{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Er_P6ziRGCXH",
        "outputId": "8ae9ff41-a91c-46e5-8dc1-8a83d336acae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Cloning into 'starcoder2'...\n",
            "remote: Enumerating objects: 44, done.\u001b[K\n",
            "remote: Counting objects: 100% (44/44), done.\u001b[K\n",
            "remote: Compressing objects: 100% (41/41), done.\u001b[K\n",
            "remote: Total 44 (delta 19), reused 9 (delta 2), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (44/44), 21.08 KiB | 3.01 MiB/s, done.\n",
            "Resolving deltas: 100% (19/19), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/bigcode-project/starcoder2.git"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mH683TEF08Gs",
        "outputId": "4b49252e-05dd-4f2f-e6f1-acb4c5358bdb"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "sample_data  starcoder2\n",
            "/content/starcoder2\n"
          ]
        }
      ],
      "source": [
        "!ls\n",
        "%cd starcoder2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZnQuOTQCGjZb",
        "outputId": "ea9302d5-e401-4980-a350-223bb88c3d79"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
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            "  Cloning https://github.com/huggingface/transformers.git to /tmp/pip-req-build-r9ck654w\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/transformers.git /tmp/pip-req-build-r9ck654w\n",
            "  Resolved https://github.com/huggingface/transformers.git to commit 76a33a10923ccc1074917f6b6a1e719e626b7dc9\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
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            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25hCollecting nvidia-cufft-cu12==11.0.2.54 (from torch>=1.10.0->accelerate==0.27.1->-r requirements.txt (line 2))\n",
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            "\u001b[?25hRequirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.10.0->accelerate==0.27.1->-r requirements.txt (line 2)) (2.2.0)\n",
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            "\u001b[?25hCollecting docstring-parser>=0.14.1 (from tyro>=0.5.11->trl==0.7.10->-r requirements.txt (line 6))\n",
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            "Requirement already satisfied: rich>=11.1.0 in /usr/local/lib/python3.10/dist-packages (from tyro>=0.5.11->trl==0.7.10->-r requirements.txt (line 6)) (13.7.1)\n",
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            "Collecting smmap<6,>=3.0.1 (from gitdb<5,>=4.0.1->GitPython!=3.1.29,>=1.0.0->wandb==0.16.3->-r requirements.txt (line 7))\n",
            "  Downloading smmap-5.0.1-py3-none-any.whl (24 kB)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl==0.7.10->-r requirements.txt (line 6)) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl==0.7.10->-r requirements.txt (line 6)) (2.16.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.10.0->accelerate==0.27.1->-r requirements.txt (line 2)) (2.1.5)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.10.0->accelerate==0.27.1->-r requirements.txt (line 2)) (1.3.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl==0.7.10->-r requirements.txt (line 6)) (0.1.2)\n",
            "Building wheels for collected packages: transformers\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for transformers: filename=transformers-4.40.0.dev0-py3-none-any.whl size=8802691 sha256=114bda17deca705bc7d115c456c221930feb03621c99e3cdfef186e3adbcb645\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-85oztzfi/wheels/e7/9c/5b/e1a9c8007c343041e61cc484433d512ea9274272e3fcbe7c16\n",
            "Successfully built transformers\n",
            "Installing collected packages: bitsandbytes, xxhash, smmap, shtab, setproctitle, sentry-sdk, nvidia-nvtx-cu12, nvidia-nvjitlink-cu12, nvidia-nccl-cu12, nvidia-curand-cu12, nvidia-cufft-cu12, nvidia-cuda-runtime-cu12, nvidia-cuda-nvrtc-cu12, nvidia-cuda-cupti-cu12, nvidia-cublas-cu12, docstring-parser, docker-pycreds, dill, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, gitdb, tyro, nvidia-cusolver-cu12, GitPython, wandb, transformers, datasets, accelerate, trl, peft\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.38.2\n",
            "    Uninstalling transformers-4.38.2:\n",
            "      Successfully uninstalled transformers-4.38.2\n",
            "Successfully installed GitPython-3.1.42 accelerate-0.27.1 bitsandbytes-0.41.3 datasets-2.18.0 dill-0.3.8 docker-pycreds-0.4.0 docstring-parser-0.16 gitdb-4.0.11 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.99 nvidia-nvtx-cu12-12.1.105 peft-0.8.2 sentry-sdk-1.43.0 setproctitle-1.3.3 shtab-1.7.1 smmap-5.0.1 transformers-4.40.0.dev0 trl-0.7.10 tyro-0.7.3 wandb-0.16.3 xxhash-3.4.1\n"
