RLFH_part1.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pogSwJQ8jHqh",
        "outputId": "28957ae1-0276-4dda-89dc-8eec6390ab15"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.4/7.4 MB\u001b[0m \u001b[31m43.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m29.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m519.1/519.1 kB\u001b[0m \u001b[31m43.7 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[31m21.8 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[31m101.2 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[31m68.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m13.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m194.1/194.1 kB\u001b[0m \u001b[31m15.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m16.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m18.9 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[31m5.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h<<<<Necessary Package Installed>>>>\n"
          ]
        }
      ],
      "source": [
        "#installing necessary package\n",
        "def get_necessay_package_installed():\n",
        "  #function to install necessary package\n",
        "  !pip install -q -U trl transformers accelerate\n",
        "  !pip install -q datasets bitsandbytes einops scipy\n",
        "  print(\"<<<<Necessary Package Installed>>>>\")\n",
        "get_necessay_package_installed()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
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          "height": 145,
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        },
        "id": "S8uG2987jiH1",
        "outputId": "019fa013-3bf8-4957-e5f8-0d4c686549e9"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig, AutoTokenizer"
      ],
      "metadata": {
        "id": "L-1vV6PEjrpZ"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"meta-llama/Llama-2-7b-chat-hf\"\n",
        "\n",
        "bnb_config = BitsAndBytesConfig(\n",
        "    load_in_4bit=True,\n",
        "    bnb_4bit_quant_type=\"nf4\",\n",
        "    bnb_4bit_compute_dtype=torch.float16,\n",
        ")\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    quantization_config=bnb_config,\n",
        "    trust_remote_code=True\n",
        ")\n",
        "model.config.use_cache = False"
      ],
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          "data": {
            "text/plain": [
              "Downloading (…)of-00002.safetensors:   0%|          | 0.00/9.98G [00:00<?, ?B/s]"
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            "application/vnd.jupyter.widget-view+json": {
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)of-00002.safetensors:   0%|          | 0.00/3.50G [00:00<?, ?B/s]"
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        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/2 [00:00<?, ?it/s]"
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          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/167 [00:00<?, ?B/s]"
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        "id": "s6UJCCsrj9Xp",
        "outputId": "fd101ea6-8978-47e6-f5a7-fd33cd05f9b3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/776 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f7f902509ef14fda80b5b59aec194508"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading tokenizer.model:   0%|          | 0.00/500k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c5e5f80ed06741febf568b5cb8cb31ca"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.84M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)cial_tokens_map.json:   0%|          | 0.00/414 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "c7dcc3cfd48c445c8e627501ebd572df"
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          "metadata": {}
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_zeroshot_inference():\n",
        "  text='''[INST]<<SYS>>\n",
        "  You are a helpful  assistant that provides information based on the given query in context.\n",
        "  <</SYS>>\n",
        "  Can you summarize the following text  :\n",
        "  Question answering (QA) systems are a type of natural language processing (NLP) technology that provide\n",
        "  precise and concise answers to questions posed in natural language. These systems have the potential to\n",
        "  revolutionize the way we access information and can be applied in a wide range of fields including education,\n",
        "  customer service, and health care.There are several approaches to building QA systems, including rule-based,\n",
        "  information retrieval, and machine learning-based approaches. Rule-based systems rely on predefined rules and\n",
        "  patterns to extract answers from a given text, while information retrieval systems use search algorithms to\n",
        "  retrieve relevant information from a large database. Machine learning-based systems, on the other hand, use\n",
        "  training data to learn to extract answers from text.One of the main challenges faced by QA systems is the need\n",
        "  to understand the context and intent behind a question. This requires the system to have a deep understanding of\n",
        "  the language and the ability to make inferences based on the given information. Another challenge is the need to\n",
        "  extract relevant information from a large and potentially unstructured dataset.Despite these challenges, QA\n",
        "  systems have a wide range of applications, including education, customer service, and health care.\n",
        "  .[/INST]'''\n",
        "  device = \"cuda:0\"\n",
        "\n",
        "  inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "  outputs = model.generate(**inputs, max_new_tokens=400)\n",
        "  print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "id": "0MKw88DIkA3A"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_zeroshot_inference()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gq2genvwlOPu",
        "outputId": "a31e2562-4480-4816-831c-78f2d633ffd3"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST]<<SYS>>\n",
            "  You are a helpful  assistant that provides information based on the given query in context.\n",
            "  <</SYS>>\n",
            "  Can you summarize the following text  :\n",
            "  Question answering (QA) systems are a type of natural language processing (NLP) technology that provide\n",
            "  precise and concise answers to questions posed in natural language. These systems have the potential to\n",
            "  revolutionize the way we access information and can be applied in a wide range of fields including education,\n",
            "  customer service, and health care.There are several approaches to building QA systems, including rule-based,\n",
            "  information retrieval, and machine learning-based approaches. Rule-based systems rely on predefined rules and\n",
            "  patterns to extract answers from a given text, while information retrieval systems use search algorithms to\n",
            "  retrieve relevant information from a large database. Machine learning-based systems, on the other hand, use\n",
            "  training data to learn to extract answers from text.One of the main challenges faced by QA systems is the need\n",
            "  to understand the context and intent behind a question. This requires the system to have a deep understanding of\n",
            "  the language and the ability to make inferences based on the given information. Another challenge is the need to\n",
            "  extract relevant information from a large and potentially unstructured dataset.Despite these challenges, QA\n",
            "  systems have a wide range of applications, including education, customer service, and health care.\n",
            "  .[/INST]  Sure, I'd be happy to summarize the text for you! Here's a concise summary of the main points:\n",
            "Question answering (QA) systems are a type of natural language processing (NLP) technology that provide precise and concise answers to questions posed in natural language. These systems have the potential to revolutionize the way we access information and can be applied in various fields such as education, customer service, and healthcare. There are different approaches to building QA systems, including rule-based, information retrieval, and machine learning-based approaches.\n",
            "The main challenges faced by QA systems include the need to understand the context and intent behind a question, as well as the need to extract relevant information from a large and potentially unstructured dataset. Despite these challenges, QA systems have a wide range of applications and can be used in various industries.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_oneshot_inference():\n",
        "  text='''[INST]<<SYS>>\n",
        "  You are a helpful  assistant that provides information based on the given query in context.\n",
        "  <</SYS>>\n",
        "  Can you summarize the following text  :\n",
        "  Context: Question answering (QA) systems are a type of natural language processing (NLP) technology that provide\n",
        "  precise and concise answers to questions posed in natural language. These systems have the potential to\n",
        "  revolutionize the way we access information and can be applied in a wide range of fields including education,\n",
        "  customer service, and health care.There are several approaches to building QA systems, including rule-based,\n",
        "  information retrieval, and machine learning-based approaches. Rule-based systems rely on predefined rules and\n",
        "  patterns to extract answers from a given text, while information retrieval systems use search algorithms to\n",
        "  retrieve relevant information from a large database. Machine learning-based systems, on the other hand, use\n",
        "  training data to learn to extract answers from text.One of the main challenges faced by QA systems is the need\n",
        "  to understand the context and intent behind a question. This requires the system to have a deep understanding of\n",
        "  the language and the ability to make inferences based on the given information. Another challenge is the need to\n",
        "  extract relevant information from a large and potentially unstructured dataset.Despite these challenges, QA\n",
        "  systems have a wide range of applications, including education, customer service, and health care.\n",
        "\n",
        "  summary: Question answering (QA) systems are a type of natural language processing (NLP) technology that provide precise and concise answers to questions posed in natural language. rule-based,\n",
        "  information retrieval, and machine learning-based are different approaches in building QA systems.One of the main challenges faced by QA systems is the need\n",
        "  to understand the context and intent behind a question. Another challenge is the need toextract relevant information from a large and potentially unstructured dataset. QA\n",
        "  systems have a wide range of applications, including education, customer service, and health care. By understanding the current state of development and the potential impact of QA systems, we can better utilize these technologies to\n",
