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  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/BohdanPetryshyn/code-llama-fim-fine-tuning/blob/main/code_llama_fim_fine_tuning_inference_and_merging.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Code Llama FIM fine-tuning inference and merging\n",
        "\n",
        "If you found a problem with this notebook, please report it in the original GitHub [repo](https://github.com/BohdanPetryshyn/code-llama-fim-fine-tuning?tab=readme-ov-file) as an issue."
      ],
      "metadata": {
        "id": "pEqJLJ4hDs2H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Install dependenices\n"
      ],
      "metadata": {
        "id": "e4Ceg3KAD_iy"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DeAZEXHn3cxq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf063204-3a94-4ddd-ac2a-b0101d53e710"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Cloning into 'repo'...\n",
            "remote: Enumerating objects: 37, done.\u001b[K\n",
            "remote: Counting objects: 100% (37/37), done.\u001b[K\n",
            "remote: Compressing objects: 100% (27/27), done.\u001b[K\n",
            "remote: Total 37 (delta 16), reused 27 (delta 10), pack-reused 0\u001b[K\n",
            "Receiving objects: 100% (37/37), 60.53 KiB | 5.04 MiB/s, done.\n",
            "Resolving deltas: 100% (16/16), done.\n"
          ]
        }
      ],
      "source": [
        "!git clone https://github.com/BohdanPetryshyn/code-llama-fim-fine-tuning.git repo"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install ninja\n",
        "!ninja --version"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NFZ7te9C4mYy",
        "outputId": "5e8ac6bc-50a5-4922-e514-33f956f70e79"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting ninja\n",
            "  Downloading ninja-1.11.1.1-py2.py3-none-manylinux1_x86_64.manylinux_2_5_x86_64.whl (307 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/307.2 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━\u001b[0m \u001b[32m204.8/307.2 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m307.2/307.2 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: ninja\n",
            "Successfully installed ninja-1.11.1.1\n",
            "1.11.1.git.kitware.jobserver-1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd repo\n",
        "!pip install -r requirements.txt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xSdimuEj4q0-",
        "outputId": "a7ca0a08-b543-4917-dc42-eb9be3a2d40f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/repo\n",
            "Collecting unsloth[colab-new]@ git+https://github.com/unslothai/unsloth.git@27fa021a7bb959a53667dd4e7cdb9598c207aa0d (from -r requirements.txt (line 25))\n",
            "  Cloning https://github.com/unslothai/unsloth.git (to revision 27fa021a7bb959a53667dd4e7cdb9598c207aa0d) to /tmp/pip-install-omnjgycd/unsloth_0b19d6cab6e54f449cf599c4b4ea808e\n",
            "  Running command git clone --filter=blob:none --quiet https://github.com/unslothai/unsloth.git /tmp/pip-install-omnjgycd/unsloth_0b19d6cab6e54f449cf599c4b4ea808e\n",
            "  Running command git rev-parse -q --verify 'sha^27fa021a7bb959a53667dd4e7cdb9598c207aa0d'\n",
            "  Running command git fetch -q https://github.com/unslothai/unsloth.git 27fa021a7bb959a53667dd4e7cdb9598c207aa0d\n",
            "  Running command git checkout -q 27fa021a7bb959a53667dd4e7cdb9598c207aa0d\n",
            "  Resolved https://github.com/unslothai/unsloth.git to commit 27fa021a7bb959a53667dd4e7cdb9598c207aa0d\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting PyGithub==2.3.0 (from -r requirements.txt (line 1))\n",
            "  Downloading PyGithub-2.3.0-py3-none-any.whl (354 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m354.4/354.4 kB\u001b[0m \u001b[31m6.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting accelerate==0.30.1 (from -r requirements.txt (line 2))\n",
            "  Downloading accelerate-0.30.1-py3-none-any.whl (302 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m302.6/302.6 kB\u001b[0m \u001b[31m11.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hCollecting bitsandbytes==0.43.1 (from -r requirements.txt (line 3))\n",
            "  Downloading bitsandbytes-0.43.1-py3-none-manylinux_2_24_x86_64.whl (119.8 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m119.8/119.8 MB\u001b[0m \u001b[31m4.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\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==0.17.0->-r requirements.txt (line 26))\n",
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            "Building wheels for collected packages: deepspeed, flash-attn, unsloth\n",
            "  Building wheel for deepspeed (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for deepspeed: filename=deepspeed-0.14.2-py3-none-any.whl size=1432246 sha256=c5529582e37b200514b2b35c0f170f2d343259c59c3a0231c6418b69c5cc069b\n",
            "  Stored in directory: /root/.cache/pip/wheels/ea/7c/43/bed44d8414c099ff962b754f425f7ff77cc623cc8a98e0da70\n",
