Copy_of_fine_tuning_code_llm_on_single_gpu.ipynb

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      "source": [
        "# Fine-tuning a Code LLM on Custom Code on a single GPU\n",
        "\n",
        "_Authored by: [Maria Khalusova](https://github.com/MKhalusova)_\n",
        "\n",
        "Publicly available code LLMs such as Codex, StarCoder, and Code Llama are great at generating code that adheres to general programming principles and syntax, but they may not align with an organization's internal conventions, or be aware of proprietary libraries.\n",
        "\n",
        "In this notebook, we'll see show how you can fine-tune a code LLM on private code bases to enhance its contextual awareness and improve a model's usefulness to your organization's needs. Since the code LLMs are quite large, fine-tuning them in a traditional manner can be resource-draining. Worry not! We will show how you can optimize fine-tuning to fit on a single GPU.\n",
        "\n",
        "\n",
        "## Dataset\n",
        "\n",
        "For this example, we picked the top 10 Hugging Face public repositories on GitHub. We have excluded non-code files from the data, such as images, audio files, presentations, and so on. For Jupyter notebooks, we've kept only cells containing code. The resulting code is stored as a dataset that you can find on the Hugging Face Hub under [`smangrul/hf-stack-v1`](https://huggingface.co/datasets/smangrul/hf-stack-v1). It contains repo id, file path, and file content.\n",
        "\n",
        "\n",
        "## Model\n",
        "\n",
        "We'll finetune [`bigcode/starcoderbase-1b`](https://huggingface.co/bigcode/starcoderbase-1b), which is a 1B parameter model trained on 80+ programming languages. This is a gated model, so if you plan to run this notebook with this exact model, you'll need to gain access to it on the model's page. Log in to your Hugging Face account to do so:"
      ]
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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          "metadata": {}
        }
      ],
      "source": [
        "from huggingface_hub import notebook_login\n",
        "\n",
        "notebook_login()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WMVe_c8q43Qo"
      },
      "source": [
        "To get started, let's install all the necessary libraries. As you can see, in addition to `transformers` and `datasets`, we'll be using `peft`, `bitsandbytes`, and `flash-attn` to optimize the training.\n",
        "\n",
        "By employing parameter-efficient training techniques, we can run this notebook on a single A100 High-RAM GPU."
      ]
    },
    {
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            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/542.1 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━\u001b[0m\u001b[90m╺\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m122.9/542.1 kB\u001b[0m \u001b[31m3.7 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m542.1/542.1 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
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            "\u001b[?25h  Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\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[0m"
          ]
        }
      ],
      "source": [
        "!pip install -q transformers datasets peft bitsandbytes flash-attn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "16EdABzt3_Ig"
      },
      "source": [
        "Let's define some variables now. Feel free to play with these."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hru3G-CLmqis"
      },
      "outputs": [],
      "source": [
        "MODEL=\"mistralai/Codestral-22B-v0.1\" # Model checkpoint on the Hugging Face Hub\n",
        "DATASET=\"smangrul/hf-stack-v1\"   # Dataset on the Hugging Face Hub\n",
        "DATA_COLUMN=\"content\"            # Column name containing the code content\n",
        "\n",
        "SEQ_LENGTH=2048                  # Sequence length\n",
        "\n",
        "# Training arguments\n",
        "MAX_STEPS=500                   # max_steps\n",
        "BATCH_SIZE=16                    # batch_size\n",
        "GR_ACC_STEPS=1                   # gradient_accumulation_steps\n",
        "LR=5e-4                          # learning_rate\n",
        "LR_SCHEDULER_TYPE=\"cosine\"       # lr_scheduler_type\n",
        "WEIGHT_DECAY=0.01                # weight_decay\n",
        "NUM_WARMUP_STEPS=30              # num_warmup_steps\n",
        "EVAL_FREQ=100                    # eval_freq\n",
        "SAVE_FREQ=100                    # save_freq\n",
        "LOG_FREQ=25                      # log_freq\n",
        "OUTPUT_DIR=\"mistralai/Codestral-22B-v0.1\" # output_dir\n",
        "BF16=True                        # bf16\n",
        "FP16=False                       # no_fp16\n",
        "\n",
        "# FIM trasformations arguments\n",
        "FIM_RATE=0.5                     # fim_rate\n",
        "FIM_SPM_RATE=0.5                 # fim_spm_rate\n",
        "\n",
        "# LORA\n",
        "LORA_R=8                         # lora_r\n",
        "LORA_ALPHA=32                    # lora_alpha\n",
        "LORA_DROPOUT=0.0                 # lora_dropout\n",
        "LORA_TARGET_MODULES=\"c_proj,c_attn,q_attn,c_fc,c_proj\"    # lora_target_modules\n",
        "\n",
        "# bitsandbytes config\n",
        "USE_NESTED_QUANT=True            # use_nested_quant\n",
        "BNB_4BIT_COMPUTE_DTYPE=\"bfloat16\"# bnb_4bit_compute_dtype\n",
        "\n",
        "SEED=0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "FyZSXTbJrcnC"
      },
      "outputs": [],
      "source": [
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    Trainer,\n",
        "    TrainingArguments,\n",
        "    logging,\n",
        "    set_seed,\n",
        "    BitsAndBytesConfig,\n",
        ")\n",
        "\n",
        "set_seed(SEED)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pO7F5L5AtKo1"
      },
      "source": [
        "## Prepare the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1LmrIZqP0oUE"
      },
      "source": [
        "Begin by loading the data. As the dataset is likely to be quite large, make sure to enable the streaming mode. Streaming allows us to load the data progressively as we iterate over the dataset instead of downloading the whole dataset at once.\n",
        "\n",
        "We'll reserve the first 4000 examples as the validation set, and everything else will be the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "4oJZvZb-1J88",
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      "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%|          | 0.00/478 [00:00<?, ?B/s]"
            ],
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      ],
      "source": [
        "from datasets import load_dataset\n",
        "import torch\n",
        "from tqdm import tqdm\n",
        "\n",
        "\n",
        "dataset = load_dataset(\n",
        "    DATASET,\n",
        "    data_dir=\"data\",\n",
        "    split=\"train\",\n",
        "    streaming=True,\n",
        ")\n",
        "\n",
        "valid_data = dataset.take(4000)\n",
        "train_data = dataset.skip(4000)\n",
        "train_data = train_data.shuffle(buffer_size=5000, seed=SEED)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sLQ8t0LM2GR6"
      },
      "source": [
        "At this step, the dataset still contains raw data with code of arbitraty length. For training, we need inputs of fixed length. Let's create an Iterable dataset that would return constant-length chunks of tokens from a stream of text files.\n",
        "\n",
        "First, let's estimate the average number of characters per token in the dataset, which will help us later estimate the number of tokens in the text buffer later. By default, we'll only take 400 examples (`nb_examples`) from the dataset. Using only a subset of the entire dataset will reduce computational cost while still providing a reasonable estimate of the overall character-to-token ratio."
      ]
    },
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        "outputId": "68e1c2e4-d420-4596-f566-f1a6e56a533c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/960 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a9d78355bc4d43df8e98490f5fe250a1"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.model:   0%|          | 0.00/587k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ff97419eff5f4993ad32ea48f32cfb35"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/1.82M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "bd9446ccae5a4501844651dc338ae798"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "823b1c55b7174d6190e3220a6d522ce1"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 400/400 [00:11<00:00, 33.98it/s] "
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The character to token ratio of the dataset is: 2.25\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n",
        "\n",
        "def chars_token_ratio(dataset, tokenizer, data_column, nb_examples=400):\n",
        "    \"\"\"\n",
        "    Estimate the average number of characters per token in the dataset.\n",
        "    \"\"\"\n",
        "\n",
        "    total_characters, total_tokens = 0, 0\n",
        "    for _, example in tqdm(zip(range(nb_examples), iter(dataset)), total=nb_examples):\n",
        "        total_characters += len(example[data_column])\n",
        "        total_tokens += len(tokenizer(example[data_column]).tokens())\n",
        "\n",
        "    return total_characters / total_tokens\n",
        "\n",
        "\n",
        "chars_per_token = chars_token_ratio(train_data, tokenizer, DATA_COLUMN)\n",
        "print(f\"The character to token ratio of the dataset is: {chars_per_token:.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6F13VGobB3Ma"
      },
      "source": [
        "The character-to-token ratio can also be used as an indicator of the quality of text tokenization. For instance, a character-to-token ratio of 1.0 would mean that each character is represented with a token, which is not very meaningful. This would indicate poor tokenization. In standard English text, one token is typically equivalent to approximately four characters, meaning the character-to-token ratio is around 4.0. We can expect a lower ratio in the code dataset, but generally speaking, a number between 2.0 and 3.5 can be considered good enough."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rcwYFRPpwxea"
      },
      "source": [
        "**Optional FIM transformations**\n",
        "\n",
        "\n",
        "Autoregressive language models typically generate sequences from left to right. By applying the FIM transformations, the model can also learn to infill text.  Check out [\"Efficient Training of Language Models to Fill in the Middle\" paper](https://arxiv.org/pdf/2207.14255.pdf) to learn more about the technique.\n",
        "We'll define the FIM transformations here and will use them when creating the Iterable Dataset. However, if you want to omit transformations, feel free to set `fim_rate` to 0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zmejYvEKw1E-"
      },
      "outputs": [],
      "source": [
        "import functools\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "# Helper function to get token ids of the special tokens for prefix, suffix and middle for FIM transformations.\n",
        "@functools.lru_cache(maxsize=None)\n",
        "def get_fim_token_ids(tokenizer):\n",
        "    try:\n",
        "        FIM_PREFIX, FIM_MIDDLE, FIM_SUFFIX, FIM_PAD = tokenizer.special_tokens_map[\"additional_special_tokens\"][1:5]\n",
        "        suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = (\n",
        "            tokenizer.vocab[tok] for tok in [FIM_SUFFIX, FIM_PREFIX, FIM_MIDDLE, FIM_PAD]\n",
        "        )\n",
        "    except KeyError:\n",
        "        suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id = None, None, None, None\n",
        "    return suffix_tok_id, prefix_tok_id, middle_tok_id, pad_tok_id\n",
        "\n",
        "\n",
        "## Adapted from https://github.com/bigcode-project/Megatron-LM/blob/6c4bf908df8fd86b4977f54bf5b8bd4b521003d1/megatron/data/gpt_dataset.py\n",
        "def permute(\n",
        "    sample,\n",
        "    np_rng,\n",
        "    suffix_tok_id,\n",
        "    prefix_tok_id,\n",
        "    middle_tok_id,\n",
        "    pad_tok_id,\n",
        "    fim_rate=0.5,\n",
        "    fim_spm_rate=0.5,\n",
        "    truncate_or_pad=False,\n",
        "):\n",
        "    \"\"\"\n",
        "    Take in a sample (list of tokens) and perform a FIM transformation on it with a probability of fim_rate, using two FIM modes:\n",
        "    PSM and SPM (with a probability of fim_spm_rate).\n",
        "    \"\"\"\n",
        "\n",
        "    # The if condition will trigger with the probability of fim_rate\n",
        "    # This means FIM transformations will apply to samples with a probability of fim_rate\n",
        "    if np_rng.binomial(1, fim_rate):\n",
        "\n",
        "        # Split the sample into prefix, middle, and suffix, based on randomly generated indices stored in the boundaries list.\n",
        "        boundaries = list(np_rng.randint(low=0, high=len(sample) + 1, size=2))\n",
        "        boundaries.sort()\n",
        "\n",
        "        prefix = np.array(sample[: boundaries[0]], dtype=np.int64)\n",
        "        middle = np.array(sample[boundaries[0] : boundaries[1]], dtype=np.int64)\n",
        "        suffix = np.array(sample[boundaries[1] :], dtype=np.int64)\n",
        "\n",
        "        if truncate_or_pad:\n",
        "            # calculate the new total length of the sample, taking into account tokens indicating prefix, middle, and suffix\n",
        "            new_length = suffix.shape[0] + prefix.shape[0] + middle.shape[0] + 3\n",
        "            diff = new_length - len(sample)\n",
        "\n",
        "            # trancate or pad if there's a difference in length between the new length and the original\n",
        "            if diff > 0:\n",
        "                if suffix.shape[0] <= diff:\n",
        "                    return sample, np_rng\n",
        "                suffix = suffix[: suffix.shape[0] - diff]\n",
        "            elif diff < 0:\n",
        "                suffix = np.concatenate([suffix, np.full((-1 * diff), pad_tok_id)])\n",
        "\n",
        "        # With the probability of fim_spm_rateapply SPM variant of FIM transformations\n",
        "        # SPM: suffix, prefix, middle\n",
        "        if np_rng.binomial(1, fim_spm_rate):\n",
        "            new_sample = np.concatenate(\n",
        "                [\n",
        "                    [prefix_tok_id, suffix_tok_id],\n",
        "                    suffix,\n",
        "                    [middle_tok_id],\n",
        "                    prefix,\n",
        "                    middle,\n",
        "                ]\n",
        "            )\n",
        "        # Otherwise, apply the PSM variant of FIM transformations\n",
        "        # PSM: prefix, suffix, middle\n",
        "        else:\n",
        "\n",
        "            new_sample = np.concatenate(\n",
        "                [\n",
        "                    [prefix_tok_id],\n",
        "                    prefix,\n",
        "                    [suffix_tok_id],\n",
        "                    suffix,\n",
        "                    [middle_tok_id],\n",
        "                    middle,\n",
        "                ]\n",
        "            )\n",
        "    else:\n",
        "        # don't apply FIM transformations\n",
        "        new_sample = sample\n",
        "\n",
        "    return list(new_sample), np_rng\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwW5FviD9xBH"
      },
      "source": [
        "Let's define the `ConstantLengthDataset`, an Iterable dataset that will return constant-length chunks of tokens. To do so, we'll read a buffer of text from the original dataset until we hit the size limits and then apply tokenizer to convert the raw text into tokenized inputs. Optionally, we'll perform FIM transformations on some sequences (the proportion of sequences affected is controlled by `fim_rate`).\n",
        "\n",
        "Once defined, we can create instances of the `ConstantLengthDataset` from both training and validation data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AgDW-692wzOl",
        "outputId": "50a2f345-496e-45a9-b6af-ba29de71aba8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FIM is not supported by tokenizer, disabling FIM\n",
            "FIM is not supported by tokenizer, disabling FIM\n"
          ]
        }
      ],
      "source": [
        "from torch.utils.data import IterableDataset\n",
        "from torch.utils.data.dataloader import DataLoader\n",
        "import random\n",
        "\n",
        "# Create an Iterable dataset that returns constant-length chunks of tokens from a stream of text files.\n",
        "\n",
        "class ConstantLengthDataset(IterableDataset):\n",
        "    \"\"\"\n",
        "    Iterable dataset that returns constant length chunks of tokens from stream of text files.\n",
        "        Args:\n",
        "            tokenizer (Tokenizer): The processor used for proccessing the data.\n",
        "            dataset (dataset.Dataset): Dataset with text files.\n",
        "            infinite (bool): If True the iterator is reset after dataset reaches end else stops.\n",
        "            seq_length (int): Length of token sequences to return.\n",
        "            num_of_sequences (int): Number of token sequences to keep in buffer.\n",
        "            chars_per_token (int): Number of characters per token used to estimate number of tokens in text buffer.\n",
        "            fim_rate (float): Rate (0.0 to 1.0) that sample will be permuted with FIM.\n",
        "            fim_spm_rate (float): Rate (0.0 to 1.0) of FIM permuations that will use SPM.\n",
        "            seed (int): Seed for random number generator.\n",
        "    \"\"\"\n",
        "\n",
        "    def __init__(\n",
        "        self,\n",
        "        tokenizer,\n",
        "        dataset,\n",
        "        infinite=False,\n",
        "        seq_length=1024,\n",
        "        num_of_sequences=1024,\n",
        "        chars_per_token=3.6,\n",
        "        content_field=\"content\",\n",
        "        fim_rate=0.5,\n",
        "        fim_spm_rate=0.5,\n",
        "        seed=0,\n",
        "    ):\n",
        "        self.tokenizer = tokenizer\n",
        "        self.concat_token_id = tokenizer.eos_token_id\n",
        "        self.dataset = dataset\n",
        "        self.seq_length = seq_length\n",
        "        self.infinite = infinite\n",
        "        self.current_size = 0\n",
        "        self.max_buffer_size = seq_length * chars_per_token * num_of_sequences\n",
        "        self.content_field = content_field\n",
        "        self.fim_rate = fim_rate\n",
        "        self.fim_spm_rate = fim_spm_rate\n",
        "        self.seed = seed\n",
        "\n",
        "        (\n",
        "            self.suffix_tok_id,\n",
        "            self.prefix_tok_id,\n",
        "            self.middle_tok_id,\n",
        "            self.pad_tok_id,\n",
        "        ) = get_fim_token_ids(self.tokenizer)\n",
        "        if not self.suffix_tok_id and self.fim_rate > 0:\n",
        "            print(\"FIM is not supported by tokenizer, disabling FIM\")\n",
        "            self.fim_rate = 0\n",
        "\n",
        "    def __iter__(self):\n",
        "        iterator = iter(self.dataset)\n",
        "        more_examples = True\n",
        "        np_rng = np.random.RandomState(seed=self.seed)\n",
        "        while more_examples:\n",
        "            buffer, buffer_len = [], 0\n",
        "            while True:\n",
        "                if buffer_len >= self.max_buffer_size:\n",
        "                    break\n",
        "                try:\n",
        "                    buffer.append(next(iterator)[self.content_field])\n",
        "                    buffer_len += len(buffer[-1])\n",
        "                except StopIteration:\n",
        "                    if self.infinite:\n",
        "                        iterator = iter(self.dataset)\n",
        "                    else:\n",
        "                        more_examples = False\n",
        "                        break\n",
        "            tokenized_inputs = self.tokenizer(buffer, truncation=False)[\"input_ids\"]\n",
        "            all_token_ids = []\n",
        "\n",
        "            for tokenized_input in tokenized_inputs:\n",
        "                # optionally do FIM permutations\n",
        "                if self.fim_rate > 0:\n",
        "                    tokenized_input, np_rng = permute(\n",
        "                        tokenized_input,\n",
        "                        np_rng,\n",
        "                        self.suffix_tok_id,\n",
        "                        self.prefix_tok_id,\n",
        "                        self.middle_tok_id,\n",
        "                        self.pad_tok_id,\n",
        "                        fim_rate=self.fim_rate,\n",
        "                        fim_spm_rate=self.fim_spm_rate,\n",
        "                        truncate_or_pad=False,\n",
        "                    )\n",
        "\n",
        "                all_token_ids.extend(tokenized_input + [self.concat_token_id])\n",
        "            examples = []\n",
        "            for i in range(0, len(all_token_ids), self.seq_length):\n",
        "                input_ids = all_token_ids[i : i + self.seq_length]\n",
        "                if len(input_ids) == self.seq_length:\n",
        "                    examples.append(input_ids)\n",
        "            random.shuffle(examples)\n",
        "            for example in examples:\n",
        "                self.current_size += 1\n",
        "                yield {\n",
        "                    \"input_ids\": torch.LongTensor(example),\n",
        "                    \"labels\": torch.LongTensor(example),\n",
        "                }\n",
        "\n",
        "\n",
        "train_dataset = ConstantLengthDataset(\n",
        "        tokenizer,\n",
        "        train_data,\n",
        "        infinite=True,\n",
        "        seq_length=SEQ_LENGTH,\n",
        "        chars_per_token=chars_per_token,\n",
        "        content_field=DATA_COLUMN,\n",
        "        fim_rate=FIM_RATE,\n",
        "        fim_spm_rate=FIM_SPM_RATE,\n",
        "        seed=SEED,\n",
        ")\n",
        "eval_dataset = ConstantLengthDataset(\n",
        "        tokenizer,\n",
        "        valid_data,\n",
        "        infinite=False,\n",
        "        seq_length=SEQ_LENGTH,\n",
        "        chars_per_token=chars_per_token,\n",
        "        content_field=DATA_COLUMN,\n",
        "        fim_rate=FIM_RATE,\n",
        "        fim_spm_rate=FIM_SPM_RATE,\n",
        "        seed=SEED,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rxev1sk6tRW9"
      },
      "source": [
        "## Prepare the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UCtWV-U42Eq_"
      },
      "source": [
        "Now that the data is prepared, it's time to load the model! We're going to load the quantized version of the model.\n",
        "\n",
        "This will allow us to reduce memory usage, as quantization represents data with fewer bits. We'll use the `bitsandbytes` library to quantize the model, as it has a nice integration with `transformers`. All we need to do is define a `bitsandbytes` config, and then use it when loading the model.\n",
        "\n",
        "There are different variants of 4bit quantization, but generally, we recommend using NF4 quantization for better performance (`bnb_4bit_quant_type=\"nf4\"`).\n",
        "\n",
        "The `bnb_4bit_use_double_quant` option adds a second quantization after the first one to save an additional 0.4 bits per parameter.\n",
        "\n",
        "To learn more about quantization, check out the [\"Making LLMs even more accessible with bitsandbytes, 4-bit quantization and QLoRA\" blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes).\n",
        "\n",
        "Once defined, pass the config to the `from_pretrained` method to load the quantized version of the model."
      ]
    },
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        {
          "output_type": "display_data",
          "data": {
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          "metadata": {}
        },
        {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The model was loaded with use_flash_attention_2=True, which is deprecated and may be removed in a future release. Please use `attn_implementation=\"flash_attention_2\"` instead.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/9 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3d43890e59b443debcbc8db03e62ee6f"
            }
          },
          "metadata": {}
        }
      ],
      "source": [
        "from peft import LoraConfig, get_peft_model, prepare_model_for_kbit_training\n",
        "from peft.tuners.lora import LoraLayer\n",
        "\n",
        "load_in_8bit = False\n",
        "\n",
        "# 4-bit quantization\n",
        "compute_dtype = getattr(torch, BNB_4BIT_COMPUTE_DTYPE)\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=compute_dtype,\n",
        "    bnb_4bit_use_double_quant=USE_NESTED_QUANT,\n",
        ")\n",
        "\n",
        "device_map = {\"\": 0}\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "        MODEL,\n",
        "        load_in_8bit=load_in_8bit,\n",
        "        quantization_config=bnb_config,\n",
        "        device_map=device_map,\n",
        "        use_cache=False,  # We will be using gradient checkpointing\n",
        "        trust_remote_code=True,\n",
        "        use_flash_attention_2=True,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bO9e2FV8D8ZF"
      },
      "source": [
        "When using a quantized model for training, you need to call the `prepare_model_for_kbit_training()` function to preprocess the quantized model for training."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Qb_eB4xzEDBk"
      },
      "outputs": [],
      "source": [
        "model = prepare_model_for_kbit_training(model)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmnLjPZpDVtg"
      },
      "source": [
        "Now that the quantized model is ready, we can set up a LoRA configuration. LoRA makes fine-tuning more efficient by drastically reducing the number of trainable parameters.\n",
        "\n",
        "To train a model using LoRA technique, we need to wrap the base model as a `PeftModel`. This involves definign LoRA configuration with `LoraConfig`, and wrapping the original model with `get_peft_model()` using the `LoraConfig`.\n",
        "\n",
        "To learn more about LoRA and its parameters, refer to [PEFT documentation](https://huggingface.co/docs/peft/conceptual_guides/lora)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_pAUU2FR2Gey",
        "outputId": "eefa29dd-57cc-46c1-dfd1-3886df8b3837"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trainable params: 8,716,288 || all params: 22,255,998,976 || trainable%: 0.0392\n"
          ]
        }
      ],
      "source": [
        "# Set up lora\n",
        "peft_config = LoraConfig(\n",
        "    lora_alpha=LORA_ALPHA,\n",
        "    lora_dropout=LORA_DROPOUT,\n",
        "    r=LORA_R,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\",\n",
        "    # target_modules=LORA_TARGET_MODULES.split(\",\"),\n",
        ")\n",
        "\n",
        "model = get_peft_model(model, peft_config)\n",
        "model.print_trainable_parameters()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tHe7AElXzXVV"
      },
      "source": [
        "As you can see, by applying LoRA technique we will now need to train less than 1% of the parameters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "T_CqVydc40IM"
      },
      "source": [
        "## Train the model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q_iN2khjrbD3"
      },
      "source": [
        "Now that we have prepared the data, and optimized the model, we are ready to bring everything together to start the training.\n",
        "\n",
        "To instantiate a `Trainer`, you need to define the training configuration. The most important is the `TrainingArguments`, which is a class that contains all the attributes to configure the training.\n",
        "\n",
        "These are similar to any other kind of model training you may run, so we won't go into detail here."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65QHS8l1tKQe",
        "outputId": "687654a2-d270-4a31-f23d-feb55ada9957"
      },
      "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"
          ]
        }
      ],
      "source": [
        "train_data.start_iteration = 0\n",
        "\n",
        "\n",
        "training_args = TrainingArguments(\n",
        "    output_dir=f\"Your_HF_username/{OUTPUT_DIR}\",\n",
        "    dataloader_drop_last=True,\n",
        "    evaluation_strategy=\"steps\",\n",
        "    save_strategy=\"steps\",\n",
        "    max_steps=MAX_STEPS,\n",
        "    eval_steps=EVAL_FREQ,\n",
        "    save_steps=SAVE_FREQ,\n",
        "    logging_steps=LOG_FREQ,\n",
        "    per_device_train_batch_size=BATCH_SIZE,\n",
        "    per_device_eval_batch_size=BATCH_SIZE,\n",
        "    learning_rate=LR,\n",
        "    lr_scheduler_type=LR_SCHEDULER_TYPE,\n",
        "    warmup_steps=NUM_WARMUP_STEPS,\n",
        "    gradient_accumulation_steps=GR_ACC_STEPS,\n",
        "    gradient_checkpointing=True,\n",
        "    fp16=FP16,\n",
        "    bf16=BF16,\n",
        "    weight_decay=WEIGHT_DECAY,\n",
        "    push_to_hub=True,\n",
        "    include_tokens_per_second=True,\n",
        ")\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kB_fLRex09ut"
      },
      "source": [
        "As a final step, instantiate the `Trainer` and call the `train` method.   "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 450
        },
        "id": "rS3nVwhUC69O",
        "outputId": "03907dbc-0993-4de5-8ae1-92d17dd43ff8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "max_steps is given, it will override any value given in num_train_epochs\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:464: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.4 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "error",
          "ename": "OutOfMemoryError",
          "evalue": "CUDA out of memory. Tried to allocate 768.00 MiB. GPU ",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mOutOfMemoryError\u001b[0m                          Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-13-e5e0fe5576ca>\u001b[0m in \u001b[0;36m<cell line: 6>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Training...\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mtrainer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain\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[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtrain\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m   1874\u001b[0m                 \u001b[0;31m# Disable progress bars when uploading models during checkpoints to avoid polluting stdout\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1875\u001b[0m                 \u001b[0mhf_hub_utils\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdisable_progress_bars\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-> 1876\u001b[0;31m                 return inner_training_loop(\n\u001b[0m\u001b[1;32m   1877\u001b[0m                     \u001b[0margs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1878\u001b[0m                     \u001b[0mresume_from_checkpoint\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mresume_from_checkpoint\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/trainer.py\u001b[0m in \u001b[0;36m_inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m   2214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2215\u001b[0m                 \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccelerator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0maccumulate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\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-> 2216\u001b[0;31m                     \u001b[0mtr_loss_step\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining_step\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\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   2217\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2218\u001b[0m                 if (\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/trainer.py\u001b[0m in \u001b[0;36mtraining_step\u001b[0;34m(self, model, inputs)\u001b[0m\n\u001b[1;32m   3236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3237\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss_context_manager\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-> 3238\u001b[0;31m             \u001b[0mloss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompute_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minputs\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   3239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3240\u001b[0m         \u001b[0;32mdel\u001b[0m \u001b[0minputs\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/trainer.py\u001b[0m in \u001b[0;36mcompute_loss\u001b[0;34m(self, model, inputs, return_outputs)\u001b[0m\n\u001b[1;32m   3262\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3263\u001b[0m             \u001b[0mlabels\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3264\u001b[0;31m         \u001b[0moutputs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0minputs\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   3265\u001b[0m         \u001b[0;31m# Save past state if it exists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3266\u001b[0m         \u001b[0;31m# TODO: this needs to be fixed and made cleaner later.\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/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_compiled_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# type: ignore[misc]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1531\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1532\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1533\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1534\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_call_impl\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\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/accelerate/utils/operations.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    820\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    821\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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--> 822\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    823\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m     \u001b[0;31m# To act like a decorator so that it can be popped when doing `extract_model_from_parallel`\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/accelerate/utils/operations.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    808\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    809\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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--> 810\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mconvert_to_fp32\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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[0m\u001b[1;32m    811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    812\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getstate__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\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/usr/local/lib/python3.10/dist-packages/torch/amp/autocast_mode.py\u001b[0m in \u001b[0;36mdecorate_autocast\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     15\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mautocast_instance\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m     \u001b[0mdecorate_autocast\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__script_unsupported\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"@autocast() decorator is not supported in script mode\"\u001b[0m  \u001b[0;31m# type: ignore[attr-defined]\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/peft/peft_model.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, inputs_embeds, labels, output_attentions, output_hidden_states, return_dict, task_ids, **kwargs)\u001b[0m\n\u001b[1;32m   1428\u001b[0m             \u001b[0;32mwith\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_enable_peft_forward_hooks\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1429\u001b[0m                 \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mspecial_peft_forward_args\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1430\u001b[0;31m                 return self.base_model(\n\u001b[0m\u001b[1;32m   1431\u001b[0m                     \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1432\u001b[0m                     \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\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/peft/tuners/tuners_utils.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    177\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    178\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mAny\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--> 179\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    181\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_pre_injection_hook\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmodel\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mModule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconfig\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mPeftConfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0madapter_name\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m)\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[0;32m/usr/local/lib/python3.10/dist-packages/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    167\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1137\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1138\u001b[0m         \u001b[0;31m# decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1139\u001b[0;31m         outputs = self.model(\n\u001b[0m\u001b[1;32m   1140\u001b[0m             \u001b[0minput_ids\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0minput_ids\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1141\u001b[0m             \u001b[0mattention_mask\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mattention_mask\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\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/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    167\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input_ids, attention_mask, position_ids, past_key_values, inputs_embeds, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m   1012\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1013\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgradient_checkpointing\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtraining\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1014\u001b[0;31m                 layer_outputs = self._gradient_checkpointing_func(\n\u001b[0m\u001b[1;32m   1015\u001b[0m                     \u001b[0mdecoder_layer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__call__\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m                     \u001b[0mhidden_states\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py\u001b[0m in \u001b[0;36m_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    449\u001b[0m             \u001b[0mprior\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mset_eval_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcallback\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    450\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 451\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    452\u001b[0m             \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    453\u001b[0m                 \u001b[0mset_eval_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mprior\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/torch/_dynamo/external_utils.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     34\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mfunctools\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwraps\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfn\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     35\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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---> 36\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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     37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     38\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0minner\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/torch/utils/checkpoint.py\u001b[0m in \u001b[0;36mcheckpoint\u001b[0;34m(function, use_reentrant, context_fn, determinism_check, debug, *args, **kwargs)\u001b[0m\n\u001b[1;32m    485\u001b[0m                 \u001b[0;34m\"use_reentrant=False.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m             )\n\u001b[0;32m--> 487\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mCheckpointFunction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfunction\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpreserve\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\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    488\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    489\u001b[0m         gen = _checkpoint_without_reentrant_generator(\n",
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            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1539\u001b[0m                 \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_pre_hooks\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0m_global_backward_hooks\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1540\u001b[0m                 or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1541\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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   1542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1543\u001b[0m         \u001b[0;32mtry\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/accelerate/hooks.py\u001b[0m in \u001b[0;36mnew_forward\u001b[0;34m(module, *args, **kwargs)\u001b[0m\n\u001b[1;32m    164\u001b[0m                 \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    165\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 166\u001b[0;31m             \u001b[0moutput\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_old_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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    167\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mmodule\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hf_hook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpost_forward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0moutput\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    168\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/transformers/models/mistral/modeling_mistral.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, hidden_states)\u001b[0m\n\u001b[1;32m     83\u001b[0m         \u001b[0minput_dtype\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m         \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfloat32\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 85\u001b[0;31m         \u001b[0mvariance\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeepdim\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[0m\u001b[1;32m     86\u001b[0m         \u001b[0mhidden_states\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mhidden_states\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrsqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvariance\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvariance_epsilon\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     87\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweight\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mhidden_states\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput_dtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 768.00 MiB. GPU "
          ]
        }
      ],
      "source": [
        "\n",
        "\n",
        "trainer = Trainer(\n",
        "    model=model, args=training_args, train_dataset=train_dataset, eval_dataset=eval_dataset\n",
        ")\n",
        "\n",
        "print(\"Training...\")\n",
        "trainer.train()\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aAERlCnt1PEW"
      },
      "source": [
        "Finally, you can push the fine-tuned model to your Hub repository to share with your team."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "1h7_AUTTDwE1"
      },
      "outputs": [],
      "source": [
        "trainer.push_to_hub()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KBVH7uFOM_UF"
      },
      "source": [
        "## Inference\n",
        "\n",
        "Once the model is uploaded to Hub, we can use it for inference. To do so we first initialize the original base model and its tokenizer. Next, we need to merge the fine-duned weights with the base model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jtL37piINBFe",
        "outputId": "82328ecd-1aac-4344-9f90-63313152b6b3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "GPTBigCodeForCausalLM(\n",
              "  (transformer): GPTBigCodeModel(\n",
              "    (wte): Embedding(49152, 2048)\n",
              "    (wpe): Embedding(8192, 2048)\n",
              "    (drop): Dropout(p=0.1, inplace=False)\n",
              "    (h): ModuleList(\n",
              "      (0-23): 24 x GPTBigCodeBlock(\n",
              "        (ln_1): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (attn): GPTBigCodeSdpaAttention(\n",
              "          (c_attn): Linear(in_features=2048, out_features=2304, bias=True)\n",
              "          (c_proj): Linear(in_features=2048, out_features=2048, bias=True)\n",
              "          (attn_dropout): Dropout(p=0.1, inplace=False)\n",
              "          (resid_dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "        (ln_2): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "        (mlp): GPTBigCodeMLP(\n",
              "          (c_fc): Linear(in_features=2048, out_features=8192, bias=True)\n",
              "          (c_proj): Linear(in_features=8192, out_features=2048, bias=True)\n",
              "          (act): PytorchGELUTanh()\n",
              "          (dropout): Dropout(p=0.1, inplace=False)\n",
              "        )\n",
              "      )\n",
              "    )\n",
              "    (ln_f): LayerNorm((2048,), eps=1e-05, elementwise_affine=True)\n",
              "  )\n",
              "  (lm_head): Linear(in_features=2048, out_features=49152, bias=False)\n",
              ")"
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "from peft import PeftModel\n",
        "import torch\n",
        "\n",
        "# load the original model first\n",
        "tokenizer = AutoTokenizer.from_pretrained(MODEL, trust_remote_code=True)\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    MODEL,\n",
        "    quantization_config=None,\n",
        "    device_map=None,\n",
        "    trust_remote_code=True,\n",
        "    torch_dtype=torch.bfloat16,\n",
        ").cuda()\n",
        "\n",
        "# merge fine-tuned weights with the base model\n",
        "peft_model_id = f\"Your_HF_username/{OUTPUT_DIR}\"\n",
        "model = PeftModel.from_pretrained(base_model, peft_model_id)\n",
        "model.merge_and_unload()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3USQ2suvDi9M"
      },
      "source": [
        "Now we can use the merged model for inference. For convenience, we'll define a `get_code_completion` - feel free to experiment with text generation parameters!"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RoTGpNbjDeWI"
      },
      "outputs": [],
      "source": [
        "def get_code_completion(prefix, suffix):\n",
        "    text = prompt = f\"\"\"<fim_prefix>{prefix}<fim_suffix>{suffix}<fim_middle>\"\"\"\n",
        "    model.eval()\n",
        "    outputs = model.generate(\n",
        "        input_ids=tokenizer(text, return_tensors=\"pt\").input_ids.cuda(),\n",
        "        max_new_tokens=128,\n",
        "        temperature=0.2,\n",
        "        top_k=50,\n",
        "        top_p=0.95,\n",
        "        do_sample=True,\n",
        "        repetition_penalty=1.0,\n",
        "    )\n",
        "    return tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0kMJiGDfDrBf"
      },
      "source": [
        "Now all we need to do to get code completion is call the `get_code_complete` function and pass the first few lines that we want to be completed as a prefix, and an empty string as a suffix."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nXlco2_-YcvM",
        "outputId": "38315450-52a5-4ba9-941e-ebbf17421f62"
      },
      "outputs": [
        {
          "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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "from peft import LoraConfig, TaskType, get_peft_model\n",
            "from transformers import AutoModelForCausalLM\n",
            "peft_config = LoraConfig(\n",
            "    model_name_or_path=\"hf-internal-testing/tiny-xl-peft-model\",\n",
            "    task_type=TaskType.CausalLM,\n",
            "    causal_projection_type=\"linear\",\n",
            "    causal_projection_config=dict(\n",
            "        hidden_size=128,\n",
            "        num_attention_heads=4,\n",
            "        cross_attention_dim=128,\n",
            "        num_layers=2,\n",
            "    ),\n",
            ")\n",
            "\n",
            "peft_model = get_peft_model(peft_config)\n",
            "peft_model.to(\"cuda\")\n",
            "\n",
            "#\n"
          ]
        }
      ],
      "source": [
        "prefix = \"\"\"from peft import LoraConfig, TaskType, get_peft_model\n",
        "from transformers import AutoModelForCausalLM\n",
        "peft_config = LoraConfig(\n",
        "\"\"\"\n",
        "suffix =\"\"\"\"\"\"\n",
        "\n",
        "print(get_code_completion(prefix, suffix))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ql2563kGlnmu"
      },
      "source": [
        "As someone who has just used the PEFT library earlier in this notebook, you can see that the generated result for creating a `LoraConfig` is rather good!\n",
        "\n",
        "If you go back to the cell where we instantiate the model for inference, and comment out the lines where we merge the fine-tuned weights, you can see what the original model would've generated for the exact same prefix:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29xxp1eHTgJ9",
        "outputId": "2db045f1-8d76-4e07-b956-f152207aac3f"
      },
      "outputs": [
        {
          "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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "from peft import LoraConfig, TaskType, get_peft_model\n",
            "from transformers import AutoModelForCausalLM\n",
            "peft_config = LoraConfig(\n",
            "    model_name_or_path=\"hf-internal-testing/tiny-xl-peft-model\",\n",
            "    task_type=TaskType.CausalLM,\n",
            "    causal_projection_type=\"linear\",\n",
            "    causal_projection_dim=128,\n",
            "    causal_projection_hidden_size=128,\n",
            "    causal_projection_num_heads=4,\n",
            "    causal_projection_attention_head_dim=16,\n",
            "    causal_projection_dropout=0.1,\n",
            "    causal_projection_activation=\"gelu\",\n",
            "    causal_projection_time\n"
          ]
        }
      ],
      "source": [
        "prefix = \"\"\"from peft import LoraConfig, TaskType, get_peft_model\n",
        "from transformers import AutoModelForCausalLM\n",
        "peft_config = LoraConfig(\n",
        "\"\"\"\n",
        "suffix =\"\"\"\"\"\"\n",
        "\n",
        "print(get_code_completion(prefix, suffix))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "c64520b2819e4dc08066f29f678f0758",
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            "789b14402171451aa9cb272852a9d478",
            "3bce3b1a3d2041ac924941c9f048b642",
            "a6307a77c9ad49c28e186fed3acec9f5",
            "8ce4ec7d2e4d4427b040143bbb409966",
            "c06d7a9cb77c443d8e0b77d99cde524e",
            "6592153a354846439351b2b946e2ac5f",
            "1c62eb20edfc4cb6a834fe31db265ddf",
            "bb4beabdbfca4f80a31d0b57bcbb23cf",
            "d9d0eea886c54d66a2721cc59c3c3568",
            "4d66ea1a32264a4db61fd3c4693493e2",
            "a4f969deb6824a6a9840f82aa1ee7b35",
            "c19945a817904e73b8e242d95b23c119",
            "d22c97492fcd41ce96b927f5bcad13bb",
            "129af13df0dc411f82a053f3a972f476",
            "e33a01f09aac4f91a6ca722b5d5ae5ca",
            "f05d26a31297438c89fdb6dca8720d48",
            "8fa0f71fe0f14b3faf5565895e17ed54",
            "54ec458b5e2c4e8b9d741eed285881cc",
            "74dfecb611394dd3ad18a05d05f726c2",
            "38ea44319bbc4774b24d8a810c76f42d"
          ]
        },
        "id": "UI8UIow--02d",
        "outputId": "7862043f-b3c8-4795-e6a2-2f79c774efba"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "c64520b2819e4dc08066f29f678f0758",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_config.json:   0%|          | 0.00/674 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "4d66ea1a32264a4db61fd3c4693493e2",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "adapter_model.safetensors:   0%|          | 0.00/22.2M [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"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Generate a function that converts a hex char[] to its equivalent ASCII character and returns it as unsigned char[].\n",
            "\n",
            "```python\n",
            "def hex_to_ascii(s):\n",
            "    return [ord(c) for c in s]\n",
            "```\n",
            "\n",
            "Now we can use this function to convert our hex string to an array of unsigned char.\n",
            "\n",
            "```python\n",
            "def hex_to_ascii_array(s):\n",
            "    return [hex_to_ascii(s)[i] for i in range(0, len(s), 2)]\n",
            "```\n",
            "\n",
            "Now we can use this function to convert our hex string to an array of unsigned char.\n",
            "\n",
            "```python\n",
            "def hex_to_ascii_array(s):\n",
            "    return [hex_to_ascii(s)[i] for i in range(0, len(s), 2)]\n",
            "```\n",
            "\n",
            "Now we can use this function to convert our hex string to an array of unsigned char.\n",
            "\n",
            "```python\n",
            "def hex_to_ascii_array(s):\n",
            "    return [hex_to_ascii(s)[i] for i in range(0, len(s), 2)]\n",
            "```\n",
            "\n",
            "Now we can use this function to convert our hex string to an array of unsigned char.\n",
            "\n",
            "```python\n",
            "def hex_to_ascii_array(s):\n",
            "    return [hex_to_ascii(s)[i] for i in range(0, len(s), 2)]\n",
            "```\n",
            "\n",
            "Now we can use this function to convert our hex string to an array of unsigned char.\n",
            "\n",
            "```python\n",
            "def hex_to_ascii_array(s):\n",
            "    return [hex_to_ascii(s)[i] for i in range(0, len(s), 2)]\n",
            "```\n",
            "\n",
            "Now we can use this function to convert our hex string to an array of unsigned char.\n",
            "\n",
            "```python\n",
            "def hex_to_ascii_array(s):\n",
            "    return [hex_to_ascii(s)[i] for i in range(0, len(s), 2)]\n",
            "```\n",
            "\n",
            "Now we can use this function to convert our hex string to an array of unsigned char.\n",
            "\n",
            "```python\n",
            "def hex_to_ascii_array(s):\n",
            "    return [hex_to_ascii(s)[i] for i in range(0, len(s), \n"
          ]
        }
      ],
      "source": [
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "from peft import PeftModel, PeftConfig\n",
        "import torch\n",
        "\n",
        "prompt = \"Generate a function that converts a hex char[] to its equivalent ASCII character and returns it as unsigned char[].\"\n",
        "\n",
        "\n",
        "config = PeftConfig.from_pretrained(\"zulqarnain-kernel/peft-starcoder-lora-a100-v2\")\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\"bigcode/starcoderbase-1b\")\n",
        "\n",
        "\n",
        "model = PeftModel.from_pretrained(base_model, \"zulqarnain-kernel/peft-starcoder-lora-a100-v2\")\n",
        "\n",
        "\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "model.to(device)\n",
        "\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"bigcode/starcoderbase-1b\")\n",
        "\n",
        "inputs = tokenizer.encode(prompt, return_tensors=\"pt\").to(device)\n",
        "\n",
        "outputs = model.generate(inputs, max_length=500)\n",
        "\n",
        "\n",
        "response = tokenizer.decode(outputs[0], clean_up_tokenization_spaces=False)\n",
        "print(response)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Pwy2ZC7U8Ema"
      },
      "source": [
        "While it is Python syntax, you can see that the original model has no understanding of what a `LoraConfig` should be doing."
      ]
    },
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "To learn how this kind of fine-tuning compares to full fine-tuning, and how to use a model like this as your copilot in VS Code via Inference Endpoints, or locally, check out the [\"Personal Copilot: Train Your Own Coding Assistant\" blog post](https://huggingface.co/blog/personal-copilot). This notebook complements the original blog post.\n"
      ]
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