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岡優志 / Okayuji

okayuji

On-device AI, Local LLM, Core ML, Apple Neural Engine, iOS, Swift, model conversion, quantization, multimodal AI, edge AI

Models in Library8
Datasets in Library0
Models on Hugging Face8
Followers2

Compare the published evidence

This profile connects the 8 model and dataset records shown below to their upstream cards and sources. The library counts describe SAVRN's coverage, rather than this publisher's entire output.

Published tasks: Feature extraction, Text generation, Text to speech, Token classification.

Reported licenses: apache-2.0. Open a record to review its specific license and conditions.

Compare model specifications, source dates, and hardware estimates on the individual records. Publisher identity verification is separate from a model's quality, deployment readiness, or suitability for your work.

Models

Run Qwen3.8 27B locally on an Apple silicon Mac with Core ML. With the companion Swift app it searches the web, reads pages, writes notes and answers with sources. Model inference stays on the Mac; search and page retrieval require network access. Requires: / Swift 6.2 · 96 GB+ RAM · ~30 GB disk. Measurements used a 128 GB Mac; 96 GB is untested. See Requirements. This repository contains weights and configuration: int8 weights, a 16,384-token context and the model's own multi-token-prediction head. The companion runtime supplies the stateful chain and speculation loop; the demo app supplies search, page reading, notes and tool calls. To run the agent: open CoreMLSamples.xcodeproj at the…

Open weights apache-2.0 262,144 tokens coreml
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A text-refinement model for cleaning up speech-to-text output: punctuation, polite-form conversion, translation, summarization, and filler-word removal. This repository contains a complete Q4KM GGUF text model, ready for a compatible runtime. For: developers adding local text cleanup, or Mac users who want to try it in KeyVoice. Not for: general chat, code generation, mathematical reasoning, or long-form creative writing. Platform: GGUF runtimes with Qwen3.5 support; the original performance report used an Apple Silicon M4 Mac. Use KeyVoice for Mac for the app experience. For your own integration, download the file with the Hugging Face CLI: Run it with llama.cpp using its CLI instructions.…

Open weights apache-2.0 gguf
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Run Google's Gemma 4 12B IT locally on an Apple silicon Mac with Core ML. One int4 bundle supports conversations up to 131,072 tokens, switching from its 32K mode to its 128K mode without reloading weights or copying the conversation cache. Requires: macOS 26+ · Xcode 26 / Swift 6.2 · ~11 GB disk · ~12 GB inference memory, 24 GB+ recommended. See Requirements for measured peaks. The script downloads this bundle to./models/gemma-4-12b-it-coreml-128k. The model guide also covers one-shot prompts and CLI flags. For the app, open CoreMLSamples.xcodeproj at the repository root and run the CoreMLSamples scheme on My Mac. For a standalone download, the existing Hub command is: The runtime supplies…

Open weights apache-2.0
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Three Core ML packages for decoding audio tokens, encoding reference audio, and extracting a speaker embedding from the Qwen3-TTS family. These are audio components, not a complete text-to-speech model. For: developers integrating the components into an Apple Silicon Mac application. Not for: generating speech from text with this download alone; the language model and host pipeline are separate. Platform: Apple Silicon (M1 and later), Core ML. This is a.mlpackage release, separate from Core AI.aimodel releases. Compile and load the packages with Apple's Core ML workflow, then use the exact interfaces below. For the complete upstream TTS pipeline and language-model weights, start with…

Open weights apache-2.0 coreml
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