Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.
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# Clone llama.cpp | |
git clone https://github.com/ggerganov/llama.cpp.git | |
cd llama.cpp | |
# Build it | |
make clean | |
LLAMA_METAL=1 make | |
# Download model | |
export MODEL=llama-2-13b-chat.ggmlv3.q4_0.bin |
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import requests | |
import time | |
import os | |
import sys | |
import openai | |
import tiktoken | |
from termcolor import colored | |
openai.api_key = open(os.path.expanduser('~/.openai')).read().strip() |
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<!DOCTYPE html> | |
<html lang="en"> | |
<head> | |
<meta charset="utf-8"> | |
<meta name="viewport" content="width=device-width, initial-scale=1"> | |
<title>Some plotting</title> | |
<link rel="stylesheet" href="https://pyscript.net/alpha/pyscript.css" /> | |
<script defer src="https://pyscript.net/alpha/pyscript.js"></script> | |
<py-env> |
##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan, A. Zisserman
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#!/bin/bash | |
##################################################### | |
# Name: Bash CheatSheet for Mac OSX | |
# | |
# A little overlook of the Bash basics | |
# | |
# Usage: | |
# | |
# Author: J. Le Coupanec | |
# Date: 2014/11/04 |
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/** | |
git clone https://github.com/twitter/scalding.git | |
cd scalding | |
./sbt scalding-repl/console | |
*/ | |
import scala.io.Source | |
val alice = Source.fromURL("http://www.gutenberg.org/files/11/11.txt").getLines | |
// Add the line numbers, which we might want later | |
val aliceLineNum = alice.zipWithIndex.toList |
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Latency Comparison Numbers (~2012) | |
---------------------------------- | |
L1 cache reference 0.5 ns | |
Branch mispredict 5 ns | |
L2 cache reference 7 ns 14x L1 cache | |
Mutex lock/unlock 25 ns | |
Main memory reference 100 ns 20x L2 cache, 200x L1 cache | |
Compress 1K bytes with Zippy 3,000 ns 3 us | |
Send 1K bytes over 1 Gbps network 10,000 ns 10 us | |
Read 4K randomly from SSD* 150,000 ns 150 us ~1GB/sec SSD |
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#! /usr/bin/env python | |
import redis | |
import random | |
import pylibmc | |
import sys | |
r = redis.Redis(host = 'localhost', port = 6389) | |
mc = pylibmc.Client(['localhost:11222']) |
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