Model: ykae/monarch-bert-base-mnli-hybrid
Author: ykae
Pipeline: text-classification
Library: transformers
Tags: transformers, pytorch, safetensors, bert, text-classification, fast, monarch-matrices, hybrid-architecture, mnli, efficiency, triton, hardware-efficient, sub-quadratic, fast-inference, custom_code, en, dataset:glue, dataset:wikipedia, license:apache-2.0, model-index, endpoints_compatible, region:us
Card metadata: language: en
license: apache-2.0
library_name: transformers
tags: bert, fast, monarch-matrices, hybrid-architecture, mnli, efficiency, triton, hardware-efficient, sub-quadratic, fast-inference
datasets: glue, wikipedia
metrics: accuracy, throughput, latency
pipeline_tag: text-classification
model-index: name: Monarch-BERT-Base-MNLI-Hybrid
results: task: type: text-classification
name: Natural Language Inference
dataset: name: GLUE MNLI
type: glue
config: mnli
split: validation_matched
metrics: name: Accuracy
type: accuracy
value: 83.6
description: Less than 1% accuracy drop compared to dense BERT-base.
verified: no, name: Throughput (TPS on H100)
type: throughput
value: 8119.8
description: Measured with torch.compile(mode='max-autotune') on NVIDIA H100. Represents an 11.8% increase over the optimized Triton baseline (7261.9 TPS).
verified: no, name: Latency (ms)
type: latency
value: 3.94
description: Batch size 32, sequence length 128. Achieves a 10.7% faster inference time compared to the baseline (4.41 ms).
verified: no
Likes: 0 | All-time downloads: 61
Created: 2026-01-05 12:08:33