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 config.json from ManyCamDS/ToarimEmbedder: direct link, hf CLI and curl.
- Browser
- Download file 241 Bytes
-
https://huggingface.co/ManyCamDS/ToarimEmbedder/resolve/main/config.json
- Command line
-
hf download hf://ManyCamDS/ToarimEmbedder/config.json
-
curl -L -o config.json https://huggingface.co/ManyCamDS/ToarimEmbedder/resolve/main/config.json
241 Bytes
| { | |
| "architectures": [ | |
| "T\u00f3arimEmbeddingModel" | |
| ], | |
| "model_type": "toarim", | |
| "vocab_size": 30522, | |
| "embed_dim": 768, | |
| "latent_k": 128, | |
| "auto_map": { | |
| "AutoModel": "toarim_embedder.T\u00f3arimEmbeddingModel" | |
| } | |
| } |