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d1b24ff4a26bf3b545c5177fe7b":{"model_module":"@jupyter-widgets/controls","model_name":"DescriptionStyleModel","model_module_version":"1.5.0","state":{"_model_module":"@jupyter-widgets/controls","_model_module_version":"1.5.0","_model_name":"DescriptionStyleModel","_view_count":null,"_view_module":"@jupyter-widgets/base","_view_module_version":"1.2.0","_view_name":"StyleView","description_width":""}}}},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":7299175,"sourceType":"datasetVersion","datasetId":4234284}],"dockerImageVersionId":30627,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## Installing Necessary Libraries","metadata":{"id":"C2EgqEPDQ8v6"}},{"cell_type":"code","source":"!pip install -q -U trl transformers accelerate git+https://github.com/huggingface/peft.git\n!pip install -q bitsandbytes einops wandb\n!pip install datasets==2.16.0","metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"mNnkgBq7Q3EU","outputId":"62cc3649-b8d1-42a4-cffa-5ebfa234be92","execution":{"iopub.status.busy":"2023-12-28T20:12:42.060505Z","iopub.execute_input":"2023-12-28T20:12:42.060775Z","iopub.status.idle":"2023-12-28T20:13:50.384149Z","shell.execute_reply.started":"2023-12-28T20:12:42.060749Z","shell.execute_reply":"2023-12-28T20:13:50.383164Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"Collecting datasets==2.16.0\n Obtaining dependency information for datasets==2.16.0 from https://files.pythonhosted.org/packages/a0/93/da8a22a292e51ab76f969eb87bda8fd70cc3963b4dd71f67bb92a70a7992/datasets-2.16.0-py3-none-any.whl.metadata\n Downloading datasets-2.16.0-py3-none-any.whl.metadata (20 kB)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (3.12.2)\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (1.24.3)\nRequirement already satisfied: pyarrow>=8.0.0 in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (11.0.0)\nCollecting pyarrow-hotfix (from datasets==2.16.0)\n Obtaining dependency information for pyarrow-hotfix from https://files.pythonhosted.org/packages/e4/f4/9ec2222f5f5f8ea04f66f184caafd991a39c8782e31f5b0266f101cb68ca/pyarrow_hotfix-0.6-py3-none-any.whl.metadata\n Downloading pyarrow_hotfix-0.6-py3-none-any.whl.metadata (3.6 kB)\nRequirement already satisfied: dill<0.3.8,>=0.3.0 in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (0.3.7)\nRequirement already satisfied: pandas in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (2.0.3)\nRequirement already satisfied: requests>=2.19.0 in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (2.31.0)\nRequirement already satisfied: tqdm>=4.62.1 in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (4.66.1)\nRequirement already satisfied: xxhash in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (3.4.1)\nRequirement already satisfied: multiprocess in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (0.70.15)\nCollecting fsspec[http]<=2023.10.0,>=2023.1.0 (from datasets==2.16.0)\n Obtaining dependency information for fsspec[http]<=2023.10.0,>=2023.1.0 from https://files.pythonhosted.org/packages/e8/f6/3eccfb530aac90ad1301c582da228e4763f19e719ac8200752a4841b0b2d/fsspec-2023.10.0-py3-none-any.whl.metadata\n Downloading fsspec-2023.10.0-py3-none-any.whl.metadata (6.8 kB)\nRequirement already satisfied: aiohttp in /opt/conda/lib/python3.10/site-packages (from datasets==2.16.0) (3.8.5)\nRequirement already satisfied: huggingface-hub>=0.19.4 in 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(1.9.2)\nRequirement already satisfied: frozenlist>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.16.0) (1.4.0)\nRequirement already satisfied: aiosignal>=1.1.2 in /opt/conda/lib/python3.10/site-packages (from