preTrainingStarCoder2_proxy.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "8d068150",
      "metadata": {
        "id": "8d068150"
      },
      "source": [
        "This notebook is calling [StarCoder2-3b](https://huggingface.co/bigcode/starcoder2-3b) from the huggingface platform, a code LLM pre-trained on a code dataset called [The Stack v2 smol](https://huggingface.co/datasets/bigcode/the-stack-v2-train-smol-ids) that includes 17 programming languages (C, C#, C++, Go, Java, JavaScript, Kotlin, Lua, PHP, Python, R, Ruby, Rust, SQL, Shell, Swift, TypeScript) and incorporates 7 code documentation languages (including HTML).\n",
        "\n",
        "The goal targeted when training StarCoder2 further in a continued pre-training fashion, before fine-tuning it, is to instruct the pre-trained model with more information on the Near Protocol blockchain, before teaching it how to code Near dApps in a fine-tuning process.\n",
        "\n",
        "While the fine-tuning approach will tend to instruct the model in a structure-aware method to give it a good knowledge of the whole dApp logic, the continued pre-training will consist on pursuing the next-token prediction paradigm instructed during training. Mopre details on the model StarCoder2 and its pre-training method is available in the official paper [StarCoder 2 and The Stack v2: The Next Generation](https://arxiv.org/abs/2402.19173) (2024)."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install transformers[torch]\n",
        "!pip install accelerate\n",
        "!pip install datasets transformers\n",
        "!pip install huggingface_hub"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Vsq8VupylntG",
        "outputId": "644edbda-87a6-4c75-c741-ff09140d2e64"
      },
      "id": "Vsq8VupylntG",
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "Requirement already satisfied: tzdata>=2022.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2024.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.2->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, requests, dill, multiprocess, datasets\n",
            "  Attempting uninstall: requests\n",
            "    Found existing installation: requests 2.31.0\n",
            "    Uninstalling requests-2.31.0:\n",
            "      Successfully uninstalled requests-2.31.0\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 requests==2.31.0, but you have requests 2.32.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed datasets-2.19.2 dill-0.3.8 multiprocess-0.70.16 requests-2.32.3 xxhash-3.4.1\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "requests"
                ]
              },
              "id": "d2e421c3f3a1433abce8d080a68f30ea"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: huggingface_hub in /usr/local/lib/python3.10/dist-packages (0.23.2)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (3.14.0)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2023.6.0)\n",
            "Requirement already satisfied: packaging>=20.9 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (24.0)\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: requests in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (2.32.3)\n",
            "Requirement already satisfied: tqdm>=4.42.1 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.66.4)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface_hub) (4.12.1)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->huggingface_hub) (2024.6.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "94e72c32",
      "metadata": {
        "id": "94e72c32"
      },
      "outputs": [],
      "source": [
        "# Set token parallelism to false to resolve further warning\n",
        "!export TOKENIZERS_PARALLELISM=false"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "65eab062",
      "metadata": {
        "id": "65eab062"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "from datasets import load_dataset\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "20ced6ee",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 552,
          "referenced_widgets": [
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            "28c6a2c5efbb47ecb2acfa23c8f3277c"
          ]
        },
        "id": "20ced6ee",
        "outputId": "c04dca62-0ae1-4c8d-f463-a5d656087f7f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:89: 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"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading readme:   0%|          | 0.00/1.01k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e2e3d8c44cae425a88e5e28d0dfcce06"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Repo card metadata block was not found. Setting CardData to empty.\n",
