Aziz Alto iamaziz

## automator-local-llm.md

      
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                iamaziz
                / automator-local-llm.md
            
            
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              January 24, 2024 20:49
            
          
    Using local LLMs anywhere (in text editor) - example below with Obsidian
inspired by and adopted from LLM-automator.

Code example with mixtral


    automator-llm-code.mp4
    
  
## list_local_ollama_models.py
import re
import subprocess


def _parse_names(data: str) -> list[str]:
    """
    Parses names from a multi-line string where each line contains a name and other details.

    Parameters:
    data (str): A multi-line string containing names and other details.

## trying_crewai-arabic-llm.py
# basedk on: https://github.com/joaomdmoura/crewAI#getting-started
from crewai import Agent, Task, Crew
from langchain_community.llms import Ollama
from langchain_community.tools import DuckDuckGoSearchRun


# -- model
# ollama_llm = Ollama(model="arabic_deepseek-llm")
# ollama_llm = Ollama(model="arabic_notux")
ollama_llm = Ollama(model="arabic_mixtral")

## Jais-LLM-GettingStarted.ipynb

      
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                iamaziz
                / Jais-LLM-GettingStarted.ipynb
            
            
              Created
              December 30, 2023 17:16
            
              
                Running Jais LLM on M3 Max chip with 64GB - for some reason it's very slow and too big for a 13b model
              
          
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## pytorch_device.py
import torch

# Check if CUDA is available, else check for MPS, otherwise default to CPU
if torch.cuda.is_available():
    device = torch.device("cuda") # GPU
elif torch.backends.mps.is_available():
    device = torch.device("mps")  # MacOS M-series chip
else:
    device = torch.device("cpu")  # CPU

## entropy_gain.py
# -*- coding: utf-8 -*-

# calculating the Entropy and Information Gain for: Learning with Trees
# by: Aziz Alto

# see Information Gain:
    # http://www.autonlab.org/tutorials/infogain.html


from __future__ import division

## weather.html
<!DOCTYPE html>
<html>

<head>
    <script src="https://code.jquery.com/jquery-3.1.1.js"></script>
    <title>Weather API</title>

    <!-- to be in a script.js -->
    <script type="text/javascript">

## read_csv_files_in_tar_gz_from_s3_bucket.py
# -- read csv files from tar.gz in S3 with S3FS and tarfile (https://s3fs.readthedocs.io/en/latest/)

bucket = 'mybucket'
key = 'mycompressed_csv_files.tar.gz'

import s3fs
import tarfile
import io
import pandas as pd

## cipynb.py
#!/usr/bin/env python

__author__ = 'Aziz'

"""
Convert all ipython notebook(s) in a given directory into the selected format and place output in a separate folder.
usages: python cipynb.py `directory` [-to FORMAT]

Using: ipython nbconvert and find command (Unix-like OS).

## videocolorizercolab.ipynb

      
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                iamaziz
                / videocolorizercolab.ipynb
            
            
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              April 4, 2022 03:57
            
              
                VideoColorizerColab.ipynb
              
          
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	import re
	import subprocess


	def _parse_names(data: str) -> list[str]:
	"""
	Parses names from a multi-line string where each line contains a name and other details.

	Parameters:
	data (str): A multi-line string containing names and other details.
	# basedk on: https://github.com/joaomdmoura/crewAI#getting-started
	from crewai import Agent, Task, Crew
	from langchain_community.llms import Ollama
	from langchain_community.tools import DuckDuckGoSearchRun


	# -- model
	# ollama_llm = Ollama(model="arabic_deepseek-llm")
	# ollama_llm = Ollama(model="arabic_notux")
	ollama_llm = Ollama(model="arabic_mixtral")
	import torch

	# Check if CUDA is available, else check for MPS, otherwise default to CPU
	if torch.cuda.is_available():
	device = torch.device("cuda") # GPU
	elif torch.backends.mps.is_available():
	device = torch.device("mps") # MacOS M-series chip
	else:
	device = torch.device("cpu") # CPU
	# -- coding: utf-8 --

	# calculating the Entropy and Information Gain for: Learning with Trees
	# by: Aziz Alto

	# see Information Gain:
	# http://www.autonlab.org/tutorials/infogain.html


	from __future__ import division
	<!DOCTYPE html>
	<html>

	<head>
	<script src="https://code.jquery.com/jquery-3.1.1.js"></script>
	<title>Weather API</title>

	<!-- to be in a script.js -->
	<script type="text/javascript">
	# -- read csv files from tar.gz in S3 with S3FS and tarfile (https://s3fs.readthedocs.io/en/latest/)

	bucket = 'mybucket'
	key = 'mycompressed_csv_files.tar.gz'

	import s3fs
	import tarfile
	import io
	import pandas as pd
	#!/usr/bin/env python

	__author__ = 'Aziz'

	"""
	Convert all ipython notebook(s) in a given directory into the selected format and place output in a separate folder.
	usages: python cipynb.py `directory` [-to FORMAT]

	Using: ipython nbconvert and find command (Unix-like OS).