Feature Extraction
Transformers
PyTorch
milco
sparse-retrieval
multilingual
learned-sparse
cross-lingual
custom_code
Instructions to use omai-research/milco-650m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omai-research/milco-650m with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="omai-research/milco-650m", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("omai-research/milco-650m", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from omai-research/milco-650m: direct link, hf CLI and curl.
- Browser
- Download file 2.37 GB
-
https://huggingface.co/omai-research/milco-650m/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://omai-research/milco-650m/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/omai-research/milco-650m/resolve/main/pytorch_model.bin
2.37 GB
- Xet hash:
- 1b79cbf4dcd63aa21b4cdb188839cad0797c329747f7cb82e2dc726681cc5f8d
- Size of remote file:
- 2.37 GB
- SHA256:
- ff8fdc2da6b7462a3a97fd60a978044c8291809b3e6f8a3ab1b5c566d49e9ec0
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