LFM2.5-ColBERT-350M
by LiquidAI
Edge-sized ColBERT late-interaction retriever built on LFM2.5
LiquidAI/LFM2.5-ColBERT-350Mmixpeek://reranker@v1/lfm25_colbert_350m_v1Overview
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.
Mixpeek SDK Integration
// Index documents in a collection (dense first stage)
const collection = await mx.collections.create({
collection_name: "docs-collection",
source: { type: "bucket", bucket_ids: ["bkt_your_bucket"] },
feature_extractor: {
feature_extractor_name: "text_embeddings",
version: "v1",
parameters: { model_id: "BAAI/bge-m3" },
},
});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
Benchmarks
| Dataset | Metric | Score | Source |
|---|---|---|---|
| Hugging Face | Pipeline tag | sentence-similarity (ColBERT/PyLate) | Liquid AI, 2026 Model Card |
| Hugging Face | Parameters | 350M | Liquid AI, 2026 Model Card |
| Hugging Face | Backbone | LFM2.5 | Liquid AI, 2026 Model Card |
Performance
Common Pipeline Companions
Specification
Research Paper
LFM2.5-ColBERT-350M model card
arxiv.orgBuild a pipeline with LFM2.5-ColBERT-350M
Add this model to a processing pipeline alongside other extractors. Combine with retrieval stages for end-to-end search.
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