Conrad Stack ConradStack

This worked on 14/May/23. The instructions will probably require updating in the future.

llama is a text prediction model similar to GPT-2, and the version of GPT-3 that has not been fine tuned yet. It is also possible to run fine tuned versions (like alpaca or vicuna with this. I think. Those versions are more focused on answering questions)

Note: I have been told that this does not support multiple GPUs. It can only use a single GPU.

It is possible to run LLama 13B with a 6GB graphics card now! (e.g. a RTX 2060). Thanks to the amazing work involved in llama.cpp. The latest change is CUDA/cuBLAS which allows you pick an arbitrary number of the transformer layers to be run on the GPU. This is perfect for low VRAM.

Clone llama.cpp from git, I am on commit 08737ef720f0510c7ec2aa84d7f70c691073c35d.

Introduction
Standards
Common combinations
Conversion
Verification/Inspection
Tips for recognising

Introduction

It happens that there are many standards for storing cryptography materials (key, certificate, ...) and it isn't always obvious to know which standard is used by just looking at file name extension or file content. There are bunch of questions on stackoverflow asking about how to convert from PEM to PKCS#8 or PKCS#12, while many tried to answer the questions, those answers may not help because the correct answer depends on the content inside the PEM file. That is, a PEM file can contain many different things, such as an X509 certificate, a PKCS#1 or PKCS#8 private key. The worst-case scenario is that someone just store a non-PEM content in "something.pem" file.

READ HERE

SAM and BAM filtering one-liners

@author: David Fredman, [email protected] (sans poly-A tail)
@dependencies: http://sourceforge.net/projects/bamtools/ and http://samtools.sourceforge.net/

Please extend with additional/faster/better solutions via a pull request!

BWA mapping (using piping for minimal disk I/O)

	import torch
	import torch.nn as nn
	from torch.nn import Parameter
	from torch.autograd import Variable, Function
	from collections import defaultdict
	import graphviz

	"""
	This is a rather distorted implementation of graph visualization in PyTorch.

	"""
	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)

	--[[
	Efficient LSTM in Torch using nngraph library. This code was optimized
	by Justin Johnson (@jcjohnson) based on the trick of batching up the
	LSTM GEMMs, as also seen in my efficient Python LSTM gist.
	--]]

	function LSTM.fast_lstm(input_size, rnn_size)
	local x = nn.Identity()()
	local prev_c = nn.Identity()()
	local prev_h = nn.Identity()()

	"""
	This is a batched LSTM forward and backward pass
	"""
	import numpy as np
	import code

	class LSTM:

	@staticmethod
	def init(input_size, hidden_size, fancy_forget_bias_init = 3):

	#!flask/bin/python
	from flask import Flask, jsonify, abort, request, make_response, url_for
	from flask_httpauth import HTTPBasicAuth

	app = Flask(__name__, static_url_path = "")
	auth = HTTPBasicAuth()

	@auth.get_password
	def get_password(username):
	if username == 'miguel':