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            "  Created wheel for flash-attn: filename=flash_attn-2.5.9.post1-cp310-cp310-linux_x86_64.whl size=120889689 sha256=5022ba11d48bf74926da9c16260f4ea2b9bb7f4e29bdb4bd6e1383ad1c55d16f\n",
            "  Stored in directory: /root/.cache/pip/wheels/cc/ad/f6/7ccf0238790d6346e9fe622923a76ec218e890d356b9a2754a\n",
            "  Building wheel for unsloth (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for unsloth: filename=unsloth-2024.5-py3-none-any.whl size=109360 sha256=bfa7e26061830b75c6eb4338f049de0ca781242463f8e764279b41139655b590\n",
            "  Stored in directory: /root/.cache/pip/wheels/6d/11/2a/f1be1ce0882e88e425e56e10b1081175f03e7e37f42e7c84e1\n",
            "Successfully built deepspeed flash-attn unsloth\n",
            "Installing collected packages: hjson, xxhash, unsloth, smmap, shtab, setproctitle, sentry-sdk, pynvml, pyjwt, 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, loguru, fsspec, einops, docker-pycreds, dill, Deprecated, tiktoken, pynacl, nvidia-cusparse-cu12, nvidia-cudnn-cu12, multiprocess, huggingface-hub, gitdb, tyro, nvidia-cusolver-cu12, gitpython, datatrove, wandb, transformers, PyGithub, datasets, xformers, flash-attn, evaluate, deepspeed, bitsandbytes, accelerate, trl, peft\n",
            "  Attempting uninstall: pyjwt\n",
            "    Found existing installation: PyJWT 2.3.0\n",
            "    Uninstalling PyJWT-2.3.0:\n",
            "      Successfully uninstalled PyJWT-2.3.0\n",
            "  Attempting uninstall: fsspec\n",
            "    Found existing installation: fsspec 2023.6.0\n",
            "    Uninstalling fsspec-2023.6.0:\n",
            "      Successfully uninstalled fsspec-2023.6.0\n",
            "  Attempting uninstall: huggingface-hub\n",
            "    Found existing installation: huggingface-hub 0.23.3\n",
            "    Uninstalling huggingface-hub-0.23.3:\n",
            "      Successfully uninstalled huggingface-hub-0.23.3\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.41.2\n",
            "    Uninstalling transformers-4.41.2:\n",
            "      Successfully uninstalled transformers-4.41.2\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",
            "gcsfs 2023.6.0 requires fsspec==2023.6.0, but you have fsspec 2024.3.1 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0mSuccessfully installed Deprecated-1.2.14 PyGithub-2.3.0 accelerate-0.30.1 bitsandbytes-0.43.1 datasets-2.19.1 datatrove-0.2.0 deepspeed-0.14.2 dill-0.3.8 docker-pycreds-0.4.0 einops-0.8.0 evaluate-0.4.2 flash-attn-2.5.9.post1 fsspec-2024.3.1 gitdb-4.0.11 gitpython-3.1.43 hjson-3.1.0 huggingface-hub-0.23.1 loguru-0.7.2 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-cu12-2.20.5 nvidia-nvjitlink-cu12-12.5.40 nvidia-nvtx-cu12-12.1.105 peft-0.11.1 pyjwt-2.8.0 pynacl-1.5.0 pynvml-11.5.0 sentry-sdk-2.5.1 setproctitle-1.3.3 shtab-1.7.1 smmap-5.0.1 tiktoken-0.7.0 transformers-4.41.1 trl-0.8.6 tyro-0.8.4 unsloth-2024.5 wandb-0.17.0 xformers-0.0.26.post1 xxhash-3.4.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load base Code Llama model"
      ],
      "metadata": {
        "id": "nVv7JMj3EHA5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer\n",
        ")\n",
        "\n",
        "base_model_id = \"codellama/CodeLlama-7b-hf\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(base_model_id, trust_remote_code=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252,
          "referenced_widgets": [
            "848c5f28531f45bcb77435c6cfe29809",
            "6ffff6b116604a629ea85ae5203fe00e",
            "15646e33a7164a0b98810eec1b5d125e",
            "88c7a613d3ac4079a1690c4ed6213951",
            "8344710979674a0ea259962630f2f434",
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            "d49addd586f34b4da8f993e7a175984c"
          ]
        },
        "id": "Sw0-Wdb-5uZ4",
        "outputId": "1c35ab03-31fa-4fd2-b9d1-31591d7c3a65"
      },
      "execution_count": 4,
      "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": [
              "tokenizer_config.json:   0%|          | 0.00/749 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "848c5f28531f45bcb77435c6cfe29809"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "999727abd2c6422591dfefef657362c2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bccd504dc2894317986b993c35e0e30a"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/411 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "46e4db57800742be8252948090737195"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model_id,\n",
        "    quantization_config=None,\n",
        "    device_map=None,\n",
        "    trust_remote_code=True,\n",
        "    torch_dtype=torch.bfloat16,\n",
        ")\n",
        "\n",
        "model.cuda()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 705,
          "referenced_widgets": [
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        },
        "id": "ad7cEEwK51jy",
