Model reference · open weights
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 by | software-mansion |
|---|---|
| Type | Language models |
| Task | Image→text |
| Runs with | executorch |
| Released | 2026-06-29 |
| Popularity | 554 downloads / month |
| Licence | Open weights |
From the model card
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:
| Path | Backend | Precision |
|---|---|---|
coreml/pp_ocrv6_coreml_int8.pte | coreml | int8 |
vulkan/pp_ocrv6_vulkan_fp16.pte | vulkan | fp16 |
xnnpack/pp_ocrv6_xnnpack_fp32.pte | xnnpack | fp32 |
xnnpack/pp_ocrv6_xnnpack_int8.pte | xnnpack | int8 |
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
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.
.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).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
Running it
Built for phones (ExecuTorch) — it does not run on a GPU server.