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 tokenizer.json from ManyCamDS/ToarimEmbedder: direct link, hf CLI and curl.
- Browser
- Download file 711 kB
-
https://huggingface.co/ManyCamDS/ToarimEmbedder/resolve/main/tokenizer.json
- Command line
-
hf download hf://ManyCamDS/ToarimEmbedder/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/ManyCamDS/ToarimEmbedder/resolve/main/tokenizer.json
711 kB
File too large to display, you can check the raw version instead.