            "WARNING:huggingface_hub.repocard:Repo card metadata block was not found. Setting CardData to empty.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/54.9M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff68782e86cd4bb99293f31163a89fb6"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading data:   0%|          | 0.00/7.44M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1020d44338524cad94206491942f48aa"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating train split:   0%|          | 0/2863 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a3b1058463924859a9b65777c851ec0c"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Generating val split:   0%|          | 0/319 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7352511b8578473d8ee11ae12f533d5a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DatasetDict({\n",
            "    train: Dataset({\n",
            "        features: ['repoName', 'tree', 'readme'],\n",
            "        num_rows: 2863\n",
            "    })\n",
            "    val: Dataset({\n",
            "        features: ['repoName', 'tree', 'readme'],\n",
            "        num_rows: 319\n",
            "    })\n",
            "})\n",
            "{'repoName': 'near-cli-rs_interactive-clap', 'tree': '.github\\n    workflows\\n        release-plz.yml\\nCHANGELOG.md\\nCargo.toml\\nREADME.md\\nexamples\\n    advanced_enum.rs\\n    advanced_struct.rs\\n    simple_enum.rs\\n    simple_struct.rs\\n    struct_with_context.rs\\n    struct_with_flatten.rs\\n    struct_with_named_arg.rs\\n    struct_with_subargs.rs\\n    struct_with_subcommand.rs\\n    to_cli_args.rs\\ninteractive-clap-derive\\n    CHANGELOG.md\\n    Cargo.toml\\n    src\\n        derives\\n            interactive_clap\\n                methods\\n                    choose_variant.rs\\n                    cli_field_type.rs\\n                    fields_with_skip_default_input_arg.rs\\n                    fields_with_subargs.rs\\n                    fields_with_subcommand.rs\\n                    from_cli_for_enum.rs\\n                    from_cli_for_struct.rs\\n                    input_arg.rs\\n                    interactive_clap_attrs_context.rs\\n                    mod.rs\\n                    skip_interactive_input.rs\\n                mod.rs\\n            mod.rs\\n            to_cli_args\\n                methods\\n                    interactive_clap_attrs_cli_field.rs\\n                    mod.rs\\n                mod.rs\\n        helpers\\n            mod.rs\\n            snake_case_to_camel_case.rs\\n            to_kebab_case.rs\\n        lib.rs\\n        tests\\n            mod.rs\\n            test_simple_struct.rs\\nsrc\\n    helpers\\n        mod.rs\\n        snake_case_to_camel_case.rs\\n        to_kebab_case.rs\\n    lib.rs\\n', 'readme': '# Interactive clap\\n\\n> **`interactive-clap` is a Rust crate of helpers for [`clap`](https://github.com/clap-rs/clap) that enable interactive prompts for structs.**\\n\\n[![Crates.io](https://img.shields.io/crates/v/interactive-clap?style=flat-square)](https://crates.io/crates/interactive-clap)\\n[![Crates.io](https://img.shields.io/crates/d/interactive-clap?style=flat-square)](https://crates.io/crates/interactive-clap)\\n[![License](https://img.shields.io/badge/license-Apache%202.0-blue?style=flat-square)](LICENSE-APACHE)\\n[![License](https://img.shields.io/badge/license-MIT-blue?style=flat-square)](LICENSE-MIT)\\n[![Contributors](https://img.shields.io/github/contributors/near-cli-rs/interactive-clap?style=flat-square)](https://github.com/near-cli-rs/interactive-clap/graphs/contributors)\\n\\nSee examples in the [`examples/`](https://github.com/near-cli-rs/interactive-clap/tree/master/examples) folder.\\n\\nSee it in action in [`near-cli-rs`](https://near.cli.rs) and [`bos-cli-rs`](https://bos.cli.rs).\\n'}\n",
            "{'repoName': 'NEAR-Edu_near-by-example-rust', 'tree': 'README.md\\ncontract\\n    Cargo.toml\\n    neardev\\n        dev-account.env\\n    src\\n        lib.rs\\nexercises\\n    0\\n        README.md\\n        lib.rs\\n        test.rs\\n    1\\n        README.md\\n        lib.rs\\n        test.rs\\n    2\\n        README.md\\n        lib.rs\\n        test.rs\\n    3\\n        README.md\\n        lib.rs\\n        test.rs\\nnext.config.js\\npackage.json\\npages\\n    _app.js\\n    api\\n        execute.js\\n        exercises\\n            [id]\\n                index.js\\n    exercises\\n        [id]\\n            index.js\\n        index.js\\n    index.js\\npublic\\n    vercel.svg\\nstate\\n    exercise.js\\n    near.js\\ntests::(?