Model reference · open weights

react-native-executorch-lfm2.5-embedding

Available as managed deployment Licence fee Embeddings software-mansion Embeddings 1 variants 5k dl/mo

react-native-executorch-lfm2.5-embedding is an open-weight embedding model from software-mansion. AxForge deploys and operates it for you on dedicated EU-owned hardware — with the licence handled where one is required.

Available as managed deployment — configured and operated for you on dedicated EU hardware, quoted per deployment.

What it is

Released bysoftware-mansion
TypeEmbedding models
TaskEmbeddings
Runs withexecutorch
Released2026-06-19
Popularity5k downloads / month
LicenceCommercial licence needed

About

What react-native-executorch-lfm2.5-embedding is

This repository hosts the lfm2.5-embedding-350m models exported for the React Native ExecuTorch library as ExecuTorch .pte programs, ready to run on device.

Upstream model: LFM2.5-Embedding-350M

Read the full model card

Variants

PathBackendPrecision
mlx/lfm_2_5_embedding_350m_mlx_int4.ptemlxint4
xnnpack/lfm_2_5_embedding_350m_xnnpack_8da4w.ptexnnpack8da4w

Unshipped backends

A backend directory carrying a NOTES.md and no .pte was exported and not published. Its note records what went wrong.

Repository structure

config.json                                       43 B
coreml/NOTES.md                                   1.6 kB
mlx/config.json                                   1015 B
mlx/lfm_2_5_embedding_350m_mlx_int4.pte           180 MB
tokenizer.json                                    4.5 MB
tokenizer_config.json                             89.9 kB
xnnpack/config.json                               1.0 kB
xnnpack/lfm_2_5_embedding_350m_xnnpack_8da4w.pte  548 MB

Compatibility

These files are published for the ExecuTorch v1.4.1 runtime. ExecuTorch gives no forward compatibility guarantee, so an older runtime may fail to load them.

To use them in React Native ExecuTorch, pass the model constant shipped in the library's model registry to the corresponding task pipeline. See the documentation.

To load these files in your own ExecuTorch runtime, read the compatibility note first.

Model details

  • Architecture: LFM2.5-350M bidirectional backbone (hybrid conv + attention, hidden size 1024) + CLS pooling + L2 normalize. The exported graph bakes in CLS pooling and L2 normalization, so the runner consumes (input_ids, attention_mask) and receives the final unit-norm embedding directly.
  • Output dimension: 1024.
  • Similarity metric: cosine (embeddings are L2-normalized, so a dot product equals cosine).
  • Prompts: the model is trained with asymmetric query: / document: text prefixes. Prepend query: to search queries and document: to indexed passages for best retrieval quality.

From the published model card. Full card on the HuggingFace links in the sidebar.

How it works

How embedding models work

Your textsentence / documentEncodermaps meaningVectorlist of numbersAn embedding model turns text into a vector, so similar meanings sit close together — the basis of search and RAG.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys react-native-executorch-lfm2-5-embedding for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (react-native-executorch-lfm2-5-embedding below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"react-native-executorch-lfm2-5-embedding","input":"text to embed"}'

Create an account — your API key is available in the console. 3M free tokens every 30 days with every new account.

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