Model reference · open weights

react-native-executorch-pp-ocrv6

Not for GPU servers LLMs software-mansion Image→text 1 build Open weights 554 dl/mo

react-native-executorch-pp-ocrv6 is an open-weight language model from software-mansion. Built for phones (ExecuTorch) — it does not run on a GPU server.

What it is

Released bysoftware-mansion
TypeLanguage models
TaskImage→text
Runs withexecutorch
Released2026-06-29
Popularity554 downloads / month
LicenceOpen weights

From the model card

What software-mansion says about react-native-executorch-pp-ocrv6

This repository hosts the pp-ocrv6 models exported for the React Native ExecuTorch library as ExecuTorch .pte programs, ready to run on device.

Upstream models:

Read the full model card

Variants

PathBackendPrecision
coreml/pp_ocrv6_coreml_int8.ptecoremlint8
vulkan/pp_ocrv6_vulkan_fp16.ptevulkanfp16
xnnpack/pp_ocrv6_xnnpack_fp32.ptexnnpackfp32
xnnpack/pp_ocrv6_xnnpack_int8.ptexnnpackint8

Repository structure

charset.json                       128 kB
config.json                        30 B
coreml/config.json                 1.3 kB
coreml/pp_ocrv6_coreml_int8.pte    7.9 MB
vulkan/config.json                 1.3 kB
vulkan/pp_ocrv6_vulkan_fp16.pte    25.0 MB
xnnpack/config.json                2.3 kB
xnnpack/pp_ocrv6_xnnpack_fp32.pte  29.6 MB
xnnpack/pp_ocrv6_xnnpack_int8.pte  22.8 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.

CoreML notes (iOS)

  • The CoreML .pte is a multifunction Core ML model (detect + recognize share one precompiled .mlmodelc). Requires iOS 18+ and an ExecuTorch runtime ≥ 1.3 (multifunction loading via functionName).
  • First-ever load on a device triggers a one-time per-shape ANE specialization (OS-cached afterwards) — warm each model once after install.

Model details

The recognizer's output is a probability distribution over the charset with softmax already baked in. Index 0 is the CTC blank, so charset[i] corresponds to logit i + 1.

Quoted from the model card on Hugging Face — the full card is behind the Hugging Face link above.

How it works

How language models work

Your prompttext / messagesTransformerattention over tokensNext-token loopgenerate + streamResponsetext · tool callsA language model reads your tokens and predicts the next one, again and again, streaming the reply back.

Running it

Where it runs

Built for phones (ExecuTorch) — it does not run on a GPU server.

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