BERT-Base (hadamard_incoherent_svd) — NeuralOps Compressed & LoRA-Healed Model Zoo

This repository provides a physically compressed and LoRA-healed checkpoint of google-bert/bert-base-uncased fine-tuned on GLUE SST-2, compressed using the hadamard_incoherent_svd (HadamardIncoherentSVDCompressor) engine from the NeuralOps Library at Pareto-selected rank k=32 (3.73x compression ratio, -73.2% parameter reduction).

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Compression & Architecture Profile

HadamardIncoherentSVDCompressor: Incoherent Orthogonal SVD & Quantization Preprocessing Engine. Smears activation and weight outliers uniformly across channels: W_rot = W H, SVD(W_rot) = U_r S_r V_r^T When folded back into V_r^T <- V_r^T H^T, the linear layer achieves 40%-65% lower truncation/quantization error with ZERO extra runtime parameters. Factorizes dense linear layers into SVDLinear representations.

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Multi-Rank Pareto Evaluation (k=256, 128, 64, 32)

Evaluated physically on NVIDIA GeForce RTX 3090 (24GB) on the GLUE SST-2 validation split (872 samples). Whenever zero-shot compression drops accuracy by > 2.0% alongside > 1.10x reduction, NeuralOps applies targeted r=8 LoRA healing (50 steps, lr=2e-4, 45s budget):

(⭐ indicates the serialized checkpoint packaged in this Hugging Face repository.)

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Packaged Artifacts

• pytorch_model.bin: Full serialized compressed BERT state dictionary (with low-rank factors / quantization buffers / LoRA healing adapters baked in).
• config.json: Model configuration tagged with compression_method: "hadamard_incoherent_svd" and compression_rank: 32.
• compression_summary.json: Complete machine-readable telemetry across all evaluated ranks [256, 128, 64, 32].
• Tokenizer Assets: Standard bert-base-uncased vocabulary (vocab.txt, tokenizer.json, tokenizer_config.json).

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Quick Start (Reconstruction & Inference)

import torch
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, BertForSequenceClassification
from neuralops.core.compression_pipeline import compress_model

repo_id = "tahamajs/bert-comp-hadamard-incoherent-svd-sst2"
tokenizer = AutoTokenizer.from_pretrained(repo_id)

# Reconstruct compressed NeuralOps topology and load serialized compressed + healed weights
model = BertForSequenceClassification.from_pretrained("google-bert/bert-base-uncased", num_labels=2)
model = compress_model(model, {"compressors": [{"method": "hadamard_incoherent_svd", "rank": 32}]})

ckpt_path = hf_hub_download(repo_id=repo_id, filename="pytorch_model.bin")
state_dict = torch.load(ckpt_path, map_location="cpu")
model.load_state_dict(state_dict, strict=False)
model.eval()

inputs = tokenizer("NeuralOps achieves strong compression without sacrificing downstream accuracy.", return_tensors="pt")
with torch.no_grad():
    logits = model(**inputs).logits
    pred = logits.argmax(dim=-1).item()
print("Predicted SST-2 Sentiment (0=Negative, 1=Positive):", pred)

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Citation

@software{neuralops2026compressorzoo,
  title   = {NeuralOps: Multi-Rank Compressed BERT-SST2 Zoo with Pareto LoRA Recovery},
  author  = {Majlesi, Taha},
  year    = {2026},
  url     = {https://github.com/tahamajs/NeuralOps}
}