<name>\\\\w+) stdout\\nstyles\\n    globals.css\\n', 'readme': \"# Saturating Counter Contract\\n\\nIn the last exercise we implemented a simple `Counter` smart contract. In this exercise we'll improve that contract by adding some overflow safety.\\n\\nSince the `Counter` uses an `i8` the minimum `self.val` is `-128` and the maximum is `127`. If we try to `decrement` or `increment` outside the minimum or maximum an overflow will occur. This can be prevented by using the `saturating_*` methods.\\n\\n## Improve increment\\n\\nUpdate the `increment` method so that it saturates at the `i8::MAX` value.\\n\\n## Improve decrement\\n\\nUpdate the `decrement` method so that it saturates at the `i8::MIN` value.\\n\\n## Resources\\n\\n- [saturating_add](https://doc.rust-lang.org/std/primitive.i8.html#method.saturating_add)\\n- [saturating_sub](https://doc.rust-lang.org/std/primitive.i8.html#method.saturating_sub)\\nThis is a [Next.js](https://nextjs.org/) project bootstrapped with [`create-next-app`](https://github.com/vercel/next.js/tree/canary/packages/create-next-app).\\n\\n## Getting Started\\n\\nFirst, run the development server:\\n\\n```bash\\nnpm run dev\\n# or\\nyarn dev\\n```\\n\\nOpen [http://localhost:3000](http://localhost:3000) with your browser to see the result.\\n\\nYou can start editing the page by modifying `pages/index.js`. The page auto-updates as you edit the file.\\n\\n[API routes](https://nextjs.org/docs/api-routes/introduction) can be accessed on [http://localhost:3000/api/hello](http://localhost:3000/api/hello). This endpoint can be edited in `pages/api/hello.js`.\\n\\nThe `pages/api` directory is mapped to `/api/*`. Files in this directory are treated as [API routes](https://nextjs.org/docs/api-routes/introduction) instead of React pages.\\n\\n## Learn More\\n\\nTo learn more about Next.js, take a look at the following resources:\\n\\n- [Next.js Documentation](https://nextjs.org/docs) - learn about Next.js features and API.\\n- [Learn Next.js](https://nextjs.org/learn) - an interactive Next.js tutorial.\\n\\nYou can check out [the Next.js GitHub repository](https://github.com/vercel/next.js/) - your feedback and contributions are welcome!\\n\\n## Deploy on Vercel\\n\\nThe easiest way to deploy your Next.js app is to use the [Vercel Platform](https://vercel.com/new?utm_medium=default-template&filter=next.js&utm_source=create-next-app&utm_campaign=create-next-app-readme) from the creators of Next.js.\\n\\nCheck out our [Next.js deployment documentation](https://nextjs.org/docs/deployment) for more details.\\n# Status Message\\n\\nImplement a Smart Contract that can `set_status` and `get_status` using a `LookupMap`.\\n\\n## set_status\\n\\nThe `set_status` smart contract method should `insert` a message into the `self.records` `LookupMap`.\\n\\n## get_status\\n\\nThe `get_status` smart contract method should get a message stored at the `account_id` key inside the `self.records` `LookupMap`.\\n\\n## Resources\\n\\n- [LookupMap](https://docs.rs/near-sdk/3.1.0/near_sdk/collections/struct.LookupMap.html)\\n# Counter Contract\\n\\nImplement a Smart Contract that can `increment`, `decrement`, and `reset` a `val`.\\n\\n## get_num\\n\\nThe `get_num` smart contract method should return the current counter `val`. The counter struct is accessible via `self`.\\n\\n## increment\\n\\nThe `increment` smart contract method should increment `val` by `1`. It is a mutable method (because it changes `val`). In Rust we need to explicitly state that `increment` can mutate the data held in the `Counter` struct by mutably borrowing `self` with `&mut self` as the first parameter in the method signature.\\n\\n## decrement\\n\\nThe `decrement` smart contract method should decrement `val` by `1`. Like `increment`, it is a mutable method.\\n\\n## reset\\n\\nThe `reset` smart contract method should reset `val` to `0`. Like `increment` and `decrement`, it is a mutable method.\\n\\n## Resources\\n\\n- [Rust Book - Data Types](https://ferrous-systems.github.io/teaching-material/basic-types.html#)\\n- [Rust Book - Structs](https://doc.rust-lang.org/book/ch05-01-defining-structs.html)\\n- [Rust Book - Method Syntax](https://doc.rust-lang.org/book/ch05-03-method-syntax.html)\\n- [Ferrous Systems - Basic Type](https://ferrous-systems.github.io/teaching-material/basic-types.html)\\n- [Ferrous Systems - Compound Types](https://ferrous-systems.github.io/teaching-material/compound-types.html#/)\\n# Wrapping Counter Contract\\n\\nIn the last exercise we improved the simple `Counter` smart contract utilizing the `saturating_*` methods. In this exercise we'll try an alternative approach using the `wrapping_*` methods.\\n\\n## Improve increment\\n\\nUpdate the `increment` method so that it wraps at the `i8::MAX` value.\\n\\n## Improve decrement\\n\\nUpdate the `decrement` method so that it wraps at the `i8::MIN` value.\\n\\n## Resources\\n\\n- [wrapping_add](https://doc.rust-lang.org/std/primitive.i8.html#method.wrapping_add)\\n- [wrapping_sub](https://doc.rust-lang.org/std/primitive.i8.html#method.wrapping_sub)\\n\"}\n"
