Infer_with_adapters.ipynb

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          "text": [
            "Collecting autotrain-advanced\n",
            "  Downloading autotrain_advanced-0.6.31-py3-none-any.whl (129 kB)\n",
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            "  Downloading codecarbon-2.2.3-py3-none-any.whl (174 kB)\n",
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            "\u001b[?25hCollecting datasets[vision]~=2.14.0 (from autotrain-advanced)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hCollecting pandas==2.0.3 (from autotrain-advanced)\n",
            "  Downloading pandas-2.0.3-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (12.3 MB)\n",
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            "\u001b[?25hCollecting Pillow==10.0.0 (from autotrain-advanced)\n",
            "  Downloading Pillow-10.0.0-cp310-cp310-manylinux_2_28_x86_64.whl (3.4 MB)\n",
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            "\u001b[?25hCollecting pydantic==1.10.11 (from autotrain-advanced)\n",
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            "\u001b[?25hCollecting sacremoses==0.0.53 (from autotrain-advanced)\n",
            "  Downloading sacremoses-0.0.53.tar.gz (880 kB)\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting scikit-learn==1.3.0 (from autotrain-advanced)\n",
            "  Downloading scikit_learn-1.3.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (10.8 MB)\n",
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            "\u001b[?25hCollecting sentencepiece==0.1.99 (from autotrain-advanced)\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hCollecting tqdm==4.65.0 (from autotrain-advanced)\n",
            "  Downloading tqdm-4.65.0-py3-none-any.whl (77 kB)\n",
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            "\u001b[?25hCollecting werkzeug==2.3.6 (from autotrain-advanced)\n",
            "  Downloading Werkzeug-2.3.6-py3-none-any.whl (242 kB)\n",
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            "\u001b[?25hCollecting trl (from autotrain-advanced)\n",
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            "Building wheels for collected packages: ipadic, sacremoses, diffusers, ffmpy\n",
            "  Building wheel for ipadic (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "  Stored in directory: /root/.cache/pip/wheels/01/a6/d1/1c0828c304a4283b2c1639a09ad86f83d7c487ef34c6b4a1bf\n",
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            "Installing collected packages: tokenizers, sentencepiece, safetensors, pydub, ipadic, fuzzywuzzy, ffmpy, bitsandbytes, xxhash, werkzeug, websockets, tzdata, tqdm, semantic-version, rapidfuzz, python-multipart, pynvml, pydantic, protobuf, Pillow, orjson, markdown-it-py, Mako, loguru, joblib, h11, einops, dill, colorlog, cmaes, aiofiles, uvicorn, tiktoken, starlette, scikit-learn, sacremoses, responses, pandas, multiprocess, mdit-py-plugins, jiwer, huggingface-hub, httpcore, arrow, alembic, transformers, optuna, httpx, fastapi, diffusers, codecarbon, gradio-client, datasets, gradio, evaluate, accelerate, trl, peft, invisible-watermark, autotrain-advanced\n",
            "  Attempting uninstall: werkzeug\n",
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            "  Attempting uninstall: markdown-it-py\n",
            "    Found existing installation: markdown-it-py 3.0.0\n",
            "    Uninstalling markdown-it-py-3.0.0:\n",
            "      Successfully uninstalled markdown-it-py-3.0.0\n",
            "  Attempting uninstall: joblib\n",
            "    Found existing installation: joblib 1.3.2\n",
            "    Uninstalling joblib-1.3.2:\n",
            "      Successfully uninstalled joblib-1.3.2\n",
            "  Attempting uninstall: scikit-learn\n",
            "    Found existing installation: scikit-learn 1.2.2\n",
            "    Uninstalling scikit-learn-1.2.2:\n",
            "      Successfully uninstalled scikit-learn-1.2.2\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.5.3\n",
            "    Uninstalling pandas-1.5.3:\n",
            "      Successfully uninstalled pandas-1.5.3\n",
            "  Attempting uninstall: mdit-py-plugins\n",
            "    Found existing installation: mdit-py-plugins 0.4.0\n",
            "    Uninstalling mdit-py-plugins-0.4.0:\n",
            "      Successfully uninstalled mdit-py-plugins-0.4.0\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires pandas==1.5.3, but you have pandas 2.0.3 which is incompatible.\n",
            "tensorflow-metadata 1.14.0 requires protobuf<4.21,>=3.20.3, but you have protobuf 4.23.4 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Mako-1.2.4 Pillow-10.0.0 accelerate-0.22.0 aiofiles-23.2.1 alembic-1.12.0 arrow-1.2.3 autotrain-advanced-0.6.31 bitsandbytes-0.41.1 cmaes-0.10.0 codecarbon-2.2.3 colorlog-6.7.0 datasets-2.14.4 diffusers-0.20.2 dill-0.3.7 einops-0.6.1 evaluate-0.3.0 fastapi-0.103.1 ffmpy-0.3.1 fuzzywuzzy-0.18.0 gradio-3.39.0 gradio-client-0.5.0 h11-0.14.0 httpcore-0.17.3 httpx-0.24.1 huggingface-hub-0.16.4 invisible-watermark-0.2.0 ipadic-1.0.0 jiwer-3.0.2 joblib-1.3.1 loguru-0.7.0 markdown-it-py-2.2.0 mdit-py-plugins-0.3.3 multiprocess-0.70.15 optuna-3.3.0 orjson-3.9.5 pandas-2.0.3 peft-0.5.0 protobuf-4.23.4 pydantic-1.10.11 pydub-0.25.1 pynvml-11.5.0 python-multipart-0.0.6 rapidfuzz-2.13.7 responses-0.18.0 sacremoses-0.0.53 safetensors-0.3.3 scikit-learn-1.3.0 semantic-version-2.10.0 sentencepiece-0.1.99 starlette-0.27.0 tiktoken-0.4.0 tokenizers-0.13.3 tqdm-4.65.0 transformers-4.32.1 trl-0.7.1 tzdata-2023.3 uvicorn-0.23.2 websockets-11.0.3 werkzeug-2.3.6 xxhash-3.3.0\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "PIL"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.16.4)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.12.2)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.31.0)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.65.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (6.0.1)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.7.1)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (23.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.2.0)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.4)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2023.7.22)\n"
          ]
        }
      ],
      "source": [
        "# @title Install Necessary Packages\n",
        "!pip install autotrain-advanced\n",
        "!pip install huggingface_hub"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!autotrain setup --update-torch"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiyVcyAtT-D8",
        "outputId": "2f0caf74-43b0-4fd7-9432-a3227b48ce38"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "> \u001b[1mINFO    Installing latest transformers@main\u001b[0m\n",
            "> \u001b[1mINFO    Successfully installed latest transformers\u001b[0m\n",
            "> \u001b[1mINFO    Installing latest peft@main\u001b[0m\n",
            "> \u001b[1mINFO    Successfully installed latest peft\u001b[0m\n",
            "> \u001b[1mINFO    Installing latest diffusers@main\u001b[0m\n",
            "> \u001b[1mINFO    Successfully installed latest diffusers\u001b[0m\n",
            "> \u001b[1mINFO    Installing latest trl@main\u001b[0m\n",
            "> \u001b[1mINFO    Successfully installed latest trl\u001b[0m\n",
            "> \u001b[1mINFO    Installing latest xformers\u001b[0m\n",
            "> \u001b[1mINFO    Successfully installed latest xformers\u001b[0m\n",
            "> \u001b[1mINFO    Installing latest PyTorch\u001b[0m\n",
            "> \u001b[1mINFO    Successfully installed latest PyTorch\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "import transformers\n",
        "from peft import PeftModel\n",
        "import torch\n",
        "\n",
        "from huggingface_hub import snapshot_download"
      ],
      "metadata": {
        "id": "qdGUaqMdVdAF"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Downloading only checkpoint folder from my HF repo, Not downloading weights from there because of its size 25GB. Instead we use Llama2 sharded version as base model and the adapter file from checkpoint"
      ],
      "metadata": {
        "id": "AdAQU3EFphUx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "snapshot_download(repo_id=\"Sriram-Gov/Sarcastic-Headline-Llama2\", local_dir = '/content', allow_patterns=\"checkpoint-672/*\") # , local_dir = '/content/exp', revision=\"refs/pr/1\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 68,
