This is a modified RESMP.DEV research release derived from LiquidAI/LFM2.5-Encoder-230M at revision 0b649ad0c684378b03d4d8304f7577a662ab89bc. It is not an official Liquid AI release. We removed the masked-language-model head and contrastively fine-tuned the full bidirectional encoder for multilingual code retrieval.

Quantization finding

This is a research artifact, not an automatic recommendation to replace the BF16 model. Activation calibration is compared with matched native round-to-nearest quantization and the complete machine-readable receipts are included so mobile and Apple-Silicon users can evaluate the size, latency, memory, and quality tradeoff themselves.

Held-out retrieval results

All rows use the same untouched 6,995-pair multilingual test set, 1,200-character query and 4,000-character passage caps, query token cap 512, and passage token cap 2,048. Higher is better. RTN is a matched quantization control; Nomic and Jina are external service baselines, not architecture-matched controls.

Model	MRR	R@1	R@5	R@10	NDCG@10	Python MRR	TypeScript MRR	Artifact
LFM2.5 230M BF16	0.3960	0.3212	0.4756	0.5375	0.4235	0.8360	0.2527	464.2 MB
LFM2.5 230M calibrated MXFP4	0.2110	0.1620	0.2572	0.3029	0.2262	0.7113	0.0693	225.5 MB
LFM2.5 230M RTN MXFP4	0.0806	0.0609	0.0931	0.1139	0.0844	0.4435	0.0191	225.4 MB
LFM2.5 230M calibrated MXFP8	0.3934	0.3197	0.4715	0.5342	0.4207	0.8394	0.2447	306.8 MB
LFM2.5 230M RTN MXFP8	0.3878	0.3162	0.4622	0.5245	0.4139	0.8322	0.2459	306.7 MB
Nomic v1.5 service	0.5439	0.4968	0.5954	0.6236	0.5595	0.9289	0.3617	service
Jina calibrated MXFP4	0.6645	0.6133	0.7221	0.7571	0.6832	0.9462	0.5057	1167.7 MB

A separate BF16 cross-runtime run on NVIDIA GeForce RTX 3090 Ti with PyTorch 2.13.0+cu130 produced MRR 0.3963, 755.7 queries/s, 190.8 passages/s, and 736.2 MB peak CUDA allocation. CUDA throughput is reported separately and is not compared directly with Metal.

A paired 10,000-sample bootstrap estimates calibrated MXFP8 minus BF16 MRR at -0.0026, with a 95% interval of [-0.0042, -0.0010]. A point estimate whose interval crosses zero is not presented as a quality win.

Usage

bash

git clone https://github.com/RESMP-DEV/calibrated-code-embeddings
cd calibrated-code-embeddings
uv sync --extra mlx
CODE_EMBEDDING_MODEL_PATH=/path/to/this-model code-embedding-serve --port 1235

The service exposes POST /v1/embeddings. It runs the bidirectional LFM2.5 body directly with MLX; LM Studio is not required. Prefix retrieval queries with query: and candidate code with passage: when calling the model directly.

Training and data receipts

Full-backbone symmetric in-batch InfoNCE training used 24,626 language-balanced pairs selected from the 42,626-row source training split, two epochs, batch size 32, learning rate 2e-5, temperature 0.05, and seed 17. The training report records the NVIDIA RTX A6000 runtime and validation history.

train: 42,626 rows, SHA-256 426ebfaad34b14d7627ba6e668ae36e08e548c9d057b0edc208bcfa6fe527629
validation: 5,319 rows, SHA-256 9ac88b3138de4ca94c2ef3a87ccf19381fc76265c2bc9d65b4791983d0315096
test: 6,995 rows, SHA-256 9ed10842a12132b6bfb5421df1e2f88dbcfbf6f6e960f36b22eb9ea6e3c72315
calibration: 4,096 rows, SHA-256 ee9edaf80a6854c18053b96521090a51bdb76642abeb98618d7aed36e70b6de9

The corpus combines pinned CodeSearchNet data with pinned permissively licensed code repositories. Exact and token 8-gram near-duplicates were removed with test-before- validation-before-train precedence. See corpus_receipt.json, source_receipt.json, training_report.json, quantization_report.json when present, benchmarks/, and artifact_manifest.json for machine-readable evidence.

License and attribution

The weights retain the LFM Open License v1.0 in LICENSE, including its attribution and commercial-use conditions. MODIFICATIONS.md identifies RESMP.DEV's changes. The training and quantization workbench is separately MIT licensed.

Citation

bibtex

@article{liquidAI2026Encoders,
  author = {Liquid AI},
  title = {LFM2.5-Encoders: Fast at Long Context, Even on CPU},
  journal = {Liquid AI Blog},
  year = {2026},
  note = {www.liquid.ai/blog/lfm2-5-encoders},
}

RESMP-DEV/LFM2.5-Encoder-230M-Code-MXFP4-GPTQ

LFM2.5 Encoder 230M Code MXFP4-GPTQ