adrienbrault

# 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

younesbelkada

# coding=utf-8
# Copyright 2023 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software

xinqiu

from langchain import PromptTemplate, LLMChain
from langchain.document_loaders import UnstructuredHTMLLoader
from langchain.embeddings import LlamaCppEmbeddings
from langchain.llms import LlamaCpp
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.vectorstores.faiss import FAISS

loader = UnstructuredHTMLLoader("langchain/docs/_build/html/index.html")
embedding = LlamaCppEmbeddings(model_path="path/models/ggml-model-q4_0.bin")
llm = LlamaCpp(model_path="path/models/ggml-model-q4_0.bin")

rain-1

The reverse turing test

I asked GPT-4 to come up with 10 questions to determine if the answerer was AI or human.

I provided my own answers for these questions and I also asked ChatGPT to answer them.

The result is that GPT-4 was able to correctly differentiate between AI and Human.

snikch

PLOTS_TMP_DIR=/mnt/scratch/plots
PLOTS_FINAL_DIR=/mnt/storage/plots

adamelliotfields

version: "3.5"

services:
  mongo:
    image: mongo:latest
    container_name: mongo
    environment:
      MONGO_INITDB_ROOT_USERNAME: admin
      MONGO_INITDB_ROOT_PASSWORD: admin
    ports:

hluaces

#!/bin/bash

# References
# https://devopsideas.com/benchmarking-disk-iops-aws-vs-digitalocean/
# https://wiki.mikejung.biz/Benchmarking#Fio_Test_Options_and_Examples
# https://tobert.github.io/post/2014-04-17-fio-output-explained.html


# Requires linux packages: fio ioping

zhu327

wrk.path  = "/rest/ping"
wrk.method = "POST"
wrk.body   = "id=7579764573763278289&platformid=ANDROID&version=1.0.0"
wrk.headers["Content-Type"] = "application/x-www-form-urlencoded"
wrk.headers["deviceid"] = "354435052821931"
wrk.headers["Accept"] = "application/json"

logfile = io.open("wrk.log", "w");
local cnt = 0;

andreybolonin

Libuv and libev, two I/O libraries with similar names, recently had the privilege to use both libraries to write something. Now let's talk about my own subjective expression of common and different points.

The topic of high-performance network programming has been discussed. Asynchronous, asynchronous, or asynchronous. Whether it is epoll or kqueue, it is always indispensable to the asynchronous topic.

Libuv is asynchronous, and libev is synchronous multiplexing IO multiplexing.

Libev is a simple encapsulation of system I/O reuse. Basically, it solves the problem of different APIs between epoll and kqueuq. Ensure that programs written using livev's API can run on most *nix platforms. However, the disadvantages of libev are also obvious. Because it basically just encapsulates the Event Library, it is inconvenient to use. For example, accept(3) requires manual setnonblocking after connection. EAGAIN, EWOULDBLOCK, and EINTER need to be detected when reading from a socket. This is a

ferventcoder

$googlePoliciesKey = 'HKLM:\Software\Policies\Google'
if (!(Test-Path $googlePoliciesKey)) {
  New-Item -Path $googlePoliciesKey  -Force | Out-Null
}

New-ItemProperty $googlePoliciesKey -Name 'UpdateDefault' -Value 0 -PropertyType DWORD -Force | Out-Null