Local AI is revolutionizing how developers tackle coding challenges by bringing powerful models directly to their devices. Private LLM supports the Qwen 2.5 Coder family, which includes some of the best coding AI models for offline use on iPhone, iPad, and Mac. Whether you're debugging complex algorithms, writing new code, or optimizing existing solutions, these models ensure speed, precision, and privacy—all while running locally.

People often ask us, "What is the best coding model to use with Private LLM?" To help you make the most of these tools, we’ve written this guide. Our model recommendations evolve over time, and we will keep this page updated as we continue to add newer, state-of-the-art models to Private LLM.

Currently, Private LLM supports over 60+ AI models, but we recommend the Qwen 2.5 Coder family due to its stellar performance in coding benchmarks.

Why Choose Qwen 2.5 Coder?

Developed by Alibaba's Qwen research team, Qwen 2.5 Coder is a specialized large language model designed for coding tasks. It supports 92+ programming languages, including Python, Java, C++, Ruby, and Rust, making it versatile for various development needs.

Model Features:

Multiple Sizes: Ranging from 0.5 billion to 32 billion parameters, Qwen 2.5 Coder caters to devices with varying computational resources.
Exceptional Benchmark Performance: The Qwen 2.5-32B-Instruct model has achieved state-of-the-art results in coding benchmarks, rivaling proprietary models like GPT-4o and Gemini. It excels in:
Code generation
Code repair
Reasoning tasks
Open-Source Flexibility: Licensed under Apache 2.0, Qwen 2.5 Coder allows developers to freely customize and integrate the model into their projects.

The Qwen 2.5 Coder family represents the best in local coding AI, ensuring reliable performance across a wide range of devices and tasks. Whether you're on a Mac, iPad, or iPhone, Qwen 2.5 Coder delivers precision, speed, and privacy, setting the gold standard for local coding AI solutions.

In this guide, we’ll explore:

The top Qwen 2.5 Coder models available on Private LLM
How parameter size impacts performance
Tips for setting optimal sampling settings (temperature and top-p)
Crafting effective system prompts for coding tasks
Why Private LLM outperforms alternatives like Ollama and LM Studio

Top Qwen 2.5 Coder Models on Private LLM

The Qwen 2.5 Coder series offers a range of models optimized for coding tasks, each catering to different device capabilities and memory constraints. Here's an overview of the available models, their parameter sizes, and corresponding device requirements:

Model	Parameters	Device Compatibility	Minimum RAM
Qwen 2.5 Coder 32B	32 billion	High-end Apple Silicon Macs	24GB+
Qwen 2.5 Coder 14B	14 billion	Macs and iPads with substantial memory	16GB+
Qwen 2.5 Coder 7B	7 billion	iPhones, iPads, and Macs	8GB+
Qwen 2.5 Coder 3B	3 billion	iPhones, iPads, and Macs	4GB+
Qwen 2.5 Coder 1.5B	1.5 billion	iPhones, iPads, and Macs	4GB+
Qwen 2.5 Coder 0.5B	0.5 billion	iPhones, iPads, and Macs	4GB+

Performance Benchmarks:

Qwen 2.5 Coder 32B: This flagship model has achieved state-of-the-art performance among open-source code models. It excels in code generation, repair, and reasoning tasks, matching the coding capabilities of GPT-4o. For instance, on the Aider code repair benchmark, it scored 74%, performing comparably to GPT-4o.
Qwen 2.5 Coder 14B: Balances performance and resource efficiency, making it suitable for devices with 16GB RAM. It delivers strong results across various coding benchmarks, outperforming many larger models.
Qwen 2.5 Coder 7B: Designed for broader device compatibility, including iPhones and iPads with 8GB RAM. Despite its smaller size, it demonstrates impressive coding abilities, achieving high scores on benchmarks like MBPP.

Choosing the Right Model:

Larger Models (14B and 32B): Ideal for complex coding tasks, offering superior reasoning and context handling. Best suited for high-end devices with ample RAM.
Smaller Models (0.5B to 7B): Suitable for devices with limited memory, these models handle standard coding tasks efficiently, ensuring responsiveness on iPhones and iPads.

Selecting a model that aligns with your device's capabilities will ensure optimal performance and a seamless coding experience.

How Parameter Size Affects Model Performance

Parameter size refers to the number of connections (weights) in an AI model. Larger models have more parameters, enabling them to:

Understand and process complex instructions.
Generate code with higher accuracy and fewer bugs.
Handle longer contexts for in-depth code analysis.

However, they also demand more RAM and processing power. If you’re running Private LLM on a device with limited memory, opt for smaller models but set clear prompts for best results.

Setting the Right Sampling Settings for Coding

When using Private LLM for coding, precision is crucial. You can fine-tune the model's behavior using temperature and top-p settings.

Temperature

Controls randomness in AI outputs.
For coding, use a low temperature (0.1–0.3) to produce consistent, reliable results.

Top-p

Limits the probability space considered by the AI.
Set top-p to 0.1–0.3 for coding to focus on the most likely completions and avoid errors.

Recommended Sampling Settings for Coding

Task	Temperature	Top-p
Basic Programming Tasks	0.1	0.1
Algorithm Implementation	0.2	0.2
Debugging/Refactoring	0.0	0.1
Creative Coding Projects	0.4	0.3

Tip: Start with lower settings and adjust if you need more variety or creativity in the AI’s suggestions.

Designing Effective System Prompts for Coding Tasks

System prompts are the foundation for guiding AI behavior. A well-crafted prompt ensures the model generates accurate, context-aware code. Here are examples and best practices:

Example 1: Debugging Focus

You are a Python expert specializing in debugging. Your task is to:
1. Explain the code's intended functionality.
2. Identify bugs or performance issues.
3. Suggest fixes with clear explanations.
4. Provide a refactored version of the code.

Example 2: Teaching-Oriented

You are teaching Python to beginners. Your task is to:
1. Break down complex concepts into simple steps.
2. Write code examples with comments.
3. Explain why each approach works.
4. Suggest practice exercises.

Why These Prompts Work

Clarity of Purpose: They define what the AI should focus on.
Context Setting: Background information helps the AI understand the task.
Quality Guidelines: Specific requirements (e.g., comments, PEP 8) lead to better outputs.

Why Private LLM Outperforms Ollama and LM Studio

Superior Quantization Techniques

Private LLM uses OmniQuant and GPTQ, which optimize performance and reduce resource demands. This results in:

Faster inference speeds.
Higher accuracy compared to RTN-quantized models used by Ollama or LM Studio.

Full Apple Ecosystem Integration

Works seamlessly across iPhone, iPad, and Mac.
Takes full advantage of Apple Silicon’s hardware acceleration.

Offline Functionality with Zero Data Collection

No tracking, no logging, no telemetry—fully private.
All processing happens locally on your device, ensuring nothing ever leaves your device.

Commercial Use and Subscription-Free

Unlike free alternatives like LM Studio, which prohibit commercial use, Private LLM imposes no such restrictions.
Pay once and enjoy lifetime access without recurring fees, making it ideal for personal and commercial projects alike.

Code Better, Faster, and Locally with Private LLM

Private LLM makes cutting-edge AI coding assistants accessible on your iPhone, iPad, and Mac. Whether you're a beginner learning to code or an experienced developer, the Qwen 2.5 Coder models offer precision, speed, and privacy.

Ready to harness the power of local coding AI? Download Private LLM and explore the best coding AI models tailored for your device.