aiohttp->datasets==2.16.0) (1.3.1)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub>=0.19.4->datasets==2.16.0) (4.5.0)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging->datasets==2.16.0) (3.0.9)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.16.0) (3.4)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.16.0) (1.26.15)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests>=2.19.0->datasets==2.16.0) (2023.11.17)\nRequirement already satisfied: python-dateutil>=2.8.2 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.16.0) (2.8.2)\nRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.16.0) (2023.3)\nRequirement already satisfied: tzdata>=2022.1 in /opt/conda/lib/python3.10/site-packages (from pandas->datasets==2.16.0) (2023.3)\nRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas->datasets==2.16.0) (1.16.0)\nDownloading datasets-2.16.0-py3-none-any.whl (507 kB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m507.1/507.1 kB\u001b[0m \u001b[31m17.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hDownloading pyarrow_hotfix-0.6-py3-none-any.whl (7.9 kB)\nDownloading fsspec-2023.10.0-py3-none-any.whl (166 kB)\n\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m166.4/166.4 kB\u001b[0m \u001b[31m15.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n\u001b[?25hInstalling collected packages: pyarrow-hotfix, fsspec, datasets\n Attempting uninstall: fsspec\n Found existing installation: fsspec 2023.12.2\n Uninstalling fsspec-2023.12.2:\n Successfully uninstalled fsspec-2023.12.2\n Attempting uninstall: datasets\n Found existing installation: datasets 2.1.0\n Uninstalling datasets-2.1.0:\n Successfully uninstalled datasets-2.1.0\n\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.\ncudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\ncuml 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\ndask-cudf 23.8.0 requires cupy-cuda11x>=12.0.0, which is not installed.\ncudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.0.3 which is incompatible.\ncudf 23.8.0 requires protobuf<5,>=4.21, but you have protobuf 3.20.3 which is incompatible.\ncuml 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.0 which is incompatible.\ncuml 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.0 which is incompatible.\ndask-cuda 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.0 which is incompatible.\ndask-cuda 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.0 which is incompatible.\ndask-cuda 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.0.3 which is incompatible.\ndask-cudf 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.0 which is incompatible.\ndask-cudf 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.0 which is incompatible.\ndask-cudf 23.8.0 requires pandas<1.6.0dev0,>=1.3, but you have pandas 2.0.3 which is incompatible.\ngcsfs 2023.6.0 requires fsspec==2023.6.0, but you have fsspec 2023.10.0 which is incompatible.\nraft-dask 23.8.0 requires dask==2023.7.1, but you have dask 2023.12.0 which is incompatible.\nraft-dask 23.8.0 requires distributed==2023.7.1, but you have distributed 2023.12.0 which is incompatible.\ns3fs 2023.12.2 requires fsspec==2023.12.2, but you have fsspec 2023.10.0 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed datasets-2.16.0 fsspec-2023.10.0 pyarrow-hotfix-0.6\n","output_type":"stream"}]},{"cell_type":"markdown","source":"# Dataset details\nInstacart Data can be downloaded from [here](https://www.kaggle.com/competitions/instacart-market-basket-analysis/data). We just need product & department csv files","metadata":{"id":"e2OTXLba3bnJ"}},{"cell_type":"code","source":"import pandas as pd\ndf_product = pd.read_csv(\"/kaggle/input/llft-datasets/departments.csv\")\ndf_dept = pd.read_csv('/kaggle/input/llft-datasets/products.csv')","metadata":{"id":"KJmHtRksTUmv","execution":{"iopub.status.busy":"2023-12-28T20:13:50.386015Z","iopub.execute_input":"2023-12-28T20:13:50.386336Z","iopub.status.idle":"2023-12-28T20:13:50.880343Z","shell.execute_reply.started":"2023-12-28T20:13:50.386306Z","shell.execute_reply":"2023-12-28T20:13:50.879539Z"},"trusted":true},"execution_count":2,"outputs":[]},{"cell_type":"code","source":"df_joined = pd.merge(df_product, df_dept, on = ['department_id'])\ndf_joined['text'] = df_joined.apply(lambda row: row['product_name'] + \" ->: \" + row['department'], axis = 1)","metadata":{"id":"WHWZSGxsUSkX","execution":{"iopub.status.busy":"2023-12-28T20:13:50.881464Z","iopub.execute_input":"2023-12-28T20:13:50.881750Z","iopub.status.idle":"2023-12-28T20:13:51.634943Z","shell.execute_reply.started":"2023-12-28T20:13:50.881725Z","shell.execute_reply":"2023-12-28T20:13:51.634158Z"},"trusted":true},"execution_count":3,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\ntrain_df, test_df = train_test_split(df_joined, test_size=0.2, random_state=42)","metadata":{"id":"GkmdTDF0Wc3e","execution":{"iopub.status.busy":"2023-12-28T20:13:51.636543Z","iopub.execute_input":"2023-12-28T20:13:51.636862Z","iopub.status.idle":"2023-12-28T20:13:52.142370Z","shell.execute_reply.started":"2023-12-28T20:13:51.636836Z","shell.execute_reply":"2023-12-28T20:13:52.141379Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/scipy/__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.3\n warnings.warn(f\"A NumPy version >={np_minversion} and <{np_maxversion}\"\n","output_type":"stream"}]},{"cell_type":"code","source":"train_df.head(10)","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"le1r4sDSWjba","outputId":"e181ba00-3fe4-43ad-d373-45053d4edbd9","execution":{"iopub.status.busy":"2023-12-28T20:14:03.369807Z","iopub.execute_input":"2023-12-28T20:14:03.370583Z","iopub.status.idle":"2023-12-28T20:14:03.389009Z","shell.execute_reply.started":"2023-12-28T20:14:03.370545Z","shell.execute_reply":"2023-12-28T20:14:03.388057Z"},"trusted":true},"execution_count":5,"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":" department_id department product_id \\\n7361 4 produce 38057 \n32534 15 canned goods 32461 \n1457 1 frozen 17540 \n5201 3 bakery 22335 \n38539 17 household 29431 \n18377 11 personal care 9014 \n28627 13 pantry 37330 \n30283 14 breakfast 11892 \n32030 15 canned goods 20544 \n28655 13 pantry 37534 \n\n product_name aisle_id \\\n7361 Spicy Organic Microgreens 83 \n32534 Spaghetti Cool Shapes Star Wars 59 \n1457 Burnt Sugar Vanilla Ice Cream 37 \n5201 Sliced Italian Bread 112 \n38539 Air Effects Value Pack Hawaiian Aloha 101 \n18377 Mint Mouthwash 20 \n28627 Pure Cane Washed Raw Sugar 17 \n30283 Mixed Berry BelVita Bites 48 \n32030 Peeled Whole Tomatoes 81 \n28655 Unprocessed Wheat Bran 17 \n\n text \n7361 Spicy Organic Microgreens ->: produce \n32534 Spaghetti Cool Shapes Star Wars ->: canned goods \n1457 Burnt Sugar Vanilla Ice Cream ->: frozen \n5201 Sliced Italian Bread ->: bakery \n38539 Air Effects Value Pack Hawaiian Aloha ->: hous... \n18377 Mint Mouthwash ->: personal care \n28627 Pure Cane Washed Raw Sugar ->: pantry \n30283 Mixed Berry BelVita Bites ->: breakfast \n32030 Peeled Whole Tomatoes ->: canned goods \n28655 Unprocessed Wheat Bran ->: pantry ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
department_iddepartmentproduct_idproduct_nameaisle_idtext
73614produce38057Spicy Organic Microgreens83Spicy Organic Microgreens ->: produce
3253415canned goods32461Spaghetti Cool Shapes Star Wars59Spaghetti Cool Shapes Star Wars ->: canned goods
14571frozen17540Burnt Sugar Vanilla Ice Cream37Burnt Sugar Vanilla Ice Cream ->: frozen
52013bakery22335Sliced Italian Bread112Sliced Italian Bread ->: bakery
3853917household29431Air Effects Value Pack Hawaiian Aloha101Air Effects Value Pack Hawaiian Aloha ->: hous...
