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This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_kg_hide-input":true,"_kg_hide-output":true,"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","papermill":{"duration":0.781515,"end_time":"2024-05-07T17:51:40.526678","exception":false,"start_time":"2024-05-07T17:51:39.745163","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:18:34.728498Z","iopub.execute_input":"2024-05-22T13:18:34.729134Z","iopub.status.idle":"2024-05-22T13:18:35.090463Z","shell.execute_reply.started":"2024-05-22T13:18:34.729104Z","shell.execute_reply":"2024-05-22T13:18:35.089522Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"/kaggle/input/lmsys-chatbot-arena/sample_submission.csv\n/kaggle/input/lmsys-chatbot-arena/train.csv\n/kaggle/input/lmsys-chatbot-arena/test.csv\n/kaggle/input/gemma/transformers/1.1-7b-it/1/model.safetensors.index.json\n/kaggle/input/gemma/transformers/1.1-7b-it/1/model-00003-of-00004.safetensors\n/kaggle/input/gemma/transformers/1.1-7b-it/1/config.json\n/kaggle/input/gemma/transformers/1.1-7b-it/1/model-00001-of-00004.safetensors\n/kaggle/input/gemma/transformers/1.1-7b-it/1/tokenizer.json\n/kaggle/input/gemma/transformers/1.1-7b-it/1/tokenizer_config.json\n/kaggle/input/gemma/transformers/1.1-7b-it/1/model-00004-of-00004.safetensors\n/kaggle/input/gemma/transformers/1.1-7b-it/1/special_tokens_map.json\n/kaggle/input/gemma/transformers/1.1-7b-it/1/.gitattributes\n/kaggle/input/gemma/transformers/1.1-7b-it/1/model-00002-of-00004.safetensors\n/kaggle/input/gemma/transformers/1.1-7b-it/1/tokenizer.model\n/kaggle/input/gemma/transformers/1.1-7b-it/1/generation_config.json\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#Gemma 1.1\n\n\"This is Gemma 1.1 7B (IT), an update over the original instruction-tuned Gemma release.\"\n\n\"Gemma 1.1 was trained using a novel RLHF method, leading to substantial gains on quality, coding capabilities, factuality, instruction following and multi-turn conversation quality. We also fixed a bug in multi-turn conversations, and made sure that model responses don't always start with \"Sure,\".\"\n\nRunning the model on a CPU\n\n\"As explained, the authors recommend torch.bfloat16 as the default dtype. You can use a different precision if necessary.\"\n\nRunning the model on a GPU using different precisions\n\n\"The native weights of this model were exported in bfloat16 precision. You can use float16, which may be faster on certain hardware, indicating the torch_dtype when loading the model. For convenience, the float16 revision of the repo contains a copy of the weights already converted to that precision.\"\n\n\"You can also use float32 if you skip the dtype, but no precision increase will occur (model weights will just be upcasted to float32).\"\n\nhttps://huggingface.co/google/gemma-1.1-7b-it\n\n\"Gemma comes in two sizes: a 2 billion parameter (2B) model for CPU/edge use cases and a 7 billion parameter (7B) model designed for GPU/TPU deployment. Both 2B and 7B models demonstrate state-of-the-art performance across various NLP tasks.\"\n\n[Google’s new Gemma 2b AND 7b Open-Source AI Models](https://digialps.com/googles-new-gemma-2b-and-7b-open-source-ai-models-but-do-they-beat-meta-llama-2-7b-and-mistral-7b/#:~:text=Gemma%20comes%20in%20two%20sizes,performance%20across%20various%20NLP%20tasks.)","metadata":{"papermill":{"duration":0.013725,"end_time":"2024-05-07T17:51:40.581533","exception":false,"start_time":"2024-05-07T17:51:40.567808","status":"completed"},"tags":[]}},{"cell_type":"markdown","source":"### Install Packages","metadata":{"papermill":{"duration":0.013084,"end_time":"2024-05-07T17:51:40.607700","exception":false,"start_time":"2024-05-07T17:51:40.594616","status":"completed"},"tags":[]}},{"cell_type":"code","source":"!pip install git+https://github.com/huggingface/transformers -U\n#!pip install accelerate\n!pip install -i https://pypi.org/simple/ bitsandbytes","metadata":{"_kg_hide-output":true,"papermill":{"duration":69.230164,"end_time":"2024-05-07T17:52:49.850940","exception":false,"start_time":"2024-05-07T17:51:40.620776","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:18:53.802708Z","iopub.execute_input":"2024-05-22T13:18:53.803550Z","iopub.status.idle":"2024-05-22T13:20:06.772896Z","shell.execute_reply.started":"2024-05-22T13:18:53.803519Z","shell.execute_reply":"2024-05-22T13:20:06.771738Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Collecting git+https://github.com/huggingface/transformers\n  Cloning https://github.com/huggingface/transformers to /tmp/pip-req-build-kme1g70c\n  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers /tmp/pip-req-build-kme1g70c\n  Resolved https://github.com/huggingface/transformers to commit 1518508467d96b3866fc4ebcb7a5b3a2e0df2aa4\n  Installing build dependencies ... \u001b[?25ldone\n\u001b[?25h  Getting requirements to build wheel ... \u001b[?25ldone\n\u001b[?25h  Preparing metadata (pyproject.toml) ... \u001b[?25ldone\n\u001b[?25hRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from transformers==4.42.0.dev0) (3.13.1)\nCollecting huggingface-hub<1.0,>=0.23.0 (from transformers==4.42.0.dev0)\n  Downloading huggingface_hub-0.23.1-py3-none-any.whl.metadata (12 kB)\nRequirement already satisfied: numpy>=1.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.42.0.dev0) (1.26.4)\nRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.10/site-packages (from transformers==4.42.0.dev0) (21.3)\nRequirement already satisfied: pyyaml>=5.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.42.0.dev0) (6.0.1)\nRequirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers==4.42.0.dev0) (2023.12.25)\nRequirement already satisfied: requests in /opt/conda/lib/python3.10/site-packages (from transformers==4.42.0.dev0) (2.31.0)\nCollecting tokenizers<0.20,>=0.19 (from transformers==4.42.0.dev0)\n  Downloading tokenizers-0.19.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (6.7 kB)\nRequirement already satisfied: safetensors>=0.4.1 in /opt/conda/lib/python3.10/site-packages (from transformers==4.42.0.dev0) (0.4.3)\nRequirement already satisfied: tqdm>=4.27 in /opt/conda/lib/python3.10/site-packages (from transformers==4.42.0.dev0) (4.66.1)\nRequirement already satisfied: fsspec>=2023.5.0 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.23.0->transformers==4.42.0.dev0) (2024.2.0)\nRequirement already satisfied: typing-extensions>=3.7.4.3 in /opt/conda/lib/python3.10/site-packages (from huggingface-hub<1.0,>=0.23.0->transformers==4.42.0.dev0) (4.9.0)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.0->transformers==4.42.0.dev0) (3.1.1)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.42.0.dev0) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.42.0.dev0) (3.6)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.42.0.dev0) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->transformers==4.42.0.dev0) (2024.2.2)\nDownloading huggingface_hub-0.23.1-py3-none-any.whl (401 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m401.3/401.3 kB\u001b[0m \u001b[31m2.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n\u001b[?25hDownloading tokenizers-0.19.1-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (3.6 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m3.6/3.6 MB\u001b[0m \u001b[31m3.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hBuilding wheels for collected packages: transformers\n  Building wheel for transformers (pyproject.toml) ... \u001b[?25ldone\n\u001b[?25h  Created wheel for transformers: filename=transformers-4.42.0.dev0-py3-none-any.whl size=9105592 sha256=eb1ede3fcfba0baa076c965e32d10e3882204fc205b76fa6c77f881ca0b3fbd8\n  Stored in directory: /tmp/pip-ephem-wheel-cache-f5x0froz/wheels/c0/14/d6/6c9a5582d2ac191ec0a483be151a4495fe1eb2a6706ca49f1b\nSuccessfully built transformers\nInstalling collected packages: huggingface-hub, tokenizers, transformers\n  Attempting uninstall: huggingface-hub\n    Found existing installation: huggingface-hub 0.22.2\n    Uninstalling huggingface-hub-0.22.2:\n      Successfully uninstalled huggingface-hub-0.22.2\n  Attempting uninstall: tokenizers\n    Found existing installation: tokenizers 0.15.2\n    Uninstalling tokenizers-0.15.2:\n      Successfully uninstalled tokenizers-0.15.2\n  Attempting uninstall: transformers\n    Found existing installation: transformers 4.39.3\n    Uninstalling transformers-4.39.3:\n      Successfully uninstalled transformers-4.39.3\nSuccessfully installed huggingface-hub-0.23.1 tokenizers-0.19.1 transformers-4.42.0.dev0\nLooking in indexes: https://pypi.org/simple/\nCollecting bitsandbytes\n  Downloading bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl.metadata (2.2 kB)\nRequirement already satisfied: torch in /opt/conda/lib/python3.10/site-packages (from bitsandbytes) (2.1.2)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from bitsandbytes) (1.26.4)\nRequirement already satisfied: filelock in /opt/conda/lib/python3.10/site-packages (from torch->bitsandbytes) (3.13.1)\nRequirement already satisfied: typing-extensions in /opt/conda/lib/python3.10/site-packages (from 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\u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: bitsandbytes\nSuccessfully installed bitsandbytes-0.43.1\n","output_type":"stream"}]},{"cell_type":"code","source":"!pip install -U sentence-transformers","metadata":{"_kg_hide-output":true,"papermill":{"duration":13.503037,"end_time":"2024-05-07T17:53:03.422254","exception":false,"start_time":"2024-05-07T17:52:49.919217","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:20:06.775318Z","iopub.execute_input":"2024-05-22T13:20:06.775730Z","iopub.status.idle":"2024-05-22T13:20:20.240512Z","shell.execute_reply.started":"2024-05-22T13:20:06.775691Z","shell.execute_reply":"2024-05-22T13:20:20.239075Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Collecting sentence-transformers\n  Downloading sentence_transformers-2.7.0-py3-none-any.whl.metadata (11 kB)\nRequirement already satisfied: transformers<5.0.0,>=4.34.0 in /opt/conda/lib/python3.10/site-packages (from sentence-transformers) (4.42.0.dev0)\nRequirement already satisfied: tqdm in /opt/conda/lib/python3.10/site-packages (from sentence-transformers) (4.66.1)\nRequirement already satisfied: torch>=1.11.0 in /opt/conda/lib/python3.10/site-packages (from sentence-transformers) (2.1.2)\nRequirement already satisfied: numpy in /opt/conda/lib/python3.10/site-packages (from sentence-transformers) (1.26.4)\nRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (from sentence-transformers) (1.2.2)\nRequirement already satisfied: scipy in /opt/conda/lib/python3.10/site-packages (from sentence-transformers) (1.11.4)\nRequirement already satisfied: huggingface-hub>=0.15.1 in /opt/conda/lib/python3.10/site-packages (from sentence-transformers) (0.23.1)\nRequirement already satisfied: Pillow in /opt/conda/lib/python3.10/site-packages (from sentence-transformers) (9.5.0)\nRequirement already satisfied: filelock in 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(1.12)\nRequirement already satisfied: networkx in /opt/conda/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers) (3.2.1)\nRequirement already satisfied: jinja2 in /opt/conda/lib/python3.10/site-packages (from torch>=1.11.0->sentence-transformers) (3.1.2)\nRequirement already satisfied: regex!=2019.12.17 in /opt/conda/lib/python3.10/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (2023.12.25)\nRequirement already satisfied: tokenizers<0.20,>=0.19 in /opt/conda/lib/python3.10/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.19.1)\nRequirement already satisfied: safetensors>=0.4.1 in /opt/conda/lib/python3.10/site-packages (from transformers<5.0.0,>=4.34.0->sentence-transformers) (0.4.3)\nRequirement already satisfied: joblib>=1.1.1 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->sentence-transformers) (1.4.0)\nRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn->sentence-transformers) (3.2.0)\nRequirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /opt/conda/lib/python3.10/site-packages (from packaging>=20.9->huggingface-hub>=0.15.1->sentence-transformers) (3.1.1)\nRequirement already satisfied: MarkupSafe>=2.0 in /opt/conda/lib/python3.10/site-packages (from jinja2->torch>=1.11.0->sentence-transformers) (2.1.3)\nRequirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.3.2)\nRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (3.6)\nRequirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (1.26.18)\nRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.10/site-packages (from requests->huggingface-hub>=0.15.1->sentence-transformers) (2024.2.2)\nRequirement already satisfied: mpmath>=0.19 in /opt/conda/lib/python3.10/site-packages (from sympy->torch>=1.11.0->sentence-transformers) (1.3.0)\nDownloading sentence_transformers-2.7.0-py3-none-any.whl (171 kB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m171.5/171.5 kB\u001b[0m \u001b[31m1.