{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "efMok_6HFs_d"
      },
      "source": [
        "# **QWQ SuperNatual**\n",
        "*Qwen2ForCausalLM*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JCBdkSmxFg4y",
        "outputId": "336c52a0-b301-4742-f8d6-672d9245ff89"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "Installing collected packages: pydub, uvicorn, tomlkit, semantic-version, ruff, python-multipart, markupsafe, ffmpy, aiofiles, starlette, safehttpx, gradio-client, fastapi, gradio, spaces\n",
            "  Attempting uninstall: markupsafe\n",
            "    Found existing installation: MarkupSafe 3.0.2\n",
            "    Uninstalling MarkupSafe-3.0.2:\n",
            "      Successfully uninstalled MarkupSafe-3.0.2\n",
            "Successfully installed aiofiles-23.2.1 fastapi-0.115.6 ffmpy-0.5.0 gradio-5.12.0 gradio-client-1.5.4 markupsafe-2.1.5 pydub-0.25.1 python-multipart-0.0.20 ruff-0.9.2 safehttpx-0.1.6 semantic-version-2.10.0 spaces-0.32.0 starlette-0.41.3 tomlkit-0.13.2 uvicorn-0.34.0\n"
          ]
        }
      ],
      "source": [
        "!pip install huggingface_hub transformers gradio spaces requests safetensors accelerate torch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "NGdDFriEHEIl"
      },
      "outputs": [],
      "source": [
        "# Authenticate with Hugging Face\n",
        "from huggingface_hub import login\n",
        "\n",
        "# Log in to Hugging Face using the provided token\n",
        "hf_token = '---your--token--goes--here---'\n",
        "login(hf_token)\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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          "text": [
            "Running Gradio in a Colab notebook requires sharing enabled. Automatically setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n",
            "\n",
            "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n",
            "* Running on public URL: https://d5ea3770d51a12d374.gradio.live\n",
            "\n",
            "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from the terminal in the working directory to deploy to Hugging Face Spaces (https://huggingface.co/spaces)\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div><iframe src=\"https://d5ea3770d51a12d374.gradio.live\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import os\n",
        "from collections.abc import Iterator\n",
        "from threading import Thread\n",
        "import gradio as gr\n",
        "import spaces\n",
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer\n",
        "\n",
        "DESCRIPTION = \"\"\"\n",
        "# QwQ SuperNatural\n",
        "\"\"\"\n",
        "\n",
        "css='''\n",
        "h1 {\n",
        "  text-align: center;\n",
        "  display: block;\n",
        "}\n",
        "\n",
        "#duplicate-button {\n",
        "  margin: auto;\n",
        "  color: #fff;\n",
        "  background: #1565c0;\n",
        "  border-radius: 100vh;\n",
        "}\n",
        "'''\n",
        "\n",
        "MAX_MAX_NEW_TOKENS = 2048\n",
        "DEFAULT_MAX_NEW_TOKENS = 1024\n",
        "MAX_INPUT_TOKEN_LENGTH = int(os.getenv(\"MAX_INPUT_TOKEN_LENGTH\", \"4096\"))\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "model_id = \"prithivMLmods/QwQ-SuperNatural-3B\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_id,\n",
        "    device_map=\"auto\",\n",
        "    torch_dtype=torch.bfloat16,\n",
        ")\n",
        "model.config.sliding_window = 4096\n",
        "model.eval()\n",
        "\n",
        "\n",
        "@spaces.GPU(duration=120)\n",
        "def generate(\n",
        "    message: str,\n",
        "    chat_history: list[dict],\n",
        "    max_new_tokens: int = 1024,\n",
        "    temperature: float = 0.6,\n",
        "    top_p: float = 0.9,\n",
        "    top_k: int = 50,\n",
        "    repetition_penalty: float = 1.2,\n",
        ") -> Iterator[str]:\n",
        "    conversation = chat_history.copy()\n",
        "    conversation.append({\"role\": \"user\", \"content\": message})\n",
        "\n",
        "    input_ids = tokenizer.apply_chat_template(conversation, add_generation_prompt=True, return_tensors=\"pt\")\n",
        "    if input_ids.shape[1] > MAX_INPUT_TOKEN_LENGTH:\n",
        "        input_ids = input_ids[:, -MAX_INPUT_TOKEN_LENGTH:]\n",
        "        gr.Warning(f\"Trimmed input from conversation as it was longer than {MAX_INPUT_TOKEN_LENGTH} tokens.\")\n",
        "    input_ids = input_ids.to(model.device)\n",
        "\n",
        "    streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)\n",
        "    generate_kwargs = dict(\n",
        "        {\"input_ids\": input_ids},\n",
        "        streamer=streamer,\n",
        "        max_new_tokens=max_new_tokens,\n",
        "        do_sample=True,\n",
        "        top_p=top_p,\n",
        "        top_k=top_k,\n",
        "        temperature=temperature,\n",
        "        num_beams=1,\n",
        "        repetition_penalty=repetition_penalty,\n",
        "    )\n",
        "    t = Thread(target=model.generate, kwargs=generate_kwargs)\n",
        "    t.start()\n",
        "\n",
        "    outputs = []\n",
        "    for text in streamer:\n",
