LLAMA_2_Fine_Tuning_Lora.ipynb

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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/shiftbug/blog/blob/main/LLAMA_2_Fine_Tuning_Lora.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
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        "!pip install -q  torch peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 accelerate\n",
        "\n",
        "import torch\n",
        "from datasets import load_dataset\n",
        "from transformers import (\n",
        "    AutoModelForCausalLM,\n",
        "    AutoTokenizer,\n",
        "    BitsAndBytesConfig,\n",
        "    TrainingArguments,\n",
        "    pipeline\n",
        ")\n",
        "from peft import LoraConfig\n",
        "from trl import SFTTrainer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n5kH6OUjdubg",
        "outputId": "64186f8c-7b95-401c-85c1-1bd71e2c0067"
      },
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      "outputs": [
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            ],
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          "metadata": {}
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      ],
      "source": [
        "# Dataset\n",
        "data_name = \"mlabonne/guanaco-llama2-1k\"\n",
        "training_data = load_dataset(data_name, split=\"train\")\n",
        "\n",
        "# Model and tokenizer names\n",
        "base_model_name = \"NousResearch/Llama-2-7b-chat-hf\"\n",
        "refined_model = \"llama-2-7b-mlabonne-enhanced\" #You can give it your own name\n",
        "\n",
        "# Tokenizer\n",
        "llama_tokenizer = AutoTokenizer.from_pretrained(base_model_name, trust_remote_code=True)\n",
        "llama_tokenizer.pad_token = llama_tokenizer.eos_token\n",
        "llama_tokenizer.padding_side = \"right\"  # Fix for fp16\n",
        "\n",
        "# Quantization Config\n",
        "quant_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        "    bnb_4bit_use_double_quant=False\n",
        ")\n",
        "\n",
        "# Model\n",
        "base_model = AutoModelForCausalLM.from_pretrained(\n",
        "    base_model_name,\n",
        "    quantization_config=quant_config,\n",
        "    device_map={\"\": 0}\n",
        ")\n",
        "base_model.config.use_cache = False\n",
        "base_model.config.pretraining_tp = 1"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# LoRA Config\n",
        "peft_parameters = LoraConfig(\n",
        "    lora_alpha=16,\n",
        "    lora_dropout=0.1,\n",
        "    r=8,\n",
        "    bias=\"none\",\n",
        "    task_type=\"CAUSAL_LM\"\n",
        ")\n",
        "\n",
        "# Training Params\n",
        "train_params = TrainingArguments(\n",
        "    output_dir=\"./results_modified\",\n",
        "    num_train_epochs=1,\n",
        "    per_device_train_batch_size=4,\n",
        "    gradient_accumulation_steps=1,\n",
        "    optim=\"paged_adamw_32bit\",\n",
        "    save_steps=25,\n",
        "    logging_steps=25,\n",
        "    learning_rate=2e-4,\n",
        "    weight_decay=0.001,\n",
        "    fp16=False,\n",
        "    bf16=False,\n",
        "    max_grad_norm=0.3,\n",
        "    max_steps=-1,\n",
        "    warmup_ratio=0.03,\n",
        "    group_by_length=True,\n",
        "    lr_scheduler_type=\"constant\",\n",
        "    report_to=\"tensorboard\"\n",
        ")\n",
        "\n",
        "# Trainer\n",
        "fine_tuning = SFTTrainer(\n",
        "    model=base_model,\n",
        "    train_dataset=training_data,\n",
        "    peft_config=peft_parameters,\n",
        "    dataset_text_field=\"text\",\n",
        "    tokenizer=llama_tokenizer,\n",
        "    args=train_params\n",
        ")\n",
        "\n",
        "# Training\n",
        "fine_tuning.train()\n",
        "\n",
        "# Save Model\n",
        "fine_tuning.model.save_pretrained(refined_model)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 508,
          "referenced_widgets": [
            "636a931c33cd415ba1eb6c4721080fd2",
            "dda7944038c34f70803c926436ffcfaf",
            "d25d9974d04344509944bbea7b8e3268",
            "8a836fa75d6b400fb3187ec1bf87946a",
            "f5404ba31d8a4295b9da0c26db9b2439",
            "1f14aff2634a48b493cd94733c356a2a",
            "edcdc867e2f44851a25fca997a0944de",
            "1ed8684669ed48629790b5b902c577ba",
            "c55e6bd1be2643d1b846f2d7b8cbf234",
            "d03a30aa2db84d29a8c4a3e34466644d",
            "12c4eef0a8c640349e257d0973a58a41"
          ]
        },
        "id": "TSsPNqspeFt5",
        "outputId": "58856f44-014b-478e-edd1-fef917d76766"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/peft/utils/other.py:102: FutureWarning: prepare_model_for_int8_training is deprecated and will be removed in a future version. Use prepare_model_for_kbit_training instead.\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/trl/trainer/sft_trainer.py:159: UserWarning: You didn't pass a `max_seq_length` argument to the SFTTrainer, this will default to 1024\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Map:   0%|          | 0/1000 [00:00<?, ? examples/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "636a931c33cd415ba1eb6c4721080fd2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "You're using a LlamaTokenizerFast tokenizer. Please note that with a fast tokenizer, using the `__call__` method is faster than using a method to encode the text followed by a call to the `pad` method to get a padded encoding.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "    <div>\n",
              "      \n",
              "      <progress value='250' max='250' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
              "      [250/250 27:15, Epoch 1/1]\n",
              "    </div>\n",
              "    <table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              " <tr style=\"text-align: left;\">\n",
              "      <th>Step</th>\n",
              "      <th>Training Loss</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td>25</td>\n",
              "      <td>1.345800</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>50</td>\n",
              "      <td>1.644300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>75</td>\n",
              "      <td>1.207900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>100</td>\n",
              "      <td>1.440000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>125</td>\n",
              "      <td>1.171600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>150</td>\n",
              "      <td>1.360000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>175</td>\n",
              "      <td>1.168700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>200</td>\n",
              "      <td>1.458100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>225</td>\n",
              "      <td>1.150900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td>250</td>\n",
              "      <td>1.525900</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table><p>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Generate Text\n",
        "query = \"How do I use the OpenAI API?\"\n",
        "text_gen = pipeline(task=\"text-generation\", model=refined_model, tokenizer=llama_tokenizer, max_length=200)\n",
        "output = text_gen(f\"<s>[INST] {query} [/INST]\")\n",
        "print(output[0]['generated_text'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtXBD2kreFmp",
        "outputId": "42364379-9444-4832-9b6d-dfd27f28c0e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Xformers is not installed correctly. If you want to use memory_efficient_attention to accelerate training use the following command to install Xformers\n",
            "pip install xformers.\n",
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1270: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation )\n",
            "  warnings.warn(\n",
            "/usr/local/lib/python3.10/dist-packages/torch/utils/checkpoint.py:31: UserWarning: None of the inputs have requires_grad=True. Gradients will be None\n",
            "  warnings.warn(\"None of the inputs have requires_grad=True. Gradients will be None\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<s>[INST] How do I use the OpenAI API? [/INST] The OpenAI API is a RESTful API that allows you to interact with the OpenAI models. OpenAI provides a Python SDK that makes it easy to use the API. You can install the SDK using pip:\n",
            "```\n",
            "pip install openai-sdk\n",
            "```\n",
            "Once you have installed the SDK, you can use the following code to get a token for the API:\n",
            "```\n",
            "from openai.api import OpenAPI\n",
            "\n",
            "api_key = \"YOUR_API_KEY\"\n",
            "\n",
            "open_api = OpenAPI(api_key)\n",
            "```\n",
            "You can then use the `open_api` object to make API requests. For example, you can use the `open_api.get_model()` method to get a model:\n",
            "```\n",
            "model = open_api.get_model(\"bert-base-uncased\")\n",
            "```\n",
            "You can then use the\n"
          ]
        }
      ]
    }
  ]
}