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  "cells": [
    {
      "cell_type": "code",
      "source": [
        "!pip install -U transformers bitsandbytes accelerate -q"
      ],
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            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.7/60.7 MB\u001b[0m \u001b[31m16.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Local Inference on GPU\n",
        "Model page: https://huggingface.co/microsoft/phi-2\n",
        "\n",
        "⚠️ If the generated code snippets do not work, please open an issue on either the [model repo](https://huggingface.co/microsoft/phi-2)\n",
        "\t\t\tand/or on [huggingface.js](https://github.com/huggingface/huggingface.js/blob/main/packages/tasks/src/model-libraries-snippets.ts) 🙏"
      ],
      "metadata": {
        "id": "qhYDm6yk-UEj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig\n",
        "import torch\n",
        "\n",
        "model_name = \"AlexKitipov/phi-2\"\n",
        "\n",
        "# Load tokenizer and set pad_token\n",
        "tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Load model configuration and set pad_token_id\n",
        "config = AutoConfig.from_pretrained(model_name, trust_remote_code=True)\n",
        "config.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "# Configure 8-bit quantization\n",
        "quantization_config = BitsAndBytesConfig(load_in_8bit=True)\n",
        "\n",
        "# Load model with the modified configuration, device_map, and quantization config\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    model_name,\n",
        "    config=config,\n",
        "    trust_remote_code=True,\n",
        "    device_map=\"auto\", # Automatically manage device placement (GPU/CPU)\n",
        "    quantization_config=quantization_config # Apply 8-bit quantization\n",
        ")\n",
        "\n",
        "# Create the pipeline with the correctly loaded model and tokenizer\n",
        "# device_map=\"auto\" already handles device placement, so explicit .to('cuda') is not needed\n",
        "pipe = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
        "print(\"Pipeline initialized successfully.\")"
      ],
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      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/453 [00:00<?, ?it/s]"
            ],
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          "text": [
            "Pipeline initialized successfully.\n"
          ]
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig\n",
        "import torch\n",
        "\n",
        "# Load tokenizer first\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"AlexKitipov/phi-2\", trust_remote_code=True)\n",
        "\n",
        "# Set pad_token for tokenizer using eos_token\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Load model configuration separately to ensure pad_token_id is set before model initialization\n",
        "config = AutoConfig.from_pretrained(\"AlexKitipov/phi-2\", trust_remote_code=True)\n",
        "\n",
        "# Explicitly set pad_token_id in the config\n",
        "config.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "# Configure 8-bit quantization with CPU offload for 32-bit modules if needed\n",
        "quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"AlexKitipov/phi-2\",\n",
        "    config=config, # Pass the modified config here\n",
        "    trust_remote_code=True,\n",
        "    device_map=\"cpu\", # Force loading entirely on CPU for testing memory constraints\n",
        "    quantization_config=quantization_config # Apply 8-bit quantization with potential CPU offload\n",
        ")"
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      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/453 [00:00<?, ?it/s]"
            ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Remote Inference via Inference Providers\n",
        "Ensure you have a valid **HF_TOKEN** set in your environment. You can get your token from [your settings page](https://huggingface.co/settings/tokens). Note: running this may incur charges above the free tier.\n",
        "The following Python example shows how to run the model remotely on HF Inference Providers, automatically selecting an available inference provider for you.\n",
        "For more information on how to use the Inference Providers, please refer to our [documentation and guides](https://huggingface.co/docs/inference-providers/en/index)."