          ]
        }
      ],
      "source": [
        "!pip install -r requirements.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
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          "height": 556,
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        },
        "id": "sSwkDbbaHK3E",
        "outputId": "f6d31671-8e28-4b63-9739-1338d80e4d25"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "a59be920211d4a629619061b4156604e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/7.88k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "44dfb705d7c3486ea41dd6b6a721ecd1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "vocab.json:   0%|          | 0.00/777k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4070a4ddb21649a8813f2556d836a27c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "merges.txt:   0%|          | 0.00/442k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "bfd830a4eded4bd6ac5ff67a989624e6",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/2.06M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "405322bdc22648bc921fe2d5b4c60c53",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/958 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5ad0db860358493ea4b0a142cebdb933",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "config.json:   0%|          | 0.00/700 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "`low_cpu_mem_usage` was None, now set to True since model is quantized.\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "b14725886e7c48d0ad56016b172fff4c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/12.1G [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1132: UserWarning: Using the model-agnostic default `max_length` (=20) to control the generation length. We recommend setting `max_new_tokens` to control the maximum length of the generation.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/nn/modules.py:228: UserWarning: Input type into Linear4bit is torch.float16, but bnb_4bit_compute_type=torch.float32 (default). This will lead to slow inference or training speed.\n",
            "  warnings.warn(f'Input type into Linear4bit is torch.float16, but bnb_4bit_compute_type=torch.float32 (default). This will lead to slow inference or training speed.')\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "def print_hello_world():\n",
            "    print(\"Hello World\")\n",
            "\n",
            "print_hello_world\n"
          ]
        }
      ],
      "source": [
        "# pip install bitsandbytes accelerate\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
        "\n",
        "# to use 4bit use `load_in_4bit=True` instead\n",
        "quantization_config = BitsAndBytesConfig(load_in_4bit=True)\n",
        "\n",
        "checkpoint = \"bigcode/starcoder2-3b\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n",
        "model = AutoModelForCausalLM.from_pretrained(checkpoint, quantization_config=quantization_config)\n",
        "\n",
        "inputs = tokenizer.encode(\"def print_hello_world():\", return_tensors=\"pt\").to(\"cuda\")\n",
        "outputs = model.generate(inputs)\n",
        "print(tokenizer.decode(outputs[0]))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6usz-PG6HvTC",
        "outputId": "8ce003c3-7957-4b2e-8acd-ee2e4388e64a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Memory footprint: 1994.90 MB\n"
          ]
        }
      ],
      "source": [
        "print(f\"Memory footprint: {model.get_memory_footprint() / 1e6:.2f} MB\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "ppQs3vTAI2sP"
      },
      "outputs": [],
      "source": [
        "import locale\n",
        "def getpreferredencoding(do_setlocale = True):\n",
        "    return \"UTF-8\"\n",
        "locale.getpreferredencoding = getpreferredencoding"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "id": "JBNCVmGlJM8t",
        "outputId": "67b1d19c-5f1a-422b-9025-5df71c1b3392"
      },
      "outputs": [
        {
          "data": {
            "application/javascript": "\n        window._wandbApiKey = new Promise((resolve, reject) => {\n            function loadScript(url) {\n            return new Promise(function(resolve, reject) {\n                let newScript = document.createElement(\"script\");\n                newScript.onerror = reject;\n                newScript.onload = resolve;\n                document.body.appendChild(newScript);\n                newScript.src = url;\n            });\n            }\n            loadScript(\"https://cdn.jsdelivr.net/npm/postmate/build/postmate.min.js\").then(() => {\n            const iframe = document.createElement('iframe')\n            iframe.style.cssText = \"width:0;height:0;border:none\"\n            document.body.appendChild(iframe)\n            const handshake = new Postmate({\n                container: iframe,\n                url: 'https://wandb.ai/authorize'\n            });\n            const timeout = setTimeout(() => reject(\"Couldn't auto authenticate\"), 5000)\n            handshake.then(function(child) {\n                child.on('authorize', data => {\n                    clearTimeout(timeout)\n                    resolve(data)\n                });\n            });\n            })\n        });\n    ",
