diff --git "a/Orca_Mini_Chat_4bit_Phi_4.ipynb" "b/Orca_Mini_Chat_4bit_Phi_4.ipynb" new file mode 100644--- /dev/null +++ "b/Orca_Mini_Chat_4bit_Phi_4.ipynb" @@ -0,0 +1,5899 @@ +{ + "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", + 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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": { + 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