Sentence Similarity
sentence-transformers
Safetensors
Transformers
bidirlm_omni
mteb
embedding
bidirectional
custom_code
Instructions to use BidirLM/BidirLM-Omni-2.5B-Embedding with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use BidirLM/BidirLM-Omni-2.5B-Embedding with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("BidirLM/BidirLM-Omni-2.5B-Embedding", trust_remote_code=True) 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 BidirLM/BidirLM-Omni-2.5B-Embedding with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("BidirLM/BidirLM-Omni-2.5B-Embedding", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
ggml (gguf) CrispEmbed support
#3
by cstr - opened
Very cool model! Thank you for the release and permissive license! We experimentally support it - also quantized - in https://github.com/CrispStrobe/CrispEmbed now, via ggml. We still lack several obvious optimizations yet, and probably there will be errors here or there. So it would be super nice if you could notify about any you encounter.
cstr changed discussion status to closed
cstr changed discussion status to open
Nice initiative, I'll take a look ๐
Nicolas-BZRD changed discussion status to closed