        "outputId": "fc8704a4-938f-4256-973c-54aa2de5f03f"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/637 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5e60ce67d6bf4d089cac177acbdaba43"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/25.1k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "56f74ed0a3d74784bbc1b111797b019b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e0b6e3e46b0847b6bd1cf26c25dadff4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b9a132927d2341079f3dff423abfb17e"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0199d3eab594fbaa5c81f6320fab3a1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "adfd6bb7eefd41a99b20d738156fa225"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/116 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f6d355a7b9ec4e81a9eb60e541959f23"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LlamaForCausalLM(\n",
              "  (model): LlamaModel(\n",
              "    (embed_tokens): Embedding(32016, 4096)\n",
              "    (layers): ModuleList(\n",
              "      (0-31): 32 x LlamaDecoderLayer(\n",
              "        (self_attn): LlamaSdpaAttention(\n",
              "          (q_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (k_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (v_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (o_proj): Linear(in_features=4096, out_features=4096, bias=False)\n",
              "          (rotary_emb): LlamaRotaryEmbedding()\n",
              "        )\n",
              "        (mlp): LlamaMLP(\n",
              "          (gate_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "          (up_proj): Linear(in_features=4096, out_features=11008, bias=False)\n",
              "          (down_proj): Linear(in_features=11008, out_features=4096, bias=False)\n",
              "          (act_fn): SiLU()\n",
              "        )\n",
              "        (input_layernorm): LlamaRMSNorm()\n",
              "        (post_attention_layernorm): LlamaRMSNorm()\n",
              "      )\n",
              "    )\n",
              "    (norm): LlamaRMSNorm()\n",
              "  )\n",
              "  (lm_head): Linear(in_features=4096, out_features=32016, bias=False)\n",
              ")"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Load fine-tuned adapter"
      ],
      "metadata": {
        "id": "QlgMtcM2ENkc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from peft import PeftModel\n",
        "\n",
        "# Replace with your adapter ID\n",
        "adapter_id = \"BohdanPetryshyn/codellama-7b-openapi-completion-ctx-lvl-fim-05-spm\"\n",
        "# Latest revision\n",
        "revision = None\n",
        "model = PeftModel.from_pretrained(model, adapter_id, revision=revision, adapter_name=\"my-adapter\")\n",
        "model.set_adapter(\"my-adapter\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81,
          "referenced_widgets": [
            "e94e25860f1041a988c0663f76180dbd",
            "8bf8657a7330450db3eba29f0a7d66a2",
            "4a9c661a99e94611aff2f6166324fbb4",
            "e0c6399329b442b49edcf6df76966732",
            "099bd052d4764830ac2d2e1dfe2ed19f",
            "43981584fd664fe4921d1c12a36826ee",
            "311815d8c9a14bcdbc81d0982482dd92",
            "649e986cd58e4fde932b294015d3e873",
            "7814674b6b1a4bfa8e46b5b38a9c243d",
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            "c4de1822dc1d47e58cad9c13114b22ed",
            "ad24c2b343a2464cad43491d2dfb541c",
            "14db99e1c7bd4d7aaa89fd937a8fffdc",
            "66b3c2e8a9254d87ac962a4756e93bb8",
            "a32816405e9e454b9c4fd316ecfea762",
            "960ddde9b77f4e4a803873f8c08782fe",
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            "b15deacc1ee84bbb8b934f5fe220e86d",
            "4dce7df155594ae6ae1b3044abc8b01b",
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            "c75384afd7a149bfbdb63cae94268292",
            "5ec17b0a63c24baab7f311023f789578"
          ]
        },
        "id": "ADvVah2-6EEb",
        "outputId": "b29659ad-d049-4301-e38c-c092949e3f17"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_config.json:   0%|          | 0.00/727 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e94e25860f1041a988c0663f76180dbd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/320M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ad24c2b343a2464cad43491d2dfb541c"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Inference test"
      ],
      "metadata": {
        "id": "C7KirdjsEe4B"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_completion(prefix, suffix, prompt = None):\n",
        "    if prompt == None:\n",
        "      prompt = f\"\"\"<PRE> {prefix} <SUF>{suffix} <MID>\"\"\"\n",
        "    if not isinstance(prompt, list):\n",
        "      prompt = tokenizer(prompt).input_ids\n",
        "\n",
        "    model.eval()\n",
        "    outputs = model.generate(\n",
        "        input_ids=torch.tensor([prompt]).cuda(),\n",
        "        max_new_tokens=256,\n",
        "    )\n",
