Streamlit_Deployment.ipynb

{"cells":[{"cell_type":"markdown","metadata":{"id":"view-in-github"},"source":["<a href=\"https://colab.research.google.com/github/Sdmr12012003/Neural_Networks_and_Large_Language_Models/blob/main/Generative_AI__Using_LLMs.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"]},{"cell_type":"code","execution_count":1,"metadata":{"id":"8NrBt2htxgQI","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5ab2bddc-b29e-4265-b7aa-8623e8979656","executionInfo":{"status":"ok","timestamp":1723555544491,"user_tz":-330,"elapsed":105939,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: transformers[torch] in /usr/local/lib/python3.10/dist-packages (4.42.4)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (3.15.4)\n","Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.23.5)\n","Requirement already satisfied: numpy<2.0,>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (1.26.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (24.1)\n","Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (6.0.2)\n","Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2024.5.15)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.32.3)\n","Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.4.4)\n","Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.19.1)\n","Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (4.66.5)\n","Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (0.32.1)\n","Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (from transformers[torch]) (2.3.1+cu121)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate>=0.21.0->transformers[torch]) (5.9.5)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers[torch]) (2024.6.1)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers[torch]) (4.12.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (1.13.1)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (3.3)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (3.1.4)\n","Collecting nvidia-cuda-nvrtc-cu12==12.1.105 (from torch->transformers[torch])\n","  Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-runtime-cu12==12.1.105 (from torch->transformers[torch])\n","  Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cuda-cupti-cu12==12.1.105 (from torch->transformers[torch])\n","  Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cudnn-cu12==8.9.2.26 (from torch->transformers[torch])\n","  Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cublas-cu12==12.1.3.1 (from torch->transformers[torch])\n","  Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cufft-cu12==11.0.2.54 (from torch->transformers[torch])\n","  Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-curand-cu12==10.3.2.106 (from torch->transformers[torch])\n","  Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl.metadata (1.5 kB)\n","Collecting nvidia-cusolver-cu12==11.4.5.107 (from torch->transformers[torch])\n","  Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-cusparse-cu12==12.1.0.106 (from torch->transformers[torch])\n","  Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl.metadata (1.6 kB)\n","Collecting nvidia-nccl-cu12==2.20.5 (from torch->transformers[torch])\n","  Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl.metadata (1.8 kB)\n","Collecting nvidia-nvtx-cu12==12.1.105 (from torch->transformers[torch])\n","  Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl.metadata (1.7 kB)\n","Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch->transformers[torch]) (2.3.1)\n","Collecting nvidia-nvjitlink-cu12 (from nvidia-cusolver-cu12==11.4.5.107->torch->transformers[torch])\n","  Using cached nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl.metadata (1.5 kB)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers[torch]) (2024.7.4)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch->transformers[torch]) (2.1.5)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch->transformers[torch]) (1.3.0)\n","Using cached nvidia_cublas_cu12-12.1.3.1-py3-none-manylinux1_x86_64.whl (410.6 MB)\n","Using cached nvidia_cuda_cupti_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (14.1 MB)\n","Using cached nvidia_cuda_nvrtc_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (23.7 MB)\n","Using cached nvidia_cuda_runtime_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (823 kB)\n","Using cached nvidia_cudnn_cu12-8.9.2.26-py3-none-manylinux1_x86_64.whl (731.7 MB)\n","Using cached nvidia_cufft_cu12-11.0.2.54-py3-none-manylinux1_x86_64.whl (121.6 MB)\n","Using cached nvidia_curand_cu12-10.3.2.106-py3-none-manylinux1_x86_64.whl (56.5 MB)\n","Using cached nvidia_cusolver_cu12-11.4.5.107-py3-none-manylinux1_x86_64.whl (124.2 MB)\n","Using cached nvidia_cusparse_cu12-12.1.0.106-py3-none-manylinux1_x86_64.whl (196.0 MB)\n","Using cached nvidia_nccl_cu12-2.20.5-py3-none-manylinux2014_x86_64.whl (176.2 MB)\n","Using cached nvidia_nvtx_cu12-12.1.105-py3-none-manylinux1_x86_64.whl (99 kB)\n","Using cached nvidia_nvjitlink_cu12-12.6.20-py3-none-manylinux2014_x86_64.whl (19.7 MB)\n","Installing collected packages: nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, nvidia-cusparse-cu12, nvidia-cudnn-cu12, nvidia-cusolver-cu12\n","Successfully installed nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.20.5 nvidia-nvjitlink-cu12-12.6.20 nvidia-nvtx-cu12-12.1.105\n"]}],"source":["!pip install transformers[torch]"]},{"cell_type":"code","execution_count":2,"metadata":{"id":"GUwy2eq-xlNh","colab":{"base_uri":"https://localhost:8080/"},"outputId":"3cfc30e7-5cd7-4e3d-b5e6-b157a0ae8b47","executionInfo":{"status":"ok","timestamp":1723555552063,"user_tz":-330,"elapsed":7577,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: torch in /usr/local/lib/python3.10/dist-packages (2.3.1+cu121)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch) (3.15.4)\n","Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch) (4.12.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch) (1.13.1)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch) (3.3)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch) (3.1.4)\n","Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch) (2024.6.1)\n","Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n","Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n","Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n","Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch) (8.9.2.26)\n","Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.3.1)\n","Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch) (11.0.2.54)\n","Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch) (10.3.2.106)\n","Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch) (11.4.5.107)\n","Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.0.106)\n","Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch) (2.20.5)\n","Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch) (12.1.105)\n","Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch) (2.3.1)\n","Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch) (12.6.20)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch) (2.1.5)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch) (1.3.0)\n"]}],"source":["pip install torch"]},{"cell_type":"code","execution_count":3,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"KEhgaVSvx04z","outputId":"909e95d6-2d1f-4ef6-8c5b-85ffa13f5dcd","executionInfo":{"status":"ok","timestamp":1723555558704,"user_tz":-330,"elapsed":6646,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting torchdata\n","  Downloading torchdata-0.8.0-cp310-cp310-manylinux1_x86_64.whl.metadata (5.4 kB)\n","Requirement already satisfied: urllib3>=1.25 in /usr/local/lib/python3.10/dist-packages (from torchdata) (2.0.7)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from torchdata) (2.32.3)\n","Requirement already satisfied: torch>=2 in /usr/local/lib/python3.10/dist-packages (from torchdata) (2.3.1+cu121)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (3.15.4)\n","Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (4.12.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (1.13.1)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (3.3)\n","Requirement already satisfied: jinja2 in 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in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (11.0.2.54)\n","Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (10.3.2.106)\n","Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (11.4.5.107)\n","Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (12.1.0.106)\n","Requirement already satisfied: nvidia-nccl-cu12==2.20.5 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (2.20.5)\n","Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (12.1.105)\n","Requirement already satisfied: triton==2.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=2->torchdata) (2.3.1)\n","Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=2->torchdata) (12.6.20)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->torchdata) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->torchdata) (3.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->torchdata) (2024.7.4)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=2->torchdata) (2.1.5)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=2->torchdata) (1.3.0)\n","Downloading torchdata-0.8.0-cp310-cp310-manylinux1_x86_64.whl (2.7 MB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.7/2.7 MB\u001b[0m \u001b[31m31.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: torchdata\n","Successfully installed torchdata-0.8.0\n"]}],"source":["!pip install torchdata"]},{"cell_type":"code","source":["!pip install streamlit"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"LkQwUYChwNs3","executionInfo":{"status":"ok","timestamp":1723555571342,"user_tz":-330,"elapsed":12642,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"a937d73c-065f-48f2-da9f-6de3b15c2e69"},"execution_count":4,"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting streamlit\n","  Downloading streamlit-1.37.1-py2.py3-none-any.whl.metadata (8.5 kB)\n","Requirement already satisfied: altair<6,>=4.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (4.2.2)\n","Requirement already satisfied: blinker<2,>=1.0.0 in /usr/lib/python3/dist-packages (from streamlit) (1.4)\n","Requirement already satisfied: cachetools<6,>=4.0 in 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(from streamlit) (2.32.3)\n","Requirement already satisfied: rich<14,>=10.14.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (13.7.1)\n","Collecting tenacity<9,>=8.1.0 (from streamlit)\n","  Downloading tenacity-8.5.0-py3-none-any.whl.metadata (1.2 kB)\n","Requirement already satisfied: toml<2,>=0.10.1 in /usr/local/lib/python3.10/dist-packages (from streamlit) (0.10.2)\n","Requirement already satisfied: typing-extensions<5,>=4.3.0 in /usr/local/lib/python3.10/dist-packages (from streamlit) (4.12.2)\n","Collecting gitpython!=3.1.19,<4,>=3.0.7 (from streamlit)\n","  Downloading GitPython-3.1.43-py3-none-any.whl.metadata (13 kB)\n","Collecting pydeck<1,>=0.8.0b4 (from streamlit)\n","  Downloading pydeck-0.9.1-py2.py3-none-any.whl.metadata (4.1 kB)\n","Requirement already satisfied: tornado<7,>=6.0.3 in /usr/local/lib/python3.10/dist-packages (from streamlit) (6.3.3)\n","Collecting watchdog<5,>=2.1.5 (from streamlit)\n","  Downloading 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watchdog-4.0.2\n"]}]},{"cell_type":"code","execution_count":5,"metadata":{"id":"zdhyoOyTx7Kz","executionInfo":{"status":"ok","timestamp":1723555581331,"user_tz":-330,"elapsed":9998,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"colab":{"base_uri":"https://localhost:8080/","height":269,"referenced_widgets":["86f5e94eae6c4c09b48763a33cee663c","599151945c164aef9908cbc849298b1e","fd3700b1c3254a9ea433ddae8ab9fff8","b5d9006ccd9e40e09e523d8461b307f1","c607cfcfc27e482b8c2ebf78dc3ad920","f50513b30c844e91b5075b3e002e4610","cd2bc409b3854fdea16d5688843cfc0d","066d33fa5b814991bc5ef5e007972f86","1a7f08e015a844d794f1100655a7aa5a","a05138a380fb4a099cddb3bfbd5c2173","e897e69e6761446c868a53d22b558372","45641f9e74104ca3a4e8272c43107dd5","3a125fc5f82b42e5904672764c5acddb","65778c286799418cb155bc12a9846ea9","9de3cf9363af48819415239d17b0f712","282e2398efb24976a964c74b5415de79","f82b14f32d274a0aa54f7fe984395288","fde7474967cc450eab0bc7383301fded","e71501cdecaf4ed9a9690b7d240da9e3","9a9728ad6d8d4c20b7dadeeb25ca3b02","7275b1b46d534e8aa5f018b82a332b00","f432bf5df432475fb4bed0bb044abe83","b8b8377161234b739a8df1946732c7f6","425e17c510354c2a9ff64e3ba2f16485","8b3bc31fa8c14db0a414db997eb67813","ea4efa6b336340c396b79fdac4f3b20f","af0c6b76b25843599aa231d933a5ec9c","50c814bcd57a4d539f37249f347c12d3","343367db068f43b4b4f26485dd35427c","ed09192c906b46d2b89dde2855c04f47","0a862bf4cc0541a092690de02c9aaa8a","62017031fb0542e39e00e7c07932be95","2cb4a77f720b49418b14ce91c49cbb0d","39f8dc2ccd9a4dc78cc38a221d9fee86","b893a56ce459475bbeb7bf7cff533151","d64c6a8d31e346fcab6f27751c7391a4","504d19cab8444a88afd64ec1c6295373","73f7a9f9e63a44b393f3379dfc83a4d8","a59eaf6ce6544adeafd2f79f3d2ef160","bcbab55e8c2d4b1898b1d2ed5de2fec8","9f0740a5c96f4f218f0f218b72ceed36","2a36fa1ee85f42cd958da1c764d5e105","b12c2a8c6b124a9ca5c4c39efdcce68e","3788e158536143d0af87a6d36bc71fd8"]},"outputId":"3af465d0-e32c-4e8e-83da-31fc2bf4bb19"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: UserWarning: \n","The secret `HF_TOKEN` does not exist in your Colab secrets.\n","To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n","You will be able to reuse this secret in all of your notebooks.\n","Please note that authentication is recommended but still optional to access public models or datasets.