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Logging into wandb.ai. (Learn how to deploy a W&B server locally: https://wandb.me/wandb-server)\n",
            "\u001b[34m\u001b[1mwandb\u001b[0m: You can find your API key in your browser here: https://wandb.ai/authorize\n",
            "wandb: Paste an API key from your profile and hit enter, or press ctrl+c to quit:"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " ··········\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import wandb\n",
        "wandb.login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tFIqEZpCJVNe",
        "outputId": "32755bb1-2067-4708-a5a9-4c4a10ce6f64"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Token will not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234,
          "referenced_widgets": [
            "309e953b62954bacb8922437d257a1f1",
            "b5f237544dde45c59de10da182608941",
            "dc90e05c4f7045d7aea863618ccadad2",
            "584d7ec353ff4722a2d77585f4839115",
            "9496fb1192374db7943f79c5a232b2b4",
            "6245c9499721471881b6a0265c43be8e",
            "4941882fb90244d39a12f183aa92da25",
            "cc7b43477eb7408eb4de24392a2a907f",
            "2096c7fe4d6c4a7eb313a0d95d2e9844",
            "d1858dc2ed284af6ac41446b7f07ea6e",
            "22092f32014e438fa73fa880a6ec600c",
            "80a83bae2f3343aea7477a08a571fb1b",
            "a716af012fdf470c9f08baa9efd6454e",
            "cddef4db899849adac12bf086f49f01a",
            "afc9a06ecc16456db6f624fed7ecfeb6",
            "acda9da77e7444b8bd769f1ed61f95b5",
            "7a0bdb9dd604436ab77bf3681960f70f",
            "eceb2ad161f14c159cda898e883ea239",
            "f61726660f4b4b898e05ed9d7b54209b",
            "f74eb867afc842dda1642c4c7066a908",
            "67aca1d36bf04f2d8ab56a6c597d3eb7",
            "29132b57a52746eb9e51f5f6aa6bd05b",
            "d464effbf7f34d0fa7b846af3a7f9af0",
            "9bcfdcfcfc6e4652bed5ff3b99336040",
            "4d24d968f2ad44c0a157b4e2c0476f1d",
            "7bf72e9ea3cc4231b62b1a3f201724c1",
            "abb1ccb4fa7042939d395bfbb2361f42",
            "b2787ecb49cf44fe8b3510981c5cd659",
            "d7d4a84bf52540fda77e1d3e6c4d376e",
            "9706ef6c7a3147e9bc2805cd5e6ac2ae",
            "e58a2c12f9ba4b599e0bff5c3d9ce561",
            "6b7b0b1d545a4aef986d29feedda7f4d",
            "bf000603106047daa85b3caf2e927d40"
          ]
        },
        "id": "0b0QYsoCNOGm",
        "outputId": "59156f6c-b686-40fe-b267-dada1e94825f"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "309e953b62954bacb8922437d257a1f1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/3.30k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "80a83bae2f3343aea7477a08a571fb1b",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading data:   0%|          | 0.00/57.8M [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "d464effbf7f34d0fa7b846af3a7f9af0",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Generating train split: 0 examples [00:00, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/plain": [
              "DatasetDict({\n",
              "    train: Dataset({\n",
              "        features: ['content', 'avg_line_length', 'max_line_length', 'alphanum_fraction', 'licenses', 'repository_name', 'path', 'size', 'lang'],\n",
              "        num_rows: 10000\n",
              "    })\n",
              "})"
            ]
          },
          "execution_count": 9,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from datasets import load_dataset\n",
        "load_dataset(\"bigcode/the-stack-smol\", data_dir=\"data/php\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tt_aO8p7Q5is",
        "outputId": "02b841ed-8e09-43be-fb0b-d42e37083120"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting git+https://github.com/huggingface/trl.git\n",
            "  Cloning https://github.com/huggingface/trl.git to /tmp/pip-req-build-1i__mkrj\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/huggingface/trl.git /tmp/pip-req-build-1i__mkrj\n",
            "  Resolved https://github.com/huggingface/trl.git to commit dc6a934269a6d42cceb1565829030fc73e00e8cf\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",
            "Requirement already satisfied: torch>=1.4.0 in /usr/local/lib/python3.10/dist-packages (from trl==0.8.2.dev0) (2.2.1+cu121)\n",
            "Requirement already satisfied: transformers>=4.31.0 in /usr/local/lib/python3.10/dist-packages (from trl==0.8.2.dev0) (4.40.0.dev0)\n",
            "Requirement already satisfied: numpy>=1.18.2 in /usr/local/lib/python3.10/dist-packages (from trl==0.8.2.dev0) (1.25.2)\n",
            "Requirement already satisfied: accelerate in /usr/local/lib/python3.10/dist-packages (from trl==0.8.2.dev0) (0.27.1)\n",
            "Requirement already satisfied: datasets in /usr/local/lib/python3.10/dist-packages (from trl==0.8.2.dev0) (2.18.0)\n",
            "Requirement already satisfied: tyro>=0.5.11 in /usr/local/lib/python3.10/dist-packages (from trl==0.8.2.dev0) (0.7.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (3.13.1)\n",
            "Requirement already satisfied: typing-extensions>=4.8.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (4.10.0)\n",
            "Requirement already satisfied: sympy in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (1.12)\n",
            "Requirement already satisfied: networkx in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (3.2.1)\n",
            "Requirement already satisfied: jinja2 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (3.1.3)\n",
            "Requirement already satisfied: fsspec in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (2023.6.0)\n",
            "Requirement already satisfied: nvidia-cuda-nvrtc-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-runtime-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cuda-cupti-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (12.1.105)\n",
            "Requirement already satisfied: nvidia-cudnn-cu12==8.9.2.26 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (8.9.2.26)\n",
            "Requirement already satisfied: nvidia-cublas-cu12==12.1.3.1 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (12.1.3.1)\n",
            "Requirement already satisfied: nvidia-cufft-cu12==11.0.2.54 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (11.0.2.54)\n",
            "Requirement already satisfied: nvidia-curand-cu12==10.3.2.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (10.3.2.106)\n",
            "Requirement already satisfied: nvidia-cusolver-cu12==11.4.5.107 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (11.4.5.107)\n",
            "Requirement already satisfied: nvidia-cusparse-cu12==12.1.0.106 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (12.1.0.106)\n",
            "Requirement already satisfied: nvidia-nccl-cu12==2.19.3 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (2.19.3)\n",
            "Requirement already satisfied: nvidia-nvtx-cu12==12.1.105 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (12.1.105)\n",
            "Requirement already satisfied: triton==2.2.0 in /usr/local/lib/python3.10/dist-packages (from torch>=1.4.0->trl==0.8.2.dev0) (2.2.0)\n",
            "Requirement already satisfied: nvidia-nvjitlink-cu12 in /usr/local/lib/python3.10/dist-packages (from nvidia-cusolver-cu12==11.4.5.107->torch>=1.4.0->trl==0.8.2.dev0) (12.4.99)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.19.3 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.8.2.dev0) (0.20.3)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.8.2.dev0) (24.0)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.8.2.dev0) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.8.2.dev0) (2023.12.25)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.8.2.dev0) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.19,>=0.14 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.8.2.dev0) (0.15.2)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.8.2.dev0) (0.4.2)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers>=4.31.0->trl==0.8.2.dev0) (4.66.2)\n",
            "Requirement already satisfied: docstring-parser>=0.14.1 in /usr/local/lib/python3.10/dist-packages (from tyro>=0.5.11->trl==0.8.2.dev0) (0.16)\n",
            "Requirement already satisfied: rich>=11.1.0 in /usr/local/lib/python3.10/dist-packages (from tyro>=0.5.11->trl==0.8.2.dev0) (13.7.1)\n",
            "Requirement already satisfied: shtab>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from tyro>=0.5.11->trl==0.8.2.dev0) (1.7.1)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from accelerate->trl==0.8.2.dev0) (5.9.5)\n",
            "Requirement already satisfied: pyarrow>=12.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.8.2.dev0) (14.0.2)\n",
            "Requirement already satisfied: pyarrow-hotfix in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.8.2.dev0) (0.6)\n",
            "Requirement already satisfied: dill<0.3.9,>=0.3.0 in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.8.2.dev0) (0.3.8)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.8.2.dev0) (1.5.3)\n",
