Text Classification
Transformers
PyTorch
Safetensors
English
bert
fast
monarch-matrices
mnli
efficiency
triton
hardware-efficient
sub-quadratic
fast-inference
h100-optimized
custom_code
Eval Results (legacy)
text-embeddings-inference
Instructions to use ykae/monarch-bert-base-mnli with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ykae/monarch-bert-base-mnli with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-classification", model="ykae/monarch-bert-base-mnli", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForSequenceClassification tokenizer = AutoTokenizer.from_pretrained("ykae/monarch-bert-base-mnli", trust_remote_code=True) model = AutoModelForSequenceClassification.from_pretrained("ykae/monarch-bert-base-mnli", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
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README.md
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* **Training Time:** A few hours on **1x NVIDIA H100**.
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* **Data:** Only **MNLI** + **500k Wikipedia Samples**.
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* **Math over Brute Force:** By replacing all FFNs with **Monarch Matrices** $O(N \log N)$, we reduced the mathematical complexity (GFLOPs) by **66%**.
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* **Trade-off:** This extreme compression comes with a moderate accuracy drop (~5%). *Need higher accuracy? Check out our [Hybrid Version](https://huggingface.co/ykae/monarch-bert-base-mnli-hybrid) (<1% loss).*
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## 🚀 Key Benchmarks
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* **Training Time:** A few hours on **1x NVIDIA H100**.
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* **Data:** Only **MNLI** + **500k Wikipedia Samples**.
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* **Trade-off:** This extreme compression comes with a moderate accuracy drop (~5%). *Need higher accuracy? Check out our [Hybrid Version](https://huggingface.co/ykae/monarch-bert-base-mnli-hybrid) (<1% loss).*
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## 🚀 Key Benchmarks
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