Dolly_LoRA_GPT_J_6b_+_Cleaned_Alpaca_Dataset.ipynb

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  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Dolly - LoRA - GPT-J-6b \n",
        "\n",
        "About Dolly - https://www.databricks.com/blog/2023/03/24/hello-dolly-democratizing-magic-chatgpt-open-models.html\n",
        "\n",
        "Cleaned Alpaca Dataset - https://github.com/gururise/AlpacaDataCleaned"
      ],
      "metadata": {
        "id": "mIF8Tbcbr95m"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hDcH7FqJQssL",
        "outputId": "89479c46-2cbd-465d-9b76-c189d1f4eabb"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m469.0/469.0 KB\u001b[0m \u001b[31m12.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m50.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m132.9/132.9 KB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m264.6/264.6 KB\u001b[0m \u001b[31m16.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m158.8/158.8 KB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  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[31m67.9 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[32m215.3/215.3 KB\u001b[0m \u001b[31m8.6 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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m84.2/84.2 MB\u001b[0m \u001b[31m21.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install datasets loralib sentencepiece\n",
        "!pip -q install git+https://github.com/huggingface/transformers # need to install from github\n",
        "!pip -q install git+https://github.com/huggingface/peft.git\n",
        "!pip -q install bitsandbytes"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"EleutherAI/gpt-j-6B\")\n",
        "\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "tokenizer.pad_token_id = tokenizer.eos_token_id"
      ],
      "metadata": {
        "colab": {
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          "height": 209,
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/619 [00:00<?, ?B/s]"
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    {
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        "from peft import PeftModel\n",
        "from transformers import AutoTokenizer, GPTJForCausalLM, GenerationConfig\n",
        "\n",
        "model = GPTJForCausalLM.from_pretrained(\n",
        "    \"EleutherAI/gpt-j-6B\",\n",
        "    load_in_8bit=True,\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "model = PeftModel.from_pretrained(model, \"samwit/dolly-lora\")\n"
      ],
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        "outputId": "d486a98b-39d8-4a3b-b9af-9dc3589da069"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "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('//colab.research.google.com/tun/m/cc48301118ce562b961b3c22d803539adc1e0c19/gpu-a100-s-30g9vmngxfz4t --tunnel_background_save_delay=10s --tunnel_periodic_background_save_frequency=30m0s --enable_output_coalescing=true --output_coalescing_required=true'), PosixPath('--listen_host=172.28.0.12 --target_host=172.28.0.12 --tunnel_background_save_url=https')}\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('module'), PosixPath('//ipykernel.pylab.backend_inline')}\n",
            "  warn(msg)\n",
            "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"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "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: 8.0\n",
            "CUDA SETUP: Detected CUDA version 118\n",
            "CUDA SETUP: Loading binary /usr/local/lib/python3.9/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "119f6c8e218e4a18b6388641ccb04866"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading pytorch_model.bin:   0%|          | 0.00/24.2G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6071dce2ffcb4a69a86e92254fd8ac41"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/adapter_config.json:   0%|          | 0.00/360 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "ebd58a328cd94158a9361c90a95e00cc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading adapter_model.bin:   0%|          | 0.00/7.38M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "da184b8559e04332b051c5bde406f767"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "PROMPT = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "### Instruction:\n",
        "What are the differences between alpacas and sheep?\n",
        "### Response:\"\"\"\n",
        "\n",
        "def dolly_generate(text):\n",
        "    inputs = tokenizer(\n",
        "        text,\n",
        "        return_tensors=\"pt\",\n",
        "    )\n",
        "    input_ids = inputs[\"input_ids\"].cuda()\n",
        "\n",
        "    generation_config = GenerationConfig(\n",
        "        temperature=0.6,\n",
        "        top_p=0.95,\n",
        "        repetition_penalty=1.2,\n",
        "    )\n",
        "\n",
        "    print(\"Generating...\")\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=128,\n",
        "        pad_token_id = 0,\n",
        "        eos_token_id = 50256\n",
        "    )\n",
        "\n",
        "    for s in generation_output.sequences:\n",
        "        print(tokenizer.decode(s))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9M6HnRDLTXqG",
        "outputId": "8867c83a-faad-42fa-81ee-b1a417842847"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 2 µs, sys: 2 µs, total: 4 µs\n",
            "Wall time: 7.39 µs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "dolly_generate(PROMPT)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0FYBEuY3eAbE",
        "outputId": "92b46720-b4ea-4e6d-e461-42cf4c6fc6f4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating...\n",
            "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
            "### Instruction:\n",
            "What are the differences between alpacas and sheep?\n",
            "### Response:\n",
            "Alpaca wool has softer, finer fibers than sheep's wool; it also tends to be more durable and less prone to shrinkage. Alpacas have longer hair coats with thicker undercoats compared to sheep which tend to have shorter fur coats with thinner undercoats. Additionally, alpacas produce milk for their young while sheep do not. Sheep typically graze on grasslands whereas alpacas prefer mountainous areas where they can find food in abundance. Lastly, alpacas require much more space per animal due to their larger size. \n",
            "\n",
            "<|endoftext|>\n",
            "CPU times: user 30 s, sys: 2.13 s, total: 32.2 s\n",
            "Wall time: 33.9 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PROMPT = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "### Instruction:\n",
        "Write me a python function that checks if a number is prime.\n",
        "### Response:\"\"\"\n",
        "\n",
        "dolly_generate(PROMPT)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2QGFEnteQre",
        "outputId": "9b5eee2e-740b-46be-efb7-6f9b548e050a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating...\n",
            "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
            "### Instruction:\n",
            "Write me a python function that checks if a number is prime.\n",
            "### Response:\n",
            "def check_prime(n):  # n - input value to be checked for primeness\n",
            "\n",
            "    \"\"\"Check whether or not given integer is prime\"\"\"\n",
            "\n",
            "    def divisibleby2(x, y=1):\n",
            "        return x % y == 0\n",
            "\n",
            "    def evens(x):\n",
            "        return (x & 1) == 0\n",
            "\n",
            "    def oddness(x):\n",
            "        return (x & 1)!= 0\n",
            "\n",
            "    while True:\n",
            "    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "PROMPT = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
        "### Instruction:\n",
        "Write an email explaining why GPT-4 should be open source.\n",
        "### Response:\"\"\"\n",
        "\n",
        "dolly_generate(PROMPT)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qs44oBh6k57a",
        "outputId": "c299a198-5122-4a1c-aa02-2a5553f2037f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating...\n",
            "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
            "### Instruction:\n",
            "Write an email explaining why GPT-4 should be open source.\n",
            "### Response:\n",
            "GPT-4 has been developed by Google and it's important to make sure its algorithms are accessible for everyone, including researchers who want to study them in order to improve AI systems. Open sourcing this technology will also help ensure that there aren't any potential security vulnerabilities or privacy concerns with using these models. Additionally, making GPT-4 available as an API would allow developers to build their own applications based on the model without having to worry about licensing issues. Finally, releasing GPT-4 under an open license such as Apache 2.0 ensures that anyone can use the code freely while still maintaining ownership of their work. \n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "u5nItwnfB92A"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}