            "Requirement already satisfied: xxhash in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.8.2.dev0) (3.4.1)\n",
            "Requirement already satisfied: multiprocess in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.8.2.dev0) (0.70.16)\n",
            "Requirement already satisfied: aiohttp in /usr/local/lib/python3.10/dist-packages (from datasets->trl==0.8.2.dev0) (3.9.3)\n",
            "Requirement already satisfied: aiosignal>=1.1.2 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.8.2.dev0) (1.3.1)\n",
            "Requirement already satisfied: attrs>=17.3.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.8.2.dev0) (23.2.0)\n",
            "Requirement already satisfied: frozenlist>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.8.2.dev0) (1.4.1)\n",
            "Requirement already satisfied: multidict<7.0,>=4.5 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.8.2.dev0) (6.0.5)\n",
            "Requirement already satisfied: yarl<2.0,>=1.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.8.2.dev0) (1.9.4)\n",
            "Requirement already satisfied: async-timeout<5.0,>=4.0 in /usr/local/lib/python3.10/dist-packages (from aiohttp->datasets->trl==0.8.2.dev0) (4.0.3)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->trl==0.8.2.dev0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->trl==0.8.2.dev0) (3.6)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->trl==0.8.2.dev0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers>=4.31.0->trl==0.8.2.dev0) (2024.2.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl==0.8.2.dev0) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich>=11.1.0->tyro>=0.5.11->trl==0.8.2.dev0) (2.16.1)\n",
            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.4.0->trl==0.8.2.dev0) (2.1.5)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl==0.8.2.dev0) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets->trl==0.8.2.dev0) (2023.4)\n",
            "Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.4.0->trl==0.8.2.dev0) (1.3.0)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich>=11.1.0->tyro>=0.5.11->trl==0.8.2.dev0) (0.1.2)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets->trl==0.8.2.dev0) (1.16.0)\n",
            "Building wheels for collected packages: trl\n",
            "  Building wheel for trl (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for trl: filename=trl-0.8.2.dev0-py3-none-any.whl size=237720 sha256=b18a84474ea863787fb9781dea9dd28a2a8b13faaa0d4075b0edc8de500d0ac1\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-sv1ib3mn/wheels/22/0e/42/319b77b2648bb6140ef2b08b0478ede9ca3cc7879fcd022d36\n",
            "Successfully built trl\n",
            "Installing collected packages: trl\n",
            "  Attempting uninstall: trl\n",
            "    Found existing installation: trl 0.7.10\n",
            "    Uninstalling trl-0.7.10:\n",
            "      Successfully uninstalled trl-0.7.10\n",
            "Successfully installed trl-0.8.2.dev0\n",
            "Requirement already satisfied: peft in /usr/local/lib/python3.10/dist-packages (0.8.2)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from peft) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from peft) (24.0)\n",
            "Requirement already satisfied: psutil in /usr/local/lib/python3.10/dist-packages (from peft) (5.9.5)\n",
            "Requirement already satisfied: pyyaml in /usr/local/lib/python3.10/dist-packages (from peft) (6.0.1)\n",
            "Requirement already satisfied: torch>=1.13.0 in /usr/local/lib/python3.10/dist-packages (from peft) (2.2.1+cu121)\n",
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (from peft) (4.40.0.dev0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from peft) (4.66.2)\n",
            "Requirement already satisfied: accelerate>=0.21.0 in /usr/local/lib/python3.10/dist-packages (from peft) (0.27.1)\n",
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          ]
        }
      ],
      "source": [
        "!pip install git+https://github.com/huggingface/trl.git\n",
        "!pip install peft"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "8vN-eLgVQryO"
      },
      "outputs": [],
      "source": [
        "import argparse\n",
        "import multiprocessing\n",
        "import os\n",
        "\n",
        "import torch\n",
        "import transformers\n",
        "from accelerate import PartialState\n",
        "from datasets import load_dataset\n",
        "from peft import LoraConfig\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    BitsAndBytesConfig,\n",
        "    logging,\n",
        "    set_seed,\n",
        ")\n",