1837711personal care9014Mint Mouthwash20Mint Mouthwash ->: personal care
2862713pantry37330Pure Cane Washed Raw Sugar17Pure Cane Washed Raw Sugar ->: pantry
3028314breakfast11892Mixed Berry BelVita Bites48Mixed Berry BelVita Bites ->: breakfast
3203015canned goods20544Peeled Whole Tomatoes81Peeled Whole Tomatoes ->: canned goods
2865513pantry37534Unprocessed Wheat Bran17Unprocessed Wheat Bran ->: pantry
\n
"},"metadata":{}}]},{"cell_type":"code","source":"test_df.head(10)","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":973},"id":"sYD0jpyYWr4D","outputId":"d92e1106-2cd8-4374-d2d8-72f95085f3d5","execution":{"iopub.status.busy":"2023-12-28T20:14:04.325152Z","iopub.execute_input":"2023-12-28T20:14:04.325528Z","iopub.status.idle":"2023-12-28T20:14:04.338243Z","shell.execute_reply.started":"2023-12-28T20:14:04.325499Z","shell.execute_reply":"2023-12-28T20:14:04.337206Z"},"trusted":true},"execution_count":6,"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":" department_id department product_id \\\n33626 16 dairy eggs 5570 \n18192 11 personal care 7582 \n47099 19 snacks 49614 \n48183 20 deli 39968 \n22197 11 personal care 37799 \n31573 15 canned goods 9984 \n45362 19 snacks 35795 \n14131 7 beverages 47666 \n26903 13 pantry 21534 \n39417 17 household 43688 \n\n product_name aisle_id \\\n33626 Coffee Rich Original Non-Dairy Creamer 53 \n18192 Cold Snap 11 \n47099 Sandies Pecan Shortbread Cookies 61 \n48183 Miso Soup 1 \n22197 Body Envy Volumizing Shampoo 22 \n31573 Bean Salad 81 \n45362 Mexican Restaurant Style Corn Tortilla Chips 107 \n14131 Grapefruit No Sugar Added 100% Juice 98 \n26903 Salsa, Diablo, Hot 51 \n39417 Toilet Bowl Cleaner with Lime & Rust Remover 114 \n\n text \n33626 Coffee Rich Original Non-Dairy Creamer ->: dai... \n18192 Cold Snap ->: personal care \n47099 Sandies Pecan Shortbread Cookies ->: snacks \n48183 Miso Soup ->: deli \n22197 Body Envy Volumizing Shampoo ->: personal care \n31573 Bean Salad ->: canned goods \n45362 Mexican Restaurant Style Corn Tortilla Chips -... \n14131 Grapefruit No Sugar Added 100% Juice ->: bever... \n26903 Salsa, Diablo, Hot ->: pantry \n39417 Toilet Bowl Cleaner with Lime & Rust Remover -... ","text/html":"
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
department_iddepartmentproduct_idproduct_nameaisle_idtext
3362616dairy eggs5570Coffee Rich Original Non-Dairy Creamer53Coffee Rich Original Non-Dairy Creamer ->: dai...
1819211personal care7582Cold Snap11Cold Snap ->: personal care
4709919snacks49614Sandies Pecan Shortbread Cookies61Sandies Pecan Shortbread Cookies ->: snacks
4818320deli39968Miso Soup1Miso Soup ->: deli
2219711personal care37799Body Envy Volumizing Shampoo22Body Envy Volumizing Shampoo ->: personal care
3157315canned goods9984Bean Salad81Bean Salad ->: canned goods
4536219snacks35795Mexican Restaurant Style Corn Tortilla Chips107Mexican Restaurant Style Corn Tortilla Chips -...
141317beverages47666Grapefruit No Sugar Added 100% Juice98Grapefruit No Sugar Added 100% Juice ->: bever...
2690313pantry21534Salsa, Diablo, Hot51Salsa, Diablo, Hot ->: pantry
3941717household43688Toilet Bowl Cleaner with Lime & Rust Remover114Toilet Bowl Cleaner with Lime & Rust Remover -...
\n
