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Model Serving Tutorial

This guide will help you setup your model serving after your Kernel is started. By default, the kernel should be served at 127.0.0.1:9000. If you've already started your Kernel, you should be ready to host your own model on your kernel.

You can check the current serving status of the Kernel by connecting to http://127.0.0.1:9000/v1.0/worker/debug

Basic Usage

1. Install Required Packages

Make sure you've installed the required packages by running:

pip install -r requirements.txt

The executor is packaged so that after installation you can directly use the kuwa-executor command to start up your executor. You can get more detailed parameters with:

> kuwa-executor --list

Available model executors:

debug      : [Tool] Debugging executor. It will reflect the last input.
dummy      : [Tool] Dummy executor. It will reply fixed message regardless of the user prompt.
geminipro  : [Cloud model] Google Gemini-Pro. Need API key.
chatgpt    : [Cloud model] OpenAI ChatGPT. Need API key.
huggingface: [On-premises model] Download and run Huggingface model locally.
llamacpp   : [On-premises model] Run the GGUF model locally.

Use "kuwa-executor [executor] --help" to get more information.

You can get more detailed instructions for a specific executor type as well:

> kuwa-executor debug --help

usage: kuwa-executor debug [-h] [--access_code ACCESS_CODE] [--version VERSION] [--ignore_kernel] [--https]
                           [--host HOST] [--port PORT] [--executor_path EXECUTOR_PATH] [--kernel_url KERNEL_URL]
                           [--log {NOTSET,DEBUG,INFO,WARNING,ERROR,CRITICAL}] [--delay DELAY]

LLM model executor, Please make sure your kernel is working before use.

optional arguments:
  -h, --help            show this help message and exit
  --delay DELAY         Inter-token delay (default: 0.02)

General Options:
  --access_code ACCESS_CODE
                        Access code (default: None)
  --version VERSION     Version of the executor interface (default: v1.0)
  --ignore_kernel       Ignore kernel (default: False)
  --https               Register the executor endpoint with https scheme (default: False)
  --host HOST           The hostname or IP address that will be stored in Kernel, Make sure the location are
                        accessible by Kernel (default: None)
  --port PORT           The port to serve. By choosing None, it'll assign an unused port (default: None)
  --executor_path EXECUTOR_PATH
                        The path this model executor is going to use (default: /chat)
  --kernel_url KERNEL_URL
                        Base URL of Kernel's executor management API (default: http://127.0.0.1:9000/)
  --log {NOTSET,DEBUG,INFO,WARNING,ERROR,CRITICAL}
                        The logging level. (default: INFO)

2. Prepare Your Model

If your model is in .gguf format:

kuwa-executor llamacpp --model_path <PATH_TO_YOUR_GGUF> --visible_gpu <CUDA_VISIBLE_DEVICES>

For models loadable by transformers (.safetensors, .bin, .model, etc.) or hosted by Huggingface:

kuwa-executor huggingface --model_path <PATH_TO_MODEL_FOLDER/HUGGINGFACE_MODEL_NAME> --visible_gpu <CUDA_VISIBLE_DEVICES>

3. Connect to Cloud Model

You can use API Keys to connect to cloud models such as Gemini or ChatGPT.

  • Start the executor with the following commands. The api_key is optional and will default to the global value.

    kuwa-executor geminipro --api_key <YOUR_API_KEY>
kuwa-executor chatgpt --api_key <YOUR_API_KEY> --model <gpt-3.5-turbo/gpt-4/gpt-4-32k/...>

  • By default, these will set up the executor with geminipro and chatgpt as the access_code respectively. If you'd like to adjust the access_code the executor is setup with, you can use --access_code <your_desired_access_code>.

Advanced Usage

Detailed Generation Args

In addition to the debug executor, other executors allow you to specify detailed generation args, either through config file or command line arguments. On-premises models also allow you to specify a system prompt and prompt template. For details, use kuwa-executor [executor] --help.

Custom Executors

Kuwa Executor can be viewed as a function or server that provides a specific functionality. The interface is defined in kuwa.executor.LLMExecutor, which is a function that takes in the user's chat history and outputs text. You can extend this class to define your own custom executor.

The simplest implementation of LLMExecutor can be seen in debug.py and dummy.py. Here is an explanation of each API:

  • __init__: Initialize the service. Make sure to call super().__init__() to complete the initialization.
  • extend_arguments: Optionally add command-line arguments. Use the argparse built-in library for parsing arguments.
  • setup: Initialize anything you need. The command line arguments have been parsed at this stage and can be accessed via the self.args variable.
  • llm_compute: The main method for handling requests. Please use an asynchronous iterator to implement this method.
  • abort: Called when the request is aborted by the user. It is expected to interrupt the current request in progress.

Connecting to other Inference Environments

Kuwa Executor can be easily connected to other inference environments, making it easy to integrate with existing open-source software.

Currently, any OpenAI API compatible inference server can be used with the ChatGPT Executor.

Here's an example using vLLM, a high-throughput inference engine.

1. Start vLLM Server (Shown using the Google Gemma 7B Instruct model as an example)

docker run --runtime nvidia --gpus all \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HUGGING_FACE_HUB_TOKEN=<secret>" \
    -p 8000:8000 \
    --ipc=host \
    vllm/vllm-openai:latest \
    --model google/gemma-7b-it --dtype half

2. Start Kuwa ChatGPT Executor

kuwa-executor chatgpt --access_code vllm --log debug \
    --base_url "http://localhost:8000/v1" `# Change the API base URL to vLLM` \
    --api_key dummy `# Dummy API Key` \
    --no_override_api_key `#Disable override the system API key with user API key.` \
    --model "google/gemma-7b-it" `# Specify Gemma 7B model`

Integrating the TAIDE API

The TAIDE API provided by the National Center for High-performance Computing (NCHC) can also be easily integrated with our ChatGPT Executor.

kuwa-executor chatgpt --access_code taide-api --log debug \
    --base_url "https://td.nchc.org.tw/api/v1/" `#TAIDE API path on NCHC` \
    --no_override_api_key `#Disable override the system API key with user API key.` \
    --api_key "YOUR_API_KEY" `#Input the API key that you have applied for` \
    --model "TAIDE/a.2.0.0-SG" `#Using the a.2.0.0 model` \
    --system_prompt "你是一個來自台灣的AI助理，你的名字是TAIDE，樂於以台灣人的立場幫助使用者，會用繁體中文回答問題。" `#TAIDE's default system prompt`