YT_LangChain_Running_HuggingFace_Models_Locally.ipynb

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            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m64.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m11.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Building wheel for sentence_transformers (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ]
        }
      ],
      "source": [
        "!pip -q install langchain huggingface_hub transformers sentence_transformers"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## HuggingFace\n",
        "\n",
        "There are two Hugging Face LLM wrappers, one for a local pipeline and one for a model hosted on Hugging Face Hub. Note that these wrappers only work for models that support the following tasks: text2text-generation, text-generation\n"
      ],
      "metadata": {
        "id": "VkVTT54xNq8T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "\n",
        "os.environ['HUGGINGFACEHUB_API_TOKEN'] = ''"
      ],
      "metadata": {
        "id": "2PpqSc3cG7jJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Use the HuggingFaceHub"
      ],
      "metadata": {
        "id": "UhauDrynY0cj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain import PromptTemplate, HuggingFaceHub, LLMChain\n",
        "\n",
        "template = \"\"\"Question: {question}\n",
        "\n",
        "Answer: Let's think step by step.\"\"\"\n",
        "\n",
        "prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
      ],
      "metadata": {
        "id": "Derb_0t-ZESh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm_chain = LLMChain(prompt=prompt, \n",
        "                     llm=HuggingFaceHub(repo_id=\"google/flan-t5-xl\", \n",
        "                                        model_kwargs={\"temperature\":0, \n",
        "                                                      \"max_length\":64}))"
      ],
      "metadata": {
        "id": "lvO31GCGKhHs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What is the capital of France?\"\n",
        "\n",
        "print(llm_chain.run(question))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uWIc38V4t5gA",
        "outputId": "4cdbb850-96db-4cf2-8cb4-064410d4d9c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Paris is the capital of France. The final answer: Paris.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "question = \"What area is best for growing wine in France?\"\n",
        "\n",
        "print(llm_chain.run(question))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uK61DssCdCmr",
        "outputId": "ed633d93-c8ce-4133-b6cb-2220b3635766"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The best area for growing wine in France is the Loire Valley. The Loire Valley is located in the south of France. The area of France that is best for growing wine is the Loire Valley. The final answer: Loire Valley.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BlenderBot\n",
        "\n",
        "Doesn't work on the Hub"
      ],
      "metadata": {
        "id": "bsg2rRuNwNb9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "blenderbot_chain = LLMChain(prompt=prompt, \n",
        "                     llm=HuggingFaceHub(repo_id=\"facebook/blenderbot-1B-distill\", \n",
        "                                        model_kwargs={\"temperature\":0, \n",
        "                                                      \"max_length\":64}))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 322
        },
        "id": "Io-4j_aat_Ll",
        "outputId": "cb3e4cd8-5fac-4856-9815-a4e961bf1d9e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValidationError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValidationError\u001b[0m                           Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-7-3837f914266c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m blenderbot_chain = LLMChain(prompt=prompt, \n\u001b[0;32m----> 2\u001b[0;31m                      llm=HuggingFaceHub(repo_id=\"facebook/blenderbot-1B-distill\", \n\u001b[0m\u001b[1;32m      3\u001b[0m                                         model_kwargs={\"temperature\":0, \n\u001b[1;32m      4\u001b[0m                                                       \"max_length\":64}))\n",
            "\u001b[0;32m/usr/local/lib/python3.8/dist-packages/pydantic/main.cpython-38-x86_64-linux-gnu.so\u001b[0m in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValidationError\u001b[0m: 1 validation error for HuggingFaceHub\n__root__\n  Got invalid task conversational, currently only ('text2text-generation', 'text-generation') are supported (type=value_error)"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# question = \"What is the capital of France?\"\n",
        "# question = \"What area is best for growing wine in France?\"\n",
        "\n",
        "# print(blenderbot_chain = LLMChain(prompt=prompt, \n",
        "# .run(question))"
      ],
      "metadata": {
        "id": "F8hXzlWZuOhE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## With Local model from HF \n",
        "\n",
        "### Why would you want to use local mode?\n",
        "\n",
        "- fine-tuned models\n",
        "- GPU hosted etc\n",
        "- some models only work locally"
      ],
      "metadata": {
        "id": "-xUmcqkUf9Dv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## T5-Flan - Encoder-Decoder"
      ],
      "metadata": {
        "id": "WXOZ_Un6e1oo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.llms import HuggingFacePipeline\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM\n",
        "\n",
        "model_id = 'google/flan-t5-large'# go for a smaller model if you dont have the VRAM\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_id, load_in_8bit=True)\n",
        "\n",
        "pipe = pipeline(\n",
        "    \"text2text-generation\",\n",
        "    model=model, \n",
        "    tokenizer=tokenizer, \n",
        "    max_length=100\n",
        ")\n",
        "\n",
        "local_llm = HuggingFacePipeline(pipeline=pipe)\n"
      ],
      "metadata": {
        "id": "GMg2xiRnfm21",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241,
          "referenced_widgets": [
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          ]
        },
        "outputId": "96d9272e-a5d4-4f68-9cee-623e7469d936"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/2.54k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/2.42M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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            }
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          "metadata": {}
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        {
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            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(local_llm('What is the capital of France? '))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LvsLFQxehfZe",
        "outputId": "d7e7c1b8-f22d-4b91-95b4-7c237b6693cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "paris\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm_chain = LLMChain(prompt=prompt, \n",
        "                     llm=local_llm\n",
        "                     )\n",
        "\n",