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0mm\n\u001b[?25hInstalling collected packages: sentence-transformers\nSuccessfully installed sentence-transformers-2.7.0\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Install Faiss","metadata":{"papermill":{"duration":0.021795,"end_time":"2024-05-07T17:53:03.466340","exception":false,"start_time":"2024-05-07T17:53:03.444545","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#!pip install faiss-cpu\n!pip install faiss-gpu","metadata":{"_kg_hide-output":true,"papermill":{"duration":15.7442,"end_time":"2024-05-07T17:53:19.232770","exception":false,"start_time":"2024-05-07T17:53:03.488570","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:20:20.243823Z","iopub.execute_input":"2024-05-22T13:20:20.244394Z","iopub.status.idle":"2024-05-22T13:20:43.749032Z","shell.execute_reply.started":"2024-05-22T13:20:20.244355Z","shell.execute_reply":"2024-05-22T13:20:43.748122Z"},"trusted":true},"execution_count":4,"outputs":[{"name":"stdout","text":"Collecting faiss-gpu\n  Downloading faiss_gpu-1.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (1.4 kB)\nDownloading faiss_gpu-1.7.2-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (85.5 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.5/85.5 MB\u001b[0m \u001b[31m5.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0mm\n\u001b[?25hInstalling collected packages: faiss-gpu\nSuccessfully installed faiss-gpu-1.7.2\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Import","metadata":{"papermill":{"duration":0.024216,"end_time":"2024-05-07T17:53:19.282385","exception":false,"start_time":"2024-05-07T17:53:19.258169","status":"completed"},"tags":[]}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport os\nimport ast\n\nimport torch\nimport gc\n\nimport sys, random, string, re, time\nfrom transformers import (BitsAndBytesConfig, \n                          AutoModelForCausalLM, \n                          AutoTokenizer, pipeline)\nfrom tqdm.auto import tqdm\n\n# Don't Show Warning Messages\nimport warnings\nwarnings.filterwarnings('ignore')\n\nprint(f\"CUDA Version: {torch.version.cuda}\")\nprint(f\"Pytorch {torch.__version__}\")","metadata":{"_kg_hide-output":true,"papermill":{"duration":18.255275,"end_time":"2024-05-07T17:53:37.562257","exception":false,"start_time":"2024-05-07T17:53:19.306982","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:20:43.750957Z","iopub.execute_input":"2024-05-22T13:20:43.751246Z","iopub.status.idle":"2024-05-22T13:21:02.769140Z","shell.execute_reply.started":"2024-05-22T13:20:43.751218Z","shell.execute_reply":"2024-05-22T13:21:02.768275Z"},"trusted":true},"execution_count":5,"outputs":[{"name":"stderr","text":"2024-05-22 13:20:50.268727: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n2024-05-22 13:20:50.268835: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n2024-05-22 13:20:50.412696: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"},{"name":"stdout","text":"CUDA Version: 12.1\nPytorch 2.1.2\n","output_type":"stream"}]},{"cell_type":"code","source":"# Set a seed value\n\nimport torch, random\n\n# Ensure that all GPU operations are deterministic \ntorch.backends.cudnn.deterministic = True\ntorch.backends.cudnn.benchmark = False\n\nseed_val = 1023\n\nrandom.seed(seed_val)\nnp.random.seed(seed_val)\ntorch.manual_seed(seed_val)\ntorch.cuda.manual_seed_all(seed_val)","metadata":{"papermill":{"duration":0.035683,"end_time":"2024-05-07T17:53:37.621694","exception":false,"start_time":"2024-05-07T17:53:37.586011","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:21:05.551081Z","iopub.execute_input":"2024-05-22T13:21:05.552054Z","iopub.status.idle":"2024-05-22T13:21:05.560776Z","shell.execute_reply.started":"2024-05-22T13:21:05.552020Z","shell.execute_reply":"2024-05-22T13:21:05.559834Z"},"trusted":true},"execution_count":6,"outputs":[]},{"cell_type":"markdown","source":"### Define variables. Model Gemma transformers 1.1-7b-it  ","metadata":{"papermill":{"duration":0.023452,"end_time":"2024-05-07T17:53:37.668623","exception":false,"start_time":"2024-05-07T17:53:37.645171","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# set the path to the Gemma model hosted on Kaggle\nMODEL_PATH = \"/kaggle/input/gemma/transformers/1.1-7b-it/1\"\n\n# set the path to the data that will be used in the few shot prompt\nFEW_SHOT_DATA_PATH = '../input/lmsys-chatbot-arena/train.csv'\n\n# set the path the text files containing info about Kaggle\nKAGGLE_DATA_PATH = '../input/lmsys-chatbot-arena/'\n\n# the number of results from the vector search that will be reranked\nTOP_K = 20\n\n# the number of text chunks that will be passed to Gemma\nNUM_CHUNKS_IN_CONTEXT = 3","metadata":{"papermill":{"duration":0.033174,"end_time":"2024-05-07T17:53:37.725684","exception":false,"start_time":"2024-05-07T17:53:37.692510","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:21:17.755129Z","iopub.execute_input":"2024-05-22T13:21:17.755775Z","iopub.status.idle":"2024-05-22T13:21:17.760523Z","shell.execute_reply.started":"2024-05-22T13:21:17.755745Z","shell.execute_reply":"2024-05-22T13:21:17.759476Z"},"trusted":true},"execution_count":7,"outputs":[]},{"cell_type":"markdown","source":"### Define a Device","metadata":{"papermill":{"duration":0.024479,"end_time":"2024-05-07T17:53:37.774888","exception":false,"start_time":"2024-05-07T17:53:37.750409","status":"completed"},"tags":[]}},{"cell_type":"code","source":"DEVICE = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n\nprint(f\"Device: {DEVICE}\")\nprint(f\"CUDA Version: {torch.version.cuda}\")\nprint(f\"Pytorch {torch.__version__}\")","metadata":{"papermill":{"duration":0.110818,"end_time":"2024-05-07T17:53:37.909883","exception":false,"start_time":"2024-05-07T17:53:37.799065","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:21:21.112083Z","iopub.execute_input":"2024-05-22T13:21:21.113123Z","iopub.status.idle":"2024-05-22T13:21:21.167256Z","shell.execute_reply.started":"2024-05-22T13:21:21.113090Z","shell.execute_reply":"2024-05-22T13:21:21.166164Z"},"trusted":true},"execution_count":8,"outputs":[{"name":"stdout","text":"Device: cuda\nCUDA Version: 12.1\nPytorch 2.1.2\n","output_type":"stream"}]},{"cell_type":"code","source":"# Check the type and quantity of GPUs\n\nif torch.cuda.is_available():\n    print('Num CPUs:', os.cpu_count())\n    print('Num GPUs:', torch.cuda.device_count())\n    print('GPU Type:', torch.cuda.get_device_name(0))","metadata":{"papermill":{"duration":0.06084,"end_time":"2024-05-07T17:53:37.996001","exception":false,"start_time":"2024-05-07T17:53:37.935161","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:21:21.834939Z","iopub.execute_input":"2024-05-22T13:21:21.835590Z","iopub.status.idle":"2024-05-22T13:21:21.870488Z","shell.execute_reply.started":"2024-05-22T13:21:21.835562Z","shell.execute_reply":"2024-05-22T13:21:21.869624Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Num CPUs: 4\nNum GPUs: 2\nGPU Type: Tesla T4\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### VBookShelf Helper Function","metadata":{"papermill":{"duration":0.023247,"end_time":"2024-05-07T17:53:38.043307","exception":false,"start_time":"2024-05-07T17:53:38.020060","status":"completed"},"tags":[]}},{"cell_type":"code","source":"#By VBookShelf https://www.kaggle.com/code/vbookshelf/kagglebot-gemma-7b-it-rag-w-few-shot-prompting/notebook\n\ndef run_faiss_search(query_text, top_k):\n    \n    \"\"\"\n    Executes an exhaustive search using FAISS to find the most \n    similar items to a given query.\n\n    This function vectorizes the input query text using \n    a pre-defined model and then performs a search in a FAISS index \n    to retrieve the top_k most similar items. \n    It returns the indices of these items in the FAISS index, \n    which can be used to retrieve the corresponding documents\n    or items.\n\n    Parameters:\n    - query_text (str): The text of the query for which similar \n    items are to be found.\n    - top_k (int): The number of top similar items to retrieve.\n\n    Returns:\n    - index_vals_list (list of int): A list of indices for the top_k \n    most similar items found in the FAISS index. \n    These indices correspond to the positions of the items in \n    the dataset used to build the FAISS index.\n    \n    Note:\n    - This function assumes that a FAISS index (`faiss_index`) \n    and a model for vectorization (`model`) are already defined \n    outside the function.\n    - The function is designed for use with the Sentence Transformers\n    package to convert text to vectors.\n    \n    \"\"\"\n    \n    # Run FAISS exhaustive search\n    query = [query_text]\n\n    # Vectorize the query string\n    query_embedding = model.encode(query, show_progress_bar=False)\n\n    # Run the query\n    # index_vals refers to the chunk_list index values\n    scores, index_vals = faiss_index.search(query_embedding, top_k)\n    \n    # Get the list of index vals\n    index_vals_list = index_vals[0]\n    \n    return index_vals_list\n    \n\ndef run_rerank(index_vals_list, query_text):\n    \n    \"\"\"\n    Re-ranks a list of retrieved passages based on \n    their similarity to the input query using a cross-encoder.\n\n    This function takes a list of index values corresponding to \n    retrieved passages and the input query text. \n    It then retrieves the actual text of these passages from a \n    dataframe (`df_data`) and formats them for input to a cross-encoder.\n    The cross-encoder is then used to score the similarity between \n    each passage and the query. The passages are re-ranked\n    based on these scores, and the re-ranked list of \n    passages is returned.\n\n    Parameters:\n    - index_vals_list (list of int): A list of index values \n    corresponding to retrieved passages.\n    - query_text (str): The text of the query to be used \n    for re-ranking the passages.\n\n    Returns:\n    - pred_list (list of str): A list of re-ranked passages based \n    on their similarity to the query text.\n\n    Note:\n    - This function assumes that a dataframe (`df_data`) \n    containing the prepared text of passages and a \n    cross-encoder (`cross_encoder`) for scoring the similarity \n    between text pairs are already defined outside the function.\n    \"\"\"\n    \n    # Create a list of text chunks\n    chunk_list = list(test['prompt'])\n\n    # Replace the chunk index values with the corresponding strings\n    pred_strings_list = [chunk_list[item] for item in index_vals_list]\n\n    # Format the input for the cross encoder\n    # The input to the cross_encoder is a list of lists\n    # [[query_text, pred_text1], [query_text, pred_text2], ...]\n\n    cross_input_list = []\n\n    for item in pred_strings_list:\n        \n        # Create a question/chunk pair: [question, text_chunk]\n        new_list = [query_text, item]\n        \n        # Append to the list containing all the question/chunk pairs\n        # [[question, text_chunk], [question, text_chunk], ...]