{"cells":[{"cell_type":"code","source":["!pip3 install -q -U bitsandbytes==0.42.0\n"],"metadata":{"id":"lyKNAYy28EUb","executionInfo":{"status":"ok","timestamp":1722264450907,"user_tz":-330,"elapsed":3078,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":1,"outputs":[]},{"cell_type":"code","source":["!pip3 install -q -U peft==0.8.2"],"metadata":{"id":"bSx4H_QZ9MTh","executionInfo":{"status":"ok","timestamp":1722264453232,"user_tz":-330,"elapsed":2327,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","source":["pip install transformers"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"K8fm9jsd899E","executionInfo":{"status":"ok","timestamp":1722264455773,"user_tz":-330,"elapsed":2544,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}},"outputId":"ed30973f-2bb4-4d7b-f0dc-dced482eab70"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.38.0)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.32.3)\n","Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.15.2)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (2023.10.0)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.19.3->transformers) (4.12.2)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n"]}]},{"cell_type":"code","source":["\n","!pip3 install -q -U trl==0.7.10"],"metadata":{"id":"AhFYnEE69OQi","executionInfo":{"status":"ok","timestamp":1722264458130,"user_tz":-330,"elapsed":2359,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["\n","!pip3 install -q -U accelerate==0.27.1"],"metadata":{"id":"Hy7M87jr9LZU","executionInfo":{"status":"ok","timestamp":1722264460757,"user_tz":-330,"elapsed":2629,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":5,"outputs":[]},{"cell_type":"code","source":["\n","!pip3 install -q -U datasets==2.17.0\n"],"metadata":{"id":"mQspBPUY9SBl","executionInfo":{"status":"ok","timestamp":1722264463023,"user_tz":-330,"elapsed":2268,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["!pip3 install -q -U transformers==4.38.0"],"metadata":{"id":"ub393jDn9Y1S","executionInfo":{"status":"ok","timestamp":1722264465647,"user_tz":-330,"elapsed":2626,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":7,"outputs":[]},{"cell_type":"code","source":["import os\n","import transformers\n","import torch\n","from google.colab import userdata\n","from datasets import load_dataset\n","from trl import SFTTrainer\n","from peft import LoraConfig\n","from transformers import AutoTokenizer, AutoModelForCausalLM\n","from transformers import BitsAndBytesConfig, GemmaTokenizer"],"metadata":{"id":"Us39UwUk8E1c","executionInfo":{"status":"ok","timestamp":1722264473703,"user_tz":-330,"elapsed":8058,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["os.environ[\"HF_TOKEN\"] = userdata.get('HF_TOKEN')"],"metadata":{"id":"RDINWDYy8R_s","executionInfo":{"status":"ok","timestamp":1722264475791,"user_tz":-330,"elapsed":2098,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["model_id = \"google/gemma-2b\"\n","bnb_config = BitsAndBytesConfig(\n"," load_in_4bit=True,\n"," bnb_4bit_quant_type=\"nf4\",\n"," bnb_4bit_compute_dtype=torch.bfloat16\n",")"],"metadata":{"id":"5__-WOdl8Wzq","executionInfo":{"status":"ok","timestamp":1722264603261,"user_tz":-330,"elapsed":437,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":10,"outputs":[]},{"cell_type":"code","source":["tokenizer = AutoTokenizer.from_pretrained(model_id, token=os.environ['HF_TOKEN'])\n","model = AutoModelForCausalLM.from_pretrained(model_id,\n"," quantization_config=bnb_config,\n"," device_map={\"\":0},\n"," token=os.environ['HF_TOKEN'])"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424,"referenced_widgets":["5a0c320331994e9491793687b7105a17","a7ce8f0ec13a4d64b2324e6e827187cd","e1035211960f4f3cb78d382cdf567205","1cc62b8bf28441fab5705e26a9e14a3d","2d81d1e8619f438985e38f937d45a848","b1c5d7de1834477597819d9f5f85ee0f","acacf053fb9f4de8ae3c6d0b26ba3a12","7776e378996c4da2b086f0eff3bcfc5d","718d3e15be944257a1f88e250f79d747","a3ecf5d3ea974b37a90deb0b7d78e58c","d01721a69adf43e38730ac05127d4445","63b4566038af4406a449407dc93280c9","f7495569d4f7499b94841600cffa3676","c8b3f4889f8d4f619a9b431a797482b6","5fe516ce9ebe49a0aea35240162a7d49","ca99b47f03fa4c0390d082f913e717b2","fad67383145142fb84938452d88db82c","9ad9007b0483443097e395850590725a","43a43b0fd722409987f3347f0b73f7ec","efab5b35d6ee40fbb789c1cbce4f3bff","4b5b583aa9f6441da957e422c157b038","a05baa1914ad4eb89a6d01c38ee8d3e5","79606c61bb9e44b197a463535e1cd0c4","b809297f87a447799b1a712e1d8de4cc","71e67185f9cc4d148f5fc7a6a356bc58","03f96a7a82fd43a48a503d095c1bdc6b","69888882b5eb472b8ab08a9469c9c7a4","c0a815df40fb4e2698ce305543bb7e0c","897722b5601c45e38cfa58cfab15ddf4","a984247c468842d2ab50db8aa2577752","cc51a62556064f9d8689dc12e963781b","3f1f8c492bee4fae8a58866fd992fa8a","e24f5434136349f4b733a0cb325b80d2","79a4f7ff269d44709b1e7ad3405c669d","9992d8c38306473fb474fcd4c4bb8222","38fcb40026fd43148a3d08cf2b0862db","7b3427f65e7543dd87d9ed18ac5b9d27","e6691b61a85d40318a360ba9c9b51b5c","ffc8658bae5145cd8ce763d29a2efa6e","dfcaa295e8494115add4b38741928ecd","a08fe52ae1994bf48c73283ec826caeb","5827855aac834eaea998af141173974b","e651010e200e464ebc5564761ddd781e","109425cbdb7949cea4d7ce485ddb72e3","1f3c8465e7b04c0aaa89f98c830a4e2d","6148e5541edc4836aa3f3646f5b2765b","c4c1a40125874e95bbf3664c3667332f","9bed3566f1294581b73a00dd7f061c13","19d28bd007cf4a099469682f025695ff","5f2f07e5f5454eff851abd540b90fdc0","720bdb62c3f045cfa7043a2b629346e1","920b3a030d484288ac1bf12cf94cbe80","ff5cbc41ea5d4c29b0432fb438da962f","481b313ab819471dba556d3b69a3bd38","d91d5a225c3d46a381186db0efb4ca52","3662d75825404501a7b8b862fa31ddf7","8104529ba246490db4a2605cc8f4c355","44f8b5d987b74e8cbef7cc71cfa33127","9762c680d5314448917fb73c8720fabd","92d7bbc21a6443df9851255391185562","7b874fc5afcf4718b29c9297d6165c07","b98859959ac44a9fbfa15ea1f7bee2a7","c39632098c1a4a79920800937cb80e25","c9ebe8c93f444316b17f763ed0e48c30","36f0f396c0ab463b94b692b8eb73def7","195e0a9aa1284456a41a75d029788aa8","a961f815a1e64e4f9ee66af9f8675c73","e68e7a476d574119bb62864a2232e244","cb8b43cc766747578db577089c35f080","754065855913408c9f1f93168ad180fa","b494804ff08d496cb31c40608d515547","86a1afb2a4c34ef68deb3771acc7ae76","a8f2bf8447e64634a19e7e66a994a73d","a733906e8bac47e3a719a908c33dcd43","0ea98342351e44529d7aee0963d76034","fb88de8c844b419093043bf49e8300fa","4d3330aaf0d4484aa59e77cafb7a718b","790acd4b77b44a6db5943731be7d4f28","2bf628ea7a9a4a239cb119e7ec53a8a1","79ff93b1a29444c08810f98bc8758545","f99f150183ff4a9e9853d56f252b19e0","99935956c5924e6d9136f938f438f996","ed779e6af2af494