      ],
      "metadata": {
        "id": "IRyu8RyM-UF-"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "029ed965"
      },
      "source": [
        "## Gradio Interface for Phi-2 Chat"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b8b69d12"
      },
      "source": [
        "# Install Gradio library\n",
        "!pip install gradio -q"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "id": "b5a619ac",
        "outputId": "9013177a-1d7f-4b37-b18b-b3a3bd478c7c"
      },
      "source": [
        "import gradio as gr\n",
        "from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig\n",
        "import torch\n",
        "\n",
        "print(\"Loading Phi-2 model and tokenizer...\")\n",
        "\n",
        "# Load tokenizer first\n",
        "tokenizer = AutoTokenizer.from_pretrained(\"AlexKitipov/phi-2\", trust_remote_code=True)\n",
        "\n",
        "# Set pad_token for tokenizer using eos_token\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Load model configuration separately to ensure pad_token_id is set before model initialization\n",
        "config = AutoConfig.from_pretrained(\"AlexKitipov/phi-2\", trust_remote_code=True)\n",
        "\n",
        "# Explicitly set pad_token_id in the config, as Phi-2's config might not have it by default\n",
        "# The model's internal structure expects this attribute to be present.\n",
        "config.pad_token_id = tokenizer.eos_token_id\n",
        "\n",
        "# Configure 8-bit quantization with CPU offload for 32-bit modules if needed\n",
        "quantization_config = BitsAndBytesConfig(load_in_8bit=True, llm_int8_enable_fp32_cpu_offload=True)\n",
        "\n",
        "model = AutoModelForCausalLM.from_pretrained(\n",
        "    \"AlexKitipov/phi-2\",\n",
        "    config=config, # Pass the modified config here\n",
        "    trust_remote_code=True,\n",
        "    device_map=\"cpu\", # Force loading entirely on CPU for testing memory constraints\n",
        "    quantization_config=quantization_config # Apply 8-bit quantization with potential CPU offload\n",
        ")\n",
        "\n",
        "# device_map=\"auto\" already handles device placement, so explicit .to('cuda') is not needed\n",
        "print(\"Model loaded successfully, device placement handled by device_map='cpu'.\")\n",
        "\n",
        "# Create a text generation pipeline and rename it to avoid conflict\n",
        "# No need to specify device here, as model placement is handled by device_map\n",
        "text_generator_pipeline = pipeline(\"text-generation\", model=model, tokenizer=tokenizer)\n",
        "\n",
        "print(\"Phi-2 model loaded successfully.\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Loading Phi-2 model and tokenizer...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Loading weights:   0%|          | 0/453 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "5f74f3acaa9840dfb91de8336143e488"
            }
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61f7b058"
      },
      "source": [
        "def predict(message, history):\n",
        "    conversation_history = \"\"\n",
        "    for human, assistant in history:\n",
        "        conversation_history += f\"Human: {human}\\nAssistant: {assistant}\\n\"\n",
        "    conversation_history += f\"Human: {message}\\nAssistant:\"\n",
        "\n",
        "    outputs = text_generator_pipeline(\n",
        "        conversation_history, # Pass conversation_history directly as a string to the renamed pipeline object\n",
        "        max_new_tokens=200, # Generate up to 200 new tokens\n",
        "        do_sample=True,\n",
        "        temperature=0.7,\n",
        "        top_k=50,\n",
        "        top_p=0.95,\n",
        "        eos_token_id=tokenizer.eos_token_id # Stop generation at end-of-sequence token\n",
        "    )\n",
        "    generated_text = outputs[0]['generated_text']\n",
        "\n",
        "    # Extract only the assistant's response part\n",
        "    assistant_response = generated_text.split(\"Assistant:\")[-1].strip()\n",
        "    # Remove the last user input from the response if the model repeats it\n",
        "    if assistant_response.startswith(message):\n",
        "        assistant_response = assistant_response[len(message):].strip()\n",
        "\n",
        "    return assistant_response"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cbdf81f5"
      },
      "source": [
        "Now, let's launch the Gradio chat interface. Click the public URL to interact with the model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 228
        },
        "id": "63e656fe",
        "outputId": "9c4a86ef-106d-4c40-fd4d-2df52958007e"
      },
      "source": [
        "gr.ChatInterface(\n",
        "    predict,\n",
        "    chatbot=gr.Chatbot(height=500), # Make the chatbot window larger\n",
        "    textbox=gr.Textbox(placeholder=\"Ask me a question\", container=False, scale=7),\n",
        "    title=\"Chat with Phi-2\",\n",
        "    description=\"Interact with the Microsoft Phi-2 model. Ask questions, have conversations, or experiment with its generative capabilities!\",\n",
        "    theme=\"soft\", # A pleasant theme\n",
        "    examples=[\"Tell me a short story.\", \"Explain quantum physics simply.\", \"What is the capital of France?\"],\n",
        "    cache_examples=False\n",
        ").launch(debug=True, share=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'gr' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipykernel_11865/638742562.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m gr.ChatInterface(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mpredict\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0mchatbot\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mChatbot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mheight\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m500\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m# Make the chatbot window larger\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0mtextbox\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mgr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mTextbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mplaceholder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Ask me a question\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcontainer\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mscale\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mtitle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"Chat with Phi-2\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'gr' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61f7b008"
