Running deepseek at home with only 5$

Introduction

The Raspberry Pi Pico is a cost-effective microcontroller that is ideal for resource-constrained machine learning applications. By leveraging lightweight AI models such as DeepSeek, you can perform local inference tasks without relying on cloud services. This guide will walk you through deploying and running the DeepSeek model on the Raspberry Pi Pico.

Prerequisites

A Raspberry Pi Pico or Pico W microcontroller.
A computer with Python or MicroPython installed.
Basic knowledge of programming with microcontrollers.
The quantized DeepSeek model files optimized for embedded systems.
Edge Impulse or TensorFlow Lite Micro libraries for deployment.

Step 1: Setting Up the Raspberry Pi Pico

Before deploying the DeepSeek model, ensure your Raspberry Pi Pico is ready for development:

Install MicroPython: Flash MicroPython firmware onto your Pico using tools like Thonny IDE or esptool.py.
Set Up Libraries: Install required libraries such as TensorFlow Lite Micro or Edge Impulse SDK for running machine learning models.
Connect Hardware: Connect your Pico to your computer via USB and verify communication using a serial terminal.

Step 2: Preparing the DeepSeek Model

The DeepSeek model must be optimized to fit within the constraints of the Raspberry Pi Pico:

Select a Lightweight Model: Use a distilled version of the DeepSeek model (e.g., 1.5b parameters) suitable for low-power devices.
Quantize the Model: Convert the model to an 8-bit integer format using TensorFlow Lite's quantization tools to reduce memory usage.
Export as C Array: Convert the quantized model into a C array that can be embedded directly into your firmware.


python
# Example: Converting a TensorFlow Lite model to a C array
import tensorflow as tf

model = tf.lite.TFLiteConverter.from_saved_model("path_to_model")
model.optimizations = [tf.lite.Optimize.DEFAULT]
tflite_quantized_model = model.convert()

# Save as .c array
with open("deepseek_model.cc", "wb") as f:
    f.write(tflite_quantized_model)

Step 3: Deploying the Model to Raspberry Pi Pico

Create a program that loads and runs the DeepSeek model on your Pico:


c
// Example code for running a TensorFlow Lite Micro model on Raspberry Pi Pico
#include "tensorflow/lite/micro/all_ops_resolver.h"
#include "tensorflow/lite/micro/micro_interpreter.h"
#include "tensorflow/lite/schema/schema_generated.h"
#include "tensorflow/lite/version.h"

// Include the quantized model
extern const unsigned char deepseek_model[];
extern const int deepseek_model_len;

void setup() {
    Serial.begin(115200);
    Serial.println("Initializing TensorFlow Lite...");

    // Load model
    const tflite::Model* model = tflite::GetModel(deepseek_model);
    if (model->version() != TFLITE_SCHEMA_VERSION) {
        Serial.println("Model schema version mismatch!");
        return;
    }

    // Set up interpreter
    static tflite::MicroInterpreter interpreter(model, tensor_arena, tensor_arena_size);
    interpreter.AllocateTensors();

    Serial.println("Model loaded successfully!");
}

void loop() {
    // Perform inference here
}

Steps:

Add dependencies: Include TensorFlow Lite Micro libraries in your project.
Compile and flash: Use tools like PlatformIO or Arduino IDE to compile and upload the firmware to your Pico.

Step 4: Running Inference

Once deployed, you can interact with the DeepSeek model via UART or GPIO inputs. For example, send a query through Serial Monitor and receive an AI-generated response in real time.


python
# Example Python script to send queries via UART
import serial

ser = serial.Serial('/dev/ttyUSB0', 115200)
ser.write(b"What is AI?\n")
response = ser.readline()
print("AI Response:", response.decode('utf-8'))

Troubleshooting Tips

If you encounter memory issues, reduce the size of your tensor arena or use an even smaller model variant.
Ensure that your firmware includes all necessary libraries and dependencies for TensorFlow Lite Micro.
If performance is slow, consider optimizing input data preprocessing steps or using hardware accelerators like external ML chips.

Conclusion

The Raspberry Pi Pico offers an affordable platform for deploying lightweight AI models like DeepSeek. By following this guide, you can run local inference tasks efficiently on this microcontroller, opening doors to various IoT and edge computing applications. Experiment with different use cases and explore how this integration can enhance your projects!

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