        "from trl import SFTTrainer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "WGUK5keeTHoO"
      },
      "outputs": [],
      "source": [
        "parser = argparse.ArgumentParser()\n",
        "parser.add_argument(\"--model_id\", type=str, default=\"bigcode/starcoder2-3b\")\n",
        "parser.add_argument(\"--dataset_name\", type=str, default=\"bigcode/the-stack-smol\")\n",
        "parser.add_argument(\"--subset\", type=str, default=\"data/php\")\n",
        "parser.add_argument(\"--split\", type=str, default=\"train\")\n",
        "parser.add_argument(\"--dataset_text_field\", type=str, default=\"content\")\n",
        "\n",
        "parser.add_argument(\"--max_seq_length\", type=int, default=512)\n",
        "parser.add_argument(\"--max_steps\", type=int, default=1000)\n",
        "parser.add_argument(\"--micro_batch_size\", type=int, default=1)\n",
        "parser.add_argument(\"--gradient_accumulation_steps\", type=int, default=4)\n",
        "parser.add_argument(\"--weight_decay\", type=float, default=0.01)\n",
        "parser.add_argument(\"--fp16\", type=bool, default=True)\n",
        "\n",
        "parser.add_argument(\"--attention_dropout\", type=float, default=0.1)\n",
        "parser.add_argument(\"--learning_rate\", type=float, default=2e-4)\n",
        "parser.add_argument(\"--lr_scheduler_type\", type=str, default=\"cosine\")\n",
        "parser.add_argument(\"--warmup_steps\", type=int, default=100)\n",
        "parser.add_argument(\"--seed\", type=int, default=0)\n",
        "parser.add_argument(\"--output_dir\", type=str, default=\"finetunedPHP_starcoder2\")\n",
        "parser.add_argument(\"--num_proc\", type=int, default=2)#T4 gpu of google colab\n",
        "parser.add_argument(\"--push_to_hub\", type=bool, default=True)\n",
        "\n",
        "args = parser.parse_args(args=[])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sRJpl3NoTUqk",
        "outputId": "52c0e1ed-8a22-469a-c730-9fcd212960aa"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "Namespace(model_id='bigcode/starcoder2-3b', dataset_name='bigcode/the-stack-smol', subset='data/php', split='train', dataset_text_field='content', max_seq_length=512, max_steps=1000, micro_batch_size=1, gradient_accumulation_steps=4, weight_decay=0.01, fp16=True, attention_dropout=0.1, learning_rate=0.0002, lr_scheduler_type='cosine', warmup_steps=100, seed=0, output_dir='finetunedPHP_starcoder2', num_proc=2, push_to_hub=True)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "args"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "WEJKhbrOTWwW"
      },
      "outputs": [],
      "source": [
        "def print_trainable_parameters(model):\n",
        "    \"\"\"\n",
        "    Prints the number of trainable parameters in the model.\n",
        "    \"\"\"\n",
        "    trainable_params = 0\n",
        "    all_param = 0\n",
        "    for _, param in model.named_parameters():\n",
        "        all_param += param.numel()\n",
        "        if param.requires_grad:\n",
        "            trainable_params += param.numel()\n",
        "    print(\n",
        "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "Dd0D-RwtTaxG"
      },
      "outputs": [],
      "source": [
        "def main(args):\n",
        "    # config\n",
        "    bnb_config = BitsAndBytesConfig(\n",
        "        load_in_4bit=True,\n",
        "        bnb_4bit_quant_type=\"nf4\",\n",
        "        bnb_4bit_compute_dtype=torch.bfloat16,\n",
        "    )\n",
        "    lora_config = LoraConfig(\n",
        "        r=8,\n",
        "        target_modules=[\n",
        "            \"q_proj\",\n",
        "            \"o_proj\",\n",
        "            \"k_proj\",\n",
        "            \"v_proj\",\n",
        "            \"gate_proj\",\n",
        "            \"up_proj\",\n",
        "            \"down_proj\",\n",
        "        ],\n",
        "        task_type=\"CAUSAL_LM\",\n",
        "    )\n",
        "\n",
        "    # load model and dataset\n",
        "    token = os.environ.get(\"HF_TOKEN\", None)\n",
        "    model = AutoModelForCausalLM.from_pretrained(\n",
        "        args.model_id,\n",
        "        quantization_config=bnb_config,\n",
        "        device_map={\"\": PartialState().process_index},\n",
        "        attention_dropout=args.attention_dropout,\n",
        "    )\n",
        "    print_trainable_parameters(model)\n",
        "\n",
        "    data = load_dataset(\n",
        "        args.dataset_name,\n",
        "        data_dir=args.subset,\n",
        "        split=args.split,\n",
        "        token=token,\n",
        "        num_proc=args.num_proc if args.num_proc else multiprocessing.cpu_count(),\n",
        "    )\n",
        "\n",
        "    # setup the trainer\n",
        "    trainer = SFTTrainer(\n",
