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tokenizer for our session.\n", "4. **Creating the Llama Pipeline**: Prepare our model for generating responses.\n", "5. **Interacting with Llama through Gradio's ChatInterface**: Prompt the model for answers and explore its capabilities.\n", "\n", "Let's dive in!\n", "\n", "**First, change runtime to GPU.**\n", "\n", "\n", "You can play with Llama-2 7B Chat here: https://huggingface.co/spaces/huggingface-projects/llama-2-7b-chat" ], "metadata": { "id": "bWOx7lgoYHZx" } }, { "cell_type": "markdown", "source": [ "## Installations\n", "\n", "Before we proceed, we need to ensure that the essential libraries are installed:\n", "- `Hugging Face Transformers`: Provides us with a straightforward way to use pre-trained models.\n", "- `PyTorch`: Serves as the backbone for deep learning operations.\n", "- `Accelerate`: Optimizes PyTorch operations, especially on GPU." ], "metadata": { "id": "DNrZtnUyYheg" } }, { "cell_type": "code", "source": [ "!pip install transformers torch accelerate" ], 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Restart Runtime: `Runtime -> Restart Runtime`" ], "metadata": { "id": "0eCcR2mVVkfW" } }, { "cell_type": "code", "source": [ "# import locale\n", "# locale.setlocale(locale.LC_ALL, 'en_US.UTF-8')" ], "metadata": { "id": "ktVvj4nuVcn_" }, "execution_count": 3, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Prerequisites\n", "\n", "To load our desired model, `meta-llama/Llama-2-7b-chat-hf`, we first need to authenticate ourselves on Hugging Face. This ensures we have the correct permissions to fetch the model.\n", "\n", "1. Gain access to the model on Hugging Face: [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf).\n", "2. Use the Hugging Face CLI to login and verify your authentication status.\n", "\n" ], "metadata": { "id": "BO2pb-EeZA95" } }, { "cell_type": "code", "source": [ "import huggingface_hub\n", "\n", "huggingface_hub.login(os.environ[\"API_TOKEN\"])" ], "metadata": { "id": "_6AfgF3_arYL", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "cdbbf9f2-c8a8-45fa-e1ac-83502796f4ef" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", " _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_|\n", " _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_|\n", " _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _|\n", " _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_|\n", "\n", " To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens .\n", "Token: \n", "Add token as git credential? (Y/n) n\n", "Token is valid (permission: write).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Loading Model & Tokenizer\n", "\n", "Here, we are preparing our session by loading both the Llama model and its associated tokenizer.\n", "\n", "The tokenizer will help in converting our text prompts into a format that the model can understand and process." ], "metadata": { "id": "xmJHSjx4abta" } }, { "cell_type": "code", "source": [ "from transformers import AutoTokenizer\n", "import transformers\n", "import torch\n", "\n", "model = \"meta-llama/Llama-2-7b-chat-hf\" # meta-llama/Llama-2-7b-chat-hf\n", "\n", "tokenizer = AutoTokenizer.from_pretrained(model, use_auth_token=True)" ], "metadata": { "id": "jsBrtGpZYmcQ", "colab": { "base_uri": "https://localhost:8080/", "height": 304, "referenced_widgets": [ "e954ac50a726411485dc410ac211fa7b", "f3aff02b70b2427e90578a8eb33905fc", "0de7b7ecf8ae455e900d7bf3e169ee2f", "c084382d022d45c0bd37ca457e67a792", "6562a59b56ce47788228634fb564973d", "f560457164604f959b239f356cf0cb93", "124526b83d254e54828bafbb6ac22091", "82a253d6c6c54903854762ebe4aa2bc6", "f5b85734a5ca48daa8676685fd1e9077", "028634bbc89d40849cda529b75fda65c", "88e7fca3fc3e408f9aef829a8fb165df", "c766b203e8ab4d36a2ec143c5bdd1763", "13d8bea21555471989b9f6962e536f97", "ad66fab068224313a1f8f7a326af8da0", "78e00283519d4abd952347528f2fce88", "57b0e18bdfaf4c8991b35aa4a39d6450", "f22e0d2654c043eca362725656444e2d", "e985691424124c24a35651fae067cbb3", "e9db190f196b47528574f6d0c50fb589", "a2eaca402c6d4168aa31322585da1877", "22911bf19b8048a6a10b448696dba4d6", "1951a121f1a04a358a5a2e0b7eabcf50", "333d346279db48c984360baece953692", "8f2f091873bb4cfa9d5ce418d1b2c90b", "a85754bbe6fc4d8c80bab278bff3b2bc", "ffc25125a2094e8aa52a41e579a98921", "0767c9956e6d4ea0aee5846fa40432f3", "4ed5541013704b718c1e8f3cb6c92fab", "ca064f950ca4493398df8140106a9287", "317f3e680ce644aa84d0df741f992e16", "252c8d2ff6a04daab7129e1e13b797ab", "20f1b917afbe49218443b5d5547b73d3", "d3b8bd1665ea40cb900ff8a1c34617b8", "e1155c8852b940c7875a78e04f9bfa59", "b2805e92ff534f84b7e5a200fee793e2", "2eb8a10661174029a6b10c32365594d0", "ef2f55112ecf402ebbe2c47b2d4a95b5", "9726710ac2114ab1a9dc003036e03a36", "615084b651084e44a00e7f6c48731a3e", "59480f01dc164817b919e4d1de68a72b", "be20096769744455af5c3ece4603a6e6", "dde86087277d482ba629328c75c0f6a3", "fc1bf2c0e39e4a19b3e0282b4e1a3041", "e50c7c386803400fbd76767a06f82210" ] }, "outputId": "1f53ec60-1d02-4c01-c6bc-d139bea32256" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/models/auto/tokenization_auto.py:671: FutureWarning: The `use_auth_token` argument is deprecated and will be removed in v5 of Transformers. Please use `token` instead.\n", " warnings.warn(\n", "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: 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" ] }, { "output_type": "display_data", "data": { "text/plain": [ "tokenizer_config.json: 0%| | 0.00/1.62k [00:00 None:\n", " \"\"\"\n", " Generate a response from the Llama model.\n", "\n", " Parameters:\n", " prompt (str): The user's input/question for the model.\n", "\n", " Returns:\n", " None: Prints the model's response.\n", " \"\"\"\n", " sequences = llama_pipeline(\n", " prompt,\n", " do_sample=True,\n", " top_k=10,\n", " num_return_sequences=1,\n", " eos_token_id=tokenizer.eos_token_id,\n", " max_length=256,\n", " )\n", " print(\"Chatbot:\", sequences[0]['generated_text'])\n", "\n" ], "metadata": { "id": "bT9kSzLHhZ-d" }, "execution_count": 8, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Drawbacks of `get_response()`\n", "\n", "1. **Lack of Conversation History**: The basic approach does not account for past interactions, making it less effective for maintaining a coherent conversation.\n", "2. **Limited Customization**: The function doesn't allow for advanced prompt formatting or handling system-level instructions.\n", "3. **Not Ready for UI Integration**: This basic approach isn't designed for easy integration with user interface libraries like Gradio." ], "metadata": { "id": "QD3Wr8F9oGjj" } }, { "cell_type": "markdown", "source": [ "## Improved Prompts\n", "\n", "The right structure of Llama 2 prompts:\n", "\n", "```\n", "[INST] <>\n", "{{ system_prompt }}\n", "<>\n", "\n", "{{ user_message }} [/INST]\n", "```" ], "metadata": { "id": "BbCnlXQ9iQIx" } }, { "cell_type": "markdown", "source": [ "### Building the Prompt\n", "\n", "Explaining the parameters:\n", "- `message` is the current message we're sending\n", "- `history` is the history of conversation as a list of tupples `[(user_msg1, bot_msg1), (usr_msg2, bot_msg2), ...]`\n" ], "metadata": { "id": "0-J2F3zzWkcU" } }, { "cell_type": "code", "source": [ "SYSTEM_PROMPT = \"\"\"[INST] <>\n", "You are a medical expert who suggests medical screenings and tests to users when they describe their symptoms. Make your answer short and concise. Split the tests into most important and optional according to given user history and symptoms\n", "<>\n", "\n", "\"\"\"\n", "\n", "# Formatting function for message and history\n", "def format_message(message: str, history: list, memory_limit: int = 3) -> str:\n", " \"\"\"\n", " Formats the message and history for the Llama model.