          ]
        }
      ],
      "source": [
        "# Load the dataset from Hugging Face\n",
        "#dataset = load_dataset('jcarbonnell/preTrainingNEAR')\n",
        "dataset = load_dataset('jcarbonnell/structTuningNEAR')\n",
        "\n",
        "\n",
        "# Verify the loaded dataset\n",
        "print(dataset)\n",
        "\n",
        "# Print the first example from the training dataset\n",
        "print(dataset['train'][0])  # Print the first example from the train dataset\n",
        "print(dataset['val'][0])  # Print the first example from the validation dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6571149a",
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            "88936e3508524488a812dbb654b65d96"
          ]
        },
        "id": "6571149a",
        "outputId": "a3374edb-e227-470b-db13-c0237e810ccf"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/1.87k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a278e591429b47a5a5cf260131aadc90"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.37M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c52a50262abd44c3bd450335f33f997e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/631 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e2231009b12e48c1a28a697fde008083"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors:   0%|          | 0.00/2.69G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9b0a12c1d56142b38aa69b3ecc56bb27"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/119 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "90973767347d446bab516b1536df4683"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/2863 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ba6497315e53474cab05546121552874"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/319 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f686467dd81b4e47a624675bfc008777"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "# Initialize the tokenizer and the model\n",
        "\n",
        "#model_name = \"bigcode/starcoder2-3b\" # RAM + SESSION RESTARTED\n",
        "#model_name = \"distilgpt2\" #SUCCESS !!\n",
        "#model_name = \"deepseek-ai/deepseek-coder-1.3b-base\" # GPU-RAM AGAIN\n",
        "#model_name = \"deepseek-ai/deepseek-coder-1.3b-instruct\" # # GPU-RAM AGAIN\n",
        "#model_name = \"RWKV/rwkv-6-world-1b6\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_name)\n",
        "\n",
        "# Set the pad_token to eos_token if not already set\n",
        "if tokenizer.pad_token is None:\n",
        "    tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "def tokenize_function(examples):\n",
        "    tokenized_inputs = tokenizer(examples['readme'], truncation=True, padding=\"longest\", max_length=64, return_special_tokens_mask=True)\n",
        "    tokenized_inputs['labels'] = tokenized_inputs['input_ids'].copy()\n",
        "    return tokenized_inputs\n",
        "\n",
        "# Apply the tokenization\n",
        "tokenized_datasets = dataset.map(tokenize_function, batched=True, remove_columns=[\"repoName\", \"tree\", \"readme\"])\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "34a8fa62",
      "metadata": {
        "id": "34a8fa62"
      },
      "source": [
        "#### Chunk the tokenized dataset into 64 tokens to avoid crashing\n",
        "def group_texts(examples):\n",
        "    concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}\n",
        "    total_length = len(concatenated_examples[list(examples.keys())[0]])\n",
        "    if total_length >= 64:\n",
        "        total_length = (total_length // 64) * 64\n",
        "    result = {\n",
        "        k: [t[i:i + 64] for i in range(0, total_length, 64)]\n",
        "        for k, t in concatenated_examples.items()\n",
        "    }\n",
        "    result[\"labels\"] = result[\"input_ids\"].copy()\n",
        "    return result\n",
        "\n",
        "#### Apply chunking\n",
        "tokenized_datasets = tokenized_datasets.map(group_texts, batched=True)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import TrainingArguments"
      ],
      "metadata": {
        "id": "KeE3Iw-Qlzu-"
      },
      "id": "KeE3Iw-Qlzu-",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a5141fbb",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 599
        },
        "id": "a5141fbb",
