llama3_1.ipynb

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      "execution_count": 1,
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        "outputId": "43813c8c-828b-40d4-ddac-366fd437de79"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: transformers in /usr/local/lib/python3.10/dist-packages (4.42.4)\n",
            "Collecting transformers\n",
            "  Downloading transformers-4.43.1-py3-none-any.whl (9.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m9.4/9.4 MB\u001b[0m \u001b[31m11.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from transformers) (3.15.4)\n",
            "Requirement already satisfied: huggingface-hub<1.0,>=0.23.2 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.23.5)\n",
            "Requirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (1.25.2)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from transformers) (24.1)\n",
            "Requirement already satisfied: pyyaml>=5.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (6.0.1)\n",
            "Requirement already satisfied: regex!=2019.12.17 in /usr/local/lib/python3.10/dist-packages (from transformers) (2024.5.15)\n",
            "Requirement already satisfied: requests in /usr/local/lib/python3.10/dist-packages (from transformers) (2.31.0)\n",
            "Requirement already satisfied: tokenizers<0.20,>=0.19 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.19.1)\n",
            "Requirement already satisfied: safetensors>=0.4.1 in /usr/local/lib/python3.10/dist-packages (from transformers) (0.4.3)\n",
            "Requirement already satisfied: tqdm>=4.27 in /usr/local/lib/python3.10/dist-packages (from transformers) (4.66.4)\n",
            "Requirement already satisfied: fsspec>=2023.5.0 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (2023.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.7.4.3 in /usr/local/lib/python3.10/dist-packages (from huggingface-hub<1.0,>=0.23.2->transformers) (4.12.2)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests->transformers) (2024.7.4)\n",
            "Installing collected packages: transformers\n",
            "  Attempting uninstall: transformers\n",
            "    Found existing installation: transformers 4.42.4\n",
            "    Uninstalling transformers-4.42.4:\n",
            "      Successfully uninstalled transformers-4.42.4\n",
            "Successfully installed transformers-4.43.1\n"
          ]
        }
      ],
      "source": [
        "!pip install --upgrade transformers"
      ]
    },
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        "from huggingface_hub import notebook_login\n",
        "\n",
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        "id": "Dq7wdeBwvhDV",
        "outputId": "69b9b5d0-d758-4352-b58a-30fcee4fdc35"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
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            "text/plain": [
              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
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    },
    {
      "cell_type": "code",
      "source": [
        "import transformers\n",
        "import torch\n",
        "\n",
        "model_id = \"meta-llama/Meta-Llama-3.1-8B-Instruct\"\n",
        "\n",
        "pipeline = transformers.pipeline(\n",
        "    \"text-generation\",\n",
        "    model=model_id,\n",
        "    model_kwargs={\"torch_dtype\": torch.bfloat16},\n",
        "    device_map=\"auto\",\n",
        ")\n",
        "prompt=f'''\n",
        "Summarise the following content and give the main point in bullets:\n",
        "Our Llama 3.1 405B is now openly available! After a year of dedicated effort, from project planning to launch reviews, we are thrilled to open-source the Llama 3 herd of models and share our findings through the paper:\n",
        "\n",
        "🔹Llama 3.1 405B, continuously trained with a 128K context length following pre-training with an 8K context length, supports multilinguality and tool usage. It offers performance comparable to leading language models, such as GPT-4, across a range of tasks.\n",
        "🔹Compared to previous Llama models, we have enhanced the preprocessing and curation pipelines for pre-training data, as well as the quality assurance and filtering methods for post-training data.\n",
        "🔹Pre-training 405B on 15.6T tokens (3.8x10^25 FLOPs) was a significant challenge. We optimized our entire training stack and used over 16K H100 GPUs.\n",
        "🔹To support large-scale production inference for the 405B model, we quantized from 16-bit (BF16) to 8-bit (FP8), reducing compute requirements and enabling the model to run on a single server node.\n",
        "🔹We leveraged the 405B model to improve the post-training quality of our 70B and 8B models.\n",
