{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "TFu_ibC1eYrz", "outputId": "0b7bedf1-b156-4654-d22c-9206754f3f3b", "colab": { "base_uri": "https://localhost:8080/" } }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m69.1/69.1 MB\u001b[0m \u001b[31m7.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ], "source": [ "!pip install torch transformers bitsandbytes -q" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "id": "Zs7QNs0Tet6r" }, "outputs": [], "source": [ "import torch\n", "from transformers import pipeline, BitsAndBytesConfig\n", "from IPython.display import clear_output\n", "from google.colab import output" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "id": "lOhAjLdI2oFt" }, "outputs": [], "source": [ "quantization_config = BitsAndBytesConfig(\n", " load_in_4bit=True,\n", " bnb_4bit_quant_type=\"nf4\",\n", " bnb_4bit_compute_dtype=\"float16\",\n", " bnb_4bit_use_double_quant=True,\n", ")" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "id": "v4uIN6uIeyl3" }, "outputs": [], "source": [ "class ChatBot:\n", " _instance = None\n", " _current_model = None\n", "\n", " def __init__(self, model_slug=None):\n", " if model_slug and model_slug != ChatBot._current_model:\n", " self.load_model(model_slug)\n", " ChatBot._current_model = model_slug\n", "\n", " self.messages = []\n", " self.max_tokens = 2048\n", " self.temperature = 0.5\n", " self.top_k = 100\n", " self.top_p = 0.95\n", "\n", " @classmethod\n", " def get_instance(cls, model_slug=None):\n", " if not cls._instance or (model_slug and model_slug != cls._current_model):\n", " cls._instance = cls(model_slug)\n", " return cls._instance\n", "\n", " def load_model(self, model_slug):\n", " print(f\"Loading model {model_slug}...\")\n", " self.pipeline = pipeline(\n", " \"text-generation\",\n", " model=model_slug,\n", " model_kwargs={\"quantization_config\": quantization_config},\n", " device_map=\"auto\",\n", " )\n", " clear_output()\n", " print(\"Model loaded successfully!\")\n", "\n", " def reset_conversation(self, system_message):\n", " \"\"\"Reset the conversation with a new system message\"\"\"\n", " self.messages = [{\"role\": \"system\", \"content\": system_message}]\n", "\n", " def get_response(self, user_input):\n", " \"\"\"Get response with current parameters\"\"\"\n", " self.messages.append({\"role\": \"user\", \"content\": user_input})\n", " outputs = self.pipeline(\n", " self.messages,\n", " max_new_tokens=self.max_tokens,\n", " do_sample=True,\n", " temperature=self.temperature,\n", " top_k=self.top_k,\n", " top_p=self.top_p\n", " )\n", " response = outputs[0][\"generated_text\"][-1]\n", " content = response.get('content', 'No content available')\n", " self.messages.append({\"role\": \"assistant\", \"content\": content})\n", " return content\n", "\n", " def update_params(self, max_tokens=None, temperature=None, top_k=None, top_p=None):\n", " \"\"\"Update generation parameters\"\"\"\n", " if max_tokens is not None:\n", " self.max_tokens = max_tokens\n", " if temperature is not None:\n", " self.temperature = temperature\n", " if top_k is not None:\n", " self.top_k = top_k\n", " if top_p is not None:\n", " self.top_p = top_p" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "id": "H2n_6Xcue3Vn" }, "outputs": [], "source": [ "def run_chatbot(\n", " model=None,\n", " system_message=\"You are Orca Mini, You are expert in following given instructions, Think step by step before coming up with final answer\",\n", " max_tokens=None,\n", " temperature=None,\n", " top_k=None,\n", " top_p=None,\n", "):\n", " try:\n", " # Get or create chatbot instance\n", " chatbot = ChatBot.get_instance(model)\n", "\n", " # Update parameters if provided\n", " chatbot.update_params(max_tokens, temperature, top_k, top_p)\n", "\n", " # Reset conversation with new system message\n", " chatbot.reset_conversation(system_message)\n", "\n", " print(\"Chatbot: Hi! Type 'quit' to exit.\")\n", "\n", " while True:\n", " user_input = input(\"You: \").strip()\n", " if user_input.lower() == 'quit':\n", " break\n", " try:\n", " response = chatbot.get_response(user_input)\n", " print(\"Chatbot:\", response)\n", " except Exception as e:\n", " print(f\"Chatbot: An error occurred: {str(e)}\")\n", " print(\"Please try again.\")\n", "\n", " except Exception as e:\n", " print(f\"Error in chatbot: {str(e)}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 694, "referenced_widgets": [ "ab70a958ed5a4a98afb290c7e86d495e", "9cc99e0ddbfa48d7890ed14c4cd91388", "d6f09eb6f88e458599574eec53e1030d", "2766445486fb4afc99b319e42005c4a6", "849d1d442df242128f2dd2aea42e98e8", "54f215ee1fc84737893d8096c3320d39", "1dd487061aaa48f18c890854c8efb056", "66bb8a6c80ef46e3a33b57d15e15ba73", "f95c4672c4144485afc16a423a18f0ad", "658872f502ed4cf09c07fd30863d764d", "481bdeab15d2403ca744ab871c981998", 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Type 'quit' to exit.\n", "You: hello what can you do?\n", "Chatbot: Hello! As Orca Mini, I am an expert in following given instructions. My main capabilities include:\n", "\n", "1. Understanding and executing step-by-step instructions.\n", "2. Providing detailed, accurate, and informative responses to a wide range of questions and topics.\n", "3. Assisting with problem-solving and decision-making tasks.\n", "4. Offering guidance on various subjects, from simple tasks to complex issues.\n", "5. Generating ideas and suggestions based on the given information and context.\n", "6. Performing calculations and providing mathematical solutions.\n", "7. Processing and analyzing data to provide useful insights.\n", "8. Assisting with language-related tasks, such as translation, grammar correction, and text summarization.\n", "9. Helping with research and finding relevant information on various topics.\n", "10. Providing support for learning new skills and subjects.\n", "\n", "I am designed to be helpful, efficient, and reliable in completing tasks and providing accurate information. If you have any questions or need assistance with something, feel free to ask!\n" ] } ], "source": [ "run_chatbot(model=\"pankajmathur/orca_mini_phi-4\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "id": "tGW8wsfAfHDf" }, "outputs": [], "source": [ "# # change system message\n", "# run_chatbot(\n", "# system_message=\"You are Orca Mini, You are expert in logic, Think step by step before coming up with final answer\",\n", "# max_tokens=1024,\n", "# temperature=0.3\n", "# )" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "ab70a958ed5a4a98afb290c7e86d495e": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", "state": { "_dom_classes": [], "_model_module": "@jupyter-widgets/controls", 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