"},"metadata":{}}]},{"cell_type":"code","source":"from datasets import Dataset,DatasetDict\ntrain_dataset_dict = DatasetDict({\n \"train\": Dataset.from_pandas(train_df),\n})","metadata":{"id":"I1BL-ToXXbaI","execution":{"iopub.status.busy":"2023-12-28T20:14:04.629037Z","iopub.execute_input":"2023-12-28T20:14:04.629371Z","iopub.status.idle":"2023-12-28T20:14:05.837030Z","shell.execute_reply.started":"2023-12-28T20:14:04.629345Z","shell.execute_reply":"2023-12-28T20:14:05.835722Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"## Loading the model","metadata":{"id":"rjOMoSbGSxx9"}},{"cell_type":"code","source":"import torch\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer\n\nmodel_name = \"TinyPixel/Llama-2-7B-bf16-sharded\"\n\nbnb_config = BitsAndBytesConfig(\n load_in_4bit=True,\n bnb_4bit_quant_type=\"nf4\",\n bnb_4bit_compute_dtype=torch.float16,\n)\n\nmodel = AutoModelForCausalLM.from_pretrained(\n model_name,\n quantization_config=bnb_config,\n trust_remote_code=True\n)\nmodel.config.use_cache = False","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["45e6f3b9e40a42cd91a00c413bb6c900","1c17c9fcd7d54875b71aa6d336e79267","1fa6fe86893748b089bcf1a4819337fe","4491f57ddfee4e76a8d23cfbd013b1f0","5a5d2c84eae749b1b8cd7209728ed2d1","cefc0062b98f41aebae5e0723b98393d","4b48b8aaa54f4b468c0a6de081d020dd","97b4b743f51b4b7dac7111dbdde6f22f","3e31a8f74d9249ad963d9f6fa3a33b4f","72c11ce6c3ef4baa83ba03315872fb62","ef048ebcd49b4d0b90407ba3eaee60bc","c9dade0bfd4245fc9d7ab839696e0adf","0b5cb932f7334dbc82a4ef948b3b751c","49ee4ac6b57e4c79a429f709c0066c04","f587c6cfe63f4bfb840fe7bd7ded72c4","bc79611644d84978af93567c99fb137e","23d326d019124c52b454ddd555a2557e","1f337bbeb5db482a9b314c35febe749e","943c1134a29c46d7b4d2a50605cac264","567349c105484fa3989c3c9a409e6ab4","8aeea097d79648858a9c2003fab5da2d","e733f657cd0c4087ab35c4b682399433","c5484b2e1f814815bd262a47874721e0","8cc87f0dcb3c4958b949c4d682791e5a","4d62a0a0333f4b7e868b48188a736f13","d1f8150cf4704d39864d5d056bcc9888","a6529c5e6f73402ebba9deff5d1d7050","6b1a0b0a15f44d08aed05dc62dea6574","06af2747c1674238b7bb628f637b67e7","ab11e4ad9e154789bd86f7c210760d27","48aa8622b0b3439480c800de6941f930","35403e39d9424d57b0a5a7efb23a30c2","e51348c4e02a4f76a614348b14d4d5c3","65c4d9db8c674251a9f5ade72401a6a1","dda29d15c5a148dcb59dbacd43985412","dd7f6bd94ca74af0bb955fb5a3dd8b84","8d59a24764124ef98509fc64b74afe20","50b6fc2de04549679e4e6fdeca11b17c","be5a47acba0a46e1a8e205895f5034c3","732d2b0f09d84d1383aaad24fd6429d1","a8be3423cac84e009e5dc83eafddf210","bcf51e0c44ca48a68152d01165fd38ad","dddbd33f13d248bdab72974a3922455a","0c88e55bcd6b4e88a59c54b68cd861d5","902ce6d6ef254df986541795403e4108","2948e591892b4e8dbba291581b11ae77","e649f2d09e5a46259e5de19842ba6d28","f0c0f44378b84b3c985e44db1368dde7","bd7457a5353c4a04b00bf23c1ca9eab3","d2ebc85adb87482aa38cb159141ff11e","076c2e16629a40409527cb1aa90d926e","acf5c611b6494980bf334194858ead54","a8ac4b02a9374f2ca24553168b86d2cc","a4bc75e6b1c74be0ab1d09b10f64694c","71329c56317a421caca9538b18810970","2b0a45bf40644ed6b3f9d73c745e2922","a20c3ce32ddc4b14a7b59236e45814c6","393a528e704e4fd9b206bc3e86863e20","76e974fe5e5644ec8898f875011f8ee7","0ce2b6f8fb4144488c621acce8d08393","c173da6e476448e1b872f2a67fd058fd","3a744c1317324399804ac0f9f886eb2c","ae21cb175a354974b9f9cda1c1472d35","8ea516e0dd1741d498d223cb19865c1e","95cc904f6f5c4a10b247106920b361bd","3b4e1c3b32e44726b14bd249b11961d3","0a76b24120db4766901614c57a62784a","edeaaa428430475a865f5bc19f84c6c2","0f94d194a5874ba1909eb26d9de17b7c","7655c783c53045dca4d81411aa943dcd","a4b1b7873a6c4e4e8f59293eee97524d","