alpaca-lora-de-demo.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/snipaid-nlg/models/blob/main/Alpaca_LoRa_DE.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Testing Alpaca-LoRA DE\n",
        "\n",
        "This notebook contains minimal code for running German LoRA Alpaca for evaluation purposes."
      ],
      "metadata": {
        "id": "6ExAe1UCWn5E"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Setup"
      ],
      "metadata": {
        "id": "7l8NpVZ484ij"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q bitsandbytes datasets loralib sentencepiece peft\n",
        "!pip install -q git+https://github.com/huggingface/transformers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_pz8MuY84Qh",
        "outputId": "4a44af6d-c626-4ad6-e223-d437c914708c"
      },
      "execution_count": null,
      "outputs": [
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.37.2-py3-none-any.whl (84.2 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.2/84.2 MB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.37.2\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m468.7/468.7 KB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h\u001b[33m  WARNING: Did not find branch or tag 'c3dc391', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m89.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m215.3/215.3 KB\u001b[0m \u001b[31m11.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "from transformers import LlamaTokenizer, LlamaForCausalLM, GenerationConfig\n",
        "\n",
        "tokenizer = LlamaTokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
        "model = LlamaForCausalLM.from_pretrained(\n",
        "    \"decapoda-research/llama-7b-hf\",\n",
        "    load_in_8bit=True,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, \"thisserand/alpaca_lora_german\") # Standford Alpaca (en): tloen/alpaca-lora-7b"
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        "id": "VucO3HSMoJkz",
        "outputId": "13968d3c-aaab-47f9-f5d7-223c1c2122e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /usr/lib64-nvidia did not contain libcudart.so as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('8013'), PosixPath('http'), PosixPath('//172.28.0.1')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-s-3dfg3em3ruki2 --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n"
          ]
        },
        {
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          "text": [
            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
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          },
          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def generate_prompt(instruction, input=None):\n",
        "    if input:\n",
        "        return f\"\"\"Nachfolgend finden Sie eine Anweisung, die eine Aufgabe beschreibt, und eine Eingabe, die weiteren Kontext liefert. Schreiben Sie eine Antwort, die die Aufgabe angemessen erfüllt.\n",
        "\n",
        "### Anweisung:\n",
        "{instruction}\n",
        "\n",
        "### Eingabe:\n",
        "{input}\n",
        "\n",
        "### Antwort:\"\"\"\n",
        "    else:\n",
        "        return f\"\"\"Nachfolgend finden Sie eine Anweisung, die eine Aufgabe beschreibt. Schreiben Sie eine Antwort, die die Aufgabe angemessen erfüllt.\n",
        "\n",
        "### Anweisung:\n",
        "{instruction}\n",
        "\n",
        "### Antwort:\"\"\""
      ],
      "metadata": {
        "id": "w3_lzwcqermJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generation_config = GenerationConfig(\n",
        "    temperature=0.1,\n",
        "    top_p=0.75,\n",
        "    num_beams=4,\n",
        ")\n",
        "\n",
        "def evaluate(instruction, input=None):\n",
        "    prompt = generate_prompt(instruction, input)\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"].cuda()\n",
        "    generation_output = model.generate(\n",
        "        input_ids=input_ids,\n",
        "        generation_config=generation_config,\n",
        "        return_dict_in_generate=True,\n",
        "        output_scores=True,\n",
        "        max_new_tokens=256\n",
        "    )\n",
        "    for s in generation_output.sequences:\n",
        "        output = tokenizer.decode(s)\n",
        "        print(\"Antwort:\", output.split(\"### Antwort:\")[1].strip())"
      ],
      "metadata": {
