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.

[!NOTE] 💻 Demo: https://huggingface.co/spaces/LiquidAI/colbert-tool-selection

📄 Model details

Property	LFM2.5-ColBERT-350M	LFM2.5-Embedding-350M
Type	Late interaction (per-token vectors)	Dense bi-encoder (single vector)
Total parameters	~353M	~354M
Backbone	LFM2.5-350M-Base + bi-directional patches	LFM2.5-350M-Base + bi-directional patches
Layers	17 (10 conv + 6 attn + 1 dense)	17 (10 conv + 6 attn + 1 pool)
Vocabulary size	64,402	65,536
Output	128-dim per token	1024-dim CLS vector
Similarity	MaxSim	Cosine
Training precision	BF16	BF16
License	LFM Open License v1.0	LFM Open License v1.0

Document length: 512 tokens

Query length: 32 tokens

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

Architecture:

text

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

Using Sentence Transformers

This model can be used as a multi-vector (ColBERT-style late interaction) retriever directly with Sentence Transformers via the MultiVectorEncoder.

pip install "sentence-transformers>=6.0.0"

python

from sentence_transformers import MultiVectorEncoder

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

query = "Which planet is known as the Red Planet?"
documents = [
    "Venus is often called Earth's twin because of its similar size and proximity.",
    "Mars, known for its reddish appearance, is often referred to as the Red Planet.",
    "Jupiter, the largest planet in our solar system, has a prominent red spot.",
    "Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
]

query_embeddings = model.encode_query([query])
document_embeddings = model.encode_document(documents)
print(query_embeddings[0].shape, document_embeddings[0].shape)
# (32, 128) (17, 128)

# MaxSim late-interaction scoring (the Mars document ranks highest)
scores = model.similarity(query_embeddings, document_embeddings)
print(scores)
# tensor([[27.1621, 28.2578, 27.7266, 28.1992]])

Using PyLate

Use this model with PyLate to index and retrieve documents. The index uses FastPLAID for efficient similarity search. First, install PyLate and transformers:

pip install -U pylate

Indexing documents

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

python

from pylate import indexes, models, retrieve

# Step 1: Load the ColBERT model (trust_remote_code applies the bidirectional patches)
model = models.ColBERT(
    model_name_or_path="LiquidAI/LFM2.5-ColBERT-350M",
    trust_remote_code=True,
)
model.tokenizer.pad_token = model.tokenizer.eos_token

# Step 2: Initialize the PLAID index
index = indexes.PLAID(
    index_folder="pylate-index",
    index_name="index",
    override=True,  # This overwrites the existing index if any
)

# Step 3: Encode the documents
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,
)

# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
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:

python

# To load an index, simply instantiate it with the correct folder/name and without overriding 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:

python

# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)

# Step 2: Encode the queries
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,
)

# Step 3: Retrieve top-k documents
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:

python

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.

Model	Type	AVG	ar	de	en	es	fr	it	ja	ko	no	pt	sv
LiquidAI/LFM2.5-ColBERT-350M	late	0.605	0.551	0.606	0.687	0.607	0.622	0.606	0.614	0.590	0.570	0.613	0.586
LiquidAI/LFM2.5-Embedding-350M	dense	0.577	0.529	0.581	0.644	0.581	0.592	0.583	0.575	0.563	0.557	0.581	0.566
Qwen/Qwen3-Embedding-0.6B	dense	0.556	0.514	0.560	0.649	0.568	0.565	0.565	0.551	0.530	0.516	0.571	0.525
LiquidAI/LFM2-ColBERT-350M	late	0.540	0.491	0.563	0.661	0.563	0.564	0.543	0.557	0.527	0.449	0.547	0.480
Alibaba-NLP/gte-multilingual-base	dense	0.528	0.477	0.523	0.624	0.537	0.542	0.528	0.511	0.494	0.516	0.534	0.526
lightonai/GTE-ModernColBERT-v1	late	0.489	0.309	0.499	0.680	0.525	0.546	0.516	0.459	0.368	0.465	0.530	0.483
lightonai/LateOn	late	0.484	0.307	0.505	0.690	0.531	0.537	0.514	0.442	0.326	0.465	0.533	0.475
lightonai/DenseOn	dense	0.432	0.178	0.474	0.676	0.496	0.520	0.487	0.378	0.197	0.422	0.493	0.433
Alibaba-NLP/gte-modernbert-base	dense	0.383	0.112	0.449	0.666	0.448	0.475	0.408	0.275	0.180	0.376	0.431	0.391
BAAI/bge-large-en-v1.5	dense	0.359	0.059	0.419	0.642	0.445	0.475	0.431	0.198	0.132	0.358	0.434	0.353