05232d6d2fe84f2d9557b56b01fa7cf1","4eb937886c9d4de7b86e3a1c6a7170f0","badbbd3d72a848edaee5903598b5550c","119eb3c2057442b382e3df9bf9064364","4b7e1ea545d5417592cf20531b621a9b","cc2e106112cf444a9e05917138c95ba5","ecec03a29b1545bdb903ebf8ba5629f3","884a626382b84640aa12fcf790ff36e5","aa05140d795c45e0a6ea100b28c63582","cf18d4277b2d49fba2be0d14e04b4605","85a30860b7d249938022872316634b43","cc3d4a30fca548f7ac19250589b9e729","c72a5401599a4476a55d7df1dcff2758","491bfc2c3070412d97389150d05b8326","1ce0359255cf44a696b331ebb1b620fa","f303a3b7f6454b8c92627bdb6001355f","fa52c86b55f04e8a86e0ea629ff077af","3c8a7bb0a64a4486980b5082960aa2ce","e84f259c0a85435d88a5fd5e5bc7f6c3","ad1d2e4d2836477eb6048f5b4ea2ee4a","4b635317795842aea0656be9ff4635cf","07e4822a40d046eea2545279844158b5","b4022e069bd0452fad1f9ac5bc397e21","6519b503e8c2482cb05fdb3ca6d05b19","328c636745cd4cc5b67f5ff3ff72f71a","4f268c8ed6a1408aaa3cdcef428fbc80","8a039f5830fe4328aef97d39537746e0","3db52c22b20e40fa94a02e22daf8dc69","e9b27c132a2249cfbc450eb9bf98c4d3","3380f86be23d44b0a7a2bffce53a1d0d","27623cf7dae645068937bb9eb6d69e89","9390b2f8f627431a983a33963bec392b","b7fed22f9bc54785a7283963a5426722","9961102a99724ec4aa68c21fbc3736b6","408170a1f199430ba098be0536201c36","7174852a2da3499baa9969f5ffdb49ec","0577ccd6dddf468cb9fedf2260a0ec9e","5075165aaf28469f97f95deded7483fd","1f4cfd377f894f42bed8829186ed76c6","2916f65cd8ca4f8ca322d715c2e4ad16","512a72d399004379acad5a6390172fc4","edfbd59c41404d0facd57d2e77aa1d6a","83c5095dcbc74e10b151117a70a4ef1f","561d158a06554d419d44eba88574e4b3","233e2bdbfd7d439db6f94908477c505e","823ccc4edce4450eb443d1d03da104ac","ab4d2b8bcc8644b5910d07ac9896bc50","625d10091e5e4603aa2db9d3f10bb3f6","a663ff291b384f0baece8431fc2fca81","42826497cb9e4e0b98348de4ae193d1e","01f960f61a7f4918b87a5c4dc049ebd0","27c4aea464a34525ad0edc72f214a520","ce1f6eec13cb41f0ba4fb0ae2e23dc01","c6e220b99ac444de943eb49d2022750d","dcc71d6086364c7bae25ed9d11c73598","989223fda3db464687178b862d3cf3ce","aaee4e89a69244ffb2592afc505a717b","75673b13d0e549818f0e05d743173efe","44d6e49d09e54a62be2c64a1a3a11de2","03c463255f8c49fa9f11a79f68b38ec5","7284ff8e6b924ee898fe93a4ebdc03b5","dd730101d6224f45a0ddbb9bea57983e","3bf4e260e3fc48658b7f7dfcb964c907","b523c5c35cd64f9abe1c926b0252bad4","eb92b7cd47db42e89b419f5ac86a482a","379b59f841154cbbbdecb2b1a0d6d6ef","b16bbe3621a74a499ed781008f0b4fa1","ea608650acb7439f80c97bd6af129591","ea7c455ac8f84e19b637b1c247c0fd8c"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0%| | 0.00/626 [00:00","text/html":"Tracking run with wandb version 0.16.1"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","text/html":"Run data is saved locally in /kaggle/working/wandb/run-20231228_201634-78xwk7sq"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","text/html":"Syncing run swift-oath-8 to Weights & Biases (docs)
"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","text/html":" View project at https://wandb.ai/bhavya-cloud30/huggingface"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"","text/html":" View run at https://wandb.ai/bhavya-cloud30/huggingface/runs/78xwk7sq"},"metadata":{}},{"name":"stderr","text":"You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"","text/html":"\n
\n \n \n [120/120 10:20, Epoch 0/1]\n
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StepTraining Loss
14.688800
23.075500
34.108800
45.894700
54.070500
64.192300
76.405300
84.363500
94.253400
