Julian Qian qxj

Chat GPT "DAN" (and other "Jailbreaks")

Comparison of ZSH frameworks and plugin managers

Changelog

update 1: add a FAQ section
update 2: benchmark chart and feature comparison table
update 3:
- improve the table with missing features for antigen
- new zplg times result

TLDR

Build tensorflow on OSX with NVIDIA CUDA support (GPU acceleration)

These instructions are based on Mistobaan's gist but expanded and updated to work with the latest tensorflow OSX CUDA PR.

Requirements

OS X 10.10 (Yosemite) or newer

Project Title

One Paragraph of project description goes here

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

	"""Convert jupyter notebook to sphinx gallery notebook styled examples.

	Usage: python ipynb_to_gallery.py <notebook.ipynb>

	Dependencies:
	pypandoc: install using `pip install pypandoc`
	"""
	import pypandoc as pdoc
	import json

	"""
	Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy)
	BSD License
	"""
	import numpy as np

	# data I/O
	data = open('input.txt', 'r').read() # should be simple plain text file
	chars = list(set(data))
	data_size, vocab_size = len(data), len(chars)

	#!/usr/bin/env python
	# -- coding: utf-8 --

	# This is a simplified implementation of the LSTM language model (by Graham Neubig)
	#
	# LSTM Neural Networks for Language Modeling
	# Martin Sundermeyer, Ralf Schlüter, Hermann Ney
	# InterSpeech 2012
	#
	# The structure of the model is extremely simple. At every time step we

	package com.rishav.hbase.union;

	import java.util.ArrayList;
	import java.util.List;

	import org.apache.hadoop.conf.Configuration;
	import org.apache.hadoop.conf.Configured;
	import org.apache.hadoop.hbase.client.Scan;
	import org.apache.hadoop.hbase.mapreduce.TableMapReduceUtil;
	import org.apache.hadoop.hbase.util.Bytes;

	SLIDES := $(patsubst %.md,%.md.slides.pdf,$(wildcard *.md))
	HANDOUTS := $(patsubst %.md,%.md.handout.pdf,$(wildcard *.md))

	all : $(SLIDES) $(HANDOUTS)

	%.md.slides.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -o $@

	%.md.handout.pdf : %.md
	pandoc $^ -t beamer --slide-level 2 -V handout -o $@

	To replicate run

	python cem.py --algorithm pcem --outdir CartPole-v0-pcem

	The --outdir argument is optional. If left as is results will be written to /tmp/CartPole-v0-pcem.