This repository hosts the **all-MiniLM-L6-v2** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. Upstream model: all-MiniLM-L6-v2 `xnnpack/all_minilm_l6_v2_xnnpack_int8.pte` is 8da8w (int8 dynamic activations, int8 per-channel weights) on the Linear layers, with an int8 word-embedding table. SQuAD v1.1 dev passage retrieval (2000 questions against their 315 paragraphs), dot product:
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View details ↗This repository hosts the **clip-vit-base-patch32** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. The `xnnpack/*_int8.pte` towers are 8da8w (int8 dynamic activations, int8 per-channel weights) on the Linear layers; the text tower also has an int8 token embedding table. ImageNetV2 zero-shot top-1 (1000 images, one per class, "a photo of a {class}."): 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.
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View details ↗This repository hosts the craft detector model for the React Native ExecuTorch library. It includes the model exported for xnnpack backend in `.pte` format, ready for use in the **ExecuTorch** runtime. If you'd like to run these models in your own ExecuTorch runtime, refer to the official documentation for setup instructions. If you intend to use this models outside of React Native ExecuTorch, make sure your runtime is compatible with the **ExecuTorch** version used to export the `.pte` files. For more details, see the compatibility note in the ExecuTorch GitHub repository. If you work with React Native ExecuTorch, the constants from the library will guarantee compatibility with runtime used behind the scenes.
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View details ↗This repository hosts the crnn recognizer model for the React Native ExecuTorch library. It includes the model exported for xnnpack backend in `.pte` format, ready for use in the **ExecuTorch** runtime. If you'd like to run these models in your own ExecuTorch runtime, refer to the official documentation for setup instructions. If you intend to use this models outside of React Native ExecuTorch, make sure your runtime is compatible with the **ExecuTorch** version used to export the `.pte` files. For more details, see the compatibility note in the ExecuTorch GitHub repository. If you work with React Native ExecuTorch, the constants from the library will guarantee compatibility with runtime used behind the scenes.
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View details ↗This repository hosts the **efficientnet-v2-s** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. A backend directory carrying a `NOTES.md` and no `.pte` was exported and not published. Its note records what went wrong. 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.
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View details ↗This repository hosts the **fsmn-vad** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. A backend directory carrying a `NOTES.md` and no `.pte` was exported and not published. Its note records what went wrong. - `coreml` (why) - `mlx` (why)
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View details ↗This repository hosts the **gemma-4-multimodal** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. The XNNPACK export uses mixed precision: MLP and language-model head linears use 8da4w, while attention projections and the PLE residual output projections remain fp16. Token embeddings remain int4. The small PLE context projection weights are materialized from their quantized values before lowering to preserve dequantization semantics. The model filename and runtime API remain unchanged; maximum prefill is 512 tokens and context length is 4,096 tokens. 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.
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View details ↗This repository hosts the **gemma-4** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. The XNNPACK export uses mixed precision: MLP and language-model head linears use 8da4w, while attention projections and the PLE residual output projections remain fp16. Token embeddings remain int4. The small PLE context projection weights are materialized from their quantized values before lowering to preserve dequantization semantics. The model filename and runtime API remain unchanged; maximum prefill is 512 tokens and context length is 4,096 tokens. 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.
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View details ↗This repository hosts the **lfm-2.5** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. `2_6b/` contains the instruction-tuned LiquidAI/LFM2.5-2.6B checkpoint, exported for chat assistants with an 8,192-token context and 128-token prefill chunks. Its chat template strips thinking from every assistant message when rendering history, matching Qwen3 and keeping incremental KV-cache prompts monotonic. Generation still starts with ` `. `tokenizer_config.json` contains the corrected template. Both XNNPACK and MLX artifacts were executed on a physical Apple Silicon Mac, including generation and the maximum prefill chunk size. See `2_6b/validation.json` for smoke-test results and `2_6b/export_manifest.json` for checkpoint provenance and SHA-256 hashes. The MLX artifact requires the React Native ExecuTorch MLX runtime from the software-mansion-labs ExecuTorch fork. The embedding lookup uses a portable int4 kernel; the decoder runs in one MLX partition.
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View details ↗This repository hosts the **llama-3.2** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. 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.
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View details ↗This repository hosts the **paraphrase-multilingual-MiniLM-L12-v2** models exported for the React Native ExecuTorch library as ExecuTorch `.pte` programs, ready to run on device. 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.
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