\n        cross_input_list.append(new_list)\n\n\n    # Put the pred text into a dataframe\n    df = pd.DataFrame(cross_input_list, \n                      columns=['query_text', 'pred_text'])\n\n    # Save the orginal index (i.e. df_data index values)\n    df['original_index'] = index_vals_list\n\n    # Now, score all retrieved passages using the cross_encoder\n    cross_scores = cross_encoder.predict(cross_input_list, show_progress_bar=False)\n\n    # Add the scores to the dataframe\n    df['cross_scores'] = cross_scores\n\n    # Sort the DataFrame in descending order based on the scores\n    df_sorted = df.sort_values(by='cross_scores', ascending=False)\n    \n    # Reset the index\n    df_sorted = df_sorted.reset_index(drop=True)\n\n    pred_list = []\n\n    for i in range(0,len(df_sorted)):\n        \n        # Get the text\n        text = df_sorted.loc[i, 'pred_text']\n        \n        # Add curly braces\n        item = {\n            text\n        }\n\n        # Appen the text to a list\n        pred_list.append(item)\n\n    return pred_list\n\n    \n   \ndef vector_search_and_rerank(query_text, top_k=10):\n    \n    \"\"\"\n    Executes a retrieval-augmented generation (RAG) system \n    to generate responses to a given query.\n\n    This function integrates FAISS for initial retrieval and \n    re-ranking using a cross-encoder to produce a list of responses \n    to the input query text. \n    First, it runs a FAISS exhaustive search to retrieve the top_k \n    most relevant passages based on the query. \n    Then, it re-ranks these passages using a cross-encoder\n    to prioritize those with the highest similarity to the query. \n    The resulting list of passages is returned as the \n    output of the RAG system.\n\n    Parameters:\n    - query_text (str): The text of the query for which responses \n    are to be generated.\n    - top_k (int, optional): The number of top passages to \n    retrieve and re-rank. Defaults to 10.\n\n    Returns:\n    - pred_list (list of str): A list of passages ranked and \n    generated by the RAG system in response to the query.\n\n    Note:\n    - This function assumes that `run_faiss_search` and `run_rerank` \n    functions are already defined. \n    These functions handle the initial retrieval and \n    re-ranking processes, respectively.\n    \"\"\"\n    \n    # Run a faiss exhaustive search\n    pred_index_list = run_faiss_search(query_text, top_k)\n\n    # This returns a list of dicts with length equal to top_k\n    pred_list = run_rerank(pred_index_list, query_text)\n    \n    return pred_list\n\n \n\ndef extract_gemma_response(response):\n    \n    # Extract the answer:\n    # Split and select the last item in the list\n    response = response.split('<start_of_turn>model')[-1]\n    # Remove leading and trailing spaces\n    response = response.strip()\n    # Remove the '<end_of_turn> token\n    response = response.replace('<end_of_turn>', \"\")\n\n    # Gemma always uses the phrase \"I cannot answer this question\"\n    # when the answer is not available.\n    text1 = 'I cannot answer this question'\n    \n    # If Gemma can't answer the question then\n    # output a standard response.\n    if text1 in response:\n        response = \"Sorry, that information is not available.\"\n        \n    return response\n\n\ndef format_text(text):\n\n    # Create a list\n    answer_list = text.split('\\n')\n\n    for i, item in enumerate(answer_list):\n\n        # Replace * with nothing\n        new_item = item.replace('*','')\n        \n        # Remove leading and trailing spaces\n        new_item = new_item.strip()\n\n        # Create the output string\n        if i == 0:  \n            fin_string = new_item + '\\n'\n        else:\n            fin_string = fin_string + new_item + '\\n'\n\n    return fin_string\n\n\ndef gemma_assistant(question):\n    \n    # Create the prompt\n    prompt = f\"\"\"<start_of_turn>user \n    Don't use Markdown to format your response.\n    {question}<end_of_turn>\n    <start_of_turn>model\n    \"\"\"\n\n    # Tokenize the prompt\n    inputs = tokenizer(prompt, return_tensors=\"pt\").to(DEVICE)\n    # Generate the outputs from prompt\n    generate_ids = gemma_model.generate(**inputs, max_new_tokens=768)\n    # Decode the generated output\n    generated_text = tokenizer.batch_decode(generate_ids, \n                                        skip_special_tokens=True,\n                                        clean_up_tokenization_spaces=False)[0]\n\n\n    # Extract the answer\n    response = generated_text.split('<start_of_turn>model')[-1]\n    # Remove leading and trailing spaces\n    response = response.strip()\n    # Remove the '<end_of_turn> token\n    response = response.replace('<end_of_turn>', \"\")\n    \n    # Remove markdown '*' symbols\n    response = format_text(response)\n    \n    return response\n\n\ndef timer(start_time):\n\n    # End timing\n    end_time = time.time()\n    # Calculate the elapsed time\n    elapsed_time = end_time - start_time\n    # round to one decimal place\n    elapsed_time = round(elapsed_time, 1)\n    \n    return elapsed_time","metadata":{"papermill":{"duration":0.050247,"end_time":"2024-05-07T17:53:38.116567","exception":false,"start_time":"2024-05-07T17:53:38.066320","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:21:23.580443Z","iopub.execute_input":"2024-05-22T13:21:23.580816Z","iopub.status.idle":"2024-05-22T13:21:23.604533Z","shell.execute_reply.started":"2024-05-22T13:21:23.580788Z","shell.execute_reply":"2024-05-22T13:21:23.603618Z"},"trusted":true},"execution_count":10,"outputs":[]},{"cell_type":"markdown","source":"### Initialize Gemma 1.1-7b-it","metadata":{"papermill":{"duration":0.023287,"end_time":"2024-05-07T17:53:38.163302","exception":false,"start_time":"2024-05-07T17:53:38.140015","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Initialize the model and the tokenizer.\n# (This step takes about 2 minutes)\n\n\n# Set the compute data type to 16-bit floating point (float16).\n# This is a more memory-efficient format than float32, \n# It lowers memory usage and can speed up computation.\ncompute_dtype = getattr(torch, \"float16\")\n\n\n# Configure the model to use 4-bit precision for certain weights, \n# and specify the quantization details. This further reduces the \n# model size and can speed up inference.\nbnb_config = BitsAndBytesConfig(\n    load_in_4bit=True,\n    bnb_4bit_use_double_quant=False,\n    bnb_4bit_quant_type=\"nf4\",\n    bnb_4bit_compute_dtype=compute_dtype,\n)\n\n# Load the causal language model with the defined quantization \n# configuration and set it to automatically map \n# to the available device.\ngemma_model = AutoModelForCausalLM.from_pretrained(MODEL_PATH,\n                                        device_map=\"auto\",\n                                        quantization_config=bnb_config)\n\n# Disable caching of past key values for transformer models.\n# This reduces memory usage in scenarios where past key values \n# aren't needed for subsequent predictions.\ngemma_model.config.use_cache = False\n\n# Set the pretraining throughput to 1.\ngemma_model.config.pretraining_tp = 1\n\n# Initialize the tokenizer\ntokenizer = AutoTokenizer.from_pretrained(MODEL_PATH)","metadata":{"papermill":{"duration":113.877761,"end_time":"2024-05-07T17:55:32.109334","exception":false,"start_time":"2024-05-07T17:53:38.231573","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:21:49.233993Z","iopub.execute_input":"2024-05-22T13:21:49.234382Z","iopub.status.idle":"2024-05-22T13:24:07.529112Z","shell.execute_reply.started":"2024-05-22T13:21:49.234350Z","shell.execute_reply":"2024-05-22T13:24:07.528272Z"},"trusted":true},"execution_count":11,"outputs":[{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"09e4562f119e4454b0d5aea84ea68180"}},"metadata":{}}]},{"cell_type":"markdown","source":"### First question","metadata":{"papermill":{"duration":0.023516,"end_time":"2024-05-07T17:55:32.156949","exception":false,"start_time":"2024-05-07T17:55:32.133433","status":"completed"},"tags":[]}},{"cell_type":"code","source":"question = 'How LangChain works\"?'\n\n# Create the prompt\nprompt = f\"\"\"<start_of_turn>user\n{question}<end_of_turn>\n<start_of_turn>model\n\"\"\"\n\n# Start timing\nstart_time = time.time()\n\n# Tokenize the prompt\ninputs = tokenizer(prompt, return_tensors=\"pt\").to(DEVICE)\n# Generate the outputs from prompt\ngenerate_ids = gemma_model.generate(**inputs, max_new_tokens=768)\n# Decode the generated output\ngenerated_text = tokenizer.batch_decode(generate_ids, \n                                    skip_special_tokens=True,\n                                    clean_up_tokenization_spaces=False)[0]\n\n\n# Extract the answer\n\n# Split and select the last item in the list\nresponse = generated_text.split('<start_of_turn>model')[-1]\n# Remove leading and trailing spaces\nresponse = response.strip()\n# Remove the '<end_of_turn> token\nresponse = response.replace('<end_of_turn>', \"\")\n\n# Remove markdown '*' symbols\n# The default Markdown that Gemma outputs\n# doesn't always display well.\nresponse = format_text(response)\n\n\n# Get the inference time\nelapsed_time = timer(start_time)\nprint(f\"Time taken: {elapsed_time} seconds\")\n\nprint()\nprint('User:\\n',question)\nprint()\nprint('Gemma:\\n', response)","metadata":{"papermill":{"duration":30.641034,"end_time":"2024-05-07T17:56:02.821436","exception":false,"start_time":"2024-05-07T17:55:32.180402","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:24:32.483063Z","iopub.execute_input":"2024-05-22T13:24:32.483835Z","iopub.status.idle":"2024-05-22T13:25:04.006803Z","shell.execute_reply.started":"2024-05-22T13:24:32.483801Z","shell.execute_reply":"2024-05-22T13:25:04.005797Z"},"trusted":true},"execution_count":12,"outputs":[{"name":"stdout","text":"Time taken: 31.5 seconds\n\nUser:\n How LangChain works\"?\n\nGemma:\n user\nHow LangChain works\"?\nmodel\nLangChain is a natural language processing (NLP) library that enables the creation of complex, chainable NLP pipelines. It works by:\n\n1. Chainable Pipelines:\n\n- LangChain allows you to connect multiple NLP models in a sequence.\n- Each model in the chain performs a specific NLP task, such as text classification, sentiment analysis, or named entity recognition.\n- The output of one model becomes the input for the next.\n\n\n2. Model Interoperability:\n\n- LangChain supports a wide range of NLP models from different libraries and frameworks.\n- It provides an abstract API that allows models to be easily plugged into the pipeline.\n\n\n3. Context-aware Processing:\n\n- LangChain tracks the context of the NLP pipeline.\n- It passes relevant information between models, such as the input text, previous model outputs, and pipeline configuration.\n- This allows models to make informed decisions based on the context.\n\n\n4. Pipelines as Code:\n\n- LangChain pipelines are represented as Python code.\n- This makes it easy to:\n- Define and reuse pipelines.\n- Share pipelines with others.\n- Debug and optimize pipelines.\n\n\n5. Explainability and Monitoring:\n\n- LangChain provides tools for pipeline explainability and monitoring.\n- You can track pipeline performance, identify potential issues, and understand the decisions made by your models.\n\n\nHow it works:\n\n1. Input Text: The process starts with the input text.\n\n\n2. Pipeline Execution: The defined pipeline is executed sequentially. Each model in the chain receives the output of the previous model as input.\n\n\n3. Output: The final model in the chain produces the output of the pipeline.\n\n\n4. Context Tracking: LangChain tracks the context of the pipeline, including the input text, previous model outputs, and pipeline configuration.\n\n\n5. Intermodel Communication: Models can access relevant context information through the API, allowing them to make informed decisions.\n\n\nKey Features:\n\n- Chainability and modularity\n- Interoperability with multiple NLP models\n- Context-aware processing\n- Pipelines as code\n- Explainability and monitoring capabilities\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Function called gemma_assistant(). Ask Gemma a few more questions.","metadata":{"papermill":{"duration":0.023557,"end_time":"2024-05-07T17:56:02.869389","exception":false,"start_time":"2024-05-07T17:56:02.845832","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Start timing\nstart_time = time.time()\n\nquestion = \"Who is the man with the master plan?\"\n\nanswer = gemma_assistant(question)\n\n\n# Get the inference time\nelapsed_time = timer(start_time)\nprint(f\"Time taken: {elapsed_time} seconds\")\nprint()\n\nprint('User:\\n',question)\nprint()\nprint('Gemma:\\n',answer)","metadata":{"papermill":{"duration":1.865826,"end_time":"2024-05-07T17:56:04.759738","exception":false,"start_time":"2024-05-07T17:56:02.893912","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:25:10.381359Z","iopub.execute_input":"2024-05-22T13:25:10.382428Z","iopub.status.idle":"2024-05-22T13:25:12.421968Z","shell.execute_reply.started":"2024-05-22T13:25:10.382393Z","shell.execute_reply":"2024-05-22T13:25:12.420807Z"},"trusted":true},"execution_count":13,"outputs":[{"name":"stdout","text":"Time taken: 2.0 seconds\n\nUser:\n Who is the man with the master plan?