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, AutoModelForImageTextToText tokenizer = AutoTokenizer.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-7B") model = AutoModelForImageTextToText.from_pretrained("LCO-Embedding/LCO-Embedding-Omni-7B") - Notebooks
- Google Colab
- Kaggle
Expand examples, remove trust_remote_code fully
#3
by tomaarsen HF Staff - opened
Hello!
Pull Request overview
- Drop the
auto_mapshim andmodeling_lco_omni.pyre-export - Rewrite the Sentence Transformers usage section with per-modality retrieval examples (text, image, audio, video) and verified expected outputs
- Switch the recommended
model_kwargstotorch_dtype+attn_implementation="flash_attention_2"
Details
This is the 7B mirror of the https://huggingface.co/LCO-Embedding/LCO-Embedding-Omni-3B/discussions/2 PR. See that PR for some more details!
- Tom Aarsen
tomaarsen changed pull request status to open
gowitheflow changed pull request status to merged