rasbt

# Sebastian Raschka 2025
#
#
# Usage:
# python llama-vs-gemma.py \
#  --auth_token hf_... \
#  --model_name meta-llama/Llama-3.2-1B \
#  --prompt medium

import argparse

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import torch.nn as nn


class Transformer(nn.Module):
    def __init__(self, ...):
        super().__init__()
        ...
        self.encoder_layers = nn.ModuleList(...)
        self.decoder_layers = nn.ModuleList(...)
        ...

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# pip install openai

import openai
import os

# export API key environment variable before
# running this script (see https://platform.openai.com/account/api-keys)
# E.g.,
#
# export OPENAI_API_KEY=copy_paste_key_from_your_account

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# Setup:
# conda create -n whisper python=3.9
# conda activate whisper
# https://github.com/openai/whisper
# pip install git+https://github.com/openai/whisper.git 

# Usage:
# python whisper-audio-to-text.py --audio_dir my_files --out_dir texts

import argparse

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# 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

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import numpy as np
from sklearn.datasets import make_classification
from sklearn.model_selection import cross_val_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.calibration import CalibratedClassifierCV


X, y = make_classification(
    n_samples=1000, n_features=5, n_redundant=2, 
    n_clusters_per_class=1, random_state=123)

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### EYECANDY ###
autoload -U colors && colors
PS1="%{$fg[red]%}%n%{$reset_color%}@%{$fg[blue]%}%m %{$fg[yellow]%}%~ %{$reset_color%}%% "
export CLICOLOR=1
#export LSCOLORS=ExFxBxDxCxegedabagacad
#alias ls='ls -GFh'
### END

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name: base
channels:
  - conda-forge
dependencies:
  - absl-py=1.0.0=pyhd8ed1ab_0
  - aiohttp=3.8.1=py39h5161555_0
  - aiosignal=1.2.0=pyhd8ed1ab_0
  - anyio=3.5.0=py39h2804cbe_0
  - appnope=0.1.2=py39h2804cbe_2
  - argh=0.26.2=pyh9f0ad1d_1002

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import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader


if torch.cuda.is_available():