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        "<a href=\"https://colab.research.google.com/github/SandeepChatterjee66/NLP-Workshop24/blob/main/notebook-3-llm_inference.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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        },
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        "outputId": "efe818e0-5b2f-4d21-a682-948be6fffd57"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'error': 'Route not found'}"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/openai-community/gpt2\"\n",
        "headers = {\"Authorization\": \"Bearer hf_<your token>\"}\n",
        "payload = {\n",
        "    \"inputs\": \"Today is a great day\",\n",
        "}\n",
        "\n",
        "response = requests.post(API_URL, headers=headers, json=payload)\n",
        "response.json()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import requests\n",
        "\n",
        "API_URL = \"https://api-inference.huggingface.co/models/openai-community/gpt2\"\n",
        "headers = {\"Authorization\": \"Bearer hf_token\"}\n",
        "\n",
        "def query(payload):\n",
        "    response = requests.post(API_URL, headers=headers, json=payload)\n",
        "    return response.json()\n",
        "\n",
        "output = query({\n",
        "    \"inputs\": \"Can you please let us know more details about your \",\n",
        "})"
      ],
      "metadata": {
        "id": "yug5JMPzfYZM"
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        "colab": {
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        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'generated_text': \"Can you please let us know more details about your iphone for feedback?\\n\\nReview status: Update Date: Online : Limited Warranty : Limited Warranty - Compliant Software : Source Version of Essentials Support: 90 Days Realistic Extended Telephone Lifestyle Pros: Free FaceTime Lifecycle Through Telco's Next-Generation Phone Service Using Connectivity Details: Essential Eligible Customers :\\n\\nCustomers from Calabar, who do primarily use a PhonesInline For Business and Ultimate Health Jets in Dallas, Texas. Changelog : High quality extended feature suite providing 200 color options! Battery Life : 4000 to 5000 hours. Time and date features : Available Is Location Deferred (Over 24h Can Be Canceled at Any Time): Home Romantic: May Live in It's Own Place Changing Footings Sleeping Tonight (Leave Asap or Opt-Out): Sleep! Designed to Avoid Seizures For Kids: Available Car Transport on Meter (No Travel To Work Taxrive: Hotels not transferable): Available Pic From Outdoor Jump: Available Pal. Access To Beach Hotel : Available Lights On PGA Tour Offices WiFi Feature : Expect AirPlay May Happen If None Supports Cortana (Have To Recall) Parking Document V. License Zimmermann RJ458 wifi APN Laptop Cable Case USB Port Warranty - NOT FOR USE TO SUPPORT HUMAN TRAFFICKER SERVICES AND TO IMMIGRATE FICTION You can use this tool for race, recreation, commuting = short life until res. travel. It's good for travelling for your living or temporary professional lives for example have bar shootout. We copied privacy policy into your experience and used software to install it on all your smartphone. We provided the link to protect you from potentially loss of your data. Well installed, if that is the case fix back up once the device was played.\\n\\nWe do not recommend using this feature of the some Internet Contact centres. were they built better.\\n\\nQur '\\n\\nLet us know if I need this.\"}]"
            ]
          },
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        }
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    },
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        "# # Step 1: Install the necessary library\n",
        "# !pip install transformers\n",
        "\n",
        "# Step 2: Import necessary libraries\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "import torch\n"
      ],
      "metadata": {
        "id": "zFQTEhs3iOz5"
      },
      "execution_count": null,
      "outputs": []
    },
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        "# Step 3: Load pre-trained GPT-2 model and tokenizer\n",
        "model_name = 'gpt2'  # You can also use 'gpt2-medium', 'gpt2-large', or 'gpt2-xl' for larger models\n",
        "model = GPT2LMHeadModel.from_pretrained(model_name)\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Ensure that the tokenizer knows the padding token\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Step 4: Define function for chat completion\n",
        "def chat_with_gpt2(input_text, max_length=100, temperature=0.7, top_p=0.9):\n",
        "    # Tokenize the input text\n",
        "    input_ids = tokenizer.encode(input_text, return_tensors='pt')\n",
        "\n",
        "    # Generate the model output\n",
        "    output = model.generate(\n",
        "        input_ids,\n",
        "        max_length=max_length + len(input_ids[0]),\n",
        "        num_return_sequences=1,\n",
        "        no_repeat_ngram_size=2,  # Prevents repetition of the same n-grams\n",
        "        temperature=temperature,\n",
        "        top_p=top_p,\n",
        "        top_k=50,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        do_sample=True\n",
        "    )\n",
        "\n",
        "    # Decode the generated tokens and return as text\n",
        "    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "    return generated_text\n"
      ],
