diff --git "a/LLM_Base_model_Pretraining.ipynb" "b/LLM_Base_model_Pretraining.ipynb"
new file mode 100644--- /dev/null
+++ "b/LLM_Base_model_Pretraining.ipynb"
@@ -0,0 +1,2974 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "S4ltaEGQkF_6"
+      },
+      "outputs": [],
+      "source": [
+        "# !pip install transformers\n",
+        "# !pip install tokenizers\n",
+        "!pip install --upgrade accelerate"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "94gs7C92kXAT"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import GPT2LMHeadModel, GPT2Tokenizer, GPT2Config\n",
+        "from transformers import TextDataset, DataCollatorForLanguageModeling\n",
+        "from transformers import Trainer, TrainingArguments"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "7_0iV8aukXDL",
+        "colab": {
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+          "height": 365,
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+          ]
+        },
+        "outputId": "36592b8e-70b7-4458-d90a-b6c0d66d3217"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n",
+            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
+            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
+            "You will be able to reuse this secret in all of your notebooks.\n",
+            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json:   0%|          | 0.00/665 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "bcfd379cf54246d7a0ff0e3e4c92c55a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/548M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "454909f6b63a44e394ca5b2059f0f493"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3f6ee03355134c8fabfaa16a55cae109"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/26.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "03da5853bd834306ab71aeecf79b1c2c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "e073123ac3634dec848b8b6762087a3b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "merges.txt:   0%|          | 0.00/456k [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "f3ae5d3032aa4cec82e95072028cffb3"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6196ae414a624763a8b4851ce0e6163d"
+            }
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "# load the pre trained model\n",
+        "#currently got the GPT 2 (Base model) working\n",
+        "model_name = 'gpt2' # https://huggingface.co/openai-community/gpt2\n",
+        "model = GPT2LMHeadModel.from_pretrained(model_name)\n",
+        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
+        "config = GPT2Config.from_pretrained(model_name)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# testing without finetuning base model\n",
+        "input_text = 'Hello how are you?'\n",
+        "input_ids = tokenizer.encode(input_text, return_tensors='pt')\n",
+        "\n",
+        "output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)\n",
+        "generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
+        "\n",
+        "print(generated_text)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "JMrs6H6u7tsp",
+        "outputId": "6ddda0f0-da39-4dea-b80e-0e13b8ec94f4"
+      },
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n",
+            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token.As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Hello how are you?\n",
+            "\n",
+            "I'm a little bit of a nerd. I'm not a big fan of the show, but I've been watching it for a while now and I think it's a great show. It's funny, it has a lot of great characters, and it does a good job of making you feel like you're part of something bigger than yourself.\n",
+            "...\n",
+            " (Laughs.)\n",
+            "\n",
+            "\n",
+            "\n",
+            "What's your favorite part about the series?\n",
+            "\n",
+            ".\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# download book by the author.\n",
+        "# In our case students might filter the gutenberg library to get books by their favourite author and train the model to answer in\n",
+        "# the authors tone\n",
+        "\n",
+        "# Sample books -for testing\n",
+        "#!wget https://raw.githubusercontent.com/brunoklein99/deep-learning-notes/master/shakespeare.txt -O shakespeare.txt\n",
+        "\n",
+        "# All shakespeare books\n",
+        "!wget https://gist.githubusercontent.com/blakesanie/dde3a2b7e698f52f389532b4b52bc254/raw/76fe1b5e9efcf0d2afdfd78b0bfaa737ad0a67d3/shakespeare.txt -O shakespeare.txt"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "GuuBStGA8knX",
+        "outputId": "c47541cf-1807-4eec-d077-c108270e3bf8"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "--2024-08-31 05:45:45--  https://gist.githubusercontent.com/blakesanie/dde3a2b7e698f52f389532b4b52bc254/raw/76fe1b5e9efcf0d2afdfd78b0bfaa737ad0a67d3/shakespeare.txt\n",
+            "Resolving gist.githubusercontent.com (gist.githubusercontent.com)... 185.199.110.133, 185.199.108.133, 185.199.111.133, ...\n",
+            "Connecting to gist.githubusercontent.com (gist.githubusercontent.com)|185.199.110.133|:443... connected.\n",
+            "HTTP request sent, awaiting response... 200 OK\n",
+            "Length: 5436475 (5.2M) [text/plain]\n",
+            "Saving to: ‘shakespeare.txt’\n",
+            "\n",