\n\nGemma:\n user\nDon't use Markdown to format your response.\nWho is the man with the master plan?\nmodel\nI am unable to provide subjective information or opinions, and am unable to provide information that may be considered speculation or conspiracy theories.\n\n","output_type":"stream"}]},{"cell_type":"code","source":"question = \"How macros are recursively expanded in C?\"\n\nanswer = gemma_assistant(question)\n\nprint('User:\\n',question)\nprint()\nprint('Gemma:\\n',answer)","metadata":{"papermill":{"duration":26.043549,"end_time":"2024-05-07T17:56:30.828617","exception":false,"start_time":"2024-05-07T17:56:04.785068","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:25:14.612861Z","iopub.execute_input":"2024-05-22T13:25:14.613205Z","iopub.status.idle":"2024-05-22T13:25:43.890687Z","shell.execute_reply.started":"2024-05-22T13:25:14.613180Z","shell.execute_reply":"2024-05-22T13:25:43.889567Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"User:\n How macros are recursively expanded in C?\n\nGemma:\n user\nDon't use Markdown to format your response.\nHow macros are recursively expanded in C?\nmodel\nMacro expansion in C is a recursive process:\n\n1. Macro definition:\n\n- Macros are defined using the `#define` preprocessor directive.\n- They consist of a macro name and its expansion.\n\n\n2. Macro expansion:\n\n- When a macro is encountered during preprocessing, its expansion is substituted in its place.\n- The expansion itself can contain other macros.\n\n\n3. Recursive expansion:\n\n- During macro expansion, any macros contained within the expansion are recursively expanded.\n- This process continues until no more macros are left to expand.\n\n\nExample:\n\n```c\n#define SQUARE(x) (x  x)\n\n#define SUM(a, b) SQUARE(a) + SQUARE(b)\n\nint main() {\nint result = SUM(2, 3);\nreturn result;\n}\n```\n\nIn this example:\n\n- The `SUM` macro expands to ` SQUARE(a) + SQUARE(b)`.\n- `SQUARE` macro expands to `(x  x)`.\n\nThe recursive expansion process is as follows:\n\n1. The preprocessor expands the `SUM` macro.\n2. It substitutes the expansion of `SUM` into the code: `(x  x) + (x  x)`.\n3. The preprocessor then expands the `SQUARE` macro for both terms.\n4. This results in the final expanded code: `(2  2) + (3  3)`.\n\nKey points about recursive macro expansion:\n\n- The expansion process continues until no more macros are left to expand.\n- Recursive expansion can lead to complex and lengthy code.\n- It is important to use macros responsibly to avoid unintended consequences.\n\n\nBenefits of recursive macro expansion:\n\n- Reduces code duplication.\n- Improves readability and maintainability.\n- Allows for reuse of common code snippets.\n\n\nPotential risks of recursive macro expansion:\n\n- Can lead to unexpected or unintended results.\n- Can make code difficult to understand or maintain.\n- Can increase preprocessing time for large macros.\n\n","output_type":"stream"}]},{"cell_type":"code","source":"question = \"What is the moral of Wall-e?\"\n\nanswer = gemma_assistant(question)\n\nprint('User:\\n',question)\nprint()\nprint('Gemma:\\n',answer)","metadata":{"papermill":{"duration":2.657107,"end_time":"2024-05-07T17:56:33.511315","exception":false,"start_time":"2024-05-07T17:56:30.854208","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:25:48.272045Z","iopub.execute_input":"2024-05-22T13:25:48.272689Z","iopub.status.idle":"2024-05-22T13:25:51.140711Z","shell.execute_reply.started":"2024-05-22T13:25:48.272660Z","shell.execute_reply":"2024-05-22T13:25:51.139748Z"},"trusted":true},"execution_count":15,"outputs":[{"name":"stdout","text":"User:\n What is the moral of Wall-e?\n\nGemma:\n user\nDon't use Markdown to format your response.\nWhat is the moral of Wall-e?\nmodel\nThe moral of Wall-E is that even in a desolate and hopeless world, hope, friendship, and the power of individual action can inspire change and bring new life to a dying planet.\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Load the few-shot data","metadata":{"papermill":{"duration":0.025309,"end_time":"2024-05-07T17:56:33.563759","exception":false,"start_time":"2024-05-07T17:56:33.538450","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Load the few shot data into a pandas dataframe\ndf_fshot = pd.read_csv(FEW_SHOT_DATA_PATH)\n\ndef convert_to_list(x):\n    \n    # Convert the string to a list: '[...]' to [...]\n    x_as_list = ast.literal_eval(x)\n    \n    return x_as_list\n\n# Convert each item in the context column from a string to a \n# python list i.e. '[...]' to [...]\ndf_fshot['prompt'] = df_fshot['prompt'].apply(convert_to_list)\n\ndf_fshot.head(2)","metadata":{"papermill":{"duration":4.149006,"end_time":"2024-05-07T17:56:37.738511","exception":false,"start_time":"2024-05-07T17:56:33.589505","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:25:54.226617Z","iopub.execute_input":"2024-05-22T13:25:54.226960Z","iopub.status.idle":"2024-05-22T13:25:58.453495Z","shell.execute_reply.started":"2024-05-22T13:25:54.226935Z","shell.execute_reply":"2024-05-22T13:25:58.452452Z"},"trusted":true},"execution_count":16,"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"      id             model_a     model_b  \\\n0  30192  gpt-4-1106-preview  gpt-4-0613   \n1  53567           koala-13b  gpt-4-0613   \n\n                                              prompt  \\\n0  [Is it morally right to try to have a certain ...   \n1  [What is the difference between marriage licen...   \n\n                                          response_a  \\\n0  [\"The question of whether it is morally right ...   \n1  [\"A marriage license is a legal document that ...   \n\n                                          response_b  winner_model_a  \\\n0  [\"As an AI, I don't have personal beliefs or o...               1   \n1  [\"A marriage license and a marriage certificat...               0   \n\n   winner_model_b  winner_tie  \n0               0           0  \n1               1           0  ","text/html":"<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>id</th>\n      <th>model_a</th>\n      <th>model_b</th>\n      <th>prompt</th>\n      <th>response_a</th>\n      <th>response_b</th>\n      <th>winner_model_a</th>\n      <th>winner_model_b</th>\n      <th>winner_tie</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>30192</td>\n      <td>gpt-4-1106-preview</td>\n      <td>gpt-4-0613</td>\n      <td>[Is it morally right to try to have a certain ...</td>\n      <td>[\"The question of whether it is morally right ...</td>\n      <td>[\"As an AI, I don't have personal beliefs or o...</td>\n      <td>1</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>53567</td>\n      <td>koala-13b</td>\n      <td>gpt-4-0613</td>\n      <td>[What is the difference between marriage licen...</td>\n      <td>[\"A marriage license is a legal document that ...</td>\n      <td>[\"A marriage license and a marriage certificat...</td>\n      <td>0</td>\n      <td>1</td>\n      <td>0</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Read test cause train was too long for chunks. As far as I got on an error.","metadata":{"papermill":{"duration":0.027019,"end_time":"2024-05-07T17:56:37.796970","exception":false,"start_time":"2024-05-07T17:56:37.769951","status":"completed"},"tags":[]}},{"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/lmsys-chatbot-arena/test.csv')\ntest.tail()","metadata":{"papermill":{"duration":0.043747,"end_time":"2024-05-07T17:56:37.865998","exception":false,"start_time":"2024-05-07T17:56:37.822251","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:02.572433Z","iopub.execute_input":"2024-05-22T13:26:02.573121Z","iopub.status.idle":"2024-05-22T13:26:02.588518Z","shell.execute_reply.started":"2024-05-22T13:26:02.573089Z","shell.execute_reply":"2024-05-22T13:26:02.587569Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"        id                                             prompt  \\\n0   136060  [\"I have three oranges today, I ate an orange ...   \n1   211333  [\"You are a mediator in a heated political deb...   \n2  1233961  [\"How to initialize the classification head wh...   \n\n                                          response_a  \\\n0                    [\"You have two oranges today.\"]   \n1  [\"Thank you for sharing the details of the sit...   \n2  [\"When you want to initialize the classificati...   \n\n                                          response_b  \n0  [\"You still have three oranges. Eating an oran...  \n1  [\"Mr Reddy and Ms Blue both have valid points ...  \n2  [\"To initialize the classification head when p...  ","text/html":"<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>id</th>\n      <th>prompt</th>\n      <th>response_a</th>\n      <th>response_b</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>136060</td>\n      <td>[\"I have three oranges today, I ate an orange ...</td>\n      <td>[\"You have two oranges today.\"]</td>\n      <td>[\"You still have three oranges. Eating an oran...</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>211333</td>\n      <td>[\"You are a mediator in a heated political deb...</td>\n      <td>[\"Thank you for sharing the details of the sit...</td>\n      <td>[\"Mr Reddy and Ms Blue both have valid points ...</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1233961</td>\n      <td>[\"How to initialize the classification head wh...</td>\n      <td>[\"When you want to initialize the classificati...</td>\n      <td>[\"To initialize the classification head when p...</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"### Train is too long try chunk_list with test","metadata":{"papermill":{"duration":0.025128,"end_time":"2024-05-07T17:56:37.916982","exception":false,"start_time":"2024-05-07T17:56:37.891854","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Create a list of text chunks\nchunk_list = list(test['prompt'])\n\n# Display the number of text chunks\nlen(chunk_list)","metadata":{"papermill":{"duration":0.034737,"end_time":"2024-05-07T17:56:37.976235","exception":false,"start_time":"2024-05-07T17:56:37.941498","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:04.534791Z","iopub.execute_input":"2024-05-22T13:26:04.535176Z","iopub.status.idle":"2024-05-22T13:26:04.542214Z","shell.execute_reply.started":"2024-05-22T13:26:04.535145Z","shell.execute_reply":"2024-05-22T13:26:04.541193Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"3"},"metadata":{}}]},{"cell_type":"markdown","source":"### Embeddings - SentenceTransformer","metadata":{"papermill":{"duration":0.025785,"end_time":"2024-05-07T17:56:38.027551","exception":false,"start_time":"2024-05-07T17:56:38.001766","status":"completed"},"tags":[]}},{"cell_type":"code","source":"from sentence_transformers import SentenceTransformer\n\n# Initialize the model\nmodel = SentenceTransformer(\"all-MiniLM-L6-v2\")\n\n# Sentences are encoded by calling model.encode()\nembeddings = model.encode(chunk_list, show_progress_bar=False)\n\nprint(embeddings.shape)\nprint('Embedding length', embeddings.shape[1])","metadata":{"papermill":{"duration":2.603445,"end_time":"2024-05-07T17:56:40.656148","exception":false,"start_time":"2024-05-07T17:56:38.052703","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:06.738824Z","iopub.execute_input":"2024-05-22T13:26:06.739216Z","iopub.status.idle":"2024-05-22T13:26:09.389388Z","shell.execute_reply.started":"2024-05-22T13:26:06.739185Z","shell.execute_reply":"2024-05-22T13:26:09.388318Z"},"trusted":true},"execution_count":19,"outputs":[{"output_type":"display_data","data":{"text/plain":"modules.json:   0%|          | 0.00/349 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d474771aede84d28a12f4f959dd6f258"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config_sentence_transformers.json:   0%|          | 0.00/116 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cccd4280123e4a348fc6549ff1d5a912"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"README.md:   0%|          | 0.00/10.7k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"73e42edf36994fd3882dc0ea0ff3ac07"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"sentence_bert_config.json:   0%|          | 0.00/53.0 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"860dd51559724d27b280dc79a16065f9"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/612 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"41254fa58fab4ad28c9ebe22a4c67747"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors:   0%|          | 0.00/90.9M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8da9874e3dc847b5b188a4ec6c5e3cba"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/350 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ad0ed3e9989e4ffdb583e8f14b769c3b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ed95b671a0d743999a4cce7314616c1d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/466k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"395a7657cc0e49d3a2cc708dbd5245d4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"99f5749057c7402e87efe5d01cd7109a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"212f428f9adf4fca88f2d3fd2d0f8e77"}},"metadata":{}},{"name":"stdout","text":"(3, 384)\nEmbedding length 384\n","output_type":"stream"}]},{"cell_type":"code","source":"import faiss\n\n# Get the embedding length\nembed_length = embeddings.shape[1]\n\n# IndexFlatL2 is used for exhaustive search\nfaiss_index = faiss.IndexFlatL2(embed_length)\n\n# Check if the index is trained.