Inference_LoRA.ipynb

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            "  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[32m244.2/244.2 kB\u001b[0m \u001b[31m3.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "\u001b[?25h  Building wheel for peft (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting sentencepiece\n",
            "  Downloading sentencepiece-0.1.99-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (1.3 MB)\n",
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            "\u001b[?25hInstalling collected packages: sentencepiece\n",
            "Successfully installed sentencepiece-0.1.99\n",
            "Collecting bitsandbytes\n",
            "  Downloading bitsandbytes-0.40.2-py3-none-any.whl (92.5 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.5/92.5 MB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hInstalling collected packages: bitsandbytes\n",
            "Successfully installed bitsandbytes-0.40.2\n",
            "Collecting datasets\n",
            "  Downloading datasets-2.13.1-py3-none-any.whl (486 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m8.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
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            "  Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n",
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            "\u001b[?25hCollecting multiprocess (from datasets)\n",
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            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->datasets) (2022.7.1)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->datasets) (1.16.0)\n",
            "Installing collected packages: xxhash, dill, multiprocess, datasets\n",
            "Successfully installed datasets-2.13.1 dill-0.3.6 multiprocess-0.70.14 xxhash-3.2.0\n"
          ]
        }
      ],
      "source": [
        "!pip install -q git+https://github.com/huggingface/peft.git git+https://github.com/huggingface/transformers.git\n",
        "!pip install sentencepiece\n",
        "!pip install bitsandbytes\n",
        "!pip install datasets"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from huggingface_hub import notebook_login\n",
        "notebook_login()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145,
          "referenced_widgets": [
            "ee6862745e2743ef92502d5bd6d49025",
            "af188741a24b42bd94ca59e919355018",
            "45dbef990864448fae6f20aea4196502",
            "06bdd914cc474ba0b85b47f5fd95d72b",
            "c22cb773dbd74de28f601ffb6a65acd2",
            "3531f08b86504ef59d22d4e362457785",
            "228b0d860e84497f8cc1baf2bfb5e0ab",
            "9b3e417e72ef4576903a819140401068",
            "4596ecb0f02a47e4a365b6d270b5ef21",
            "911df3850b94474c99411bbe82da032e",
            "5cbd631682b44e078a22b78c37d6b373",
            "8f4dedc247c3409bbd771ad4d3f105c5",
            "928eaace45374d14aa479f191fdd7827",
            "3ddb5827ee634fcb919e6fce9b900b24",
            "f351cdaece054347abfc96171b698ab5",
            "cb2a4eafaa51483abd27d400f0720023",
            "2325a8f377fd4d8aa6e0bd717fe926b5",
            "5015f3d6279b452cb4a43b7de42ada9f",
            "43b6d9caea944c2ba0c94f3d502e8bd5",
            "5c24a2f8fdc4424e9a75d4143642671c",
            "eb98d0209a574c268492ec2443cf45cb",
            "b7a50d22cc2841d98c03ab494a610123",
            "e7cc22b3014546aba1cd2a6fec001918",
            "eeae3b80864e42b5b9bb4bea67dbfe04",
            "4c972625908b43109b01c712450d5a91",
            "bc27db3661504399823c51321c2f67b4",
            "0db26b5b182a4641b45e149bff17e4c8",
            "7241f48318a2487182c606ae9f92750a",
            "024a287355e9461fa401daa6ba6fd398",
            "be3fc8fe35744a3cb28ffbb54172e723",
            "3991d17a74994a0db789baa666ec2a3c",
            "5f2b4dc0024a40e9a3744accd185f6b2"
          ]
        },
        "id": "X8M5g4H4ZD9q",
        "outputId": "0847b96c-02c6-4d1b-da12-b0e9b4bda7e4"
      },
      "execution_count": null,
      "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,
              "version_minor": 0,
              "model_id": "ee6862745e2743ef92502d5bd6d49025"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "HUGGING_FACE_USER_NAME = \"Andron00e\""
      ],
      "metadata": {
        "id": "7kQGga7JZKJQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_name = \"Open-Llama-3B-LoRA-Corpus\""
      ],
      "metadata": {
        "id": "LxE5CE3tZKs9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import torch\n",
        "from peft import PeftModel, PeftConfig\n",
        "from transformers import AutoModelForCausalLM, AutoTokenizer\n",
        "\n",
        "peft_model_id = f\"{HUGGING_FACE_USER_NAME}/{model_name}\"\n",
        "\n",
        "tokenizer = AutoTokenizer.from_pretrained(peft_model_id)\n",
        "model = AutoModelForCausalLM.from_pretrained(peft_model_id, load_in_8bit=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 49,
          "referenced_widgets": [
            "0e9e9fe178464579bfebae844e16fc22",
            "70bf84b27ba441e593f28881f96293c9",
            "3a553dd8c8a14f2d9ccb143de4ddd485",
            "51cd122f32d949619461ac60e53182fe",
            "ee79f98040ce434aadde19986b36b7ab",
            "bc319314db1a433ba51b0cdf8e87fe70",
            "74a38959fd514705bd790b1a5c65d7e0",
            "8531fb751f8a4e95bce4582b1691b066",
            "9557d6810bcb48fd95766cec43716c0a",
            "f8b44b24d78342abad04ca533a0c0678",
            "b494abf53ba744dd8e72af6b65d224d0"
          ]
        },
        "id": "_L7_yn1wZXmr",
        "outputId": "38156427-836e-4315-d85a-59da58fb2803"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Downloading (…)neration_config.json:   0%|          | 0.00/137 [00:00<?, ?B/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0e9e9fe178464579bfebae844e16fc22"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# original style"
      ],
      "metadata": {
        "id": "MGTSDLdxdyNR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Markdown\n",
        "\n",
        "def make_inference(system_prompt):\n",
        "  batch = tokenizer(f\"### CONTEXT\\n{system_prompt}\\n\\n### RESPONSE\\n\", return_tensors='pt')\n",
        "\n",
        "  with torch.cuda.amp.autocast():\n",
        "    output_tokens = model.generate(**batch, max_new_tokens=200)\n",
        "\n",
        "  display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))"
      ],
      "metadata": {
        "id": "hmOr3eeBaMvv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Your name is Sasha and you live in Moscow. What is your name? \"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "izf6ASJ7bs6v",
        "outputId": "60a6c80e-f29e-47c3-c21e-4466f0eb78e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nYour name is Sasha and you live in Moscow. What is your name? \n\n### RESPONSE\nЯ Дмитрий\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Сыр это самая вкусная еда в мире. Какая еда в мире самая вкусная? \"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "m4dBidLXeoAk",
        "outputId": "5e6a3f01-319b-402d-be59-e126fdea40e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nСыр это самая вкусная еда в мире. Какая еда в мире самая вкусная? \n\n### RESPONSE\nЭто Большая Баллада\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Шимпанзе черный. Сколько пальцев у шимпанзе?\"\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "AjixBvexezMI",
        "outputId": "5523a5cc-5ee0-4480-a669-6425225bb005"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nШимпанзе черный. Сколько пальцев у шимпанзе?\n\n### RESPONSE\nОн же\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"У шимпанзе 5 пальцев. Сколько пальцев у шимпанзе?\"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "X9XL2gA8e__c",
        "outputId": "f29d0400-db89-416b-fe95-152d1711a8e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nУ шимпанзе 5 пальцев. Сколько пальцев у шимпанзе?\n\n### RESPONSE\nУ шимпанзе 5 пальцев. Сколько пальцев у шимпанзе?\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"В Арктике всегда \"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "cy3_711pfJ_0",
        "outputId": "558272bd-f1af-46ac-b37e-94af065697ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nВ Арктике всегда \n\n### RESPONSE\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Мальчик \"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "vrCH1xTKfXkR",
        "outputId": "49e6dd2f-b25e-4938-edc4-c385747dfb90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nМальчик \n\n### RESPONSE\nНе знаю\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"The boy \"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "my3AAge2fjb6",
        "outputId": "c3c7cba0-1278-4658-8c20-f1a650d1f3d9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nThe boy \n\n### RESPONSE\nДа\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"В Арктике всегда холодно. Где всегда холодно?\"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "VXLG5rITgNPs",
        "outputId": "fbd26f3c-28d2-4231-b145-5d36007da8d8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nВ Арктике всегда холодно. Где всегда холодно?\n\n### RESPONSE\nНе в Арктике\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Продолжи фразу. В Арктике всегда\"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 115
        },
        "id": "v1P5tRUigbO_",
        "outputId": "876f4bf5-8d09-4f49-ec04-5ccaab6b7674"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nПродолжи фразу. В Арктике всегда\n\n### RESPONSE\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Continue the sentence. In the Arctic \"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "JMqgrnQQglAh",
        "outputId": "b277c1f5-98bf-44f8-fd4d-0d29ab76f983"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nContinue the sentence. In the Arctic \n\n### RESPONSE\n\nОк\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"My name is Thomas and my \"\n",
        "\n",
        "\n",
        "make_inference(system_prompt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "n2LhMvJehip_",
        "outputId": "4fb4e1cd-2020-4940-d6b5-f521fbb14b5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nMy name is Thomas and my \n\n### RESPONSE\nЯ Томас\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Q&A style"
      ],
      "metadata": {
        "id": "9a0P4Jwnd2yi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, Markdown\n",
        "\n",
        "def make_inference(system_prompt, question):\n",
        "  batch = tokenizer(f\"### CONTEXT\\n{system_prompt}\\n\\n### QUESTION\\n{question}\\n\\n### RESPONSE\\n\", return_tensors='pt')\n",
        "\n",