        "outputId": "6ac82dcd-c704-43d2-e229-df8554f0f0ff"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/training_args.py:1474: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "ImportError",
          "evalue": "Using the `Trainer` with `PyTorch` requires `accelerate>=0.21.0`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mImportError\u001b[0m                               Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-9-786fb70ce2e5>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# setup training arguments\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m training_args = TrainingArguments(\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0moutput_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'./results'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0moverwrite_output_dir\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mnum_train_epochs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m5\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, eval_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, lr_scheduler_kwargs, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, save_only_model, restore_callback_states_from_checkpoint, no_cuda, use_cpu, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, ddp_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, dataloader_prefetch_factor, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, accelerator_config, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length,...\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m__post_init__\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1639\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mframework\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"pt\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1640\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0mis_torch_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1641\u001b[0;31m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"cpu\"\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_torch_greater_or_equal_than_2_3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1642\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"cuda\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1643\u001b[0m             \u001b[0;32mand\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdevice\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtype\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"mlu\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36mdevice\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2147\u001b[0m         \"\"\"\n\u001b[1;32m   2148\u001b[0m         \u001b[0mrequires_backends\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"torch\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2149\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_devices\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2151\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mproperty\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py\u001b[0m in \u001b[0;36m__get__\u001b[0;34m(self, obj, objtype)\u001b[0m\n\u001b[1;32m     57\u001b[0m         \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mcached\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 59\u001b[0;31m             \u001b[0mcached\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     60\u001b[0m             \u001b[0msetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobj\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     61\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mcached\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/training_args.py\u001b[0m in \u001b[0;36m_setup_devices\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2053\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_sagemaker_mp_enabled\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2054\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mis_accelerate_available\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2055\u001b[0;31m                 raise ImportError(\n\u001b[0m\u001b[1;32m   2056\u001b[0m                     \u001b[0;34mf\"Using the `Trainer` with `PyTorch` requires `accelerate>={ACCELERATE_MIN_VERSION}`: \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2057\u001b[0m                     \u001b[0;34m\"Please run `pip install transformers[torch]` or `pip install accelerate -U`\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mImportError\u001b[0m: Using the `Trainer` with `PyTorch` requires `accelerate>=0.21.0`: Please run `pip install transformers[torch]` or `pip install accelerate -U`",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: If your import is failing due to a missing package, you can\nmanually install dependencies using either !pip or !apt.\n\nTo view examples of installing some common dependencies, click the\n\"Open Examples\" button below.\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ],
