Instructions to use kssteven/ibert-roberta-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kssteven/ibert-roberta-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="kssteven/ibert-roberta-base")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("kssteven/ibert-roberta-base") model = AutoModelForMaskedLM.from_pretrained("kssteven/ibert-roberta-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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# I-BERT base model
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This model, `ibert-roberta-base`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this
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I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic.
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In particular, I-BERT replaces all floating point operations in the Transformer architectures (e.g., MatMul, GELU, Softmax, and LayerNorm) with closely approximating integer operations.
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This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU.
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# I-BERT base model
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This model, `ibert-roberta-base`, is an integer-only quantized version of [RoBERTa](https://arxiv.org/abs/1907.11692), and was introduced in [this paper](https://arxiv.org/abs/2101.01321).
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I-BERT stores all parameters with INT8 representation, and carries out the entire inference using integer-only arithmetic.
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In particular, I-BERT replaces all floating point operations in the Transformer architectures (e.g., MatMul, GELU, Softmax, and LayerNorm) with closely approximating integer operations.
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This can result in upto 4x inference speed up as compared to floating point counterpart when tested on an Nvidia T4 GPU.
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