        "  with torch.cuda.amp.autocast():\n",
        "    output_tokens = model.generate(**batch, max_new_tokens=200)\n",
        "\n",
        "  display(Markdown((tokenizer.decode(output_tokens[0], skip_special_tokens=True))))"
      ],
      "metadata": {
        "id": "NiO_VOG8dWOY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Cheese is the best food.\"\n",
        "question = \"What is the best food?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        },
        "id": "_sLwhUnZdYFV",
        "outputId": "4e7fa205-98fd-4017-a7a0-ffd9dd94bf8a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/transformers/generation/utils.py:1468: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nCheese is the best food.\n\n### QUESTION\nWhat is the best food?\n\n### RESPONSE\nI don't know\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Сыр это самая вкусная еда в мире.\"\n",
        "question = \"Какая еда в мире самая вкусная?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "U92seBo7djxC",
        "outputId": "11a7d58c-6741-431b-d693-10f78bb13188"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nСыр это самая вкусная еда в мире.\n\n### QUESTION\nКакая еда в мире самая вкусная?\n\n### RESPONSE\nЭто сыр\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Шимпанзе черный\"\n",
        "question = \"Сколько пальцев у шимпанзе?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "I_seD2rfdtsG",
        "outputId": "619540e5-394b-43a9-a9ea-a276cc70ee6a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nШимпанзе черный\n\n### QUESTION\nСколько пальцев у шимпанзе?\n\n### RESPONSE\nОн хорошо играет\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"У шимпанзе 5 пальцев\"\n",
        "question = \"Сколько пальцев у шимпанзе?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "fKDekXIQeRDT",
        "outputId": "dad7582f-7643-4cc0-a41e-ce28f2db4a80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nУ шимпанзе 5 пальцев\n\n### QUESTION\nСколько пальцев у шимпанзе?\n\n### RESPONSE\nОн 5\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Тебя зовут Саша и ты живешь в Москве\"\n",
        "question = \"Как тебя зовут?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "t2rD_tfGeYOZ",
        "outputId": "9905b445-448b-4008-e436-1476acde4256"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nТебя зовут Саша и ты живешь в Москве\n\n### QUESTION\nКак тебя зовут?\n\n### RESPONSE\nТебя зовут Саша и ты живешь в Москве\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"В Арктике всегда.\"\n",
        "question = \"Как лучше продолжить контекст?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "XN4dbeZyelyX",
        "outputId": "0825d2d6-991d-4ba7-c565-7b7e2546436f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nВ Арктике всегда.\n\n### QUESTION\nКак лучше продолжить контекст?\n\n### RESPONSE\nНе лучше\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"The boy\"\n",
        "question = \"What is the best way to continue the context?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "ELD6htKxfz76",
        "outputId": "f2b3e949-c73a-4ed9-bed7-0a2ebd81d53b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nThe boy\n\n### QUESTION\nWhat is the best way to continue the context?\n\n### RESPONSE\nI don't know\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"В Арктике всегда холодно.\"\n",
        "question = \"Где всегда холодно?\"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "2GZrTd_ff9N5",
        "outputId": "4eac9295-eca6-4e13-96e2-458e2d99d3c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nВ Арктике всегда холодно.\n\n### QUESTION\nГде всегда холодно?\n\n### RESPONSE\nНа Байкальском кряже\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "system_prompt = \"Продолжи фразу.\"\n",
        "question = \"В Арктике всегда \"\n",
        "\n",
        "make_inference(system_prompt, question)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 198
        },
        "id": "44CXtxt6g5om",
        "outputId": "864b8249-0104-4e62-bb4b-74e2a680c100"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "### CONTEXT\nПродолжи фразу.\n\n### QUESTION\nВ Арктике всегда \n\n### RESPONSE\nНе в Арктике\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "DrelCn3shcbO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# inference api"
      ],
      "metadata": {
        "id": "JnIHG4uZiYGe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/Andron00e/Open-Llama-3B-LoRA-Corpus\"\n",
        "headers = {\"Authorization\": \"Bearer #################\"}\n",
        "\n",
        "def query(payload):\n",
        "\tresponse = requests.post(API_URL, headers=headers, json=payload)\n",
        "\treturn response.json()\n",
        "\n",
        "output = query({\n",
        "\t\"inputs\": \"Can you please let us know more details about your \",\n",
        "})"
      ],
      "metadata": {
        "id": "gjOmlO2Pia-k"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PahplWAiihxp",
        "outputId": "9bcee7d6-f6e9-4b28-e12d-1d0eb42ad069"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'error': 'Model Andron00e/Open-Llama-3B-LoRA-Corpus is currently loading',\n",
              " 'estimated_time': 274.1216735839844}"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "beugpSTvik2v"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}