Feature Extraction
Transformers
Safetensors
embeddings
multimodal
vision
code
instruction-tuning
retrieval
text-matching
sentence-similarity
late-interaction
multi-vector
mteb
vidore
lora
adapter
nova
runtime-instructions
Eval Results (legacy)
Instructions to use remodlai/nova-embeddings-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use remodlai/nova-embeddings-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="remodlai/nova-embeddings-v1")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("remodlai/nova-embeddings-v1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download modules.json from remodlai/nova-embeddings-v1: direct link, hf CLI and curl.
- Browser
- Download file 168 Bytes
-
https://huggingface.co/remodlai/nova-embeddings-v1/resolve/main/modules.json
- Command line
-
hf download hf://remodlai/nova-embeddings-v1/modules.json
-
curl -L -o modules.json https://huggingface.co/remodlai/nova-embeddings-v1/resolve/main/modules.json
168 Bytes
- Xet hash:
- 42323ece4cedb3f92eb30cff34926a8368bf37d690224501d00b520b0026c199
- Size of remote file:
- 168 Bytes
- SHA256:
- b54acf92eab134d664abbbc5e42fd92f27535d3605666962c15dc3c32b2d9744
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