Instructions to use Intel/dynamic-minilmv2-L6-H384-squad1.1-int8-static with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Intel/dynamic-minilmv2-L6-H384-squad1.1-int8-static with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="Intel/dynamic-minilmv2-L6-H384-squad1.1-int8-static")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("Intel/dynamic-minilmv2-L6-H384-squad1.1-int8-static") model = AutoModelForQuestionAnswering.from_pretrained("Intel/dynamic-minilmv2-L6-H384-squad1.1-int8-static", device_map="auto") - Notebooks
- Google Colab
- Kaggle
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README.md
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### QuaLA-MiniLM training process
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Figure showing QuaLA-MiniLM training process. To run the model with the best accuracy-efficiency tradeoff per a specific computational budget, we set the length configuration to the best setting found by an evolutionary search to match our computational constraint.
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[ArchitecureQuaLA-MiniLM.jpg](ArchitecureQuaLA-MiniLM.jpg)
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| Model Detail | Description |
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### QuaLA-MiniLM training process
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To run the model with the best accuracy-efficiency tradeoff per a specific computational budget, we set the length configuration to the best setting found by an evolutionary search to match our computational constraint.
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