104.241800
114.148900
124.197100
133.969200
144.037200
153.888400
163.758000
174.755700
183.548200
193.475100
203.049900
213.012200
222.993100
232.950900
242.855900
253.164900
262.550800
272.651900
282.317600
292.483500
302.644000
312.176000
322.793700
332.830100
342.565300
352.572600
362.837900
372.072700
381.941900
393.014100
402.565200
412.129300
421.883700
433.757800
442.155100
452.436700
461.906200
471.636000
482.534600
491.725700
504.357000
511.173300
521.309800
532.660300
541.796600
553.182700
562.338700
572.140700
582.312900
592.869600
602.287000
612.014700
622.349800
632.019500
642.153200
653.176300
661.887700
671.667100
682.461500
691.965700
701.554700
711.900300
721.998500
732.101900
742.134000
751.523100
762.393500
771.955600
782.094400
791.923000
802.208700
811.893800
821.962500
831.993100
842.102000
852.067400
861.987200
871.886500
882.349000
892.203600
902.034100
912.344100
922.122800
932.491500
942.177400
952.424000
962.270300
971.473400
982.263300
992.326300
1002.064200
1012.950700
1022.645200
1033.197500
1042.332300
1052.383900
1061.784600
1072.445000
1082.185100
1092.316000
1102.474000
1112.175800
1121.757700
1132.463900
1142.078500
1152.323500
1162.148700
1172.166000
1182.218800
1192.009800
1202.456200

"},"metadata":{}},{"execution_count":15,"output_type":"execute_result","data":{"text/plain":"TrainOutput(global_step=120, training_loss=2.617857338984807, metrics={'train_runtime': 691.1477, 'train_samples_per_second': 2.778, 'train_steps_per_second': 0.174, 'total_flos': 1214487435018240.0, 'train_loss': 2.617857338984807, 'epoch': 0.05})"},"metadata":{}}]},{"cell_type":"code","source":"from huggingface_hub import notebook_login\n\nnotebook_login()","metadata":{"colab":{"base_uri":"https://localhost:8080/","height":162,"referenced_widgets":["cb58a3f86de942569216261bb6340194","8eecd4c35c6444ff80e6313ee4436ef6","e6b032ec3455413ea2cf1587950ad961","673a42ce7ba44eb48783a34cc812609a","37b2cc10a86f44fabf51c737885e6112","648962cf18484bd4b75041c17a2cbadc","8614c8bbd8ee494b8eab24d09c2d1395","79693ab777014c4fb49e78426e95ce6a","3db175f75dcf4835826f6cc07f1a7db6","bba35e61f2614df7ac2dab8303a8fee2","62ab6ac382e948abbf13512cde7d9e8d","e35dda384d244d779f17fafb1b3b5bf7","c06fd2227e8440c993a45e638c79b948","a37e417602484b58ad6095f0f2e8ba1e","87ee00bdda164ac6afea0426427baad1","c870bae8bd7b4e669157803e4355399c","2f3bbac2a6ec4e6d8b27c8d5ae48e252","11c6648e232a4df09edebee8863d7a18","5f0bb8df178e4781aef0a9dbcbc86986","bb874a5d6f92404fa915129d03710021","ec22f1dfebb340caa805241bd8733479","2d16a70c0bee4a10b8d003f84e72db2d","6df2a1b9ca2341f5b0eecde5e09616b7","5fe2e18d5af44a878b6f22a69210fe6f","6e3d440636a0420c99a57cc6a0d825c4","088d6eb284de4a02b97cf27692c7dfdb","17f4065f404f487ead893fc12a789a8f","52ae24782f4749beb79ad79d50014ebb","cbfb14a0803a41b6951e481d64567dad","fa8c66c95c8149d18244c180f80fc662","34b9f0e06b764fa2bbc766df183aa76f","d1819a10d5d94dfa8d019b796fb4b40f"]},"id":"_aN7vPN-5P8N","outputId":"52d694cf-3b2b-405d-f00e-3292b467d0f1","execution":{"iopub.status.busy":"2023-12-28T20:27:35.505147Z","iopub.execute_input":"2023-12-28T20:27:35.505559Z","iopub.status.idle":"2023-12-28T20:27:35.538934Z","shell.execute_reply.started":"2023-12-28T20:27:35.505520Z","shell.execute_reply":"2023-12-28T20:27:35.537113Z"},"trusted":true},"execution_count":16,"outputs":[{"output_type":"display_data","data":{"text/plain":"VBox(children=(HTML(value='