\n", "\n", " Parameters:\n", " message (str): Current message to send.\n", " history (list): Past conversation history.\n", " memory_limit (int): Limit on how many past interactions to consider.\n", "\n", " Returns:\n", " str: Formatted message string\n", " \"\"\"\n", " # always keep len(history) <= memory_limit\n", " if len(history) > memory_limit:\n", " history = history[-memory_limit:]\n", "\n", " if len(history) == 0:\n", " return SYSTEM_PROMPT + f\"{message} [/INST]\"\n", "\n", " formatted_message = SYSTEM_PROMPT + f\"{history[0][0]} [/INST] {history[0][1]} \"\n", "\n", " # Handle conversation history\n", " for user_msg, model_answer in history[1:]:\n", " formatted_message += f\"[INST] {user_msg} [/INST] {model_answer} \"\n", "\n", " # Handle the current message\n", " formatted_message += f\"[INST] {message} [/INST]\"\n", "\n", " return formatted_message" ], "metadata": { "id": "CiWBmmV6OWVw" }, "execution_count": 15, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Getting Responses\n", "\n", "We need the function to generate responses." ], "metadata": { "id": "tlbsuGikcsB8" } }, { "cell_type": "code", "source": [ "# Generate a response from the Llama model\n", "def get_llama_response(message: str, history: list) -> str:\n", " \"\"\"\n", " Generates a conversational response from the Llama model.\n", "\n", " Parameters:\n", " message (str): User's input message.\n", " history (list): Past conversation history.\n", "\n", " Returns:\n", " str: Generated response from the Llama model.\n", " \"\"\"\n", " query = format_message(message, history)\n", " response = \"\"\n", "\n", " sequences = llama_pipeline(\n", " query,\n", " do_sample=True,\n", " top_k=10,\n", " num_return_sequences=1,\n", " eos_token_id=tokenizer.eos_token_id,\n", " max_length=1024,\n", " )\n", "\n", " generated_text = sequences[0]['generated_text']\n", " response = generated_text[len(query):] # Remove the prompt from the output\n", "\n", " print(\"Chatbot:\", response.strip())\n", " return response.strip()\n" ], "metadata": { "id": "PeEh17FDLzEe" }, "execution_count": 16, "outputs": [] }, { "cell_type": "code", "source": [ "import gradio as gr\n", "\n", "gr.ChatInterface(get_llama_response).launch()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 625 }, "id": "cHk0MVvdUF3S", "outputId": "dbca356d-103e-4728-f7ea-63e081a31585" }, "execution_count": 17, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Setting queue=True in a Colab notebook requires sharing enabled. Setting `share=True` (you can turn this off by setting `share=False` in `launch()` explicitly).\n", "\n", "Colab notebook detected. To show errors in colab notebook, set debug=True in launch()\n", "Running on public URL: https://e0acdb7d28cbbcd580.gradio.live\n", "\n", "This share link expires in 72 hours. For free permanent hosting and GPU upgrades, run `gradio deploy` from Terminal to deploy to Spaces (https://huggingface.co/spaces)\n" ] }, { "output_type": "display_data", "data": { "text/plain": [ "" ], "text/html": [ "
" ] }, "metadata": {} }, { "output_type": "execute_result", "data": { "text/plain": [] }, "metadata": {}, "execution_count": 17 } ] }, { "cell_type": "markdown", "source": [ "### Conclusion\n", "\n", "Thanks to the Hugging Face Library, creating a pipeline to chat with llama 2 (or any other open-source LLM) is quite easy.\n", "\n", "But if you worked a lot with much larger models such as GPT-4, you need to adjust your expectations." ], "metadata": { "id": "s3lxvhWKqfFd" } }, { "cell_type": "code", "source": [], "metadata": { "id": "CWD3HFWlr2BE" }, "execution_count": 17, "outputs": [] } ] }