          "errorDetails": {
            "actions": [
              {
                "action": "open_url",
                "actionText": "Open Examples",
                "url": "/notebooks/snippets/importing_libraries.ipynb"
              }
            ]
          }
        }
      ],
      "source": [
        "# setup training arguments\n",
        "training_args = TrainingArguments(\n",
        "    output_dir='./results',\n",
        "    overwrite_output_dir=True,\n",
        "    num_train_epochs=5,\n",
        "    per_device_train_batch_size=1,  # reduced batch size 4 to 1 to avoid crashing\n",
        "    per_device_eval_batch_size=1,\n",
        "    evaluation_strategy=\"epoch\",\n",
        "    save_strategy=\"epoch\",\n",
        "    learning_rate=2e-5,\n",
        "    logging_dir='./logs',\n",
        "    logging_steps=10,\n",
        "    gradient_accumulation_steps=8,  # Simulate larger batch size\n",
        "    #fp16=True,  # mixed precision training not supported on mps\n",
        ")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import Trainer"
      ],
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        "id": "-OzW0fajmml7"
      },
      "id": "-OzW0fajmml7",
      "execution_count": null,
      "outputs": []
    },
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      "execution_count": null,
      "id": "de1d227b",
      "metadata": {
        "id": "de1d227b"
      },
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      "source": [
        "# create the trainer and start training\n",
        "trainer = Trainer(\n",
        "    model=model,\n",
        "    args=training_args,\n",
        "    train_dataset=tokenized_datasets['train'],\n",
        "    eval_dataset=tokenized_datasets['val'],\n",
        ")\n",
        "\n",
        "trainer.train()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3c2d90ea",
      "metadata": {
        "id": "3c2d90ea"
      },
      "outputs": [],
      "source": [
        "# model evaluation\n",
        "import math\n",
        "\n",
        "eval_results = trainer.evaluate()\n",
        "perplexity = math.exp(eval_results['eval_loss'])\n",
        "print(f\"Perplexity: {perplexity}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Assuming you have access to the training logs\n",
        "training_loss = [log['loss'] for log in trainer.state.log_history if 'loss' in log]\n",
        "validation_loss = [log['eval_loss'] for log in trainer.state.log_history if 'eval_loss' in log]\n",
        "\n",
        "# Pad or trim the lists to ensure they have the same length\n",
        "min_length = min(len(training_loss), len(validation_loss))\n",
        "training_loss = training_loss[:min_length]\n",
        "validation_loss = validation_loss[:min_length]\n",
        "\n",
        "epochs = range(len(training_loss))\n",
        "\n",
        "plt.plot(epochs, training_loss, label='Training Loss')\n",
        "plt.plot(epochs, validation_loss, label='Validation Loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.title(f'Training and Validation Loss for {model_name}')\n",
        "plt.show()"
      ],
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        "id": "bGioHn8MLofE"
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      "id": "bGioHn8MLofE",
      "execution_count": null,
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      "cell_type": "code",
      "execution_count": null,
      "id": "280f9190",
      "metadata": {
        "id": "280f9190"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "\n",
        "prompt = \"Once upon a time\"\n",
        "inputs = tokenizer(prompt, return_tensors='pt')\n",
        "\n",
        "# Move input_ids to the same device as the model\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "inputs = {key: value.to(device) for key, value in inputs.items()}\n",
        "model.to(device)\n",
        "\n",
        "# Ensure pad_token_id is set\n",
        "if tokenizer.pad_token_id is None:\n",
        "    tokenizer.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "# Generate text with attention_mask\n",
        "output = model.generate(\n",
        "    inputs['input_ids'],\n",
        "    attention_mask=inputs['attention_mask'],\n",
        "    max_length=50,\n",
        "    pad_token_id=tokenizer.pad_token_id\n",
        ")\n",
        "\n",
        "generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "print(generated_text)\n"
      ]
    }
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