        "  improve various industries and enhance the way we access information.[/INST]\n",
        "  [INST]<<SYS>>\n",
        "  You are a helpful  assistant that provides information based on the given query in context.\n",
        "  <</SYS>>\n",
        "  Can you summarize the following text\n",
        "  Context : Jupiter is the fifth planet from the Sun and the largest in the Solar System. It is a gas giant with a mass one-thousandth that of the Sun, but two-and-a-half times that of all the other planets in the Solar System combined. Jupiter is one of the brightest objects visible to the naked eye in the night sky, and has been known to ancient civilizations since before recorded history. It is named after the Roman god Jupiter.[19] When viewed from Earth, Jupiter can be bright enough for its reflected light to cast visible shadows,[20] and is on average the third-brightest natural object in the night sky after the Moon and Venus.\n",
        "  Jupiter is primarily composed of hydrogen with a quarter of its mass being helium, though helium comprises only about a tenth of the number of molecules. It may also have a rocky core of heavier elements,[21] but like the other giant planets, Jupiter lacks a well-defined solid surface. Because of its rapid rotation, the planet's shape is that of an oblate spheroid (it has a slight but noticeable bulge around the equator)\n",
        "  Summary :\n",
        "  .[/INST]'''\n",
        "  device = \"cuda:0\"\n",
        "\n",
        "  inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "  outputs = model.generate(**inputs, max_new_tokens=400)\n",
        "  print(tokenizer.decode(outputs[0], skip_special_tokens=True))"
      ],
      "metadata": {
        "id": "n-_JGgVxlmiF"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_oneshot_inference()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jw4qMG50m1PZ",
        "outputId": "9f6c841c-6589-49ec-fc57-02babf31aaa8"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST]<<SYS>>\n",
            "  You are a helpful  assistant that provides information based on the given query in context.\n",
            "  <</SYS>>\n",
            "  Can you summarize the following text  :\n",
            "  Context: Question answering (QA) systems are a type of natural language processing (NLP) technology that provide\n",
            "  precise and concise answers to questions posed in natural language. These systems have the potential to\n",
            "  revolutionize the way we access information and can be applied in a wide range of fields including education,\n",
            "  customer service, and health care.There are several approaches to building QA systems, including rule-based,\n",
            "  information retrieval, and machine learning-based approaches. Rule-based systems rely on predefined rules and\n",
            "  patterns to extract answers from a given text, while information retrieval systems use search algorithms to\n",
            "  retrieve relevant information from a large database. Machine learning-based systems, on the other hand, use\n",
            "  training data to learn to extract answers from text.One of the main challenges faced by QA systems is the need\n",
            "  to understand the context and intent behind a question. This requires the system to have a deep understanding of\n",
            "  the language and the ability to make inferences based on the given information. Another challenge is the need to\n",
            "  extract relevant information from a large and potentially unstructured dataset.Despite these challenges, QA\n",
            "  systems have a wide range of applications, including education, customer service, and health care.\n",
            "\n",
            "  summary: Question answering (QA) systems are a type of natural language processing (NLP) technology that provide precise and concise answers to questions posed in natural language. rule-based,\n",
            "  information retrieval, and machine learning-based are different approaches in building QA systems.One of the main challenges faced by QA systems is the need\n",
            "  to understand the context and intent behind a question. Another challenge is the need toextract relevant information from a large and potentially unstructured dataset. QA\n",
            "  systems have a wide range of applications, including education, customer service, and health care. By understanding the current state of development and the potential impact of QA systems, we can better utilize these technologies to\n",
            "  improve various industries and enhance the way we access information.[/INST]\n",
            "  [INST]<<SYS>>\n",
            "  You are a helpful  assistant that provides information based on the given query in context.\n",
            "  <</SYS>>\n",
            "  Can you summarize the following text \n",
            "  Context : Jupiter is the fifth planet from the Sun and the largest in the Solar System. It is a gas giant with a mass one-thousandth that of the Sun, but two-and-a-half times that of all the other planets in the Solar System combined. Jupiter is one of the brightest objects visible to the naked eye in the night sky, and has been known to ancient civilizations since before recorded history. It is named after the Roman god Jupiter.[19] When viewed from Earth, Jupiter can be bright enough for its reflected light to cast visible shadows,[20] and is on average the third-brightest natural object in the night sky after the Moon and Venus.\n",
