s_teja saitejamalyala

## hftg_demo_guanaco-33b-merged_qlora.py
import os

import dotenv
from langchain import HuggingFaceTextGenInference

dotenv.load_dotenv()

os.environ[
    "HUGGINGFACEHUB_API_TOKEN"
] = os.getenv("HF_API_TOKEN")

## µrwkv.py
import numpy as np
import torch


class MyModule(torch.nn.Module):

    def add_param(self, key, shape):
        val = torch.randn(*shape)/np.prod(shape)
        setattr(self, key, torch.nn.Parameter(val))

## 00_risk_budgeting.md

      
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                yhilpisch
                / 00_risk_budgeting.md
            
            
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              May 8, 2024 12:03
            
          
    Risk Parity & Budgeting

With Python
Dr. Yves J. Hilpisch | The Python Quants & The AI Machine
Python for Quant Finance Meetup, London, 16. November 2022
(short link to this Gist: http://bit.ly/pqf_risk)

  
## video-subtitles-via-whisper.py
# Sebastian Raschka 09/24/2022
# Create a new conda environment and packages
#   conda create -n whisper python=3.9
#   conda activate whisper
#   conda install mlxtend -c conda-forge

# Install ffmpeg
# macOS & homebrew
#   brew install ffmpeg
# Ubuntu

## calculate_trainset_mean_std.py
"""
in this script, we calculate the image per channel mean and standard
deviation in the training set, do not calculate the statistics on the
whole dataset, as per here http://cs231n.github.io/neural-networks-2/#datapre
"""

import numpy as np
from os import listdir
from os.path import join, isdir
from glob import glob

## ffmpeg-convert-mp3-to-wave
ffmpeg -i input.mp3 -acodec pcm_s16le -ac 1 -ar 16000 output.wav

# To convert all mp3 files in a directory in Linux:

for f in *.mp3; do ffmpeg -i "$f" -acodec pcm_s16le -ac 1 -ar 16000 "${f%.mp3}.wav"; done

# Or Windows:

for /r %i in (*) do ffmpeg -i %i -acodec pcm_s16le -ac 1 -ar 16000 %i.wav

## CANTest.ino
/*
  MCP2515 CAN Interface Using SPI

  Author: David Harding

  Created: 11/08/2010
  Modified: 6/26/12 by RechargeCar Inc.

  For further information see:
	import os

	import dotenv
	from langchain import HuggingFaceTextGenInference

	dotenv.load_dotenv()

	os.environ[
	"HUGGINGFACEHUB_API_TOKEN"
	] = os.getenv("HF_API_TOKEN")
	import numpy as np
	import torch


	class MyModule(torch.nn.Module):

	def add_param(self, key, shape):
	val = torch.randn(*shape)/np.prod(shape)
	setattr(self, key, torch.nn.Parameter(val))
	# Sebastian Raschka 09/24/2022
	# Create a new conda environment and packages
	# conda create -n whisper python=3.9
	# conda activate whisper
	# conda install mlxtend -c conda-forge

	# Install ffmpeg
	# macOS & homebrew
	# brew install ffmpeg
	# Ubuntu
	"""
	in this script, we calculate the image per channel mean and standard
	deviation in the training set, do not calculate the statistics on the
	whole dataset, as per here http://cs231n.github.io/neural-networks-2/#datapre
	"""

	import numpy as np
	from os import listdir
	from os.path import join, isdir
	from glob import glob
	ffmpeg -i input.mp3 -acodec pcm_s16le -ac 1 -ar 16000 output.wav

	# To convert all mp3 files in a directory in Linux:

	for f in *.mp3; do ffmpeg -i "$f" -acodec pcm_s16le -ac 1 -ar 16000 "${f%.mp3}.wav"; done

	# Or Windows:

	for /r %i in (*) do ffmpeg -i %i -acodec pcm_s16le -ac 1 -ar 16000 %i.wav
	/*
	MCP2515 CAN Interface Using SPI

	Author: David Harding

	Created: 11/08/2010
	Modified: 6/26/12 by RechargeCar Inc.

	For further information see: