Birch-san

Loading LLaMA via Huggingface + Safetensors, with 4-bit quantization

Let's say we're trying to load a LLaMA model via AutoModelForCausalLM.from_pretrained with 4-bit quantization in order to inference from it:

python -m generate.py

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, LlamaTokenizerFast, LlamaForCausalLM
import transformers

grongierisc

#!/bin/sh

set -e

ARCH="amd64"
U_NAME=$(uname -m)

if [ "${U_NAME}" = "aarch64" ]; then
    ARCH="arm64"
fi

hardyscc

Gitlab MicroK8s CI/CD Integration

Install microk8s

sudo apt-get update
sudo apt-get upgrade -y

sudo snap install microk8s --classic --channel=1.25/stable
sudo microk8s status --wait-ready

p3jitnath

# WARNING : This gist in the current form is a collection of command examples. Please exercise caution where mentioned.

# Docker
sudo apt-get update
sudo apt-get remove docker docker-engine docker.io
sudo apt install docker.io
sudo systemctl start docker
sudo systemctl enable docker
docker --version

slowkow

#!/usr/bin/env python
"""
The Needleman-Wunsch Algorithm
==============================

This is a dynamic programming algorithm for finding the optimal alignment of
two strings.

Example
-------

wottpal

#! -*- coding: utf-8 -*-

import sys
import time
import numpy as np
import gensim
import matplotlib.pyplot as plt
from matplotlib import font_manager, rc
from sklearn.manifold import TSNE
from sklearn.cluster import MiniBatchKMeans

cneud

Image Preprocessing

• ImageMagick C
• kraken Python
• ocrobin Python
• ocrodeg Python
• ocropy Python
• ocropy2 Python
• ocrorot Python
• prima-image-lib C++
• unpaper C

lxxstc

基本是按照文档顺序，此处对INSTALL.md做补充

安装BLAS/LAPACK

Ubuntu

不必按照INSTALL.md里面的编译安装方法，只要安装源里的包即可

$ sudo apt install libopenblas-dev
$ sudo apt install liblapack
$ sudo apt install swig

CentOS7

loretoparisi

#!bin/bash

# grab labeled dataset
wget http://downloads.tatoeba.org/exports/sentences.tar.bz2
bunzip2 sentences.tar.bz2
tar xvf sentences.tar

# macos only for gshuf
brew install coreutils
awk -F"\t" '{print"__label__"$2" "$3}' < sentences.csv | gshuf > all.txt

norswap

package demo;

public interface Node {}