tgo-app-dev/vpipe-supplement

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. Four are Apache-2.0 and two (Silero VAD, BEATs) are MIT. See 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:

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 @ 512×320 (Core ML)	Vision encoder for a multimodal LLM	Apache-2.0
qwen3_5_mlx_4b_vision_vid_768x480_w8.tar	Qwen3.5-4B vision tower @ 768×480 (Core ML)	Vision encoder for a multimodal LLM	Apache-2.0
gemma4_mlx_e4b_vision_768x480_w8.tar	Gemma 4 (e4b) vision tower @ 768×480 (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
beats_as2m_10s.tar	BEATs iter3+ (AS2M, cpt1) (Core ML)	Audio tagging / sound-event classification	MIT

qwen3_5_mlx_4b_vision_vid_512x320_w8

Qwen3.5-4B vision tower (variant=tensor_video_rgb), the vision encoder that turns a pair of video frames into the embedding sequence consumed by the Qwen3.5-4B language model.

• Task: video → vision feature embeddings (multimodal LLM front-end).
• Inputs: image0, image1 — the two consecutive video frames that form one temporal patch, each a planar RGB fp16 tensor (1, 3, 320, 512) (1×3×H×W, i.e. 512×320 W×H) with raw pixels in 0..255. The x/127.5 - 1 normalisation is baked into the graph (applied on the ANE).
• Output: image_features — vision features of shape (160, 2560), fp16.
• Precision: 8-bit weight quantization, FP16 compute.
• Upstream: Qwen3.5 (Qwen team, Alibaba Cloud) — Apache-2.0.
• Conversion: coremltools 9.0 from TorchScript (torch==2.12.0).

qwen3_5_mlx_4b_vision_vid_768x480_w8

The same Qwen3.5-4B vision tower (variant=tensor_video_rgb) at a higher 768×480 input — identical I/O contract to the 512×320 build, with a longer output token sequence.

• Task: video → vision feature embeddings (multimodal LLM front-end).
• Inputs: image0, image1 — the two consecutive video frames that form one temporal patch, each a planar RGB fp16 tensor (1, 3, 480, 768) (1×3×H×W, i.e. 768×480 W×H) with raw pixels in 0..255. The x/127.5 - 1 normalisation is baked into the graph (applied on the ANE).
• Output: image_features — vision features of shape (360, 2560), fp16.
• Precision: 8-bit weight quantization, FP16 compute.
• Upstream: Qwen3.5 (Qwen team, Alibaba Cloud) — Apache-2.0.
• Conversion: coremltools 9.0 from TorchScript (torch==2.12.0).

gemma4_mlx_e4b_vision_768x480_w8

Gemma 4 (e4b) vision tower, converted to Core ML. It pools and projects the visual features into the language model's 2560-dim text embedding space, emitting "soft tokens" ready to splice into the Gemma 4 prompt.

• Task: image → LLM soft tokens (multimodal LLM front-end).
• Input: image — a single planar RGB fp16 MLMultiArray [1, 3, 480, 768] (1×3×H×W, i.e. 768×480 W×H), raw pixels in 0..255 (channel order R, G, B). Preprocessing is baked in: x/255 then 2*(x - 0.5).
• Output: soft_tokens — shape [1, 160, 2560], fp16.
• Precision: int8 weight-only quantization (per-block), FP16 compute.
• Upstream: Gemma 4 (e4b) (Google DeepMind) — Apache-2.0.
• Conversion: coremltools 9.0 from TorchScript (torch==2.12.1).

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 (Megvii Inc.) — Apache-2.0.
• Conversion: coremltools 9.0 from TorchScript (torch==2.12.1); all operations placed on the Apple Neural Engine (no CPU fallback ops).

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 (Silero Team) — MIT; Core ML conversion from FluidInference/silero-vad-coreml — MIT.
• Conversion: coremltools 9.0b1 from TorchScript (torch==2.7.0).

beats_as2m_10s

BEATs iter3+ audio representation model, fine-tuned on AudioSet-2M (the cpt1 checkpoint), converted to Core ML over a fixed 10-second window. It classifies audio into the AudioSet sound-event ontology.

• Task: audio tagging / sound-event classification.
• Input: waveform — a mono 16 kHz waveform over a fixed 10-second window (160,000 samples).
• Output: probs — per-class probabilities over the AudioSet ontology (527 sound-event classes).
• Precision: FP16 compute (weights not quantized).
• Upstream: BEATs (Microsoft, microsoft/unilm) — MIT.
• Conversion: coremltools 9.0 from TorchScript (torch==2.7.0).

Verifying downloads

SHA-256 checksums of the archives:

ed27924a97a873a019d1c8a1e1f9826bd30da2588683843d6c59cfb0b2289f30  qwen3_5_mlx_4b_vision_vid_512x320_w8.tar
3e928ea0c3e6848896278b68436d46f1aa306d95d8ca4a1faadfad354d705b87  qwen3_5_mlx_4b_vision_vid_768x480_w8.tar
17bebe9fdb416a760df4d00ed39c7edf296ba768d54139c8183c1323da646d3e  gemma4_mlx_e4b_vision_768x480_w8.tar
7f7bf85947bb228f978470284e16a9d00c906a6cb07359f9a51c68806fa9c609  yolox_l_1024x640_w8.tar
ee14d006d1cea87f15a4f0ac5f3e1e00b80a10c40eca855e8ff6741f389b4423  silero-vad-unified-v6.tar
47c63f83bfa47e248b199e820ae6a01a5ed40a5f6ef4b85f42f12d7ac5754638  beats_as2m_10s.tar

Licenses

These are third-party models redistributed in converted form. Each retains its upstream license; see THIRD_PARTY_LICENSES.md for full attribution and verbatim license texts.