        "    return (outputs, tokenizer.batch_decode(outputs, skip_special_tokens=False)[0])"
      ],
      "metadata": {
        "id": "iKk5IOuB6HPP"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prefix = \"\"\"\n",
        "  /special-events:\n",
        "    post:\n",
        "      summary: Create special events\n",
        "      description: Creates a new special event for the museum.\n",
        "      operationId: createSpecialEvent\n",
        "      tags:\n",
        "        - Events\n",
        "      requestBody:\n",
        "        required: true\n",
        "        content:\n",
        "          application/json:\n",
        "            schema:\n",
        "              $ref: '#/components/schemas/CreateSpecialEventRequest'\n",
        "            examples:\n",
        "              default_example:\n",
        "                $ref: '#/components/examples/CreateSpecialEventRequestExample'\n",
        "      responses:\n",
        "        '200':\n",
        "          description: Success\n",
        "          content:\n",
        "            application/json:\n",
        "              schema:\n",
        "                $ref: '#/components/schemas/SpecialEventResponse'\n",
        "              examples:\n",
        "                default_example:\n",
        "                  $ref: '#/components/examples/CreateSpecialEventResponseExample'\n",
        "        '400':\n",
        "          description: Bad request\n",
        "        '404':\n",
        "          description: Not found\n",
        "    get:\n",
        "      summary: List special events\n",
        "      description: Return a list of upcoming special events at the museum.\n",
        "      operationId: listSpecialEvents\n",
        "      tags:\n",
        "        - Events\n",
        "      parameters:\"\"\"\n",
        "\n",
        "suffix = \"\"\"  /special-events/{eventId}:\n",
        "    get:\n",
        "      summary: Get special event\n",
        "      description: Get details about a special event.\n",
        "      operationId: getSpecialEvent\n",
        "      tags:\n",
        "        - Events\n",
        "      parameters:\n",
        "        - $ref: '#/components/parameters/EventId'\n",
        "      responses:\n",
        "        '200':\n",
        "          description: Success\n",
        "          content:\n",
        "            application/json:\n",
        "              schema:\n",
        "                $ref: '#/components/schemas/SpecialEventResponse'\n",
        "              examples:\n",
        "                default_example:\n",
        "                  $ref: '#/components/examples/GetSpecialEventResponseExample'\n",
        "        '400':\n",
        "          description: Bad request\n",
        "        '404':\n",
        "          description: Not found\"\"\"\n",
        "\n",
        "tokens, result = get_completion(prefix, suffix)\n",
        "\n",
        "print(result)\n",
        "print([tokenizer.decode(token) for token in tokens[0]])\n",
        "print(tokens[0].tolist())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aBgZitjS6sRZ",
        "outputId": "b15a800f-ef80-4ad9-87ea-be3d70c51c8e"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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`:2 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s> <PRE> \n",
            "  /special-events:\n",
            "    post:\n",
            "      summary: Create special events\n",
            "      description: Creates a new special event for the museum.\n",
            "      operationId: createSpecialEvent\n",
            "      tags:\n",
            "        - Events\n",
            "      requestBody:\n",
            "        required: true\n",
            "        content:\n",
            "          application/json:\n",
            "            schema:\n",
            "              $ref: '#/components/schemas/CreateSpecialEventRequest'\n",
            "            examples:\n",
            "              default_example:\n",
            "                $ref: '#/components/examples/CreateSpecialEventRequestExample'\n",
            "      responses:\n",
            "        '200':\n",
            "          description: Success\n",
            "          content:\n",
            "            application/json:\n",
            "              schema:\n",
            "                $ref: '#/components/schemas/SpecialEventResponse'\n",
            "              examples:\n",
            "                default_example:\n",
            "                  $ref: '#/components/examples/CreateSpecialEventResponseExample'\n",
            "        '400':\n",
            "          description: Bad request\n",
            "        '404':\n",
            "          description: Not found\n",
            "    get:\n",
            "      summary: List special events\n",
            "      description: Return a list of upcoming special events at the museum.\n",
            "      operationId: listSpecialEvents\n",
