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
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license: mit
library_name: transformers
---
# Aurora-Coder-Base
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<img src="figures/fig1.png" width="60%" alt="Aurora-Coder-Base" />
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<a href="LICENSE" style="margin: 2px;">
<img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/>
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## 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
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| | 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 |
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### 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.
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