Instructions to use sinequa/vectorizer.hazelnut with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sinequa/vectorizer.hazelnut with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("sinequa/vectorizer.hazelnut") model = AutoModelForMaskedLM.from_pretrained("sinequa/vectorizer.hazelnut") - Notebooks
- Google Colab
- Kaggle
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## Requirements
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- Minimal Sinequa version: 11.10.0
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- Minimal Sinequa version for using FP16 models and GPUs with CUDA compute capability of 8.9+ (like NVIDIA L4): 11.11.0
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- [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
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## Model Details
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## Requirements
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- Minimal Sinequa version: 11.10.0
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- [Cuda compute capability](https://developer.nvidia.com/cuda-gpus): above 5.0 (above 6.0 for FP16 use)
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## Model Details
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