diff --git "a/TRAINER.ipynb" "b/TRAINER.ipynb"
new file mode 100644--- /dev/null
+++ "b/TRAINER.ipynb"
@@ -0,0 +1,3878 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "rqX9hLfEziis"
+      },
+      "source": [
+        "Further documentation for the libraries used can be found at https://unsloth.ai/"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "9eJjsg0_D2Dj"
+      },
+      "source": [
+        "For information on hugging face tokens go to https://huggingface.co/docs/hub/en/security-tokens"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "pdymVP95XA4_",
+        "outputId": "9dd22bfa-2e4f-4c5f-f544-c73431671713"
+      },
+      "outputs": [],
+      "source": [
+        "# Add your hugging face token to secret keys or store your huggingface token as an environment variable.\n",
+        "# It is used to download or upload models to your account.\n",
+        "from google.colab import userdata\n",
+        "from huggingface_hub import login\n",
+        "login(userdata.get('TOKEN'))"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "cBEG5Pq1lG-G"
+      },
+      "outputs": [],
+      "source": [
+        "%%capture\n",
+        "# Installs Unsloth, Xformers (Flash Attention) and all other packages!\n",
+        "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
+        "!pip install --upgrade transformers\n",
+        "\n",
+        "# We have to check which Torch version for Xformers (2.3 -> 0.0.27)\n",
+        "from torch import __version__; from packaging.version import Version as V\n",
+        "xformers = \"xformers==0.0.27\" if V(__version__) < V(\"2.4.0\") else \"xformers\"\n",
+        "!pip install --no-deps {xformers} trl peft accelerate bitsandbytes triton"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "aRz4XCANNy8b",
+        "outputId": "0ccf0b95-5daf-452f-8bf7-37e224f5ce81"
+      },
+      "outputs": [],
+      "source": [
+        "from unsloth import FastLanguageModel\n",
+        "import torch\n",
+        "\n",
+        "max_seq_length = 2048\n",
+        "\n",
+        "def load_model(model_name, max_seq_length=max_seq_length, dtype=None, load_in_4bit=True):\n",
+        "    model, tokenizer = FastLanguageModel.from_pretrained(\n",
+        "        model_name=model_name,\n",
+        "        max_seq_length=max_seq_length,\n",
+        "        dtype=dtype,\n",
+        "        load_in_4bit=load_in_4bit,\n",
+        "    )\n",
+        "    return model, tokenizer"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "gShO0BiJIq1P"
+      },
+      "source": [
+        "#Load the base model and attach LoRa adapters.\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 362,
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+          ]
+        },
+        "id": "urDIg3QRlCMx",
+        "outputId": "b45d8a03-31ab-4d4a-ea31-bb9f7e6314fc"
+      },
+      "outputs": [],
+      "source": [
+        "models = [\n",
+        "    'Phi-3.5-mini-instruct-bnb-4bit', # |Min Training Gpu : T4, Max Model size : 14.748 GB|\n",
+        "    'gemma-2-27b-it-bnb-4bit',  # |Min Training Gpu: A100, Min Testing GPU: T4, Max Model size: 39.564 GB|\n",
+        "    'Meta-Llama-3.1-8B-Instruct-bnb-4bit' # |Min Training Gpu: T4, Min Testing GPU: T4, Max Model size : 14.748 GB|\n",
+        "    ]\n",
+        "\n",
+        "chat_templates = [\n",
+        "    'phi-3',\n",
+        "    'alpaca',\n",
+        "    'llama-3.1'\n",
+        "]\n",
+        "\n",
+        "# Select model and corresponding template.\n",
+        "selection = 2\n",
+        "model_name = models[selection]\n",
+        "chat_template = chat_templates[selection]\n",
+        "HfUsername = \"CooperW\"\n",
+        "\n",
+        "base_model = f\"unsloth/{model_name}\"\n",
+        "LoRa_Adapters = f\"{HfUsername}/{model_name.replace('-', '_').replace('.', '_')}_128prompt\"\n",
+        "\n",
+        "# Load the base model.\n",
+        "model, tokenizer = load_model(base_model)\n",
+        "\n",
+        "try:\n",
+        "  # For continued training load LoRa adapters to model.\n",
+        "  from peft import PeftModel\n",
+        "  model = PeftModel.from_pretrained(model, LoRa_Adapters)\n",
+        "\n",
+        "  from huggingface_hub import snapshot_download\n",
+        "  import os\n",
+        "\n",
+        "  download_path = snapshot_download(repo_id=LoRa_Adapters, ignore_patterns=[\"*.md\", \"*.safetensors\"])\n",
+        "  last_checkpoint_path = os.path.join(download_path, \"last-checkpoint\")\n",
+        "except:\n",
+        "  # Initialise LoRa adapters.\n",
+        "  model = FastLanguageModel.get_peft_model(\n",
+        "    model,\n",
+        "    r = 128,\n",
+        "    target_modules = [\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\",\n",
+        "                      \"gate_proj\", \"up_proj\", \"down_proj\",],\n",
+        "    lora_alpha = 8,\n",
+        "    lora_dropout = 0,\n",
+        "    bias = \"none\",\n",
+        "    use_gradient_checkpointing = \"unsloth\",\n",
+        "    random_state = 3407,\n",
+        "    use_rslora = True,\n",