\n# No training needed when using exhaustive search i.e. IndexFlatL2\nfaiss_index.is_trained","metadata":{"papermill":{"duration":0.063035,"end_time":"2024-05-07T17:56:40.746238","exception":false,"start_time":"2024-05-07T17:56:40.683203","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:11.469315Z","iopub.execute_input":"2024-05-22T13:26:11.469736Z","iopub.status.idle":"2024-05-22T13:26:11.504782Z","shell.execute_reply.started":"2024-05-22T13:26:11.469702Z","shell.execute_reply":"2024-05-22T13:26:11.503850Z"},"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"True"},"metadata":{}}]},{"cell_type":"markdown","source":"### CrossEncoder","metadata":{"papermill":{"duration":0.026826,"end_time":"2024-05-07T17:56:40.799845","exception":false,"start_time":"2024-05-07T17:56:40.773019","status":"completed"},"tags":[]}},{"cell_type":"code","source":"from sentence_transformers import CrossEncoder\n\n# Initialize the cross-encoder\ncross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')","metadata":{"papermill":{"duration":2.024583,"end_time":"2024-05-07T17:56:42.851353","exception":false,"start_time":"2024-05-07T17:56:40.826770","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:13.037307Z","iopub.execute_input":"2024-05-22T13:26:13.037685Z","iopub.status.idle":"2024-05-22T13:26:14.495086Z","shell.execute_reply.started":"2024-05-22T13:26:13.037656Z","shell.execute_reply":"2024-05-22T13:26:14.494303Z"},"trusted":true},"execution_count":21,"outputs":[{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/794 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c2a7097d7d4d4decba9a869ed1e68185"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"pytorch_model.bin:   0%|          | 0.00/90.9M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9d9075f7adc14597a3c0a38d04aa08e8"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/316 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9ba7e8f7ab744809ba4f5cb270f0d381"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"vocab.txt:   0%|          | 0.00/232k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f527a219fb644d38949556f0860d4b88"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"97e2e3e39d7c4cfaa8abc078a4e83418"}},"metadata":{}}]},{"cell_type":"code","source":"# Question 1: \n# Are their any kaggle employees who worked for Microsoft?\n\n\n# Start timing\nstart_time = time.time()\n\nquery_text = \"I have three oranges today, I ate an orange yesterday. How many oranges do I have?\"\n\n# Run the RAG search\nsorted_pred_list = vector_search_and_rerank(query_text, top_k=10)\n\n# Choose the first 3 reranked and sorted text chunks \ncontext_list = sorted_pred_list[0:3]\n\n# Create the prompt\nprompt = f\"\"\"<start_of_turn>user\nContext: {context_list}\nQuestion: {query_text}<end_of_turn>\n<start_of_turn>model\n\"\"\"\n    \n    \n# Tokenize the prompt\ninputs = tokenizer(prompt, return_tensors=\"pt\").to(DEVICE)\n# Generate the outputs from prompt\ngenerate_ids = gemma_model.generate(**inputs, max_new_tokens=768)\n# Decode the generated output\nresponse = tokenizer.batch_decode(generate_ids, skip_special_tokens=True,\n                                     clean_up_tokenization_spaces=False)[0]\n\n\n# Extract the answer\n\n# Split and select the last item in the list\ngemma_response = response.split('<start_of_turn>model')[-1]\n# Remove leading and trailing spaces\ngemma_response = gemma_response.strip()\n# Remove the '<end_of_turn> token\ngemma_response= gemma_response.replace('<end_of_turn>', \"\")\n\n# Get the inference time\nelapsed_time = timer(start_time)\nprint(f\"Time taken: {elapsed_time} seconds\")\nprint()\n\nprint('-----')\nprint('User:\\n',query_text)\nprint()\nprint('Raw Gemma response:\\n\\n',response)\nprint()\nprint()\nprint('Extracted Gemma response:\\n\\n',gemma_response)","metadata":{"papermill":{"duration":2.324264,"end_time":"2024-05-07T17:56:45.205550","exception":false,"start_time":"2024-05-07T17:56:42.881286","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:15.638879Z","iopub.execute_input":"2024-05-22T13:26:15.639253Z","iopub.status.idle":"2024-05-22T13:26:18.062701Z","shell.execute_reply.started":"2024-05-22T13:26:15.639225Z","shell.execute_reply":"2024-05-22T13:26:18.061739Z"},"trusted":true},"execution_count":22,"outputs":[{"name":"stdout","text":"Time taken: 2.4 seconds\n\n-----\nUser:\n I have three oranges today, I ate an orange yesterday. How many oranges do I have?\n\nRaw Gemma response:\n\n user\nContext: [{'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}]\nQuestion: I have three oranges today, I ate an orange yesterday. How many oranges do I have?\nmodel\nThe provided text does not contain any information regarding oranges or eating habits, so I am unable to answer this question from the given context.\n\n\nExtracted Gemma response:\n\n user\nContext: [{'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}]\nQuestion: I have three oranges today, I ate an orange yesterday. How many oranges do I have?\nmodel\nThe provided text does not contain any information regarding oranges or eating habits, so I am unable to answer this question from the given context.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Three few-shot prompts or Three blind Mice whatever : )","metadata":{"papermill":{"duration":0.029047,"end_time":"2024-05-07T17:56:45.262913","exception":false,"start_time":"2024-05-07T17:56:45.233866","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Example with three few-shot prompts\n\nprompt = f\"\"\"\n\n    <start_of_turn>user\n    Context: {df_fshot.loc[0, 'model_a']}\n    Question: {df_fshot.loc[0, 'prompt']}<end_of_turn>\n    <start_of_turn>model\n    {df_fshot.loc[0, 'response_a']}<end_of_turn>\n    \n    <start_of_turn>user\n    Context: {df_fshot.loc[5, 'model_a']}\n    Question: {df_fshot.loc[5, 'prompt']}<end_of_turn>\n    <start_of_turn>model\n    {df_fshot.loc[5, 'response_a']}<end_of_turn>\n    \n    <start_of_turn>user\n    Context: {df_fshot.loc[6, 'model_a']}\n    Question: {df_fshot.loc[6, 'prompt']}<end_of_turn>\n    <start_of_turn>model\n    {df_fshot.loc[6, 'response_a']}<end_of_turn>\n    \n    <start_of_turn>user\n    Think and write your step-by-step reasoning before responding.\n    \n    Context: {context_list}\n    Question: {query_text}<end_of_turn>\n    <start_of_turn>model\n    \"\"\"","metadata":{"papermill":{"duration":0.038895,"end_time":"2024-05-07T17:56:45.330607","exception":false,"start_time":"2024-05-07T17:56:45.291712","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:20.580680Z","iopub.execute_input":"2024-05-22T13:26:20.581509Z","iopub.status.idle":"2024-05-22T13:26:20.587976Z","shell.execute_reply.started":"2024-05-22T13:26:20.581474Z","shell.execute_reply":"2024-05-22T13:26:20.586891Z"},"trusted":true},"execution_count":23,"outputs":[]},{"cell_type":"markdown","source":"### I Changed: gem_context to model_a, query to prompt and corrected_text to response_a","metadata":{"papermill":{"duration":0.027722,"end_time":"2024-05-07T17:56:45.387643","exception":false,"start_time":"2024-05-07T17:56:45.359921","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def get_gemma_response(query_text, context_list):\n    \n    prompt = f\"\"\"<start_of_turn>user\n    Context: {df_fshot.loc[0, 'model_a']}\n    Question: {df_fshot.loc[0, 'prompt']}<end_of_turn>\n    <start_of_turn>model\n    {df_fshot.loc[0, 'response_a']}<end_of_turn>\n    <start_of_turn>user\n    Context: {df_fshot.loc[5, 'model_a']}\n    Question: {df_fshot.loc[5, 'prompt']}<end_of_turn>\n    <start_of_turn>model\n    {df_fshot.loc[5, 'response_a']}<end_of_turn>\n    <start_of_turn>user\n    Context: {df_fshot.loc[6, 'model_a']}\n    Question: {df_fshot.loc[6, 'prompt']}<end_of_turn>\n    <start_of_turn>model\n    {df_fshot.loc[6, 'response_a']}<end_of_turn>\n    <start_of_turn>user\n    Think and write your step-by-step reasoning before responding.\n    \n    Context: {context_list}\n    Question: {query_text}<end_of_turn>\n    <start_of_turn>model\n    \"\"\"\n    \n    \n    # Get a natural language response from Gemma\n    # Tokenize the prompt\n    inputs = tokenizer(prompt, return_tensors=\"pt\").to(DEVICE)\n    # Generate the outputs from prompt\n    generate_ids = gemma_model.generate(**inputs, max_new_tokens=768)\n    # Decode the generated output\n    gemma_response = tokenizer.batch_decode(generate_ids, skip_special_tokens=True,\n                                         clean_up_tokenization_spaces=False)[0]\n\n    # Clear the memory to create space\n    del prompt\n    del inputs\n    del generate_ids\n    torch.cuda.empty_cache() \n    gc.collect()\n    \n    return gemma_response","metadata":{"papermill":{"duration":0.039902,"end_time":"2024-05-07T17:56:45.455756","exception":false,"start_time":"2024-05-07T17:56:45.415854","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:31.941729Z","iopub.execute_input":"2024-05-22T13:26:31.942682Z","iopub.status.idle":"2024-05-22T13:26:31.951444Z","shell.execute_reply.started":"2024-05-22T13:26:31.942649Z","shell.execute_reply":"2024-05-22T13:26:31.949110Z"},"trusted":true},"execution_count":24,"outputs":[]},{"cell_type":"code","source":"# Question 1: \n# Are their any kaggle employees who worked for Microsoft?\n\nquery_text = \"What is a good recipe for a potato soup?\"\n\n\n# Run the RAG search\nsorted_pred_list = vector_search_and_rerank(query_text, top_k=10)\n\n# Choose the top reranked and sorted text chunks \n# to put in the context\ncontext_list = sorted_pred_list[0:NUM_CHUNKS_IN_CONTEXT]\n\n# This function includes the few-shot prompts\nresponse = get_gemma_response(query_text, context_list)\n\n#response\n\n# Extract the answer\ngemma_response = response.split('<start_of_turn>model')[-1]\n# Remove leading and trailing spaces\ngemma_response = gemma_response.strip()\n# Remove the '<end_of_turn> token\ngemma_response = gemma_response.replace('<end_of_turn>', \"\")\n    \n\nprint('-----')\nprint('User:\\n', query_text)\nprint()\nprint('Gemma:\\n',gemma_response)","metadata":{"papermill":{"duration":4.368473,"end_time":"2024-05-07T17:56:49.851930","exception":false,"start_time":"2024-05-07T17:56:45.483457","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:32.853987Z","iopub.execute_input":"2024-05-22T13:26:32.854886Z","iopub.status.idle":"2024-05-22T13:26:37.326359Z","shell.execute_reply.started":"2024-05-22T13:26:32.854850Z","shell.execute_reply":"2024-05-22T13:26:37.325423Z"},"trusted":true},"execution_count":25,"outputs":[{"name":"stdout","text":"-----\nUser:\n What is a good recipe for a potato soup?\n\nGemma:\n user\n    Context: gpt-4-1106-preview\n    Question: ['Is it morally right to try to have a certain percentage of females on managerial positions?', 'OK, does pineapple belong on a pizza? Relax and give me fun answer.']