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)

afiodorov

Docker Image  : pytorch/pytorch
Image Runtype : jupyter_direc ssh_direc ssh_proxy
Environment : [["JUPYTER_DIR", "/"], ["-p 41654:41654", "1"]]

pip install torch bitsandbytes sentencepiece "protobuf<=3.20.2" git+https://github.com/huggingface/transformers flask python-dotenv Flask-HTTPAuth accelerate

!mv /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda116.so /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so

primus852

#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check

renevo

Updating/Installing Go in WSL

Remove

Removes older versions

sudo rm -rf /usr/local/go* && sudo rm -rf /usr/local/go

cbaziotis

def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

cbaziotis

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args: