StarCoder_LLM.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qE5xxNpN9zmz"
      },
      "source": [
        "# **StarCoder**\n",
        "\n",
        "StarCoder, a cutting-edge Large Language Model (LLM) for code, has been crafted through a collaboration between Hugging Face and ServiceNow as a key component of the BigCode Initiative. This advanced model is trained on a vast corpus of permissively licensed data sourced from over 80 programming languages and textual content extracted from GitHub repositories. This comprehensive training dataset encompasses diverse sources such as documentation and Jupyter programming notebooks, ensuring StarCoder's proficiency across a wide range of coding tasks.\n",
        "\n",
        "You can find more detailed information about StarCoder in the blog post available at [StarCoder Blog.]( https://huggingface.co/blog/starcoder)\n",
        "\n",
        "\n",
        "The models are available at: Hugging Face - [BigCode Models](https://huggingface.co/bigcode)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c08fa11c-3b32-46a5-c37b-dbae9c55918c",
        "id": "8JEO72g0Lf8L"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.1/7.1 MB\u001b[0m \u001b[31m79.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m104.3/104.3 MB\u001b[0m \u001b[31m17.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m72.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m108.2/108.2 MB\u001b[0m \u001b[31m16.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install langchain transformers\n",
        "!pip -q install accelerate bitsandbytes sentencepiece Xformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## StarChatAlpha\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "OOOsVInVLf8M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
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          ]
        },
        "outputId": "f0bf189a-ec3f-4302-e0ba-a3a0f79f0272",
        "id": "bZ9ugNvPLf8M"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0d2a2df74be149ad867ca9bdaf253940"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load the model\n"
      ],
      "metadata": {
        "id": "aAIZ21XzLf8M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import transformers\n",
        "from transformers import GenerationConfig, pipeline\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"HuggingFaceH4/starchat-alpha\")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceH4/starchat-alpha\",\n",
        "                                              load_in_8bit=True,\n",
        "                                              device_map='auto',\n",
        "                                              torch_dtype=torch.float16,\n",
        "                                             use_auth_token= True\n",
        "                                              )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
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            "6173482a569b4e3c8d116c1d470fa4e4",
            "6604cf0a528b46c8ab45c92e6beb014b",
            "ce2458ff24ae4edd8bf38ca4438648f6",
            "a70808911b5a403195a6f089b9474d9f",
            "e7c1fbbb12644cfea43e9710fbde5ba5",
            "c04491e0e91f4d7a8b092ce6f39d707e",
            "81d01905c45143869cfa64cde2fc795e",
            "5f283c0511a44401a5b3b5b3f2d63aa3",
            "f18beeae4bf84d1cb5ac419ee52a02fb",
            "219ba0b4ee7548898148515c6f779132",
            "4c7984a517894c40a534db88d779c175",
            "18f3d37dfc454614b9554914bb616a7b",
            "9a9b4397306349d5919bfe71e365c241",
            "4c71dd1688e949668d0444e9556638a1"
          ]
        },
        "outputId": "21e5fc79-6e33-489b-f77b-710ca12822d6",
        "id": "qaKDoi0MLf8M"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/717 [00:00<?, ?B/s]"
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            }
          },
          "metadata": {}
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            }
          },
          "metadata": {}
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          },
          "metadata": {}
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          "metadata": {}
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          "metadata": {}
        },
        {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: 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/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('http'), PosixPath('8013'), PosixPath('//172.28.0.1')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-10ky6q9n610nk --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: 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/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:145: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so'), PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 8.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "26ebdc89f6b4439ea727746f181c1804"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/111 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a70808911b5a403195a6f089b9474d9f"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.eos_token, tokenizer.pad_token"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee51658a-4221-444a-c675-1595b9e61864",
        "id": "J8Fs4bRvLf8M"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('<|endoftext|>', '<|endoftext|>')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.eos_token_id, tokenizer.pad_token_id"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c19d5630-17f6-44e8-d96c-2ee2d20f46f1",
        "id": "DM2udnPXLf8M"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(0, None)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tokenizer.pad_token_id = 0"
      ],
      "metadata": {
        "id": "ZrvAp6fKLf8N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Basic Text generation"
      ],
      "metadata": {
        "id": "ON_2BqFDLf8N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "full_prompt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "e4aed901-1ddc-49a4-85a9-1385d249755f",
        "id": "uL58ho_ALf8N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'<|system|>\\nBelow is a conversation between a human user and a helpful AI coding assistant.<|end|>\\n<|user|>\\nWrite a python function that reverses an input string<|end|>\\n<|assistant|>'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import textwrap\n",
        "\n",