      },
      "source": [
        "def predict(message, history):\n",
        "    conversation_history = \"\"\n",
        "    for human, assistant in history:\n",
        "        conversation_history += f\"Human: {human}\\nAssistant: {assistant}\\n\"\n",
        "    conversation_history += f\"Human: {message}\\nAssistant:\"\n",
        "\n",
        "    outputs = pipe(\n",
        "        conversation_history, # Pass conversation_history directly as a string\n",
        "        max_new_tokens=200, # Generate up to 200 new tokens\n",
        "        do_sample=True,\n",
        "        temperature=0.7,\n",
        "        top_k=50,\n",
        "        top_p=0.95,\n",
        "        eos_token_id=tokenizer.eos_token_id # Stop generation at end-of-sequence token\n",
        "    )\n",
        "    generated_text = outputs[0]['generated_text']\n",
        "\n",
        "    # Extract only the assistant's response part\n",
        "    assistant_response = generated_text.split(\"Assistant:\")[-1].strip()\n",
        "    # Remove the last user input from the response if the model repeats it\n",
        "    if assistant_response.startswith(message):\n",
        "        assistant_response = assistant_response[len(message):].strip()\n",
        "\n",
        "    return assistant_response"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ['HF_TOKEN'] = 'YOUR_TOKEN_HERE'"
      ],
      "metadata": {
        "id": "leeUFqBD-UF-"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "from huggingface_hub import InferenceClient\n",
        "\n",
        "client = InferenceClient(\n",
        "    provider=\"auto\",\n",
        "    api_key=os.environ[\"HF_TOKEN\"],\n",
        ")\n",
        "\n",
        "completion = client.chat.completions.create(\n",
        "    model=\"AlexKitipov/phi-2\",\n",
        "    messages=\"\\\"Can you please let us know more details about your \\\"\",\n",
        ")\n",
        "\n",
        "print(completion.choices[0].message)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "Zmof51oV-UGN",
        "outputId": "0b2ad44e-84f6-49a4-c4b7-0267d91f0438"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "Cannot select auto-router when using non-Hugging Face API key.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m/tmp/ipykernel_11865/780418629.py\u001b[0m in \u001b[0;36m<cell line: 0>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m )\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m completion = client.chat.completions.create(\n\u001b[0m\u001b[1;32m     10\u001b[0m     \u001b[0mmodel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"AlexKitipov/phi-2\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mmessages\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"\\\"Can you please let us know more details about your \\\"\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/inference/_client.py\u001b[0m in \u001b[0;36mchat_completion\u001b[0;34m(self, messages, model, stream, frequency_penalty, logit_bias, logprobs, max_tokens, n, presence_penalty, response_format, seed, stop, stream_options, temperature, tool_choice, tool_prompt, tools, top_logprobs, top_p, extra_body)\u001b[0m\n\u001b[1;32m    920\u001b[0m             \u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mextra_body\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    921\u001b[0m         }\n\u001b[0;32m--> 922\u001b[0;31m         request_parameters = provider_helper.prepare_request(\n\u001b[0m\u001b[1;32m    923\u001b[0m             \u001b[0minputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmessages\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    924\u001b[0m             \u001b[0mparameters\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/inference/_providers/_common.py\u001b[0m in \u001b[0;36mprepare_request\u001b[0;34m(self, inputs, parameters, headers, model, api_key, extra_payload)\u001b[0m\n\u001b[1;32m    100\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    101\u001b[0m         \u001b[0;31m# routed URL if HF token, or direct URL (to customize in '_prepare_route' in subclasses)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 102\u001b[0;31m         \u001b[0murl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapi_key\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprovider_mapping_info\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mprovider_id\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    103\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    104\u001b[0m         \u001b[0;31m# prepare payload (to customize in subclasses)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/inference/_providers/_common.py\u001b[0m in \u001b[0;36m_prepare_url\u001b[0;34m(self, api_key, mapped_model)\u001b[0m\n\u001b[1;32m    214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    215\u001b[0m         Usually not overwritten in subclasses.\"\"\"\n\u001b[0;32m--> 216\u001b[0;31m         \u001b[0mbase_url\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_base_url\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mapi_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    217\u001b[0m         \u001b[0mroute\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_prepare_route\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmapped_model\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    218\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0;34mf\"{base_url.rstrip('/')}/{route.lstrip('/')}\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.12/dist-packages/huggingface_hub/inference/_providers/_common.py\u001b[0m in \u001b[0;36m_prepare_base_url\u001b[0;34m(self, api_key)\u001b[0m\n\u001b[1;32m    300\u001b[0m         \u001b[0;31m# Route to the proxy if the api_key is a HF TOKEN\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    301\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstartswith\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"hf_\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 302\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot select auto-router when using non-Hugging Face API key.\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    303\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    304\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbase_url\u001b[0m  \u001b[0;31m# No `/auto` suffix in the URL\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: Cannot select auto-router when using non-Hugging Face API key."
          ]
        }
      ]
    }
  ]
}