\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"86f5e94eae6c4c09b48763a33cee663c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["spiece.model:   0%|          | 0.00/792k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"45641f9e74104ca3a4e8272c43107dd5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b8b8377161234b739a8df1946732c7f6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/2.20k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"39f8dc2ccd9a4dc78cc38a221d9fee86"}},"metadata":{}}],"source":["from transformers import AutoTokenizer\n","tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-base\")"]},{"cell_type":"code","execution_count":6,"metadata":{"id":"S5s2lkKox_B8","executionInfo":{"status":"ok","timestamp":1723555582995,"user_tz":-330,"elapsed":1678,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"colab":{"base_uri":"https://localhost:8080/","height":177,"referenced_widgets":["6e2c43612dc3442cb5ce9d62baee7d8c","5a77d8ddb5e04356ad80812899507b27","2aac16d924b44fecb463b723be7a9f34","3e1b446992e6481181d21b70eece44e9","d0921eae5e17484d995a5880f9726191","1d9d162f4f0a4f82ab72c3ee9e31d32a","a3a625d7db334ca3b672bb5f594b68a9","ee0f95f0414d46e68eda38d369d7c609","98c01f8e7d844d5b8313c616ce4be5a6","d5613edb11f14deca2005b86fb544aca","5a1660a806cc43968511f1cd0181d4a5","7b77fcb3fc4843b6a0630dce41968195","af673d496d914c0bb1171ac55d58a18f","a99f34cce23f4752ad0b7c884737b60a","a01a2a36aa814bbcb78ad52bad4a385d","5a07359daf9844c9a2e629bbd911db4b","9a8547f6327e40c593768fca095a6e5b","6c0120fea2c14b598625b1ee2815260b","eb8de81983664070918c9e4fcab4dc71","26ddb6499a4f46d5b7b59bef28f5334f","f20f79f58ac445c786a60289fbac20ad","d88d986c10af46b1893174b3e5f61c0f","42acf14b62f04b80b6b6f10120d2ad5c","413daed7b4e5434b8b13632f695fc4ec","cfd45982e9534defb136eec569b2fc18","067403b1905c4eb8afa7460064180a4d","39915f95343349b786ed0d7d45e2b471","5b9bbeebacb149a48ae6295a310bc2ea","ea760e755b8840c8a542fe232545a4bb","094fa8f771c54c2cbe13fe514dcd9f43","bc625ff85796456f8d149fea38442d32","d229449d6d794999bf5d668e9b0b9ab2","7c12b9b15c8f4f8588d41059bbbc714c","f17622e30df04d2fac1eafcfa0598ec6","ddf15d82a58448eca110b54859f3039d","ceb0b969207948dfa1535ce67117368b","a95e4b863bb547268152807a65718b41","ac94703f049849ea8f3051ded450b2a4","9b26a175473b4cf59ca23fd65a689fb7","17c5771415774a7ba995ae357185f6d9","1c04aa7474a248b7b59192ad5c6db4ef","57c1f6ac77bf49b3b6a1a997f7508e5c","8727d60cddc24641a7558ee7609c8a0d","f2309863207447b7a159218f0bbbdc93","34b02d321f354a14a1b9620c6db53f10","da70399f9c314b2ea1a7e870edd1dc22","e4a24c20bf664634b426b9435228d578","2247a0766515465888890e6c1d991955","de99301d7bf04588aaf49d90c6b9d35b","c2349d602a52438e828d2e94516d0ffb","bd2e3ff8586d4d68bce3f15f5532edd6","cbdc54e0fff34887a706c3f2c3b6cb39","f40a51d441b245ad918c9a5ccdea4c05","227455ee3ca6403f8c85044ee0c5f65c","d3752871edc9445a8681cada6dd07991"]},"outputId":"6ba72ef3-9274-4f32-8ae9-06bcb8e86e43"},"outputs":[{"output_type":"display_data","data":{"text/plain":["tokenizer_config.json: 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?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f17622e30df04d2fac1eafcfa0598ec6"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["special_tokens_map.json:   0%|          | 0.00/12.5k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"34b02d321f354a14a1b9620c6db53f10"}},"metadata":{}}],"source":["tokenizer1 = AutoTokenizer.from_pretrained(\"Salesforce/codet5-small\")"]},{"cell_type":"code","execution_count":7,"metadata":{"id":"D2mfcjb5yDO7","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1ba9199a-186d-4d3f-e043-3fe32ba79d04","executionInfo":{"status":"ok","timestamp":1723555582995,"user_tz":-330,"elapsed":14,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': tensor([[  531,    59,   140,  8437,    16,     8, 12748,    13, 25027,     7,\n","             6,     1],\n","        [   21,    79,    33,  9240,    11,  1704,    12, 11213,     5,     1,\n","             0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n","        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0]])}\n"]}],"source":["example = [\"Do not meddle in the affairs of wizards,\",\"for they are subtle and quick to anger.\"]\n","encoded_input = tokenizer(example,padding = True, return_tensors = 'pt')\n","print(encoded_input)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"k9QRDXBByGYG","colab":{"base_uri":"https://localhost:8080/"},"outputId":"dff75f78-7b97-40c0-de58-e738bc95c755","executionInfo":{"status":"ok","timestamp":1723553648894,"user_tz":-330,"elapsed":6,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["{'input_ids': tensor([[    1,  3244,   486,  6735,    72,   298,   316,   326, 10073,   507,\n","         10539,   434, 24204,    87,    16,     2],\n","        [    1,  1884,  2898,   854,   720,  5929,   471,  9549,   358,   392,\n","           693,    18,     2,     0,     0,     0]]), 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],\n","        [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0]])}\n"]}],"source":["encoded_input1 = tokenizer1(example,padding = True, return_tensors = 'pt')\n","print(encoded_input1)"]},{"cell_type":"code","execution_count":8,"metadata":{"id":"ahjlkaY7yJad","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["e526a77ec2494b929fec1f118ab3cc16","6c1f584f07e8418c9bb6e8729eb45c34","2e98dd26cef74a7eb4357b3979538373","13512ed18a0a42f38458c4fb7bb01833","6f1931c844c344ffbc6a443b81ce5671","81215c87c76c4ea9887c6f26b98b5d98","285b21ba5eb344df8158e885ba40cf21","ecd242b5e9cf41f6b131d0740ebdcee6","4d8df62cd07c4727a94178769f205659","02f3b253a32c47a68ce36c0add76fa34","047dc1d06b3b4c5baf9e7ac3ab08cb03"]},"outputId":"cb19a1ab-fd42-4fe0-aca2-4aa65b3dfd7f","executionInfo":{"status":"ok","timestamp":1723555583785,"user_tz":-330,"elapsed":798,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["config.json:   0%|          | 0.00/1.40k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e526a77ec2494b929fec1f118ab3cc16"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["T5Config {\n","  \"_name_or_path\": \"google/flan-t5-base\",\n","  \"architectures\": [\n","    \"T5ForConditionalGeneration\"\n","  ],\n","  \"classifier_dropout\": 0.0,\n","  \"d_ff\": 2048,\n","  \"d_kv\": 64,\n","  \"d_model\": 768,\n","  \"decoder_start_token_id\": 0,\n","  \"dense_act_fn\": \"gelu_new\",\n","  \"dropout_rate\": 0.1,\n","  \"eos_token_id\": 1,\n","  \"feed_forward_proj\": \"gated-gelu\",\n","  \"initializer_factor\": 1.0,\n","  \"is_encoder_decoder\": true,\n","  \"is_gated_act\": true,\n","  \"layer_norm_epsilon\": 1e-06,\n","  \"model_type\": \"t5\",\n","  \"n_positions\": 512,\n","  \"num_decoder_layers\": 12,\n","  \"num_heads\": 12,\n","  \"num_layers\": 12,\n","  \"output_past\": true,\n","  \"pad_token_id\": 0,\n","  \"relative_attention_max_distance\": 128,\n","  \"relative_attention_num_buckets\": 32,\n","  \"task_specific_params\": {\n","    \"summarization\": {\n","      \"early_stopping\": true,\n","      \"length_penalty\": 2.0,\n","      \"max_length\": 200,\n","      \"min_length\": 30,\n","      \"no_repeat_ngram_size\": 3,\n","      \"num_beams\": 4,\n","      \"prefix\": \"summarize: \"\n","    },\n","    \"translation_en_to_de\": {\n","      \"early_stopping\": true,\n","      \"max_length\": 300,\n","      \"num_beams\": 4,\n","      \"prefix\": \"translate English to German: \"\n","    },\n","    \"translation_en_to_fr\": {\n","      \"early_stopping\": true,\n","      \"max_length\": 300,\n","      \"num_beams\": 4,\n","      \"prefix\": \"translate English to French: \"\n","    },\n","    \"translation_en_to_ro\": {\n","      \"early_stopping\": true,\n","      \"max_length\": 300,\n","      \"num_beams\": 4,\n","      \"prefix\": \"translate English to Romanian: \"\n","    }\n","  },\n","  \"tie_word_embeddings\": false,\n","  \"transformers_version\": \"4.42.4\",\n","  \"use_cache\": true,\n","  \"vocab_size\": 32128\n","}"]},"metadata":{},"execution_count":8}],"source":["from transformers import AutoConfig\n","details = AutoConfig.from_pretrained(\"google/flan-t5-base\")\n","details"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"Vn3uXiZPyUm8","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["4f9c7a0cfdf94560b274a83e01f59f63","25254c2f7fb543b5bd67690a2e476f7b","22bf83a637ab40aa9bf8111fbf140a01","7c3c473649834182be3fd177e182efff","21793c7303a14b839bafe2d843b7e053","68f93cfdc8ea4a7fa5e4a9dcdda8dde4","d86295d9b70f4719ba15801dd77a4988","44ead3deb1c2495bb72646ed46706605","4b6ed99bbd064809bcf13244c3910404","a493c63cacd74237845c1766eaa2a75a","3ceed3c67e494b62b4d3bb16ace26890"]},"outputId":"3ec62f56-4348-4892-c3f2-9a25b4ea8147","executionInfo":{"status":"ok","timestamp":1723555598262,"user_tz":-330,"elapsed":14488,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["model.safetensors:   0%|          | 0.00/990M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4f9c7a0cfdf94560b274a83e01f59f63"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["T5Model(\n","  (shared): Embedding(32128, 768)\n","  (encoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (decoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n",")"]},"metadata":{},"execution_count":9}],"source":["from transformers import AutoModel\n","model = AutoModel.from_pretrained('google/flan-t5-base')\n","model"]},{"cell_type":"code","execution_count":10,"metadata":{"id":"_dgXV6W8yXvM","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1f6ca667-3e38-409f-9ffc-9078785b5ada","executionInfo":{"status":"ok","timestamp":1723555609092,"user_tz":-330,"elapsed":10860,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["T5Model(\n","  (shared): Embedding(32128, 768)\n","  (encoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (decoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n",")"]},"metadata":{},"execution_count":10}],"source":["okk = AutoConfig.from_pretrained('google/flan-t5-base')\n","okk1 = AutoModel.from_config(okk)\n","okk1"]},{"cell_type":"code","execution_count":11,"metadata":{"id":"yp4sPGnFybit","colab":{"base_uri":"https://localhost:8080/"},"outputId":"cebece97-01b0-4d3f-cdf0-c43400bb2a34","executionInfo":{"status":"ok","timestamp":1723555620056,"user_tz":-330,"elapsed":10974,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["T5ForConditionalGeneration(\n","  (shared): Embedding(32128, 768)\n","  (encoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (decoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n",")"]},"metadata":{},"execution_count":11}],"source":["from transformers import AutoModelForPreTraining\n","pre_okk = AutoModelForPreTraining.from_config(okk)\n","pre_okk"]},{"cell_type":"code","execution_count":12,"metadata":{"id":"KVY-BSpIyqlv","colab":{"base_uri":"https://localhost:8080/"},"outputId":"037e5cb2-be94-4ed9-9b3b-35a1d23e091e","executionInfo":{"status":"ok","timestamp":1723555632592,"user_tz":-330,"elapsed":12550,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting datasets\n","  Downloading datasets-2.20.0-py3-none-any.whl.metadata (19 kB)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from datasets) (3.15.4)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.26.4)\n","Collecting pyarrow>=15.0.0 (from datasets)\n","  Downloading 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This behaviour is the source of the following dependency conflicts.\n","cudf-cu12 24.4.1 requires pyarrow<15.0.0a0,>=14.0.1, but you have pyarrow 17.0.0 which is incompatible.\n","gcsfs 2024.6.1 requires fsspec==2024.6.1, but you have fsspec 2024.5.0 which is incompatible.\n","ibis-framework 8.0.0 requires pyarrow<16,>=2, but you have pyarrow 17.0.0 which is incompatible.\u001b[0m\u001b[31m\n","\u001b[0mSuccessfully installed datasets-2.20.0 dill-0.3.8 fsspec-2024.5.0 multiprocess-0.70.16 pyarrow-17.0.0 xxhash-3.4.1\n"]}],"source":["pip install datasets"]},{"cell_type":"code","execution_count":13,"metadata":{"id":"w4LgSVCDytZT","colab":{"base_uri":"https://localhost:8080/","height":484,"referenced_widgets":["70d57f05bc5b4f089aeae1d0b6a9bed5","fe6ca3bee05346a59c5b6ec7b5d2d558","bc774b4b694c4216bff99aeb682f7f15","87c2b47916c7458091bee6455440da15","35d7cbaa80e147e6a4b49175a0586556","05bbfb47026e42d090f975b9a8db1b3f","6e5e3627ed4f40749ba799beff4d9928","53149b52a10846f89fadea2b1567132b","4446b8a4089a4def961ca7690f5d9000","6ad4c5484eb644ec8bbede08386d9782","f5a8c91849654c1f96c71c8c6bc627fe","921b542133d54677aedae96d423f4a6d","8e03e717db094c2dbd45401d4685f3ae","ef09abb9c4814796bccba9aea97c2c32","93c5ec21ff17404fb351520332790b00","fcca88e722ba485ab0cea0362f5561a4","e118e2ba09544170a188712a7fa1408a","d4171451c35b4b51bff3ee01eb957d82","6448861e500a411c9544c1bf1b85a53c","b41ae9eb86f544f48f7c14f600dd9747","620bb653da1a41928cf1e43a30b9d3ff","4de8061a464f4d58a2d682eec1d098ba","babdc6e46385411ca11cbe755ec6f53c","3e898492aca84710a717490ba539ae8c","4ea7fc746fc84e48af9e075de1c0af76","f7cdeb2dafdf4db5bae3c396f78bb207","3da01b4ed4bd4b99a0f5ebc3f6ec9365","d67386773b2747598966b8de6f6bafd2","427b0ec1cd8c486ba48702d066ca21fa","b30a19764bad4748959e1936f9666c7a","1cbeea36eb8c418980d1bfc472243bf5","3b7e5b0d2a694a6bbfed96065355095b","e8f1d7daf0f74fb0b90cfbda2ffa6ce4","6597331fa8544ab5ac44e58fe352a7ab","49cb47c905374fcf9bb2daca778db335","28a9b69fe7324a528ca001d58aab3506","bd1a069461d645bfb81c92530c4e2d7c","2582c6a3c42946fd9d1dbf1ab215bf1c","7246e4e8cfbd40398c05ed064afcf186","562d02f9d6e341eb80f634f17e7f0e98","1d14faf0ca3b4095a4fe9a47a7d68c49","b3b8731bf022488aaed2ec62cba0d7e3","a18538ec01314378ab8e1581c7366f48","39ffdf0570084cc69e80be001c8b03d6","5d231a5880e54c94b2a833a54841aab9","8cd4a62e1eed492da91e1cbbc616f31d","5c1da1dc332442a8978be8a4b547e4bb","4d9cce3948d64c0e95ae82d843d4c846","0dbc5d186afb41bd8e7ff8ca1ccd1e7b","d23d9f2bf5534ee78ba573a3185ad37e","ceeab6280f2d4a139c19a7646d4a3f56","f96c5de22ec844e699d395f7b2579506","1a671e13ccb94d0caadfb2baef4c4d30","7b9397c58774445fa42748a06b0433dd","8862ac135aac49feb021cd909e5e8ab3","43eb33ba551d4cd58015c5710f19a399","c6adb67776374f8391bd087132075e5a","b529fece1740448a98bd3641cec0f2df","19bf6ddb53f14e1388208c955c93957a","860596977f6a4552995b3f8da0304b70","5e4e35f748c44b3c959d571f14c1d244","4e378b1e1d7d4ed2aa36b850cb304096","39954a6ee9404a998add184c0a3a5721","9f7c9ce4ee4c4eeca151b8d774772197","72d26974db5947eb9d19ee03d1b28932","bf0cf50e2d32490ba3eb89f27b75a4a0","fcbff2214e6c442eb2d29a3cb787ed0e","b689a233fa6a4c16a37389ae64a69996","cc70da7b22944e8eadf8ef1a70a84f4b","9cfaee3cbe994897935b83ce9422d220","9929b4e3487d4b6d90555667dd22a9a6","8c2d6a421a1b4ddf97dfc34f5e225d7b","6f12711b39364083a43d7b6d4f7e15e3","3c3563d3ae9e44c1812b2fa94ec17be4","11712b644ef54c8cad2153911baf6acc","0246e3c717f74d66a4f57e5f4fb21dfe","e05ac33f67fd4461b1669034aecd0189"]},"outputId":"f5ee6362-74fb-4d5e-f8da-741866fa7716","executionInfo":{"status":"ok","timestamp":1723555638802,"user_tz":-330,"elapsed":6265,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading readme:   0%|          | 0.00/4.65k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"70d57f05bc5b4f089aeae1d0b6a9bed5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/11.3M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"921b542133d54677aedae96d423f4a6d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/442k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"babdc6e46385411ca11cbe755ec6f53c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading data:   0%|          | 0.00/1.35M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"6597331fa8544ab5ac44e58fe352a7ab"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating train split:   0%|          | 0/12460 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"5d231a5880e54c94b2a833a54841aab9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating validation split:   0%|          | 0/500 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"43eb33ba551d4cd58015c5710f19a399"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Generating test split:   0%|          | 0/1500 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"fcbff2214e6c442eb2d29a3cb787ed0e"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['id', 'dialogue', 'summary', 'topic'],\n","        num_rows: 12460\n","    })\n","    validation: Dataset({\n","        features: ['id', 'dialogue', 'summary', 'topic'],\n","        num_rows: 500\n","    })\n","    test: Dataset({\n","        features: ['id', 'dialogue', 'summary', 'topic'],\n","        num_rows: 1500\n","    })\n","})"]},"metadata":{},"execution_count":13}],"source":["from datasets import load_dataset\n","\n","data = load_dataset('knkarthick/dialogsum')\n","data"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oY0gqWZ6yw4s","colab":{"base_uri":"https://localhost:8080/"},"outputId":"92d372ee-e81b-4bc6-9e2f-2410d078ad9f","executionInfo":{"status":"ok","timestamp":1723532529480,"user_tz":-330,"elapsed":7,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["#Person1#: I have a problem with my cable.