How to use LCO-Embedding/LCO-Embedding-Omni-3B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LCO-Embedding/LCO-Embedding-Omni-3B") sentences = [ "The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium." ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [3, 3]
How to use LCO-Embedding/LCO-Embedding-Omni-3B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LCO-Embedding/LCO-Embedding-Omni-3B")
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-3B") model = AutoModelForMultimodalLM.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-3B", device_map="auto")
https://huggingface.co/LCO-Embedding/LCO-Embedding-Omni-3B/resolve/main/vocab.json
hf download hf://LCO-Embedding/LCO-Embedding-Omni-3B/vocab.json
curl -L -o vocab.json https://huggingface.co/LCO-Embedding/LCO-Embedding-Omni-3B/resolve/main/vocab.json