MKQA-11 — Recall@20

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

Model	Type	AVG	ar	de	en	es	fr	it	ja	ko	no	pt	sv
LiquidAI/LFM2.5-ColBERT-350M	late	0.694	0.608	0.709	0.748	0.711	0.715	0.707	0.703	0.640	0.689	0.703	0.700
LiquidAI/LFM2.5-Embedding-350M	dense	0.691	0.610	0.709	0.738	0.708	0.715	0.703	0.685	0.630	0.691	0.710	0.708
Alibaba-NLP/gte-multilingual-base	dense	0.675	0.567	0.692	0.741	0.705	0.703	0.697	0.655	0.563	0.698	0.700	0.699
LiquidAI/LFM2-ColBERT-350M	late	0.646	0.554	0.696	0.754	0.711	0.710	0.667	0.658	0.558	0.541	0.669	0.589
Qwen/Qwen3-Embedding-0.6B	dense	0.638	0.520	0.671	0.723	0.678	0.672	0.671	0.635	0.543	0.620	0.667	0.620
lightonai/GTE-ModernColBERT-v1	late	0.459	0.092	0.532	0.754	0.552	0.615	0.510	0.275	0.166	0.503	0.524	0.524
lightonai/LateOn	late	0.454	0.157	0.492	0.755	0.537	0.577	0.481	0.316	0.209	0.472	0.502	0.501
lightonai/DenseOn	dense	0.435	0.165	0.482	0.751	0.491	0.553	0.457	0.325	0.222	0.438	0.443	0.453
BAAI/bge-large-en-v1.5	dense	0.413	0.133	0.471	0.748	0.450	0.531	0.461	0.208	0.172	0.456	0.443	0.467
Alibaba-NLP/gte-modernbert-base	dense	0.295	0.060	0.333	0.736	0.273	0.417	0.291	0.100	0.052	0.332	0.326	0.330

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).

Model	Stage	Docs cached	p50	p95
LFM2.5-Embedding-350M	Query embedding	yes	7.3 ms	9.6 ms
LFM2.5-ColBERT-350M	Query embedding	yes	8.1 ms	8.5 ms
LFM2.5-ColBERT-350M	Query embedding + MaxSim	yes	8.2 ms	15.2 ms
LFM2.5-ColBERT-350M	Query embedding + Doc embedding + MaxSim	no	34.3 ms	36.3 ms

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.

Workload	Setup	p50	p95	p99
LFM2.5-Embedding-350M	Query embedding	1.5 ms	1.6 ms	1.7 ms
LFM2.5-ColBERT-350M	Query embedding	1.3 ms	1.4 ms	1.5 ms
LFM2.5-ColBERT-350M	Query embedding + MaxSim	2.5 ms	2.7 ms	2.8 ms
LFM2.5-ColBERT-350M	Query embedding + Doc embedding + MaxSim	22.8 ms	24.1 ms	26.4 ms

📬 Contact

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Citation

@article{liquidai2025lfm2,
  title={LFM2 Technical Report},
  author={Liquid AI},
  journal={arXiv preprint arXiv:2511.23404},
  year={2025}
}

@misc{PyLate,
  title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
  author={Chaffin, Antoine and Sourty, Raphaël},
  url={https://github.com/lightonai/pylate},
  year={2024}
}