Search Results for "apple"
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32 projects for "apple" with 2 filters applied:
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Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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whisper.cpp
Port of OpenAI's Whisper model in C/C++
whisper.cpp is a lightweight, C/C++ reimplementation of OpenAI’s Whisper automatic speech recognition (ASR) model—designed for efficient, standalone transcription without external dependencies. The entire high-level implementation of the model is contained in whisper.h and whisper.cpp. The rest of the code is part of the ggml machine learning library. The command downloads the base.en model converted to custom ggml format and runs the inference on all .wav samples in the folder samples.... -
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kokoro-onnx
TTS with kokoro and onnx runtime
kokoro-onnx is a text-to-speech toolkit that wraps the Kokoro neural TTS model in an easy-to-use ONNX Runtime interface, so you can generate speech from Python with minimal setup. It focuses on running efficiently on commodity hardware, including macOS with Apple Silicon, while still delivering near real-time performance for many use cases. The project ships prebuilt model files and a simple example script, so you can go from installation to producing an audio.wav file in just a few steps. It supports multiple languages and voices, with a curated voice list and configuration via a VOICES file hosted alongside the models. ... -
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BaseRT
Fastest LLM inference runtime for Apple Silicon
BaseRT is a local large language model inference runtime optimized for Apple Silicon computers. It accelerates model execution through hand-written Metal kernels and requires an M1 or newer Mac running macOS 14 or later. A unified command-line interface can download models from Hugging Face, convert checkpoints, launch chats, benchmark performance, and inspect model packages. Its server implements OpenAI-compatible chat, completion, embedding, transcription, tool-calling, and multimodal endpoints. ... -
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MLX Engine
LM Studio Apple MLX engine
MLX Engine is the Apple MLX-based inference backend used by LM Studio to run large language models efficiently on Apple Silicon hardware. Built on top of the mlx-lm and mlx-vlm ecosystems, the engine provides a unified architecture capable of supporting both text-only and multimodal models. Its design focuses on high-performance on-device inference, leveraging Apple’s MLX stack to accelerate computation on M-series chips. -
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Phi-3-MLX
Phi-3.5 for Mac: Locally-run Vision and Language Models
Phi-3-Vision-MLX is an Apple MLX (machine learning on Apple silicon) implementation of Phi-3 Vision, a lightweight multi-modal model designed for vision and language tasks. It focuses on running vision-language AI efficiently on Apple hardware like M1 and M2 chips. -
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serve-sim
The `npx serve` of Apple Simulators
serve-sim is a developer tool for hosting Apple Simulators through a local or network-accessible web interface. It is described as the npx serve equivalent for Apple Simulators, allowing users to preview and control a simulator from a browser. The tool is especially useful for AI coding agents such as Codex, Cursor, and Claude Desktop because they can interact with a simulator through browser-driven workflows. -
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Gemma Chat
Local AI chat + coding agent for Apple Silicon, powered by Gemma 4
Gemma Chat is a local-first AI chat and coding assistant designed to run fully on-device, particularly optimized for Apple Silicon machines. It leverages Google’s Gemma family of lightweight language models, which are built on the same underlying technology as Gemini and designed for efficient local inference and reasoning tasks. The project enables users to interact with AI through a chat interface while also supporting code generation and editing workflows. -
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ds4.c
DeepSeek 4 Flash local inference engine for Metal
ds4.c is a specialized local inference engine created by antirez for running DeepSeek V4 Flash models directly on Apple Silicon hardware using Metal acceleration. Unlike general-purpose inference runtimes, the project is intentionally optimized for a specific model family, enabling highly efficient execution and simplified architecture. The engine includes DS4-specific model loading, KV cache management, prompt rendering, and OpenAI-compatible server APIs for local deployment workflows. ... -
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uzu
A high-performance inference engine for AI models