        "        model=model,\n",
        "        train_dataset=data,\n",
        "        max_seq_length=args.max_seq_length,\n",
        "        args=transformers.TrainingArguments(\n",
        "            per_device_train_batch_size=args.micro_batch_size,\n",
        "            gradient_accumulation_steps=args.gradient_accumulation_steps,\n",
        "            warmup_steps=args.warmup_steps,\n",
        "            max_steps=args.max_steps,\n",
        "            learning_rate=args.learning_rate,\n",
        "            lr_scheduler_type=args.lr_scheduler_type,\n",
        "            weight_decay=args.weight_decay,\n",
        "            fp16=args.fp16,\n",
        "            logging_strategy=\"steps\",\n",
        "            logging_steps=10,\n",
        "            output_dir=args.output_dir,\n",
        "            optim=\"paged_adamw_8bit\",\n",
        "            seed=args.seed,\n",
        "            run_name=f\"train-{args.model_id.split('/')[-1]}\",\n",
        "            report_to=\"wandb\",\n",
        "        ),\n",
        "        peft_config=lora_config,\n",
        "        dataset_text_field=args.dataset_text_field,\n",
        "    )\n",
        "\n",
        "    # launch\n",
        "    print(\"Training...\")\n",
        "    trainer.train()\n",
        "\n",
        "    print(\"Saving the last checkpoint of the model\")\n",
        "    model.save_pretrained(os.path.join(args.output_dir, \"final_checkpoint/\"))\n",
        "    if args.push_to_hub:\n",
        "        trainer.push_to_hub(\"Upload model\")\n",
        "    print(\"Training Done! 💥\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
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        },
        "id": "GjuYlUNdTqnq",
        "outputId": "8e9ed1ef-55d4-48cb-b59f-22e3c0de3fe8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "trainable params: 151369728 || all params: 1591200768 || trainable%: 9.5129245186488\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "72eb5b869a3d4208be0794d88cf1273a",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Map:   0%|          | 0/10000 [00:00<?, ? examples/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33mcodehimanshu24\u001b[0m (\u001b[33mstarcoder2\u001b[0m). Use \u001b[1m`wandb login --relogin`\u001b[0m to force relogin\n"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Training...\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "70174cbd47fd4651b21b2e37a3d67e74",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(Label(value='Waiting for wandb.init()...\\r'), FloatProgress(value=0.011112673922222408, max=1.0…"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "wandb version 0.16.4 is available!  To upgrade, please run:\n",
              " $ pip install wandb --upgrade"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Tracking run with wandb version 0.16.3"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Run data is saved locally in <code>/content/starcoder2/wandb/run-20240325_044236-yaifaow3</code>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              "Syncing run <strong><a href='https://wandb.ai/starcoder2/huggingface/runs/yaifaow3' target=\"_blank\">train-starcoder2-3b</a></strong> to <a href='https://wandb.ai/starcoder2/huggingface' target=\"_blank\">Weights & Biases</a> (<a href='https://wandb.me/run' target=\"_blank\">docs</a>)<br/>"
            ],
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              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View project at <a href='https://wandb.ai/starcoder2/huggingface' target=\"_blank\">https://wandb.ai/starcoder2/huggingface</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "text/html": [
              " View run at <a href='https://wandb.ai/starcoder2/huggingface/runs/yaifaow3' target=\"_blank\">https://wandb.ai/starcoder2/huggingface/runs/yaifaow3</a>"
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{'loss': 2.1512, 'grad_norm': 2.374952793121338, 'learning_rate': 2e-05, 'epoch': 0.0}\n",
            "{'loss': 2.8922, 'grad_norm': 1.7982090711593628, 'learning_rate': 3.8e-05, 'epoch': 0.01}\n",
            "{'loss': 2.9155, 'grad_norm': 2.2329304218292236, 'learning_rate': 5.4000000000000005e-05, 'epoch': 0.01}\n",
            "{'loss': 2.7554, 'grad_norm': 1.7585422992706299, 'learning_rate': 7.4e-05, 'epoch': 0.02}\n",
            "{'loss': 1.9353, 'grad_norm': 0.5500292181968689, 'learning_rate': 9.200000000000001e-05, 'epoch': 0.02}\n",
            "{'loss': 1.9264, 'grad_norm': 0.4516673684120178, 'learning_rate': 0.00011200000000000001, 'epoch': 0.02}\n",
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            "Saving the last checkpoint of the model\n"
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