          "referenced_widgets": [
            "b14157e9d7214b16b24371ed05c45c1b",
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        "id": "RqJzSPoOT-Gr",
        "outputId": "c4fcc1ff-cf51-4a79-d312-777df881be13"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Fetching 12 files:   0%|          | 0/12 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b14157e9d7214b16b24371ed05c45c1b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'/content'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are taking sharded Llama2 7B sharded version as base model here, because it is compressed of size 13GB instead of 25GB.<br>\n",
        "However, if you want to download the 25GB [version](https://huggingface.co/Sriram-Gov/Sarcastic-Headline-Llama2) available in HF repo, you can also do inference in that way"
      ],
      "metadata": {
        "id": "qhzAcn2gVyoU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer = AutoTokenizer.from_pretrained('/content/checkpoint-672')\n",
        "model = AutoModelForCausalLM.from_pretrained('TinyPixel/Llama-2-7B-bf16-sharded', torch_dtype = torch.float16, device_map=\"auto\") #Base_Model for example: meta-llama/Llama-2-13b-chat-hf\n",
        "model = PeftModel.from_pretrained(model, \"/content/checkpoint-672\", device_map=\"auto\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 625,
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        "id": "bbywheLYT-Jb",
        "outputId": "7d1363cb-aeb2-4a2b-b6c9-31324ebcfd8c"
      },
      "execution_count": 6,
      "outputs": [
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# @title Prompt template\n",
        "format_text = \"You are a savage, disrespectful and witty agent. You convert below news headline into a funny, humiliating, creatively sarcastic news headline while still maintaining the original context.\\n### headline: {}\\n### sarcastic_headline: {}\"\n",
        "print(format_text)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v1Wws9R2T-MD",
        "outputId": "8b483b00-f441-4c05-8912-6d46194ee72d"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a savage, disrespectful and witty agent. You convert below news headline into a funny, humiliating, creatively sarcastic news headline while still maintaining the original context.\n",
            "### headline: {}\n",
            "### sarcastic_headline: {}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "t1 = \"a couple sitting on a desk having the time of their life\"\n",
        "t2 = \"steriods are good for lungs\"\n",
        "t3 = 'mansoons are best for mosquitoes'\n",
        "t4 = 'i love to eat pizza'\n",
        "formatted_input = format_text.format(t2, \"\")\n",
        "print(formatted_input)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LYfG87ZnT-Ob",
        "outputId": "3d66428e-6148-4173-f0c7-6dd542f34a8f"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a savage, disrespectful and witty agent. You convert below news headline into a funny, humiliating, creatively sarcastic news headline while still maintaining the original context.\n",
            "### headline: steriods are good for lungs\n",
            "### sarcastic_headline: \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "device = \"cuda:0\"\n",
        "\n",
        "inputs = tokenizer(formatted_input, return_tensors=\"pt\").to(device)\n",
        "outputs = model.generate(**inputs, max_length=300) # max_length=300, temperature=1\n",
        "print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hJ6ytatIT-Qz",
        "outputId": "06eb638e-1a73-4fba-d75d-e8f06aefee95"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are a savage, disrespectful and witty agent. You convert below news headline into a funny, humiliating, creatively sarcastic news headline while still maintaining the original context.\n",
            "### headline: steriods are good for lungs\n",
            "### sarcastic_headline:  steroids are good for lungs, because they make you breathe more heavily and deeply, which is just like exercise\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "iz15krXFWifC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "bEBHcd1RWiYD"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}