        "🔹In post-training, we refined chat models with multiple rounds of alignment involving supervised fine-tuning (SFT), rejection sampling, and direct preference optimization. We generate most SFT examples using synthetic data.\n",
        "🔹We integrated image, video, and speech capabilities into Llama 3 using a compositional approach, enabling models to recognize images and videos and support interaction via speech. They are under development and not yet ready for release.\n",
        "🔹We've updated our license to allow developers to use outputs from Llama models to enhance other models.\n",
        "\n",
        "There is nothing more rewarding than working at the forefront of AI development alongside some of the brightest minds in the field and publishing our research transparently. I'm excited about the innovations our open-source models enable and the potential of the future herd of Llamas!\n",
        "'''\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": prompt}\n",
        "]\n",
        "\n",
        "result = pipeline(\n",
        "    messages,\n",
        "    max_new_tokens=500,\n",
        ")\n",
        "print(result[0][\"generated_text\"][-1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473,
          "referenced_widgets": [
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          ]
        },
        "id": "IUMt_oVXvhMO",
        "outputId": "de6e40eb-a287-404d-e2fa-992ca5048f60"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "config.json:   0%|          | 0.00/855 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "a6236e622c79456088da7bb2703c15d3"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62331b448b684477bec5b38cdaf5323b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b8890f6883a34d8db9f71d184afa61a0"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00001-of-00004.safetensors:   0%|          | 0.00/4.98G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "f0ad1b75e4984b4d9ee9ef6a2be22dc2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00002-of-00004.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "00cdee66a2644644b47c913c64693bbd"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00003-of-00004.safetensors:   0%|          | 0.00/4.92G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "d054de54089d406184b69003d0eaf0e2"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "model-00004-of-00004.safetensors:   0%|          | 0.00/1.17G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "77e5d48c6ccd45ea941ceaa1cebaf422"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "cb20af72c4504cbd8333d62904a5532d"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "generation_config.json:   0%|          | 0.00/184 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "16caca6357204535a81681b5a9d2b5a4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:accelerate.big_modeling:Some parameters are on the meta device device because they were offloaded to the cpu.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer_config.json:   0%|          | 0.00/50.9k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2134db784fef4066a9f1d83392db5d4f"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "tokenizer.json:   0%|          | 0.00/9.08M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "fdb78a3254534fe4afa2ebee337f5996"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "special_tokens_map.json:   0%|          | 0.00/68.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "62926fc453cb4492bf0a5da4f15cac48"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'role': 'assistant', 'content': \"However, I need to clarify that I'm a large language model, I don't have the ability to directly access external links. But I can help you extract the data from the provided link and present it in a tabular format based on the information you provide.\\n\\nPlease paste the content from the link, or describe the content in detail, and I'll help you create a table with the specified columns: **Section**, **Subsection**, and **Content Summary**.\\n\\nAlternatively, if you can provide the content from the link, I can also use a simulated version of the website to extract the data. Please let me know which approach you prefer.\\n\\nOnce I have the content, I'll create a table that summarizes the key points from the specified sections.\"}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prompt=f'''\n",
        "Please use the content provided below to create 5 multiple choice questions. Each question should include:\n",
        "\n",
        "1. A clear and concise question statement.\n",
        "2. Four answer options, with one correct answer and three plausible distractors.\n",
        "3. Identification of the correct answer.\n",
        "\n",
