LFM2.5-ColBERT-350M

← Back to Liquid Nanos LFM2.5-ColBERT-350M is a late-interaction retrieval model. It creates 128-dimensional vectors per token and scores query/document matches with MaxSim, which improves retrieval quality and generalization at the cost of a larger index.

HF GGUF

Use LFM2.5-ColBERT-350M when you want stronger retrieval or reranking quality and can afford a larger per-token index. Use LFM2.5-Embedding-350M when you need the smallest, fastest dense-vector index.

Specifications

Property	Value
Parameters	~353M
Type	Late interaction
Document Length	512 tokens
Query Length	32 tokens
Output	128-dimensional vector per token
Similarity	MaxSim
Supported Languages	English, Spanish, German, French, Italian, Portuguese, Arabic, Swedish, Norwegian, Japanese, Korean

High-Quality Retrieval

Better matching from token-level interactions.

Reranking

Reorder candidates from a first-stage retriever.

Enterprise RAG

Strong multilingual document matching.

Quick Start

This model uses PyLate for indexing, retrieval, and reranking.

PyLate
Reranking
GGUF

Install:

pip install -U pylate

Index and retrieve 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,
)

documents_ids = ["1", "2", "3"]
documents = [
    "Paris is the capital of France.",
    "Tokyo is the capital of Japan.",
    "Berlin is the capital of Germany.",
]

document_embeddings = model.encode(
    documents,
    batch_size=32,
    is_query=False,
    show_progress_bar=True,
)
index.add_documents(
    documents_ids=documents_ids,
    documents_embeddings=document_embeddings,
)

query_embeddings = model.encode(
    ["Which city is Japan's capital?"],
    batch_size=32,
    is_query=True,
    show_progress_bar=True,
)

retriever = retrieve.ColBERT(index=index)
results = retriever.retrieve(query_embeddings=query_embeddings, k=10)
print(results)

from pylate import models, rank

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

queries = ["Which city is Japan's capital?"]
documents = [[
    "Paris is the capital of France.",
    "Tokyo is the capital of Japan.",
    "Berlin is the capital of Germany.",
]]
document_ids = [["fr", "jp", "de"]]

query_embeddings = model.encode(queries, is_query=True)
document_embeddings = model.encode(documents, is_query=False)

reranked = rank.rerank(
    documents_ids=document_ids,
    queries_embeddings=query_embeddings,
    documents_embeddings=document_embeddings,
)
print(reranked)

Download GGUF:

hf download LiquidAI/LFM2.5-ColBERT-350M-GGUF \
  --local-dir ./LFM2.5-ColBERT-350M-GGUF

Use the GGUF files with a llama.cpp build that supports LFM2.5 ColBERT models.

Models

Fine-tuning

Edge Inference

GPU Inference

Cloud Inference

Guides

Help

Specifications

High-Quality Retrieval

Reranking

Enterprise RAG

Quick Start

​Specifications

High-Quality Retrieval

Reranking

Enterprise RAG

​Quick Start

Specifications

Quick Start