Feature Extraction
Transformers
Safetensors
Indonesian
English
llama
text-generation
text-embedding
retrieval
matryoshka
sea-lion
text-embeddings-inference
8-bit precision
bitsandbytes
Instructions to use evoreign/sea-lion-8b-mrl-embedding-merged with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use evoreign/sea-lion-8b-mrl-embedding-merged with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="evoreign/sea-lion-8b-mrl-embedding-merged")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("evoreign/sea-lion-8b-mrl-embedding-merged") model = AutoModelForMultimodalLM.from_pretrained("evoreign/sea-lion-8b-mrl-embedding-merged") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 30371c383c8ba901391f5f055dc288c5d9c001de1be0a22ee80322f346d8006a
- Size of remote file:
- 17.2 MB
- SHA256:
- 7b440973616209d024d4c8cff713c7564a0ee79e257667e5e3ab556467c3be8e
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