Instructions to use Tevatron/OmniEmbed-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use Tevatron/OmniEmbed-v0.1 with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("Tevatron/OmniEmbed-v0.1") 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] - PEFT
How to use Tevatron/OmniEmbed-v0.1 with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
All OmniEmbed examples appear to be broken
Using transformers version 4.56.2 and torch version 2.8.0 the examples for OmniEmbed from the model card seem to be broken. I am getting ´ValueError: Videos features and imagve tokens do not match: tokens: 0, features 16744´ for video retrieval and a similar error for the image document retrieval example.
Which version of transformers was originally used to demonstrate functionality? This may be related to known issues concerning the Qwen base-model, see https://github.com/QwenLM/Qwen3-VL/issues/556, but this is already known for a year so I am not sure.
Nevermind, found the issue. texts = processor.apply_chat_template(message, tokenize=False, add_generation_prompt=True) now returns a string (maybe it returned a list in earlier versions?), so slicing it with [0] always makes texts equal to <<|endoftext|>" as only the first '<' from the original string is appended to the EOS string.