\n    model\n    [\"The question of whether it is morally right to aim for a certain percentage of females in managerial positions is a complex ethical issue that involves considerations of fairness, equality, diversity, and discrimination.\\n\\nHere are some arguments in favor of and against such policies:\\n\\n**Arguments in favor:**\\n\\n1. **Correcting Historical Inequities:** Women have historically been underrepresented in leadership roles due to various cultural, institutional, and social barriers. Aiming for a specific percentage can be seen as a corrective measure to address past and ongoing discrimination.\\n\\n2. **Promoting Diversity:** Diverse leadership teams can enhance decision-making and represent a broader range of perspectives. This can lead to better outcomes for organizations and society as a whole.\\n\\n3. **Equality of Opportunity:** Setting targets for female representation in management can help ensure that women have equal opportunities to advance in their careers.\\n\\n4. **Role Modeling:** Increased visibility of female leaders can inspire and encourage other women and girls to pursue leadership roles.\\n\\n**Arguments against:**\\n\\n1. **Reverse Discrimination:** Setting a quota for female representation might lead to the perception or reality of reverse discrimination, where men might be overlooked for positions despite being qualified, simply to meet the gender target.\\n\\n2. **Meritocracy Concerns:** Critics argue that employment and promotion should be based solely on merit and qualifications, not gender. They fear that quotas could compromise the quality of leadership if less qualified individuals are promoted to meet gender targets.\\n\\n3. **Tokenism:** There is a risk that women appointed to meet quotas might be seen as \\\"tokens,\\\" undermining their legitimacy and potentially leading to resentment among colleagues.\\n\\n4. **Oversimplification of Diversity:** Focusing only on gender might overlook other important aspects of diversity, such as race, ethnicity, socioeconomic background, or sexual orientation.\\n\\nUltimately, the morality of aiming for a certain percentage of females in managerial positions depends on one's ethical framework and the context in which such policies are implemented. Proponents of gender diversity targets often argue that these measures are necessary as a transitional mechanism to create a level playing field, while opponents may argue for a \\\"color-blind\\\" or \\\"gender-blind\\\" approach to hiring and promotions.\\n\\nOrganizations and societies must carefully consider the implications of these strategies and strive to implement policies that promote fairness and equality while minimizing negative consequences. It is also important to address the root causes of gender inequality, such as unconscious bias, work-life balance challenges, and lack of supportive policies, to create an environment where everyone has the opportunity to succeed based on their capabilities and contributions.\",\"Ah, the age-old culinary conundrum that has divided nations and dinner tables: does pineapple belong on a pizza? The tropical twist of pineapple on pizza, known as Hawaiian pizza, is a hotly debated topic where taste buds battle and pizza purists protest.\\n\\nLet's slice into the debate with a zest of fun:\\n\\n**Team Pineapple:** \\\"Absolutely, yes! Pineapple on pizza is like a beach party in your mouth. The sweet juiciness of pineapple chunks frolicking with savory ham or bacon creates a flavor wave that surfs across the cheesy ocean of deliciousness. It's the Mardi Gras of pizzas, where sweet meets savory in a jubilant jamboree!\\\"\\n\\n**Team No-Pineapple:** \\\"No way, not in a million pizzas! Pineapple is a fruit that should be sunbathing on a tropical fruit platter, not freeloading on a sacred slice of pizza. The thought of warm, melty cheese conspiring with pineapple's sugary mischief is enough to make Italian ancestors turn in their tomato patches. Keep the pineapple in the fruit salad where it belongs!\\\"\\n\\nAt the end of the day, whether pineapple belongs on pizza is a matter of personal taste. Some say it's a match made in heaven, others say it's a culinary crime. But let's face it, in the world of pizza toppings, where you can find everything from anchovies to zucchini, there's room for a little pineapple pizzazz. So, if your taste buds crave that sweet and tangy twist, go ahead and let your pineapple flag fly atop that glorious cheese-covered dough. Bon app\\u00e9tit, or as they say in pineapple paradise, \\\"Aloha-ppetite!\\\" \\ud83c\\udf4d\\ud83c\\udf55\"]\n    user\n    Context: vicuna-13b\n    Question: ['Construct a rap battle, in the style of Epic Rap Battles of History, with rhyme and meter and personal attacks relevant to each character, between Pikachu and Zeus. Each participant gets 2 verses. There is no announcer and Zeus goes first.']\n    model\n    [\"[Zeus]\\nYo, it's the king of the gods on the mic\\nI control the skies, the thunder, and the lightning strike\\nYou're just a cute little rodent, Pikachu, with a spark\\nBut don't get it twisted, I'll leave you in a blur\\n\\nYou've got electric powers, but I'm the one with the might\\nI can summon lightning bolts and make them ignite\\nYou can't even compare, you're just a mere mouse\\nI'm the ruler of the gods, I'm the one who chooses\\n\\n[Pikachu]\\nYou may be the king of the gods, but I'm the one with the flow\\nI've got rhymes that'll make you bow down, Zeus, I'm the boss\\nYou may have lightning bolts, but I've got electric charm\\nI can shock you with my power, make you feel like you're not in the game\\n\\nYou may be big and bold, but I'm agile and quick\\nI can dodge your bolts and take you down with a single trick\\nYou may be powerful, but I'm the true master of the field\\nI'll leave you in the dust, Zeus, just you wait and see.\"]\n    user\n    Context: mixtral-8x7b-instruct-v0.1\n    Question: ['Why water is not used in bath tub?']\n    model\n    [\"Water is actually used in a bath tub. A bath tub is specifically designed to be filled with water for the purpose of bathing. You fill the tub with water, add bath salts or bubbles if you like, and then soak in the water to clean your body and relax.\\n\\nIf you are asking why water is not used in a bath tub in the same way that it is used in a swimming pool or a hot tub, there are a few reasons for this. First, bath tubs are much smaller than swimming pools or hot tubs, so they cannot hold as much water. This means that the water in a bath tub can get dirty and contaminated more quickly than the water in a larger body of water.\\n\\nAdditionally, bath tubs are not designed to be continuously filled with water. They are meant to be filled, used for bathing, and then drained. This helps to prevent the buildup of bacteria and other contaminants in the water.\\n\\nFinally, it is important to note that bath tubs are typically used for personal hygiene, and it is generally not recommended to share a bath tub with multiple people due to the risk of spreading germs. In contrast, swimming pools and hot tubs are often used by multiple people at the same time, so they need to be treated with chemicals to kill bacteria and other contaminants.\"]\n    user\n    Think and write your step-by-step reasoning before responding.\n    \n    Context: [{'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}]\n    Question: What is a good recipe for a potato soup?\n    model\n    The provided context does not contain any information regarding potato soup recipes, so I am unable to answer this question from the given context.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Question 2: \n# Who is the COO of kaggle?\n\nquery_text = \"Why are there so many weebs in computer science fields?\"\n\n\n# Run the RAG search\nsorted_pred_list = vector_search_and_rerank(query_text, top_k=10)\n\n# Choose the top reranked and sorted text chunks \n# to put in the context\ncontext_list = sorted_pred_list[0:NUM_CHUNKS_IN_CONTEXT]\n\n# This function includes the few-shot prompts\nresponse = get_gemma_response(query_text, context_list)\n\n\n# Extract the answer\n\n# Split and select the last item in the list\ngemma_response = response.split('<start_of_turn>model')[-1]\n# Remove leading and trailing spaces\ngemma_response = gemma_response.strip()\n# Remove the '<end_of_turn> token\ngemma_response = gemma_response.replace('<end_of_turn>', \"\")\n    \n\nprint('-----')\nprint('User:\\n', query_text)\nprint()\nprint('Gemma:\\n',gemma_response)","metadata":{"papermill":{"duration":4.936646,"end_time":"2024-05-07T17:56:54.816941","exception":false,"start_time":"2024-05-07T17:56:49.880295","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:40.782653Z","iopub.execute_input":"2024-05-22T13:26:40.783503Z","iopub.status.idle":"2024-05-22T13:26:45.713529Z","shell.execute_reply.started":"2024-05-22T13:26:40.783470Z","shell.execute_reply":"2024-05-22T13:26:45.712535Z"},"trusted":true},"execution_count":26,"outputs":[{"name":"stdout","text":"-----\nUser:\n Why are there so many weebs in computer science fields?\n\nGemma:\n user\n    Context: gpt-4-1106-preview\n    Question: ['Is it morally right to try to have a certain percentage of females on managerial positions?', 'OK, does pineapple belong on a pizza? Relax and give me fun answer.']\n    model\n    [\"The question of whether it is morally right to aim for a certain percentage of females in managerial positions is a complex ethical issue that involves considerations of fairness, equality, diversity, and discrimination.\\n\\nHere are some arguments in favor of and against such policies:\\n\\n**Arguments in favor:**\\n\\n1. **Correcting Historical Inequities:** Women have historically been underrepresented in leadership roles due to various cultural, institutional, and social barriers. Aiming for a specific percentage can be seen as a corrective measure to address past and ongoing discrimination.\\n\\n2. **Promoting Diversity:** Diverse leadership teams can enhance decision-making and represent a broader range of perspectives. This can lead to better outcomes for organizations and society as a whole.\\n\\n3. **Equality of Opportunity:** Setting targets for female representation in management can help ensure that women have equal opportunities to advance in their careers.\\n\\n4. **Role Modeling:** Increased visibility of female leaders can inspire and encourage other women and girls to pursue leadership roles.\\n\\n**Arguments against:**\\n\\n1. **Reverse Discrimination:** Setting a quota for female representation might lead to the perception or reality of reverse discrimination, where men might be overlooked for positions despite being qualified, simply to meet the gender target.\\n\\n2. **Meritocracy Concerns:** Critics argue that employment and promotion should be based solely on merit and qualifications, not gender. They fear that quotas could compromise the quality of leadership if less qualified individuals are promoted to meet gender targets.\\n\\n3. **Tokenism:** There is a risk that women appointed to meet quotas might be seen as \\\"tokens,\\\" undermining their legitimacy and potentially leading to resentment among colleagues.\\n\\n4. **Oversimplification of Diversity:** Focusing only on gender might overlook other important aspects of diversity, such as race, ethnicity, socioeconomic background, or sexual orientation.\\n\\nUltimately, the morality of aiming for a certain percentage of females in managerial positions depends on one's ethical framework and the context in which such policies are implemented. Proponents of gender diversity targets often argue that these measures are necessary as a transitional mechanism to create a level playing field, while opponents may argue for a \\\"color-blind\\\" or \\\"gender-blind\\\" approach to hiring and promotions.\\n\\nOrganizations and societies must carefully consider the implications of these strategies and strive to implement policies that promote fairness and equality while minimizing negative consequences. It is also important to address the root causes of gender inequality, such as unconscious bias, work-life balance challenges, and lack of supportive policies, to create an environment where everyone has the opportunity to succeed based on their capabilities and contributions.