\n","#Person2#: What about it?\n","#Person1#: My cable has been out for the past week or so.\n","#Person2#: The cable is down right now. I am very sorry.\n","#Person1#: When will it be working again?\n","#Person2#: It should be back on in the next couple of days.\n","#Person1#: Do I still have to pay for the cable?\n","#Person2#: We're going to give you a credit while the cable is down.\n","#Person1#: So, I don't have to pay for it?\n","#Person2#: No, not until your cable comes back on.\n","#Person1#: Okay, thanks for everything.\n","#Person2#: You're welcome, and I apologize for the inconvenience.\n"]}],"source":["print(data['train'][100]['dialogue'])"]},{"cell_type":"code","execution_count":14,"metadata":{"id":"ty0SAcQoyz1Z","colab":{"base_uri":"https://localhost:8080/","height":1000,"referenced_widgets":["a05afa795ce54f439e206449a802fb32","1d4c8920741c4437968847ba755ef8e1","eff3ec807ad3448fa4742f0ecc6f5bc2","2f076658ca704dd687e471fcca0e8ebb","291e381e89ef417796edf53d61e6b14f","3fb5f473f0fe41ce89cb5258f7e37832","d02154398200404b9a6c12c543bdf572","7372f2d1ec044419b891c8b51ac47e70","75d09d4a1def49559c92513608bb134a","d80d8d3ae3b64473868ffd3de293acd1","0fda171534e74fb0be53eb69a7ff0930"]},"outputId":"89b3c026-333e-48b8-b9ce-17e528731e1e","executionInfo":{"status":"ok","timestamp":1723555642089,"user_tz":-330,"elapsed":3303,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["generation_config.json:   0%|          | 0.00/147 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a05afa795ce54f439e206449a802fb32"}},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["T5ForConditionalGeneration(\n","  (shared): Embedding(32128, 768)\n","  (encoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (decoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n",")"]},"metadata":{},"execution_count":14}],"source":["from transformers import AutoModelForSeq2SeqLM\n","models = AutoModelForSeq2SeqLM.from_pretrained('google/flan-t5-base')\n","models"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"baRlz7Ery2Im","colab":{"base_uri":"https://localhost:8080/","height":36},"outputId":"ed8169eb-871b-4e54-e22d-fef480848b57","executionInfo":{"status":"ok","timestamp":1723532535880,"user_tz":-330,"elapsed":12,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["'for they are subtle and quick to anger.'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":19}],"source":["decoded_input = tokenizer1.decode(encoded_input1['input_ids'][1], skip_special_tokens = True)\n","decoded_input"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VABShc5iy47M","colab":{"base_uri":"https://localhost:8080/"},"outputId":"4407f3e0-b252-4513-9bfd-accddd6c3411","executionInfo":{"status":"ok","timestamp":1723532541666,"user_tz":-330,"elapsed":5793,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["#Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\n","Person1: It's ten to nine.\n","\n","\n","#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","#Person1#: I'm thinking of upgrading my computer.\n","\n","\n"]}],"source":["d = [40,200]\n","for i,index in enumerate(d):\n","  dialogue = data['test'][index]['dialogue']\n","  summary = data['test'][index]['summary']\n","\n","  inputs = tokenizer(dialogue, return_tensors = 'pt')\n","  output = tokenizer.decode(models.generate(inputs['input_ids'], max_new_tokens = 50)[0], skip_special_tokens = True)\n","\n","  print(summary)\n","  print(output)\n","  print('\\n')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8PTqAbzVy8eA","colab":{"base_uri":"https://localhost:8080/"},"outputId":"39e2e252-b321-4696-c7ca-2092cc7e1cc7","executionInfo":{"status":"ok","timestamp":1723532550244,"user_tz":-330,"elapsed":8584,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["#Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\n","The train is about to leave.\n","\n","\n","#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","#Person1#: You'd probably want to upgrade your computer. #Person2#: You could also upgrade your hardware. #Person1#: You'd probably want a faster processor, more memory and a\n","\n","\n"]}],"source":["for i,index in enumerate(d):\n","  dialogue = data['test'][index]['dialogue']\n","  summary = data['test'][index]['summary']\n","\n","  prompt = f\"\"\"\n","\n","  Summarize the following conversation:\n","  {dialogue}\n","\n","  Summary:\n","\n","   \"\"\"\n","\n","  inputs = tokenizer(prompt, return_tensors = 'pt')\n","  output = tokenizer.decode(models.generate(inputs['input_ids'], max_new_tokens = 50)[0], skip_special_tokens = True)\n","\n","  print(summary)\n","  print(output)\n","  print('\\n')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Iin9FBy4zBd3","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c546a0b5-fa8f-45ce-dd3e-e6e383700060","executionInfo":{"status":"ok","timestamp":1723532558673,"user_tz":-330,"elapsed":8433,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["#Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\n","The train is about to leave.\n","\n","\n","#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","#Person1#: You'd probably want to upgrade your system. #Person2#: You'd probably want to upgrade your hardware. #Person1#: You'd probably want to upgrade your computer. #P\n","\n","\n"]}],"source":["for i,index in enumerate(d):\n","  dialogue = data['test'][index]['dialogue']\n","  summary = data['test'][index]['summary']\n","\n","  prompt = f\"\"\"\n","\n","  Summarize the following conversation:\n","  {dialogue}\n","\n","  What is going on?\n","\n","   \"\"\"\n","\n","  inputs = tokenizer(prompt, return_tensors = 'pt')\n","  output = tokenizer.decode(models.generate(inputs['input_ids'], max_new_tokens = 50)[0], skip_special_tokens = True)\n","\n","  print(summary)\n","  print(output)\n","  print('\\n')"]},{"cell_type":"code","execution_count":45,"metadata":{"id":"OORgOHCgzdDc","executionInfo":{"status":"ok","timestamp":1723555896638,"user_tz":-330,"elapsed":468,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[],"source":["def prompt(example_full_index,example_summarize_index):\n","  prompt = ''\n","  for i,index in enumerate(example_full_index):\n","    dialogue = data['test'][index]['dialogue']\n","    summary = data['test'][index]['summary']\n","\n","    prompt += f\"\"\"\n","\n","    Summarize the following conversation:\n","    {dialogue}\n","\n","    What is going on?\n","    {summary}\n","\n","    \"\"\"\n","  dialogue1 = data['test'][example_summarize_index]['dialogue']\n","\n","  prompt += f\"\"\"\n","\n","  Summarize the following conversation:\n","  {dialogue1}\n","\n","  What is going on?\n","  \"\"\"\n","\n","  return prompt"]},{"cell_type":"code","execution_count":46,"metadata":{"id":"8oKS6dwOzgkU","colab":{"base_uri":"https://localhost:8080/","height":157},"outputId":"1c2c392b-3560-4b4c-9b57-af6343648b75","executionInfo":{"status":"ok","timestamp":1723555897109,"user_tz":-330,"elapsed":11,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"\\n\\n    Summarize the following conversation:\\n    #Person1#: What time is it, Tom?\\n#Person2#: Just a minute. It's ten to nine by my watch.\\n#Person1#: Is it? I had no idea it was so late. I must be off now.\\n#Person2#: What's the hurry?\\n#Person1#: I must catch the nine-thirty train.\\n#Person2#: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there.\\n\\n    What is going on?\\n    #Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\\n\\n    \\n\\n  Summarize the following conversation:\\n  #Person1#: Have you considered upgrading your system?\\n#Person2#: Yes, but I'm not sure what exactly I would need.\\n#Person1#: You could consider adding a painting program to your software. It would allow you to make up your own flyers and banners for advertising.\\n#Person2#: That would be a definite bonus.\\n#Person1#: You might also want to upgrade your hardware because it is pretty outdated now.\\n#Person2#: How can we do that?\\n#Person1#: You'd probably need a faster processor, to begin with. And you also need a more powerful hard disc, more memory and a faster modem. Do you have a CD-ROM drive?\\n#Person2#: No.\\n#Person1#: Then you might want to add a CD-ROM drive too, because most new software programs are coming out on Cds.\\n#Person2#: That sounds great. Thanks.\\n\\n  What is going on?\\n  \""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":46}],"source":["example_full_index = [40]\n","example_summarize_index = 200\n","\n","one_shot_prompt = prompt(example_full_index,example_summarize_index)\n","one_shot_prompt"]},{"cell_type":"code","execution_count":44,"metadata":{"id":"28u9aEU6zjlk","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b087a2e3-7f64-4e5c-ee54-880fd186e405","executionInfo":{"status":"ok","timestamp":1723555888945,"user_tz":-330,"elapsed":5169,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","#Person1#: You'd probably want to upgrade your system. #Person2#: You'd probably want to upgrade your hardware. #Person1#: You'd probably want a faster processor, more memory\n"]}],"source":["input = tokenizer(one_shot_prompt, return_tensors = 'pt')\n","output = tokenizer.decode(models.generate(input['input_ids'] , max_new_tokens = 50)[0], skip_special_tokens = True)\n","summary = data['test'][example_summarize_index]['summary']\n","print(summary)\n","print(output)"]},{"cell_type":"code","execution_count":47,"metadata":{"id":"jaVXt13Tzm6p","colab":{"base_uri":"https://localhost:8080/","height":157},"outputId":"6e434ead-fd9a-4fde-a4e3-1790451e5e71","executionInfo":{"status":"ok","timestamp":1723555902262,"user_tz":-330,"elapsed":544,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["\"\\n\\n    Summarize the following conversation:\\n    #Person1#: What time is it, Tom?\\n#Person2#: Just a minute. It's ten to nine by my watch.\\n#Person1#: Is it? I had no idea it was so late. I must be off now.\\n#Person2#: What's the hurry?\\n#Person1#: I must catch the nine-thirty train.\\n#Person2#: You've plenty of time yet. The railway station is very close. It won't take more than twenty minutes to get there.\\n\\n    What is going on?\\n    #Person1# is in a hurry to catch a train. Tom tells #Person1# there is plenty of time.\\n\\n    \\n\\n    Summarize the following conversation:\\n    #Person1#: May, do you mind helping me prepare for the picnic?\\n#Person2#: Sure. Have you checked the weather report?\\n#Person1#: Yes. It says it will be sunny all day. No sign of rain at all. This is your father's favorite sausage. Sandwiches for you and Daniel.\\n#Person2#: No, thanks Mom. I'd like some toast and chicken wings.\\n#Person1#: Okay. Please take some fruit salad and crackers for me.\\n#Person2#: Done. Oh, don't forget to take napkins disposable plates, cups and picnic blanket.\\n#Person1#: All set. May, can you help me take all these things to the living room?\\n#Person2#: Yes, madam.\\n#Person1#: Ask Daniel to give you a hand?\\n#Person2#: No, mom, I can manage it by myself. His help just causes more trouble.\\n\\n    What is going on?\\n    Mom asks May to help to prepare for the picnic and May agrees.\\n\\n    \\n\\n    Summarize the following conversation:\\n    #Person1#: OK, that's a cut! Let's start from the beginning, everyone.\\n#Person2#: What was the problem that time?\\n#Person1#: The feeling was all wrong, Mike. She is telling you that she doesn't want to see you any more, but I want to get more anger from you. You're acting hurt and sad, but that's not how your character would act in this situation.\\n#Person2#: But Jason and Laura have been together for three years. Don't you think his reaction would be one of both anger and sadness?\\n#Person1#: At this point, no. I think he would react the way most guys would, and then later on, we would see his real feelings.\\n#Person2#: I'm not so sure about that.\\n#Person1#: Let's try it my way, and you can see how you feel when you're saying your lines. After that, if it still doesn't feel right, we can try something else.\\n\\n    What is going on?\\n    #Person1# and Mike have a disagreement on how to act out a scene. #Person1# proposes that Mike can try to act in #Person1#'s way.\\n\\n    \\n\\n  Summarize the following conversation:\\n  #Person1#: Have you considered upgrading your system?\\n#Person2#: Yes, but I'm not sure what exactly I would need.\\n#Person1#: You could consider adding a painting program to your software. It would allow you to make up your own flyers and banners for advertising.\\n#Person2#: That would be a definite bonus.\\n#Person1#: You might also want to upgrade your hardware because it is pretty outdated now.\\n#Person2#: How can we do that?\\n#Person1#: You'd probably need a faster processor, to begin with. And you also need a more powerful hard disc, more memory and a faster modem. Do you have a CD-ROM drive?\\n#Person2#: No.\\n#Person1#: Then you might want to add a CD-ROM drive too, because most new software programs are coming out on Cds.\\n#Person2#: That sounds great. Thanks.\\n\\n  What is going on?\\n  \""],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":47}],"source":["example_full_index = [40,80,100]\n","example_summarize_index = 200\n","\n","few_shot_prompt = prompt(example_full_index,example_summarize_index)\n","few_shot_prompt"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"8VB0MRkC1ZHo","colab":{"base_uri":"https://localhost:8080/"},"outputId":"7d6a75b7-b46b-4fef-e144-d5988206e1ab","executionInfo":{"status":"ok","timestamp":1723532585489,"user_tz":-330,"elapsed":14746,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["Token indices sequence length is longer than the specified maximum sequence length for this model (930 > 512). Running this sequence through the model will result in indexing errors\n"]},{"output_type":"stream","name":"stdout","text":["#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","#Person1#: You'd probably want to upgrade your system. #Person2#: You'd probably want to upgrade your hardware. #Person1#: You'd probably want to upgrade your computer.