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
Upload folder using huggingface_hub
Browse files- README.md +68 -0
- config.json +6 -0
- figures/fig1.png +0 -0
- figures/fig2.png +0 -0
- pytorch_model.bin +3 -0
README.md
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---
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license: mit
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library_name: transformers
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---
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# Aurora-Coder-Base
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<!-- markdownlint-disable first-line-h1 -->
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<!-- markdownlint-disable html -->
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<!-- markdownlint-disable no-duplicate-header -->
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<div align="center">
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<img src="figures/fig1.png" width="60%" alt="Aurora-Coder-Base" />
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</div>
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<hr>
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<div align="center" style="line-height: 1;">
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<a href="LICENSE" style="margin: 2px;">
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<img alt="License" src="figures/fig2.png" style="display: inline-block; vertical-align: middle;"/>
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</a>
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</div>
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## 1. Introduction
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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.
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## 2. Evaluation Results
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### Comprehensive Benchmark Results
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<div align="center">
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| | Benchmark | StarBase | CodeNova | Aurora-mini | Aurora-Coder-Base |
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|---|---|---|---|---|---|
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| **Core Reasoning Tasks** | Math Reasoning | 0.579 | 0.535 | 0.552 | 0.592 |
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| | Logical Reasoning | 0.810 | 0.831 | 0.824 | 0.846 |
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| | Common Sense | 0.731 | 0.734 | 0.750 | 0.761 |
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| **Language Understanding** | Reading Comprehension | 0.681 | 0.711 | 0.673 | 0.732 |
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| | Question Answering | 0.607 | 0.592 | 0.591 | 0.628 |
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| | Text Classification | 0.833 | 0.808 | 0.832 | 0.843 |
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| | Sentiment Analysis | 0.785 | 0.780 | 0.756 | 0.806 |
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| **Generation Tasks** | Code Generation | 0.676 | 0.665 | 0.642 | 0.692 |
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| | Creative Writing | 0.609 | 0.623 | 0.639 | 0.656 |
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| | Dialogue Generation | 0.657 | 0.616 | 0.618 | 0.673 |
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| | Summarization | 0.762 | 0.780 | 0.748 | 0.787 |
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| **Specialized Capabilities** | Translation | 0.808 | 0.778 | 0.808 | 0.816 |
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| | Knowledge Retrieval | 0.655 | 0.645 | 0.691 | 0.697 |
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| | Instruction Following | 0.725 | 0.743 | 0.742 | 0.779 |
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| | Safety Evaluation | 0.715 | 0.700 | 0.703 | 0.759 |
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</div>
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### Overall Performance Summary
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The Aurora-Coder-Base demonstrates strong performance across all evaluated benchmark categories, with particularly notable results in reasoning and generation tasks.
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## 3. Chat Website & API Platform
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We offer a chat interface and API for you to interact with Aurora-Coder-Base. Please check our official website for more details.
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## 4. How to Run Locally
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Please refer to our code repository for more information about running Aurora-Coder-Base locally.
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### Temperature
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We recommend setting the temperature parameter to 0.6.
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## 5. License
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This repository is released under the mit license. The model supports commercial use.
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## 6. Contact
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If you have any questions, please contact us at hello@aurora.dev.
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config.json
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{
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"model_type": "bert",
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"architectures": [
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"BertModel"
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]
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}
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figures/fig1.png
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figures/fig2.png
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pytorch_model.bin
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version https://git-lfs.github.com/spec/v1
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oid sha256:965362299a238de576a92dfdd3e32aea7a2bacc94b2c41541c8c9258b923f587
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size 23
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