DeepSeek R-1 is the most powerful open-source reasoning model that performs on par with OpenAI's o1 model. Run the 1.58-bit Dynamic GGUF version by Unsloth.

Msty is the perfect alternative to LM Studio. Msty offers a powerful and intuitive interface that makes it easy to get started, even for beginners. Say goodbye to complexities and embrace the simplicity with Msty. With innovative features like Folders, Vapor Mode, and Workspaces, Msty makes you more productive than you ever got with LM Studio.

🚀 Want to fine-tune AI models on your Mac without cloud services? As an ex-Ollama developer, I'll show you how to use Apple's MLX framework to fine-tune mod...

BoltAI is a native, high-performance AI app for Mac. It allows you to integrate AI assistances into your daily tasks.

Meta's new Llama 3.3 70B is a genuinely GPT-4 class Large Language Model that runs on my laptop.

MLX-VLM is a package for running Vision LLMs locally on your Mac using MLX. - Blaizzy/mlx-vlm

Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚ - exo-explore/exo

Setup your own private Generative AI environment using Ollama

Run your own AI cluster at home with everyday devices 📱💻 🖥️⌚ - exo-explore/exo

There’s a whole lot of buzz around the new Qwen2.5-Coder Series of open source (Apache 2.0 licensed) LLM releases from Alibaba’s Qwen research team. On first impression it looks like …

The ultimate AI productivity app that protects your privacy. Bring all your apps and data into one AI-powered search and assistant. Get it for you and for your teams today.

MacWhisper has been updated to version 8 with some new features, including a video player. Multiple apps use the Whisper model to perform transcription. I bought a license for MacWhisper early, and I’ve been using it a lot ever since. One example: We use a Notion database to manage all the MacSparky content (this blog, the... Continue reading →

MindMac let you use ChatGPT efficient on macOS

AI powered voice to text for macOS

An open source approach to locally record and enable searching everything you view on your Apple Silicon. - jasonjmcghee/rem: An open source approach to locally record and enable searching everythi...

archived 5 Nov 2023 01:42:48 UTC

Press a hotkey to chat anywhere on your Mac. No subscription fees. No logins. Your own API key.

MLX: An array framework for Apple silicon. Contribute to ml-explore/mlx development by creating an account on GitHub.

Enhanced ChatGPT Clone: Features OpenAI, GPT-4 Vision, Bing, Anthropic, OpenRouter, PaLM 2, AI model switching, message search, langchain, DALL-E-3, ChatGPT Plugins, OpenAI Functions, Secure Multi-...

Run Latent Consistency Models on your Mac. Contribute to replicate/latent-consistency-model development by creating an account on GitHub.

How to run a latent consistency model on your M1 or M2 Mac

Contribute to danielgross/localpilot development by creating an account on GitHub.

AI Chat Browser: Fast, Full webapp access to ChatGPT / Claude / Bard / Bing / Llama2! I use this 20 times a day. - GitHub - smol-ai/GodMode: AI Chat Browser: Fast, Full webapp access to ChatGPT / C...

Run Llama-2-13B-chat locally on your M1/M2 Mac with GPU inference. Uses 10GB RAM - llama2-mac-gpu.sh

Prepare your M1, M1 Pro, M1 Max, M1 Ultra or M2 Mac for data science and machine learning with accelerated PyTorch for Mac.

archived 31 Mar 2023 11:16:52 UTC

CogSci 131 (Computational Modeling of Cognition) is one of my favorite classes at Berkeley when I took it with Prof. Tom Griffiths in 2016. Now I’m teaching it in my last semester. Before getting to the fun parts (e.g., implementing recurrent nets and classic RL algorithms like SARSA using vanilla NumPy), one needs to set up the right Python environment. I hope this tutorial can save students some headaches. Option #1: Conda Virtual Environment To avoid nightmares down the line👇, use virtual environments for your local stuff.

You can install PyTorch for GPU support with a Mac M1/M2 using CONDA. It is very important that you install an ARM version of Python. In this video I walk you through all the steps necessary to prepare an Apple Metal Mac for my deep learning course in PyTorch. 0:27 Overview 1:00 Install Miniconda 1:49 Uninstall Anaconda (optional) 3:33 Install Miniconda 5:03 Install Jupyter 5:24 Install PyTorch 7:07 Create Jupyter Kernel Code for This Video: https://github.com/jeffheaton/t81_558_deep_learning/blob/pytorch/install/pytorch-install-aug-2022.ipynb ~~~~~~~~~~~~~~~ COURSE MATERIAL ~~~~~~~~~~~~~~~ 📖 Textbook - coming soon 😸🐙 GitHub - https://github.com/jeffheaton/t81_558_deep_learning/tree/pytorch ▶️ Play List - https://www.youtube.com/playlist?list=PLjy4p-07OYzuy_lHcRW8lPTLPTTOmUpmi 🏫 WUSTL Course Site - https://sites.wustl.edu/jeffheaton/t81-558/ ~~~~~~~~~~~~~~~ CONNECT ~~~~~~~~~~~~~~~ 🖥️ Website: https://www.heatonresearch.com/ 🐦 Twitter - https://twitter.com/jeffheaton 😸🐙 GitHub - https://github.com/jeffheaton 📸 Instagram - https://www.instagram.com/jeffheatondotcom/ 🦾 Discord: https://discord.gg/3bjthYv ▶️ Subscribe: https://www.youtube.com/c/heatonresearch?sub_confirmation=1 ~~~~~~~~~~~~~~ SUPPORT ME 🙏~~~~~~~~~~~~~~ 🅿 Patreon - https://www.patreon.com/jeffheaton 🙏 Other Ways to Support (some free) - https://www.heatonresearch.com/support.html ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ #Python #Tensorflow #Keras