\n"]}],"source":["input = tokenizer(few_shot_prompt, return_tensors = 'pt')\n","output = tokenizer.decode(models.generate(input['input_ids'] , max_new_tokens = 50)[0], skip_special_tokens = True)\n","summary = data['test'][example_summarize_index]['summary']\n","print(summary)\n","print(output)"]},{"cell_type":"code","execution_count":48,"metadata":{"id":"LeChwU6T1cqd","colab":{"base_uri":"https://localhost:8080/"},"outputId":"9116a332-d56d-45b1-dc21-e3509d97d40c","executionInfo":{"status":"ok","timestamp":1723555915171,"user_tz":-330,"elapsed":9495,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stderr","text":["Token indices sequence length is longer than the specified maximum sequence length for this model (930 > 512). Running this sequence through the model will result in indexing errors\n"]},{"output_type":"stream","name":"stdout","text":["#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","#Person1#: You'd probably want to upgrade your system. #Person2#: You'd probably want to upgrade your hardware. #Person1#: You'd probably want to upgrade your computer.\n"]}],"source":["from transformers import GenerationConfig\n","\n","generation_config = GenerationConfig(max_new_tokens = 50)\n","generation_config1 = GenerationConfig(max_new_tokens = 50, do_sample = True , temperature = 0.1)\n","generation_config2 = GenerationConfig(max_new_tokens = 50, do_sample = True , temperature = 0.5)\n","generation_config3 = GenerationConfig(max_new_tokens = 50, do_sample = True , temperature = 1.0)\n","\n","input = tokenizer(few_shot_prompt, return_tensors = 'pt')\n","output = tokenizer.decode(models.generate(input['input_ids'] , generation_config = generation_config)[0], skip_special_tokens = True)\n","summary = data['test'][example_summarize_index]['summary']\n","print(summary)\n","print(output)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C2z4nuuA1hHK","colab":{"base_uri":"https://localhost:8080/"},"outputId":"1e4a9c3c-71b4-4f4e-8cb9-81f05eed9f0d","executionInfo":{"status":"ok","timestamp":1723532605865,"user_tz":-330,"elapsed":8165,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","#Person1#: You'd probably want to upgrade your computer. #Person2#: You'd probably want to upgrade your hardware. #Person1#: You'd probably want to upgrade your computer.\n"]}],"source":["input = tokenizer(few_shot_prompt, return_tensors = 'pt')\n","output = tokenizer.decode(models.generate(input['input_ids'] , generation_config = generation_config1)[0], skip_special_tokens = True)\n","summary = data['test'][example_summarize_index]['summary']\n","print(summary)\n","print(output)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"m-oIykn71lED","colab":{"base_uri":"https://localhost:8080/"},"outputId":"214f9df2-6841-4a44-b18f-b70557f5b82c","executionInfo":{"status":"ok","timestamp":1723532612112,"user_tz":-330,"elapsed":6250,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","#Person1#: You need to upgrade your computer. #Person2#: OK.\n"]}],"source":["input = tokenizer(few_shot_prompt, return_tensors = 'pt')\n","output = tokenizer.decode(models.generate(input['input_ids'] , generation_config = generation_config2)[0], skip_special_tokens = True)\n","summary = data['test'][example_summarize_index]['summary']\n","print(summary)\n","print(output)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mRNax-eG1nMU","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b18a394b-2391-466e-efc2-94c1e03e945e","executionInfo":{"status":"ok","timestamp":1723532616911,"user_tz":-330,"elapsed":4803,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","During the presentation, the speaker wants to discuss what upgrades he has considered.\n"]}],"source":["input = tokenizer(few_shot_prompt, return_tensors = 'pt')\n","output = tokenizer.decode(models.generate(input['input_ids'] , generation_config = generation_config3)[0], skip_special_tokens = True)\n","summary = data['test'][example_summarize_index]['summary']\n","print(summary)\n","print(output)"]},{"cell_type":"code","execution_count":17,"metadata":{"id":"nkxPdHS31ozh","colab":{"base_uri":"https://localhost:8080/"},"outputId":"a2a89a38-dc81-4a73-d171-e2a02b01115c","executionInfo":{"status":"ok","timestamp":1723555652558,"user_tz":-330,"elapsed":10485,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting rouge_score\n","  Downloading rouge_score-0.1.2.tar.gz (17 kB)\n","  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n","Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.4.0)\n","Requirement already satisfied: nltk in /usr/local/lib/python3.10/dist-packages (from rouge_score) (3.8.1)\n","Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.26.4)\n","Requirement already satisfied: six>=1.14.0 in /usr/local/lib/python3.10/dist-packages (from rouge_score) (1.16.0)\n","Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (8.1.7)\n","Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (1.4.2)\n","Requirement already satisfied: regex>=2021.8.3 in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (2024.5.15)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from nltk->rouge_score) (4.66.5)\n","Building wheels for collected packages: rouge_score\n","  Building wheel for rouge_score (setup.py) ... \u001b[?25l\u001b[?25hdone\n","  Created wheel for rouge_score: filename=rouge_score-0.1.2-py3-none-any.whl size=24935 sha256=5491544d6e43536b09ff4fea1a8f7237b6ca80f264ff4178b7eea84e72126f91\n","  Stored in directory: /root/.cache/pip/wheels/5f/dd/89/461065a73be61a532ff8599a28e9beef17985c9e9c31e541b4\n","Successfully built rouge_score\n","Installing collected packages: rouge_score\n","Successfully installed rouge_score-0.1.2\n"]}],"source":["pip install rouge_score"]},{"cell_type":"code","execution_count":18,"metadata":{"id":"hv8kee2i1u55","colab":{"base_uri":"https://localhost:8080/"},"outputId":"482412a3-d05b-46d3-8467-f3e587b2be21","executionInfo":{"status":"ok","timestamp":1723555659913,"user_tz":-330,"elapsed":7387,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting evaluate\n","  Downloading evaluate-0.4.2-py3-none-any.whl.metadata (9.3 kB)\n","Requirement already satisfied: datasets>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from evaluate) (2.20.0)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from evaluate) (1.26.4)\n","Requirement already satisfied: dill in /usr/local/lib/python3.10/dist-packages (from evaluate) (0.3.8)\n","Requirement already 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(from requests>=2.19.0->evaluate) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests>=2.19.0->evaluate) (2024.7.4)\n","Requirement already satisfied: python-dateutil>=2.8.2 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2024.1)\n","Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->evaluate) (2024.1)\n","Requirement already satisfied: aiohappyeyeballs>=2.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) (2.3.4)\n","Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets>=2.0.0->evaluate) 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\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.1/84.1 kB\u001b[0m \u001b[31m3.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: evaluate\n","Successfully installed evaluate-0.4.2\n"]}],"source":["pip install evaluate"]},{"cell_type":"code","execution_count":19,"metadata":{"id":"RbsINxa11x8_","colab":{"base_uri":"https://localhost:8080/"},"outputId":"7af1eff7-fb78-4ee8-a4a9-d4358a82a554","executionInfo":{"status":"ok","timestamp":1723555666453,"user_tz":-330,"elapsed":6575,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting peft\n","  Downloading peft-0.12.0-py3-none-any.whl.metadata (13 kB)\n","Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft) (1.26.4)\n","Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft) (24.1)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft) (5.9.5)\n","Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft) (6.0.2)\n","Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft) (2.3.1+cu121)\n","Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft) (4.42.4)\n","Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft) (4.66.5)\n","Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from peft) (0.32.1)\n","Requirement already satisfied: safetensors in /usr/local/lib/python3.10/dist-packages (from peft) (0.4.4)\n","Requirement already satisfied: huggingface-hub>=0.17.0 in /usr/local/lib/python3.10/dist-packages (from peft) (0.23.5)\n","Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->peft) (3.15.4)\n","Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->peft) (2024.5.0)\n","Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->peft) (2.32.3)\n","Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub>=0.17.0->peft) (4.12.2)\n","Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (1.13.1)\n","Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.3)\n","Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (3.1.4)\n","Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.13.0->peft) (12.1.105)\n","Requirement already 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transformers->peft) (0.19.1)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13.0->peft) (2.1.5)\n","Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (3.3.2)\n","Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (3.7)\n","Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (2.0.7)\n","Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface-hub>=0.17.0->peft) (2024.7.4)\n","Requirement already satisfied: mpmath<1.4,>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13.0->peft) (1.3.0)\n","Downloading peft-0.12.0-py3-none-any.whl (296 kB)\n","\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m296.4/296.4 kB\u001b[0m \u001b[31m7.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hInstalling collected packages: peft\n","Successfully installed peft-0.12.0\n"]}],"source":["pip install peft"]},{"cell_type":"code","execution_count":20,"metadata":{"id":"bqSpWTSR109f","colab":{"base_uri":"https://localhost:8080/"},"outputId":"6a42fc2a-50b0-4287-8323-a75917a4aa9c","executionInfo":{"status":"ok","timestamp":1723555671972,"user_tz":-330,"elapsed":5543,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Collecting loralib\n","  Downloading loralib-0.1.2-py3-none-any.whl.metadata (15 kB)\n","Downloading loralib-0.1.2-py3-none-any.whl (10 kB)\n","Installing collected packages: loralib\n","Successfully installed loralib-0.1.2\n"]}],"source":["pip install loralib"]},{"cell_type":"code","execution_count":21,"metadata":{"id":"5mlSw52_13zv","executionInfo":{"status":"ok","timestamp":1723555690190,"user_tz":-330,"elapsed":18231,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[],"source":["from transformers import TrainingArguments, Trainer\n","import evaluate\n","import pandas as pd\n","import numpy as np\n","import time"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"fWqfWbiR2FVL","executionInfo":{"status":"ok","timestamp":1723555690193,"user_tz":-330,"elapsed":56,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[],"source":["#lets print number of trainable parameter\n","\n","def print_number_of_trainable_parameters(model):\n","  trainable_parameters = 0\n","  all_parameters = 0\n","  for _,param in models.named_parameters():\n","    all_parameters += param.numel()\n","    if param.requires_grad:\n","      trainable_parameters += param.numel()\n","  print(f\"Number of Trainable Parameters : {trainable_parameters} \\nNumber of All Parameters : {all_parameters}\")"]},{"cell_type":"code","execution_count":23,"metadata":{"id":"C-D9uxFU2JFb","colab":{"base_uri":"https://localhost:8080/"},"outputId":"5319c9fc-a680-49eb-a1fe-eda5221c62be","executionInfo":{"status":"ok","timestamp":1723555690194,"user_tz":-330,"elapsed":43,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Number of Trainable Parameters : 247577856 \n","Number of All Parameters : 247577856\n"]}],"source":["print_number_of_trainable_parameters(models)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"HNrKgkzI2NkG","colab":{"base_uri":"https://localhost:8080/"},"outputId":"c8f0b16e-87bd-473b-d086-1ed16a004ea0","executionInfo":{"status":"ok","timestamp":1723532660550,"user_tz":-330,"elapsed":13,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Tue Aug 13 07:04:19 2024       \n","+---------------------------------------------------------------------------------------+\n","| NVIDIA-SMI 535.104.05             Driver Version: 535.104.05   CUDA Version: 12.2     |\n","|-----------------------------------------+----------------------+----------------------+\n","| GPU  Name                 Persistence-M | Bus-Id        Disp.A | Volatile Uncorr. ECC |\n","| Fan  Temp   Perf          Pwr:Usage/Cap |         Memory-Usage | GPU-Util  Compute M. |\n","|                                         |                      |               MIG M. |\n","|=========================================+======================+======================|\n","|   0  Tesla T4                       Off | 00000000:00:04.0 Off |                    0 |\n","| N/A   45C    P8               9W /  70W |      0MiB / 15360MiB |      0%      Default |\n","|                                         |                      |                  N/A |\n","+-----------------------------------------+----------------------+----------------------+\n","                                                                                         \n","+---------------------------------------------------------------------------------------+\n","| Processes:                                                                            |\n","|  GPU   GI   CI        PID   Type   Process name                            GPU Memory |\n","|        ID   ID                                                             Usage      |\n","|=======================================================================================|\n","|  No running processes found                                                           |\n","+---------------------------------------------------------------------------------------+\n"]}],"source":["!nvidia-smi"]},{"cell_type":"code","execution_count":24,"metadata":{"id":"I45G-PDW2R4-","executionInfo":{"status":"ok","timestamp":1723555690194,"user_tz":-330,"elapsed":35,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[],"source":["import