e9993a29bf99f3180","624750be576a46a39209069e8a948315","3ed4cfe9af1b4b65870269b7d7c6a183","c43bd204c9d541f7b37a6cc0c502a798","4d690c790d89414e88074e72cee3cdbb","4ab1eed7af014f5c9dc8f18d88b9f752","e19cf69a57c04ab79868a666d2b7f8ff","df60c89510db4504b0a4174fcae33f81","19b5f861c9504687a3b2f9ea1dd222b3","102ff82320d5416ebb16dc32284aa7fe","0a314b1008244c06815cb637ec7db2ff","1f284fbfe79b4dd59120e41c0f2b077e","615a7d010dcd4d359913aa426e2fe787","585ef06cf9724afaae5949f3c0a31425","4ac76d65925c4c64a29bb98df10aa295","6ef186fbe036422189d6f253f2f6f3af","a2c9485050b141a58a6782869b21f0cf","95c4859c9c894be9916f2b4e1e9e174e","71c95db5812a4a8599b8bc8e5e1ab559","689dfb56ef53414fbec42694cb01de59","d5c7f7e030314ae6a7aa7907c713ba78","652944be92ff42268b2fe255c086b0b1","d344e9267cca483aaef8785e7019f4ce","f67638c96e45430586b870ecd206f556","e8e835c4c61e4c72b848d323df65d2ad","e45367c9cfbf4e46992c81b67c6dcefa","13d339c64ccb41f38b53ddfccca6aa10","476dbfef1d384e5f8108884b08010c34","5a3fcfc173734c52a23961ca6ce07a39","aae51948f75540f6b96505f428136ffa","4b9f5899817e45ea8da020fa32fe15e4","a1ec4eb4db704ad7a307d867f9cf5e08","042506233e894f12b411d71ca7a818b1","7b929ecbf6154c3bbb2804faebf66fbb","51a0e52b5a45444695510c59e40f9b61","57b5f44bca1b4a15864fc9af0d2cb79c","ea59d1a318cd4ec3ae8c00aa6453cf9f","de0a63be6f554ba1aa6f243b44059082","d5a7c557c38b4fc78dd7d22bf0e094d6"]},"id":"-r3l-BVp-EZI","executionInfo":{"status":"ok","timestamp":1722264641905,"user_tz":-330,"elapsed":27092,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}},"outputId":"85c9a239-3e68-405d-b268-b4ecc8aab0b3"},"execution_count":11,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n"," warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 0%| | 0.00/33.6k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5a0c320331994e9491793687b7105a17"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.model: 0%| | 0.00/4.24M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"63b4566038af4406a449407dc93280c9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json: 0%| | 0.00/17.5M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"79606c61bb9e44b197a463535e1cd0c4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json: 0%| | 0.00/636 [00:00<?, 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0.00/4.95G [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"790acd4b77b44a6db5943731be7d4f28"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["model-00002-of-00002.safetensors: 0%| | 0.00/67.1M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e19cf69a57c04ab79868a666d2b7f8ff"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Loading checkpoint shards: 0%| | 0/2 [00:00<?, ?it/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"95c4859c9c894be9916f2b4e1e9e174e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["generation_config.json: 0%| | 0.00/137 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5a3fcfc173734c52a23961ca6ce07a39"}},"metadata":{}}]},{"cell_type":"code","source":["os.environ[\"WANDB_DISABLED\"] = \"false\""],"metadata":{"id":"hjf5HvHL-HUc","executionInfo":{"status":"ok","timestamp":1722264660980,"user_tz":-330,"elapsed":1,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":13,"outputs":[]},{"cell_type":"code","source":["lora_config = LoraConfig(\n"," r = 8,\n"," target_modules = [\"q_proj\", \"o_proj\", \"k_proj\", \"v_proj\",\n"," \"gate_proj\", \"up_proj\", \"down_proj\"],\n"," task_type = \"CAUSAL_LM\",\n",")"],"metadata":{"id":"HkV9A-xA-Sk-","executionInfo":{"status":"ok","timestamp":1722264670015,"user_tz":-330,"elapsed":566,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":14,"outputs":[]},{"cell_type":"code","source":["from datasets import load_dataset\n","\n","data = load_dataset(\"aitamilnadu/thirukkural_instruct\")\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":113,"referenced_widgets":["aee356d6cee841dc8684a2bffaf7cf8a","84fa85abdd784ae89e3af18acdb5a4ad","a62509428c274028b5922d8532296859","c9817851a2eb41cca2565a4766c81cbe","66cb05d653774275a6ab18aacf28fc84","5f486b462e9246d087cfe63dde12e1c0","35aeb0d863744738954958693bf5efcd","38419433e2604140be57ecffadd25478","6e51928380d94127ae5479a11f4b8195","81c0c3f3658d47088fe1ac1886c1a995","5032ed3dfb78492394badbfbcfcb32de","79b9b60cbfe0444c8a61fee323a8ee5c","6a5a1cf8cebf42b298d5c50d438a04f1","c70c13b677484fefbbf187179ad0ae93","5b1fdbd9a6ea4e1496405e5b27a69630","d24e8e1e9c1945c8ba1b948485c940ab","1bd75c49792a4d60b315723c50e6daa2","20b11507354640819095e5f19dba034d","2c48a196ae834793a78f481b084f1834","57e905e20ad04c61ae1361446a9196fa","34a4c8db7bb048a1a7b4ae67d1f3c78f","55c5a3c72e6741ccbcc4ac6b429af223","0b9086455505427da81b06b781441653","c968141dbf8e4927b29b971acca80a36","63a93b9d09024855b433064e10aaa6a7","a52a31b5c1804d768cf9db25e4aa0f19","f39816c7ba1a479cb695ad3726201fef","3926fcc55a2444d89b21ba8caaaa99bd","3c15a9d863354375a4ec4c2360c5ddde","9075dda8fc9a4a7e813ec00522d71214","3ec75d5898cc49f893ef3c61eb584ded","a8839eacb4aa4e179f369dadc5e58b8f","c029d85c6c484dca8d806b62c3f32c6b"]},"id":"94cLWt59-Upu","executionInfo":{"status":"ok","timestamp":1722264753490,"user_tz":-330,"elapsed":4342,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}},"outputId":"a9721e1c-fab3-42eb-e2fe-bcb967079d62"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading readme: 0%| | 0.00/8.06k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"aee356d6cee841dc8684a2bffaf7cf8a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data: 0%| | 0.00/538k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"79b9b60cbfe0444c8a61fee323a8ee5c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split: 0 examples [00:00, ? 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Ayyappan","userId":"16084323207539095249"}}},"execution_count":19,"outputs":[]},{"cell_type":"code","source":["df = pd.DataFrame(data[\"train\"])"],"metadata":{"id":"7zZWC2TO-set","executionInfo":{"status":"ok","timestamp":1722264815785,"user_tz":-330,"elapsed":494,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"4aR2BXVx-4Q4","executionInfo":{"status":"ok","timestamp":1722264819869,"user_tz":-330,"elapsed":655,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}},"outputId":"7e2bd686-8723-413c-aed6-4150deace05e"},"execution_count":21,"outputs":[{"output_type":"execute_result","data":{"text/plain":[" template_id template_lang \\\n","0 2 ['tam'] \n","1 2 ['tam'] \n","2 2 ['tam'] \n","3 1 ['tam'] \n","4 2 ['tam'] \n","... ... ... \n","3985 1 ['tam'] \n","3986 1 ['tam'] \n","3987 1 ['tam'] \n","3988 3 