Add VPIPE supplementary CoreML models with docs and licenses

Release three pre-converted Core ML model packages consumed by the VPIPE
runtime, each shipped as a Git LFS .tar of an .mlpackage:

- qwen3_5_mlx_4b_vision_vid_512x320_w8 Qwen3.5-4B vision tower (Apache-2.0)
- yolox_l_1024x640_w8 YOLOX-L object detector (Apache-2.0)
- silero-vad-unified-v6 Silero VAD v6, unified (MIT)

Document each model in README.md (task, I/O, precision, upstream source,
conversion toolchain, SHA-256 checksums) and record per-model attribution
with verbatim license texts in THIRD_PARTY_LICENSES.md.

The bundle mixes two licenses, so the README front matter declares
`license: other` (apache-2.0 and mit) rather than a single identifier.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>

README.md

@@ -1,3 +1,111 @@
 ---
-license: apache-2.0
+license: other
+license_name: mixed-apache-2.0-and-mit
+license_link: https://huggingface.co/tgo-app-dev/vpipe-supplement/blob/main/THIRD_PARTY_LICENSES.md
+tags:
+  - coreml
+  - vpipe
+  - apple-silicon
+  - object-detection
+  - voice-activity-detection
+  - vision-language-model
 ---
+
+# VPIPE supplementary CoreML models
+
+This repository distributes the supplementary, pre-converted **Core ML**
+model packages consumed by the VPIPE runtime — a concurrent pipeline
+framework for real-time video tasks on Apple silicon, by T-Go LLC. These
+are third-party models converted to Core ML; they are released here so the
+VPIPE runtime can fetch a pinned, ready-to-run artifact instead of
+re-running the conversion.
+
+> **Licensing note:** the bundled models are **not** all under a single
+> license. Two are Apache-2.0 and one (Silero VAD) is MIT. See
+> [THIRD_PARTY_LICENSES.md](THIRD_PARTY_LICENSES.md) for the per-model
+> attribution and verbatim license texts.
+
+## Distribution format
+
+Each archive is a `.tar` (tracked with Git LFS) containing a single
+top-level `*.mlpackage` directory — an unflattened Core ML model package
+with its `Manifest.json`, the `model.mlmodel` spec, and the
+`weights/weight.bin` blob. Unpack before use:
+
+```sh
+tar xf yolox_l_1024x640_w8.tar      # -> yolox_l_1024x640_w8.mlpackage/
+```
+
+The VPIPE runtime loads the unpacked `.mlpackage` via its `model_path`
+configuration. In the filenames, `w8` denotes 8-bit weight quantization
+(activations run in FP16 on the Apple Neural Engine); resolutions are
+written `WxH`.
+
+## Models included
+
+| Archive | Model | Task | License |
+| --- | --- | --- | --- |
+| `qwen3_5_mlx_4b_vision_vid_512x320_w8.tar` | Qwen3.5-4B vision tower (Core ML) | Vision encoder for a multimodal LLM | Apache-2.0 |
+| `yolox_l_1024x640_w8.tar` | YOLOX-L (Core ML) | Object detection | Apache-2.0 |
+| `silero-vad-unified-v6.tar` | Silero VAD v6, unified (Core ML) | Voice activity detection | MIT |
+
