Using GPU on Apple Silicon (Tensorflow、Pytorch)
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Updated
Apr 22, 2024 - Python
Using GPU on Apple Silicon (Tensorflow、Pytorch)
save space by turning universal binaries to native
Explore machine learning techniques with Gradio interfaces for Stable Diffusion image generation and LoRA text generation with the Apple MLX framework.
Real-time object detection and counting with YOLOv3. Includes a user-friendly GUI for selecting image and video inputs.
Script to perform some hashcracking logic automagically
Lightweight Hashcat automatisation with base dictionaries
This project is an Implementation of the Paper DDAMFN 2023. This is implemented in PyTorch with FER+ and CK+ dataset using the mps device on Apple Silicon.
Examples for using the SiLLM framework for training and running Large Language Models (LLMs) on Apple Silicon
the small distributed language model toolkit; fine-tune state-of-the-art LLMs anywhere, rapidly
Blender Guide
Hard-burned subtitles OCR to SRT extractor
Rasa on ARM-based Macs (Native/Docker)
MLX-VLM is a package for running Vision LLMs locally on your Mac using MLX.
Benchmark of Apple MLX operations on all Apple Silicon chips (GPU, CPU) + MPS and CUDA.
Black-box tool that uses Deep Reinforcement Learning to test and explore Android applications
SiLLM simplifies the process of training and running Large Language Models (LLMs) on Apple Silicon by leveraging the MLX framework.
A simple UI / Web / Frontend for MLX mlx-lm using Streamlit.
Finetune llama2-70b and codellama on MacBook Air without quantization
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