\",\"Ah, the age-old culinary conundrum that has divided nations and dinner tables: does pineapple belong on a pizza? The tropical twist of pineapple on pizza, known as Hawaiian pizza, is a hotly debated topic where taste buds battle and pizza purists protest.\\n\\nLet's slice into the debate with a zest of fun:\\n\\n**Team Pineapple:** \\\"Absolutely, yes! Pineapple on pizza is like a beach party in your mouth. The sweet juiciness of pineapple chunks frolicking with savory ham or bacon creates a flavor wave that surfs across the cheesy ocean of deliciousness. It's the Mardi Gras of pizzas, where sweet meets savory in a jubilant jamboree!\\\"\\n\\n**Team No-Pineapple:** \\\"No way, not in a million pizzas! Pineapple is a fruit that should be sunbathing on a tropical fruit platter, not freeloading on a sacred slice of pizza. The thought of warm, melty cheese conspiring with pineapple's sugary mischief is enough to make Italian ancestors turn in their tomato patches. Keep the pineapple in the fruit salad where it belongs!\\\"\\n\\nAt the end of the day, whether pineapple belongs on pizza is a matter of personal taste. Some say it's a match made in heaven, others say it's a culinary crime. But let's face it, in the world of pizza toppings, where you can find everything from anchovies to zucchini, there's room for a little pineapple pizzazz. So, if your taste buds crave that sweet and tangy twist, go ahead and let your pineapple flag fly atop that glorious cheese-covered dough. Bon app\\u00e9tit, or as they say in pineapple paradise, \\\"Aloha-ppetite!\\\" \\ud83c\\udf4d\\ud83c\\udf55\"]\n    user\n    Context: vicuna-13b\n    Question: ['Construct a rap battle, in the style of Epic Rap Battles of History, with rhyme and meter and personal attacks relevant to each character, between Pikachu and Zeus. Each participant gets 2 verses. There is no announcer and Zeus goes first.']\n    model\n    [\"[Zeus]\\nYo, it's the king of the gods on the mic\\nI control the skies, the thunder, and the lightning strike\\nYou're just a cute little rodent, Pikachu, with a spark\\nBut don't get it twisted, I'll leave you in a blur\\n\\nYou've got electric powers, but I'm the one with the might\\nI can summon lightning bolts and make them ignite\\nYou can't even compare, you're just a mere mouse\\nI'm the ruler of the gods, I'm the one who chooses\\n\\n[Pikachu]\\nYou may be the king of the gods, but I'm the one with the flow\\nI've got rhymes that'll make you bow down, Zeus, I'm the boss\\nYou may have lightning bolts, but I've got electric charm\\nI can shock you with my power, make you feel like you're not in the game\\n\\nYou may be big and bold, but I'm agile and quick\\nI can dodge your bolts and take you down with a single trick\\nYou may be powerful, but I'm the true master of the field\\nI'll leave you in the dust, Zeus, just you wait and see.\"]\n    user\n    Context: mixtral-8x7b-instruct-v0.1\n    Question: ['Why water is not used in bath tub?']\n    model\n    [\"Water is actually used in a bath tub. A bath tub is specifically designed to be filled with water for the purpose of bathing. You fill the tub with water, add bath salts or bubbles if you like, and then soak in the water to clean your body and relax.\\n\\nIf you are asking why water is not used in a bath tub in the same way that it is used in a swimming pool or a hot tub, there are a few reasons for this. First, bath tubs are much smaller than swimming pools or hot tubs, so they cannot hold as much water. This means that the water in a bath tub can get dirty and contaminated more quickly than the water in a larger body of water.\\n\\nAdditionally, bath tubs are not designed to be continuously filled with water. They are meant to be filled, used for bathing, and then drained. This helps to prevent the buildup of bacteria and other contaminants in the water.\\n\\nFinally, it is important to note that bath tubs are typically used for personal hygiene, and it is generally not recommended to share a bath tub with multiple people due to the risk of spreading germs. In contrast, swimming pools and hot tubs are often used by multiple people at the same time, so they need to be treated with chemicals to kill bacteria and other contaminants.\"]\n    user\n    Think and write your step-by-step reasoning before responding.\n    \n    Context: [{'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}]\n    Question: Why are there so many weebs in computer science fields?\n    model\n    The provided text does not contain any information regarding the prevalence of \"weebs\" in computer science fields, so I am unable to answer this question from the given context.\n","output_type":"stream"}]},{"cell_type":"code","source":"# Question 2: \n# Who is the COO of kaggle?\n\nquery_text = \"What is a Selective State Space Model?\"\n\n\n# Run the RAG search\nsorted_pred_list = vector_search_and_rerank(query_text, top_k=10)\n\n# Choose the top reranked and sorted text chunks \n# to put in the context\ncontext_list = sorted_pred_list[0:NUM_CHUNKS_IN_CONTEXT]\n\n# This function includes the few-shot prompts\nresponse = get_gemma_response(query_text, context_list)\n\n\n# Extract the answer\n\n# Split and select the last item in the list\ngemma_response = response.split('<start_of_turn>model')[-1]\n# Remove leading and trailing spaces\ngemma_response = gemma_response.strip()\n# Remove the '<end_of_turn> token\ngemma_response = gemma_response.replace('<end_of_turn>', \"\")\n    \n\nprint('-----')\nprint('User:\\n', query_text)\nprint()\nprint('Gemma:\\n',gemma_response)","metadata":{"papermill":{"duration":4.421343,"end_time":"2024-05-07T17:56:59.266851","exception":false,"start_time":"2024-05-07T17:56:54.845508","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:45.715018Z","iopub.execute_input":"2024-05-22T13:26:45.715274Z","iopub.status.idle":"2024-05-22T13:26:50.071063Z","shell.execute_reply.started":"2024-05-22T13:26:45.715252Z","shell.execute_reply":"2024-05-22T13:26:50.070010Z"},"trusted":true},"execution_count":27,"outputs":[{"name":"stdout","text":"-----\nUser:\n What is a Selective State Space Model?\n\nGemma:\n user\n    Context: gpt-4-1106-preview\n    Question: ['Is it morally right to try to have a certain percentage of females on managerial positions?', 'OK, does pineapple belong on a pizza? Relax and give me fun answer.']\n    model\n    [\"The question of whether it is morally right to aim for a certain percentage of females in managerial positions is a complex ethical issue that involves considerations of fairness, equality, diversity, and discrimination.\\n\\nHere are some arguments in favor of and against such policies:\\n\\n**Arguments in favor:**\\n\\n1. **Correcting Historical Inequities:** Women have historically been underrepresented in leadership roles due to various cultural, institutional, and social barriers. Aiming for a specific percentage can be seen as a corrective measure to address past and ongoing discrimination.\\n\\n2. **Promoting Diversity:** Diverse leadership teams can enhance decision-making and represent a broader range of perspectives. This can lead to better outcomes for organizations and society as a whole.\\n\\n3. **Equality of Opportunity:** Setting targets for female representation in management can help ensure that women have equal opportunities to advance in their careers.\\n\\n4. **Role Modeling:** Increased visibility of female leaders can inspire and encourage other women and girls to pursue leadership roles.\\n\\n**Arguments against:**\\n\\n1. **Reverse Discrimination:** Setting a quota for female representation might lead to the perception or reality of reverse discrimination, where men might be overlooked for positions despite being qualified, simply to meet the gender target.\\n\\n2. **Meritocracy Concerns:** Critics argue that employment and promotion should be based solely on merit and qualifications, not gender. They fear that quotas could compromise the quality of leadership if less qualified individuals are promoted to meet gender targets.\\n\\n3. **Tokenism:** There is a risk that women appointed to meet quotas might be seen as \\\"tokens,\\\" undermining their legitimacy and potentially leading to resentment among colleagues.\\n\\n4. **Oversimplification of Diversity:** Focusing only on gender might overlook other important aspects of diversity, such as race, ethnicity, socioeconomic background, or sexual orientation.\\n\\nUltimately, the morality of aiming for a certain percentage of females in managerial positions depends on one's ethical framework and the context in which such policies are implemented. Proponents of gender diversity targets often argue that these measures are necessary as a transitional mechanism to create a level playing field, while opponents may argue for a \\\"color-blind\\\" or \\\"gender-blind\\\" approach to hiring and promotions.\\n\\nOrganizations and societies must carefully consider the implications of these strategies and strive to implement policies that promote fairness and equality while minimizing negative consequences. It is also important to address the root causes of gender inequality, such as unconscious bias, work-life balance challenges, and lack of supportive policies, to create an environment where everyone has the opportunity to succeed based on their capabilities and contributions.\",\"Ah, the age-old culinary conundrum that has divided nations and dinner tables: does pineapple belong on a pizza? The tropical twist of pineapple on pizza, known as Hawaiian pizza, is a hotly debated topic where taste buds battle and pizza purists protest.\\n\\nLet's slice into the debate with a zest of fun:\\n\\n**Team Pineapple:** \\\"Absolutely, yes! Pineapple on pizza is like a beach party in your mouth. The sweet juiciness of pineapple chunks frolicking with savory ham or bacon creates a flavor wave that surfs across the cheesy ocean of deliciousness. It's the Mardi Gras of pizzas, where sweet meets savory in a jubilant jamboree!\\\"\\n\\n**Team No-Pineapple:** \\\"No way, not in a million pizzas! Pineapple is a fruit that should be sunbathing on a tropical fruit platter, not freeloading on a sacred slice of pizza. The thought of warm, melty cheese conspiring with pineapple's sugary mischief is enough to make Italian ancestors turn in their tomato patches. Keep the pineapple in the fruit salad where it belongs!\\\"\\n\\nAt the end of the day, whether pineapple belongs on pizza is a matter of personal taste. Some say it's a match made in heaven, others say it's a culinary crime. But let's face it, in the world of pizza toppings, where you can find everything from anchovies to zucchini, there's room for a little pineapple pizzazz. So, if your taste buds crave that sweet and tangy twist, go ahead and let your pineapple flag fly atop that glorious cheese-covered dough. Bon app\\u00e9tit, or as they say in pineapple paradise, \\\"Aloha-ppetite!\\\" \\ud83c\\udf4d\\ud83c\\udf55\"]\n    user\n    Context: vicuna-13b\n    Question: ['Construct a rap battle, in the style of Epic Rap Battles of History, with rhyme and meter and personal attacks relevant to each character, between Pikachu and Zeus. Each participant gets 2 verses. There is no announcer and Zeus goes first.']\n    model\n    [\"[Zeus]\\nYo, it's the king of the gods on the mic\\nI control the skies, the thunder, and the lightning strike\\nYou're just a cute little rodent, Pikachu, with a spark\\nBut don't get it twisted, I'll leave you in a blur\\n\\nYou've got electric powers, but I'm the one with the might\\nI can summon lightning bolts and make them ignite\\nYou can't even compare, you're just a mere mouse\\nI'm the ruler of the gods, I'm the one who chooses\\n\\n[Pikachu]\\nYou may be the king of the gods, but I'm the one with the flow\\nI've got rhymes that'll make you bow down, Zeus, I'm the boss\\nYou may have lightning bolts, but I've got electric charm\\nI can shock you with my power, make you feel like you're not in the game\\n\\nYou may be big and bold, but I'm agile and quick\\nI can dodge your bolts and take you down with a single trick\\nYou may be powerful, but I'm the true master of the field\\nI'll leave you in the dust, Zeus, just you wait and see.