diff --git a/features/issue_generation/model/Llama_3_FineTuning.ipynb b/features/issue_generation/model/Llama_3_FineTuning.ipynb
new file mode 100644
index 0000000..9090f27
--- /dev/null
+++ b/features/issue_generation/model/Llama_3_FineTuning.ipynb
@@ -0,0 +1,5695 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "ZsLn8NqVlwm6",
+        "outputId": "1c240e43-bb9a-40fc-cf4f-4b5b998b8911"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m11.6/11.6 MB\u001b[0m \u001b[31m67.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m503.6/503.6 kB\u001b[0m \u001b[31m24.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m564.7/564.7 kB\u001b[0m \u001b[31m38.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.1/60.1 MB\u001b[0m \u001b[31m12.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.8/42.8 MB\u001b[0m \u001b[31m17.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
+            "pylibcudf-cu12 25.6.0 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 21.0.0 which is incompatible.\n",
+            "cudf-cu12 25.6.0 requires pyarrow<20.0.0a0,>=14.0.0; platform_machine == \"x86_64\", but you have pyarrow 21.0.0 which is incompatible.\u001b[0m\u001b[31m\n",
+            "\u001b[0m"
+          ]
+        }
+      ],
+      "source": [
+        "# requirements\n",
+        "!pip install -q -U \\\n",
+        "    transformers==4.56.2 \\\n",
+        "    datasets==4.1.1 \\\n",
+        "    accelerate==1.10.1 \\\n",
+        "    peft==0.17.1 \\\n",
+        "    trl==0.23.0 \\\n",
+        "    bitsandbytes==0.48.0 \\\n",
+        "    triton==3.4.0"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "rlAQIPKpLIpV"
+      },
+      "outputs": [],
+      "source": [
+        "import os\n",
+        "os.environ[\"WANDB_MODE\"] = \"disabled\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 331,
+          "referenced_widgets": [
+            "e4e948d9de8d4e078cdcb68987ae1ebd",
+            "f6f54169154345888bac939655f14841",
+            "b0255f2fecf04522ada626ce01592336",
+            "c6bfc009542a4086b151af31fe87aa5d",
+            "731618a2c0014180886b82d615dac293",
+            "b79bc2e94e6e477ba955dbf529b774a0",
+            "7e7cbf45eeb04394bdedbb8058f1a8bd",
+            "082ce1305a0745eb8a34508275cd896a",
+            "93112582ff3d412093ddab36eeffca58",
+            "aacf1d336f224017b7d291b87bd5eead",
+            "813c8089c03c486cb3f58e34e9d10090",
+            "9fb3279b9b2344ee849ad24667e31c0c",
+            "1ce0db88622246d28421d812a7d4a559",
+            "82e2d1ab2d4844f08d14edc8cbc7a451",
+            "cb5487c595ee43ca808b2ac084c384b3",
+            "0f970505f62641159715f52678dbb580",
+            "f9462c513b0a47488586cf415da9de76"
+          ]
+        },
+        "id": "r-JmC_S3m1AX",
+        "outputId": "8065b647-dec3-4cb3-8d1a-4714f1bda68f"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "e4e948d9de8d4e078cdcb68987ae1ebd",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "from huggingface_hub import login\n",
+        "login()"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "i1BTKHU9ohgJ"
+      },
+      "source": [
+        "## loading data\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 225,
+          "referenced_widgets": [
+            "6818a7d117c14ed7a8d71231e229fea1",
+            "bcd655a6bab3437eba4932210f1beda8",
+            "6352e64a98654e84b8243ff16240a264",
+            "a83d821b73a04e4183eb5e2c29717e56",
+            "42418000399548bdbf43a3ef0dee4ac8",
+            "e2a2ca8c6111451483bc5aeb0a49deee",
+            "266e6acc42774b0595f5d4b82fffb631",
+            "00fa5897db204d518f92d70c4ee5c4fc",
+            "792ae0cf8e4042b3beeb1fc76b0f126b",
+            "833cc8562a5d4f24bac9d9e8d0fdb152",
+            "afe20d5387cd492a8ea9d4d578215663"
+          ]
+        },
+        "id": "eqrpQWbRnOUJ",
+        "outputId": "f4f75b14-3267-4402-d734-fdab84cb060b"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:94: 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"
+          ]
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "6818a7d117c14ed7a8d71231e229fea1",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Resolving data files:   0%|          | 0/457 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Dataset loaded successfully from 'ronit-26/project-docs'\n",
+            "Training dataset size: 411\n",
+            "Evaluation dataset size: 46\n"
+          ]
+        }
+      ],
+      "source": [
+        "import json\n",
+        "from datasets import load_dataset\n",
+        "\n",
+        "\n",
+        "dataset_id = \"ronit-26/project-docs\"\n",
+        "\n",
+        "dataset = load_dataset(dataset_id, split=\"train\")\n",
+        "\n",
+        "# formating the datapoint in the input and output with input doc containing a prompt for instruction\n",
+        "def format_prompt(example):\n",
+        "    prompt = f\"\"\"<|start_header_id|>user<|end_header_id|>\n",
+        "\n",
+        "**Analyze the following project document to identify the key tasks, dependencies, and requirements. Your goal is to interpret the author's intent, even if the document is informal or contains minor errors.\n",
+        "\n",
+        "Based on your analysis, generate a structured list of actionable GitHub issues in JSON format. Follow these principles:**\n",
+        "**- Decompose large goals into smaller, atomic tasks.**\n",
+        "**- Infer logical labels (e.g., 'frontend', 'backend', 'database') based on the task description.**\n",
+        "**- Infer dependencies from the logical workflow if they are not explicitly stated.**\n",
+        "\n",
+        "**Project Document:**\n",
+        "{example['doc']}\n",