        "def generate_response(input_prompt):\n",
        "    system_prompt = \"<|system|>\\nBelow is a conversation between a human user and a helpful AI coding assistant.<|end|>\\n\"\n",
        "\n",
        "    user_prompt = f\"<|user|>\\n{input_prompt}<|end|>\\n\"\n",
        "\n",
        "    assistant_prompt = \"<|assistant|>\"\n",
        "\n",
        "    full_prompt = system_prompt + user_prompt + assistant_prompt\n",
        "\n",
        "    inputs = tokenizer.encode(full_prompt, return_tensors=\"pt\").to('cuda')\n",
        "    outputs = model.generate(inputs,\n",
        "                            eos_token_id = 0,\n",
        "                            pad_token_id = 0,\n",
        "                            max_length=256,\n",
        "                            early_stopping=True)\n",
        "    output =  tokenizer.decode(outputs[0])\n",
        "    output = output[len(full_prompt):]\n",
        "    if \"<|end|>\" in output:\n",
        "        cutoff = output.find(\"<|end|>\")\n",
        "        output = output[:cutoff]\n",
        "    print(input_prompt+'\\n')\n",
        "    # wrapped_text = textwrap.fill(output, width=100)\n",
        "    # print(wrapped_text +'\\n\\n')\n",
        "    print(output)\n"
      ],
      "metadata": {
        "id": "BTjZPOzSLf8N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "generate_response(\"Write a python function that reverses each word in an input string\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ea029a8-6e8e-4150-9f99-63054dfb71c1",
        "id": "S1vZ_XKNLf8N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Write a python function that reverses each word in an input string\n",
            "\n",
            "\n",
            "Here's a Python function that reverses each word in an input string:\n",
            "\n",
            "def reverse_words(input_str):\n",
            "    words = input_str.split()\n",
            "    reversed_words = [word[::-1] for word in words]\n",
            "    return \" \".join(reversed_words)\n",
            "\n",
            "This function takes an input string and splits it into a list of words using the split() method. It then uses a list comprehension to reverse each word in the list using the [::-1] slicing notation. Finally, the reversed words are joined together using the join() method and the result is returned.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generate_response(\"What is Flask?\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1aa98a80-e656-4ffd-9094-362a7b2d93fc",
        "id": "IMMYESb4Lf8N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "What is Flask?\n",
            "\n",
            "\n",
            "Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions. However, Flask supports extensions that can add application features as if they were implemented in Flask itself.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generate_response(\"def scrape_url(url):\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "20a63af3-7436-43a8-f926-59db8a13a4a5",
        "id": "tXiRTsQgLf8N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "def scrape_url(url):\n",
            "\n",
            "\n",
            "def scrape_url(url):\n",
            "    response = requests.get(url)\n",
            "    soup = BeautifulSoup(response.text, 'html.parser')\n",
            "    # extract data from soup and return it\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "generate_response(\"write a JS function that sorts a list alphabetically\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "305a389b-8526-4a14-e816-ef454b166136",
        "id": "0li8yL8ELf8N"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "write a JS function that sorts a list alphabetically\n",
            "\n",
            "\n",
            "function sortListAlphabetically(list) {\n",
            "  list.sort();\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yAdw3ADcS8Ph"
      },
      "source": [
        "## StarChatAlpha\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "5d5d4d26fc41400d8db601d6d7fadcab",
            "1ac9c2219d3e48d9992e9bb6894d298e",
            "5ac6beb6beed4510acaeabf4892d53e1",
            "4b50991873d741fa8c4e5e8945a8bae1",
            "785b27c912784b4c8252a3515131ed65",
            "40fef7228e2745f6af1fe244314512d7",
            "73bf0e04682643d6829289fe9dd5abec",
            "3d28cc08043e45c7b177410640b372da",
            "fa771af1ea3d4a5486b8b9f87bb91f97",
            "ba2932edec7548cdbefef2303700da89",
            "9032f20231014091aca41e35f984fe23",
            "a5a3710f4b0a4d9081924925c6276aa4",
            "b1844d5c5f954c48bd7455d19e0f0601",
            "02ec3aff764a4f6d871eb2ff3db943a0"
          ]
        },
        "id": "4nKQTQigaK_n",
        "outputId": "477c329c-2c86-445c-ec02-f8f30c647dc1"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5d5d4d26fc41400d8db601d6d7fadcab",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a34mOuQFxyIC"
      },
      "source": [
        "## Load the model\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "background_save": true,
          "base_uri": "https://localhost:8080/",
          "height": 118,
          "referenced_widgets": [
            "8161eab9785b40e49e6a4d5f184b3f04",
            "4225238bbb1247b58cc4a5f90a5c7d81",
            "0bcd974588a54b72b2c1b701534c4132",
            "b7b655917d854e9894e1668b31245c86",
            "15d7fc05c3954492af6d2a1c7a357e3f",
            "7b8209582c9f4447b9e865e854c97899",
            "854a6ae2dd82481f812ec0a22f2e55b9",
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        "outputId": "15a9c542-8bc3-4234-9d75-634f469d3e89"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The `load_in_4bit` and `load_in_8bit` arguments are deprecated and will be removed in the future versions. Please, pass a `BitsAndBytesConfig` object in `quantization_config` argument instead.\n"
          ]
        },
        {
          "data": {
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              "model_id": "8161eab9785b40e49e6a4d5f184b3f04",
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              "version_minor": 0
            },
            "text/plain": [
              "Downloading shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
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              "model-00003-of-00004.safetensors:   0%|          | 0.00/9.86G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
