@mkopcins/release0.9.1#1228
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Updates Whisper models URLs to match the recently uploaded CoreML fp16 models. The newly uploaded fp16 models are roughly 50% reduced in size and ~30% faster than the old fp32 ones. - [ ] Yes - [x] No - [ ] Bug fix (change which fixes an issue) - [ ] New feature (change which adds functionality) - [ ] Documentation update (improves or adds clarity to existing documentation) - [x] Other (chores, tests, code style improvements etc.) - [x] iOS - [ ] Android <!-- Provide step-by-step instructions on how to test your changes. Include setup details if necessary. --> <!-- Add screenshots here, if applicable --> <!-- Link related issues here using #issue-number --> - [x] I have performed a self-review of my code - [ ] I have commented my code, particularly in hard-to-understand areas - [ ] I have updated the documentation accordingly - [x] My changes generate no new warnings <!-- Include any additional information, assumptions, or context that reviewers might need to understand this PR. -->
<!-- Provide a concise and descriptive summary of the changes implemented in this PR. --> - [ ] Yes - [x] No - [ ] Bug fix (change which fixes an issue) - [x] New feature (change which adds functionality) - [x] Documentation update (improves or adds clarity to existing documentation) - [ ] Other (chores, tests, code style improvements etc.) - [x] iOS - [x] Android Test by running apps/llm app on llm screen (for text only model) and multimodal screen (for audio-vision-text model). Text model should work as any other llm model. Multimodal can process up-to-30sec audio chunks as well as image inputs, should be able to transcribe audio, describe pictures or similar. <!-- Add screenshots here, if applicable --> - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [x] I have updated the documentation accordingly - [ ] My changes generate no new warnings
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…emma 4 E2B (#1223) Bumps ExecuTorch to 1.3 and adds two GPU backends with Gemma 4 E2B support: - **MLX (iOS / Apple GPU)** — new backend, with metadata-driven chunked prefill. The MLX `forward` is exported with a sliding-window cap on the sequence dimension and a one-shot prefill spikes Metal memory, so MLX models are prefilled in steps of the forward's declared max input length (read from the method metadata). Non-MLX backends keep the original one-shot path. - **Vulkan (Android GPU)** — Gemma 4 E2B now runs on Vulkan. The prebuilt `libexecutorch.so` (arm64-v8a, x86_64) is rebuilt from the labs 1.3 fork with the Gemma4 Vulkan support: the `aten.rms_norm` lowering and the Gemma SDPA shaders, ported onto 1.3's tile-load helper API with the DHSB Q/K/V layout the Gemma4 export uses. `models.llm.gemma4_e2b` is registered with `mlx` / `xnnpack` / `vulkan` variants and defaults to **MLX on iOS** and **Vulkan on Android**. - [ ] Yes - [x] No - [x] Bug fix (change which fixes an issue) - [x] New feature (change which adds functionality) - [ ] Documentation update (improves or adds clarity to existing documentation) - [x] Other (chores, tests, code style improvements etc.) - [x] iOS - [x] Android 1. Build and run the LLM example app (`apps/llm`) on a physical device (Vulkan/MLX need a real GPU — not the simulator/emulator). 2. In the model picker, select **Gemma 4 E2B**. 3. Send a prompt and confirm coherent generation: - iOS → runs on the MLX backend. - Android → runs on the Vulkan backend. 4. Confirm generation does not stop immediately after prefill and produces multiple tokens. <!-- Add screenshots here, if applicable --> <!-- Link related issues here using #issue-number --> - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [ ] I have updated the documentation accordingly - [x] My changes generate no new warnings The vulkan gemma won't work until @mkopcins PR is merged. --------- Co-authored-by: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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barhanc
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Jun 11, 2026
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Release 0.9.1
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