        "Ensure that the questions cover different aspects of the content, testing comprehension, key details, and critical thinking.\n",
        "Content:\n",
        "\n",
        "This prompt ensures that the questions are well-rounded and cover various aspects of the content.\n",
        "Our Llama 3.1 405B is now openly available! After a year of dedicated effort, from project planning to launch reviews, we are thrilled to open-source the Llama 3 herd of models and share our findings through the paper:\n",
        "\n",
        "🔹Llama 3.1 405B, continuously trained with a 128K context length following pre-training with an 8K context length, supports multilinguality and tool usage. It offers performance comparable to leading language models, such as GPT-4, across a range of tasks.\n",
        "🔹Compared to previous Llama models, we have enhanced the preprocessing and curation pipelines for pre-training data, as well as the quality assurance and filtering methods for post-training data.\n",
        "🔹Pre-training 405B on 15.6T tokens (3.8x10^25 FLOPs) was a significant challenge. We optimized our entire training stack and used over 16K H100 GPUs.\n",
        "🔹To support large-scale production inference for the 405B model, we quantized from 16-bit (BF16) to 8-bit (FP8), reducing compute requirements and enabling the model to run on a single server node.\n",
        "🔹We leveraged the 405B model to improve the post-training quality of our 70B and 8B models.\n",
        "🔹In post-training, we refined chat models with multiple rounds of alignment involving supervised fine-tuning (SFT), rejection sampling, and direct preference optimization. We generate most SFT examples using synthetic data.\n",
        "🔹We integrated image, video, and speech capabilities into Llama 3 using a compositional approach, enabling models to recognize images and videos and support interaction via speech. They are under development and not yet ready for release.\n",
        "🔹We've updated our license to allow developers to use outputs from Llama models to enhance other models.\n",
        "\n",
        "There is nothing more rewarding than working at the forefront of AI development alongside some of the brightest minds in the field and publishing our research transparently. I'm excited about the innovations our open-source models enable and the potential of the future herd of Llamas!\n",
        "'''\n",
        "messages = [\n",
        "    {\"role\": \"user\", \"content\": prompt}\n",
        "]\n",
        "\n",
        "result = pipeline(\n",
        "    messages,\n",
        "    max_new_tokens=500,\n",
        ")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z-7kWjG2x2H8",
        "outputId": "3099efa6-9b2b-4fb6-83c0-f8d335146ca2"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-iQETfDw0Veo",
        "outputId": "b310dcd2-48ff-465c-d231-cc946aceecf8"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': [{'role': 'user',\n",
              "    'content': \"\\nPlease use the content provided below to create 5 multiple choice questions. Each question should include:\\n\\n1. A clear and concise question statement.\\n2. Four answer options, with one correct answer and three plausible distractors.\\n3. Identification of the correct answer.\\n\\nEnsure that the questions cover different aspects of the content, testing comprehension, key details, and critical thinking.\\nContent:\\n\\nThis prompt ensures that the questions are well-rounded and cover various aspects of the content.\\nOur Llama 3.1 405B is now openly available! After a year of dedicated effort, from project planning to launch reviews, we are thrilled to open-source the Llama 3 herd of models and share our findings through the paper:\\n\\n🔹Llama 3.1 405B, continuously trained with a 128K context length following pre-training with an 8K context length, supports multilinguality and tool usage. It offers performance comparable to leading language models, such as GPT-4, across a range of tasks.\\n🔹Compared to previous Llama models, we have enhanced the preprocessing and curation pipelines for pre-training data, as well as the quality assurance and filtering methods for post-training data.\\n🔹Pre-training 405B on 15.6T tokens (3.8x10^25 FLOPs) was a significant challenge. We optimized our entire training stack and used over 16K H100 GPUs.\\n🔹To support large-scale production inference for the 405B model, we quantized from 16-bit (BF16) to 8-bit (FP8), reducing compute requirements and enabling the model to run on a single server node.\\n🔹We leveraged the 405B model to improve the post-training quality of our 70B and 8B models.\\n🔹In post-training, we refined chat models with multiple rounds of alignment involving supervised fine-tuning (SFT), rejection sampling, and direct preference optimization. We generate most SFT examples using synthetic data.