os\n","from datetime import datetime\n","import argparse\n","import torch.multiprocessing as mp\n","import torchvision\n","import torchvision.transforms as transforms\n","import torch\n","import torch.nn as nn\n","import torch.distributed as dist\n"]},{"cell_type":"code","execution_count":26,"metadata":{"id":"hxmhcZC22qnW","colab":{"base_uri":"https://localhost:8080/"},"outputId":"b7b53f3e-2b10-4f37-b3d2-248d77060f66","executionInfo":{"status":"ok","timestamp":1723555711605,"user_tz":-330,"elapsed":463,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["cpu\n"]}],"source":["device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n","print(device)"]},{"cell_type":"code","execution_count":27,"metadata":{"id":"bU0dii2y3IWK","executionInfo":{"status":"ok","timestamp":1723555712989,"user_tz":-330,"elapsed":695,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[],"source":["models = models.to(device)"]},{"cell_type":"code","execution_count":28,"metadata":{"id":"o8Z1TqzI3d7H","colab":{"base_uri":"https://localhost:8080/","height":480,"referenced_widgets":["53d8d7bddd6842958428b93798d65519","99de12a3e5e043c28cefafc55eddb5ea","ea0e620f39754f73bc903b636e4cfd4d","bff19b533d3d4acd89a6d43aaefacaf4","d7f4e36aab334ad188b4489f21821431","2cc1e25134984a9a9c0fc5a47556face","8864dd0bfcd74846bc1ed42cb97dd455","5ab71d3dc60e4d1785c7ad76a5f2de1e","ec5dd71742e549b69bf774b30cdfff66","ed85205d578a447eb5b32e44c9119a87","b010e667f894440885695ff3394a4bc2","984eac4f2b69403ba1734ab097f62259","2abbe89859174233878a59bad2cacec8","c17da2290288414cb7fab0d7b02a5f28","b1913d1bd5124101a4756b0be4d5bd21","cd709c41d23b407185d2bdcb2f1d1f17","1627bbf8d56b474cab646401bd265f0a","c974d23b1bfd4c208fd47d68533f8312","265839dba6974e228435a015e63eaf16","45f0aa9091d6465ebd6272eb243cf926","cbd3f6c9d5f8426e80f197221901b133","06b21daf7f35447f972991363a8308b4","7c1f33b260d54c90bf9b59d291407c29","fd56839aa4f7459397d1ade0fd8483cc","0a6fe20a212040ff9149e524d19085d4","1fd00af7e3144737b96626268a898941","0dd0e996f633431da16b39cde7f99d01","9b1b8be75d6a4ed883057e63067f00cc","c943153535d6459aa18ba9717d0dfc13","4e531a283b19487caf7a5e53e68dbd2d","f2b260a39b654bb0bcb93df27ad70a02","7b8e77b8b2a14bccbca3ace9519cca30","e7607c1d0ba54e82b00460a514169b9f"]},"outputId":"ab97654f-fa40-4a85-96b0-0779ce1110b2","executionInfo":{"status":"ok","timestamp":1723555759512,"user_tz":-330,"elapsed":45078,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/12460 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"53d8d7bddd6842958428b93798d65519"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["<ipython-input-28-3666f0c13f95>:6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n","  example['input_ids'] = torch.tensor(tokenizer(prompt, padding='max_length', truncation=True, return_tensors='pt').input_ids).to(device).long()\n","<ipython-input-28-3666f0c13f95>:7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n","  example['label_ids'] = torch.tensor(tokenizer(example['summary'], padding='max_length', truncation=True, return_tensors='pt').input_ids).to(device).long()\n"]},{"output_type":"stream","name":"stdout","text":["torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n","torch.int64\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/500 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"984eac4f2b69403ba1734ab097f62259"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["torch.int64\n"]},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1500 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"7c1f33b260d54c90bf9b59d291407c29"}},"metadata":{}},{"output_type":"stream","name":"stdout","text":["torch.int64\n","torch.int64\n"]}],"source":["def tokenize_function(example):\n","    start_prompt = 'Summarize the following conversation:'\n","    end_prompt = '\\n\\nSummary:'\n","\n","    prompt = [start_prompt + dialogue + end_prompt for dialogue in example['dialogue']]\n","    example['input_ids'] = torch.tensor(tokenizer(prompt, padding='max_length', truncation=True, return_tensors='pt').input_ids).to(device).long()\n","    example['label_ids'] = torch.tensor(tokenizer(example['summary'], padding='max_length', truncation=True, return_tensors='pt').input_ids).to(device).long()\n","\n","    print(example['input_ids'].dtype)\n","    return example\n","\n","tokenized_dataset = data.map(tokenize_function, batched=True)\n","tokenized_dataset = tokenized_dataset.remove_columns(['id','topic','dialogue','summary'])\n"]},{"cell_type":"markdown","metadata":{"id":"i_vB5icqbKJ5"},"source":[]},{"cell_type":"code","execution_count":29,"metadata":{"id":"m4D8hfur3tW2","colab":{"base_uri":"https://localhost:8080/","height":424},"outputId":"2f47cd8b-f846-43df-e85a-d78832ab1997","executionInfo":{"status":"ok","timestamp":1723555770336,"user_tz":-330,"elapsed":10844,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                               input_ids  \\\n","0      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","1      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","2      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","3      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","4      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","...                                                  ...   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7, 3912, 151...  \n","12459  [1713, 345, 13515, 536, 4663, 987, 7, 21, 1713...  \n","\n","[12460 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-78e8056c-bf49-40a3-9998-a3c587ad162d\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>input_ids</th>\n","      <th>label_ids</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>[12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...</td>\n","      <td>[1363, 5, 3931, 31, 7, 652, 3, 9, 691, 18, 413...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>[12198, 1635, 1737, 8, 826, 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Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids', 'label_ids'],\n","        num_rows: 12460\n","    })\n","    validation: Dataset({\n","        features: ['input_ids', 'label_ids'],\n","        num_rows: 500\n","    })\n","    test: Dataset({\n","        features: ['input_ids', 'label_ids'],\n","        num_rows: 1500\n","    })\n","})"]},"metadata":{},"execution_count":30}],"source":["tokenized_dataset"]},{"cell_type":"code","execution_count":31,"metadata":{"id":"Y9IvO7Lf34aD","colab":{"base_uri":"https://localhost:8080/","height":424},"outputId":"4359d287-6875-47fb-b6d7-5d08dda09502","executionInfo":{"status":"ok","timestamp":1723555778467,"user_tz":-330,"elapsed":8150,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["                                               input_ids  \\\n","0      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","1      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","2      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","3      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","4      [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","...                                                  ...   \n","12455  [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","12456  [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","12457  [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","12458  [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","12459  [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...   \n","\n","                                               label_ids  \n","0      [1363, 5, 3931, 31, 7, 652, 3, 9, 691, 18, 413...  \n","1      [8667, 13156, 1217, 11066, 63, 21, 112, 12956,...  \n","2      [1713, 345, 13515, 536, 4663, 31, 7, 479, 21, ...  \n","3      [1713, 345, 13515, 536, 4663, 31, 7, 12603, 25...  \n","4      [1534, 8654, 5484, 7, 2504, 8511, 23, 12, 2595...  \n","...                                                  ...  \n","12455  [8331, 301, 53, 1432, 7, 1363, 5, 1862, 95, 11...  \n","12456  [1713, 345, 13515, 536, 4663, 11, 1713, 345, 1...  \n","12457  [1713, 345, 13515, 357, 4663, 3170, 7, 3, 9, 4...  \n","12458  [1713, 345, 13515, 357, 4663, 31, 7, 3912, 151...  \n","12459  [1713, 345, 13515, 536, 4663, 987, 7, 21, 1713...  \n","\n","[12460 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-6279ee81-a71e-4a0f-ad3a-5f40ea6651bb\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr 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34...</td>\n","      <td>[1534, 8654, 5484, 7, 2504, 8511, 23, 12, 2595...</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>12455</th>\n","      <td>[12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...</td>\n","      <td>[8331, 301, 53, 1432, 7, 1363, 5, 1862, 95, 11...</td>\n","    </tr>\n","    <tr>\n","      <th>12456</th>\n","      <td>[12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...</td>\n","      <td>[1713, 345, 13515, 536, 4663, 11, 1713, 345, 1...</td>\n","    </tr>\n","    <tr>\n","      <th>12457</th>\n","      <td>[12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...</td>\n","      <td>[1713, 345, 13515, 357, 4663, 3170, 7, 3, 9, 4...</td>\n","    </tr>\n","    <tr>\n","      <th>12458</th>\n","      <td>[12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...</td>\n","      <td>[1713, 345, 13515, 357, 4663, 31, 7, 3912, 151...</td>\n","    </tr>\n","    <tr>\n","      <th>12459</th>\n","      <td>[12198, 1635, 1737, 8, 826, 3634, 10, 4663, 34...</td>\n","      <td>[1713, 345, 13515, 536, 4663, 987, 7, 21, 1713...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>12460 rows × 2 columns</p>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6279ee81-a71e-4a0f-ad3a-5f40ea6651bb')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    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Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Data Type of input_ids: <class 'list'>\n","Length of input_ids list: 512\n","Example input_ids: [12198, 1635, 1737, 8, 826, 3634, 10, 4663, 345, 13515, 536, 4663, 10, 2018, 6, 1363, 5, 3931, 5, 27, 31, 51, 7582, 12833, 77, 7, 5, 1615, 33, 25, 270, 469, 58, 1713, 345, 13515, 357, 4663, 10, 27, 435, 34, 133, 36, 3, 9, 207, 800, 12, 129, 3, 9, 691, 18, 413, 5, 1713, 345, 13515, 536, 4663, 10, 2163, 6, 168, 6, 25, 43, 29, 31, 17, 141, 80, 21, 305, 203, 5, 148, 225, 43, 80, 334, 215, 5, 1713, 345, 13515, 357, 4663, 10, 27, 214, 5, 27, 2320, 38, 307, 38, 132, 19, 1327, 1786, 6, 572, 281, 217, 8, 2472, 58, 1713, 345, 13515, 536, 4663, 10, 1548, 6, 8, 200, 194, 12, 1792, 2261, 21154, 19, 12, 253, 91, 81, 135, 778, 5, 264, 653, 12, 369, 44, 709, 728, 3, 9, 215, 21, 39, 293, 207, 5, 1713, 345, 13515, 357, 4663, 10, 8872, 5, 1713, 345, 13515, 536, 4663, 10, 1563, 140, 217, 270, 5, 696, 2053, 11, 11581, 320, 1399, 5, 2321, 3, 9, 1659, 6522, 6, 754, 5, 531, 25, 7269, 6, 1363, 5, 3931, 58, 1713, 345, 13515, 357, 4663, 10, 2163, 5, 1713, 345, 13515, 536, 4663, 10, 14627, 53, 19, 8, 1374, 1137, 13, 5084, 1874, 11, 842, 1994, 6, 25, 214, 5, 148, 310, 225, 10399, 5, 1713, 345, 13515, 357, 4663, 10, 27, 31, 162, 1971, 3986, 13, 648, 6, 68, 27, 131, 54, 31, 17, 1727, 12, 4583, 8, 7386, 5, 1713, 345, 13515, 536, 4663, 10, 1548, 6, 62, 43, 2287, 11, 128, 11208, 24, 429, 199, 5, 27, 31, 195, 428, 25, 72, 251, 274, 25, 1175, 5, 1713, 345, 13515, 357, 4663, 10, 8872, 6, 2049, 2472, 5, 20698, 10, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n"]}],"source":["# Example to check the format of input_ids in the training dataset\n","training_sample = tokenized_dataset['train'][0]  # Get the first sample from the training set\n","\n","# Access and print the input_ids from the sample\n","input_ids_sample = training_sample['input_ids']\n","print(f\"Data Type of input_ids: {type(input_ids_sample)}\")\n","\n","# Check the length of the list (number of tokens in input_ids)\n","print(f\"Length of input_ids list: {len(input_ids_sample)}\")\n","\n","# Print an example token sequence from the input_ids list\n","print(f\"Example input_ids: {input_ids_sample}\")\n","\n"]},{"cell_type":"code","execution_count":33,"metadata":{"id":"d-7evZLFhV9i","executionInfo":{"status":"ok","timestamp":1723555781657,"user_tz":-330,"elapsed":3212,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[],"source":["pip install accelerate>=0.20.1"]},{"cell_type":"code","execution_count":34,"metadata":{"id":"qopXQeSg38UU","colab":{"base_uri":"https://localhost:8080/"},"outputId":"6d2b9cac-772d-4a5b-a240-1c89e5cf5980","executionInfo":{"status":"ok","timestamp":1723555781658,"user_tz":-330,"elapsed":27,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["Shape of Dataset:\n","Shape of train dataset : (12460, 2)\n","Shape of train dataset : (500, 2)\n","Shape of train dataset : (1500, 2)\n","DatasetDict({\n","    train: Dataset({\n","        features: ['input_ids', 'label_ids'],\n","        num_rows: 12460\n","    })\n","    validation: Dataset({\n","        features: ['input_ids', 'label_ids'],\n","        num_rows: 500\n","    })\n","    test: Dataset({\n","        features: ['input_ids', 'label_ids'],\n","        num_rows: 1500\n","    })\n","})\n"]}],"source":["print(f\"Shape of Dataset:\")\n","print(f\"Shape of train dataset : {tokenized_dataset['train'].shape}\")\n","print(f\"Shape of train dataset : {tokenized_dataset['validation'].shape}\")\n","print(f\"Shape of train dataset : {tokenized_dataset['test'].shape}\")\n","\n","print(tokenized_dataset)"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount(\"/content/drive/\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ta9ZvkbAhuMl","executionInfo":{"status":"ok","timestamp":1723555804388,"user_tz":-330,"elapsed":22748,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"5613d2a1-0e01-4d4d-e64d-e8d5f8cfe75d"},"execution_count":35,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/drive/\n"]}]},{"cell_type":"code","source":["dataset_dir = \"/content/drive/MyDrive/Generative AI with LLMs/Flan_T5_Model_Results&Weights\""],"metadata":{"id":"FrZAPHmThsOA","executionInfo":{"status":"ok","timestamp":1723555804389,"user_tz":-330,"elapsed":24,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"execution_count":36,"outputs":[]},{"cell_type":"markdown","source":[],"metadata":{"id":"krufMuirH-pG"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"JmVeTZKdcXHL","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1723532717631,"user_tz":-330,"elapsed":6,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"cbd47c89-d49f-4e87-a2b9-682a8d386ecd"},"outputs":[{"output_type":"stream","name":"stderr","text":["max_steps is given, it will override any value given in