            "      tags:\n",
            "        - Events\n",
            "      parameters: <SUF>  /special-events/{eventId}:\n",
            "    get:\n",
            "      summary: Get special event\n",
            "      description: Get details about a special event.\n",
            "      operationId: getSpecialEvent\n",
            "      tags:\n",
            "        - Events\n",
            "      parameters:\n",
            "        - $ref: '#/components/parameters/EventId'\n",
            "      responses:\n",
            "        '200':\n",
            "          description: Success\n",
            "          content:\n",
            "            application/json:\n",
            "              schema:\n",
            "                $ref: '#/components/schemas/SpecialEventResponse'\n",
            "              examples:\n",
            "                default_example:\n",
            "                  $ref: '#/components/examples/GetSpecialEventResponseExample'\n",
            "        '400':\n",
            "          description: Bad request\n",
            "        '404':\n",
            "          description: Not found <MID>\n",
            "        - $ref: '#/components/parameters/Page'\n",
            "        - $ref: '#/components/parameters/PageSize'\n",
            "      responses:\n",
            "        '200':\n",
            "          description: Success\n",
            "          content:\n",
            "            application/json:\n",
            "              schema:\n",
            "                $ref: '#/components/schemas/SpecialEventListResponse'\n",
            "              examples:\n",
            "                default_example:\n",
            "                  $ref: '#/components/examples/ListSpecialEventsResponseExample'\n",
            "        '400':\n",
            "          description: Bad request\n",
            "        '404':\n",
            "          description: Not found\n",
            "   <EOT></s>\n",
            "['<s>', '<PRE>', '', '\\n', '', '/', 'special', '-', 'events', ':', '\\n', '  ', 'post', ':', '\\n', '    ', 'summary', ':', 'Create', 'special', 'events', '\\n', '    ', 'description', ':', 'Cre', 'ates', 'a', 'new', 'special', 'event', 'for', 'the', 'museum', '.', '\\n', '    ', 'operation', 'Id', ':', 'create', 'Special', 'Event', '\\n', '    ', 'tags', ':', '\\n', '      ', '-', 'Events', '\\n', '    ', 'request', 'Body', ':', '\\n', '      ', 'required', ':', 'true', '\\n', '      ', 'content', ':', '\\n', '        ', 'application', '/', 'json', ':', '\\n', '          ', 'schema', ':', '\\n', '            ', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'schemas', '/', 'Create', 'Special', 'Event', 'Request', \"'\", '\\n', '          ', 'examples', ':', '\\n', '            ', 'default', '_', 'example', ':', '\\n', '              ', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'examples', '/', 'Create', 'Special', 'Event', 'Request', 'Example', \"'\", '\\n', '    ', 'responses', ':', '\\n', '      ', \"'\", '2', '0', '0', \"':\", '\\n', '        ', 'description', ':', 'Success', '\\n', '        ', 'content', ':', '\\n', '          ', 'application', '/', 'json', ':', '\\n', '            ', 'schema', ':', '\\n', '              ', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'schemas', '/', 'Special', 'Event', 'Response', \"'\", '\\n', '            ', 'examples', ':', '\\n', '              ', 'default', '_', 'example', ':', '\\n', '               ', '', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'examples', '/', 'Create', 'Special', 'Event', 'Response', 'Example', \"'\", '\\n', '      ', \"'\", '4', '0', '0', \"':\", '\\n', '        ', 'description', ':', 'Bad', 'request', '\\n', '      ', \"'\", '4', '0', '4', \"':\", '\\n', '        ', 'description', ':', 'Not', 'found', '\\n', '  ', 'get', ':', '\\n', '    ', 'summary', ':', 'List', 'special', 'events', '\\n', '    ', 'description', ':', 'Return', 'a', 'list', 'of', 'up', 'coming', 'special', 'events', 'at', 'the', 'museum', '.', '\\n', '    ', 'operation', 'Id', ':', 'list', 'Special', 'Events', '\\n', '    ', 'tags', ':', '\\n', '      ', '-', 'Events', '\\n', '    ', 'parameters', ':', '<SUF>', '', '/', 'special', '-', 'events', '/{', 'event', 'Id', '}:', '\\n', '  ', 'get', ':', '\\n', '    ', 'summary', ':', 'Get', 'special', 'event', '\\n', '    ', 'description', ':', 'Get', 'details', 'about', 'a', 'special', 'event', '.', '\\n', '    ', 'operation', 'Id', ':', 'get', 'Special', 'Event', '\\n', '    ', 'tags', ':', '\\n', '      ', '-', 'Events', '\\n', '    ', 'parameters', ':', '\\n', '      ', '-', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'parameters', '/', 'Event', 'Id', \"'\", '\\n', '    ', 'responses', ':', '\\n', '      ', \"'\", '2', '0', '0', \"':\", '\\n', '        ', 'description', ':', 'Success', '\\n', '        ', 'content', ':', '\\n', '          ', 'application', '/', 'json', ':', '\\n', '            ', 'schema', ':', '\\n', '              ', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'schemas', '/', 