Feature Extraction
Transformers
Safetensors
Spanish
English
toarim
sentence-similarity
stateful-embeddings
nested-learning
rag
industrial-rag
cpu-optimized
sherman-morrison
low-rank-svd
custom_code
Instructions to use ManyCamDS/ToarimEmbedder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ManyCamDS/ToarimEmbedder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ManyCamDS/ToarimEmbedder", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ManyCamDS/ToarimEmbedder", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download .gitattributes from ManyCamDS/ToarimEmbedder: direct link, hf CLI and curl.
- Browser
- Download file 133 Bytes
-
https://huggingface.co/ManyCamDS/ToarimEmbedder/resolve/main/.gitattributes
- Command line
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hf download hf://ManyCamDS/ToarimEmbedder/.gitattributes
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curl -L -o .gitattributes https://huggingface.co/ManyCamDS/ToarimEmbedder/resolve/main/.gitattributes
133 Bytes
| *.safetensors filter=lfs diff=lfs merge=lfs -text | |
| *.bin filter=lfs diff=lfs merge=lfs -text | |
| *.pt filter=lfs diff=lfs merge=lfs -text | |