        "id": "Egh3beCVRpW5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Artikel per URL einlesen"
      ],
      "metadata": {
        "id": "suWRf2CP8tHj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q newspaper3k\n",
        "from newspaper import Article"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hZQqncqP8pVp",
        "outputId": "9a735b85-0505-4445-f122-7380169dfe63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/211.1 KB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m211.1/211.1 KB\u001b[0m \u001b[31m11.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m93.9/93.9 KB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m65.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m81.1/81.1 KB\u001b[0m \u001b[31m10.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for tinysegmenter (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for feedfinder2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for jieba3k (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for sgmllib3k (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#@markdown Lade einen Artikel aus dem Internet.\n",
        "url = 'https://t3n.de/news/hippocamera-app-gegen-demenz-1535827/' #@param {type:\"string\"}\n",
        "article = Article(url, browser_user_agent=\"Googlebot-News\")\n",
        "article.download()\n",
        "article.parse()\n",
        "article.text"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "ZG9E-NmU8j8v",
        "outputId": "1fecf3e7-5540-48e6-c688-e455516c4438"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'Das Gedächtnis lässt sich genauso trainieren, wie ein Muskel. (Bild: LightField Studios)\\n\\nUnser Gehirn ist wie ein Muskel: Wird es nicht trainiert, verliert es auf Dauer an Funktionalität. Damit steigt im Alter das Risiko, an degenerativen Erkrankungen wie Demenz zu leiden.\\n\\nUmso wichtiger ist es daher, dass wir unserem Gehirn regelmäßig Anreize setzen, um sich zu erinnern. Genau dafür wurde die App Hippocamera entwickelt.\\n\\nSo arbeitet Hippocamera\\n\\nHippocamera setzt dafür – wie es der Name vermuten lässt – am sogenannten Hippocampus an, dem Bereich unseres Gehirns, der für das Kurzzeitgedächtnis zuständig ist. Damit solche Erinnerungen abrufbar sind, arbeitet die App mit Videos, die mit einer kurzen Tonspur unterlegt sind.\\n\\nUnd das funktioniert so: Zunächst werden die Nutzer:innen bei der Aufnahme in mehreren Schritten speziell angeleitet, um das Kurzzeitgedächtnis zu stimulieren.\\n\\nIm nächsten Schritt kreiert die App aus den kurzen Clips Hinweise und Anreize, die dann nach einem bestimmten Muster den Nutzer:innen vorgelegt werden, damit sie sich wieder an das Ereignis erinnern.\\n\\nHippocamera operiert dabei nach einem ähnlichen Prinzip wie der Hippocampus selbst. Auf diese Weise soll das Gedächtnis dreimal effektiver trainiert werden als durch das wiederholte Ansehen von Standardvideos, wie die Firma auf ihrer Website dazu schreibt.\\n\\nStudie bestätigt Wirksamkeit der App\\n\\nUnd das wurde jetzt sogar von einer Studie der Universität Toronto bestätigt. Die Wissenschaftler:innen fanden heraus, dass diejenigen Teilnehmer:innen, die regelmäßig die Hippocamera-App nutzten, sich an bis zu 50 Prozent mehr Details erinnern konnten.\\n\\nDie Forscher:innen sind überzeugt, dass mit dem Verlust des Gedächtnisses auch ein Stück weit der Verlust der eigenen Persönlichkeit einhergeht. Mit der Hippocamera-App sollen sich Events wieder besser mit Emotionen verknüpfen lassen.\\n\\nZahl der Demenzerkrankungen wird zunehmen\\n\\nLaut der Deutschen Alzheimer Gesellschaft leiden derzeit etwa 1,8 Millionen Menschen in Deutschland an einer Demenzerkrankung. Bis zum Jahr 2050 wird sich die Zahl laut Schätzungen sogar auf 2,4 bis 2,8 Millionen erhöhen. Neben einer gesunden Ernährung empfiehlt die DAG unter anderem „geistige, körperliche und soziale Aktivität“, um das Risiko einer Erkrankung zu verringern.\\n\\nMehr zu diesem Thema Studie Apps So arbeitet'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Generate"
      ],
      "metadata": {
        "id": "Uw5I3AzT8zh6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(input(\"Anweisung: \"), article.text[:400])"
      ],
      "metadata": {
        "id": "upOB2AQJSW9-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "evaluate(\"Generiere eine Liste von SEO Schlagworten zum Thema KI Sprachmodelle.\")"
      ],
      "metadata": {
        "id": "zhgBkR-ijjSj"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}