        "question = \"What is the capital of England?\"\n",
        "\n",
        "print(llm_chain.run(question))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3UVVbwLegmU8",
        "outputId": "2e898281-3c88-4e6c-926b-1508697acbc1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The capital of England is London. London is the capital of England. So the answer is London.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## GPT2-medium - Decoder Only Model\n",
        "\n",
        "microsoft/DialoGPT-large"
      ],
      "metadata": {
        "id": "ccAHJARfnKSK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_id = \"gpt2-medium\"\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(model_id)\n",
        "\n",
        "pipe = pipeline(\n",
        "    \"text-generation\", \n",
        "    model=model, \n",
        "    tokenizer=tokenizer, \n",
        "    max_length=100\n",
        ")\n",
        "\n",
        "local_llm = HuggingFacePipeline(pipeline=pipe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 209,
          "referenced_widgets": [
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            "1df6d73632974f1dbe7fd0e6dd47379d",
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          ]
        },
        "id": "wEKTalGcgxRg",
        "outputId": "75a6ebee-83d2-40cc-8292-98c90d511927"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)lve/main/config.json:   0%|          | 0.00/718 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)olve/main/vocab.json:   0%|          | 0.00/1.04M [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)/main/tokenizer.json:   0%|          | 0.00/1.36M [00:00<?, ?B/s]"
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            "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 (…)\"pytorch_model.bin\";:   0%|          | 0.00/1.52G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7a9d3be502e048b1a61d16c78b3370c4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/124 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "e4d1778ff46c4881bd6e6d6a1ac35b07"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm_chain = LLMChain(prompt=prompt, \n",
        "                     llm=local_llm\n",
        "                     )\n",
        "\n",
        "question = \"What is the capital of France?\"\n",
        "\n",
        "print(llm_chain.run(question))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MNMzu_bznhBp",
        "outputId": "441a2390-a583-42de-a372-226e0ec7ddde"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/transformers/generation/utils.py:1186: UserWarning: You have modified the pretrained model configuration to control generation. This is a deprecated strategy to control generation and will be removed soon, in a future version. Please use a generation configuration file (see https://huggingface.co/docs/transformers/main_classes/text_generation)\n",
            "  warnings.warn(\n",
            "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "\n",
            "The French Empire is made up of 8 provinces from the Atlantic to Pacific land masses.\n",
            "\n",
            "From the Atlantic, France extends westward.\n",
            "\n",
            "From the Pacific to Europe, France extends west.\n",
            "\n",
            "From Europe to Asia, France spreads along the middle.\n",
            "\n",
            "From Asia to the Mediterranean Sea, France follows the southward-southeast direction.\n",
            "\n",
            "The coast line\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## BlenderBot - Encoder-Decoder"
      ],
      "metadata": {
        "id": "UHikkKC0u3dV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.llms import HuggingFacePipeline\n",
        "import torch\n",
        "from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, AutoModelForSeq2SeqLM\n",
        "\n",
        "model_id = 'facebook/blenderbot-1B-distill'\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_id)\n",
        "model = AutoModelForSeq2SeqLM.from_pretrained(model_id)\n",
        "\n",
        "pipe = pipeline(\n",
        "    \"text2text-generation\",\n",
        "    model=model, \n",
        "    tokenizer=tokenizer, \n",
        "    max_length=100\n",
        ")\n",
        "\n",
        "local_llm = HuggingFacePipeline(pipeline=pipe)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 273,
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            "c5bb9b5b0883427882e845548d84c43e",
            "1900d8df4b0c474686f51018ed9a9341"
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        },
        "id": "45AQKsOfu5WN",
        "outputId": "2e87f8c3-325c-4e79-8cd0-d543a13deafe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)okenizer_config.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            "application/vnd.jupyter.widget-view+json": {
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)in/added_tokens.json:   0%|          | 0.00/16.0 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "b5db34ced1574697a94558b21f109f82"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "43fce9989a7f449ea09e5b0992997fc7"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)\"pytorch_model.bin\";:   0%|          | 0.00/2.87G [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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              "model_id": "36a9784d59ab4da78b835b96f100cf91"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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              "Downloading (…)neration_config.json:   0%|          | 0.00/347 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "7e308f5eecb84f8e9f750638e6c95f36"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "llm_chain = LLMChain(prompt=prompt, \n",
        "                     llm=local_llm\n",
        "                     )\n",
        "\n",
        "question = \"What area is best for growing wine in France?\"\n",
        "\n",
        "print(llm_chain.run(question))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bpNLf2lovgO7",
        "outputId": "08689f24-7076-40fa-e429-c83e3b13046f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " I'm not sure, but I do know that France is one of the largest producers of wine in the world.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## SentenceTransformers"
      ],
      "metadata": {
        "id": "z66tHc3V5yv-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain.embeddings import HuggingFaceEmbeddings\n",
        "\n",
        "model_name = \"sentence-transformers/all-mpnet-base-v2\"\n",
        "\n",
        "hf = HuggingFaceEmbeddings(model_name=model_name)"
      ],
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hf.embed_documents(['this is an embedding','this another embedding'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-weIzgnSys2M",
        "outputId": "a4a71927-81dc-44b1-819b-ec3541695383"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "hf = HuggingFaceHubEmbeddings(\n",
        "    repo_id=model_name,\n",
        "    task=\"feature-extraction\",\n",
        "    # huggingfacehub_api_token=\"my-api-key\",\n",
        ")"
      ],
      "metadata": {
        "id": "KBwmKrk4smhr"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}