Instructions to use binqiangliu/EmbeddingModelallMiniLML6v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use binqiangliu/EmbeddingModelallMiniLML6v2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="binqiangliu/EmbeddingModelallMiniLML6v2")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("binqiangliu/EmbeddingModelallMiniLML6v2") model = AutoModel.from_pretrained("binqiangliu/EmbeddingModelallMiniLML6v2", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 340c711f718dcb27266d7d6584492af3d2384e6f5ce3cbdc8d11761cefb033d5
- Size of remote file:
- 90.9 MB
- SHA256:
- c3a85f238711653950f6a79ece63eb0ea93d76f6a6284be04019c53733baf256
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