LFM2.5-Embedding-350M — MLX (4-bit)#
MLX build of LiquidAI/LFM2.5-Embedding-350M, a multilingual dense bi-encoder (1024-dim CLS embedding, cosine similarity), 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 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. - Reload applies the quant from
config.json["quantization"] before loading weights (see retrieval.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 | 0.728 | 100.0% | 0.775 | 100.0% | 709 MB |
| 8-bit | 0.729 | 100.1% | 0.775 | 100.0% | 377 MB |
| 4-bit ◄ | 0.730 | 100.0% | 0.766 | 98.6% | 200 MB |
| mxfp4 | 0.725 | 99.8% | 0.764 | 98.4% | — |
NDCG@10 by dataset#
| dataset | bf16 | 8-bit | 4-bit ◄ | mxfp4 |
|---|
| NanoNQ · en | 0.704 | 0.704 | 0.703 | 0.703 |
| NanoFiQA2018 · en | 0.504 | 0.511 | 0.502 | 0.498 |
| NanoSciFact · en | 0.716 | 0.717 | 0.714 | 0.712 |
| NanoNFCorpus · en | 0.342 | 0.340 | 0.335 | 0.345 |
| MIRACL · es | 0.891 | 0.892 | 0.895 | 0.893 |
| MIRACL · de | 0.809 | 0.810 | 0.819 | 0.812 |
| MIRACL · ja | 0.929 | 0.928 | 0.940 | 0.922 |
| MIRACL · ar | 0.926 | 0.926 | 0.928 | 0.916 |
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-Embedding-350M
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