+        "    loftq_config = None,\n",
+        "  )"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "2IrwuiZgVQ6h"
+      },
+      "source": [
+        "#Data Mapping\n",
+        "This function will take the given dataset and map the contents to the [\"text\"] field, this format is what is needed to be input to the model.\n",
+        "\n",
+        "Example:\n",
+        "```\n",
+        "<|user|>\n",
+        "Hi!<|end|>\n",
+        "<|assistant|>\n",
+        "Hello! How are you?<|end|>\n",
+        "<|user|>\n",
+        "I'm doing great! And you?<|end|>\n",
+        "\n",
+        "```"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "QVEYv-pnlh9s"
+      },
+      "outputs": [],
+      "source": [
+        "from unsloth.chat_templates import get_chat_template\n",
+        "\n",
+        "tokenizer = get_chat_template(\n",
+        "    tokenizer,\n",
+        "    chat_template =  chat_template,\n",
+        "    mapping = {\"role\" : \"from\", \"content\" : \"value\", \"user\" : \"human\", \"assistant\" : \"gpt\"},\n",
+        ")\n",
+        "\n",
+        "def formatting_prompts_func(examples):\n",
+        "    convos = examples[\"conversations\"]\n",
+        "    texts = [tokenizer.apply_chat_template(convo, tokenize = False, add_generation_prompt = False) for convo in convos]\n",
+        "    return { \"text\" : texts, }\n",
+        "pass"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 113,
+          "referenced_widgets": [
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+          ]
+        },
+        "id": "iVqRqJrqlpvD",
+        "outputId": "d4ab65e0-7c53-476c-e351-2c73f891a556"
+      },
+      "outputs": [],
+      "source": [
+        "# This will load datasets from your hugging face repository, alternatively load locally stored datasets.\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "dataset_repo = f\"{HfUsername}/jadidi\"\n",
+        "dataset_name = \"train_network.jsonl\"\n",
+        "\n",
+        "train_dataset = load_dataset(dataset_repo, data_files=dataset_name, split='train')\n",
+        "train_dataset = train_dataset.map(formatting_prompts_func, batched = True,)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "U9TEfn5l290J"
+      },
+      "outputs": [],
+      "source": [
+        "from transformers import Trainer, TrainingArguments\n",
+        "from trl import SFTTrainer\n",
+        "import os\n",
+        "\n",
+        "training_args = TrainingArguments(\n",
+        "    output_dir=\"outputs\",\n",
+        "    per_device_train_batch_size=8,\n",
+        "    gradient_accumulation_steps=16,\n",
+        "    lr_scheduler_type = \"cosine_with_restarts\",\n",
+        "    lr_scheduler_kwargs = { \"num_cycles\": len(trainer.get_train_dataloader()) / 100 },\n",
+        "    optim=\"adamw_8bit\",\n",
+        "    weight_decay=0.01,\n",
+        "    seed=3407,\n",
+        "    warmup_steps=10,\n",
+        "    # max_steps=100,\n",
+        "    num_train_epochs=3,\n",
+        "    learning_rate=2e-4,\n",
+        "    fp16=not torch.cuda.is_bf16_supported(),\n",
+        "    bf16=torch.cuda.is_bf16_supported(),\n",
+        "    logging_steps=1,\n",
+        ")\n",
+        "\n",
+        "# Define the name of the repo your model is uploaded. By default : (YourHuggingFaceAccount/baseModelName)\n",
+        "training_args = training_args.set_push_to_hub(\n",
+        "    model_id=LoRa_Adapters,\n",
+        "    strategy=\"checkpoint\",\n",
+        "    private_repo=True,\n",
+        "    always_push=False\n",
+        ")\n",
+        "\n",
+        "# Define how often your model is uploaded during training.\n",
+        "# Only LoRa adapters are saved.\n",
+        "training_args = training_args.set_save(\n",
+        "    strategy='steps',\n",
+        "    steps=50,\n",
+        "    total_limit=2,\n",
+        ")\n",
+        "\n",
+        "trainer = SFTTrainer(\n",
+        "    model=model,\n",
+        "    tokenizer=tokenizer,\n",
+        "    train_dataset=train_dataset,\n",
+        "    dataset_text_field=\"text\",\n",
+        "    max_seq_length=max_seq_length,\n",
+        "    dataset_num_proc=os.cpu_count(),\n",
+        "    packing=False,\n",
+        "    args=training_args,\n",
+        ")\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "0ajwRxIR4GXc",
+        "outputId": "183abc90-a621-4882-be16-52b11e44d4c1"
+      },
+      "outputs": [],
+      "source": [
+        "try:\n",
+        "  trainer_stats = trainer.train(resume_from_checkpoint=True)\n",
+        "except:\n",
+        "  trainer_stats = trainer.train()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "h44slV_NISp2"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import runtime\n",
+        "runtime.unassign()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "j1M3mCf8fogx"
+      },
+      "source": []
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "machine_shape": "hm",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
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+      "name": "python"
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