tori29umai0123

import argparse
import csv
import glob
import os
from pathlib import Path

import cv2
import numpy as np
import torch
from PIL import Image

thesamesam

FAQ on the xz-utils backdoor (CVE-2024-3094)

This is a living document. Everything in this document is made in good faith of being accurate, but like I just said; we don't yet know everything about what's going on.

Background

On March 29th, 2024, a backdoor was discovered in xz-utils, a suite of software that

alfredplpl

# Reference #1: https://note.com/npaka/n/nc55e44e407ff
# Reference #2: https://huggingface.co/blog/gemma-peft
# Licence: MIT

from peft import LoraConfig

lora_config = LoraConfig(
    r=8,
    target_modules=["q_proj", "o_proj", "k_proj", "v_proj", "gate_proj", "up_proj", "down_proj"],
    task_type="CAUSAL_LM",

takeokunn

[
    {
        "word": "Asshole",
        "kana": "アスホール",
        "meaning": "いやな奴（Ass=お尻、Hole=穴）",
        "notice": "「うざい野郎」「ろくでなし」"
    },
    {
        "word": "あばずれ",
        "kana": "あばずれ",

yyya-nico

KonomiTVですべての地上波チャンネルを同時視聴する

KonomiTVにファイルを足すことで、地上波多チャンネル同時視聴ができるかもしれない。
当然チューナーは必要数用意
まずフォルダーを以下の場所に作る。

• {KonomiTVインストールフォルダー}/client/dist/wholech/
• {KonomiTVインストールフォルダー}/client/dist/wholech/css/
• {KonomiTVインストールフォルダー}/client/dist/wholech/js/

htmlファイルはwholechフォルダー、cssファイルはcssフォルダー、jsファイルはjsフォルダーに置く。

subaru-shoji

litagin02

import os

import gradio as gr
import torch

from infer import get_net_g, infer
import utils

voice_keys = ["dec", "flow"]
speech_style_keys = ["enc_p"]

alfredplpl

# MIT License
# This code will run on VRAM 12GB+ GPU such as T4, RTX 3060
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.document_loaders import PyPDFLoader
from langchain.vectorstores import FAISS
from langchain.chains import RetrievalQA
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.llms.huggingface_pipeline import HuggingFacePipeline

opparco

#
# debug tokenizer of lmsys/vicuna-13b-v1.3
#

from transformers import AutoTokenizer


tokenizer = AutoTokenizer.from_pretrained("lmsys/vicuna-13b-v1.3")

def encode_decode(string: str):

opparco

#
# correct vocab of matsuo-lab/weblab-10b
#

vocab = {}

# 95 -> (none)
# 96 -> \xa1
# ...
# 107 -> \xac