            "  Jupiter is primarily composed of hydrogen with a quarter of its mass being helium, though helium comprises only about a tenth of the number of molecules. It may also have a rocky core of heavier elements,[21] but like the other giant planets, Jupiter lacks a well-defined solid surface. Because of its rapid rotation, the planet's shape is that of an oblate spheroid (it has a slight but noticeable bulge around the equator)\n",
            "  Summary : \n",
            "  .[/INST]  Sure, here's a summary of the text you provided:\n",
            "Jupiter is the largest planet in the Solar System, a gas giant with a mass one-thousandth that of the Sun but two-and-a-half times that of all other planets combined. It is named after the Roman god Jupiter and is known to ancient civilizations. Jupiter is bright enough to cast visible shadows when viewed from Earth and is on average the third-brightest natural object in the night sky after the Moon and Venus. The planet is primarily composed of hydrogen with a quarter of its mass being helium, and may also have a rocky core of heavier elements. Due to its rapid rotation, Jupiter has an oblate spheroid shape, with a slight bulge around the equator.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_few_shot_inference():\n",
        "  text='''[INST]<<SYS>>\n",
        "  You are a helpful  assistant that provides information based on the given query in context.\n",
        "  <</SYS>>\n",
        "  Context : Context: NLP Cloud was founded in 2021 when the team realized there was no easy way to reliably leverage Natural Language Processing in production.\n",
        "  Question: When was NLP Cloud founded?\n",
        "  Answer: 2021 [/INST]\n",
        "  [INST]<<SYS>>\n",
        "  You are a helpful  assistant that provides information based on the given query in context.\n",
        "  <</SYS>>\n",
        "  Context: NLP Cloud developed their API by mid-2020 and they added many pre-trained open-source models since then.\n",
        "  Question: What did NLP Cloud develop?\n",
        "  Answer: API [/INST]\n",
        "  [INST]<<SYS>>\n",
        "  You are a helpful  assistant that provides information based on the given query in context.\n",
        "  <</SYS>>\n",
        "  Context: All plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice.\n",
        "  Question: When can plans be stopped?\n",
        "  Answer: Anytime [/INST]\n",
        "  [INST]<<SYS>>\n",
        "  You are a helpful  assistant that provides information based on the given query in context.\n",
        "  <</SYS>>\n",
        "  Context: The main challenge with GPT-J is memory consumption. Using a GPU plan is recommended.\n",
        "  Question: Which plan is recommended for GPT-J?\n",
        "  Answer: GPU plan [/INST]\n",
        "  [INST]<<SYS>>\n",
        "  You are a helpful  assistant that provides information based on the given query in context.\n",
        "  <</SYS>>\n",
        "  Context:The main advantage with GPT-J is less #. .\n",
        "  Question:What is the main advantage for GPT-J?\n",
        "  Answer:\n",
        "  .[/INST]'''\n",
        "  device = \"cuda:0\"\n",
        "\n",
        "  inputs = tokenizer(text, return_tensors=\"pt\").to(device)\n",
        "  outputs = model.generate(**inputs, min_length=1,max_new_tokens=1000)\n",
        "  print(tokenizer.decode(outputs[0], skip_special_tokens=True))\n"
      ],
      "metadata": {
        "id": "co7gg8sXm4Yg"
      },
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_few_shot_inference()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VQAhhwfTqH8Y",
        "outputId": "026d3406-52da-4264-e4cb-1f4788c1d521"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[INST]<<SYS>>\n",
            "  You are a helpful  assistant that provides information based on the given query in context.\n",
            "  <</SYS>>\n",
            "  Context : Context: NLP Cloud was founded in 2021 when the team realized there was no easy way to reliably leverage Natural Language Processing in production.\n",
            "  Question: When was NLP Cloud founded?\n",
            "  Answer: 2021 [/INST]\n",
            "  [INST]<<SYS>>\n",
            "  You are a helpful  assistant that provides information based on the given query in context.\n",
            "  <</SYS>>\n",
            "  Context: NLP Cloud developed their API by mid-2020 and they added many pre-trained open-source models since then.\n",
            "  Question: What did NLP Cloud develop?\n",
            "  Answer: API [/INST]\n",
            "  [INST]<<SYS>>\n",
            "  You are a helpful  assistant that provides information based on the given query in context.\n",
            "  <</SYS>>\n",
            "  Context: All plans can be stopped anytime. You only pay for the time you used the service. In case of a downgrade, you will get a discount on your next invoice.\n",
            "  Question: When can plans be stopped?\n",
            "  Answer: Anytime [/INST]\n",
            "  [INST]<<SYS>>\n",
            "  You are a helpful  assistant that provides information based on the given query in context.\n",
            "  <</SYS>>\n",
            "  Context: The main challenge with GPT-J is memory consumption. Using a GPU plan is recommended.\n",
            "  Question: Which plan is recommended for GPT-J?\n",
            "  Answer: GPU plan [/INST]\n",
            "  [INST]<<SYS>>\n",
            "  You are a helpful  assistant that provides information based on the given query in context.\n",
            "  <</SYS>>\n",
            "  Context:The main advantage with GPT-J is less #. .\n",
            "  Question:What is the main advantage for GPT-J?\n",
            "  Answer:\n",
            "  .[/INST]  Less #\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "gJz3U2WXqKFn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}