{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# {{ model_class }} Tutorial\n",
    "\n",
    "Welcome to the comprehensive tutorial on `{{ model_class }}` from Hugging Face's Transformers library. This notebook will guide you through the process of loading, configuring, and utilizing the `{{ model_class }}` for generating text. We'll cover several key aspects including model loading, setting up the tokenizer, configuring the model for your hardware, and performing text generation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting Up the Environment\n",
    "\n",
    "Before we begin, let's set up our environment by importing the necessary libraries and checking for GPU availability."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Importing the Transformers library and torch\n",
    "from transformers import {{ model_class }}, {{ tokenizer_class }}\n",
    "import torch\n",
    "\n",
    "# Checking for GPU availability\n",
    "device = 'cuda' if torch.cuda.is_available() and {{ use_cuda }} else 'cpu'\n",
    "print(f'Using device: {device}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Model and Tokenizer\n",
    "\n",
    "In this section, we will load the `{{ model_class }}` and its corresponding tokenizer. We will use the `{{ model_name }}` model along with its respective tokenizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the model\n",
    "model = {{ model_class }}.from_pretrained('{{ model_name }}')\n",
    "tokenizer = {{ tokenizer_class }}.from_pretrained('{{ tokenizer_name }}')\n",
    "\n",
    "# Configuring the device\n",
    "model = model.to(device)\n",
    "\n",
    "# Precision configuration (optional)\n",
    "if '{{ precision }}' == 'float16':\n",
    "    model = model.half()\n",
    "\n",
    "# Explain the importance of each configuration..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Text with the Model\n",
    "\n",
    "Now that our model and tokenizer are set up, we can use them to generate text. This section will demonstrate how to provide a prompt to the model and generate a response."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Text generation\n",
    "prompt = 'Today is a beautiful day'\n",
    "inputs = tokenizer(prompt, return_tensors='pt')\n",
    "inputs = inputs.to(device)\n",
    "\n",
    "# Generating a response\n",
    "with torch.no_grad():\n",
    "    outputs = model.generate(**inputs)\n",
    "    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
    "\n",
    "print('Generated Text:\\n', generated_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Additional Tips and Tricks\n",
    "\n",
    "This section provides additional tips and tricks for optimizing model performance, handling different types of inputs, and troubleshooting common issues."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "In this tutorial, we have covered the basics of using `{{ model_class }}` from Hugging Face's Transformers library. Remember to explore the library's extensive documentation for more advanced use cases and techniques."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine-Tuning the Model\n",
    "\n",
    "In this section, we'll fine-tune `{{ model_class }}` on a specific task. Fine-tuning involves training the model on a smaller dataset for a particular task. We'll use a sample dataset for this demonstration."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Preparing the Dataset\n",
    "\n",
    "We'll start by preparing our dataset for fine-tuning. We'll load a sample dataset, preprocess it, and prepare it in a format suitable for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Sample dataset preparation\n",
    "from datasets import load_dataset\n",
    "dataset = load_dataset('sample_dataset_name')\n",
    "\n",
    "# Preprocessing the data\n",
    "def preprocess_data(example):\n",
    "    # Add your preprocessing steps here\n",
    "    return example\n",
    "\n",
    "dataset = dataset.map(preprocess_data)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Setting Up Training Parameters\n",
    "\n",
    "Now, let's set up our training parameters, including the optimizer, learning rate, and training epochs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AdamW\n",
    "\n",
    "# Optimizer\n",
    "optimizer = AdamW(model.parameters(), lr=5e-5)\n",
    "\n",
    "# Training parameters\n",
    "num_epochs = 3\n",
    "batch_size = 16\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training Loop\n",
    "\n",
    "We'll now define and run the training loop. This loop will update the model's weights based on our dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tqdm import tqdm\n",
    "\n",
    "model.train()\n",
    "for epoch in range(num_epochs):\n",
    "    loop = tqdm(dataset['train'], leave=True)\n",
    "    for batch in loop:\n",
    "        # Add your training steps here\n",
    "        pass\n",
    "\n",
    "    # Optionally add validation and saving checkpoints"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluating the Fine-Tuned Model\n",
    "\n",
    "After fine-tuning, let's evaluate our model on a validation set to see the improvements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.eval()\n",
    "# Add code to evaluate the model on the validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion\n",
    "\n",
    "This tutorial covered how to fine-tune `{{ model_class }}` for a specific task. Remember to explore various datasets, hyperparameters, and techniques to get the best results for your use case."
   ]
  }
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