uzu is a high-performance inference engine designed to run artificial intelligence models efficiently on Apple Silicon hardware. Written primarily in Rust and leveraging Apple’s Metal framework, the project focuses on maximizing performance when executing large language models and other AI workloads on devices such as Mac computers with M-series chips. The engine implements a hybrid architecture in which model layers can be executed either as custom GPU kernels or through Apple’s MPSGraph API, allowing it to balance performance and compatibility depending on the workload. ... -
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ANE Training
Training neural networks on Apple Neural Engine via APIs
...The repository implements a from-scratch transformer training pipeline capable of running both forward and backward passes on ANE hardware without relying on CoreML, Metal, or GPU acceleration. It explores the internal software stack of the Apple Neural Engine by interfacing with private classes such as _ANEClient and compiling custom compute graphs in the MIL format. The project includes performance benchmarks and kernel breakdowns that show how different components of the training loop are distributed between the ANE and CPU. It is primarily intended as a research and educational proof of concept rather than a production library, highlighting what is technically possible with undocumented hardware access. -
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TurboFieldfare
Gemma 4 26B-A4B inference in ~2 GB of RAM on any M-series MacBook
...Quantized weights, custom Metal kernels, chunked prefill, and a bounded expert cache improve efficiency. The installer downloads and repacks model ranges without staging a second full checkpoint. Its current scope is text-only Gemma inference on macOS 26 and Apple Silicon hardware. -
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h3-metal
MiniMax H3 inference engine for Mac computers
h3-metal is a native MiniMax-H3 inference engine for Apple Silicon focused on generating video and audio locally with Metal. It supports prompt-to-video and prompt-to-audio workflows as well as first-frame, last-frame, image, video, and audio references. An interactive terminal session keeps prompt conditioning, the diffusion transformer, and the video decoder in memory for faster repeated generations. -
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Core AI Models
Model export recipes, Python primitives, and Swift runtime utilities
...It provides export recipes that convert supported open-source models into Core AI model files. It also includes Python primitives for authoring custom PyTorch models that are better suited for Apple platform deployment. The Swift package adds runtime utilities that help developers integrate exported models into macOS and iOS apps. The repository also contains agent skills that guide coding assistants through Core AI workflows, model authoring, and compression exploration. It is useful for developers who want a curated path from model preparation to local app integration on Apple hardware. -
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Depth Pro
Sharp Monocular Metric Depth in Less Than a Second
Depth Pro is a foundation model for zero-shot metric monocular depth estimation, producing sharp, high-frequency depth maps with absolute scale from a single image. Unlike many prior approaches, it does not require camera intrinsics or extra metadata, yet still outputs metric depth suitable for downstream 3D tasks. Apple highlights both accuracy and speed: the model can synthesize a ~2.25-megapixel depth map in around 0.3 seconds on a standard GPU, enabling near real-time applications. The repo and research page emphasize boundary fidelity and crisp geometry, addressing a common weakness in monocular depth where edges can blur. Community integrations (e.g., inference wrappers and UI nodes) have sprung up around the model, reflecting practical interest in video, AR, and generative pipelines. ... -
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tiny-llm
A course of learning LLM inference serving on Apple Silicon
tiny-llm is an educational open-source project designed to teach system engineers how large language model inference and serving systems work by building them from scratch. The project is structured as a guided course that walks developers through the process of implementing the core components required to run a modern language model, including attention mechanisms, token generation, and optimization techniques. Rather than relying on high-level machine learning frameworks, the codebase uses... -
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Agent Stuff
These are commands I use with agents, mostly Claude
...The repository includes commands for planning discussions, code review, changelog updates, summarization, browser automation, GitHub work, tmux control, Sentry analysis, Apple Mail search, and Google Workspace access. Its Pi extensions add workflow tools such as session control, file browsing, long-running goals, notifications, enhanced edits, task lists, and prompt-mode switching. Many items are tuned for the author’s own environment, so users should expect to adjust paths and defaults. It is best treated as a practical personal agent toolkit and reference for building reusable AI coding workflows. -
