{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "seVkK-3FfCxK" }, "source": [ "## Mistral-7B-Instruct\n", "Sources:\n", "\n", "- https://mistral.ai/news/announcing-mistral-7b/\n", "- https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1\n", "- https://huggingface.co/TheBloke/Mistral-7B-Instruct-v0.1-GGUF\n", "- https://huggingface.co/spaces/osanseviero/mistral-super-fast/blob/main/Mistral_7B.ipynb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Setup" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "sR_OMSFLdwpa", "outputId": "c00d8f31-97a7-4453-f587-4a1bdd6acee6" }, "outputs": [], "source": [ "%pip install -U openai" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pip show openai" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "E22K1DuniBkx", "outputId": "d0c60f18-14ae-42b4-ee0f-63f87f2c54f8" }, "outputs": [], "source": [ "import openai\n", "import os\n", "\n", "openai.api_key = os.getenv('HF_API_KEY')\n", "openai.api_base = \"https://limcheekin-mistral-7b-instruct-v0-1-gguf.hf.space/v1\"\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Prompt Format\n", "Sources: \n", "- https://colab.research.google.com/drive/1F2PeWl5FOHv4sjd7XTEu40JjqbFhC3LB?usp=sharing\n", "- https://huggingface.co/spaces/osanseviero/mistral-super-fast/blob/main/Mistral_7B.ipynb\n", "\n", "```\n", "[INST] [/INST]\n", " \n", "[INST] [/INST]\n", "```\n", "---\n", "```\n", "[INST] {{ user_msg_1 }} [/INST] {{ model_answer_1 }} [INST] {{ user_msg_2 }} [/INST] {{ model_answer_2 }}\n", "```" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "def get_prompt(system_prompt: str, instruction: str):\n", " prompt = f\"[INST]System: {system_prompt}[/INST] [INST]User: {instruction}[/INST]\"\n", " return prompt" ] }, { "cell_type": "markdown", "metadata": { "id": "8qv6czMrmRdg" }, "source": [ "### Completion Function" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "import time\n", "\n", "DEFAULT_SYSTEM_PROMPT = \"You are a helpful assistant, you will complete the task by follow the instructions given.\"\n", "\n", "# Source: https://platform.openai.com/docs/api-reference/chat/create\n", "def get_completion(prompt, system_prompt=DEFAULT_SYSTEM_PROMPT, temperature=0.0, max_tokens=512):\n", " messages = [{\"role\": \"user\", \"content\": get_prompt(system_prompt=system_prompt, instruction=prompt)}]\n", " start_time = time.time()\n", " response = openai.ChatCompletion.create(\n", " model=\"\",\n", " messages=messages,\n", " temperature=temperature, \n", " max_tokens=max_tokens,\n", " request_timeout=600,\n", " stream=True\n", " )\n", " return combine_chunks(start_time, response)\n", "\n", "# Source: https://github.com/openai/openai-cookbook/blob/main/examples/How_to_stream_completions.ipynb\n", "def combine_chunks(start_time, response):\n", " # create variables to collect the stream of chunks\n", " collected_messages = []\n", " # iterate through the stream of events\n", " for chunk in response:\n", " chunk_time = time.time() - start_time # calculate the time delay of the chunk\n", " chunk_message = chunk['choices'][0]['delta'] # extract the message\n", " collected_messages.append(chunk_message) # save the message\n", " print(chunk_message.get('content', ''), end='')\n", " # print(f\"Message received {chunk_time:.2f} seconds after request: {chunk_message}\") # print the delay and text\n", "\n", " # print the time delay and text received\n", " print(f\"\\nResponse time taken: {chunk_time:.2f}s\")\n", " full_reply_content = ''.join([m.get('content', '') for m in collected_messages])\n", " return full_reply_content " ] }, { "cell_type": "code", "execution_count": 49, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DYKGCpH3mJZ3", "outputId": "fd4560b7-ee53-43df-fb39-ffb5002c04f0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1. AI stands for Artificial Intelligence. It refers to the development of computer systems that can perform tasks that typically require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.\n", "2. AI technology involves machine learning algorithms, deep learning neural networks, natural language processing (NLP), and other techniques that enable computers to learn from data and improve their performance over time.\n", "3. AI has many applications in various industries, including healthcare, finance, transportation, education, and entertainment. It is used for tasks such as predictive analytics, recommendation systems, chatbots, and autonomous vehicles.\n", "4. AI is a rapidly evolving field with new advancements being made all the time. It has the potential to revolutionize many aspects of our lives and improve our quality of life in various ways.\n", "Response time taken: 44.19s\n" ] } ], "source": [ "text = \"What is AI?\"\n", "response = get_completion(text)" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "A100", "machine_shape": "hm", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.10.12" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "089398100f7842d7ad213f45e4263106": { "model_module": "@jupyter-widgets/base", "model_module_version": "1.2.0", "model_name": "LayoutModel", "state": { "_model_module": "@jupyter-widgets/base", "_model_module_version": "1.2.0", "_model_name": "LayoutModel", "_view_count": null, "_view_module": 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