Deploying GGUF Models with vLLM on Inferless

This repository provides a template for seamlessly deploying GGUF models using vLLM on the Inferless platform.

Fork the Repository

Get started by forking the repository. You can do this by clicking on the fork button in the top right corner of the repository page.

This will create a copy of the repository in your own GitHub account, allowing you to make changes and customize it according to your needs.

Create a Custom Runtime in Inferless

To access the custom runtime window in Inferless, simply navigate to the sidebar and click on the Create new Runtime button. A pop-up will appear.

Next, provide a suitable name for your custom runtime and proceed by uploading the inferless-runtime-config.yaml file given above. Finally, ensure you save your changes by clicking on the save button.

Add Your NFS Volume

Go into the inferless.yaml and replace <YOUR_NFS_VOLUME_MOUNT_PATH> with your volume mount path.

Import the Model in Inferless

Log in to your inferless account, select the workspace you want the model to be imported into and click the Add Model button.

Choose Github as your model source from the provider list, and use the forked repo URL as the GitHub Repository and select the Branch.

Enter all the required details to Import your model. Refer this link for more information on model import.

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Curl Command

Following is an example of the curl command you can use to make inference. You can find the exact curl command in the Model's API page in Inferless.

curl --location '<your_inference_url>' \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer <your_api_key>' \
    --data '{
      "inputs": [
        {
          "name": "prompt",
          "shape": [1],
          "data": ["What is deep learning?"],
          "datatype": "BYTES"
        },
        {
          "name": "system_prompt",
          "optional": true,
          "shape": [1],
          "data": ["You are a friendly bot."],
          "datatype": "STRING"
        },
        {
          "name": "temperature",
          "optional": true,
          "shape": [1],
          "data": [0.7],
          "datatype": "FP32"
        },
        {
          "name": "top_p",
          "optional": true,
          "shape": [1],
          "data": [0.1],
          "datatype": "FP32"
        },
        {
          "name": "repetition_penalty",
          "optional": true,
          "shape": [1],
          "data": [1.18],
          "datatype": "FP32"
        },
        {
          "name": "max_tokens",
          "optional": true,
          "shape": [1],
          "data": [512],
          "datatype": "INT16"
        },
        {
          "name": "top_k",
          "optional": true,
          "shape": [1],
          "data": [40],
          "datatype": "INT8"
        }
      ]
    }'

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Customizing the Code

Open the app.py file. This contains the main code for inference. It has three main functions, initialize, infer and finalize.

Initialize - This function is executed during the cold start and is used to initialize the model. If you have any custom configurations or settings that need to be applied during the initialization, make sure to add them in this function.

Infer - This function is where the inference happens. The argument to this function inputs, is a dictionary containing all the input parameters. The keys are the same as the name given in inputs. Refer to input for more.

def infer(self, inputs):
    prompt = inputs["prompt"]
    system_prompt = inputs.get("system_prompt","You are a friendly bot.")
    temperature = inputs.get("temperature",0.7)
    top_p = inputs.get("top_p",0.1)
    top_k = int(inputs.get("top_k",40))
    repetition_penalty = inputs.get("repetition_penalty",1.18)
    max_tokens = inputs.get("max_tokens",256)

Finalize - This function is used to perform any cleanup activity for example you can unload the model from the gpu by setting to None.

def finalize(self):
    self.llm = None

For more information refer to the Inferless docs.