Model Template: Multi_LoRA_Adapter_Template

The model template supports multiple LoRA (Low-Rank Adaptation) adapters, allowing you to perform a variety of natural language processing tasks with ease. Built on the Mistral-7B model and utilizing the transformers and PEFT libraries, this template enables dynamic switching between different adapters. It has 4 different LoRA adapters(french,sql,dpo,orca) which can be change with each inference request.

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 Hugging Face Access Token

Go into the inferless.yaml and replace <YOUR_HUGGINGFACE_ACCESS_TOKEN> with your hugging face access token. Make sure to check the repo is private to protect your hugging face token.

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.

Select the PyTorch as framework and choose Repo(custom code) as your model source and select your provider, and use the forked repo URL as the Model URL.

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": ["Who are you?"],
          "datatype": "BYTES"
        },
        {
          "name": "adapter_name",
          "shape": [1],
          "data": ["dpo"],
          "datatype": "BYTES"
        },
        {
          "name": "temperature",
          "optional": true,
          "shape": [1],
          "data": [0.7],
          "datatype": "FP32"
        },
        {
          "name": "repetition_penalty",
          "optional": true,
          "shape": [1],
          "data": [1.18],
          "datatype": "FP32"
        },
        {
          "name": "max_new_tokens",
          "optional": true,
          "shape": [1],
          "data": [128],
          "datatype": "INT16"
        }
      ]
    }'

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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"]
    adapter_name = inputs.pop("adapter_name")
    temperature = inputs.get("temperature",0.7)
    repetition_penalty = float(inputs.get("repetition_penalty",1.18))
    max_new_tokens = inputs.get("max_new_tokens",128)

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.model = None

For more information refer to the Inferless docs.