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# Usecase | |
def make_prompt(input_text: str, system_prompt="", max_length=512) -> str: | |
""" | |
Generates text using a large language model, given a prompt and a device. | |
Args: | |
input_text (str): The input text for prompting. | |
system_prompt (str): The system prompt (not used in the function). | |
max_length (int): The maximum length of the generated text. |
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import random | |
import textwrap | |
import torch # You missed importing the torch module. | |
device = 'cuda' if torch.cuda.is_available() else 'cpu' | |
import textwrap | |
def text_wrapper(text, width=90): | |
# Wraps the input text to the specified width |
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import torch | |
from datasets import load_dataset | |
from peft import LoraConfig | |
from transformers import ( | |
AutoModelForCausalLM, | |
AutoTokenizer, | |
BitsAndBytesConfig, | |
HfArgumentParser, | |
TrainingArguments, |
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import argparse | |
import bitsandbytes as bnb | |
from datasets import load_dataset | |
from functools import partial | |
import os | |
from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training, AutoPeftModelForCausalLM | |
import torch | |
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed, Trainer, TrainingArguments, BitsAndBytesConfig, \ | |
DataCollatorForLanguageModeling, Trainer, TrainingArguments | |
from datasets import load_dataset |
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def train_one_epoch(model, optimizer, scheduler, dataloader, device, epoch): | |
model.train() | |
dataset_size = 0 | |
running_loss = 0.0 | |
bar = tqdm(enumerate(dataloader), total=len(dataloader)) | |
""" The total argument in tqdm specifies the total number of iterations (or updates to the progress bar). In this case, len(dataloader) is used as the total which is the total number of batches in the dataloader. """ | |
for step, data in bar: | |
ids = data['input_ids'].to(device, dtype = torch.long) |
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from tensorflow import keras | |
import tensorflow as tf | |
class ChildDense(keras.layers.Layer): | |
def __init__(self, units, activation=None): | |
super().__init__() | |
self.units = units | |
self.activation = activation | |
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from statsmodels.tsa.stattools import adfuller | |
from statsmodels.tsa.seasonal import seasonal_decompose | |
import statsmodels.api as sm | |
from sklearn.metrics import mean_squared_error, mean_absolute_error | |
import math | |
#Test for staionarity | |
def test_stationarity(timeseries): | |
#Determing rolling statistics | |
rolling_mean = timeseries.rolling(12).mean() |
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# Now generate TPR, FPR and ROC data | |
tpr,fpr,roc_auc = ([[]]*number_of_classes for _ in range(3)) | |
f,ax = plt.subplots() | |
for i in range(number_of_classes): | |
fpr[i], tpr[i], _ = roc_curve(labels==i, predictions[:, i]) | |
roc_auc[i] = auc(fpr[i], tpr[i]) |
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import numpy as np | |
from sklearn.metrics import roc_auc_score | |
from sklearn.metrics import roc_curve, auc | |
import matplotlib.pyplot as plt | |
#generating synthetic data | |
number_of_classes = 5 | |
samples_per_class= 70 |
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from sklearn.datasets import make_classification | |
from sklearn.model_selection import train_test_split | |
import numpy as np | |
from tqdm import tqdm | |
import random | |
from sklearn.metrics import accuracy_score | |
from sklearn.neighbors import KNeighborsClassifier | |
from matplotlib.colors import ListedColormap | |
import matplotlib.pyplot as plt | |
import warnings |
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