Deploy the DeepSeek-R1-Distill-Qwen-32B using Inferless

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Introduction

The DeepSeek-R1-Distill-Qwen-32B is a latest model from the DeepSeek team, renowned for its advanced reasoning capabilities. Released in January 2025, this model is part of the DeepSeek-R1 series, which stands out for its innovative use of large-scale reinforcement learning (RL) without relying on traditional supervised fine-tuning.

The distillation process incorporated supervised fine-tuning on 800k carefully curated samples from DeepSeek R1, leveraging the powerful Qwen2.5-32B as the base model. This meticulous approach has resulted in a highly efficient and robust model that exemplifies state-of-the-art performance.

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Our Observations

We have deployed the model on an A100 GPU(80GB). Here are our observations:

Note: The inference time and cold start time are average values.

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Defining Dependencies

We are using the vLLM to serve the model on a single A100 (80GB).

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Constructing the GitHub/GitLab Template

Now quickly construct the GitHub/GitLab template, this process is mandatory and make sure you don’t add any file named model.py.

DeepSeek-R1-Distill-Qwen-32B/
├── app.py
├── inferless-runtime-config.yaml
└── inferless.yaml

You can also add other files to this directory.

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Create the Input Schema with Pydantic

Using the inferless Python client and Pydantic, you can define structured schemas directly in your code for input and output, eliminating the need for external file.

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Input Schema

When defining an input schema with Pydantic, you need to annotate your class attributes with the appropriate types, such as str, float, int, etc. These type annotations specifys what type of data each field should contain. The default value serves as the example input for testing with the infer function.

@inferless.request
class RequestObjects(BaseModel):
    prompt: str = Field(default="Explain Deep Learning.")
    temperature: Optional[float] = 0.7
    top_p: Optional[float] = 0.1
    repetition_penalty: Optional[float] = 1.18
    top_k: Optional[int] = 40
    max_tokens: Optional[int] = 256

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Output Schema

The @inferless.response decorator helps you define structured output schemas.

@inferless.response
class ResponseObjects(BaseModel):
    generated_text: str = Field(default='Test output')

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Usage in the infer Function

Once you have annotated the objects you can expect the infer function to receive RequestObjects as input, and returns a ResponseObjects instance as output, ensuring the results adhere to a defined structure.

class InferlessPythonModel:
    def infer(self, request: RequestObjects) -> ResponseObjects:
        
        return ResponseObjects(generated_text=result[0])

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Create the class for inference

In the app.py we will define the class and import all the required functions

1. def initialize: In this function, you will initialize your model and define any variable that you want to use during inference.

2. def infer: This function gets called for every request that you send. Here you can define all the steps that are required for the inference. You can also pass custom values for inference and pass it through inputs(dict) parameter.

3. def finalize: This function cleans up all the allocated memory.

from vllm import LLM
from vllm.sampling_params import SamplingParams
from transformers import AutoTokenizer
import inferless
from pydantic import BaseModel, Field
from typing import Optional

@inferless.request
class RequestObjects(BaseModel):
  prompt: str = Field(default="Explain Deep Learning.")
  temperature: Optional[float] = 0.7
  top_p: Optional[float] = 0.1
  repetition_penalty: Optional[float] = 1.18
  top_k: Optional[int] = 40
  max_tokens: Optional[int] = 256

@inferless.response
class ResponseObjects(BaseModel):
  generated_text: str = Field(default='Test output')

class InferlessPythonModel:
  def initialize(self):
    model_id = "deepseek-ai/DeepSeek-R1-Distill-Qwen-32B"
    self.llm = LLM(model=model_id,gpu_memory_utilization=0.9,max_model_len=5000,dtype="float16")
    self.tokenizer = AutoTokenizer.from_pretrained(model_id)

  def infer(self, request: RequestObjects) -> ResponseObjects:
    sampling_params = SamplingParams(temperature=request.temperature,top_p=request.top_p,repetition_penalty=request.repetition_penalty,
                     top_k=request.top_k,max_tokens=request.max_tokens)
    input_text = self.tokenizer.apply_chat_template([{"role": "user", "content": request.prompt}], tokenize=False)
    result = self.llm.generate(input_text, sampling_params)
    result_output = [output.outputs[0].text for output in result]

    generateObject = ResponseObjects(generated_text = result_output[0])        
    return generateObject

  def finalize(self):
    self.llm = None

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Creating the Custom Runtime

This is a mandatory step where we allow the users to upload their custom runtime through inferless-runtime-config.yaml.

build:
  cuda_version: "12.1.1"
  python_packages:
    - vllm==0.6.6.post1
    - inferless==0.2.6
    - pydantic==2.10.2

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Method A: Deploying the model on Inferless Platform

Inferless supports multiple ways of importing your model. For this tutorial, we will use GitHub.

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Step 1: Login to the inferless dashboard can click on Import model button

Navigate to your desired workspace in Inferless and Click on Add a custom model button that you see on the top right. An import wizard will open up.

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Step 2: Follow the UI to complete the model Import

• Select the GitHub/GitLab Integration option to connect your source code repository with the deployment environment.
• Navigate to the specific GitHub repository that contains your model’s code. Here, you will need to identify and enter the name of the model you wish to import.
• Choose the appropriate type of machine that suits your model’s requirements. Additionally, specify the minimum and maximum number of replicas to define the scalability range for deploying your model.
• Optionally, you have the option to enable automatic build and deployment. This feature triggers a new deployment automatically whenever there is a new code push to your repository.
• If your model requires additional software packages, configure the Custom Runtime settings by including necessary pip or apt packages. Also, set up environment variables such as Inference Timeout, Container Concurrency, and Scale Down Timeout to tailor the runtime environment according to your needs.
• Wait for the validation process to complete, ensuring that all settings are correct and functional. Once validation is successful, click on the “Import” button to finalize the import of your model.

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Step 3: Wait for the model build to complete usually takes ~5-10 minutes

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Step 4: Use the APIs to call the model

Once the model is in ‘Active’ status you can click on the ‘API’ page to call the model

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Here is the Demo:

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Method B: Deploying the model on Inferless CLI

Inferless allows you to deploy your model using Inferless-CLI. Follow the steps to deploy using Inferless CLI.

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Clone the repository of the model

Let’s begin by cloning the model repository:

git clone https://github.com/inferless/DeepSeek-R1-Distill-Qwen-32B.git

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Deploy the Model

To deploy the model using Inferless CLI, execute the following command:

inferless deploy --gpu A100 --runtime inferless-runtime-config.yaml

Explanation of the Command:

• --gpu A100: Specifies the GPU type for deployment. Available options include A10, A100, and T4.
• --runtime inferless-runtime-config.yaml: Defines the runtime configuration file. If not specified, the default Inferless runtime is used.