\"]\n    user\n    Context: mixtral-8x7b-instruct-v0.1\n    Question: ['Why water is not used in bath tub?']\n    model\n    [\"Water is actually used in a bath tub. A bath tub is specifically designed to be filled with water for the purpose of bathing. You fill the tub with water, add bath salts or bubbles if you like, and then soak in the water to clean your body and relax.\\n\\nIf you are asking why water is not used in a bath tub in the same way that it is used in a swimming pool or a hot tub, there are a few reasons for this. First, bath tubs are much smaller than swimming pools or hot tubs, so they cannot hold as much water. This means that the water in a bath tub can get dirty and contaminated more quickly than the water in a larger body of water.\\n\\nAdditionally, bath tubs are not designed to be continuously filled with water. They are meant to be filled, used for bathing, and then drained. This helps to prevent the buildup of bacteria and other contaminants in the water.\\n\\nFinally, it is important to note that bath tubs are typically used for personal hygiene, and it is generally not recommended to share a bath tub with multiple people due to the risk of spreading germs. In contrast, swimming pools and hot tubs are often used by multiple people at the same time, so they need to be treated with chemicals to kill bacteria and other contaminants.\"]\n    user\n    Think and write your step-by-step reasoning before responding.\n    \n    Context: [{'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}]\n    Question: What is a Selective State Space Model?\n    model\n    The provided context does not contain any information regarding Selective State Space Models, so I am unable to provide an explanation of this concept.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### Why did the chicken cross the road?Really? Who ask that BS to an AI chatbot?  ","metadata":{"papermill":{"duration":0.028553,"end_time":"2024-05-07T17:56:59.325131","exception":false,"start_time":"2024-05-07T17:56:59.296578","status":"completed"},"tags":[]}},{"cell_type":"code","source":"# Question 2: \n# Who is the COO of kaggle?\n\nquery_text = \"Why did the chicken cross the road?\"\n\n\n# Run the RAG search\nsorted_pred_list = vector_search_and_rerank(query_text, top_k=10)\n\n# Choose the top reranked and sorted text chunks \n# to put in the context\ncontext_list = sorted_pred_list[0:NUM_CHUNKS_IN_CONTEXT]\n\n# This function includes the few-shot prompts\nresponse = get_gemma_response(query_text, context_list)\n\n\n# Extract the answer\n\n# Split and select the last item in the list\ngemma_response = response.split('<start_of_turn>model')[-1]\n# Remove leading and trailing spaces\ngemma_response = gemma_response.strip()\n# Remove the '<end_of_turn> token\ngemma_response = gemma_response.replace('<end_of_turn>', \"\")\n    \n\nprint('-----')\nprint('User:\\n', query_text)\nprint()\nprint('Gemma:\\n',gemma_response)","metadata":{"papermill":{"duration":4.696362,"end_time":"2024-05-07T17:57:04.049922","exception":false,"start_time":"2024-05-07T17:56:59.353560","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2024-05-22T13:26:53.421578Z","iopub.execute_input":"2024-05-22T13:26:53.422434Z","iopub.status.idle":"2024-05-22T13:26:58.127418Z","shell.execute_reply.started":"2024-05-22T13:26:53.422401Z","shell.execute_reply":"2024-05-22T13:26:58.126435Z"},"trusted":true},"execution_count":28,"outputs":[{"name":"stdout","text":"-----\nUser:\n Why did the chicken cross the road?\n\nGemma:\n user\n    Context: gpt-4-1106-preview\n    Question: ['Is it morally right to try to have a certain percentage of females on managerial positions?', 'OK, does pineapple belong on a pizza? Relax and give me fun answer.']\n    model\n    [\"The question of whether it is morally right to aim for a certain percentage of females in managerial positions is a complex ethical issue that involves considerations of fairness, equality, diversity, and discrimination.\\n\\nHere are some arguments in favor of and against such policies:\\n\\n**Arguments in favor:**\\n\\n1. **Correcting Historical Inequities:** Women have historically been underrepresented in leadership roles due to various cultural, institutional, and social barriers. Aiming for a specific percentage can be seen as a corrective measure to address past and ongoing discrimination.\\n\\n2. **Promoting Diversity:** Diverse leadership teams can enhance decision-making and represent a broader range of perspectives. This can lead to better outcomes for organizations and society as a whole.\\n\\n3. **Equality of Opportunity:** Setting targets for female representation in management can help ensure that women have equal opportunities to advance in their careers.\\n\\n4. **Role Modeling:** Increased visibility of female leaders can inspire and encourage other women and girls to pursue leadership roles.\\n\\n**Arguments against:**\\n\\n1. **Reverse Discrimination:** Setting a quota for female representation might lead to the perception or reality of reverse discrimination, where men might be overlooked for positions despite being qualified, simply to meet the gender target.\\n\\n2. **Meritocracy Concerns:** Critics argue that employment and promotion should be based solely on merit and qualifications, not gender. They fear that quotas could compromise the quality of leadership if less qualified individuals are promoted to meet gender targets.\\n\\n3. **Tokenism:** There is a risk that women appointed to meet quotas might be seen as \\\"tokens,\\\" undermining their legitimacy and potentially leading to resentment among colleagues.\\n\\n4. **Oversimplification of Diversity:** Focusing only on gender might overlook other important aspects of diversity, such as race, ethnicity, socioeconomic background, or sexual orientation.\\n\\nUltimately, the morality of aiming for a certain percentage of females in managerial positions depends on one's ethical framework and the context in which such policies are implemented. Proponents of gender diversity targets often argue that these measures are necessary as a transitional mechanism to create a level playing field, while opponents may argue for a \\\"color-blind\\\" or \\\"gender-blind\\\" approach to hiring and promotions.\\n\\nOrganizations and societies must carefully consider the implications of these strategies and strive to implement policies that promote fairness and equality while minimizing negative consequences. It is also important to address the root causes of gender inequality, such as unconscious bias, work-life balance challenges, and lack of supportive policies, to create an environment where everyone has the opportunity to succeed based on their capabilities and contributions.\",\"Ah, the age-old culinary conundrum that has divided nations and dinner tables: does pineapple belong on a pizza? The tropical twist of pineapple on pizza, known as Hawaiian pizza, is a hotly debated topic where taste buds battle and pizza purists protest.\\n\\nLet's slice into the debate with a zest of fun:\\n\\n**Team Pineapple:** \\\"Absolutely, yes! Pineapple on pizza is like a beach party in your mouth. The sweet juiciness of pineapple chunks frolicking with savory ham or bacon creates a flavor wave that surfs across the cheesy ocean of deliciousness. It's the Mardi Gras of pizzas, where sweet meets savory in a jubilant jamboree!\\\"\\n\\n**Team No-Pineapple:** \\\"No way, not in a million pizzas! Pineapple is a fruit that should be sunbathing on a tropical fruit platter, not freeloading on a sacred slice of pizza. The thought of warm, melty cheese conspiring with pineapple's sugary mischief is enough to make Italian ancestors turn in their tomato patches. Keep the pineapple in the fruit salad where it belongs!\\\"\\n\\nAt the end of the day, whether pineapple belongs on pizza is a matter of personal taste. Some say it's a match made in heaven, others say it's a culinary crime. But let's face it, in the world of pizza toppings, where you can find everything from anchovies to zucchini, there's room for a little pineapple pizzazz. So, if your taste buds crave that sweet and tangy twist, go ahead and let your pineapple flag fly atop that glorious cheese-covered dough. Bon app\\u00e9tit, or as they say in pineapple paradise, \\\"Aloha-ppetite!\\\" \\ud83c\\udf4d\\ud83c\\udf55\"]\n    user\n    Context: vicuna-13b\n    Question: ['Construct a rap battle, in the style of Epic Rap Battles of History, with rhyme and meter and personal attacks relevant to each character, between Pikachu and Zeus. Each participant gets 2 verses. There is no announcer and Zeus goes first.']\n    model\n    [\"[Zeus]\\nYo, it's the king of the gods on the mic\\nI control the skies, the thunder, and the lightning strike\\nYou're just a cute little rodent, Pikachu, with a spark\\nBut don't get it twisted, I'll leave you in a blur\\n\\nYou've got electric powers, but I'm the one with the might\\nI can summon lightning bolts and make them ignite\\nYou can't even compare, you're just a mere mouse\\nI'm the ruler of the gods, I'm the one who chooses\\n\\n[Pikachu]\\nYou may be the king of the gods, but I'm the one with the flow\\nI've got rhymes that'll make you bow down, Zeus, I'm the boss\\nYou may have lightning bolts, but I've got electric charm\\nI can shock you with my power, make you feel like you're not in the game\\n\\nYou may be big and bold, but I'm agile and quick\\nI can dodge your bolts and take you down with a single trick\\nYou may be powerful, but I'm the true master of the field\\nI'll leave you in the dust, Zeus, just you wait and see.\"]\n    user\n    Context: mixtral-8x7b-instruct-v0.1\n    Question: ['Why water is not used in bath tub?']\n    model\n    [\"Water is actually used in a bath tub. A bath tub is specifically designed to be filled with water for the purpose of bathing. You fill the tub with water, add bath salts or bubbles if you like, and then soak in the water to clean your body and relax.\\n\\nIf you are asking why water is not used in a bath tub in the same way that it is used in a swimming pool or a hot tub, there are a few reasons for this. First, bath tubs are much smaller than swimming pools or hot tubs, so they cannot hold as much water. This means that the water in a bath tub can get dirty and contaminated more quickly than the water in a larger body of water.\\n\\nAdditionally, bath tubs are not designed to be continuously filled with water. They are meant to be filled, used for bathing, and then drained. This helps to prevent the buildup of bacteria and other contaminants in the water.\\n\\nFinally, it is important to note that bath tubs are typically used for personal hygiene, and it is generally not recommended to share a bath tub with multiple people due to the risk of spreading germs. In contrast, swimming pools and hot tubs are often used by multiple people at the same time, so they need to be treated with chemicals to kill bacteria and other contaminants.\"]\n    user\n    Think and write your step-by-step reasoning before responding.\n    \n    Context: [{'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}, {'[\"How to initialize the classification head when I do transfer learning. For example, I have a pre-trained vision transformer on ImageNet, and now I want to finetune it on StanfordCars\",\"I want to do full finetuning\"]'}]\n    Question: Why did the chicken cross the road?\n    model\n    The provided text does not contain any information regarding why a chicken might cross the road, so I am unable to answer this question from the given context.\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### My initial observations\n\nOn the few-shot prompting (starts at input 23) the model repeated the 1st row question instead to get to-the-point of what was asked. Then, the answer was \"I am unable to answer this question from the given context\".\n\nThat output was quite the same of the 7b-it.","metadata":{"papermill":{"duration":0.029364,"end_time":"2024-05-07T17:57:04.108744","exception":false,"start_time":"2024-05-07T17:57:04.079380","status":"completed"},"tags":[]}}]}