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https://github.com/amrrs/LLM-QA-Bot/blob/main/LLM_Q%26A_with_Open_Source_Hugging_Face_Models_with-UI.ipynb\n", + "\n", + "Notebook rationale: \n", + "* Free access to GPUs in Google Colab\n" + ], + "metadata": { + "id": "1zhrsuqKEqAA" + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "i_g3-tUqCEQN", + "outputId": "6fdc0d31-a2ca-4896-ea47-8ff7cfde008e" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Fri Mar 10 06:08:27 2023 \n", + "+-----------------------------------------------------------------------------+\n", + "| NVIDIA-SMI 525.85.12 Driver Version: 525.85.12 CUDA Version: 12.0 |\n", + "|-------------------------------+----------------------+----------------------+\n", + "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", + "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", + "| | | MIG M. |\n", + "|===============================+======================+======================|\n", + "| 0 Tesla T4 Off | 00000000:00:04.0 Off | 0 |\n", + "| N/A 67C P0 29W / 70W | 2275MiB / 15360MiB | 0% Default |\n", + "| | | N/A |\n", + "+-------------------------------+----------------------+----------------------+\n", + " \n", + "+-----------------------------------------------------------------------------+\n", + "| Processes: |\n", + "| GPU GI CI PID Type Process name GPU Memory |\n", + "| ID ID Usage |\n", + "|=============================================================================|\n", + "+-----------------------------------------------------------------------------+\n" + ] + } + ], + "source": [ + "# Runtime > Change runtime type > Hardware accelerator: GPU\n", + "# Install Driver\n", + "!nvidia-smi" + ] + }, + { + "cell_type": "code", + "source": [ + "# Install LLM tools\n", + "! pip install -q langchain gpt-index llama-index transformers sentence_transformers" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "8EkL3CcxFtjh", + "outputId": "bd44b1fb-33e4-45cb-9b77-917a6eca0743" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", + " Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n", + " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m87.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", + "\u001b[2K 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"\u001b[?25h Building wheel for llama-index (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for openai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "# Install visualization tools\n", + "!pip install -q gradio" + ], + "metadata": { + "id": "C8nj-t_YFuSH" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Import Libraries\n", + "import gradio as gr\n", + "\n", + "## Features\n", + "from llama_index import Document\n", + "\n", + "## Model\n", + "from langchain.llms.base import LLM\n", + "from llama_index import LLMPredictor, GPTSimpleVectorIndex\n", + "from transformers import pipeline\n", + "import torch\n", + "\n", + "from langchain.embeddings.huggingface import HuggingFaceEmbeddings\n", + "from llama_index import LangchainEmbedding" + ], + "metadata": { + "id": "Xqn1yfpEDy8D" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Feature Functions\n", + "\n", + "## Text to Document\n", + "def text_to_document(input_text):\n", + " text_list = [input_text]\n", + " return [Document(t) for t in text_list] # documents" + ], + "metadata": { + "id": "sL4ORAReD766" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Model Functions & Class\n", + "class FlanLLM(LLM):\n", + " model_name = \"google/flan-t5-base\"\n", + " pipeline = pipeline(\"text2text-generation\", model=model_name,\n", + " device=0, model_kwargs={\"torch_dtype\": torch.bfloat16})\n", + "\n", + " def _call(self, prompt, stop=None):\n", + " return self.pipeline(prompt, max_length=9999)[0][\"generated_text\"]\n", + "\n", + " def _identifying_params(self):\n", + " return {\"name_of_model\": self.model_name}\n", + "\n", + " def _llm_type(self):\n", + " return \"custom\"\n", + "\n", + "llm_predictor = LLMPredictor(llm=FlanLLM())\n", + "hfemb = HuggingFaceEmbeddings()\n", + "embed_model = LangchainEmbedding(hfemb)\n", + "\n", + "def document_to_index(documents):\n", + " global index\n", + " index = GPTSimpleVectorIndex(\n", + " documents, embed_model=embed_model, llm_predictor=llm_predictor)\n", + " return('Index successfully saved.')\n", + "\n", + "def query_and_response(chat_history, user_query):\n", + " bot_response = index.query(user_query)\n", + " response = \"\"\n", + " for letter in ''.join(bot_response.response):\n", + " response += f'{letter}'\n", + " yield chat_history + [(user_query, response)]" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 689, + "referenced_widgets": [ + "a62752f8b273454099934062834b43aa", + "5ea8251fe45146919104e7c6ba120e54", + "a6f984b2a739477eb6854061a3ab2508", + "827465ed846a4acd9221d974005d3cde", + "becf45a954004ff4b7b74a308982ccc4", + "8f4523a674534550a606438526b110ca", + "33d24d5a2b43499283fe3174148f22f6", 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