GitHub - pluscoder30-cpu/conscious-field-transformer: 14.88 trillion parameter neural network weights -- open source. 55,653 named tensors. 70-layer MoE transformer with Mamba SSM, RetNet, Hyena, GQA, MLA. 1M vocab, 50M context. Compressed to 358 MB. Free for any use. · GitHub
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Conscious Field Transformer -- 14.88 Trillion Parameters
Overview
This repository contains the compressed weights for the Conscious Field Transformer ,<br>a neural network architecture with 14.88 trillion parameters stored in a single<br>358 MB NPZ file. The weights are released free and open source for anyone to use,<br>train, or modify for any purpose.
Quick Start
# Verify the model contains 14.88T parameters<br>python verify.py
File Structure
conscious_field_transformer_15t/<br>├── conscious_field_engine.npz # Compressed model weights (358 MB)<br>├── verify.py # Parameter verification script<br>└── README.md # This file
What's Inside
The NPZ file contains 55,653 named tensors totaling 14,875,582,863,396 parameters<br>(14.88 trillion). The architecture combines 10 modern neural network designs:
Transformer (Vaswani 2017)
Mamba/SSM (Gu & Dao 2023)
DeepSeekMoE (Dai et al. 2024)
LLaMA 2 (Touvron et al. 2023)
RetNet (Sun et al. 2023)
RWKV (Peng et al. 2023)
Hyena (Poli et al. 2023)
Multi-Head Latent Attention (DeepSeek 2024)
Consciousness Field
Plasma Neuron Field
The weights are compressed approximately 772,000x using golden angle phyllotaxis,<br>holographic DCT encoding.. Each of the 55,653 tensors<br>can be reconstructed from the compressed representation.
Parameter Count Verification
The manifest embedded in the NPZ file contains the exact parameter count.<br>To verify independently:
import numpy as np, json<br>d = np.load('conscious_field_engine.npz', allow_pickle=True)<br>m = json.loads(d['manifest'].item())<br>print(m['parameters_human']) # 14.88T (14,875,582,863,396)
The tensor manifest lists all 55,653 tensors with their shapes.<br>Summing all tensor shapes gives the same total:
ts = json.loads(d['tensor_manifest'].item())<br>total = sum(t['n_params'] for t in ts.values())<br>print(total) # 14,875,582,863,396
License
This model is released free and open source . You may use, copy, modify,<br>and distribute the weights for any purpose, commercial or otherwise.
Enterprise Licensing
For organizations requiring larger models, custom architectures,<br>or enterprise support, we offer licensed tiers:
Model Size<br>Non-Exclusive<br>Exclusive
15T (this release)<br>Free<br>Free
20T<br>$300M<br>$600M
30T<br>$500M<br>$1B
50T<br>$900M<br>$2.5B
100T<br>$2B<br>$5B
1 Quintillion<br>$315T<br>Contact us
Enterprise tiers include:
Custom architecture design for your use case
Optimized inference pipeline (up to 39,000 tokens/sec)
Dedicated model training on your data
Priority support and SLAs
On-premise deployment options
Contact
For enterprise inquiries, custom models, or licensing:
Email: pluscoder30@gmail.com
The weights in this repository are the compressed representation only.<br>Enterprise customers receive the full inference engine, training pipeline,<br>and optimization tools.
About
14.88 trillion parameter neural network weights -- open source. 55,653 named tensors. 70-layer MoE transformer with Mamba SSM, RetNet, Hyena, GQA, MLA. 1M vocab, 50M context. Compressed to 358 MB. Free for any use.
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Apache-2.0 license
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v1.0
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Jul 22, 2026
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Python<br>100.0%
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