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apfel
Apple Intelligence from the command line
apfel is a lightweight and likely experimental development project focused on building efficient and minimal tools or frameworks, typically emphasizing simplicity, performance, and clean abstractions. The project appears to follow a philosophy of reducing unnecessary complexity while still enabling practical functionality for developers who prefer lean systems over heavy frameworks. It is designed to be adaptable, allowing developers to extend or modify its behavior depending on their... -
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SwiftUI Agent Skill
SwiftUI agent skill for Claude Code, Codex, and other AI tools
...The system includes a comprehensive review process that evaluates code across multiple dimensions, including navigation, data flow, design compliance, and performance optimization. It is specifically tailored to align with current Apple development standards, including modern Swift versions and accessibility requirements like VoiceOver support. The tool integrates seamlessly with multiple AI coding environments, allowing developers to invoke it as part of their coding or review process. -
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OuteTTS
Interface for OuteTTS models
...The project supports multiple backends including llama.cpp (Python bindings and server), Hugging Face Transformers, ExLlamaV2, VLLM and a JavaScript interface via Transformers.js, allowing it to run on CPUs, NVIDIA CUDA GPUs, AMD ROCm, Vulkan-capable GPUs, and Apple Metal. It also includes a notion of speaker profiles: you can create a speaker from a short audio sample, save it as JSON, and reuse it for consistent voice identity across generations and sessions. For best quality, the model is designed to work with a reference speaker clip and will inherit emotion, style, and accent from that reference. -
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Just download the above zip file, extract it and then open the index.html file on internet browsers like Firefox ( preferable ) or Google Chrome.
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Softwares For Blind, Deaf, Handicap
Easy AI Softwares for Blind, Deaf, Handicapped, Disabled People
Just download the above zip file, extract it first and then open the index.html file on internet browsers like Firefox ( preferable ) or Google Chrome. Also, keep NumLock ON while using the Numeric Keypad of any Keyboard. Can also attach an external USB keyboard, with seperate Numeric Keypad, if required. I have added some general guidelines for students, using these softwares, on the Wiki Page of this website. Please refer them for more instructions. -
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Voice Accounting For Blind & Mute People
Free & Easy AI Voice Accounting Software For Blind & Speechless People
Just download the above zip file, extract it and then open the index.html file on internet browsers like Firefox ( preferable ) or Google Chrome. Also, please view and download my full collection of softwares for people with disabilities, here : https://sourceforge.net/projects/softwares-for-disabled-people/ This full collection also includes the Voice Accounting Software as well. -
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Riffusion
Real-time music generation using stable diffusion techniques AI
Riffusion (hobby) is a Python-based open source library designed for real-time music and audio generation using stable diffusion techniques. Riffusion (hobby) works by generating and manipulating spectrogram images, which are then converted into playable audio clips, effectively bridging image-based diffusion models with sound synthesis. It implements a diffusion pipeline that supports prompt interpolation, allowing smooth transitions between different musical styles or prompts over time.... -
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VoiceShot API - CF SDK
ColdFusion SDK for the VoiceShot API.
VoiceShot's API allows you to quickly integrate both incoming and outgoing phone calling and text messaging services into your applications. From your own applications, you can easily place and receive interactive telephone calls and text messages. Put callers in touch with the data and people they want when they want it. Send notification phone calls and text messages, automate customer service calls/texts, provide order status and integrate with your own custom applications to provide... -
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VoiceShot API - PHP SDK
PHP SDK for processing phone calls and SMS through the VoiceShot API.
VoiceShot's API allows you to quickly integrate both incoming and outgoing phone calling and text messaging services into your applications. From your own applications, you can easily place and receive interactive telephone calls and text messages. Put callers in touch with the data and people they want when they want it. Send notification phone calls and text messages, automate customer service calls/texts, provide order status and integrate with your own custom applications to provide...