+        "\n",
+        "<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n",
+        "{json.dumps(example['issues'])}<|eot_id|>\"\"\"\n",
+        "    example['text'] = prompt\n",
+        "    return example\n",
+        "\n",
+        "# Apply the formatting function\n",
+        "dataset = dataset.map(format_prompt)\n",
+        "\n",
+        "# Split the dataset for training and evaluation\n",
+        "dataset_split = dataset.train_test_split(test_size=0.1, seed=42)\n",
+        "\n",
+        "train_dataset = dataset_split[\"train\"]\n",
+        "eval_dataset = dataset_split[\"test\"]\n",
+        "\n",
+        "# Check the size of your datasets\n",
+        "print(f\"Dataset loaded successfully from '{dataset_id}'\")\n",
+        "print(f\"Training dataset size: {len(train_dataset)}\")\n",
+        "print(f\"Evaluation dataset size: {len(eval_dataset)}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "AyxLhXpmHOh1"
+      },
+      "outputs": [],
+      "source": [
+        "import torch\n",
+        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
+        "\n",
+        "# Base model that we want to fine tune on from hugging face.\n",
+        "# model_id = \"meta-llama/Meta-Llama-3-8B-Instruct\"\n",
+        "model_id=\"ronit-26/finetuned-issues-generator\""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "LZtH5aU4FS5n"
+      },
+      "outputs": [],
+      "source": [
+        "# A configuration object for 4-bit quantization `QLoRA`....(for fitting the model into the GPU's memory)\n",
+        "bnb_config = BitsAndBytesConfig(\n",
+        "    load_in_4bit=True, # to load the model's weights in 4-bit precision\n",
+        "    bnb_4bit_quant_type=\"nf4\",# Normal Float 4\n",
+        "    bnb_4bit_compute_dtype=torch.bfloat16 # While the model is stored in 4-bit, the actual math during training is performed in a higher 16-bit precision\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 401,
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+        "id": "Ipy_apnRHVW1",
+        "outputId": "80c5194c-7cde-4320-effd-0768a5750930"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+            ]
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+          "output_type": "display_data"
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+              "Fetching 4 files:   0%|          | 0/4 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+              "model-00002-of-00004.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
+            ]
+          },
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+          "output_type": "display_data"
+        },
+        {
+          "data": {
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+        },
+        {
+          "data": {
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+            },
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
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+              "version_minor": 0
+            },
+            "text/plain": [
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+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "bbc34b07e1d54ea3a965a7b39c6e42a4",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "d2cd954bc116403396019709d0d9c8ba",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/187 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7527539da3db4f3bb5c8697b1e66dda8",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer_config.json:   0%|          | 0.00/51.0k [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "0eb6f88e12f44575a996059af0cd8450",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "f0c90ed93d664459ab49de8931503e48",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "special_tokens_map.json:   0%|          | 0.00/73.0 [00:00<?, ?B/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "# Load the pre-trained model from the Hub with our quantization settings.\n",
+        "model = AutoModelForCausalLM.from_pretrained(\n",
+        "    model_id,\n",
+        "    quantization_config=bnb_config,\n",
+        "    device_map=\"auto\" # Automatically places the model on the GPU\n",
+        ")\n",
+        "\n",
+        "# Loads the tokenizer associated with the Llama 3 model.\n",
+        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
+        "tokenizer.pad_token = tokenizer.eos_token"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "gUL8bUb8I0CD"
+      },
+      "outputs": [],
+      "source": [
+        "from peft import LoraConfig\n",
+        "from trl import SFTConfig\n",
+        "from transformers import TrainingArguments\n",
+        "\n",
+        "# configuration object for the LoRA adapters\n",
+        "lora_config = LoraConfig(\n",
+        "    r=16, # rank of matrices for training\n",
+        "    lora_alpha=32, # scaling factor\n",
+        "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\"],\n",
+        "    lora_dropout=0.05,\n",
+        "    bias=\"none\",\n",
+        "    task_type=\"CAUSAL_LM\"\n",
+        ")\n",
+        "\n",
+        "# configuration object for all the training hyperparameters\n",
+        "training_args = SFTConfig(\n",
+        "    output_dir=\"./results\",\n",
+        "    # --- Training Hyperparameters ---\n",
+        "    num_train_epochs=1,\n",
+        "    per_device_train_batch_size=2,\n",
+        "    gradient_accumulation_steps=4,\n",
+        "    learning_rate=2e-4,\n",
+        "    logging_steps=10,\n",
+        "    # --- Other Trainer Settings ---\n",
+        "    save_strategy=\"epoch\",\n",
+        "    eval_strategy=\"epoch\",\n",
+        "    load_best_model_at_end=True,\n",
+        "    push_to_hub=True,\n",
