Sentence Similarity
sentence-transformers
Safetensors
lfm2
liquid
lfm2.5
edge
feature-extraction
custom_code
Instructions to use LiquidAI/LFM2.5-Embedding-350M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- sentence-transformers
How to use LiquidAI/LFM2.5-Embedding-350M with sentence-transformers:
from sentence_transformers import SentenceTransformer model = SentenceTransformer("LiquidAI/LFM2.5-Embedding-350M", trust_remote_code=True) sentences = [ "That is a happy person", "That is a happy dog", "That is a very happy person", "Today is a sunny day" ] embeddings = model.encode(sentences) similarities = model.similarity(embeddings, embeddings) print(similarities.shape) # [4, 4] - Notebooks
- Google Colab
- Kaggle
Maximum Sequence Length
#1
by tomaarsen - opened
Hello @EdoardoMosca and co!
The model card mentions both a Context Length of 32,768 tokens, as well as a "Document length" of 512 tokens. Sentence Transformers uses the "max_seq_length": 512, from https://huggingface.co/LiquidAI/LFM2.5-Embedding-350M/blob/main/sentence_bert_config.json#L2 and adopts the 512. Is this indeed the expected behaviour? Just making sure π€
- Tom Aarsen
Hey @tomaarsen thanks for flagging this!
512 is indeed the right number. The 32k context length refers to the original LFM2.5 backbone. But I do agree it can be confusing, I'll remove it across cards