num_train_epochs\n"]}],"source":["output_dir = f'{dataset_dir}/dialogue-summary-training-{str(int(time.time()))}'\n","\n","training_args = TrainingArguments(\n","    output_dir=output_dir,\n","    learning_rate=1e-5,\n","    num_train_epochs=1,\n","    weight_decay=0.01,\n","    logging_steps=1,\n","    max_steps=1,\n","    per_device_train_batch_size=2,  # Small batch size\n","    gradient_accumulation_steps=4,  # Accumulate gradients over 4 steps\n","    report_to=None,\n",")\n","\n","trainer = Trainer(\n","    model=models,\n","    args=training_args,\n","    train_dataset=tokenized_dataset['train'],\n","    eval_dataset=tokenized_dataset['validation']\n",")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pktZo9Rr4R0_","colab":{"base_uri":"https://localhost:8080/","height":158},"outputId":"49a6c711-3c5f-460c-df77-e9bfc66433e6","executionInfo":{"status":"ok","timestamp":1723532776957,"user_tz":-330,"elapsed":57513,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1/1 00:47, Epoch 0/1]\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>48.900300</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=1, training_loss=48.9002571105957, metrics={'train_runtime': 57.3249, 'train_samples_per_second': 0.14, 'train_steps_per_second': 0.017, 'total_flos': 5478058819584.0, 'train_loss': 48.9002571105957, 'epoch': 0.0006420545746388443})"]},"metadata":{},"execution_count":58}],"source":["trainer.train()"]},{"cell_type":"code","execution_count":37,"metadata":{"id":"wzZWUULF4WFt","executionInfo":{"status":"ok","timestamp":1723555804390,"user_tz":-330,"elapsed":17,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[],"source":["instruct_model_paths = f'{dataset_dir}/instruct_model'\n","\n","# trainer.save_model(instruct_model_paths)"]},{"cell_type":"code","execution_count":38,"metadata":{"id":"-5tWvEt7oLr_","colab":{"base_uri":"https://localhost:8080/"},"outputId":"3270a640-3c0d-41a7-c621-e23301f45c22","executionInfo":{"status":"ok","timestamp":1723555823243,"user_tz":-330,"elapsed":18867,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"execute_result","data":{"text/plain":["T5ForConditionalGeneration(\n","  (shared): Embedding(32128, 768)\n","  (encoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (decoder): T5Stack(\n","    (embed_tokens): Embedding(32128, 768)\n","    (block): ModuleList(\n","      (0): T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","              (relative_attention_bias): Embedding(32, 12)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","      (1-11): 11 x T5Block(\n","        (layer): ModuleList(\n","          (0): T5LayerSelfAttention(\n","            (SelfAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (1): T5LayerCrossAttention(\n","            (EncDecAttention): T5Attention(\n","              (q): Linear(in_features=768, out_features=768, bias=False)\n","              (k): Linear(in_features=768, out_features=768, bias=False)\n","              (v): Linear(in_features=768, out_features=768, bias=False)\n","              (o): Linear(in_features=768, out_features=768, bias=False)\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","          (2): T5LayerFF(\n","            (DenseReluDense): T5DenseGatedActDense(\n","              (wi_0): Linear(in_features=768, out_features=2048, bias=False)\n","              (wi_1): Linear(in_features=768, out_features=2048, bias=False)\n","              (wo): Linear(in_features=2048, out_features=768, bias=False)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","              (act): NewGELUActivation()\n","            )\n","            (layer_norm): T5LayerNorm()\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (final_layer_norm): T5LayerNorm()\n","    (dropout): Dropout(p=0.1, inplace=False)\n","  )\n","  (lm_head): Linear(in_features=768, out_features=32128, bias=False)\n",")"]},"metadata":{},"execution_count":38}],"source":["instruct_model = AutoModelForSeq2SeqLM.from_pretrained(instruct_model_paths)\n","instruct_model"]},{"cell_type":"code","execution_count":39,"metadata":{"id":"fh4VAWg0sPjo","executionInfo":{"status":"ok","timestamp":1723555827512,"user_tz":-330,"elapsed":4296,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[],"source":["orignal_model = AutoModelForSeq2SeqLM.from_pretrained('google/flan-t5-base')"]},{"cell_type":"code","execution_count":49,"metadata":{"id":"c43HLXkBoXWs","colab":{"base_uri":"https://localhost:8080/"},"outputId":"be29fe59-5267-499b-d262-1db71cf5c301","executionInfo":{"status":"ok","timestamp":1723555924140,"user_tz":-330,"elapsed":6597,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"outputs":[{"output_type":"stream","name":"stdout","text":["--------------------------------------\n","BASLINE HUMAN SUMMARY\n","\n","#Person1# and Mike have a disagreement on how to act out a scene. #Person1# proposes that Mike can try to act in #Person1#'s way.\n","--------------------------------------\n","ORIGNAL_MODEL SUMMARY\n","\n","Identify the problem.\n","--------------------------------------\n","ISTRUCT_FULL_FINE_TUNED MODEL SUMMARY\n","Identify the problem.\n"]}],"source":["index = 100\n","\n","dialogue = data['test'][index]['dialogue']\n","human_summary = data['test'][index]['summary']\n","\n","prompt = f\"\"\"\n","Summarize the following :\n","{dialogue}\n","\n","Summary:\n"," \"\"\"\n","\n","input_ids = tokenizer(prompt, truncation = True , return_tensors = 'pt').input_ids\n","orignal_model_output = orignal_model.generate(input_ids, generation_config = GenerationConfig(max_new_tokens = 50,do_sample = True , temperature = 0.1))\n","orignal_model_text = tokenizer.decode(orignal_model_output[0] , skip_special_tokens = True)\n","\n","instruct_model_output = instruct_model.generate(input_ids, generation_config = GenerationConfig(max_new_tokens = 50,do_sample = True , temperature = 0.1))\n","instruct_model_text = tokenizer.decode(instruct_model_output[0] , skip_special_tokens = True)\n","\n","dash_line = \"--------------------------------------\"\n","print(dash_line)\n","print('BASLINE HUMAN SUMMARY\\n')\n","print(human_summary)\n","print(dash_line)\n","print('ORIGNAL_MODEL SUMMARY\\n')\n","print(orignal_model_text)\n","print(dash_line)\n","print('ISTRUCT_FULL_FINE_TUNED MODEL SUMMARY')\n","print(instruct_model_text)\n"]},{"cell_type":"markdown","source":["# Evaluation"],"metadata":{"id":"9o0HPPS7Qp3W"}},{"cell_type":"code","source":["rouge = evaluate.load('rouge')"],"metadata":{"id":"8Nx0n_bPQsJ-","outputId":"21c5e9f1-2d38-4ee2-995c-184370aadeaf","colab":{"base_uri":"https://localhost:8080/","height":49,"referenced_widgets":["a3532670c24146f6b4185fcecf13604b","ea107f279e064568bb5095692fc679cb","1798842f52234934a57e51fcb855e1f8","4b75d3abaa9846fcb255cc83beed2207","830cc4d2736a47ddb61537019b000da8","a662f55180244a0b802fe61d9303bbee","fb7a7a4c098e45ceb5825cd1cab4c446","99f10cf7857148e1952ee6f2a2ea3d16","210be2e0a4ae412b9f7214516f1234b3","ffacecf62fb24abda5b5fa4e01a3fc77","d0f542d780e04d7b8e52c8f2fdcede4b"]},"executionInfo":{"status":"ok","timestamp":1723555991551,"user_tz":-330,"elapsed":1752,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"execution_count":50,"outputs":[{"output_type":"display_data","data":{"text/plain":["Downloading builder script:   0%|          | 0.00/6.27k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"a3532670c24146f6b4185fcecf13604b"}},"metadata":{}}]},{"cell_type":"code","source":["dialogues = data['test'][0:10]['dialogue']\n","summary = data['test'][0:10]['summary']\n","\n","orignal_model_summaries = []\n","instruct_model_summaries = []\n","\n","for _,dialogue in enumerate(dialogues):\n","  prompt = f\"\"\"\n","  Summarize the following:\n","  {dialogue}\n","\n","  Summary:\n","   \"\"\"\n","  input_ids = tokenizer(prompt, truncation = True , return_tensors = 'pt').input_ids\n","\n","  orignal_model_output = orignal_model.generate(input_ids, generation_config = GenerationConfig(max_new_tokens = 50,do_sample = True , temperature = 0.1))\n","  orignal_model_text = tokenizer.decode(orignal_model_output[0] , skip_special_tokens = True)\n","  orignal_model_summaries.append(orignal_model_text)\n","\n","  instruct_model_output = instruct_model.generate(input_ids, generation_config = GenerationConfig(max_new_tokens = 50,do_sample = True , temperature = 0.1))\n","  instruct_model_text = tokenizer.decode(instruct_model_output[0] , skip_special_tokens = True)\n","  instruct_model_summaries.append(instruct_model_text)\n","\n","zipped_summaries = list(zip(summary,orignal_model_summaries,instruct_model_summaries))\n","\n","df = pd.DataFrame(zipped_summaries , columns = ['human_baseline_summaries','orignal_model_summaries' , 'instruct_model_summaries'])\n","df\n"],"metadata":{"id":"u6TRCfGlQvQq","outputId":"908f191b-603e-4896-927c-95b8721265bf","colab":{"base_uri":"https://localhost:8080/","height":536},"executionInfo":{"status":"ok","timestamp":1723532903758,"user_tz":-330,"elapsed":68063,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                            human_baseline_summaries  \\\n","0  Ms. Dawson helps #Person1# to write a memo to ...   \n","1  In order to prevent employees from wasting tim...   \n","2  Ms. Dawson takes a dictation for #Person1# abo...   \n","3  #Person2# arrives late because of traffic jam....   \n","4  #Person2# decides to follow #Person1#'s sugges...   \n","5  #Person2# complains to #Person1# about the tra...   \n","6  #Person1# tells Kate that Masha and Hero get d...   \n","7  #Person1# tells Kate that Masha and Hero are g...   \n","8  #Person1# and Kate talk about the divorce betw...   \n","9  #Person1# and Brian are at the birthday party ...   \n","\n","                             orignal_model_summaries  \\\n","0  The following memo is to be distributed to all...   \n","1  This memo is to be distributed to all employee...   \n","2  This memo is to be distributed to all employee...   \n","3  Taking public transport to work is a good idea...   \n","4  The traffic jam has been a problem for Person1...   \n","5  Taking public transport to work is a good idea...   \n","6               Masha and Hero are getting divorced.   \n","7               Masha and Hero are getting divorced.   \n","8               Masha and Hero are getting divorced.   \n","9  #Person1#: Happy Birthday, Brian. #Person2#: I...   \n","\n","                            instruct_model_summaries  \n","0  This memo is to be distributed to all employee...  \n","1  This memo is to be distributed to all employee...  \n","2  This memo is to be distributed to all employee...  \n","3  Taking public transport to work is a good idea...  \n","4  Taking public transport to work is a good idea...  \n","5  Taking public transport to work is a good idea...  \n","6               Masha and Hero are getting divorced.  \n","7               Masha and Hero are getting divorced.  \n","8               Masha and Hero are getting divorced.  \n","9  #Person1#: Happy Birthday, Brian. #Person2#: I...  "],"text/html":["\n","  <div id=\"df-5c5d34ec-680c-47fb-ac5a-2e505232e48f\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>human_baseline_summaries</th>\n","      <th>orignal_model_summaries</th>\n","      <th>instruct_model_summaries</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Ms. Dawson helps #Person1# to write a memo to ...</td>\n","      <td>The following memo is to be distributed to all...</td>\n","      <td>This memo is to be distributed to all employee...</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>In order to prevent employees from wasting tim...</td>\n","      <td>This memo is to be distributed to all employee...</td>\n","      <td>This memo is to be distributed to all employee...</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Ms. Dawson takes a dictation for #Person1# abo...</td>\n","      <td>This memo is to be distributed to all employee...</td>\n","      <td>This memo is to be distributed to all employee...</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>#Person2# arrives late because of traffic jam....</td>\n","      <td>Taking public transport to work is a good idea...</td>\n","      <td>Taking public transport to work is a good idea...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>#Person2# decides to follow #Person1#'s sugges...</td>\n","      <td>The traffic jam has been a problem for Person1...</td>\n","      <td>Taking public transport to work is a good idea...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>#Person2# complains to #Person1# about the tra...</td>\n","      <td>Taking public transport to work is a good idea...</td>\n","      <td>Taking public transport to work is a good idea...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>#Person1# tells Kate that Masha and Hero get d...</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>#Person1# tells Kate that Masha and Hero are g...</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>#Person1# and Kate talk about the divorce betw...</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>#Person1# and Brian are at the birthday party ...</td>\n","      <td>#Person1#: Happy Birthday, Brian. #Person2#: I...</td>\n","      <td>#Person1#: Happy Birthday, Brian. #Person2#: I...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5c5d34ec-680c-47fb-ac5a-2e505232e48f')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n","    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n","  </svg>\n","    </button>\n","\n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      gap: 12px;\n","    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Kate feels surprised because she thought they are well matched\",\n          \"In order to prevent employees from wasting time on Instant Message programs, #Person1# decides to terminate the use of those programs and asks Ms. Dawson to send out a memo to all employees by the afternoon.\",\n          \"#Person2# complains to #Person1# about the traffic jam, #Person1# suggests quitting driving and taking public transportation instead.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"orignal_model_summaries\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"The following memo is to be distributed to all employees by this afternoon:\",\n          \"This memo is to be distributed to all employees by this afternoon.\",\n          \"#Person1#: Happy Birthday, Brian. #Person2#: I'm so happy you remember. #Person1#: This is a really wonderful party. #Person2#: You look very pretty today\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"instruct_model_summaries\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 6,\n        \"samples\": [\n          \"This memo is to be distributed to all employees by this afternoon.