+            "shakespeare.txt     100%[===================>]   5.18M  --.-KB/s    in 0.07s   \n",
+            "\n",
+            "2024-08-31 05:45:45 (79.1 MB/s) - ‘shakespeare.txt’ saved [5436475/5436475]\n",
+            "\n"
+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "PE_ak0pNkXF0",
+        "outputId": "dc120cb8-2e2e-4b49-c80c-48d88c09c479"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.10/dist-packages/transformers/data/datasets/language_modeling.py:53: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
+      "source": [
+        "# prepare the dataset\n",
+        "\n",
+        "train_path = 'shakespeare.txt'\n",
+        "train_dataset = TextDataset(\n",
+        "    tokenizer=tokenizer,\n",
+        "    file_path=train_path,\n",
+        "    block_size=128\n",
+        ")\n",
+        "\n",
+        "data_collator = DataCollatorForLanguageModeling(\n",
+        "    tokenizer=tokenizer,\n",
+        "    mlm=False\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {
+        "id": "AErVFzrakXHK"
+      },
+      "outputs": [],
+      "source": [
+        "# set up training args\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir='./results',\n",
+        "    overwrite_output_dir=True,\n",
+        "    num_train_epochs=3,\n",
+        "    per_device_eval_batch_size=32,\n",
+        "    eval_steps=100,\n",
+        "    save_steps=10_000,\n",
+        "    warmup_steps=500,\n",
+        ")\n",
+        "\n",
+        "trainer = Trainer(\n",
+        "    model=model,\n",
+        "    args=training_args,\n",
+        "    data_collator=data_collator,\n",
+        "    train_dataset=train_dataset,\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 110
+        },
+        "id": "um5prRF4kXJp",
+        "outputId": "956cb525-5bcf-4562-8986-fd145c6cd948"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
+            "text/html": [
+              "\n",
+              "    <div>\n",
+              "      \n",
+              "      <progress value='75' max='75' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [75/75 00:31, Epoch 3/3]\n",
+              "    </div>\n",
+              "    <table border=\"1\" class=\"dataframe\">\n",
+              "  <thead>\n",
+              " <tr style=\"text-align: left;\">\n",
+              "      <th>Step</th>\n",
+              "      <th>Training Loss</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "  </tbody>\n",
+              "</table><p>"
+            ]
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "TrainOutput(global_step=75, training_loss=4.883878580729166, metrics={'train_runtime': 32.7918, 'train_samples_per_second': 18.023, 'train_steps_per_second': 2.287, 'total_flos': 38605897728000.0, 'train_loss': 4.883878580729166, 'epoch': 3.0})"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 15
+        }
+      ],
+      "source": [
+        "trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 16,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "-Jwzlm_GkXND",
+        "outputId": "85a60d7a-6ab7-461e-c6dd-0c9f5e5731dd"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "('./results/tokenizer_config.json',\n",
+              " './results/special_tokens_map.json',\n",
+              " './results/vocab.json',\n",
+              " './results/merges.txt',\n",
+              " './results/added_tokens.json')"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 16
+        }
+      ],
+      "source": [
+        "output_dir = './results'\n",
+        "model.save_pretrained(output_dir)\n",
+        "tokenizer.save_pretrained(output_dir)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "del model\n",
+        "del tokenizer"
+      ],
+      "metadata": {
+        "id": "o4wHKGZV-kT7"
+      },
+      "execution_count": 17,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 18,
+      "metadata": {
+        "id": "xbYUpT1-mBdi"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
+        "\n",
+        "# Loading the new model from local dir\n",
+        "output_dir = './results'\n",
+        "model = GPT2LMHeadModel.from_pretrained(output_dir)\n",
+        "tokenizer = GPT2Tokenizer.from_pretrained(output_dir)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 19,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "KH4LNOPHmBg3",
+        "outputId": "37e4ba46-67ba-4473-dd40-a48491141571"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
+          ]
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Hello how are you?\n",
+            "\n",
+            "I am a man of the world,\n",
+            "And I am not a woman, but a child.\n",
+            "But I have a wife, and a son, who is my own.\n",
+            "\n",
+            "\n",
+            "What do you say, my dear, that I should not be so kind to you, when I love you so much?\n",
+            "\n",
+            "\n",
+            "\n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "#Testing the new model\n",
+        "input_text = 'Hello how are you?'\n",
+        "input_ids = tokenizer.encode(input_text, return_tensors='pt')\n",
+        "\n",
+        "output = model.generate(input_ids, max_length=100, num_return_sequences=1, no_repeat_ngram_size=2)\n",
+        "generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
+        "\n",
+        "print(generated_text)"
+      ]
+    }
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