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← Back to Liquid Nanos LFM2.5-Embedding-350M is a dense bi-encoder retrieval model that produces one 1024-dimensional vector per query or document. It is built for fast, compact multilingual and cross-lingual semantic search across 11 languages.
Use LFM2.5-Embedding-350M when you need a small, fast vector index or compatibility with standard dense-vector search. Use LFM2.5-ColBERT-350M when retrieval quality matters more than index size.

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Specifications

PropertyValue
Parameters~354M
TypeDense bi-encoder
Document Length512 tokens
Output1024-dimensional CLS vector
SimilarityCosine
Supported LanguagesEnglish, Spanish, German, French, Italian, Portuguese, Arabic, Swedish, Norwegian, Japanese, Korean

Semantic Search

Fast dense retrieval for documents and products.

Vector Databases

One vector per item for compact indexing.

Cross-Lingual RAG

Retrieve across 11 supported languages.

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Quick Start

Install:
pip install -U sentence-transformers
Encode queries and documents:
from sentence_transformers import SentenceTransformer

model = SentenceTransformer(
    "LiquidAI/LFM2.5-Embedding-350M",
    trust_remote_code=True,
)

queries = [
    "What is the capital of France?",
    "Which city is Japan's capital?",
]
documents = [
    "Paris is the capital and largest city of France.",
    "Tokyo is the capital of Japan.",
    "Berlin is the capital and largest city of Germany.",
]

query_embeddings = model.encode(
    queries,
    prompt_name="query",
    normalize_embeddings=True,
)
document_embeddings = model.encode(
    documents,
    prompt_name="document",
    normalize_embeddings=True,
)

scores = query_embeddings @ document_embeddings.T
print(scores)