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        }
      ],
      "source": [
        "import torch\n",
        "import transformers\n",
        "from transformers import GenerationConfig, pipeline\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"HuggingFaceH4/starchat-alpha\")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\"HuggingFaceH4/starchat-alpha\",\n",
        "                                              load_in_8bit=True,\n",
        "                                              device_map='auto',\n",
        "                                              torch_dtype=torch.float16,\n",
        "                                             use_auth_token= True\n",
        "                                              )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tl6JZCGoc_Y2",
        "outputId": "ee51658a-4221-444a-c675-1595b9e61864"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "('<|endoftext|>', '<|endoftext|>')"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.eos_token, tokenizer.pad_token"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZuiryA7byes",
        "outputId": "c19d5630-17f6-44e8-d96c-2ee2d20f46f1"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(0, None)"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "tokenizer.eos_token_id, tokenizer.pad_token_id"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "znOIo3Ztc8hA"
      },
      "outputs": [],
      "source": [
        "tokenizer.pad_token_id = 0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qc7rAtDGaVaA"
      },
      "source": [
        "### Basic Text generation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "iBRbd7XCdJSk",
        "outputId": "e4aed901-1ddc-49a4-85a9-1385d249755f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'<|system|>\\nBelow is a conversation between a human user and a helpful AI coding assistant.<|end|>\\n<|user|>\\nWrite a python function that reverses an input string<|end|>\\n<|assistant|>'"
            ]
          },
          "execution_count": 16,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "\n",
        "full_prompt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U78fp86dZnZH"
      },
      "outputs": [],
      "source": [
        "import textwrap\n",
        "\n",
        "def generate_response(input_prompt):\n",
        "    system_prompt = \"<|system|>\\nBelow is a conversation between a human user and a helpful AI coding assistant.<|end|>\\n\"\n",
        "\n",
        "    user_prompt = f\"<|user|>\\n{input_prompt}<|end|>\\n\"\n",
        "\n",
        "    assistant_prompt = \"<|assistant|>\"\n",
        "\n",
        "    full_prompt = system_prompt + user_prompt + assistant_prompt\n",
        "\n",
        "    inputs = tokenizer.encode(full_prompt, return_tensors=\"pt\").to('cuda')\n",
        "    outputs = model.generate(inputs,\n",
        "                            eos_token_id = 0,\n",
        "                            pad_token_id = 0,\n",
        "                            max_length=256,\n",
        "                            early_stopping=True)\n",
        "    output =  tokenizer.decode(outputs[0])\n",
        "    output = output[len(full_prompt):]\n",
        "    if \"<|end|>\" in output:\n",
        "        cutoff = output.find(\"<|end|>\")\n",
        "        output = output[:cutoff]\n",
        "    print(input_prompt+'\\n')\n",
        "    # wrapped_text = textwrap.fill(output, width=100)\n",
        "    # print(wrapped_text +'\\n\\n')\n",
        "    print(output)\n"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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        },
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        "outputId": "6ea029a8-6e8e-4150-9f99-63054dfb71c1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Write a python function that reverses each word in an input string\n",
            "\n",
            "\n",
            "Here's a Python function that reverses each word in an input string:\n",
            "\n",
            "def reverse_words(input_str):\n",
            "    words = input_str.split()\n",
            "    reversed_words = [word[::-1] for word in words]\n",
            "    return \" \".join(reversed_words)\n",
            "\n",
            "This function takes an input string and splits it into a list of words using the split() method. It then uses a list comprehension to reverse each word in the list using the [::-1] slicing notation. Finally, the reversed words are joined together using the join() method and the result is returned.\n"
          ]
        }
      ],
      "source": [
        "generate_response(\"Write a python function that reverses each word in an input string\")"
      ]
    },
    {
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      "metadata": {
        "colab": {
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      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "What is Flask?\n",
            "\n",
            "\n",
            "Flask is a micro web framework written in Python. It is classified as a microframework because it does not require particular tools or libraries. It has no database abstraction layer, form validation, or any other components where pre-existing third-party libraries provide common functions. However, Flask supports extensions that can add application features as if they were implemented in Flask itself.\n"
          ]
        }
      ],
      "source": [
        "generate_response(\"What is Flask?\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "def scrape_url(url):\n",
            "\n",
            "\n",
            "def scrape_url(url):\n",
            "    response = requests.get(url)\n",
            "    soup = BeautifulSoup(response.text, 'html.parser')\n",
            "    # extract data from soup and return it\n"
          ]
        }
      ],
      "source": [
        "generate_response(\"def scrape_url(url):\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "write a JS function that sorts a list alphabetically\n",
            "\n",
            "\n",
            "function sortListAlphabetically(list) {\n",
            "  list.sort();\n",
            "}\n"
          ]
        }
      ],
      "source": [
        "generate_response(\"write a JS function that sorts a list alphabetically\")"
      ]
    }
  ],
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