+### `qwen3_5_mlx_4b_vision_vid_512x320_w8`
+
+Qwen3.5-4B vision tower (`variant=ane_v1_video`), the vision encoder that
+turns video frames into the embedding sequence consumed by the Qwen3.5-4B
+language model.
+
+- **Task:** image/video → vision feature embeddings (multimodal LLM front-end).
+- **Inputs:** `image0`, `image1` — the two consecutive video frames that
+  form one temporal patch, each **512×320 (W×H)**. Fed as a BGRA8888
+  `CVPixelBuffer`; the graph drops alpha and applies a `1/127.5` scale and
+  `-1` bias, yielding a BGR tensor in `[-1, 1]`.
+- **Output:** `image_features` — vision features of shape `(160, 2560)`.
+- **Precision:** 8-bit weight quantization, FP16 compute.
+- **Upstream:** [Qwen3.5](https://huggingface.co/Qwen) (Qwen team, Alibaba Cloud) — Apache-2.0.
+- **Conversion:** coremltools 9.0 from TorchScript (`torch==2.12.0`).
+
+### `yolox_l_1024x640_w8`
+
+YOLOX-L anchor-free object detector, converted to Core ML at a fixed
+**1024×640 (W×H)** input.
+
+- **Task:** object detection (bounding boxes + per-class scores).
+- **Input:** `image` — Core ML image input at 1024×640.
+- **Output:** `output` — detection tensor `[1, N, 4 + num_classes]`
+  (`(cx, cy, w, h)` in model-input pixels plus sigmoid class scores).
+- **Precision:** 8-bit weight quantization, FP16 compute.
+- **Upstream:** [YOLOX](https://github.com/Megvii-BaseDetection/YOLOX) (Megvii Inc.) — Apache-2.0.
+- **Conversion:** coremltools 9.0 from TorchScript (`torch==2.7.0`).
+
+### `silero-vad-unified-v6`
+
+Silero VAD v6.0.0, the unified single-graph (STFT + encoder + decoder)
+Core ML conversion by FluidInference.
+
+- **Task:** voice activity detection (per-window speech probability).
+- **Inputs:** `audio_input` (mono PCM window) plus the recurrent
+  `hidden_state` / `cell_state` carried between calls.
+- **Outputs:** `vad_output` (speech probability) plus
+  `new_hidden_state` / `new_cell_state`.
+- **Precision:** FP16 compute.
+- **Upstream:** [Silero VAD](https://github.com/snakers4/silero-vad)
+  (Silero Team) — MIT; Core ML conversion from
+  [FluidInference/silero-vad-coreml](https://huggingface.co/FluidInference/silero-vad-coreml) — MIT.
+- **Conversion:** coremltools 9.0b1 from TorchScript (`torch==2.7.0`).
+
+## Verifying downloads
+
+SHA-256 checksums of the archives:
+
+```
+3940398c2518ac5f01c09f5f5eab868c2b217ab53c7d9a145d2387e76251b5d5  qwen3_5_mlx_4b_vision_vid_512x320_w8.tar
+133cd460061c943a042a4d69090825f83941725db2097b4e8ebae9802b956cd3  yolox_l_1024x640_w8.tar
+ee14d006d1cea87f15a4f0ac5f3e1e00b80a10c40eca855e8ff6741f389b4423  silero-vad-unified-v6.tar
+```
+
+## Licenses
+
+These are third-party models redistributed in converted form. Each retains
+its upstream license; see [THIRD_PARTY_LICENSES.md](THIRD_PARTY_LICENSES.md)
+for full attribution and verbatim license texts.

