LFM2.5-Embedding-350M
LFM2.5-Embedding-350M is a dense bi-encoder for fast multilingual retrieval. It produces a single vector per document — the smallest, fastest index — for reliable cross-lingual search across 11 languages.
- Best-in-class multilingual accuracy for a dense embedder of its size.
- Inference speed is on par with much smaller models, thanks to the efficient LFM2 backbone.
- You can use it as a drop-in replacement in your current RAG pipelines.
Find more information about LFM2.5-Embedding-350M in our blog post.
🏃 How to run
Example usage with llama.cpp:
Start llama-server
llama-server -hf LiquidAI/LFM2.5-Embedding-350M-GGUF --embeddings
Make requests to embed queries and documents, and rank by cosine similarity (note the asymmetric query: / document: prompt prefixes)
❯ uv run dense-retrieve.py
Score: -0.1783 | Q: What is panda? | D: hi
Score: 0.0511 | Q: What is panda? | D: it is a bear
Score: 0.5657 | Q: What is panda? | D: The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.
# /// script
# requires-python = ">=3.10"
# dependencies = ["numpy", "requests"]
# ///
# dense-retrieve.py
import numpy as np, requests
QUERY_PREFIX, DOC_PREFIX = "query: ", "document: "
def embed(text: str) -> np.ndarray:
r = requests.post(
"http://localhost:8080/v1/embeddings",
json={"input": text},
)
v = np.array(r.json()["data"][0]["embedding"])
return v / np.linalg.norm(v)
docs = [
"hi",
"it is a bear",
"The giant panda (Ailuropoda melanoleuca), sometimes called a panda bear or simply panda, is a bear species endemic to China.",
]
query = "What is panda?"
q = embed(QUERY_PREFIX + query)
for doc in docs:
d = embed(DOC_PREFIX + doc)
print(f"Score: {float(q @ d):.4f} | Q: {query} | D: {doc}")
Find more details in the original model card: https://huggingface.co/LiquidAI/LFM2.5-Embedding-350M