LFM2.5-ColBERT-350M — MLX (4-bit)
MLX build of LiquidAI/LFM2.5-ColBERT-350M, a multilingual late-interaction retriever (128-dim vector per token, scored with MaxSim), for local inference on Apple Silicon with MLX.
All weights, architecture, and behavior are LiquidAI's. This repository changes the file format (PyTorch/safetensors → MLX) and post-training quantized to 4-bit (affine, group size 64) from the bf16 MLX conversion. Every Linear and embedding layer (including the 1024→128 Dense projection head) is quantized; the non-quantized layers (conv, norms) stay bf16. See the original model card for training details and intended use.
Quantization details
- Quantized with
mlx.nn.quantize(mode='affine', bits=4, group_size=64)— the exact configuration benchmarked below. - Verified bit-exact: the reloaded checkpoint's encodings are identical (max abs diff
0) to the in-memory-quantized model used for the benchmark (verify_export.py) — the shipped artifact is the model measured below. Downstream ColBERT NDCG can wobble ≤0.002 across processes from GPU MaxSim reduction order — a scoring artifact, not the weights. - Reload applies the quant from
config.json["quantization"]before loading weights (seeretrieval.load_model).
Evaluation
Retrieval quality of this checkpoint (and its sibling precisions), measured as NDCG@10 / Recall@10 on judged pools. Retention = metric ÷ bf16 metric, averaged per-dataset.
Setup. English = the four NanoBEIR sets (full small corpora, ~2–5k passages, 50 queries each). Multilingual = MIRACL dev (the real queries and relevance judgments) for Spanish, German, Japanese, Arabic, each scored over a reduced pool of ~6k passages (judged positives + hard-mined negatives + sampled distractors, from mteb/MIRACLRetrievalHardNegatives), 100 queries each. Reduced pools make absolute scores easier than full-corpus MIRACL and not leaderboard-comparable — but every precision searches the identical pool, so the retention numbers (the point of this table) are sound. ColBERT uses brute-force MaxSim with no query augmentation, so its absolute scores sit a touch below a full PLAID setup.
Summary (mean over 8 datasets)
| precision | NDCG@10 | NDCG retention | Recall@10 | Recall retention | size |
|---|---|---|---|---|---|
| bf16 | 0.740 | 100.0% | 0.780 | 100.0% | 707 MB |
| 8-bit | 0.741 | 100.0% | 0.779 | 99.4% | 376 MB |
| 4-bit ◄ | 0.731 | 98.7% | 0.780 | 99.7% | 199 MB |
| mxfp4 | 0.730 | 98.5% | 0.773 | 98.8% | — |
NDCG@10 by dataset
| dataset | bf16 | 8-bit | 4-bit ◄ | mxfp4 |
|---|---|---|---|---|
| NanoNQ · en | 0.757 | 0.751 | 0.716 | 0.742 |
| NanoFiQA2018 · en | 0.528 | 0.512 | 0.524 | 0.520 |
| NanoSciFact · en | 0.693 | 0.712 | 0.702 | 0.682 |
| NanoNFCorpus · en | 0.345 | 0.342 | 0.335 | 0.334 |
| MIRACL · es | 0.900 | 0.901 | 0.899 | 0.900 |
| MIRACL · de | 0.823 | 0.837 | 0.826 | 0.811 |
| MIRACL · ja | 0.934 | 0.933 | 0.923 | 0.926 |
| MIRACL · ar | 0.938 | 0.941 | 0.924 | 0.926 |
License & attribution
Redistributed under the LFM Open License v1.0 (LICENSE) — the same license as the original model. Per Section 4, this notice records that the files were modified (format conversion to MLX + 4-bit quantization). The original work is by Liquid AI; this repository is an independent conversion, not affiliated with or endorsed by Liquid AI. The license includes a commercial-use threshold (Section 5) — review it for your use case.
Base model: LiquidAI/LFM2.5-ColBERT-350M