\\n🔹We integrated image, video, and speech capabilities into Llama 3 using a compositional approach, enabling models to recognize images and videos and support interaction via speech. They are under development and not yet ready for release.\\n🔹We've updated our license to allow developers to use outputs from Llama models to enhance other models.\\n\\nThere is nothing more rewarding than working at the forefront of AI development alongside some of the brightest minds in the field and publishing our research transparently. I'm excited about the innovations our open-source models enable and the potential of the future herd of Llamas!\\n\"},\n",
              "   {'role': 'assistant',\n",
              "    'content': 'Here are five multiple-choice questions based on the provided content:\\n\\n**Question 1**\\nWhat is a notable improvement in the preprocessing and curation pipelines for pre-training data in Llama 3.1 405B compared to previous Llama models?\\n\\nA) Increased context length\\nB) Enhanced filtering methods for post-training data\\nC) Improved data curation and preprocessing pipelines\\nD) Reduced token count\\n\\n**Correct answer:** C) Improved data curation and preprocessing pipelines\\n\\n**Question 2**\\nWhat was a significant challenge in pre-training the 405B model?\\n\\nA) Limited GPU resources\\nB) High computational requirements\\nC) Short context length\\nD) Low token count\\n\\n**Correct answer:** B) High computational requirements\\n\\n**Question 3**\\nWhat is a benefit of quantizing the 405B model from 16-bit (BF16) to 8-bit (FP8)?\\n\\nA) Improved performance on specific tasks\\nB) Reduced memory requirements\\nC) Reduced compute requirements and enabled single-server node deployment\\nD) Enhanced multilinguality\\n\\n**Correct answer:** C) Reduced compute requirements and enabled single-server node deployment\\n\\n**Question 4**\\nWhat is a key approach used in post-training to refine chat models in Llama 3?\\n\\nA) Supervised fine-tuning (SFT) with synthetic data\\nB) Direct preference optimization\\nC) Rejection sampling\\nD) All of the above\\n\\n**Correct answer:** D) All of the above\\n\\n**Question 5**\\nWhat change has been made to the license of Llama models to enable developers to use outputs to enhance other models?\\n\\nA) Stricter licensing terms\\nB) Limited usage rights\\nC) Updated license to allow developers to use outputs\\nD) No change to the license\\n\\n**Correct answer:** C) Updated license to allow developers to use outputs'}]}]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(result[0][\"generated_text\"][-1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D0oBDD3X2Hfq",
        "outputId": "1bc19f35-3b06-4a40-efe7-5d380c9916bd"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{'role': 'assistant', 'content': 'Here are five multiple-choice questions based on the provided content:\\n\\n**Question 1**\\nWhat is a notable improvement in the preprocessing and curation pipelines for pre-training data in Llama 3.1 405B compared to previous Llama models?\\n\\nA) Increased context length\\nB) Enhanced filtering methods for post-training data\\nC) Improved data curation and preprocessing pipelines\\nD) Reduced token count\\n\\n**Correct answer:** C) Improved data curation and preprocessing pipelines\\n\\n**Question 2**\\nWhat was a significant challenge in pre-training the 405B model?\\n\\nA) Limited GPU resources\\nB) High computational requirements\\nC) Short context length\\nD) Low token count\\n\\n**Correct answer:** B) High computational requirements\\n\\n**Question 3**\\nWhat is a benefit of quantizing the 405B model from 16-bit (BF16) to 8-bit (FP8)?\\n\\nA) Improved performance on specific tasks\\nB) Reduced memory requirements\\nC) Reduced compute requirements and enabled single-server node deployment\\nD) Enhanced multilinguality\\n\\n**Correct answer:** C) Reduced compute requirements and enabled single-server node deployment\\n\\n**Question 4**\\nWhat is a key approach used in post-training to refine chat models in Llama 3?\\n\\nA) Supervised fine-tuning (SFT) with synthetic data\\nB) Direct preference optimization\\nC) Rejection sampling\\nD) All of the above\\n\\n**Correct answer:** D) All of the above\\n\\n**Question 5**\\nWhat change has been made to the license of Llama models to enable developers to use outputs to enhance other models?\\n\\nA) Stricter licensing terms\\nB) Limited usage rights\\nC) Updated license to allow developers to use outputs\\nD) No change to the license\\n\\n**Correct answer:** C) Updated license to allow developers to use outputs'}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "JSngSUOG3nYd"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}