ModelListedExternal

Cozet

Architecture: SYNAXIM (Symbiotic Native Axim Inference Machine) Author: GRRN Research License: Proprietary Status: Phase 1 --- Architecture Validation (130M parameters)

Sourcehuggingface.co/GRRNMAKER/Cozet/tree/badcd4ae730588b83e29274675973dd46a025677

KindOtherVersionRevision badcd4aCgrade

Model source

Kind: Other
File size: 129 MB–1.61 GB
Version: vRevision badcd4a
Updated: Oct 22, 2026
Downloads: 0
Source: Hugging Face

Source description

Cozet --- Native SYNAXIM Base Model

Architecture: SYNAXIM (Symbiotic Native Axim Inference Machine) Author: GRRN Research License: Proprietary Status: Phase 1 --- Architecture Validation (130M parameters)

What is Cozet?

Cozet is a language model built from scratch on the SYNAXIM architecture --- a non-transformer design that replaces self-attention with a persistent associative memory matrix (the Symbiotic Gate).

Cozet is not a converted transformer. It is born native. Every weight is trained from random initialization through the M-matrix paradigm. No KV cache. O(1) memory. Infinite context.

Architecture: Symbiotic Gate

Standard transformers compute attention as:

output = softmax(Q @ K^T / sqrt(d)) @ V    # O(n^2) compute, O(n) KV cache

SYNAXIM computes attention as:

gate   = sigmoid(mean(Q * K))               # scalar routing
M_new  = gate * M + (1-gate) * outer(k, v)  # O(1) persistent memory update
output = q @ M_new                           # associative retrieval

The M-matrix accumulates context through gated outer product updates and never grows. Memory is O(D^2) fixed regardless of sequence length.

Model Configurations

Config	Parameters	D	Layers	Heads	Intermediate	Status
Cozet-Small	130M	1024	12	16/4 GQA	4096	Phase 1
Cozet-Medium	1.3B	2048	24	16/4 GQA	8192	Phase 2
Cozet-Large	7.2B	4096	32	32/8 GQA	14336	Phase 3

Training

Native pretraining from randomly initialized weights on FineWeb-Edu / RedPajama using truncated BPTT through the M-matrix chain.

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Model artifacts

64 artifacts

checkpoints/cozet_2.5m_final.pt

pt · 892 MB · SHA-256 89e988bb1745…aea5 · Hugging Face

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checkpoints/cozet_50k_synthetic.pt

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checkpoints/cozet_5m_final.pt

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checkpoints/cozet_grown_24L.pt

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checkpoints/cozet_lora_adapters.pt

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checkpoints/cozet_merged.pt

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checkpoints/cozet_nemotron_decay.pt

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checkpoints/cozet_p2_step10000.pt

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checkpoints/cozet_p2_step100000.pt

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checkpoints/cozet_p2_step101000.pt

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checkpoints/cozet_p2_step102000.pt

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checkpoints/cozet_p2_step103000.pt

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checkpoints/cozet_p2_step104000.pt

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checkpoints/cozet_p2_step11000.pt

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checkpoints/cozet_p2_step110000.pt

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checkpoints/cozet_p2_step111000.pt

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checkpoints/cozet_p2_step112000.pt

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checkpoints/cozet_p2_step12000.pt

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checkpoints/cozet_p2_step120000.pt

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checkpoints/cozet_p2_step121000.pt

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checkpoints/cozet_p2_step122000.pt

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checkpoints/cozet_p2_step131000.pt

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checkpoints/cozet_p2_step132000.pt

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checkpoints/cozet_p2_step14000.pt

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checkpoints/cozet_p2_step141000.pt

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checkpoints/cozet_p2_step142000.pt

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checkpoints/cozet_p2_step149000.pt

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checkpoints/cozet_p2_step15000.pt

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checkpoints/cozet_p2_step150000.pt

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Source excerpts

2 excerpts

ReadmeJul 17huggingface.co/GRRNMAKER/Cozet

# Cozet --- Native SYNAXIM Base Model **Architecture**: SYNAXIM (Symbiotic Native Axim Inference Machine) **Author**: GRRN Research **License**: Proprietary **Status**: Phase 1 --- Architecture Validation (130M parameters) --- ## What is Cozet? Cozet is a language model built from scratch on the **SYNAXIM architecture** --- a non-transformer design that replaces self-attention with a persistent associative memory matrix (the Symbiotic Gate). Cozet is **not** a converted transformer. It is born native. Every weight is trained from random initialization through the M-matrix paradigm. No KV cache. O(1) memory. Infinite context. ## Architecture: Symbiotic Gate Standard transformers compute attention as: ``` output = softmax(Q @ K^T / sqrt(d)) @ V # O(n^2) compute, O(n) KV cache ``` SYNAXIM computes attention as: ``` gate = sigmoid(mean(Q * K)) # scalar routing M_new = gate * M + (1-gate) * outer(k, v) # O(1) persistent memory update output = q @ M_new # associative retrieval ``` The M-matrix accumulates context through gated outer product updates and never grows. Memory is O(D^2) fixed regardless of sequence length. ## Model Configurations | Config | Parameters | D | Layers | Heads | Intermediate | Status | |--------|-----------|------|--------|-------|-------------|--------| | Cozet-Small | 130M | 1024 | 12 | 16/4 GQA | 4096 | Phase 1 | | Cozet-Medium | 1.3B | 2048 | 24 | 16/4 GQA | 8192 | Phase 2 | | Cozet-Large | 7.2B | 4096 | 32 | 32/8 GQA | 14336 | Phase 3 | ## Training Native pretraining from randomly initialized weights on FineWeb-Edu / RedPajama using truncated BPTT through the M-matrix chain. ## Links - **Architecture**: SYNAXIM --- https://github.com/GRRN-MAKER/SYNAXIM - **Theory**: Odyssey Meta-Cognitive Framework - **Inference Engine**: SYNAXIM `.symb` format (pure NumPy, zero dependencies) --- (c) 2026 GRRN Research. All rights reserved.

NoteJul 17huggingface.co/GRRNMAKER/Cozet

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