THIRD_PARTY_LICENSES.md

@@ -0,0 +1,343 @@
+# Third-Party Licenses
+
+This repository redistributes the following third-party machine-learning
+models in pre-converted **Core ML** form. Each entry below names the
+component and the archive it ships in, identifies the upstream source,
+reproduces the upstream copyright statement, and points to the verbatim
+license text required by that component's license.
+
+Two of the three models are licensed under the Apache License, Version 2.0;
+the third (Silero VAD) is licensed under the MIT License. The verbatim
+license texts are reproduced in the appendices at the end of this file.
+
+| Archive | Component | License |
+| --- | --- | --- |
+| `qwen3_5_mlx_4b_vision_vid_512x320_w8.tar` | Qwen3.5 vision tower | Apache-2.0 ([Appendix A](#appendix-a-apache-license-version-20)) |
+| `yolox_l_1024x640_w8.tar` | YOLOX | Apache-2.0 ([Appendix A](#appendix-a-apache-license-version-20)) |
+| `silero-vad-unified-v6.tar` | Silero VAD (FluidInference Core ML conversion) | MIT ([Appendix B](#appendix-b-mit-license)) |
+
+---
+
+## Qwen3.5 (vision tower)
+
+The archive `qwen3_5_mlx_4b_vision_vid_512x320_w8.tar` contains a Core ML
+conversion of the **Qwen3.5-4B** vision tower (the multimodal vision
+encoder), derived from the Qwen3.5 model series published by the Qwen team
+at Alibaba Cloud (<https://huggingface.co/Qwen>). The weights are
+8-bit-quantized and the graph is exported to Core ML; the model is otherwise
+a derivative of the upstream Qwen3.5 weights.
+
+Qwen3.5 open-weight models are distributed under the Apache License,
+Version 2.0.
+
+### Copyright notice
+
+Reproduced from the upstream `LICENSE`:
+
+> Copyright 2024 Alibaba Cloud
+
+### License
+
+Apache License, Version 2.0. The verbatim text is reproduced in
+[Appendix A](#appendix-a-apache-license-version-20), and is available
+upstream at
+<https://huggingface.co/Qwen/Qwen3-8B/blob/main/LICENSE> and
+<https://www.apache.org/licenses/LICENSE-2.0>.
+
+---
+
+## YOLOX
+
+The archive `yolox_l_1024x640_w8.tar` contains a Core ML conversion of
+**YOLOX-L**, the anchor-free object detector from Megvii Inc.
+(<https://github.com/Megvii-BaseDetection/YOLOX>). The weights are
+8-bit-quantized and exported to Core ML at a 1024×640 input; the model is
+otherwise a derivative of the upstream YOLOX-L weights.
+
+YOLOX is distributed under the Apache License, Version 2.0.
+
+### Copyright notice
+
+Reproduced verbatim from
+<https://github.com/Megvii-BaseDetection/YOLOX/blob/main/LICENSE>:
+
+> Copyright (c) 2021-2022 Megvii Inc. All rights reserved.
+
+### License
+
+Apache License, Version 2.0. The verbatim text is reproduced in
+[Appendix A](#appendix-a-apache-license-version-20), and is available
+upstream at
+<https://github.com/Megvii-BaseDetection/YOLOX/blob/main/LICENSE>.
+
+---
+
+## Silero VAD
+
+The archive `silero-vad-unified-v6.tar` contains the **Silero VAD v6.0.0**
+unified Core ML model (STFT + encoder + decoder in one graph). The
+underlying voice-activity-detection model is Silero VAD by the Silero Team
+(<https://github.com/snakers4/silero-vad>); the Core ML conversion
+redistributed here is from FluidInference
+(<https://huggingface.co/FluidInference/silero-vad-coreml>). Both the
+original model and the FluidInference conversion are distributed under the
+MIT License.
+
+### Copyright notice
+
+Reproduced verbatim from
+<https://github.com/snakers4/silero-vad/blob/master/LICENSE>:
+
+> Copyright (c) 2020-present Silero Team
+
+The FluidInference Core ML conversion credits both the Silero Team
+(original model) and the FluidAudio Team (conversion) and is itself
+released under the MIT License.
+
+### License
+
+MIT License. The verbatim text is reproduced in
+[Appendix B](#appendix-b-mit-license), and is available upstream at
+<https://github.com/snakers4/silero-vad/blob/master/LICENSE>.
+
+---
+
+# Appendix A: Apache License, Version 2.0
+
+The Qwen3.5 vision tower and YOLOX models above are both distributed under
+the following license, reproduced verbatim.
+
+```
+                                 Apache License
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+---
+
+# Appendix B: MIT License
+
+The Silero VAD model above is distributed under the following license,
+reproduced verbatim.
+
+```
+MIT License
+
+Copyright (c) 2020-present Silero Team
+
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+```

qwen3_5_mlx_4b_vision_vid_512x320_w8.tar

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yolox_l_1024x640_w8.tar

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