Feature Extraction
sentence-transformers
Safetensors
Transformers
qwen2_5_omni_thinker
image-text-to-text
multimodal-embedding
Instructions to use LCO-Embedding/LCO-Embedding-Omni-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LCO-Embedding/LCO-Embedding-Omni-7B with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LCO-Embedding/LCO-Embedding-Omni-7B") 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] - Transformers
How to use LCO-Embedding/LCO-Embedding-Omni-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="LCO-Embedding/LCO-Embedding-Omni-7B")# Load model directly from transformers import AutoTokenizer, AutoModelForMultimodalLM tokenizer = AutoTokenizer.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-7B") model = AutoModelForMultimodalLM.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-7B", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 6617ba7fd76a5535c9d34682d2bb886ddcf55cfb54f4aca9daa65965a6bc8d16
- Size of remote file:
- 4.99 GB
- SHA256:
- b4c4c9cfb39cbc06a1a1fd499c962ff32bd50cbb5a17aa139c59d280a8de1047
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.