NextCoder-32B: An Open Source Big Model for Code Editing and Optimization Support

NextCoder-32B is an open source editorial big model developed by Microsoft and released on the Hugging Face platform. It is based on the Qwen2.5 model, optimized with Selective Knowledge Transfer (SeleKT) technology, and designed for code generation, repair, and optimization. The model supports very long contexts of up to 32K tokens for complex programming tasks. nextCoder-32B performs well in code editing benchmarks, matching the performance of GPT-4o, with an improvement of about 44% over the baseline model. it provides an easy-to-use interface that can be quickly loaded and used by developers through Hugging Face's Transformers library. It provides an easy-to-use interface that developers can quickly load and use through Hugging Face's Transformers library. The model is suitable for developers who need to efficiently handle code-related tasks, but the potential risk of malicious hints should be noted and code review is recommended before use.

Function List

• Code Generation: Generate accurate code snippets based on natural language prompts entered by the user.
• Code Repair: Automatically detects and fixes syntax errors, logic problems and potential vulnerabilities in your code.
• Code Optimization: Improve the code structure to enhance the code running efficiency and readability.
• Long Context Support: Supports inputs of up to 32K tokens, ideal for working with large code bases or complex projects.
• Multi-language support: compatible with Python, JavaScript, C++ and other programming languages.
• Integrated Transformers Library: Load models through Hugging Face's new Transformers library for an easy development experience.

Using Help

Installation process

To use NextCoder-32B, you need to install Hugging Face's Transformers library first, and it is recommended to use the latest version (≥4.37.0 is recommended) to avoid compatibility issues. Below are the detailed installation and usage steps:

1. Installing the Python Environment
   Make sure that Python 3.8 or above is installed locally. You can check the Python version with the following command:

   python --version
   

2. Installing the Transformers Library
   Install the latest version of the Transformers library using pip:

   pip install transformers>=4.37.0
   

3. Installing PyTorch
   NextCoder-32B relies on PyTorch to run, and it is recommended to install a version with GPU support to improve performance:

   pip install torch
   

   If you are using a GPU, choose the appropriate CUDA version for your hardware, see the PyTorch website.

4. Download the model and splitter
   NextCoder-32B models and splitters can be loaded directly from Hugging Face. Make sure you have enough storage space (the model takes up about 60GB). Below is an example of the loading code:

   from transformers import AutoModelForCausalLM, AutoTokenizer
   model_name = "microsoft/NextCoder-32B"
   model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")
   tokenizer = AutoTokenizer.from_pretrained(model_name)
   

5. Verify Installation
   After running the above code, if no error is reported, it means that the model and the disambiguator are loaded successfully. It is recommended to use a GPU environment to accelerate inference.

Usage

The core function of NextCoder-32B is code generation and repair. The following is the specific operation procedure:

1. Code generation

Users can generate code with natural language hints. For example, generate a division function and handle edge cases:

prompt = """Fix the following function that divides two numbers to handle all the edge cases:
def divide(a, b):
return a/b
"""
messages = [{"role": "user", "content": prompt}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(**model_inputs, max_new_tokens=1024)
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

Sample output might be:

def divide(a, b):
if b == 0:
raise ValueError("Division by zero is not allowed")
if not isinstance(a, (int, float)) or not isinstance(b, (int, float)):
raise TypeError("Inputs must be numbers")
return a / b

2. Code fixes

NextCoder-32B automatically fixes errors in your code. For example, enter a code with a spelling error (e.g. returm), the model is corrected to return and optimize the logic. The operation is similar to code generation, only the problem code is entered as a prompt.

3. Code optimization

If you need to optimize existing code, enter the original code and state the optimization goal. Example:

prompt = """Optimize this function for better performance and readability:
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n-1)
"""

models may return more efficient iterative implementations:

def factorial(n):
if not isinstance(n, int) or n < 0:
raise ValueError("Input must be a non-negative integer")
result = 1
for i in range(1, n + 1):
result *= i
return result

4. Long contextualization

NextCoder-32B supports inputs up to 32K tokens and is suitable for processing large code bases. Users can take an entire file or multiple functions as input, and the model analyzes the context and provides accurate suggestions for changes. For example, input a complete Python file and the model can fix errors in multiple functions at once.

5. Cautions

• safety: NextCoder-32B may generate insecure code for malicious prompts. It is recommended to run the generated code in a sandboxed environment and review it manually.
• hardware requirement: High-performance hardware is required to run the model; a GPU with at least 16GB of video memory or a CPU with 64GB of RAM is recommended.
• dependency version: Using Transformers <4.37.0 may cause loading errors, it is recommended to always keep the latest version.

Advanced Use

• batch file: By batch-entering multiple prompts, the model can generate or fix multiple pieces of code at once, improving efficiency.
• Customized tips: Users can control the style of the output by adjusting the prompt template, e.g. to generate comment-rich code or language-specific code.
• Integration with IDE: Integrate the model into IDEs such as VS Code or PyCharm for real-time code completion via Hugging Face's API or local deployment.

application scenario

1. software development
   Developers can use NextCoder-32B to quickly generate sample code, fix bugs, or optimize existing code to shorten development cycles.
2. Education and learning
   Programming beginners can use the model to generate sample code, understand programming logic, or check for errors in practice code.
3. Code review
   Teams can use models to scan for potential issues before code commits, improving code quality and reducing manual review time.
4. Open Source Project Maintenance
   Open source project maintainers can use the model to batch process code contributions and automatically fix formatting errors or logic problems.

QA

1. What programming languages does NextCoder-32B support?
   The model supports Python, JavaScript, C++, Java and other mainstream programming languages, applicable to most development scenarios.
2. How to avoid generating unsafe code?
   Run generated code in a sandbox environment with manual review. Avoid direct input of hints that could trigger malicious behavior.
3. What hardware is required for the model to run?
   A GPU with at least 16GB of video memory or a CPU with 64GB of RAM is recommended to ensure smooth operation.
4. Do I need to be connected to the Internet to use it?
   The model can be run offline, but you need to download the model files and dependent libraries in advance.