DogLLAMA_LoRA_Merge.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/pathikg/DogLLaMA/blob/main/DogLLAMA_LoRA_Merge.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "DufCuzspRfJL"
      },
      "outputs": [],
      "source": [
        "!pip install -q  torch peft==0.4.0 bitsandbytes==0.40.2 transformers==4.31.0 trl==0.4.7 accelerate"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!huggingface-cli login"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "flT3DD_mbBiO",
        "outputId": "2055f4f3-6230-48a2-f7c2-32daf2b274e9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "    _|    _|  _|    _|    _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|_|_|_|    _|_|      _|_|_|  _|_|_|_|\n",
            "    _|    _|  _|    _|  _|        _|          _|    _|_|    _|  _|            _|        _|    _|  _|        _|\n",
            "    _|_|_|_|  _|    _|  _|  _|_|  _|  _|_|    _|    _|  _|  _|  _|  _|_|      _|_|_|    _|_|_|_|  _|        _|_|_|\n",
            "    _|    _|  _|    _|  _|    _|  _|    _|    _|    _|    _|_|  _|    _|      _|        _|    _|  _|        _|\n",
            "    _|    _|    _|_|      _|_|_|    _|_|_|  _|_|_|  _|      _|    _|_|_|      _|        _|    _|    _|_|_|  _|_|_|_|\n",
            "\n",
            "    A token is already saved on your machine. Run `huggingface-cli whoami` to get more information or `huggingface-cli logout` if you want to log out.\n",
            "    Setting a new token will erase the existing one.\n",
            "    To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n",
            "Token: \n",
            "Add token as git credential? (Y/n) n\n",
            "Token is valid (permission: write).\n",
            "Your token has been saved to /root/.cache/huggingface/token\n",
            "Login successful\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig"
      ],
      "metadata": {
        "id": "bRKmvO0qdzmf"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Load based model with LoRA weights"
      ],
      "metadata": {
        "id": "cIaaGDcrqM0G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "peft_model_id = \"pathikg/DogLLaMA-LoRA\"\n",
        "config = PeftConfig.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    config.base_model_name_or_path, low_cpu_mem_usage=True, return_dict=True,  torch_dtype=torch.float16, device_map={\"\": 0}\n",
        ")\n",
        "\n",
        "# Load the Lora model\n",
        "model = PeftModel.from_pretrained(model, peft_model_id)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173,
          "referenced_widgets": [
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        "outputId": "71d48af8-1f2a-4b72-caaa-e12955100916"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "8d05e2453d194a08939f9f0a4bb841d4"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Infer with LLaMA style prompt"
      ],
      "metadata": {
        "id": "R7Y9D_qWpj26"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Reload tokenizer to save it\n",
        "tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.padding_side = \"right\""
      ],
      "metadata": {
        "id": "HAxPmUHjk-VE"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch = tokenizer(\"<s>[INST] Introduce yourself [/INST]\", return_tensors=\"pt\").to(\"cuda\")\n",
        "\n",
        "with torch.cuda.amp.autocast():\n",
        "    output_tokens = model.generate(**batch, max_new_tokens=200)\n",
        "\n",
        "print(\"\\n\\n\", tokenizer.decode(output_tokens[0], skip_special_tokens=True).strip())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2BUw3rS_XhWY",
        "outputId": "09f038fc-b1fa-44aa-dc1e-06448efb8bd3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            " [INST] Introduce yourself [/INST] Bark bark! Tail wagging Hello there, I'm a friendly pup, ready to play! 🐾💕\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Merge model\n",
        "Now we'll merge the LoRA weights with the base model to use the same out of the box"
      ],
      "metadata": {
        "id": "ZXwWDkh9psPY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model = model.merge_and_unload()"
      ],
      "metadata": {
        "id": "drF_8hFldhg5"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_model.push_to_hub(\"pathikg/DogLLaMA_7b\")\n",
        "tokenizer.push_to_hub(\"pathikg/DogLLaMA_7b\")"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 148,
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      },
      "execution_count": 16,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "pytorch_model-00001-of-00002.bin:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
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          "metadata": {}
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Upload 2 LFS files:   0%|          | 0/2 [00:00<?, ?it/s]"
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          "metadata": {},
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      "metadata": {
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      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "# Use a pipeline as a high-level helper\n",
        "from transformers import pipeline\n",
        "\n",
        "pipe = pipeline(\"question-answering\", model=\"pathikg/DogLLaMA_7b\")"
      ],
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/630 [00:00<?, ?B/s]"
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          "data": {
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          "data": {
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}