google-gemma-gemini-llm-chatbots.ipynb

{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# 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":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![](https://pbs.twimg.com/media/GG3SidNWYAAljb1?format=jpg&name=4096x4096)\n\n# To Know about Google Gemini Architecture and Parameters, Watch this Video\n## Link - https://youtu.be/vabQP3UkWDc\n\n### Key Features of Gemini\n- Enhanced Contextual Understanding: Gemini is the first model to outperform human experts on MMLU (Massive Multitask Language Understanding), one of the most popular methods to test the knowledge and problem-solving abilities of AI models.\n- Multimodality: Gemini is built from the ground up for multimodality — reasoning seamlessly across text, images, video, audio, and code.\n- Anything to anything: Gemini is natively multimodal, which gives you the potential to transform any type of input into any type of output.\n- Customizability: Users can fine-tune Gemini for specific tasks or industries.\n\n\n# NoteBook - Google Gemini Gemma Models Tutorials - 2\n\n### Exmaple Code to Run Google Gemma Models","metadata":{}},{"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\n!pip install trl\n!pip install git+https://github.com/huggingface/peft.git","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"!pip install -i https://pypi.org/simple/ bitsandbytes","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from huggingface_hub import notebook_login\nnotebook_login(write_permission=True)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import torch\nimport os\nfrom transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n\n# Define conversation template\nchat_template = \"\"\"\n**You:** {user_input}\n**Gemma-2b:** {model_response}\n\"\"\"\n\n# Load pre-trained model and tokenizer\nmodel_id = \"google/gemma-2b-it\"\nlora_config = BitsAndBytesConfig(\n    load_in_4bit=True,\n    bnb_4bit_quant_type=\"nf4\",\n    bnb_4bit_compute_dtype=torch.bfloat16\n)\n\ntokenizer = AutoTokenizer.from_pretrained(model_id)\nquantized_model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=lora_config, device_map={\"\":0})","metadata":{"execution":{"iopub.status.busy":"2024-03-21T10:28:03.451824Z","iopub.execute_input":"2024-03-21T10:28:03.452568Z","iopub.status.idle":"2024-03-21T10:28:35.034770Z","shell.execute_reply.started":"2024-03-21T10:28:03.452535Z","shell.execute_reply":"2024-03-21T10:28:35.033832Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"tokenizer_config.json:   0%|          | 0.00/2.16k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"57cb71ea92d04392bc53ac24fdcc6fd5"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ab44307d848f47d0955c89e33fe296eb"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8822588cf0f945a7ba311e33ca40ff64"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"special_tokens_map.json:   0%|          | 0.00/888 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"e5c4751ec5884250bd6bc3abc24c0b2e"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"config.json:   0%|          | 0.00/627 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"d4f6ebf082ac4200bdd8a6df0b6de44b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model.safetensors.index.json:   0%|          | 0.00/13.5k [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"ca3ae0108ace4a628ae4686617672d37"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"84915837f61342608d9ea4eaa76024a4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00001-of-00002.safetensors:   0%|          | 0.00/4.95G [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"f7f060c498f048f194d3ee8324c33f1a"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"model-00002-of-00002.safetensors:   0%|          | 0.00/67.1M [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"04fb7ac49881442c8866d2588a07b626"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b95b1327122e40f189e5e042faa5bbc0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"generation_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"cb489b9710ef4b2cb643a66b6723ee9d"}},"metadata":{}}]},{"cell_type":"code","source":"import warnings\nwarnings.filterwarnings('ignore')\n\ndef chatbot():\n  \"\"\"\n  Interactive chat function with Gemma-2b chatbot.\n  \"\"\"\n  while True:\n    user_input = input(\"You: \")\n    if user_input.lower() == \"quit\":\n        break\n\n    # Encode user input with tokenizer\n    input_ids = tokenizer.encode(user_input, return_tensors=\"pt\")\n\n    # Generate model response\n    output = quantized_model.generate(input_ids, max_length=1024, do_sample=True, top_k=10, top_p=0.9)\n    model_response = tokenizer.decode(output[0], skip_special_tokens=True)\n\n    # Print conversation\n    print(\"Gemma-2b ---> \",model_response.split(\"\\n\")[-1])\n\nif __name__ == \"__main__\":\n    print(\"Welcome to the Gemma-2b Chatbot!\")\n    chatbot()\n    print(\"Goodbye!\")","metadata":{"execution":{"iopub.status.busy":"2024-03-21T10:45:50.067569Z","iopub.execute_input":"2024-03-21T10:45:50.067970Z","iopub.status.idle":"2024-03-21T10:47:15.881744Z","shell.execute_reply.started":"2024-03-21T10:45:50.067943Z","shell.execute_reply":"2024-03-21T10:47:15.880683Z"},"trusted":true},"execution_count":23,"outputs":[{"name":"stdout","text":"Welcome to the Gemma-2b Chatbot!\n","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"You:  Hi Gemma! how are you\n"},{"name":"stdout","text":"Gemma-2b --->  I hope you're having a wonderful day so far! Is there anything I can do to help you today?\n","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"You:  Yes, Tell me who is biggest animal?\n"},{"name":"stdout","text":"Gemma-2b --->  The biggest animal is the blue whale, with a length of 100 feet and a weight of 200 tons.\n","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"You:  and Biggest Bird?\n"},{"name":"stdout","text":"Gemma-2b --->  **The Biggest Bird is a must-see for anyone interested in birds, nature, and filmmaking.**\n","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"You:  Who is Biggest Bird?\n"},{"name":"stdout","text":"Gemma-2b --->  The Biggest Bird is a 126-foot-long bird of the family Megapotamidae, the largest known bird species. It is found in the Southern Hemisphere in South America and Antarctica. It is the most abundant bird in the world.\n","output_type":"stream"},{"output_type":"stream","name":"stdin","text":"You:  quit\n"},{"name":"stdout","text":"Goodbye!\n","output_type":"stream"}]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}