How can I customize the inference parameters for DeepSeek-R1 in FastAPI

Step 1: Environment Setup

First, ensure you have a suitable environment for development. This includes Python 3.8 or later, pip for package management, and a virtual environment to keep dependencies isolated.

bash
# Create a new virtual environment
python -m venv deepseek_env

# Activate the environment
# On Windows
deepseek_env\Scripts\activate
# On macOS/Linux
source deepseek_env/bin/activate

# Install required packages
pip install fastapi uvicorn deepseek-api

Step 2: Model Preparation

DeepSeek-R1 offers various model sizes, including distilled versions that are more practical for deployment. You can use tools like Ollama to manage and serve these models locally.

1. Install Ollama: Follow the instructions on the Ollama documentation to install and configure it for serving DeepSeek-R1 models.

2. Download DeepSeek-R1 Model: Use Ollama to download the desired DeepSeek-R1 model size.

Step 3: Fine-Tuning the Model

Fine-tuning allows you to adapt the model to your specific use case. Here’s how you can do it:

1. Prepare a Dataset: Create a domain-specific dataset in JSON or CSV format.

2. Fine-Tune the Model: Use DeepSeek’s fine-tuning scripts to adapt the model. You can leverage libraries like `peft`, `unsloth`, and `accelerate` for efficient fine-tuning.

python
   # Example fine-tuning command
   python finetune.py --dataset your_dataset.json --output_dir fine_tuned_model/
   

3. Save and Evaluate the Model: After fine-tuning, save the model and evaluate its performance.

python
   # Save the fine-tuned model
   model.save_pretrained("fine_tuned_model/")
   

Step 4: Integrating with FastAPI

Now, integrate the fine-tuned model with FastAPI to create a customizable API.

1. Create a FastAPI App: Initialize a FastAPI application.

python
   from fastapi import FastAPI
   from transformers import AutoModelForCausalLM, AutoTokenizer

   app = FastAPI()
   model_name = "fine_tuned_model/"
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   model = AutoModelForCausalLM.from_pretrained(model_name)
   

2. Define API Endpoints: Create endpoints for inference. You can customize the inference parameters by adjusting the model inputs or processing logic.

python
   @app.post("/generate")
   async def generate(prompt: str):
       inputs = tokenizer(prompt, return_tensors="pt")
       outputs = model.generate(**inputs, max_length=100)  # Example parameter customization
       return {"response": tokenizer.decode(outputs)}
   

3. Run the API: Use Uvicorn to run the FastAPI application.

bash
   uvicorn app:app --host 0.0.0.0 --port 8000
   

Step 5: Customizing Inference Parameters

To customize inference parameters, you can modify the model generation logic within the API endpoint. For example, you can adjust the `max_length`, `num_beams`, or `no_repeat_ngram_size` parameters based on your requirements.

python
# Customizing inference parameters
outputs = model.generate(
    inputs,
    max_length=100,  # Maximum length of the generated text
    num_beams=4,     # Number of beams for beam search
    no_repeat_ngram_size=3  # Size of n-grams to avoid repetition
)

Step 6: Streaming Responses

If you need to handle large responses or provide real-time updates, consider using FastAPI’s `StreamingResponse`. This allows the server to send chunked responses to the client.

python
from fastapi.responses import StreamingResponse

# Example of streaming response
@app.post("/api/stream")
async def stream_data(prompt: str):
    def stream():
        # Generate text in chunks
        for chunk in generate_chunks(prompt):
            yield chunk

    return StreamingResponse(stream(), media_type="text/plain")

By following these steps, you can effectively customize the inference parameters for DeepSeek-R1 in a FastAPI application, ensuring that your AI-powered service meets your specific needs.

Citations:
[1] https://blog.stackademic.com/integrating-deepseek-r1-with-fastapi-building-an-ai-powered-resume-analyzer-code-demo-4e1cc29cdc6e
[2] https://www.byteplus.com/en/topic/397556
[3] https://www.firecrawl.dev/blog/fine-tuning-deepseek
[4] https://github.com/aws-samples/sample-chatbot-lambda-snapstart
[5] https://www.youtube.com/watch?v=mtFo6uG1VgY
[6] https://blog.domainindia.com/2025/01/30/deepseek-openwebui-installation-guide/
[7] https://vadim.blog/deepseek-r1-ollama-fastapi
[8] https://api-docs.deepseek.com