Revolutionize Your Coding with DeepSeek Coder: A Step-by-Step Guide for Developers

By Samvida Labs

Why AI is the Future of Software Development

The world of software development is evolving at lightning speed, and AI-powered coding assistants are leading the charge. Developers today face increasing pressure to:
✔ Deliver high-quality code faster
✔ Reduce repetitive tasks
✔ Debug efficiently
✔ Adapt to new frameworks quickly

Traditional coding methods can’t keep up—but AI-powered tools like DeepSeek Coder can.

At Samvida Labs, we’ve been exploring how AI can supercharge development workflows. In this guide, we’ll show you how to set up DeepSeek Coder—a cutting-edge AI coding assistant—on your local machine and use it to accelerate your Java, Angular, and Spring Boot projects.

What is DeepSeek Coder?

DeepSeek Coder is an open-source AI model designed specifically for code generation, autocompletion, and debugging. Unlike generic AI tools, it understands:
✅ Complex programming logic
✅ Multiple languages (Java, Python, JavaScript, SQL, etc.)
✅ Framework-specific patterns (Spring Boot, Angular, React, etc.)

Whether you're building a multi-tenant SaaS platform (like our example project) or debugging a tricky API issue, DeepSeek Coder can cut development time in half.

Step 1: System Requirements

Before installing, ensure your Windows PC meets these specs:

• OS: Windows 10/11 (64-bit)

• RAM: 16GB+ (32GB recommended for training)

• GPU: NVIDIA (RTX 3060 or better for best performance)

• Storage: 50GB+ free space

• Python 3.9-3.11

(Don’t have a GPU? No worries—you can still run it on CPU, but responses will be slower.)

Step 2: Installing DeepSeek Coder

1 > Install Python & Git

winget install Python.Python.3.11 winget install Git.Git

2️ > Set Up CUDA (For GPU Users)

1. Download CUDA Toolkit 12.1 from NVIDIA

2. Install cuDNN (requires NVIDIA Developer account)

3. Add CUDA to PATH:

   setx PATH "%PATH%;C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v12.1\bin"

3️ > Download DeepSeek Coder Model

git lfs install git clone https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct

(This downloads the 6.7B parameter model, optimized for coding tasks.)

Step 3: Running DeepSeek Coder

Create a Python script (deepseek_interact.py) to interact with the model:

from transformers import AutoModelForCausalLM, AutoTokenizer

import torch model = AutoModelForCausalLM.from_pretrained(

"./deepseek-coder-6.7b-instruct",

device_map="auto",

torch_dtype=torch.float16

)

tokenizer = AutoTokenizer.from_pretrained(model_path)

def generate_code(prompt):

inputs = tokenizer(prompt, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=1024)

return tokenizer.decode(outputs[0], skip_special_tokens=True)

prompt = """[INST] Generate a Spring Boot controller for Support Tickets with:

- Create Ticket

- List Tickets (Paginated)

- Update Status

- Add Comments

[/INST]"""

print(generate_code(prompt))

Run it:

python deepseek_interact.py

Output:
A fully functional Spring Boot controller tailored to your needs!

Step 4: Fine-Tuning for Your Project

Want even better results? Fine-tune DeepSeek Coder on your codebase!

1. Prepare training data (training_data.jsonl):

{"input": "Create JPA entity for Tenant", "output": "@Entity public class Tenant {...}"}

{"input": "Angular service for Ticket API", "output": "@Injectable() export class TicketService {...}"}

2. Run fine-tuning:

from transformers import Trainer, TrainingArguments

trainer = Trainer(

model=model,

args=TrainingArguments(output_dir="./fine-tuned-model", per_device_train_batch_size=4), train_dataset=load_dataset("json", data_files="training_data.jsonl")["train"]

)

trainer.train()

Now, the model speaks your project’s language!

Step 5: Integrate with VS Code

1. Install the Hugging Face VS Code extension

2. Configure in settings.json:

{

"codegpt.api": "local",

"codegpt.model": "./fine-tuned-deepseek-coder",

"codegpt.temperature": 0.7

}

Now, get AI-powered suggestions right in your IDE!

Why This is a Game-Changer

At Samvida Labs, we’ve used DeepSeek Coder to:
✔ Reduce boilerplate coding by 60%
✔ Debug complex issues in minutes
✔ Onboard new developers faster

Whether you're building a tenant management system, a fintech API, or a scalable SaaS product, AI-assisted coding is the future.

Final Thoughts

DeepSeek Coder is not just another autocomplete tool—it’s a productivity multiplier. By following this guide, you can:
✅ Set it up locally
✅ Fine-tune it for your stack
✅ Integrate it into your workflow

Want to see it in action? Try generating a full CRUD API or Angular service today!

Follow Samvida Labs for more AI & DevTools insights!

Your Turn!

Have you tried DeepSeek Coder? What’s your favorite AI coding tool? Let us know in the comments! 🚀