10.MPT_Instruct_30B.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1VJ7lfTYz0qu",
        "outputId": "175b6ea4-8fb7-4fdf-aed8-4b1ff5c2d40e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  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[32m236.8/236.8 kB\u001b[0m \u001b[31m6.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m83.0 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[31m80.1 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",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m9.3 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[31m47.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m17.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m32.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.3/134.3 kB\u001b[0m \u001b[31m20.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.0/1.0 MB\u001b[0m \u001b[31m66.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m114.5/114.5 kB\u001b[0m \u001b[31m13.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m35.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m149.6/149.6 kB\u001b[0m \u001b[31m22.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m97.1/97.1 MB\u001b[0m \u001b[31m17.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m227.6/227.6 kB\u001b[0m \u001b[31m28.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109.1/109.1 MB\u001b[0m \u001b[31m15.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "!pip -q install git+https://github.com/huggingface/transformers # need to install from github\n",
        "!pip install -q datasets loralib sentencepiece\n",
        "!pip -q install bitsandbytes accelerate xformers einops"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdVSk5iZ1DVB",
        "outputId": "db9f0879-96de-4efd-a8d5-b1f6c0f6272f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Jun 26 03:28:01 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    48W / 400W |      0MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "|  No running processes found                                                 |\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ],
      "source": [
        "!nvidia-smi"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "import transformers\n",
        "from transformers import AutoTokenizer\n",
        "\n",
        "model_name = 'mosaicml/mpt-30b-instruct'\n",
        "\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained('mosaicml/mpt-30b')\n",
        "\n",
        "config = transformers.AutoConfig.from_pretrained(model_name,\n",
        "                                                 trust_remote_code=True)\n",
        "config.init_device = 'cuda:0'\n",
        "config.max_seq_len = 16384\n",
        "\n",
        "model = transformers.AutoModelForCausalLM.from_pretrained(\n",
        "  model_name,\n",
        "  config=config,\n",
        "  torch_dtype=torch.bfloat16, # Load model weights in bfloat16\n",
        "  trust_remote_code=True,\n",
        "  device_map='auto',\n",
        "  load_in_8bit=True,\n",
        ")\n"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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        {
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        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- configuration_mpt.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
        },
        {
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          "data": {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- adapt_tokenizer.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
          ]
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- norm.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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          "text": [
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            "- flash_attn_triton.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- attention.py\n",
            "- flash_attn_triton.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- blocks.py\n",
            "- norm.py\n",
            "- attention.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- hf_prefixlm_converter.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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        {
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        {
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          "text": [
            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- param_init_fns.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- custom_embedding.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- meta_init_context.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n",
            "A new version of the following files was downloaded from https://huggingface.co/mosaicml/mpt-30b-instruct:\n",
            "- modeling_mpt.py\n",
            "- adapt_tokenizer.py\n",
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            "- hf_prefixlm_converter.py\n",
            "- param_init_fns.py\n",
            "- custom_embedding.py\n",
            "- meta_init_context.py\n",
            ". Make sure to double-check they do not contain any added malicious code. To avoid downloading new versions of the code file, you can pin a revision.\n"
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            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b60b0a238a1f4e2d9b396ab1a61629ba"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "You are using config.init_device='cuda:0', but you can also use config.init_device=\"meta\" with Composer + FSDP for fast initialization.\n",
            "\n",
            "===================================BUG REPORT===================================\n",
            "Welcome to bitsandbytes. For bug reports, please run\n",
            "\n",
            "python -m bitsandbytes\n",
            "\n",
            " and submit this information together with your error trace to: https://github.com/TimDettmers/bitsandbytes/issues\n",
            "================================================================================\n",
            "bin /usr/local/lib/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so\n",
            "CUDA_SETUP: WARNING! libcudart.so not found in any environmental path. Searching in backup paths...\n",
            "CUDA SETUP: CUDA runtime path found: /usr/local/cuda/lib64/libcudart.so.11.0\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/python3.10/dist-packages/bitsandbytes/libbitsandbytes_cuda118.so...\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: /usr/lib64-nvidia did not contain ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] as expected! Searching further paths...\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: 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/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('//172.28.0.1'), PosixPath('8013'), PosixPath('http')}\n",
            "  warn(msg)\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: WARNING: The following directories listed in your path were found to be non-existent: {PosixPath('--logtostderr --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-a100-s-2vnsfvg2p0rp5 --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/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: 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/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: 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",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/cuda_setup/main.py:149: UserWarning: Found duplicate ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] files: {PosixPath('/usr/local/cuda/lib64/libcudart.so.11.0'), PosixPath('/usr/local/cuda/lib64/libcudart.so')}.. We'll flip a coin and try one of these, in order to fail forward.\n",
            "Either way, this might cause trouble in the future:\n",
            "If you get `CUDA error: invalid device function` errors, the above might be the cause and the solution is to make sure only one ['libcudart.so', 'libcudart.so.11.0', 'libcudart.so.12.0'] in the paths that we search based on your env.\n",
            "  warn(msg)\n",
            "WARNING:accelerate.utils.modeling:The model weights are not tied. Please use the `tie_weights` method before using the `infer_auto_device` function.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/7 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "23c65f7594904622993f78d6f8313ddc"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/91.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7087ea92f42b47e89122962e8342fe95"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!nvidia-smi"
      ],
      "metadata": {
        "id": "8IS_kfwtE1uo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6ed466fc-f339-4869-fc92-7ef5434394b3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mon Jun 26 03:46:49 2023       \n",
            "+-----------------------------------------------------------------------------+\n",
            "| NVIDIA-SMI 525.85.12    Driver Version: 525.85.12    CUDA Version: 12.0     |\n",
            "|-------------------------------+----------------------+----------------------+\n",
            "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
            "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
            "|                               |                      |               MIG M. |\n",
            "|===============================+======================+======================|\n",
            "|   0  NVIDIA A100-SXM...  Off  | 00000000:00:04.0 Off |                    0 |\n",
            "| N/A   33C    P0    53W / 400W |  30311MiB / 40960MiB |      0%      Default |\n",
            "|                               |                      |             Disabled |\n",
            "+-------------------------------+----------------------+----------------------+\n",
            "                                                                               \n",
            "+-----------------------------------------------------------------------------+\n",
            "| Processes:                                                                  |\n",
            "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
            "|        ID   ID                                                   Usage      |\n",
            "|=============================================================================|\n",
            "+-----------------------------------------------------------------------------+\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Wo-FSysZiVkA"
      },
      "outputs": [],
      "source": [
        "import json\n",
        "import textwrap\n",
        "\n",
        "def get_prompt(instruction):\n",
        "    prompt_template = \"Below is an instruction that describes a task. Write a response that appropriately completes the request.\\n\\n###Instruction\\n{instruction}\\n\\n### Response\\n\"\n",
        "    return prompt_template.format(instruction=instruction)\n",
        "\n",
        "def cut_off_text(text, prompt):\n",
        "    cutoff_phrase = prompt\n",
        "    index = text.find(cutoff_phrase)\n",
        "    if index != -1:\n",
        "        return text[:index]\n",
        "    else:\n",
        "        return text\n",
        "\n",
        "def remove_substring(string, substring):\n",
        "    return string.replace(substring, \"\")\n",
        "\n",
        "\n",
        "def generate(text):\n",
        "    prompt = get_prompt(text)\n",
        "    with torch.autocast('cuda', dtype=torch.bfloat16):\n",
        "        inputs = tokenizer(prompt, return_tensors=\"pt\").to('cuda')\n",
        "        outputs = model.generate(**inputs,\n",
        "                                 max_new_tokens=512,\n",
        "                                 eos_token_id=tokenizer.eos_token_id,\n",
        "                                 pad_token_id=tokenizer.pad_token_id,\n",
        "                                 )\n",
        "        final_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=False)[0]\n",
        "        final_outputs = cut_off_text(final_outputs, '<|endoftext|>')\n",
        "        final_outputs = remove_substring(final_outputs, prompt)\n",
        "\n",
        "    return final_outputs#, outputs\n",
        "\n",
        "def parse_text(text):\n",
        "        wrapped_text = textwrap.fill(text, width=100)\n",
        "        print(wrapped_text +'\\n\\n')\n",
        "        # return assistant_text\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'''\n",
        "%%time\n",
        "function = [\n",
        "    {\n",
        "        \"name\": \"get_flight_info\",\n",
        "        \"description\": \"Get the info of the cheapest flight for a given date\",\n",
        "        \"parameters\": {\n",
        "            \"type\": \"object\",\n",
        "            \"properties\": {\n",
        "                \"fly_from\": {\n",
        "                    \"type\": \"string\",\n",
        "                    \"description\": \"the 3-digit code for departure airport\"\n",
        "                },\n",
        "                \"fly_to\": {\n",
        "                    \"type\": \"string\",\n",
        "                    \"description\": \"the 3-digit code for arrival airport\"\n",
        "                },\n",
        "                \"date\": {\n",
        "                    \"type\": \"string\",\n",
        "                    \"description\": \"the dd/mm/yyyy format date for flight search\"\n",
        "                },\n",
        "            },\n",
        "            \"required\": [\"fly_from\", \"fly_to\", \"date\"]\n",
        "        }\n",
        "    }\n",
        "]\n",
        "prompt = \"My query is - What is the cheapest flight for 13/08/2023 from Shanghai to New York? Before answer you need to learn the function definition first, then give me a JSON structure describing how to call this function to get answer from this function to help answer my query, for example [{'name': 'get_flight_info', 'parameters': {'fly_from':'LAX', 'fly_to':'SFO', 'date':'11/09/2012'}]. ###Function- \" + format(function)\n",
        "print (prompt)\n",
        "generated_text = generate(prompt)\n",
        "parse_text(generated_text)\n",
        "'''\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "kscz0EXCzG2L",
        "outputId": "6ce98584-6d85-4d10-a580-5ceb20ed8aa4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'\\n%%time\\nfunction = [\\n    {\\n        \"name\": \"get_flight_info\",\\n        \"description\": \"Get the info of the cheapest flight for a given date\",\\n        \"parameters\": {\\n            \"type\": \"object\",\\n            \"properties\": {\\n                \"fly_from\": {\\n                    \"type\": \"string\",\\n                    \"description\": \"the 3-digit code for departure airport\"\\n                },\\n                \"fly_to\": {\\n                    \"type\": \"string\",\\n                    \"description\": \"the 3-digit code for arrival airport\"\\n                },\\n                \"date\": {\\n                    \"type\": \"string\",\\n                    \"description\": \"the dd/mm/yyyy format date for flight search\"\\n                },\\n            },\\n            \"required\": [\"fly_from\", \"fly_to\", \"date\"]\\n        }\\n    }\\n]\\nprompt = \"My query is - What is the cheapest flight for 13/08/2023 from Shanghai to New York? Before answer you need to learn the function definition first, then give me a JSON structure describing how to call this function to get answer from this function to help answer my query, for example [{\\'name\\': \\'get_flight_info\\', \\'parameters\\': {\\'fly_from\\':\\'LAX\\', \\'fly_to\\':\\'SFO\\', \\'date\\':\\'11/09/2012\\'}]. ###Function- \" + format(function)\\nprint (prompt)   \\ngenerated_text = generate(prompt)\\nparse_text(generated_text)\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "function = [\n",
        "    {\n",
        "        \"name\": \"get_flight_info\",\n",
        "        \"description\": \"Get the info of the cheapest flight for a given date\",\n",
        "        \"parameters\": {\n",
        "            \"type\": \"object\",\n",
        "            \"properties\": {\n",
        "                \"fly_from\": {\n",
        "                    \"type\": \"string\",\n",
        "                    \"description\": \"the 3-digit code for departure airport\"\n",
        "                },\n",
        "                \"fly_to\": {\n",
        "                    \"type\": \"string\",\n",
        "                    \"description\": \"the 3-digit code for arrival airport\"\n",
        "                },\n",
        "                \"date\": {\n",
        "                    \"type\": \"string\",\n",
        "                    \"description\": \"the dd/mm/yyyy format date for flight search\"\n",
        "                },\n",
        "            },\n",
        "            \"required\": [\"fly_from\", \"fly_to\", \"date\"]\n",
        "        }\n",
        "    }\n",
        "]\n",
        "prompt = \"My query is - What is the cheapest flight for 13/08/2023 from Shanghai to New York? Before answer you need to learn the function definition first, then give me a JSON structure describing how to call this function to get answer from this function to help answer my query, you should provide 'name' from function definition, and provide 'parameters' in 'properties' from function definition, the format should be : [{'function_name': name, 'parameters': {para1:value1, para2:value2, para3:value3...}]. ###Function- \" + format(function)\n",
        "#print (prompt)\n",
        "generated_text = generate(prompt)\n",
        "response = parse_text(generated_text.partition(\"### Response\\n\")[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TEKp9O9x4AZ1",
        "outputId": "50004c1f-0c01-4c26-eee5-c8e8de2ede7d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n",
            "/usr/local/lib/python3.10/dist-packages/bitsandbytes/autograd/_functions.py:321: UserWarning: MatMul8bitLt: inputs will be cast from torch.bfloat16 to float16 during quantization\n",
            "  warnings.warn(f\"MatMul8bitLt: inputs will be cast from {A.dtype} to float16 during quantization\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The JSON structure for calling the function is as follows:  [{ \"name\": \"get_flight_info\",\n",
            "\"parameters\": { \"fly_from\": \"SHA\", \"fly_to\": \"JFK\", \"date\": \"13/08/2023\" } }]\n",
            "\n",
            "\n",
            "CPU times: user 16.8 s, sys: 139 ms, total: 16.9 s\n",
            "Wall time: 18.1 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "#first generate usage\n",
        "prompt = \"\"\"Please summerize the below article- ##start: Since the launch of MPT-7B in May, the ML community has eagerly embraced open-source MosaicML Foundation Series models. The MPT-7B base, -Instruct, -Chat, and -StoryWriter models have collectively been downloaded over 3M times!\n",
        "We’ve been overwhelmed by what the community has built with  MPT-7B. To highlight a few: LLaVA-MPT adds vision understanding to MPT,  GGML optimizes MPT on Apple Silicon and CPUs, and GPT4All lets you run a GPT4-like chatbot on your laptop using MPT as a backend model.\n",
        "Today, we are excited to expand the MosaicML Foundation Series with MPT-30B, a new, open-source model licensed for commercial use that is significantly more powerful than MPT-7B and outperforms the original GPT-3. In addition, we are releasing two fine-tuned variants, MPT-30B-Instruct and MPT-30B-Chat, that are built on top of MPT-30B and excel at single-turn instruction following and multi-turn conversations, respectively.\n",
        "All MPT-30B models come with special features that differentiate them from other LLMs, including an 8k token context window at training time, support for even longer contexts via ALiBi, and efficient inference + training performance via FlashAttention. The MPT-30B family also has strong coding abilities thanks to its pretraining data mixture. This model was extended to an 8k context window on NVIDIA H100s, making it (to the best of our knowledge) the first LLM trained on H100s. H100s are now available to MosaicML customers!\n",
        "The size of MPT-30B was also specifically chosen to make it easy to deploy on a single GPU—either 1xA100-80GB in 16-bit precision or 1xA100-40GB in 8-bit precision. Other comparable LLMs such as Falcon-40B have larger parameter counts and cannot be served on a single datacenter GPU (today); this necessitates 2+ GPUs, which increases the minimum inference system cost.\n",
        "If you want to start using MPT-30B in production, there are several ways to customize and deploy it using the MosaicML Platform.\n",
        "MosaicML Training. Customize MPT-30B using your private data via finetuning, domain-specific pretraining, or training from scratch. You always own the final model weights,  and your data is never stored on our platform. Pricing is per-GPU-minute.\n",
        "MosaicML Inference: Starter Edition. Talk to our hosted endpoints for MPT-30B-Instruct (and MPT-7B-Instruct) using our Python API, with standard pricing per-1K-tokens.\n",
        "MosaicML Inference: Enterprise Edition. Deploy custom MPT-30B models, either on MosaicML compute or in your own private VPC, using our optimized inference stack. Pricing is per-GPU-minute, so you only pay for the compute you use.\n",
        "We are so excited to see what our community and customers build next with MPT-30B. To learn more about the models and how you can customize them using the MosaicML platform, read on!\n",
        "MPT-30B Family\n",
        "Mosaic Pretrained Transformer (MPT) models are GPT-style decoder-only transformers with several improvements including higher speed, greater stability, and longer context lengths. Thanks to these improvements, customers can train MPT models efficiently (40-60% MFU) without diverging from loss spikes and can serve MPT models with both standard HuggingFace pipelines and FasterTransformer.\n",
        "MPT-30B (Base)\n",
        "MPT-30B is a commercial Apache 2.0 licensed, open-source foundation model that exceeds the quality of GPT-3 (from the original paper) and is competitive with other open-source models such as LLaMa-30B and Falcon-40B.\n",
        "Using our publicly available LLM Foundry codebase, we trained MPT-30B over the course of 2 months, transitioning between multiple different A100 clusters as hardware availability changed, with an average MFU of >46%. In mid-June, after we received our first batch of 256xH100s from CoreWeave, we seamlessly moved MPT-30B to the new cluster to resume training on H100s with an average MFU of >35%. To the best of our knowledge, MPT-30B is the first public model to be (partially) trained on H100s! We found that throughput increased by 2.44x per GPU and we expect this speedup to increase as software matures for the H100.\n",
        "As mentioned earlier, MPT-30B was trained with a long context window of 8k tokens (vs. 2k for LLaMa and Falcon) and can handle arbitrarily long context windows via ALiBi or with finetuning. To build 8k support into MPT-30B efficiently, we first pre-trained on 1T tokens using sequences that were 2k tokens long, and continued training for an additional 50B tokens using sequences that were 8k tokens long.\n",
        "The data mix used for MPT-30B pretraining is very similar to MPT-7B (see the MPT-7B blog post for details). For the 2k context window pre-training we used 1T tokens from the same 10 data subsets as the MPT-7B model (Table 1), but in slightly different proportions.\n",
        "\n",
        "Table 1: Data mix for MPT-30B pretraining. We collected 1T tokens of pretraining data from ten different open-source text corpora. We tokenized the text using the EleutherAI GPT-NeoX-20B tokenizer and sampled according to the above ratios.\n",
        "For the 8k context window finetuning, we created two data mixes from the same 10 subsets we used for the 2k context window pretraining (Figure 1). The first 8k finetuning mix is similar to the 2k pretraining mix, but we increased the relative proportion of code by 2.5x. To create the second 8k finetuning mix, which we refer to as the “long sequence” mix, we extracted all sequences of length ≥ 4096 tokens from the 10 pretraining data subsets. We then finetuned on a combination of these two data mixes. See the Appendix for more details on the 8k context window finetuning data.\n",
        "\n",
        "Figure 1:  Data subset distribution for 8k context window finetuning. For 8k context window finetuning, we took each data subset and extracted all the samples with ≥ 4096 tokens in order to create a new “long sequence” data mix. We then finetuned on a combination of both the long sequence and original data mixes.\n",
        "In Figure 2, we measure these six core capabilities and find that MPT-30B significantly improves over MPT-7B in every respect. In Figure 3 we perform the same comparison between similarly-sized MPT, LLaMa, and Falcon models. Overall we find that the 7B models across the different families are quite similar. But LLaMa-30B and Falcon-40B are slightly higher in text capabilities than MPT-30B, which is consistent with their larger pretraining budgets:\n",
        "MPT-30B FLOPs ~= 6 * 30e9 [params] * 1.05e12 [tokens] = 1.89e23 FLOPs\n",
        "LLaMa-30B FLOPs ~= 6 * 32.5e9 [params] * 1.4e12 [tokens] = 2.73e23 FLOPs (1.44x more)\n",
        "Falcon-40B FLOPs ~= 6 * 40e9 [params] * 1e12 [tokens] = 2.40e23 FLOps (1.27x more)\n",
        "On the other hand, we find that MPT-30B is significantly better at programming, which we credit to its pretraining data mixture including a substantial amount of code. We dig into programming ability further in Table 2,  where we compare the HumanEval scores of MPT-30B, MPT-30B-Instruct, and MPT-30B-Chat to existing open source models including those designed for code generation. We find that MPT-30B models are very strong at programming and MPT-30B-Chat outperforms all models except WizardCoder. We hope that this combination of text and programming capabilities will make MPT-30B models a popular choice for the community.\n",
        "Finally in Table 3, we show how MPT-30B outperforms GPT-3 on the smaller set of eval metrics that are available from the original GPT-3 paper. Just about 3 years after the original publication, we are proud to surpass this famous baseline with a smaller model (17% of GPT-3 parameters) and significantly less training compute (60% of GPT-3 FLOPs).\n",
        "For more detailed evaluation data, or if you want to reproduce our results, you can see the raw data and scripts we used in our LLM Foundry eval harness here. Note that we are still polishing our HumanEval methodology and will release it soon via Composer and LLM-Foundry.\n",
        "\n",
        "Figure 2 -MPT-7B vs MPT-30B.  Our new MPT-30B model significantly improves over our previous MPT-7B model\n",
        "\n",
        "Figure 3 - MPT vs. LLaMa vs. Falcon models. Left: Comparing models with 7 billion parameters. Right: Comparing models with 30 to 40 billion parameters.\n",
        "\n",
        "Table 2: Zero-shot accuracy (pass @ 1) of MPT-30B models vs. general purpose and GPT-distilled code generation models on HumanEval, a corpus of Python coding problems. We find that MPT-30B models outperform LLaMa-30B and Falcon-40B by a wide margin, and even outperform many purpose-built coding models such as StarCoder. See Appendix about disclaimer about Falcon-40B-Instruct and Falcon-40B. External sources: [1], [2], [3], [4], [5]\n",
        "\n",
        "Table 3: Zero-shot accuracy of MPT-30B vs. GPT-3 on nine in-context-learning (ICL) tasks. We find that MPT-30B outperforms GPT-3 in six out of the nine metrics. GPT-3 numbers are copied from the original paper.\n",
        "\n",
        "\n",
        "‍##end\n",
        "\n",
        "\"\"\"\n",
        "#print (prompt)\n",
        "generated_text = generate(prompt)\n",
        "response = parse_text(generated_text.partition(\"### Response\\n\")[2])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sl00duQ34AXz",
        "outputId": "a0b26d16-0700-47f6-aed7-a719bdde42d7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1. What is the average MFU of MPT-30B? 2. What is MosaicML Training? 3. Which model did the\n",
            "researchers choose to compare their work with in Figure 3? 4. What is the Apache 2.0 licensed, open-\n",
            "source foundation model called? 5. What is MosaicML Inference: Starter Edition? 6. What is the size\n",
            "of MPT-30B? 7. Who do we train MPT-30B? 8. What is Mosaic Pretrained Transformer (MPT)? 9. What is\n",
            "the size of Mosaic-7B? 10. What is MosaicML Training? 11. What is the average MFU of Mosaic-7B? 12.\n",
            "What is MosaicML Inference: Enterprise Edition? 13. Who pretrained Mosaic-30B on 1T tokens? 14. What\n",
            "model was extended to an 8k context window on NVIDIA H100s? 15. Who is releasing two fine-tuned\n",
            "variants, MPT-30B-Instruct and MPT-30B-Chat? 16. What is the average MFU of Mosaic-30B?\n",
            "\n",
            "\n",
            "CPU times: user 45.2 s, sys: 71.7 ms, total: 45.3 s\n",
            "Wall time: 45.2 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"Explain the details of this code: ##code start: class GetflightInPeriodCheckInput(BaseModel):\n",
        "\n",
        "\n",
        "    fly_from: str = Field(..., description=\"the 3-digit code for departure airport\")\n",
        "    fly_to: str = Field(..., description=\"the 3-digit code for arrival airport\")\n",
        "    date_from: str = Field(..., description=\"the dd/mm/yyyy format of start date for the range of search\")\n",
        "    date_to: str = Field(..., description=\"the dd/mm/yyyy format of end date for the range of search\")\n",
        "    sort: str = Field(..., description=\"the catagory for low-to-high sorting, only support 'price', 'duration', 'date'\")\n",
        "    price_limit: int = Field(..., description=\"The price limit for the flights of search, in USD, it is set to 999 if not provided\")\n",
        "    duration_limit: int = Field(..., description=\"The flying duration limit for the flights of search, in hours, it is set to 999 if not provided\")\n",
        "\n",
        "class GetflightInPeriodTool(BaseTool):\n",
        "    name = \"get_flight_in_period\"\n",
        "    description = \\\"\\\"\\\"Useful when you need to search the flights info. You can sort the result by \"sort\" argument.\n",
        "                     You can filter the result by price_limit and duration_limit. They are default value is 999 if not set.\n",
        "                    if there is no year, you need to use 2023 for search.\n",
        "                    Try to understand the parameters of every flight\n",
        "\n",
        "                  \\\"\\\"\\\"\n",
        "    '''\n",
        "    description = \\\"\\\"\\\"Useful for when you need to find out the information from top 10 flights by sorting for certain category defined in \"sort\" with a given range of dates.\n",
        "                You should input or convert to the nearest 3-digit airport code and also input dates range in dd/mm/yyyy format from 2023 for default.\n",
        "                In the funtion return, every element means one entire flight with flight info including price means fly ticket price, duration means the traveling time, and route informtion for every connection flight.\n",
        "                \\\"\\\"\\\"\n",
        "    '''\n",
        "    def _run(self, fly_from: str, fly_to: str, date_from: str, date_to: str, sort: str, price_limit: int, duration_limit: int):\n",
        "        get_flight_in_period_response = get_flight_in_period(fly_from, fly_to, date_from, date_to, sort, price_limit, duration_limit)\n",
        "\n",
        "        return get_flight_in_period_response\n",
        "\n",
        "    def _arun(self, fly_from: str, fly_to: str, date_from: str, date_to: str, sort: str, price_limit: int, duration_limit: int):\n",
        "        raise NotImplementedError(\"This tool does not support async\")\n",
        "\n",
        "\n",
        "    args_schema: Optional[Type[BaseModel]] = GetflightInPeriodCheckInput. ##code end\"\"\"\n",
        "\n",
        "\n",
        "generated_text = generate(prompt)\n",
        "response = parse_text(generated_text)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "1d8dc69e-b033-48c6-8fd0-50d28bbb0963"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "This code defines a class called GetflightInPeriodTool, which has a method called _run. The\n",
            "description of this tool is \"Useful for when you need to find out the information from top 10\n",
            "flights by sorting for certain category defined in \"sort\" with a given range of dates. You should\n",
            "input or convert to the nearest 3-digit airport code and also input dates range in dd/mm/yyyy format\n",
            "from 2023 for default. In the funtion return, every element means one entire flight with flight info\n",
            "including price means fly ticket price, duration means the traveling time, and route informtion for\n",
            "every connection flight.\". The code also defines an args_schema for this tool, which is a class\n",
            "called GetflightInPeriodCheckInput. This class has 6 fields: fly_from, fly_to, date_from, date_to,\n",
            "sort, price_limit, duration_limit.\n",
            "\n",
            "\n"
          ]
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      "cell_type": "code",
      "source": [
        "prompt=\"complet the code start with - def get_flight_in_period(fly_from, fly_to, date_from, date_to, sort, price_limit=999, duration_limit=999):\"\n",
        "generated_text = generate(prompt)\n",
        "response = parse_text(generated_text)\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X3fY9Fyl5ZAz",
        "outputId": "86983277-d070-4195-95b0-801abf13eba3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:0 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "# sort can be 'price' or 'duration' # price_limit and duration_limit are int, which represent the\n",
            "limit of the flights' price or duration  # get all the flights from fly_from to fly_to in the\n",
            "date_from to date_to all_flights = get_all_flights(fly_from, fly_to, date_from, date_to)  # sort the\n",
            "flights by price or duration if sort == 'price':     all_flights.sort(key=lambda x: x.price,\n",
            "reverse=True) elif sort == 'duration':     all_flights.sort(key=lambda x: x.duration, reverse=True)\n",
            "# get the flights under the price or duration limit limited_flights = [] for flight in all_flights:\n",
            "if flight.price <= price_limit and flight.duration <= duration_limit:\n",
            "limited_flights.append(flight)          # return the limited flights return limited_flights\n",
            "\n",
            "\n"
          ]
        }
      ]
    }
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