Instructions to use procedure2012/Aurora-Coder-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use procedure2012/Aurora-Coder-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="procedure2012/Aurora-Coder-Base")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("procedure2012/Aurora-Coder-Base") model = AutoModel.from_pretrained("procedure2012/Aurora-Coder-Base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
| license: mit | |
| library_name: transformers | |
| # Aurora-Coder-Base | |
| <!-- markdownlint-disable first-line-h1 --> | |
| <!-- markdownlint-disable html --> | |
| <!-- markdownlint-disable no-duplicate-header --> | |
| <div align="center"> | |
| <img src="figures/fig1.png" width="60%" alt="Aurora-Coder-Base" /> | |
| </div> | |
| <hr> | |
| <div align="center" style="line-height: 1;"> | |
| <a href="LICENSE" style="margin: 2px;"> | |
| <img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/> | |
| </a> | |
| </div> | |
| ## 1. Introduction | |
| Aurora-Coder-Base is a code-specialised checkpoint fine-tuned on a deduplicated multi-language corpus. It targets repository-level code completion and agentic tool use. | |
| ## 2. Evaluation Results | |
| ### Comprehensive Benchmark Results | |
| <div align="center"> | |
| | | Benchmark | StarBase | CodeNova | Aurora-mini | Aurora-Coder-Base | | |
| |---|---|---|---|---|---| | |
| | **Core Reasoning Tasks** | Math Reasoning | 0.579 | 0.535 | 0.552 | 0.592 | | |
| | | Logical Reasoning | 0.810 | 0.831 | 0.824 | 0.846 | | |
| | | Common Sense | 0.731 | 0.734 | 0.750 | 0.761 | | |
| | **Language Understanding** | Reading Comprehension | 0.681 | 0.711 | 0.673 | 0.732 | | |
| | | Question Answering | 0.607 | 0.592 | 0.591 | 0.628 | | |
| | | Text Classification | 0.833 | 0.808 | 0.832 | 0.843 | | |
| | | Sentiment Analysis | 0.785 | 0.780 | 0.756 | 0.806 | | |
| | **Generation Tasks** | Code Generation | 0.676 | 0.665 | 0.642 | 0.692 | | |
| | | Creative Writing | 0.609 | 0.623 | 0.639 | 0.656 | | |
| | | Dialogue Generation | 0.657 | 0.616 | 0.618 | 0.673 | | |
| | | Summarization | 0.762 | 0.780 | 0.748 | 0.787 | | |
| | **Specialized Capabilities** | Translation | 0.808 | 0.778 | 0.808 | 0.816 | | |
| | | Knowledge Retrieval | 0.655 | 0.645 | 0.691 | 0.697 | | |
| | | Instruction Following | 0.725 | 0.743 | 0.742 | 0.779 | | |
| | | Safety Evaluation | 0.715 | 0.700 | 0.703 | 0.759 | | |
| </div> | |
| ### Overall Performance Summary | |
| The Aurora-Coder-Base demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks. | |
| ## 3. Chat Website & API Platform | |
| We offer a chat interface and API for you to interact with Aurora-Coder-Base. Please check our official website for more details. | |
| ## 4. How to Run Locally | |
| Please refer to our code repository for more information about running Aurora-Coder-Base locally. | |
| ### Temperature | |
| We recommend setting the temperature parameter to 0.6. | |
| ## 5. License | |
| This repository is released under the mit license. The model supports commercial use. | |
| ## 6. Contact | |
| If you have any questions, please contact us at hello@aurora.dev. | |