\",\n          \"Taking public transport to work is a good idea for Person1 because it's a good way to get to work.\",\n          \"#Person1#: Happy Birthday, Brian. #Person2#: I'm so happy you remember. #Person1#: This is really wonderful party. #Person2#: Yes, you are always popular with\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":64}]},{"cell_type":"code","source":["original_model_results = rouge.compute(\n","    predictions=orignal_model_summaries,\n","    references=summary[0:len(orignal_model_summaries)],\n","    use_aggregator=True,\n","    use_stemmer=True,\n",")\n","\n","instruct_model_results = rouge.compute(\n","    predictions=instruct_model_summaries,\n","    references=summary[0:len(instruct_model_summaries)],\n","    use_aggregator=True,\n","    use_stemmer=True,\n",")\n","\n","print('ORIGINAL MODEL:')\n","print(original_model_results)\n","print('INSTRUCT MODEL:')\n","print(instruct_model_results)"],"metadata":{"id":"NdpZe87xSK0V","colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"status":"ok","timestamp":1723532983691,"user_tz":-330,"elapsed":1708,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"417a7611-6cf2-4df9-9d97-47903b7227d2"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["ORIGINAL MODEL:\n","{'rouge1': 0.29689599155001634, 'rouge2': 0.108141392718275, 'rougeL': 0.22375687199630606, 'rougeLsum': 0.22658199987147357}\n","INSTRUCT MODEL:\n","{'rouge1': 0.32384928658765866, 'rouge2': 0.12427592967197483, 'rougeL': 0.2400478376059771, 'rougeLsum': 0.2444273319273319}\n"]}]},{"cell_type":"code","source":["print(\"Absolute percentage improvement of INSTRUCT MODEL over HUMAN BASELINE\")\n","\n","improvement = (np.array(list(instruct_model_results.values())) - np.array(list(original_model_results.values())))\n","for key, value in zip(instruct_model_results.keys(), improvement):\n","    print(f'{key}: {value*100:.2f}%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"o874nY-4kjQd","executionInfo":{"status":"ok","timestamp":1723533026852,"user_tz":-330,"elapsed":475,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"4616158d-7dc0-4d3d-c116-2eeba94e4916"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Absolute percentage improvement of INSTRUCT MODEL over HUMAN BASELINE\n","rouge1: 2.70%\n","rouge2: 1.61%\n","rougeL: 1.63%\n","rougeLsum: 1.78%\n"]}]},{"cell_type":"markdown","source":["##PEFT-LORA Finetuning"],"metadata":{"id":"rBN3KEWBlD0k"}},{"cell_type":"code","source":["from peft import LoraConfig, get_peft_model, TaskType\n","\n","lora_config = LoraConfig(\n","    r=32, # Rank\n","    lora_alpha=32,\n","    target_modules=[\"q\", \"v\"],\n","    lora_dropout=0.05,\n","    bias=\"none\",\n","    task_type=TaskType.SEQ_2_SEQ_LM # FLAN-T5\n",")"],"metadata":{"id":"TXMfvOf8kuGa","executionInfo":{"status":"ok","timestamp":1723555999274,"user_tz":-330,"elapsed":731,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"execution_count":51,"outputs":[]},{"cell_type":"code","source":["def print_number_of_trainable_parameters(model):\n","    trainable_model_params = 0\n","    all_model_params = 0\n","    for _, param in model.named_parameters():\n","        all_model_params += param.numel()\n","        if param.requires_grad:\n","            trainable_model_params += param.numel()\n","    return f\"trainable model parameters: {trainable_model_params}\\nall model parameters: {all_model_params}\\npercentage of trainable model parameters: {100 * trainable_model_params / all_model_params:.2f}%\""],"metadata":{"id":"5CmE7pu8l04f","executionInfo":{"status":"ok","timestamp":1723555999274,"user_tz":-330,"elapsed":2,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"execution_count":52,"outputs":[]},{"cell_type":"code","source":["peft_model = get_peft_model(models,\n","                            lora_config)\n","print(print_number_of_trainable_parameters(peft_model))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"8sqhoMMClJFO","executionInfo":{"status":"ok","timestamp":1723556000579,"user_tz":-330,"elapsed":715,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"eb7a371b-d449-45cb-a925-bd7ee2d407d7"},"execution_count":53,"outputs":[{"output_type":"stream","name":"stdout","text":["trainable model parameters: 3538944\n","all model parameters: 251116800\n","percentage of trainable model parameters: 1.41%\n"]}]},{"cell_type":"code","source":["output_dir = f'{dataset_dir}/peft-dialogue-summary-training-{str(int(time.time()))}'\n","\n","peft_training_args = TrainingArguments(\n","    output_dir=output_dir,\n","    auto_find_batch_size=True,\n","    learning_rate=1e-3, # Higher learning rate than full fine-tuning.\n","    num_train_epochs=1,\n","    logging_steps=1,\n","    max_steps=1,\n","    per_device_train_batch_size=2,  # Small batch size\n","    gradient_accumulation_steps=4,  # Accumulate gradients over 4 steps\n",")\n","\n","peft_trainer = Trainer(\n","    model=peft_model,\n","    args=training_args,\n","    train_dataset=tokenized_dataset['train'],\n","    eval_dataset=tokenized_dataset['validation']\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"e09CcAexlXFe","executionInfo":{"status":"ok","timestamp":1723533780157,"user_tz":-330,"elapsed":484,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"d84f52a7-b69c-487a-ac8c-adc8adfa2bdf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stderr","text":["max_steps is given, it will override any value given in num_train_epochs\n"]}]},{"cell_type":"code","source":["peft_trainer.train()\n","\n","peft_model_path=f\"{dataset_dir}/peft-dialogue-summary-checkpoint-local\"\n","\n","peft_trainer.model.save_pretrained(peft_model_path)\n","tokenizer.save_pretrained(peft_model_path)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":280},"id":"I4_FaAOTnfFR","executionInfo":{"status":"ok","timestamp":1723533819346,"user_tz":-330,"elapsed":4295,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"3cad3779-dd50-4ea1-ae37-23226faf1d7b"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='1' max='1' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [1/1 00:00, Epoch 0/1]\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>46.665800</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["('/content/drive/MyDrive/Generative AI with LLMs/Flan_T5_Model_Results&Weights/peft-dialogue-summary-checkpoint-local/tokenizer_config.json',\n"," '/content/drive/MyDrive/Generative AI with LLMs/Flan_T5_Model_Results&Weights/peft-dialogue-summary-checkpoint-local/special_tokens_map.json',\n"," '/content/drive/MyDrive/Generative AI with LLMs/Flan_T5_Model_Results&Weights/peft-dialogue-summary-checkpoint-local/spiece.model',\n"," '/content/drive/MyDrive/Generative AI with LLMs/Flan_T5_Model_Results&Weights/peft-dialogue-summary-checkpoint-local/added_tokens.json',\n"," '/content/drive/MyDrive/Generative AI with LLMs/Flan_T5_Model_Results&Weights/peft-dialogue-summary-checkpoint-local/tokenizer.json')"]},"metadata":{},"execution_count":75}]},{"cell_type":"code","source":["from peft import PeftModel, PeftConfig\n","import torch\n","\n","peft_model_base = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-base\", torch_dtype=torch.bfloat16)\n","tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-base\")\n","\n","peft_model = PeftModel.from_pretrained(peft_model_base,\n","                                       f\"{dataset_dir}/peft-dialogue-summary-checkpoint-local\",\n","                                       torch_dtype=torch.bfloat16,\n","                                       is_trainable=False)"],"metadata":{"id":"F5BS7qWDoSDk","executionInfo":{"status":"ok","timestamp":1723556016439,"user_tz":-330,"elapsed":6563,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"execution_count":54,"outputs":[]},{"cell_type":"code","source":["peft_model = peft_model.to('cpu')\n","instruct_model = instruct_model.to('cpu')\n","original_model = orignal_model.to('cpu')"],"metadata":{"id":"fRPcqlOQnubY","executionInfo":{"status":"ok","timestamp":1723556016950,"user_tz":-330,"elapsed":518,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"execution_count":55,"outputs":[]},{"cell_type":"code","source":["print(print_number_of_trainable_parameters(peft_model))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"AFUdhy1VoBcM","executionInfo":{"status":"ok","timestamp":1723556016950,"user_tz":-330,"elapsed":5,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"1444456a-26ed-44a1-b8cf-022638d11e0b"},"execution_count":56,"outputs":[{"output_type":"stream","name":"stdout","text":["trainable model parameters: 0\n","all model parameters: 251116800\n","percentage of trainable model parameters: 0.00%\n"]}]},{"cell_type":"code","source":["index = 200\n","dialogue = data['test'][index]['dialogue']\n","baseline_human_summary = data['test'][index]['summary']\n","\n","prompt = f\"\"\"\n","Summarize the following conversation.\n","\n","{dialogue}\n","\n","Summary: \"\"\"\n","\n","input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n","\n","original_model_outputs = original_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200, num_beams=1))\n","original_model_text_output = tokenizer.decode(original_model_outputs[0], skip_special_tokens=True)\n","\n","instruct_model_outputs = instruct_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200, num_beams=1))\n","instruct_model_text_output = tokenizer.decode(instruct_model_outputs[0], skip_special_tokens=True)\n","\n","peft_model_outputs = peft_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200, num_beams=1))\n","peft_model_text_output = tokenizer.decode(peft_model_outputs[0], skip_special_tokens=True)\n","\n","print(dash_line)\n","print(f'BASELINE HUMAN SUMMARY:\\n{baseline_human_summary}')\n","print(dash_line)\n","print(f'ORIGINAL MODEL:\\n{original_model_text_output}')\n","print(dash_line)\n","print(f'INSTRUCT MODEL:\\n{instruct_model_text_output}')\n","print(dash_line)\n","print(f'PEFT MODEL: {peft_model_text_output}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"UGGr8F4JoLHe","executionInfo":{"status":"ok","timestamp":1723556060173,"user_tz":-330,"elapsed":36014,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"fd286699-6d4f-4b26-8932-0e980e9a9ff9"},"execution_count":57,"outputs":[{"output_type":"stream","name":"stdout","text":["--------------------------------------\n","BASELINE HUMAN SUMMARY:\n","#Person1# teaches #Person2# how to upgrade software and hardware in #Person2#'s system.\n","--------------------------------------\n","ORIGINAL MODEL:\n","#Person1#: I'm thinking of upgrading my computer.\n","--------------------------------------\n","INSTRUCT MODEL:\n","#Person1#: I'm thinking of upgrading my computer.\n","--------------------------------------\n","PEFT MODEL: #Person1#: I'm thinking of upgrading my computer.\n"]}]},{"cell_type":"code","source":["dialogues = data['test'][0:10]['dialogue']\n","human_baseline_summaries = data['test'][0:10]['summary']\n","\n","original_model_summaries = []\n","instruct_model_summaries = []\n","peft_model_summaries = []\n","\n","for idx, dialogue in enumerate(dialogues):\n","    prompt = f\"\"\"\n","Summarize the following conversation.\n","\n","{dialogue}\n","\n","Summary: \"\"\"\n","\n","    input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n","\n","    human_baseline_text_output = human_baseline_summaries[idx]\n","\n","    original_model_outputs = original_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200))\n","    original_model_text_output = tokenizer.decode(original_model_outputs[0], skip_special_tokens=True)\n","\n","    instruct_model_outputs = instruct_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200))\n","    instruct_model_text_output = tokenizer.decode(instruct_model_outputs[0], skip_special_tokens=True)\n","\n","    peft_model_outputs = peft_model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200))\n","    peft_model_text_output = tokenizer.decode(peft_model_outputs[0], skip_special_tokens=True)\n","\n","    original_model_summaries.append(original_model_text_output)\n","    instruct_model_summaries.append(instruct_model_text_output)\n","    peft_model_summaries.append(peft_model_text_output)\n","\n","zipped_summaries = list(zip(human_baseline_summaries, original_model_summaries, instruct_model_summaries, peft_model_summaries))\n","\n","df = pd.DataFrame(zipped_summaries, columns = ['human_baseline_summaries', 'original_model_summaries', 'instruct_model_summaries', 'peft_model_summaries'])\n","df"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":536},"id":"_t1JTPBIo0dG","executionInfo":{"status":"ok","timestamp":1723534342703,"user_tz":-330,"elapsed":129490,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"502938c8-2400-44d9-e494-6950e6608022"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["                            human_baseline_summaries  \\\n","0  Ms. Dawson helps #Person1# to write a memo to ...   \n","1  In order to prevent employees from wasting tim...   \n","2  Ms. Dawson takes a dictation for #Person1# abo...   \n","3  #Person2# arrives late because of traffic jam....   \n","4  #Person2# decides to follow #Person1#'s sugges...   \n","5  #Person2# complains to #Person1# about the tra...   \n","6  #Person1# tells Kate that Masha and Hero get d...   \n","7  #Person1# tells Kate that Masha and Hero are g...   \n","8  #Person1# and Kate talk about the divorce betw...   \n","9  #Person1# and Brian are at the birthday party ...   \n","\n","                            original_model_summaries  \\\n","0     #Person1#: I need to take a dictation for you.   \n","1     #Person1#: I need to take a dictation for you.   \n","2     #Person1#: I need to take a dictation for you.   \n","3  The traffic jam at the Carrefour intersection ...   \n","4  The traffic jam at the Carrefour intersection ...   \n","5  The traffic jam at the Carrefour intersection ...   \n","6               Masha and Hero are getting divorced.   \n","7               Masha and Hero are getting divorced.   \n","8               Masha and Hero are getting divorced.   \n","9  #Person1#: Happy birthday, Brian. #Person2#: I...   \n","\n","                            instruct_model_summaries  \\\n","0     #Person1#: I need to take a dictation for you.   \n","1     #Person1#: I need to take a dictation for you.   \n","2     #Person1#: I need to take a dictation for you.   \n","3  The traffic jam at the Carrefour intersection ...   \n","4  The traffic jam at the Carrefour intersection ...   \n","5  The traffic jam at the Carrefour intersection ...   \n","6               Masha and Hero are getting divorced.   \n","7               Masha and Hero are getting divorced.   \n","8               Masha and Hero are getting divorced.   \n","9  #Person1#: Happy birthday, Brian. #Person2#: I...   \n","\n","                                peft_model_summaries  \n","0     #Person1#: I need to take a dictation for you.  \n","1     #Person1#: I need to take a dictation for you.  \n","2     #Person1#: I need to take a dictation for you.  \n","3  The traffic jam at the Carrefour intersection ...  \n","4  The traffic jam at the Carrefour intersection ...  \n","5  The traffic jam at the Carrefour intersection ...  \n","6               Masha and Hero are getting divorced.  \n","7               Masha and Hero are getting divorced.  \n","8               Masha and Hero are getting divorced.  \n","9  #Person1#: Happy birthday, Brian. #Person2#: T...  "],"text/html":["\n","  <div id=\"df-000bcf86-8672-49a9-9ba5-63829e93289b\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>human_baseline_summaries</th>\n","      <th>original_model_summaries</th>\n","      <th>instruct_model_summaries</th>\n","      <th>peft_model_summaries</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>Ms. Dawson helps #Person1# to write a memo to ...</td>\n","      <td>#Person1#: I need to take a dictation for you.</td>\n","      <td>#Person1#: I need to take a dictation for you.</td>\n","      <td>#Person1#: I need to take a dictation for you.</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>In order to prevent employees from wasting tim...</td>\n","      <td>#Person1#: I need to take a dictation for you.</td>\n","      <td>#Person1#: I need to take a dictation for you.</td>\n","      <td>#Person1#: I need to take a dictation for you.</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>Ms. Dawson takes a dictation for #Person1# abo...</td>\n","      <td>#Person1#: I need to take a dictation for you.</td>\n","      <td>#Person1#: I need to take a dictation for you.</td>\n","      <td>#Person1#: I need to take a dictation for you.</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>#Person2# arrives late because of traffic jam....</td>\n","      <td>The traffic jam at the Carrefour intersection ...</td>\n","      <td>The traffic jam at the Carrefour intersection ...</td>\n","      <td>The traffic jam at the Carrefour intersection ...</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>#Person2# decides to follow #Person1#'s sugges...</td>\n","      <td>The traffic jam at the Carrefour intersection ...</td>\n","      <td>The traffic jam at the Carrefour intersection ...</td>\n","      <td>The traffic jam at the Carrefour intersection ...</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>#Person2# complains to #Person1# about the tra...</td>\n","      <td>The traffic jam at the Carrefour intersection ...</td>\n","      <td>The traffic jam at the Carrefour intersection ...</td>\n","      <td>The traffic jam at the Carrefour intersection ...</td>\n","    </tr>\n","    <tr>\n","      <th>6</th>\n","      <td>#Person1# tells Kate that Masha and Hero get d...</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","    </tr>\n","    <tr>\n","      <th>7</th>\n","      <td>#Person1# tells Kate that Masha and Hero are g...</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","    </tr>\n","    <tr>\n","      <th>8</th>\n","      <td>#Person1# and Kate talk about the divorce betw...</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","      <td>Masha and Hero are getting divorced.</td>\n","    </tr>\n","    <tr>\n","      <th>9</th>\n","      <td>#Person1# and Brian are at the birthday party ...</td>\n","      <td>#Person1#: Happy birthday, Brian. #Person2#: I...</td>\n","      <td>#Person1#: Happy birthday, Brian. #Person2#: I...</td>\n","      <td>#Person1#: Happy birthday, Brian. #Person2#: T...</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button 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Kate feels surprised because she thought they are well matched\",\n          \"In order to prevent employees from wasting time on Instant Message programs, #Person1# decides to terminate the use of those programs and asks Ms. Dawson to send out a memo to all employees by the afternoon.\",\n          \"#Person2# complains to #Person1# about the traffic jam, #Person1# suggests quitting driving and taking public transportation instead.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"original_model_summaries\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"The traffic jam at the Carrefour intersection is a problem.\",\n          \"#Person1#: Happy birthday, Brian. #Person2#: I'm so happy you're having a good time. #Person1#: Thank you, I'm sure you look great today. #Person2#: Thank you, I'm sure you look great. #Person1#: Thank you, I'm sure you look great today. #Person2#: Thank you, I'm sure you look great today. #Person1#: Thank you, I'm sure you look great today. #Person2#: Thank you, I'm sure you look great today. #Person1#: Thank you, I'm sure you look great today.\",\n          \"#Person1#: I need to take a dictation for you.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"instruct_model_summaries\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"The traffic jam at the Carrefour intersection is a problem.\",\n          \"#Person1#: Happy birthday, Brian. #Person2#: I'm so happy you're having a good time. #Person1#: Thank you, I'm sure you look great today. #Person2#: Thank you, I'm sure you look great. #Person1#: Thank you, I'm sure you look great today. #Person2#: Thank you, I'm sure you look great today. #Person1#: Thank you, I'm sure you look great today. #Person2#: Thank you, I'm sure you look great today. #Person1#: Thank you, I'm sure you look great today.\",\n          \"#Person1#: I need to take a dictation for you.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"peft_model_summaries\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"The traffic jam at the Carrefour intersection is a problem.\",\n          \"#Person1#: Happy birthday, Brian. #Person2#: Thank you for coming.\",\n          \"#Person1#: I need to take a dictation for you.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"}},"metadata":{},"execution_count":86}]},{"cell_type":"code","source":["rouge = evaluate.load('rouge')\n","\n","original_model_results = rouge.compute(\n","    predictions=original_model_summaries,\n","    references=human_baseline_summaries[0:len(original_model_summaries)],\n","    use_aggregator=True,\n","    use_stemmer=True,\n",")\n","\n","instruct_model_results = rouge.compute(\n","    predictions=instruct_model_summaries,\n","    references=human_baseline_summaries[0:len(instruct_model_summaries)],\n","    use_aggregator=True,\n","    use_stemmer=True,\n",")\n","\n","peft_model_results = rouge.compute(\n","    predictions=peft_model_summaries,\n","    references=human_baseline_summaries[0:len(peft_model_summaries)],\n","    use_aggregator=True,\n","    use_stemmer=True,\n",")\n","\n","print('ORIGINAL MODEL:')\n","print(original_model_results)\n","print('INSTRUCT MODEL:')\n","print(instruct_model_results)\n","print('PEFT MODEL:')\n","print(peft_model_results)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xftkaRlXpPlj","executionInfo":{"status":"ok","timestamp":1723534343427,"user_tz":-330,"elapsed":733,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"696c383d-30f8-4ffb-9811-c8160513bc2e"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["ORIGINAL MODEL:\n","{'rouge1': 0.24089921652421653, 'rouge2': 0.11769053708439897, 'rougeL': 0.22001958689458687, 'rougeLsum': 0.22134175465057818}\n","INSTRUCT MODEL:\n","{'rouge1': 0.24089921652421653, 'rouge2': 0.11769053708439897, 'rougeL': 0.22001958689458687, 'rougeLsum': 0.22134175465057818}\n","PEFT MODEL:\n","{'rouge1': 0.2510566239316239, 'rouge2': 0.11535720375106562, 'rougeL': 0.229375, 'rougeLsum': 0.23316773504273502}\n"]}]},{"cell_type":"code","source":["print(\"Absolute percentage improvement of PEFT MODEL over HUMAN BASELINE\")\n","\n","improvement = (np.array(list(peft_model_results.values())) - np.array(list(original_model_results.values())))\n","for key, value in zip(peft_model_results.keys(), improvement):\n","    print(f'{key}: {value*100:.2f}%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hJSXOiiCpUXf","executionInfo":{"status":"ok","timestamp":1723534343427,"user_tz":-330,"elapsed":10,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"5a0f730e-e691-4bef-a0b9-fc82c1b0869a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Absolute percentage improvement of PEFT MODEL over HUMAN BASELINE\n","rouge1: 1.02%\n","rouge2: -0.23%\n","rougeL: 0.94%\n","rougeLsum: 1.18%\n"]}]},{"cell_type":"code","source":["print(\"Absolute percentage improvement of PEFT MODEL over INSTRUCT MODEL\")\n","\n","improvement = (np.array(list(peft_model_results.values())) - np.array(list(instruct_model_results.values())))\n","for key, value in zip(peft_model_results.keys(), improvement):\n","    print(f'{key}: {value*100:.2f}%')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"dnVH3PN_pala","executionInfo":{"status":"ok","timestamp":1723534343427,"user_tz":-330,"elapsed":6,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"4e3c607d-37c6-4c86-b271-b76d5e3730bf"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Absolute percentage improvement of PEFT MODEL over INSTRUCT MODEL\n","rouge1: 1.02%\n","rouge2: -0.23%\n","rougeL: 0.94%\n","rougeLsum: 1.18%\n"]}]},{"cell_type":"code","source":["!wget -q -O - ipv4.icanhazip.com"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tbKZPYaN9skH","executionInfo":{"status":"ok","timestamp":1723559705502,"user_tz":-330,"elapsed":490,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"655783f9-8586-4050-da3e-898d62e10b26"},"execution_count":7,"outputs":[{"output_type":"stream","name":"stdout","text":["34.83.236.158\n"]}]},{"cell_type":"code","source":["!npm install localtunnel"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"XtUSYa6GGBRX","executionInfo":{"status":"ok","timestamp":1723559707041,"user_tz":-330,"elapsed":1543,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"ac5df39e-9e05-4f28-d180-5381f509de11"},"execution_count":8,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[K\u001b[?25h\n","up to date, audited 23 packages in 539ms\n","\n","3 packages are looking for funding\n","  run `npm fund` for details\n","\n","2 \u001b[33m\u001b[1mmoderate\u001b[22m\u001b[39m severity vulnerabilities\n","\n","To address all issues (including breaking changes), run:\n","  npm audit fix --force\n","\n","Run `npm audit` for details.\n"]}]},{"cell_type":"code","source":["!streamlit run /content/app.py &>/content/logs.txt &"],"metadata":{"id":"Pc9dF5k6DMQL","executionInfo":{"status":"ok","timestamp":1723559707042,"user_tz":-330,"elapsed":6,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}}},"execution_count":9,"outputs":[]},{"cell_type":"code","source":["!npx localtunnel --port 8501"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"YFo6LlU_FG9H","outputId":"5e1accfc-03b6-4211-e261-8b10dfe6c60c"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["your url is: https://red-weeks-train.loca.lt\n"]}]},{"cell_type":"code","source":["%%writefile app.py\n","from peft import PeftModel, PeftConfig\n","from transformers import AutoTokenizer\n","from transformers import AutoModelForSeq2SeqLM\n","import torch\n","\n","dataset_dir = \"/content/drive/MyDrive/Generative AI with LLMs/Flan_T5_Model_Results&Weights\"\n","\n","orignal_model = AutoModelForSeq2SeqLM.from_pretrained('google/flan-t5-base')\n","instruct_model_paths = f'{dataset_dir}/instruct_model'\n","instruct_model = AutoModelForSeq2SeqLM.from_pretrained(instruct_model_paths)\n","peft_model_base = AutoModelForSeq2SeqLM.from_pretrained(\"google/flan-t5-base\", torch_dtype=torch.bfloat16)\n","tokenizer = AutoTokenizer.from_pretrained(\"google/flan-t5-base\")\n","\n","peft_model = PeftModel.from_pretrained(peft_model_base,\n","                                       f\"{dataset_dir}/peft-dialogue-summary-checkpoint-local\",\n","                                       torch_dtype=torch.bfloat16,\n","                                       is_trainable=False)\n","\n","import streamlit as st\n","st.set_page_config(page_title = \"Human Centric Summarizer\",\n","                   layout = \"wide\",\n","                   initial_sidebar_state = \"expanded\")\n","\n","st.title(\"Human Centric Summarizer 🥸\")\n","generic_summary = st.sidebar.checkbox(\"Google T5 generic Model Summary\")\n","instruct_summary = st.sidebar.checkbox(\"Instruction Finetuned Custom Summarizer model\")\n","peft_summary = st.sidebar.checkbox(\"PEFT LoRA tuned Summarizer Model\")\n","\n","st.write(\"About this Model\")\n","st.write(\"Developed a human-centric summarization model by instruction fine-tuning Flan T5 LLM, acheiving 27% ROUGE-1 improvements, and reduced training time by 87% using PEFT with LoRA adapters\")\n","\n","def summary(dialogue,model):\n","  prompt = f\"\"\"\n","  Summarize the following conversation.\n","\n","  {dialogue}\n","\n","  Summary: \"\"\"\n","  input_ids = tokenizer(prompt, return_tensors=\"pt\").input_ids\n","  model_outputs = model.generate(input_ids=input_ids, generation_config=GenerationConfig(max_new_tokens=200))\n","  model_text_output = tokenizer.decode(model_outputs[0], skip_special_tokens=True)\n","\n","  return model_text_output\n","\n","prompt = st.chat_input(\"Say something\")\n","messages = st.container(height=300)\n","\n","if prompt and generic_summary:\n","  summarized = summary(prompt, orignal_model)\n","  messages.chat_message(\"user\").write(prompt)\n","  messages.chat_message(\"assistant\").write(f\"Echo: {summarized}\")\n","elif prompt and instruct_summary:\n","  summarized = summary(prompt, instruct_model)\n","  messages.chat_message(\"user\").write(prompt)\n","  messages.chat_message(\"assistant\").write(f\"Echo: {summarized}\")\n","elif prompt and peft_summary:\n","  summarized = summary(prompt, peft_model)\n","  messages.chat_message(\"user\").write(prompt)\n","  messages.chat_message(\"assistant\").write(f\"Echo: {summarized}\")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"G-9hcWRjIiYX","executionInfo":{"status":"ok","timestamp":1723559701234,"user_tz":-330,"elapsed":479,"user":{"displayName":"Sahil Barbade","userId":"16182957082954187035"}},"outputId":"988acd72-72a9-4d47-9bdd-1bf2b5d31cdb"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["Overwriting app.py\n"]}]},{"cell_type":"code","source":[],"metadata":{"id":"jDstmPj1FJ_8"},"execution_count":null,"outputs":[]}],"metadata":{"colab":{"provenance":[{"file_id":"10GLbyrDFmGb8EDWBk8tx3C-6MCfAJ4pg","timestamp":1723555414022}]},"kernelspec":{"display_name":"Python 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