+        "    hub_model_id=\"ronit-26/finetuned-issues-generator\",\n",
+        "    hub_strategy=\"every_save\",\n",
+        "    report_to=None,\n",
+        "    # --- SFT Specific Settings ---\n",
+        "    max_length=2048,\n",
+        "    dataset_text_field=\"text\",\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 499
+        },
+        "id": "2ID6S0TGdda7",
+        "outputId": "3610c079-a473-4f64-b977-c41d46f4dda9"
+      },
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/peft/mapping_func.py:73: UserWarning: You are trying to modify a model with PEFT for a second time. If you want to reload the model with a different config, make sure to call `.unload()` before.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.12/dist-packages/peft/tuners/tuners_utils.py:196: UserWarning: Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "data": {
+            "application/javascript": "\n        window._wandbApiKey = new Promise((resolve, reject) => {\n            function loadScript(url) {\n            return new Promise(function(resolve, reject) {\n                let newScript = document.createElement(\"script\");\n                newScript.onerror = reject;\n                newScript.onload = resolve;\n                document.body.appendChild(newScript);\n                newScript.src = url;\n            });\n            }\n            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n            const iframe = document.createElement('iframe')\n            iframe.style.cssText = \"width:0;height:0;border:none\"\n            document.body.appendChild(iframe)\n            const handshake = new Postmate({\n                container: iframe,\n                url: 'https://wandb.ai/authorize'\n            });\n            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n            handshake.then(function(child) {\n                child.on('authorize', data => {\n                    clearTimeout(timeout)\n                    resolve(data)\n                });\n            });\n            })\n        });\n    ",
+            "text/plain": [
+              "<IPython.core.display.Javascript object>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
+            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize?ref=models\n",
+            "wandb: Paste an API key from your profile and hit enter:\n"
+          ]
+        },
+        {
+          "ename": "Abort",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mAbort\u001b[0m                                     Traceback (most recent call last)",
+            "\u001b[0;32m/tmp/ipython-input-1461895822.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     10\u001b[0m     \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mtraining_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m )\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   2317\u001b[0m                 \u001b[0;31m# Disable progress bars when uploading models during checkpoints to avoid polluting stdout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2318\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable_progress_bars\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2319\u001b[0;31m                 return inner_training_loop(\n\u001b[0m\u001b[1;32m   2320\u001b[0m                     \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2321\u001b[0m                     \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2576\u001b[0m         \u001b[0mgrad_norm\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mOptional\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2577\u001b[0m         \u001b[0mlearning_rate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2578\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallback_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2579\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2580\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0meval_on_start\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer_callback.py\u001b[0m in \u001b[0;36mon_train_begin\u001b[0;34m(self, args, state, control)\u001b[0m\n\u001b[1;32m    504\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_train_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerState\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerControl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    505\u001b[0m         \u001b[0mcontrol\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_training_stop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 506\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcall_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"on_train_begin\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    507\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    508\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerState\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainerControl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/trainer_callback.py\u001b[0m in \u001b[0;36mcall_event\u001b[0;34m(self, event, args, state, control, **kwargs)\u001b[0m\n\u001b[1;32m    554\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mcall_event\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mevent\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    555\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mcallback\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcallbacks\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 556\u001b[0;31m             result = getattr(callback, event)(\n\u001b[0m\u001b[1;32m    557\u001b[0m                 \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    558\u001b[0m                 \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/integrations/integration_utils.py\u001b[0m in \u001b[0;36mon_train_begin\u001b[0;34m(self, args, state, control, model, **kwargs)\u001b[0m\n\u001b[1;32m    966\u001b[0m             \u001b[0margs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_name\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    967\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_initialized\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 968\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msetup\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    969\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    970\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mon_train_end\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTrainingArguments\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstate\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontrol\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocessing_class\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/transformers/integrations/integration_utils.py\u001b[0m in \u001b[0;36msetup\u001b[0;34m(self, args, state, model, **kwargs)\u001b[0m\n\u001b[1;32m    893\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    894\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_wandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 895\u001b[0;31m                 self._wandb.init(\n\u001b[0m\u001b[1;32m    896\u001b[0m                     \u001b[0mproject\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgetenv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"WANDB_PROJECT\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"huggingface\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    897\u001b[0m                     \u001b[0;34m**\u001b[0m\u001b[0minit_args\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/wandb/sdk/wandb_init.py\u001b[0m in \u001b[0;36minit\u001b[0;34m(entity, project, dir, id, name, notes, tags, config, config_exclude_keys, config_include_keys, allow_val_change, group, job_type, mode, force, anonymous, reinit, resume, resume_from, fork_from, save_code, tensorboard, sync_tensorboard, monitor_gym, settings)\u001b[0m\n\u001b[1;32m   1571\u001b[0m         \u001b[0;31m# Need to build delay into this sentry capture because our exit hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1572\u001b[0m         \u001b[0;31m# mess with sentry's ability to send out errors before the program ends.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1573\u001b[0;31m         \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sentry\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreraise\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/wandb/analytics/sentry.py\u001b[0m in \u001b[0;36mreraise\u001b[0;34m(self, exc)\u001b[0m\n\u001b[1;32m    160\u001b[0m         \u001b[0;31m# this will messily add this \"reraise\" function to the stack trace,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    161\u001b[0m         \u001b[0;31m# but hopefully it's not too bad\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 162\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mexc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwith_traceback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msys\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexc_info\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    164\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0m_safe_noop\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/wandb/sdk/wandb_init.py\u001b[0m in \u001b[0;36minit\u001b[0;34m(entity, project, dir, id, name, notes, tags, config, config_exclude_keys, config_include_keys, allow_val_change, group, job_type, mode, force, anonymous, reinit, resume, resume_from, fork_from, save_code, tensorboard, sync_tensorboard, monitor_gym, settings)\u001b[0m\n\u001b[1;32m   1494\u001b[0m         \u001b[0mwi\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_WandbInit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwl\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minit_telemetry\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1495\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1496\u001b[0;31m         \u001b[0mwi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmaybe_login\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_settings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1497\u001b[0m         \u001b[0mrun_settings\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshow_warnings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmake_run_settings\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minit_settings\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1498\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/wandb/sdk/wandb_init.py\u001b[0m in \u001b[0;36mmaybe_login\u001b[0;34m(self, init_settings)\u001b[0m\n\u001b[1;32m    188\u001b[0m             \u001b[0;32mreturn\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    189\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 190\u001b[0;31m         wandb_login._login(\n\u001b[0m\u001b[1;32m    191\u001b[0m             \u001b[0manonymous\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_settings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0manonymous\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    192\u001b[0m             \u001b[0mhost\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mrun_settings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_url\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/wandb/sdk/wandb_login.py\u001b[0m in \u001b[0;36m_login\u001b[0;34m(anonymous, key, relogin, host, force, timeout, verify, referrer, update_api_key, _silent, _disable_warning)\u001b[0m\n\u001b[1;32m    316\u001b[0m             \u001b[0mkey_is_pre_configured\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    317\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 318\u001b[0;31m             \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey_status\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwlogin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprompt_api_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreferrer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mreferrer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    319\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    320\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mverify\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/wandb/sdk/wandb_login.py\u001b[0m in \u001b[0;36mprompt_api_key\u001b[0;34m(self, referrer)\u001b[0m\n\u001b[1;32m    235\u001b[0m     ) -> Tuple[Optional[str], ApiKeyStatus]:\n\u001b[1;32m    236\u001b[0m         \u001b[0;34m\"\"\"Updates the global API key by prompting the user.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 237\u001b[0;31m         \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prompt_api_key\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreferrer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    238\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mstatus\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mApiKeyStatus\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mNOTTY\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    239\u001b[0m             directive = (\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/wandb/sdk/wandb_login.py\u001b[0m in \u001b[0;36m_prompt_api_key\u001b[0;34m(self, referrer)\u001b[0m\n\u001b[1;32m    211\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    212\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 213\u001b[0;31m                 key = apikey.prompt_api_key(\n\u001b[0m\u001b[1;32m    214\u001b[0m                     \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_settings\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m                     \u001b[0mapi\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/wandb/sdk/lib/apikey.py\u001b[0m in \u001b[0;36mprompt_api_key\u001b[0;34m(settings, api, input_callback, browser_callback, no_offline, no_create, local, referrer)\u001b[0m\n\u001b[1;32m    190\u001b[0m                 )\n\u001b[1;32m    191\u001b[0m             \u001b[0mwandb\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtermlog\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0m_api_key_prompt_str\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapp_url\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreferrer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 192\u001b[0;31m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minput_callback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapi_ask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstrip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    193\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mresult\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mLOGIN_CHOICE_NOTTY\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    194\u001b[0m         \u001b[0;31m# TODO: Needs refactor as this needs to be handled by caller\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/click/termui.py\u001b[0m in \u001b[0;36mprompt\u001b[0;34m(text, default, hide_input, confirmation_prompt, type, value_proc, prompt_suffix, show_default, err, show_choices)\u001b[0m\n\u001b[1;32m    166\u001b[0m     \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    167\u001b[0m         \u001b[0;32mwhile\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 168\u001b[0;31m             \u001b[0mvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mprompt_func\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprompt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    169\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m                 \u001b[0;32mbreak\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/click/termui.py\u001b[0m in \u001b[0;36mprompt_func\u001b[0;34m(text)\u001b[0m\n\u001b[1;32m    149\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mhide_input\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    150\u001b[0m                 \u001b[0mecho\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merr\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 151\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mAbort\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    152\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    153\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mvalue_proc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mAbort\u001b[0m: "
+          ]
+        }
+      ],
+      "source": [
+        "from trl import SFTTrainer\n",
+        "\n",
+        "# SFTTrainer object, which orchestrates the entire fine-tuning process\n",
+        "trainer = SFTTrainer(\n",
+        "    model=model,\n",
+        "    train_dataset=train_dataset,\n",
+        "    eval_dataset=eval_dataset,\n",
+        "    peft_config=lora_config,\n",
+        "    processing_class=tokenizer,\n",
+        "    args=training_args,\n",
+        ")\n",
+        "trainer.train()"
+      ]
+    }
+  ],
+  "metadata": {
+    "accelerator": "GPU",
+    "colab": {
+      "gpuType": "T4",
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    },
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