'Special', 'Event', 'Response', \"'\", '\\n', '            ', 'examples', ':', '\\n', '              ', 'default', '_', 'example', ':', '\\n', '               ', '', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'examples', '/', 'Get', 'Special', 'Event', 'Response', 'Example', \"'\", '\\n', '      ', \"'\", '4', '0', '0', \"':\", '\\n', '        ', 'description', ':', 'Bad', 'request', '\\n', '      ', \"'\", '4', '0', '4', \"':\", '\\n', '        ', 'description', ':', 'Not', 'found', '<MID>', '\\n', '      ', '-', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'parameters', '/', 'Page', \"'\", '\\n', '      ', '-', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'parameters', '/', 'Page', 'Size', \"'\", '\\n', '    ', 'responses', ':', '\\n', '      ', \"'\", '2', '0', '0', \"':\", '\\n', '        ', 'description', ':', 'Success', '\\n', '        ', 'content', ':', '\\n', '          ', 'application', '/', 'json', ':', '\\n', '            ', 'schema', ':', '\\n', '              ', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'schemas', '/', 'Special', 'Event', 'List', 'Response', \"'\", '\\n', '            ', 'examples', ':', '\\n', '              ', 'default', '_', 'example', ':', '\\n', '               ', '', '$', 'ref', ':', \"'#\", '/', 'components', '/', 'examples', '/', 'List', 'Special', 'Events', 'Response', 'Example', \"'\", '\\n', '      ', \"'\", '4', '0', '0', \"':\", '\\n', '        ', 'description', ':', 'Bad', 'request', '\\n', '      ', \"'\", '4', '0', '4', \"':\", '\\n', '        ', 'description', ':', 'Not', 'found', '\\n', ' ', '<EOT>', '</s>']\n",
            "[1, 32007, 29871, 13, 29871, 847, 18732, 29899, 13604, 29901, 13, 1678, 1400, 29901, 13, 418, 15837, 29901, 6204, 4266, 4959, 13, 418, 6139, 29901, 6760, 1078, 263, 716, 4266, 1741, 363, 278, 19133, 29889, 13, 418, 5858, 1204, 29901, 1653, 24780, 2624, 13, 418, 8282, 29901, 13, 4706, 448, 28488, 13, 418, 2009, 8434, 29901, 13, 4706, 3734, 29901, 1565, 13, 4706, 2793, 29901, 13, 3986, 2280, 29914, 3126, 29901, 13, 9651, 10938, 29901, 13, 795, 395, 999, 29901, 16321, 29914, 14036, 29914, 11993, 29914, 4391, 24780, 2624, 3089, 29915, 13, 9651, 6455, 29901, 13, 795, 2322, 29918, 4773, 29901, 13, 18884, 395, 999, 29901, 16321, 29914, 14036, 29914, 19057, 29914, 4391, 24780, 2624, 3089, 14023, 29915, 13, 418, 20890, 29901, 13, 4706, 525, 29906, 29900, 29900, 2396, 13, 3986, 6139, 29901, 21397, 13, 3986, 2793, 29901, 13, 9651, 2280, 29914, 3126, 29901, 13, 795, 10938, 29901, 13, 18884, 395, 999, 29901, 16321, 29914, 14036, 29914, 11993, 29914, 24780, 2624, 5103, 29915, 13, 795, 6455, 29901, 13, 18884, 2322, 29918, 4773, 29901, 13, 462, 29871, 395, 999, 29901, 16321, 29914, 14036, 29914, 19057, 29914, 4391, 24780, 2624, 5103, 14023, 29915, 13, 4706, 525, 29946, 29900, 29900, 2396, 13, 3986, 6139, 29901, 9178, 2009, 13, 4706, 525, 29946, 29900, 29946, 2396, 13, 3986, 6139, 29901, 2216, 1476, 13, 1678, 679, 29901, 13, 418, 15837, 29901, 2391, 4266, 4959, 13, 418, 6139, 29901, 7106, 263, 1051, 310, 701, 11506, 4266, 4959, 472, 278, 19133, 29889, 13, 418, 5858, 1204, 29901, 1051, 24780, 13634, 13, 418, 8282, 29901, 13, 4706, 448, 28488, 13, 418, 4128, 29901, 32008, 29871, 847, 18732, 29899, 13604, 19248, 3696, 1204, 6177, 13, 1678, 679, 29901, 13, 418, 15837, 29901, 3617, 4266, 1741, 13, 418, 6139, 29901, 3617, 4902, 1048, 263, 4266, 1741, 29889, 13, 418, 5858, 1204, 29901, 679, 24780, 2624, 13, 418, 8282, 29901, 13, 4706, 448, 28488, 13, 418, 4128, 29901, 13, 4706, 448, 395, 999, 29901, 16321, 29914, 14036, 29914, 16744, 29914, 2624, 1204, 29915, 13, 418, 20890, 29901, 13, 4706, 525, 29906, 29900, 29900, 2396, 13, 3986, 6139, 29901, 21397, 13, 3986, 2793, 29901, 13, 9651, 2280, 29914, 3126, 29901, 13, 795, 10938, 29901, 13, 18884, 395, 999, 29901, 16321, 29914, 14036, 29914, 11993, 29914, 24780, 2624, 5103, 29915, 13, 795, 6455, 29901, 13, 18884, 2322, 29918, 4773, 29901, 13, 462, 29871, 395, 999, 29901, 16321, 29914, 14036, 29914, 19057, 29914, 2577, 24780, 2624, 5103, 14023, 29915, 13, 4706, 525, 29946, 29900, 29900, 2396, 13, 3986, 6139, 29901, 9178, 2009, 13, 4706, 525, 29946, 29900, 29946, 2396, 13, 3986, 6139, 29901, 2216, 1476, 32009, 13, 4706, 448, 395, 999, 29901, 16321, 29914, 14036, 29914, 16744, 29914, 5074, 29915, 13, 4706, 448, 395, 999, 29901, 16321, 29914, 14036, 29914, 16744, 29914, 5074, 3505, 29915, 13, 418, 20890, 29901, 13, 4706, 525, 29906, 29900, 29900, 2396, 13, 3986, 6139, 29901, 21397, 13, 3986, 2793, 29901, 13, 9651, 2280, 29914, 3126, 29901, 13, 795, 10938, 29901, 13, 18884, 395, 999, 29901, 16321, 29914, 14036, 29914, 11993, 29914, 24780, 2624, 1293, 5103, 29915, 13, 795, 6455, 29901, 13, 18884, 2322, 29918, 4773, 29901, 13, 462, 29871, 395, 999, 29901, 16321, 29914, 14036, 29914, 19057, 29914, 1293, 24780, 13634, 5103, 14023, 29915, 13, 4706, 525, 29946, 29900, 29900, 2396, 13, 3986, 6139, 29901, 9178, 2009, 13, 4706, 525, 29946, 29900, 29946, 2396, 13, 3986, 6139, 29901, 2216, 1476, 13, 259, 32010, 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Merge and upload the model"
