Instructions to use nreimers/MiniLM-L6-H384-uncased with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nreimers/MiniLM-L6-H384-uncased with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nreimers/MiniLM-L6-H384-uncased")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nreimers/MiniLM-L6-H384-uncased") model = AutoModel.from_pretrained("nreimers/MiniLM-L6-H384-uncased", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from nreimers/MiniLM-L6-H384-uncased: direct link, hf CLI and curl.
- Browser
- Download file 90.9 MB
-
https://huggingface.co/nreimers/MiniLM-L6-H384-uncased/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://nreimers/MiniLM-L6-H384-uncased/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/nreimers/MiniLM-L6-H384-uncased/resolve/main/flax_model.msgpack
90.9 MB
- Xet hash:
- 420221ae1ef91c1a1504bfc9bd89438985cd526ea36112feee77bdeb8431ec6b
- Size of remote file:
- 90.9 MB
- SHA256:
- cc64ec084e314b03470e11b4c5170bb9a64db946dc738768251ce188933dbab8
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