Anchen mzbac

## merge_peft_adapters.py
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

import os
import argparse

def get_args():
    parser = argparse.ArgumentParser()
    parser.add_argument("--base_model_name_or_path", type=str)

## instruction
Docker Image  : pytorch/pytorch
Image Runtype : jupyter_direc ssh_direc ssh_proxy
Environment : [["JUPYTER_DIR", "/"], ["-p 41654:41654", "1"]]

pip install torch bitsandbytes sentencepiece "protobuf<=3.20.2" git+https://github.com/huggingface/transformers flask python-dotenv Flask-HTTPAuth accelerate

!mv /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda116.so /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so

## cuda_11.7_installation_on_Ubuntu_22.04
#!/bin/bash

### steps ####
# verify the system has a cuda-capable gpu
# download and install the nvidia cuda toolkit and cudnn
# setup environmental variables
# verify the installation
###

### to verify your gpu is cuda enable check

## go-wsl.md

      
              1 file
            
          
              7 forks
            
          
              12 comments
            
          
              37 stars
            
          
                renevo
                / go-wsl.md
            
            
              Created
              September 5, 2018 18:56
            
              
                Easily update/install Go in wsl
              
          
    Updating/Installing Go in WSL

Remove

Removes older versions
sudo rm -rf /usr/local/go* && sudo rm -rf /usr/local/go

  
## AttentionWithContext.py
def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:
        x (): input
        kernel (): weights
    Returns:
    """
    if K.backend() == 'tensorflow':

## Attention.py

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:

## readme.md

      
              2 files
            
          
              257 forks
            
          
              182 comments
            
          
              743 stars
            
          
                baraldilorenzo
                / readme.md
            
            
              Last active
              June 13, 2024 03:07
            
              
                VGG-16 pre-trained model for Keras
              
          
    ##VGG16 model for Keras
This is the Keras model of the 16-layer network used by the VGG team in the ILSVRC-2014 competition.
It has been obtained by directly converting the Caffe model provived by the authors.
Details about the network architecture can be found in the following arXiv paper:
Very Deep Convolutional Networks for Large-Scale Image Recognition

K. Simonyan, A. Zisserman

  
## ReactCanvasDrawing.js
/** @jsx React.DOM */

var Graphic = React.createClass({

  componentDidMount: function() {
    var context = this.getDOMNode().getContext('2d');
    this.paint(context);
  },

  componentDidUpdate: function() {

## dynamicPagRepeatAngular.html
<!DOCTYPE HTML>
<html lang="en" ng-app="myApp">
	<head>
		<meta charset="utf-8">
		<title>Dynamic Pagination w/ Filtering</title>
		<meta name="viewport" content="width=device-width, initial-scale=1.0">
		<meta name="description" content="">
		<meta name="author" content="Kim Maida">

		<!-- JS Libraries -->

## bloop.js
(function() {
  // Do not use this library. This is just a fun example to prove a
  // point.

  var Bloop = window.Bloop = {};

  var mountId = 0;
  function newMountId() {
    return mountId++;
  }
	from transformers import AutoModelForCausalLM, AutoTokenizer
	from peft import PeftModel
	import torch

	import os
	import argparse

	def get_args():
	parser = argparse.ArgumentParser()
	parser.add_argument("--base_model_name_or_path", type=str)
	Docker Image : pytorch/pytorch
	Image Runtype : jupyter_direc ssh_direc ssh_proxy
	Environment : [["JUPYTER_DIR", "/"], ["-p 41654:41654", "1"]]

	pip install torch bitsandbytes sentencepiece "protobuf<=3.20.2" git+https://github.com/huggingface/transformers flask python-dotenv Flask-HTTPAuth accelerate

	!mv /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cuda116.so /opt/conda/lib/python3.10/site-packages/bitsandbytes/libbitsandbytes_cpu.so
	#!/bin/bash

	### steps ####
	# verify the system has a cuda-capable gpu
	# download and install the nvidia cuda toolkit and cudnn
	# setup environmental variables
	# verify the installation
	###

	### to verify your gpu is cuda enable check
	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:
	x (): input
	kernel (): weights
	Returns:
	"""
	if K.backend() == 'tensorflow':

	from keras import backend as K, initializers, regularizers, constraints
	from keras.engine.topology import Layer


	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:
	/** @jsx React.DOM */

	var Graphic = React.createClass({

	componentDidMount: function() {
	var context = this.getDOMNode().getContext('2d');
	this.paint(context);
	},

	componentDidUpdate: function() {
	<!DOCTYPE HTML>
	<html lang="en" ng-app="myApp">
	<head>
	<meta charset="utf-8">
	<title>Dynamic Pagination w/ Filtering</title>
	<meta name="viewport" content="width=device-width, initial-scale=1.0">
	<meta name="description" content="">
	<meta name="author" content="Kim Maida">

	<!-- JS Libraries -->
	(function() {
	// Do not use this library. This is just a fun example to prove a
	// point.

	var Bloop = window.Bloop = {};

	var mountId = 0;
	function newMountId() {
	return mountId++;
	}