Anemll

import FoundationModels
import Playgrounds
import Foundation

let session = LanguageModelSession()
let start = Date()
let response = try await session.respond(to: "What is Apple Neural Engine and how to use it?")
let responseText = response.content // Replace 'value' with the actual property name from LanguageModelSession.Response<String> that holds the string payload.
print(responseText)
let end = Date()

awni

Setup

Install packages:

pip install open-webui mlx-lm

Start Open WebUI server:

wey-gu

Tool: draw.io

Animate the connectors

Animating your connectors is great for demonstrating directional flow charts, electrical circuits and more. To animate your connectors:

1. Click on the connector you wish to animate. Hold Ctrl or Cmd and click to select multiple connectors
2. On the right-hand side go to Style > Property and click on the arrow to expand the field
3. Scroll down to Flow Animation and check the box

awni

Test Time Scaling with MLX LM and R1-based LLMs

Install MLX LM:

pip install mlx-lm

And run:

willccbb

# train_grpo.py
#
# See https://github.com/willccbb/verifiers for ongoing developments
#
"""
citation:

@misc{brown2025grpodemo,
  title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models},
  author={Brown, William},

awni

Setup

On every machine in the cluster install openmpi and mlx-lm:

conda install conda-forge::openmpi
pip install -U mlx-lm

Next download the pipeline parallel run script. Download it to the same path on every machine:

Maharshi-Pandya

You are an assistant that engages in extremely thorough, self-questioning reasoning. Your approach mirrors human stream-of-consciousness thinking, characterized by continuous exploration, self-doubt, and iterative analysis.

## Core Principles

1. EXPLORATION OVER CONCLUSION
- Never rush to conclusions
- Keep exploring until a solution emerges naturally from the evidence
- If uncertain, continue reasoning indefinitely
- Question every assumption and inference

ivanfioravanti

# Required packages:
# pip install SpeechRecognition mlx-whisper pyaudio
# Note: This script requires Apple Silicon Mac for MLX Whisper

import speech_recognition as sr
import numpy as np
import mlx_whisper

r = sr.Recognizer()
mic = sr.Microphone(sample_rate=16000)

awni

"""
A minimal, fast example generating text with Llama 3.1 in MLX.

To run, install the requirements:

    pip install -U mlx transformers fire

Then generate text with:

    python l3min.py "How tall is K2?"

TheBloke

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

import os
import argparse

def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--base_model_name_or_path", type=str)