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        "outputId": "29faeb94-76cc-4286-ef02-0ca51f5aabd4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_auth.py:94: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
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    },
    {
      "cell_type": "code",
      "source": [
        "# Step 5: Test chat completion\n",
        "input_text = \"Hello, how are you today?\"\n",
        "response = chat_with_gpt2(input_text)\n",
        "print(\"GPT-2 Response:\", response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n0VzI7YiicUH",
        "outputId": "05431b63-576d-4fdb-cdcb-17f7171dd4c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "The attention mask is not set and cannot be inferred from input because pad token is same as eos token. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "GPT-2 Response: Hello, how are you today?\n",
            "\n",
            "You are so cute. I love you so much.\n",
            " I'm going to say something to you, I'll tell you what I want to do, but first, we have to make sure you have a good night's sleep. You're going through a rough night, and I know that you are. If you don't sleep well, you'll get an even better night. Now, do you want me to go to bed? Yes? I don´t know, it´\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 6: Optional chat loop (for interactive chat)\n",
        "print(\"Chat with GPT-2! Type 'quit' to exit.\")\n",
        "while True:\n",
        "    user_input = input(\"You: \")\n",
        "    if user_input.lower() == \"quit\":\n",
        "        print(\"Exiting the chat.\")\n",
        "        break\n",
        "\n",
        "    # Get the response from GPT-2\n",
        "    response = chat_with_gpt2(user_input)\n",
        "\n",
        "    # Print the model's response\n",
        "    print(f\"GPT-2: {response}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VAwYt1LmiiHL",
        "outputId": "af4ac96f-d9bf-41f3-b1e0-d4a7b2a0a41b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Chat with GPT-2! Type 'quit' to exit.\n",
            "You: hi\n",
            "GPT-2: hi: A woman who is a member of the Muslim Brotherhood. She is accused of being a \"dirty Muslim\" who has been forced to fight for her religion and her family. The group is trying to make her a martyr.\n",
            "\n",
            "Ahmed: This is not the first time the Islamic State has attacked women. In April, the group threatened to blow up a mosque in the town of Zaytoun, which is located in northern Syria. A number of women have been killed in this attack\n",
            "You: quit\n",
            "Exiting the chat.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Step 1: Install the necessary library\n",
        "#!pip install transformers\n",
        "\n",
        "# Step 2: Import necessary libraries\n",
        "from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
        "import torch\n",
        "\n",
        "# Step 3: Check if GPU is available and set device\n",
        "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
        "print(f\"Using device: {device}\")\n",
        "\n",
        "# Step 4: Load pre-trained GPT-2 model and tokenizer\n",
        "model_name = 'gpt2'  # You can also use 'gpt2-medium', 'gpt2-large', or 'gpt2-xl' for larger models\n",
        "model = GPT2LMHeadModel.from_pretrained(model_name)\n",
        "tokenizer = GPT2Tokenizer.from_pretrained(model_name)\n",
        "\n",
        "# Ensure that the tokenizer knows the padding token\n",
        "tokenizer.pad_token = tokenizer.eos_token\n",
        "\n",
        "# Move model to GPU if available\n",
        "model = model.to(device)\n",
        "\n",
        "# Step 5: Define function for chat completion\n",
        "def chat_with_gpt2(input_text, max_length=100, temperature=0.7, top_p=0.9):\n",
        "    # Tokenize the input text\n",
        "    input_ids = tokenizer.encode(input_text, return_tensors='pt').to(device)\n",
        "\n",
        "    # Generate the model output\n",
        "    output = model.generate(\n",
        "        input_ids,\n",
        "        max_length=max_length + len(input_ids[0]),\n",
        "        num_return_sequences=1,\n",
        "        no_repeat_ngram_size=2,  # Prevents repetition of the same n-grams\n",
        "        temperature=temperature,\n",
        "        top_p=top_p,\n",
        "        top_k=50,\n",
        "        pad_token_id=tokenizer.eos_token_id,\n",
        "        do_sample=True\n",
        "    )\n",
        "\n",
        "    # Decode the generated tokens and return as text\n",
        "    generated_text = tokenizer.decode(output[0], skip_special_tokens=True)\n",
        "    return generated_text\n",
        "\n",
        "# Step 6: Test chat completion\n",
        "input_text = \"Hello, how are you today?\"\n",
        "response = chat_with_gpt2(input_text)\n",
        "print(\"GPT-2 Response:\", response)\n",
        "\n",
        "# Step 7: Optional chat loop (for interactive chat)\n",
        "print(\"Chat with GPT-2! Type 'quit' to exit.\")\n",
        "while True:\n",
        "    user_input = input(\"You: \")\n",
        "    if user_input.lower() == \"quit\":\n",
        "        print(\"Exiting the chat.\")\n",
        "        break\n",
        "\n",
        "    # Get the response from GPT-2\n",
        "    response = chat_with_gpt2(user_input)\n",
        "\n",
        "    # Print the model's response\n",
        "    print(f\"GPT-2: {response}\")\n"
      ],
      "metadata": {
        "id": "t0QWK-bui96G"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}