Practical overview
When it fits
Use NaturalCC when you need a Python framework for code-model research, dataset preparation or benchmark-oriented evaluation, and can supply the model checkpoints, data and computing environment for the chosen workflow.
Who it is for
- Researchers developing code-intelligence models and prototypes.
- Developers building model-based workflows for code generation, completion, summarization, retrieval or clone detection.
- Teams evaluating code models against software-engineering benchmarks.
Documented uses
- Train custom models for code generation or completion.
- Prepare code and documentation datasets for summarization research.
- Build and evaluate code retrieval or clone-detection experiments.
- Prototype type-inference models.
Requirements & limits
The repository is public and identifies NaturalCC as open source under the MIT license.
- The README specifies Python 3.6 or later, PyTorch 1.4 or later and GCC/G++ 5.0 or later.
- NVIDIA GPU, NCCL and CUDA Toolkit are optional but recommended for training new models; NVIDIA Apex is optional for faster training.
- The repository describes support for Code Llama, CodeT5, CodeGen and StarCoder.
- A Hugging Face token is required for certain models, including StarCoder.
- The documented generation example expects a downloaded checkpoint, a JSON prompt dataset and a CUDA device.
Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.
Run the documented code-generation example
Use a downloaded model checkpoint and JSON prompt dataset with NaturalCC’s generation task to save generated code results.
Before you start
- NaturalCC installed with its listed Python requirements and additional dependencies.
- A downloaded compatible model checkpoint; the README example uses CodeLlama-7B.
- A JSON dataset containing test cases with an input field.
- A CUDA device, as specified in the example task initialization.
Steps
Download a model checkpoint and prepare a JSON file of code prompts.
Initialize GenerationTask with the documented task name and CUDA device.
Load the checkpoint with from_pretrained and the JSON file with load_dataset.
Call task.run with an output path, batch_size=1 and max_length=50.
Expected outcome
The example writes generation results to the specified output file.
Guide limitations
- The example requires users to provide the checkpoint, dataset path and output path.
- The README specifies a CUDA device for this example; GPU, NCCL and CUDA are listed as optional but recommended for training new models.
Prepare the documented Python summarization dataset
Download, clean and preprocess the Python WAN dataset for the repository’s summarization workflow.
Before you start
- NaturalCC repository cloned locally.
- The Python environment and dependencies described in the repository installation instructions.
Steps
Run the Python WAN dataset download script.
Run the dataset cleaning module.
Run the attributes-cast module.
Run the summarization preprocessing module to save code and docstring tokens in MMAP format.
Expected outcome
The documented dataset preparation steps produce cleaned and converted data, including MMAP-formatted code and docstring tokens.
Compare options for Model inference and serving
These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.
| Project | Documented purpose | Platform | Access and requirements |
|---|---|---|---|
| Naturalcc | Train custom models for code generation or completion. | Linux, macOS, Windows | The repository is public and identifies NaturalCC as open source under the MIT license. |
| Hyperpose | Customize pose-estimation architectures and datasets. | Docker, Linux, macOS, Windows | |
| Multi Task Nlp | Define multiple tasks and their data and model settings in a YAML file. | Linux, macOS, Windows | The project is an open-source library under the Apache-2.0 license, distributed through its public GitHub repository. |
Sources & review
Source record details
- Source availability
- Active
- Sources checked
- 13 October 2026
- Original website title
- CGCL-codes/naturalcc · Official homepage title · retained in its source language