tahamajs/bert-comp-tomoe-compressor-sst2

BERT-Base (tomoe_compressor) — 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 tomoe_compressor (ToMoECompressor) engine from the NeuralOps Library at Pareto-selected rank k=128 (1.40x compression ratio, -28.8% parameter reduction).

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Compression & Architecture Profile

ToMoECompressor: ToMoE Compressor: dynamically replaces dense feed-forward and projection layers with an MoE-fied equivalent.

Property	Specification
Registry Key (COMPRESSOR_REGISTRY)	tomoe_compressor
NeuralOps Class & Family	ToMoECompressor (low_rank)
Source Implementation	neuralops/compression/low_rank/tomoe_compressor.py
Base Architecture	google-bert/bert-base-uncased (109,483,778 parameters, 12 layers, $D=768$)
Showcase Rank (k)	128 (LoRA Recovery Applied: True)
Compressed Parameter Count	77,965,346 (-28.79% reduction, 1.404x physical compression ratio)
Teacher Baseline SST-2 Acc	91.63%
Showcase Final SST-2 Acc	52.41% (Zero-Shot: 49.08%, Parity Delta: -39.22%)

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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):

Rank (k)	Physical Ratio	Compressed Params (Reduction)	Zero-Shot Acc	Healed Acc	Parity vs Teacher (91.63%)
256	1.10x	99,816,482 (-8.8%)	49.08%	49.08%	-42.55%
128 ⭐	1.40x	77,965,346 (-28.8%)	49.08%	52.41% (LoRA)	-39.22%
64	2.13x	51,423,266 (-53.0%)	49.08%	49.20% (LoRA)	-42.43%
32	2.87x	38,152,226 (-65.2%)	49.08%	49.08% (LoRA)	-42.55%

(⭐ 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: "tomoe_compressor" and compression_rank: 128.
• 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-tomoe-compressor-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": "tomoe_compressor", "rank": 128}]})

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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Quick Start (Universal NeuralOps Hub API)

import torch
from neuralops.hub import load_surgeried_model

# Load model and tokenizer/processor in one line directly from Hugging Face Hub
model, tokenizer = load_surgeried_model("tahamajs/bert-comp-tomoe-compressor-sst2")
model.eval()
print(f"Successfully loaded tahamajs/bert-comp-tomoe-compressor-sst2 with NeuralOps modular surgical weights!")

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}
}