        "        outputs.append(text)\n",
        "        yield \"\".join(outputs)\n",
        "\n",
        "\n",
        "demo = gr.ChatInterface(\n",
        "    fn=generate,\n",
        "    additional_inputs=[\n",
        "        gr.Slider(\n",
        "            label=\"Max new tokens\",\n",
        "            minimum=1,\n",
        "            maximum=MAX_MAX_NEW_TOKENS,\n",
        "            step=1,\n",
        "            value=DEFAULT_MAX_NEW_TOKENS,\n",
        "        ),\n",
        "        gr.Slider(\n",
        "            label=\"Temperature\",\n",
        "            minimum=0.1,\n",
        "            maximum=4.0,\n",
        "            step=0.1,\n",
        "            value=0.6,\n",
        "        ),\n",
        "        gr.Slider(\n",
        "            label=\"Top-p (nucleus sampling)\",\n",
        "            minimum=0.05,\n",
        "            maximum=1.0,\n",
        "            step=0.05,\n",
        "            value=0.9,\n",
        "        ),\n",
        "        gr.Slider(\n",
        "            label=\"Top-k\",\n",
        "            minimum=1,\n",
        "            maximum=1000,\n",
        "            step=1,\n",
        "            value=50,\n",
        "        ),\n",
        "        gr.Slider(\n",
        "            label=\"Repetition penalty\",\n",
        "            minimum=1.0,\n",
        "            maximum=2.0,\n",
        "            step=0.05,\n",
        "            value=1.2,\n",
        "        ),\n",
        "    ],\n",
        "    stop_btn=None,\n",
        "    examples=[\n",
        "        [\"Write a Python function to reverses a string if it's length is a multiple of 4. def reverse_string(str1): if len(str1) % 4 == 0: return ''.join(reversed(str1)) return str1 print(reverse_string('abcd')) print(reverse_string('python')) \"],\n",
        "        [\"Rectangle $ABCD$ is the base of pyramid $PABCD$. If $AB = 10$, $BC = 5$, $\\overline{PA}\\perp \\text{plane } ABCD$, and $PA = 8$, then what is the volume of $PABCD$?\"],\n",
        "        [\"Difference between List comprehension and Lambda in Python lst  =  [x ** 2  for x in range (1, 11)   if  x % 2 == 1] print(lst)\"],\n",
        "        [\"What happens when the sun goes down?\"],\n",
        "    ],\n",
        "    cache_examples=False,\n",
        "    type=\"messages\",\n",
        "    description=DESCRIPTION,\n",
        "    css=css,\n",
        "    fill_height=True,\n",
        ")\n",
        "\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    demo.queue(max_size=20).launch()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-q-qiUKLeFc"
      },
      "source": [
        "# **Sample Example👇🏻**\n",
        "\n",
        "\n",
        "In this task, you're given passages that contain mentions of names of people, places, or things. Some of these mentions refer to the same person, place, or thing. Your job is to write questions that evaluate one's understanding of such references. Good questions are expected to link pronouns (she, her, him, his, their, etc.) or other mentions to people, places, or things to which they may refer. Do not ask questions that can be answered correctly without understanding the paragraph or having multiple answers. Avoid questions that do not link phrases referring to the same entity. For each of your questions, the answer should be one or more phrases in the paragraph, and it should be unambiguous. Passage: The earthquake swarm was noted on October 12, 2007 in the Prince George Citizen by citizen staff, three days after the earthquakes began. Scientists mentioned in the report were seismologist John Cassidy of Natural Resources Canada and volcanologist Catherine Hickson, who was part of the Geological Survey of Canada at the time. At the time of the report, scientists did not know the origin of the swarm. Seismologist John Cassidy stated, \"the depth is enough to rule out hydrothermal but it's up in the air as to whether the cause is tectonic shifts or volcanic activity. If it is volcanic there are certain characteristics that we would expect, there's a tremor-like character to it. And so we'll be looking for the types of events that we see beneath volcanoes and we'll be looking to see if they're getting closer to the surface or if they're migrating at all.\"Even if the Nazko swarm were a warning of a volcanic eruption, Hickson doubted it would turn out to be a highly explosive eruption like those that can occur in subduction-zone volcanoes. \"We're not talking about an injection of tonnes of ash many kilometers into the air like the 1980 Mount St. Helens eruption or the 1991 Mount Pinatubo eruption. We're talking about something very small, relatively localized that should have a fairly limited impact... but it'll be extremely exciting\", Hickson said. If an eruption were to occur, Hickson suggested that it would be characterized by a lava fountain that sends globs of lava 100 m (330 ft) into the air. This is similar to those that occur in Hawaii. Hickson said that a Nazko eruption could be a tourist attraction, but warned that noxious gases such as carbon dioxide and sulfur dioxide would be released during the event."
      ]
    }
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