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Ayyappan","userId":"16084323207539095249"}}},"execution_count":34,"outputs":[]},{"cell_type":"code","source":["print(str(generate_fine_tuning_prompt(data[\"train\"][0])))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"zrcGj7iVBeE3","executionInfo":{"status":"ok","timestamp":1722265552782,"user_tz":-330,"elapsed":497,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}},"outputId":"b6d89d9d-5d6f-4819-8b50-82a11d55bcab"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["Task: Given a meaning or context, identify the appropriate Thirukkural and provide a brief explanation.###Input: 158 . ###Target: ' \n"," .'\n"," . . . .\n"]}]},{"cell_type":"code","source":["train=map"],"metadata":{"id":"tOckkzk8B89R"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["trainer = SFTTrainer(\n"," model=model,\n"," train_dataset=data[\"train\"],\n"," args=transformers.TrainingArguments(\n"," per_device_train_batch_size=1,\n"," gradient_accumulation_steps=4,\n"," warmup_steps=2,\n"," max_steps=100,\n"," learning_rate=2e-4,\n"," fp16=True,\n"," logging_steps=1,\n"," output_dir=\"outputs\",\n"," optim=\"paged_adamw_8bit\"\n"," ),\n"," peft_config=lora_config,\n"," formatting_func=formatting_func,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":104,"referenced_widgets":["087c957069714f7ea02dd3b1b2b22da5","da775be9010a467bb4a42022e573a4fa","15d5ece2e5a04b5c816c761906a9782c","f64153f9e86748f59009fc62b6940eea","8c46925e94804d7b85c8587e8b7b67e2","73d1560c750f45cc82420e0117d3d2dd","06b621e32328466fb71ac4da0aedd815","c58d9aa647a14e3dbea8f28e77c6d18b","e86b03429aa74d03b5c0363fc47349ee","92d7adbb067a4489b11e9e583ae607dd","2f9a23a63dc84cb4a5f4f0e9c756f824"]},"id":"JNKJ7CnsBFEp","executionInfo":{"status":"ok","timestamp":1722265965750,"user_tz":-330,"elapsed":1882,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}},"outputId":"c5305ba4-a3db-48e9-ef2e-ce4761f84b3c"},"execution_count":35,"outputs":[{"output_type":"display_data","data":{"text/plain":["Map: 0%| | 0/3990 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"087c957069714f7ea02dd3b1b2b22da5"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:290: UserWarning: You passed a tokenizer with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `tokenizer.padding_side = 'right'` to your code.\n"," warnings.warn(\n"]}]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":1000},"id":"0619zLmwBLsL","executionInfo":{"status":"ok","timestamp":1722266119360,"user_tz":-330,"elapsed":124618,"user":{"displayName":"Bala Ayyappan","userId":"16084323207539095249"}},"outputId":"09af5036-7afa-46f4-b768-a7581919929f"},"execution_count":36,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <div>\n"," \n"," <progress value='100' max='100' style='width:300px; height:20px; vertical-align: middle;'></progress>\n"," [100/100 02:01, Epoch 