pluscoder30-cpu/conscious-field-transformer — explained in plain English
Analysis updated 2026-05-18
Download and inspect a massive neural network model to study its parameter structure.
Run the verify script to confirm the claimed parameter count on your own machine.
Experiment with the compressed NPZ file and its 55,653 named tensors in Python.
Explore the mix of ten neural network architectures referenced in the model design.
| pluscoder30-cpu/conscious-field-transformer | 0marildo/imago | 0xdfi/glm-5.2-1m-4x-dgx-spark | |
|---|---|---|---|
| Stars | 3 | 3 | 3 |
| Language | Python | Python | Python |
| Setup difficulty | moderate | easy | hard |
| Complexity | 3/5 | 2/5 | 5/5 |
| Audience | researcher | general | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires downloading a 358 MB compressed NPZ file from the GitHub Releases page and having Python with NumPy installed.
{"i": 0, "repo": "pluscoder30-cpu/conscious-field-transformer", "stars": 3, "lang": "Python", "ok": true, "char_count": 1650, "text": "This repository shares a large neural network model called the Conscious Field Transformer. The project claims it contains roughly 14.88 trillion parameters, which is a way of describing the size and complexity of the model's internal settings. Rather than hosting the file directly on GitHub, the authors ask you to download it from their Releases page. The compressed file is about 358 MB in size.\n\nThe README says the weights are stored as an NPZ file, which is a NumPy archive format used in Python for saving arrays of numbers. Inside that file are 55,653 named tensors. A tensor is a multi-dimensional array of numbers, and in a neural network these hold the learned values the model uses to produce its output. The repository includes a small script called verify.py so people can check the parameter count on their own machine.\n\nThe architecture is described as a mix of ten different neural network designs, each cited with a year and author. Some of these, like the Transformer and LLaMA 2, are well known. Others, such as Mamba, RetNet, RWKV, and Hyena, are more recent research designs. Two entries, Consciousness Field and Plasma Neuron Field, do not correspond to widely recognized published architectures, so it is hard to assess what they mean from the README alone.\n\nCompression is described as approximately 772,000x using something called holographic DCT encoding. The README states each tensor can be reconstructed from the compressed form. A short code snippet shows how to load the file in Python and print the parameter total from an embedded manifest.\n\nThe license section says the weights are free for any use, commercial or otherwise. The README then lists paid enterprise tiers for larger hypothetical models, with prices ranging into billions of dollars, and one joke entry priced near global debt. These tiers appear to be marketing and are not part of the downloadable release. The full README is not longer than what was shown.", "error": null}
A Python repo sharing a large neural network model file called the Conscious Field Transformer, claiming roughly 14.88 trillion parameters compressed into a 358 MB download. Includes a verify script and describes a mix of known and novel architectures.
Mainly Python. The stack also includes Python, NumPy, PyTorch.
The model weights are free for any use including commercial purposes, though the README lists paid enterprise tiers for hypothetical larger models.
Setup difficulty is rated moderate, with roughly 30min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
double-check against the repo, no cap.