LFM2.5 ColBERT 350M

We release two new best-in-class multilingual retrieval models:

• LFM2.5-Embedding-350M — A dense bi-encoder, one vector per document. Smallest, fastest index.
• LFM2.5-ColBERT-350M — A late-interaction model. One vector per token, matched via MaxSim. Higher accuracy and better generalization at the cost of index size.

Both models are 350M params and the first bidirectional members of the LFM family, built on LFM2.5-350M-Base. They can be used as a drop-in replacement for your current RAG pipeline and target fast, cheap, and reliable multilingual / cross-lingual search across 11 languages.

Find more details about the bidirectional architecture and training recipe in our blog post.

📄 Model details

Document length: 512 tokens   

Query length: 32 tokens

Supported languages: English, Spanish, German, French, Italian, Portuguese, Arabic, Swedish, Norwegian, Japanese, Korean.

Architecture:

ColBERT(
  (0): Transformer({'max_seq_length': 511, 'do_lower_case': False}) with Transformer model: Lfm2BidirectionalModel
  (1): Dense({'in_features': 1024, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)

We recommend LFM2.5-Embedding-350M and LFM2.5-ColBERT-350M for short-context retrieval use cases, such as:

• E-commerce: find products across many languages with semantic search at scale.
• FAQ and support knowledge bases: retrieve the right answer reliably across customer-facing surfaces.
• On-device semantic search: search files, emails, and notes locally on consumer hardware.
• Enterprise knowledge assistants: retrieve internal legal, financial, and technical documents across languages.

🏃 How to run

First, install the PyLate and transformers libraries:

pip install -U pylate

Retrieval

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search.

Indexing documents

Load LFM2.5-ColBERT-350M and initialize the PLAID index, then encode and index your documents:

from pylate import indexes, models, retrieve


model = models.ColBERT(
    model_name_or_path="LiquidAI/LFM2.5-ColBERT-350M",
    trust_remote_code=True,
)
model.tokenizer.pad_token = model.tokenizer.eos_token


index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)


documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]

documents_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,  # Ensure that it is set to False to indicate that these are documents, not queries
    show_progress_bar=True,
)


index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=documents_embeddings,
)

Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:

index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
)

Retrieving top-k documents for queries

Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries. To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries, and then retrieve the top-k documents to get the top matches ids and relevance scores:

retriever = retrieve.ColBERT(index=index)


queries_embeddings = model.encode(
    ["query for document 3", "query for document 1"],
    batch_size=32,
    is_query=True,  # Ensure that it is set to True to indicate that these are queries
    show_progress_bar=True,
)


scores = retriever.retrieve(
    queries_embeddings=queries_embeddings,
    k=10,  # Retrieve the top 10 matches for each query
)

Reranking

If you only want to use LFM2.5-ColBERT-350M to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use the rank function and pass the queries and documents to rerank:

from pylate import rank, models

queries = [
    "query A",
    "query B",
]

documents = [
    ["document A", "document B"],
    ["document 1", "document C", "document B"],
]

documents_ids = [
    [1, 2],
    [1, 3, 2],
]

model = models.ColBERT(
    model_name_or_path="LiquidAI/LFM2.5-ColBERT-350M",
    trust_remote_code=True,
)

queries_embeddings = model.encode(
    queries,
    is_query=True,
)

documents_embeddings = model.encode(
    documents,
    is_query=False,
)

reranked_documents = rank.rerank(
    documents_ids=documents_ids,
    queries_embeddings=queries_embeddings,
    documents_embeddings=documents_embeddings,
)

📈 Performance

We highlight (= bold) the best bi-encoder and best late retriever for each language.

NanoBEIR Multilingual Extended — NDCG@10

LiquidAI/nanobeir-multilingual-extended. Multilingual retrieval capabilities.

MKQA-11 — Recall@20

MKQA. Cross-lingual capabilities (subset of the 11 languages we target).

Inference speed - llama.cpp

End-to-end latency on MacBook Pro M4 Max via llama.cpp at fp16, measured at 32-token queries and 256-token documents. Docs cached means that the document embeddings are pre-computed and looked up (from an index).

Both models LiquidAI/LFM2.5-ColBERT-350M-GGUF and LiquidAI/LFM2.5-Embedding-350M-GGUF are available on Hugging Face under different quantization schemas for llama.cpp.

Inference speed - Enterprise GPU

For large-scale production-grade enterprise deployments, we also experiment with an internal GPU stack to deliver extremely low-latency serving under high inbound load. We observe latencies as low as 1 ms.

📬 Contact

• Got questions or want to connect? Join our Discord community.
• If you are interested in custom solutions with edge deployment, please contact our sales team.

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