      ],
      "metadata": {
        "id": "TOK0leFI8vSX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "b77bd8b6d6ff4306b130cd5e8833144d",
            "f104ad8fa07542819e2803059d6e41f3",
            "b02df6a14c2648f5b0372a780d4427eb",
            "ab39837c19dd4ec1baa64c08c32d191c",
            "d2745d4fc31c4065870faeb5a390d5c5",
            "6ca48ce9545748ab8fc8bb91e1e53abb",
            "7a68b7a7a4db4fdebdf34659a7819b29",
            "ab27ae9a92574fa5b1c5c42882fb036e",
            "c5b66648678741dbaeaf94373f5f27be",
            "481871a1b2934b9b95ebc539d9019d4f",
            "1af429cc87a244fb9437206ab5a89291",
            "1929c52f324047e290d440880d0fcf55",
            "ed127dc725fe4e5486fb51a9270b104e",
            "f840eceaca6e4c4c8923bd8d8bd929f5",
            "e16d8c62c63a43fe8526ae8032ac1b69",
            "32be8f948f8044ee971a2a77ace84559",
            "8f7dc0bc513145a7b9c33d31b2dd89eb",
            "6daefd7b40ed4ee18f285db0b4c6ffc3",
            "567afa6f641e4a0a8342f2cae18511fb",
            "a877bfa48bbd4880a10dd8c20890761d",
            "b54a9aef01aa432a8e413e8849126133",
            "2fcbbd5530364d1db65162ddbe1fbc76",
            "4f2366e074ac4e9db851da21e86b89e2",
            "0e43611b73e04164ae8e63ef20e69776",
            "706168e6d20c4845bd78fd3b09ced800",
            "04dd70c4a5b24854ac94c2bd2f7ce3e5",
            "30cd2a5dadb4418cb5e3526b3525c3d4",
            "45043bc9516844239cbbc955690f57bd",
            "b3a2cb5e067e4a1dbeea0663fba18ce8",
            "46748afea9b8407ab63c26f1a26c670f",
            "e37e3a2974e44c67805d147c2121d521",
            "c7b04bdb7de7468aa0418de48decb979"
          ]
        },
        "id": "rK3lbTf08usw",
        "outputId": "4e3d991f-4aad-441d-986c-f06c28cf1090"
      },
      "execution_count": 9,
      "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": "b77bd8b6d6ff4306b130cd5e8833144d"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace with your desired merged model ID\n",
        "merged_model_id = \"code-llama-openapi-completion\"\n",
        "\n",
        "model = model.merge_and_unload()\n",
        "model.save_pretrained(merged_model_id)\n",
        "model.push_to_hub(merged_model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228,
          "referenced_widgets": [
            "9a3941e2f292427e92bea4db5ef76eb3",
            "41c490acf7bc445ca2e27c83d05701e4",
            "6608bd446fe84be0bdec4cff53d2641d",
            "acd38fb806774caf945d3ebffbd6a025",
            "d19b1b38924d44e89b6b29ee6217d3aa",
            "b6bf02e191744be9aac4ebe49bd56eb0",
            "864d678f0ef746579de15d2138140f36",
            "c2689c04a23b4aebaa9b38d27e0953f3",
            "373227fbbd80452a84a088b1c3e576b0",
            "0eacc11826d4480ca3afbb0a17c2f13e",
            "5652556eb8554ade8485ecbfa1c43b55"
          ]
        },
        "id": "9yYw_j-671LI",
        "outputId": "06d76cae-57dd-4767-af34-8549d97d6719"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2024-06-15 09:57:04,444] [INFO] [real_accelerator.py:203:get_accelerator] Setting ds_accelerator to cuda (auto detect)\n",
            "\u001b[93m [WARNING] \u001b[0m async_io requires the dev libaio .so object and headers but these were not found.\n",
            "\u001b[93m [WARNING] \u001b[0m async_io: please install the libaio-dev package with apt\n",
            "\u001b[93m [WARNING] \u001b[0m If libaio is already installed (perhaps from source), try setting the CFLAGS and LDFLAGS environment variables to where it can be found.\n",
            "\u001b[93m [WARNING] \u001b[0m Please specify the CUTLASS repo directory as environment variable $CUTLASS_PATH\n",
            "\u001b[93m [WARNING] \u001b[0m NVIDIA Inference is only supported on Ampere and newer architectures\n",
            "\u001b[93m [WARNING] \u001b[0m sparse_attn requires a torch version >= 1.5 and < 2.0 but detected 2.3\n",
            "\u001b[93m [WARNING] \u001b[0m using untested triton version (2.3.0), only 1.0.0 is known to be compatible\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "9a3941e2f292427e92bea4db5ef76eb3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CommitInfo(commit_url='https://huggingface.co/BohdanPetryshyn/code-llama-openapi-completion/commit/066b109f08930ee89b44f097e6a0a98201f2e9d8', commit_message='Upload LlamaForCausalLM', commit_description='', oid='066b109f08930ee89b44f097e6a0a98201f2e9d8', pr_url=None, pr_revision=None, pr_num=None)"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Upload Code Llama tokenizer to the model repository\n",
        "\n",
        "To make the merged model fully functional withing the HF Inference Endponits platform, we have to add a tokenizer to the repository. For some reason, the following approach results in the model not generating `<EOT>` token during inference:\n",
        "\n",
        "```\n",
        "tokenizer.push_to_hub(merged_model_id)\n",
        "```\n",
        "\n",
        "So we are copying the original Code Llama tokenizer files manually using git CLI."
      ],
      "metadata": {
        "id": "mP17A03ZEoWY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Replace with your Hugging Face account name\n",
        "full_merged_model_id = f\"BohdanPetryshyn/{merged_model_id}\"\n",
        "\n",
        "%cd /content\n",
        "\n",
        "# Clone the merged repo. Skipping large files\n",
        "!export GIT_LFS_SKIP_SMUDGE=1 && git clone https://huggingface.co/{full_merged_model_id} merged_model\n",
        "\n",
        "# Clone the original Code Llama repo to copy the tokenizer files\n",
        "!export GIT_LFS_SKIP_SMUDGE=1 && git clone https://huggingface.co/codellama/CodeLlama-7b-hf original_code_llama\n",
        "!cd original_code_llama && git lfs pull --include \"tokenizer*\"\n",
        "\n",
        "%cd merged_model\n",
        "\n",
        "!cp ../original_code_llama/special_tokens_map.json .\n",
        "!cp ../original_code_llama/tokenizer* .\n",
        "\n",
        "!git config --global user.email \"code-llama-fim-fine-tuning@colab.research.google.com\"\n",
        "!git config --global user.name \"Code Llama FIM Fine Tuning by Bohdan Petryshyn\"\n",
        "!git add .\n",
        "!git commit -m \"Add tokenizer from the original Code Llama model\"\n",
        "!git push origin main"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p8aPiwSV8Yl5",
        "outputId": "bb33224e-df63-4732-e5ce-982308432ae0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content\n",
            "Cloning into 'merged_model'...\n",
            "remote: Enumerating objects: 23, done.\u001b[K\n",
            "remote: Counting objects: 100% (20/20), done.\u001b[K\n",
            "remote: Compressing objects: 100% (20/20), done.\u001b[K\n",
            "remote: Total 23 (delta 3), reused 0 (delta 0), pack-reused 3 (from 1)\u001b[K\n",
            "Unpacking objects: 100% (23/23), 485.16 KiB | 3.76 MiB/s, done.\n",
            "Cloning into 'original_code_llama'...\n",
            "remote: Enumerating objects: 100, done.\u001b[K\n",
            "remote: Counting objects: 100% (45/45), done.\u001b[K\n",
            "remote: Compressing objects: 100% (25/25), done.\u001b[K\n",
            "remote: Total 100 (delta 35), reused 20 (delta 20), pack-reused 55 (from 1)\u001b[K\n",
            "Receiving objects: 100% (100/100), 505.60 KiB | 2.44 MiB/s, done.\n",
            "Resolving deltas: 100% (35/35), done.\n",
            "/content/merged_model\n",
            "[main ed459f2] Add tokenizer from the original Code Llama model\n",
            " 4 files changed, 93478 insertions(+)\n",
            " create mode 100644 special_tokens_map.json\n",
            " create mode 100644 tokenizer.json\n",
            " create mode 100644 tokenizer.model\n",
            " create mode 100644 tokenizer_config.json\n",
            "Uploading LFS objects: 100% (1/1), 500 KB | 0 B/s, done.\n",
            "Enumerating objects: 7, done.\n",
            "Counting objects: 100% (7/7), done.\n",
            "Delta compression using up to 8 threads\n",
            "Compressing objects: 100% (6/6), done.\n",
            "Writing objects: 100% (6/6), 477.36 KiB | 5.36 MiB/s, done.\n",
            "Total 6 (delta 1), reused 0 (delta 0), pack-reused 0\n",
            "To https://huggingface.co/BohdanPetryshyn/code-llama-openapi-completion\n",
            "   cf9fff0..ed459f2  main -> main\n"
          ]
        }
      ]
    }
  ]
}