LiquidAI/LFM2.5-ColBERT-350M · Hugging Face

Source: https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M

We release two newbest-in-class multilingual retrievalmodels:

• 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 pertoken, 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 onLFM2.5-350M-Base. They can be used as adrop-in replacementfor 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 ourblog post.

💻Demo:https://huggingface.co/spaces/LiquidAI/colbert-tool-selection

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#%F0%9F%93%84-model-details📄 Model details

PropertyLFM2.5-ColBERT-350M****LFM2.5-Embedding-350M****TypeLate interaction (per-token vectors)Dense bi-encoder (single vector)Total parameters~353M~354MBackboneLFM2.5-350M-Base+ bi-directional patchesLFM2.5-350M-Base+ bi-directional patchesLayers17 (10 conv + 6 attn + 1 dense)17 (10 conv + 6 attn + 1 pool)Vocabulary size64,40265,536Output128-dim per token1024-dim CLS vectorSimilarityMaxSimCosineTraining precisionBF16BF16LicenseLFM Open License v1.0LFM 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:

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.

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#%F0%9F%8F%83-how-to-run🏃 How to run

First, install the PyLate and transformers libraries:

pip install -U pylate

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#retrievalRetrieval

Use this model with PyLate to index and retrieve documents. The index usesFastPLAIDfor efficient similarity search.

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#indexing-documentsIndexing documents

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

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:

# 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",
)

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#retrieving-top-k-documents-for-queriesRetrieving 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:

# 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
)

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#rerankingReranking

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 therankfunction 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,
)

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#%F0%9F%93%88-performance📈 Performance

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

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#nanobeir-multilingual-extended–ndcg10NanoBEIR Multilingual Extended — NDCG@10

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

ModelTypeAVGardeenesfritjakonoptsvLiquidAI/LFM2.5-ColBERT-350Mlate0.6050.5510.6060.6870.6070.6220.6060.6140.5900.5700.6130.586LiquidAI/LFM2.5-Embedding-350Mdense0.5770.5290.5810.6440.5810.5920.5830.5750.5630.5570.581****0.566Qwen/Qwen3-Embedding-0.6Bdense0.5560.5140.5600.6490.5680.5650.5650.5510.5300.5160.5710.525LiquidAI/LFM2-ColBERT-350Mlate0.5400.4910.5630.6610.5630.5640.5430.5570.5270.4490.5470.480Alibaba-NLP/gte-multilingual-basedense0.5280.4770.5230.6240.5370.5420.5280.5110.4940.5160.5340.526lightonai/GTE-ModernColBERT-v1late0.4890.3090.4990.6800.5250.5460.5160.4590.3680.4650.5300.483lightonai/LateOnlate0.4840.3070.5050.6900.5310.5370.5140.4420.3260.4650.5330.475lightonai/DenseOndense0.4320.1780.4740.6760.4960.5200.4870.3780.1970.4220.4930.433Alibaba-NLP/gte-modernbert-basedense0.3830.1120.4490.6660.4480.4750.4080.2750.1800.3760.4310.391BAAI/bge-large-en-v1.5dense0.3590.0590.4190.6420.4450.4750.4310.1980.1320.3580.4340.353

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#mkqa-11–recall20MKQA-11 — Recall@20

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

ModelTypeAVGardeenesfritjakonoptsvLiquidAI/LFM2.5-ColBERT-350Mlate0.6940.6080.7090.7480.7110.7150.7070.7030.6400.6890.7030.700LiquidAI/LFM2.5-Embedding-350Mdense0.6910.6100.7090.7380.7080.7150.7030.6850.6300.6910.710****0.708Alibaba-NLP/gte-multilingual-basedense0.6750.5670.6920.7410.7050.7030.6970.6550.5630.6980.7000.699LiquidAI/LFM2-ColBERT-350Mlate0.6460.5540.6960.7540.7110.7100.6670.6580.5580.5410.6690.589Qwen/Qwen3-Embedding-0.6Bdense0.6380.5200.6710.7230.6780.6720.6710.6350.5430.6200.6670.620lightonai/GTE-ModernColBERT-v1late0.4590.0920.5320.7540.5520.6150.5100.2750.1660.5030.5240.524lightonai/LateOnlate0.4540.1570.4920.7550.5370.5770.4810.3160.2090.4720.5020.501lightonai/DenseOndense0.4350.1650.4820.7510.4910.5530.4570.3250.2220.4380.4430.453BAAI/bge-large-en-v1.5dense0.4130.1330.4710.7480.4500.5310.4610.2080.1720.4560.4430.467Alibaba-NLP/gte-modernbert-basedense0.2950.0600.3330.7360.2730.4170.2910.1000.0520.3320.3260.330

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#inference-speed—llamacppInference speed - llama.cpp

End-to-end latency onMacBook Pro M4 Maxviallama.cppatfp16, measured at32-token queriesand256-token documents.Docs cachedmeans that the document embeddings are pre-computed and looked up (from an index).

ModelStageDocs cachedp50p95LFM2.5-Embedding-350MQuery embeddingyes7.3 ms9.6 msLFM2.5-ColBERT-350MQuery embeddingyes8.1 ms8.5 msLFM2.5-ColBERT-350MQuery embedding + MaxSimyes8.2 ms15.2 msLFM2.5-ColBERT-350MQuery embedding + Doc embedding + MaxSimno34.3 ms36.3 ms Both modelsLiquidAI/LFM2.5-ColBERT-350M-GGUFandLiquidAI/LFM2.5-Embedding-350M-GGUFare available on Hugging Face under different quantization schemas for llama.cpp.

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#inference-speed—enterprise-gpuInference 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.

WorkloadSetupp50p95p99LFM2.5-Embedding-350MQuery embedding1.5 ms1.6 ms1.7 msLFM2.5-ColBERT-350MQuery embedding1.3 ms1.4 ms1.5 msLFM2.5-ColBERT-350MQuery embedding + MaxSim2.5 ms2.7 ms2.8 msLFM2.5-ColBERT-350MQuery embedding + Doc embedding + MaxSim22.8 ms24.1 ms26.4 ms

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#%F0%9F%93%AC-contact📬 Contact

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

https://huggingface.co/LiquidAI/LFM2.5-ColBERT-350M#citationCitation

@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}
}