thistleknot

import torch
import transformers
import pyreft
import os
from datasets import load_dataset
import pandas as pd
#pd.DataFrame([len(q) for q in quotes]).describe()
#pd.DataFrame([len(q) for q in quotes]).hist()
import numpy as np

thistleknot

#for data txt files see: https://github.com/TheCynosure/smmry_impl
#example use 
"""
Search_web("history of Taco Tuesday")
Tell me about this.
"""
#get google api keys'
#https://console.cloud.google.com/apis/dashboard
#https://programmablesearchengine.google.com/controlpanel/all
#could be retooled quite easily to use duckduckgo_search rather than google and you don't have to mess with getting api key's

thistleknot

def get_v1_url(symbol, period_type, crumb):
    headers = {
        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/119.0.0.0 Safari/537.36',
        'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',
        'Accept-Language': 'en-US,en;q=0.5',
    }

    period1 = 493590046
    period2 = 1913180947

thistleknot

import torch
import torch.nn as nn
from torch.nn import  functional as F
from torch.nn.parameter import Parameter
from tqdm import tqdm
from mamba_ssm import Mamba
#hyperparams
epochs = 100
lr = 1e-3
batch_size = 64

thistleknot

from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments
import wandb
from datasets import load_dataset
import torch
import os
import argparse
import numpy as np
import pandas as pd
from transformers import EvalPrediction
from torch.utils.data import DataLoader

thistleknot

#This method deducts from the list sent in (splitting the records between sample and remainder).
#Always 100% full of data until no more samples can be extracted where an empty sample along with the remainder are returned [where the remainder is to be folded into a new iteration]
    
# Function to find the combination of values that adds up to the target sum
def find_combination_to_sum(counts, target):
    #print("Target inside function (find_combination_to_sum):", target)
    values = []
    for val, count in counts.items():
        #print(f"Value (val): {val}, Type: {type(val)}")
        #print(f"Count: {count}, Type: {type(count)}")

thistleknot

from transformers import GPT2Tokenizer, GPTNeoForCausalLM
import torch
import torch.nn.functional as F

# Load the GPT-Neo 1.3B model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("EleutherAI/gpt-neo-1.3B")
model = GPTNeoForCausalLM.from_pretrained("EleutherAI/gpt-neo-1.3B")

# Your question and prompt
question = "Is a bird a mammal?"

thistleknot

from transformers import GPT2Tokenizer, GPT2LMHeadModel
import torch
import torch.nn.functional as F

# Load the GPT-2 model and tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT2LMHeadModel.from_pretrained("gpt2")

# Your question and prompt
question = "Is a bird a mammal?"

thistleknot

#!pip install --upgrade numpy
!pip install numpy==1.24


from datasets import load_dataset
from pyod.models.knn import KNN
from pyod.models.knn import KNN   # Example: You can use K-Nearest Neighbors as an ECOD model
from scipy import stats
from scipy.interpolate import UnivariateSpline
from scipy.stats import gaussian_kde

thistleknot

#!/usr/bin/env python
# coding: utf-8

# In[3]:

#!pip install --upgrade numpy
get_ipython().system('pip install numpy==1.24')

# In[4]: