notebooks/cabrita-lora.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "view-in-github"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/pedrogengo/a59bc1da07479ef101db61a99ebd1b35/alpaca-lora.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ExAe1UCWn5E"
      },
      "source": [
        "# Talk to Cabrita-LoRA\n",
        "\n",
        "This notebook contains minimal code for running [Alpaca-LoRA](https://github.com/tloen/alpaca-lora/) for demonstration purposes. Please check the repo for more details.\n",
        "The notebook is based in this another [notebook](https://colab.research.google.com/drive/1eWAmesrW99p7e1nah5bipn0zikMb8XYC)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X_pz8MuY84Qh",
        "outputId": "e94db934-452c-4b1b-f61d-e4b3f0af1e88"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.37.1-py3-none-any.whl (76.3 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m76.3/76.3 MB\u001b[0m \u001b[31m10.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.37.1\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 KB\u001b[0m \u001b[31m8.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h\u001b[33m  WARNING: Did not find branch or tag 'c3dc391', assuming revision or ref.\u001b[0m\u001b[33m\n",
            "\u001b[0m  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m50.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.8/212.8 KB\u001b[0m \u001b[31m5.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip install bitsandbytes\n",
        "!pip install -q datasets loralib sentencepiece\n",
        "!pip install -q git+https://github.com/zphang/transformers@c3dc391\n",
        "!pip install -q git+https://github.com/huggingface/peft.git"
      ]
    },
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        },
        "id": "VucO3HSMoJkz",
        "outputId": "03db26a9-c078-4a53-c129-f54c5f96adfa"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please submit your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching /usr/local/cuda/lib64...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so\n",
            "CUDA SETUP: Highest compute capability among GPUs detected: 7.5\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: /usr/lib64-nvidia did not contain libcudart.so as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/sys/fs/cgroup/memory.events /var/colab/cgroup/jupyter-children/memory.events')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https'), PosixPath('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-t4-hm-2dqhgunruoioj --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('/env/python')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.9/dist-packages/bitsandbytes/cuda_setup/main.py:136: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//ipykernel.pylab.backend_inline'), PosixPath('module')}\n",
            "  warn(msg)\n"
          ]
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "5c624f310c8746efa0ebe2a53f5ff5d1",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "ef3b86986ba548b293068f759dcb0a4c",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/2.00 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "05ea7a0c010e4562956e10e80069b660",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/141 [00:00<?, ?B/s]"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Overriding torch_dtype=None with `torch_dtype=torch.float16` due to requirements of `bitsandbytes` to enable model loading in mixed int8. Either pass torch_dtype=torch.float16 or don't pass this argument at all to remove this warning.\n"
          ]
        },
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            ]
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        {
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            ]
          },
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        }
      ],
      "source": [
        "from peft import PeftModel\n",
        "from transformers import LLaMATokenizer, LLaMAForCausalLM, GenerationConfig\n",
        "\n",
        "tokenizer = LLaMATokenizer.from_pretrained(\"decapoda-research/llama-7b-hf\")\n",
        "model = LLaMAForCausalLM.from_pretrained(\n",
        "    \"decapoda-research/llama-7b-hf\",\n",
        "    load_in_8bit=True,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, \"22h/cabrita-lora-v0-1\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "w3_lzwcqermJ"
      },
      "outputs": [],
      "source": [
        "def generate_prompt(instruction, input=None):\n",
        "    if input:\n",
        "        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa, juntamente com uma entrada que fornece mais contexto. Escreva uma resposta que complete adequadamente o pedido.\n",
        "\n",
        "### Instrução:\n",
        "{instruction}\n",
        "\n",
        "### Entrada:\n",
        "{input}\n",
        "\n",
        "### Resposta:\"\"\"\n",
        "    else:\n",
        "        return f\"\"\"Abaixo está uma instrução que descreve uma tarefa. Escreva uma resposta que complete adequadamente o pedido.\n",
        "\n",
        "### Instrução:\n",
        "{instruction}\n",
        "\n",
        "### Resposta:\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "Egh3beCVRpW5"
      },
      "outputs": [],
      "source": [
        "generation_config = GenerationConfig(\n",
        "    temperature=0.2,\n",
        "    top_p=0.75,\n",
        "    num_beams=4,\n",
        ")\n",
        "\n",
        "def evaluate(instruction, input=None):\n",
        "    prompt = generate_prompt(instruction, input)\n",
        "    inputs = tokenizer(prompt, return_tensors=\"pt\")\n",
        "    input_ids = inputs[\"input_ids\"].cuda()\n",
        "    generation_output = model.generate(\n",
        "        input_ids=input_ids,\n",
        "        generation_config=generation_config,\n",
        "        return_dict_in_generate=True,\n",
        "        output_scores=True,\n",
        "        max_new_tokens=256\n",
        "    )\n",
        "    for s in generation_output.sequences:\n",
        "        output = tokenizer.decode(s)\n",
        "        print(\"Resposta:\", output.split(\"### Resposta:\")[1].strip())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "evaluate(input(\"Instrução: \"))"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "include_colab_link": true,
      "machine_shape": "hm",
      "name": "cabrita-lora.ipynb",
      "provenance": []
    },
    "gpuClass": "premium",
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    },
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