> ## Documentation Index
> Fetch the complete documentation index at: https://liquidai-example-leonie-demo.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# LFM2.5-Embedding-350M
> 350M dense bi-encoder model for multilingual retrieval and semantic search
← 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](/lfm/models/lfm25-colbert-350m) when retrieval quality matters more than index size.
## Specifications
| Property | Value |
| ------------------- | --------------------------------------------------------------------------------------------------- |
| Parameters | \~354M |
| Type | Dense bi-encoder |
| Document Length | 512 tokens |
| Output | 1024-dimensional CLS vector |
| Similarity | Cosine |
| Supported Languages | English, Spanish, German, French, Italian, Portuguese, Arabic, Swedish, Norwegian, Japanese, Korean |
Fast dense retrieval for documents and products.
One vector per item for compact indexing.
Retrieve across 11 supported languages.
## Quick Start
**Install:**
```bash theme={null}
pip install -U sentence-transformers
```
**Encode queries and documents:**
```python theme={null}
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)
```
**Download GGUF:**
```bash theme={null}
hf download LiquidAI/LFM2.5-Embedding-350M-GGUF \
--local-dir ./LFM2.5-Embedding-350M-GGUF
```
Use the GGUF files with a llama.cpp build that supports LFM2.5 embedding models.