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HDC-AM-Master-Weights
ryomennagi/HDC-AM-Master-Weights
HDC-AM-Master-Weights is a machine learning model from ryomennagi. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as apache-2.0.
Hyperdimensional Computing Address Mapping master knowledge overlay, generated by the 444.4 MiB master fold (d=4096, strict bipolar {-1,+1}^D).
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Public
Updated Sep 22, 2026
Repo size
466 MB
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.safetensors466 MB · 100%
At a glance
- License
- apache-2.0
- Access
- Public
- Created
- Sep 22, 2026
- Updated
- Sep 22, 2026
- SHA
- 89f6b04c
Model card
From the Hugging Face model README
HDC-AM Master Fast-Weights Overlay
Hyperdimensional Computing Address Mapping master knowledge overlay, generated by the 444.4 MiB master fold (d=4096, strict bipolar {-1,+1}^D).
| field | value |
|---|---|
| artifact | fast_weights_master.safetensors |
| size | 465,985,624 B (444.4 MiB budget) |
| layout | single slots int8 tensor [113,766, 4096] |
| validations | mmap zero-copy open · TR-1 strict ±1 · associative recall top-1 |
Load (zero-copy mmap)
from huggingface_hub import hf_hub_download
from hdc_am_pipeline import MemoryMapped_OverlayLoader, HDC_Recall_Layer
path = hf_hub_download("ryomennagi/HDC-AM-Master-Weights",
"fast_weights_master.safetensors")
with MemoryMapped_OverlayLoader(path, writable=False) as mm:
names = [k for k in mm.header_keys if k.startswith("slot_")]
layer = HDC_Recall_Layer(mm, names, d=4096)
print(layer.recall("Taj Mahal mausoleum Yamuna")) # associative recall
Reproduce
python master_fill.py # folds exact 113,766 rows -> 444.4 MiB
python verify_master.py # gate checks (mmap / TR-1 / recall)
- License
- apache-2.0
- Created
- Sep 22, 2026
- Updated
- Sep 22, 2026