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, device_map="auto")# 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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Adding `safetensors` variant of this model
Browse filesThis is an automated PR created with https://huggingface.co/spaces/safetensors/convert
This new file is equivalent to `pytorch_model.bin` but safe in the sense that
no arbitrary code can be put into it.
These files also happen to load much faster than their pytorch counterpart:
https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb
The widgets on your model page will run using this model even if this is not merged
making sure the file actually works.
If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions
Feel free to ignore this PR.
- model.safetensors +3 -0
model.safetensors
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