awni

"""
An LM with a REPL

Gives an LLM a Python REPL: the model can write ```repl``` code blocks,
which get executed, with stdout/stderr fed back into the conversation.

Requires a running mlx_lm.server:
    mlx_lm.server
"""

karpathy

"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.

@karpathy
"""

import os       # os.path.exists
import math     # math.log, math.exp

awni

The following guide will show you how to connect a local model served with MLX to OpenCode for local coding.

1. Install OpenCode

curl -fsSL https://opencode.ai/install | bash

2. Install mlx-lm

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