100/100]\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"," <tr>\n"," <td>1</td>\n"," <td>2.909100</td>\n"," </tr>\n"," <tr>\n"," <td>2</td>\n"," <td>2.909100</td>\n"," </tr>\n"," <tr>\n"," <td>3</td>\n"," <td>2.855600</td>\n"," </tr>\n"," <tr>\n"," <td>4</td>\n"," <td>2.730400</td>\n"," </tr>\n"," <tr>\n"," <td>5</td>\n"," <td>2.593800</td>\n"," </tr>\n"," <tr>\n"," <td>6</td>\n"," <td>2.450000</td>\n"," </tr>\n"," <tr>\n"," <td>7</td>\n"," <td>2.303200</td>\n"," </tr>\n"," <tr>\n"," <td>8</td>\n"," <td>2.158900</td>\n"," </tr>\n"," <tr>\n"," <td>9</td>\n"," <td>2.021700</td>\n"," </tr>\n"," <tr>\n"," <td>10</td>\n"," <td>1.889400</td>\n"," </tr>\n"," <tr>\n"," <td>11</td>\n"," <td>1.755500</td>\n"," </tr>\n"," <tr>\n"," <td>12</td>\n"," <td>1.620200</td>\n"," </tr>\n"," <tr>\n"," <td>13</td>\n"," <td>1.486800</td>\n"," </tr>\n"," <tr>\n"," <td>14</td>\n"," <td>1.357500</td>\n"," </tr>\n"," <tr>\n"," <td>15</td>\n"," <td>1.235300</td>\n"," </tr>\n"," <tr>\n"," <td>16</td>\n"," <td>1.116000</td>\n"," </tr>\n"," <tr>\n"," <td>17</td>\n"," <td>1.005700</td>\n"," </tr>\n"," <tr>\n"," <td>18</td>\n"," <td>0.904500</td>\n"," </tr>\n"," <tr>\n"," <td>19</td>\n"," <td>0.808400</td>\n"," </tr>\n"," <tr>\n"," <td>20</td>\n"," <td>0.714100</td>\n"," </tr>\n"," <tr>\n"," <td>21</td>\n"," <td>0.629500</td>\n"," </tr>\n"," <tr>\n"," <td>22</td>\n"," <td>0.553000</td>\n"," </tr>\n"," <tr>\n"," <td>23</td>\n"," <td>0.481200</td>\n"," </tr>\n"," <tr>\n"," <td>24</td>\n"," <td>0.413800</td>\n"," </tr>\n"," <tr>\n"," <td>25</td>\n"," <td>0.348300</td>\n"," </tr>\n"," <tr>\n"," <td>26</td>\n"," <td>0.290700</td>\n"," </tr>\n"," <tr>\n"," <td>27</td>\n"," <td>0.239600</td>\n"," </tr>\n"," <tr>\n"," <td>28</td>\n"," <td>0.196400</td>\n"," </tr>\n"," <tr>\n"," <td>29</td>\n"," <td>0.164000</td>\n"," </tr>\n"," <tr>\n"," <td>30</td>\n"," <td>0.140100</td>\n"," </tr>\n"," <tr>\n"," <td>31</td>\n"," <td>0.122200</td>\n"," </tr>\n"," <tr>\n"," <td>32</td>\n"," <td>0.107200</td>\n"," </tr>\n"," <tr>\n"," <td>33</td>\n"," <td>0.093200</td>\n"," </tr>\n"," <tr>\n"," <td>34</td>\n"," <td>0.080400</td>\n"," </tr>\n"," <tr>\n"," <td>35</td>\n"," <td>0.072400</td>\n"," </tr>\n"," <tr>\n"," <td>36</td>\n"," <td>0.067000</td>\n"," </tr>\n"," <tr>\n"," <td>37</td>\n"," <td>0.063300</td>\n"," </tr>\n"," <tr>\n"," <td>38</td>\n"," <td>0.058600</td>\n"," </tr>\n"," <tr>\n"," <td>39</td>\n"," <td>0.056800</td>\n"," </tr>\n"," <tr>\n"," <td>40</td>\n"," <td>0.053100</td>\n"," </tr>\n"," <tr>\n"," <td>41</td>\n"," <td>0.050300</td>\n"," </tr>\n"," <tr>\n"," <td>42</td>\n"," <td>0.047100</td>\n"," </tr>\n"," <tr>\n"," <td>43</td>\n"," <td>0.043800</td>\n"," </tr>\n"," <tr>\n"," <td>44</td>\n"," <td>0.041500</td>\n"," </tr>\n"," <tr>\n"," <td>45</td>\n"," <td>0.038100</td>\n"," </tr>\n"," <tr>\n"," <td>46</td>\n"," <td>0.035100</td>\n"," </tr>\n"," <tr>\n"," <td>47</td>\n"," <td>0.032200</td>\n"," </tr>\n"," <tr>\n"," <td>48</td>\n"," <td>0.029000</td>\n"," </tr>\n"," <tr>\n"," <td>49</td>\n"," <td>0.025800</td>\n"," </tr>\n"," <tr>\n"," <td>50</td>\n"," <td>0.023000</td>\n"," </tr>\n"," <tr>\n"," <td>51</td>\n"," <td>0.019800</td>\n"," </tr>\n"," <tr>\n"," <td>52</td>\n"," <td>0.016900</td>\n"," </tr>\n"," <tr>\n"," <td>53</td>\n"," <td>0.014300</td>\n"," </tr>\n"," <tr>\n"," <td>54</td>\n"," <td>0.012100</td>\n"," </tr>\n"," <tr>\n"," <td>55</td>\n"," <td>0.010400</td>\n"," </tr>\n"," <tr>\n"," <td>56</td>\n"," <td>0.009100</td>\n"," </tr>\n"," <tr>\n"," <td>57</td>\n"," <td>0.008600</td>\n"," </tr>\n"," <tr>\n"," <td>58</td>\n"," <td>0.008200</td>\n"," </tr>\n"," <tr>\n"," <td>59</td>\n"," <td>0.007800</td>\n"," </tr>\n"," <tr>\n"," <td>60</td