Train Your Own Coder: Fine-Tuning DeepSeek on Private Repos
Quick Answer: Key Takeaways
- The Goal: Teach DeepSeek your company's specific variable names, architectural patterns, and legacy code quirks.
- The Method: We use LoRA (Low-Rank Adaptation) to fine-tune without needing a massive server farm.
- The Cost: Less than $5 in electricity if running locally, or ~$20 on a rented cloud GPU.
- The Hardware: A single NVIDIA RTX 3090/4090 (24GB VRAM) is sufficient for fine-tuning the 7B model.
Why Generic Models Aren't Enough
You have tried GitHub Copilot. You have tried standard ChatGPT. They are great at writing generic Python scripts. But they fail miserably when you ask them to interact with your proprietary, undocumented internal API from 2019.
To get an AI that truly understands your business logic, you need to stop renting and start training. This tutorial is the advanced capstone of our complete The DeepSeek Developer Ecosystem: Why Open Weights Are Winning the 2026 Code War.
We are going to take the base DeepSeek model and force it to learn your codebase.
The "Style" Problem
Every engineering team has a "dialect." Maybe you use Hungarian notation. Maybe you strictly enforce functional programming patterns in JavaScript.
A base model like DeepSeek-Coder-V2 has read the entire internet, but it hasn't read your private GitLab.
Fine-tuning solves this by reducing hallucinations regarding internal libraries, mimicking your specific commenting style, and boosting productivity by predicting your specific boilerplate.
Prerequisites: What You Need
You do not need a cluster of H100s. Because of a technique called QLoRA (Quantized Low-Rank Adaptation), we can fine-tune efficient models on consumer hardware.
The Hardware Checklist:
- GPU: NVIDIA RTX 3090 or 4090 (24GB VRAM is the sweet spot).
- RAM: 64GB System RAM recommended.
- OS: Linux (Ubuntu) or WSL2 on Windows.
If you don't have this hardware, you can rent a GPU on clouds like Lambda Labs or RunPod for pennies per hour.
Step 1: Preparing Your Data
This is the most critical step. Garbage in, garbage out. You cannot just dump your src folder into the model. You need to create "Instruction-Response" pairs.
The Format (JSONL): Your training data should look like this:
{
"instruction": "Create a user login function using our internal AuthWrapper.",
"output": "def login_user(user): \n return AuthWrapper.validate_and_session(user)"
}How to automate this:
- Write a script to scrape your high-quality repositories.
- Use a larger model (like DeepSeek R1 via API) to generate the "Instructions" for your existing code snippets.
- Save this as a
.jsonlfile.
Step 2: The Training Process (LoRA)
We will use Unsloth or Axolotl, the current industry standards for efficient fine-tuning. Why LoRA? Instead of retraining the whole brain (which costs millions), LoRA trains a tiny "adapter" layer that sits on top of the model.
The Workflow:
- Load the Base Model: Download
deepseek-coder-6.7b-instruct. - Load the Adapter: Initialize a LoRA config (Rank 64 or 128 is standard).
- Run Training: Feed your JSONL file into the trainer.
On a 4090, training on 10,000 lines of code might take less than an hour.
Step 3: Merging and Testing
Once training is done, you have a .safetensors adapter file. You can load this into Ollama or your VS Code setup immediately.
The Result: When you type def, it won't just suggest generic code. It will suggest your code.
Curious how much better a custom model performs? We compared a stock model against a fine-tuned version in our I Cancelled Copilot for DeepSeek: The Brutal 30-Day Benchmark.
The difference in syntax accuracy for proprietary functions is night and day.
Conclusion: You Are the Platform
Fine-tuning is the final step in declaring independence from Big Tech APIs. You now own a model that knows your secrets but tells them to no one.
It is cheaper, faster, and infinitely more secure.
Frequently Asked Questions (FAQ)
They serve different purposes. RAG is better for fetching up-to-date documentation. Fine-tuning is better for teaching the model a specific coding style or "muscle memory" for your internal libraries. Ideally, use both.
If you own the hardware, it is free. If you use cloud GPUs, a typical fine-tuning run for a specific repository costs between $2 and $20 in compute credits.
Yes. This is the primary use case for fine-tuning. By feeding it your best-written code, the model biases its weights to replicate that specific syntax and structure.