vatsalsaglani

import os
from requests import head
import torch as T
import torch.nn as nn
import torch.nn.functional as F
from modules import Encoder, Decoder


class RecommendationTransformer(nn.Module):
		"""Sequential recommendation model architecture

vatsalsaglani

import os
import re
import pandas as pd
from tqdm import trange, tnrange
import torch as T
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from bert4rec_dataset import Bert4RecDataset
from bert4rec_model import RecommendationModel, RecommendationTransformer

vatsalsaglani

from train_pipeline import trainer
from constants import TRAIN_CONSTANTS

from rich.table import Column, Table
from rich import box
from rich.console import Console

console = Console(record=True)

training_logger = Table(

vatsalsaglani

import torch as T
import torch.nn.functional as F
import torch.nn as nn
import numpy as np
import os
import re
from bert4rec_model import RecommendationTransformer
from constants import TRAIN_CONSTANTS
from typing import List, Dict, Tuple
import random

vatsalsaglani

<script>
  // @ts-nocheck

  import mermaid from "mermaid";
  import { onMount } from "svelte";
  import { browser } from "$app/environment";
  import MarkdownRenderer from "../components/MarkdownRenderer.svelte";
  import Spinner from "../components/Spinner.svelte";
  $: inputText = ``;
  $: thoughtProcess = ``;

vatsalsaglani

import math
import re
from urllib import request
from io import BytesIO
import base64
from PIL import Image
from typing import Literal


def getImageDimensions(image: str):  # base64 or url

vatsalsaglani

from typing import List, Dict, Literal, Union
from transformers import AutoTokenizer


class MistralAICtx:

    def __init__(self, model_name: str):
        assert "mistral" in model_name, "MistralCtx only available for Mistral models"
        self.tokenizer = AutoTokenizer.from_pretrained(
            "mistralai/Mistral-7B-Instruct-v0.2")