>\n"," <td>0.007700</td>\n"," </tr>\n"," <tr>\n"," <td>61</td>\n"," <td>0.007500</td>\n"," </tr>\n"," <tr>\n"," <td>62</td>\n"," <td>0.007400</td>\n"," </tr>\n"," <tr>\n"," <td>63</td>\n"," <td>0.007400</td>\n"," </tr>\n"," <tr>\n"," <td>64</td>\n"," <td>0.007300</td>\n"," </tr>\n"," <tr>\n"," <td>65</td>\n"," <td>0.007300</td>\n"," </tr>\n"," <tr>\n"," <td>66</td>\n"," <td>0.007300</td>\n"," </tr>\n"," <tr>\n"," <td>67</td>\n"," <td>0.007200</td>\n"," </tr>\n"," <tr>\n"," <td>68</td>\n"," <td>0.007200</td>\n"," </tr>\n"," <tr>\n"," <td>69</td>\n"," <td>0.007100</td>\n"," </tr>\n"," <tr>\n"," <td>70</td>\n"," <td>0.007200</td>\n"," </tr>\n"," <tr>\n"," <td>71</td>\n"," <td>0.007100</td>\n"," </tr>\n"," <tr>\n"," <td>72</td>\n"," <td>0.007100</td>\n"," </tr>\n"," <tr>\n"," <td>73</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>74</td>\n"," <td>0.007100</td>\n"," </tr>\n"," <tr>\n"," <td>75</td>\n"," <td>0.007100</td>\n"," </tr>\n"," <tr>\n"," <td>76</td>\n"," <td>0.007100</td>\n"," </tr>\n"," <tr>\n"," <td>77</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>78</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>79</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>80</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>81</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>82</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>83</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>84</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>85</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>86</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>87</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>88</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>89</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>90</td>\n"," <td>0.006900</td>\n"," </tr>\n"," <tr>\n"," <td>91</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>92</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>93</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>94</td>\n"," <td>0.006900</td>\n"," </tr>\n"," <tr>\n"," <td>95</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>96</td>\n"," <td>0.006900</td>\n"," </tr>\n"," <tr>\n"," <td>97</td>\n"," <td>0.007000</td>\n"," </tr>\n"," <tr>\n"," <td>98</td>\n"," <td>0.006900</td>\n"," </tr>\n"," <tr>\n"," <td>99</td>\n"," <td>0.006900</td>\n"," </tr>\n"," <tr>\n"," <td>100</td>\n"," <td>0.006900</td>\n"," </tr>\n"," </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=100, training_loss=0.4181924769748002, metrics={'train_runtime': 123.9248, 'train_samples_per_second': 3.228, 'train_steps_per_second': 0.807, 'total_flos': 1025322135552000.0, 'train_loss': 0.4181924769748002, 'epoch': 100.0})"]},"metadata":{},"execution_count":36}]},{"cell_type":"code","source":["text = \"inputts: 389 .\"\n","device = \"cuda:0\"\n","inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n","\n","outputs = model.generate(**inputs, max_new_tokens=200)\n","print(tokenizer.decode(outputs[0], skip_special_tokens=True))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"X9uuNf2PDYHy","executionInfo":{"status":"ok","timestamp":1722266225399,"user_tz":-330,"elapsed":17885,"user":{"displayName":"Bala 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