LFM2.5-ColBERT-350M

Name: LFM2.5-ColBERT-350M
Author: LiquidAI

by LiquidAI

Edge-sized ColBERT late-interaction retriever built on LFM2.5

8Kdl/month

103likes

HuggingFace Run on your data, free

Identifier

Model ID

LiquidAI/LFM2.5-ColBERT-350M

Overview

LFM2.5-ColBERT-350M is Liquid AI's compact late-interaction retriever: instead of collapsing a document into one vector, it keeps a small vector per token and scores relevance with MaxSim between query and document token vectors. That preserves term-level precision that single-vector embeddings blur away, which shows up on exact-phrase, entity-heavy, and long-document retrieval. At 350M parameters with the LFM2.5 backbone's efficiency, it runs late-interaction quality at edge and on-CPU budgets where classic ColBERT deployments were impractical.

On Mixpeek, a late-interaction model like this slots into the reranking or precision stage of a multi-stage retriever: a dense first stage recalls candidates cheaply, then token-level MaxSim re-scores the top-K. See the late interaction retrieval guide for when token-level matching beats single vectors and what it costs at the index layer.

Architecture

ColBERT-style late-interaction architecture (PyLate-compatible) on the LFM2.5 hybrid backbone: per-token contextual embeddings with MaxSim scoring, trained for sentence-similarity and retrieval. 350M parameters, English-focused, runs via sentence-transformers/PyLate with custom code enabled.

Key Capabilities

•Token-level late-interaction scoring (MaxSim) for precise term matching
•Multi-vector document representations that survive exact-phrase queries
•Edge/CPU-friendly footprint at 350M parameters
•PyLate and sentence-transformers compatible for drop-in retrieval stacks
•Strong fit as a precision reranking stage over a dense first stage

Use Cases on Mixpeek

•Reranking top-K candidates from a dense retriever with token-level precision
•Entity- and phrase-heavy corpora (legal, technical docs) where single vectors blur terms
•On-prem or edge retrieval where large rerankers do not fit
•Hybrid stacks pairing BM25 or dense recall with late-interaction re-scoring

Use LFM2.5-ColBERT-350M on Mixpeek

Build multimodal processing pipelines with this model and others. Extract features, run inference, and set up retrieval in Mixpeek Studio.

Open Studio

How It Runs on Mixpeek

On Mixpeek, LFM2.5-ColBERT-350M runs as a managed extractor inside a processing pipeline. Point a bucket of sentence similarity data at it, and Mixpeek handles GPU provisioning, batching, retries, and writing the outputs into a vector store you can query.

Extractor outputs land in the Mixpeek Vector Store (MVS), where you can combine them with retrieval, reranking, and filter stages to build end-to-end search and agent-perception pipelines, no model-serving infrastructure to maintain.