpp
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| - go
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| - patent-evidence
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| language:
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| - en
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| pipeline_tag: translation
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| title: UFO Compression Python
|
| ---
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|
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| # UFO Hyper-Compression & Self-Reconstruction Framework Kit (Python Edition)
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| <!-- Patent Pending — USPTO Provisional Application | Watermark: ip zymatica.space -->
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| This repository houses the core algorithmic framework for the **UFO (Ultra-Frequency-Optimized) 7-Level Compression & Self-Reconstruction Pipeline**.
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| 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.
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|
|
| ---
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|
|
| ## 1. Intellectual Property & Patent Claims
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| This framework implements the following proprietary claims under USPTO provisional applications:
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| 1. **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 ($R_C, R_F, R_A$).
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| 2. **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.
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| 3. **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.
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| 4. **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.
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|
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| ---
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|
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| ## 2. Tokenizer 7-Level Compression Paradigm
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|
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| 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:
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| * **Level 1 (Raw Baseline):** Loading raw `tokenizer.json`, `merges.txt`, `vocab.json` (23 MB).
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| * **Level 2 (Structured Extraction):** Isolating vocab mappings and merge tuples (15 MB).
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| * **Level 3 (Byte/ID Delta Packing):** Varint packing of contiguous IDs; merges represented as vocabulary index pairs (7 MB).
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| * **Level 4 (Prefix-Suffix Differential Compression):** Prefix character length extraction + suffix arrays (4 MB).
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| * **Level 5 (Base Oracle Reference):** Zero-delta alignment vs `Qwen/Qwen3.5-0.8B`.
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| * **Level 6 (Deflate Entropy Coding):** Zlib Level 9 hyper-deflate (2.4 MB absolute, 28 bytes reference).
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| * **Level 7 (XOR-FEC Chirp Packetization):** Packetization into 28 × 255-byte packets (27 data + 1 FEC parity).
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|
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| ---
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| ## 3. Multi-Language System Implementations
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| To ensure broad patent coverage and prevent unauthorized ports, the core algorithms have been transpiled and verified in:
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| * `python/`: Production-ready Python modules.
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| * `rust/`: Systems-level Rust implementation (`tokenizer_coder.rs`) for native speed.
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| * `cpp/`: Low-level C++ port (`tokenizer_coder.cpp` / `.hpp`) for embedded systems and edge microcontrollers.
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| * `go/`: Go port (`tokenizer_coder.go`) for high-concurrency server wrappers.
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|
|
| ---
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| *Watermark: ip zymatica.space | astronautshe.com | Patent Pending — All Rights Reserved*
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|
