{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/dbamman/anlp25/blob/main/10.llms/Prompting%20Local%20LLMs.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kM7cO8y_PKZg"
   },
   "source": [
    "In this notebook, we'll explore few-shot learning with [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B); this model can fit within the memory and processing constraints of a T4 GPU on Google Colab while also being openly available.\n",
    "\n",
    "Then, we will also use quantization to fit a larger model ([Qwen3-14B]()) on the T4 GPU by converting the model weights to 4-bits instead of the full 16-bits.\n",
    "\n",
    "Can you create a new classification task and design prompts to differentiate between the classes within it?  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "!pip install -U bitsandbytes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "pwHS9LTUReib"
   },
   "outputs": [],
   "source": [
    "from textwrap import dedent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "RrIEtQc10uej"
   },
   "outputs": [],
   "source": [
    "import torch\n",
    "from torch.nn import functional as F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "iPCwcrivPKZt"
   },
   "outputs": [],
   "source": [
    "from transformers import AutoModelForCausalLM, AutoTokenizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# check that the GPU is available\n",
    "torch.cuda.is_available()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NyVRJjCNQCKH"
   },
   "source": [
    "## Qwen3-4B\n"
   ]
  },
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    },
    "id": "yOz-lch2QK49",
    "outputId": "1b80aa61-8113-438c-a018-cf0b7a7d0065"
   },
   "outputs": [],
   "source": [
    "model = AutoModelForCausalLM.from_pretrained(\"Qwen/Qwen3-4B\", device_map=\"cuda\", dtype=\"auto\")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"Qwen/Qwen3-4B\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "q-4xaVSxO_Xb"
   },
   "outputs": [],
   "source": [
    "def classify_with_prompt(labels, shots, target_x, thinking=False):\n",
    "    system_prompt = dedent(f\"\"\"\n",
    "        You're a helpful assistant for text classification. You'll be given an input text and need to output a single choice from the following set of categories:\n",
    "        {', '.join(labels)}\n",
    "        Pick one of those labels and do not generate any other text.\n",
    "    \"\"\").strip()\n",
    "    \n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": system_prompt},\n",
    "        {\"role\": \"user\", \"content\": shots[0][\"X\"]}, {\"role\": \"assistant\", \"content\": shots[0][\"y\"]},\n",
    "        {\"role\": \"user\", \"content\": shots[1][\"X\"]}, {\"role\": \"assistant\", \"content\": shots[1][\"y\"]},\n",
    "        {\"role\": \"user\", \"content\": shots[2][\"X\"]}, {\"role\": \"assistant\", \"content\": shots[2][\"y\"]},\n",
    "        {\"role\": \"user\", \"content\": target_x}\n",
    "    ]\n",
    "    text = tokenizer.apply_chat_template(\n",
    "        messages,\n",
    "        tokenize=False,\n",
    "        add_generation_prompt=True,\n",
    "        enable_thinking=thinking # Switches between thinking and non-thinking modes. Default is True.\n",
    "    )\n",
    "    \n",
    "    model_inputs = tokenizer([text], return_tensors=\"pt\").to(model.device)\n",
    "    \n",
    "    # conduct text completion\n",
    "    generated = model.generate(\n",
    "        **model_inputs,\n",
    "        max_new_tokens=32768\n",
    "    )\n",
    "\n",
    "    # let's break this down:\n",
    "    #                      | we take the element of the batch (our batch size is 1)\n",
    "    #                      |  |-----------------------------| skip our original input\n",
    "    output_ids = generated[0][len(model_inputs.input_ids[0]):].tolist()\n",
    "\n",
    "    # decode into token space\n",
    "    print(tokenizer.decode(output_ids, skip_special_tokens=True).strip(\"\\n\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "Nq23gAVHQhjA",
    "outputId": "50688199-8bbc-4820-a685-1b856cc644c3"
   },
   "outputs": [],
   "source": [
    "shots = [\n",
    "    {\"X\":\"I love this movie\", \"y\": \"positive\"},\n",
    "    {\"X\":\"I hate this movie\", \"y\": \"negative\"},\n",
    "    {\"X\":\"I kind of like the movie\", \"y\": \"positive\"}\n",
    "]\n",
    "\n",
    "target_x = \"This is one of the best movies I've ever seen\"\n",
    "\n",
    "classify_with_prompt([\"positive\", \"negative\"], shots, target_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "yz-N_AmGYp0p",
    "outputId": "eb833b49-a9e8-4068-9853-632f07e67577"
   },
   "outputs": [],
   "source": [
    "shots = [\n",
    "    {\"X\":\"Vampires take over the planet during an eclipse\", \"y\": \"Horror\"},\n",
    "    {\"X\":\"A court sentences George to be Jerry's butler\", \"y\": \"Comedy\"},\n",
    "    {\"X\":\"John turns into a werewolf during a full moon\", \"y\": \"Horror\"}\n",
    "]\n",
    "\n",
    "target_x = \"John is a werewolf who plays basketball\"\n",
    "\n",
    "classify_with_prompt([\"Horror\", \"Comedy\"], shots, target_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "FQ2cFQIpS1Jt",
    "outputId": "7e8e4b60-174b-451e-b478-7e0e37e87716"
   },
   "outputs": [],
   "source": [
    "shots = [\n",
    "    {\"X\":\"This is a text\", \"y\": \"English\"},\n",
    "    {\"X\":\"Nel mezzo del cammin' di nostra vita\", \"y\": \"Italian\"},\n",
    "    {\"X\":\"Je ne sais pas\", \"y\": \"French\"},\n",
    "]\n",
    "\n",
    "target_x = \"Siempre imaginé que el Paraíso sería algún tipo de biblioteca\"\n",
    "\n",
    "classify_with_prompt([\"English\", \"Italian\", \"French\", \"Spanish\", \"Japanese\"], shots, target_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vpfbqxWTPKZ4"
   },
   "source": [
    "Construct a new classification task; try to find one that the 4B model fails for."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "shots = [\n",
    "    # FILL ME IN\n",
    "]\n",
    "\n",
    "target_x = \"\"\n",
    "\n",
    "classify_with_prompt([\n",
    "    # FILL ME IN\n",
    "], shots, target_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "eY_n3aPaPKZ4"
   },
   "source": [
    "## Qwen-14B with Quantization\n",
    "\n",
    "Now let's try a bigger model. A general rule of thumb is to multiply the model size by 4 to estimate how much GPU memory you will need for inference. For example, without quantization, a 14-billion parameter model would require roughly 56GB of memory for inference."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# first, delete the previous model to free up memory\n",
    "\n",
    "del model\n",
    "del tokenizer\n",
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import BitsAndBytesConfig\n",
    "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    \"Qwen/Qwen3-14B\",\n",
    "    device_map=\"cuda\",\n",
    "    dtype=\"auto\",\n",
    "    quantization_config=quantization_config\n",
    ")\n",
    "tokenizer = AutoTokenizer.from_pretrained(\"Qwen/Qwen3-14B\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rerun the prompting tasks from above. Are any of the outputs different?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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