Blaizzy/mlx-audio
A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library built on Apple's MLX framework, providing efficient speech analysis on Apple Silicon.
MLOps | LLMs | VLMs | Audio LMs | Ex - ML Research Engineer @arcee-ai
A text-to-speech (TTS), speech-to-text (STT) and speech-to-speech (STS) library built on Apple's MLX framework, providing efficient speech analysis on Apple Silicon.
Local AI, native to your Mac. Chat, serve, monitor, and connect MLX models from one macOS app.
MLX-VLM is a package for inference and fine-tuning of Vision Language Models (VLMs) on your Mac using MLX.
A modular Swift SDK for audio processing with MLX on Apple Silicon
MLX-Video is the best package for inference and finetuning of Image-Video-Audio generation models on your Mac using MLX.
MLX-Embeddings is the best package for running Vision and Language Embedding models locally on your Mac using MLX.
Run LLMs with MLX
Examples in the MLX framework
Live line counts for GitHub repositories, folders, pull requests, and side-by-side comparisons.
Ship RAG based LLM web apps in seconds.
MLX: An array framework for Apple silicon
This repository is a curated collection of resources, tutorials, and practical examples designed to guide you through the journey of mastering CUDA programming. Whether you're just starting or looking to optimize and scale your GPU-accelerated applications.
LLMs and VLMs with MLX Swift
jPlayer : HTML5 Audio & Video for jQuery
FastMLX is a high performance production ready API to host MLX models.
A framework for efficient model inference with omni-modality models
A Swift client for Hugging Face Hub and Inference Providers APIs
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
Deploy and scale Large Language Models (LLMs) in production.
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Reachy Mini's SDK
Talk with Reachy Mini!
Contains random samples referenced in the paper "Sleeper Agents: Training Robustly Deceptive LLMs that Persist Through Safety Training".
🦜🔗 Build context-aware reasoning applications
VoCoT: Unleashing Visually Grounded Multi-Step Reasoning in Large Multi-Modal Models
model activation visualiser