---
license: mit
library_name: transformers
---
# CodeGenModel

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  <img src="figures/fig1.png" width="60%" alt="CodeGenModel" />
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  <a href="LICENSE" style="margin: 2px;">
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## 1. Introduction

CodeGenModel is specialized for code generation tasks. This model has been selected as the best checkpoint based on code generation benchmark performance.

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  <img width="80%" src="figures/fig3.png">
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The model demonstrates outstanding performance in code-related tasks while maintaining strong general capabilities.

## 2. Evaluation Results

### Comprehensive Benchmark Results

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| | Benchmark | CodeModel-v1 | CodeModel-v2 | CodeGenModel |
|---|---|---|---|---|
| **Core Reasoning Tasks** | Math Reasoning | 0.510 | 0.535 | 0.550 |
| | Logical Reasoning | 0.789 | 0.801 | 0.819 |
| | Common Sense | 0.716 | 0.702 | 0.736 |
| **Language Understanding** | Reading Comprehension | 0.671 | 0.685 | 0.700 |
| | Question Answering | 0.582 | 0.599 | 0.607 |
| | Text Classification | 0.803 | 0.811 | 0.828 |
| | Sentiment Analysis | 0.777 | 0.781 | 0.792 |
| **Generation Tasks** | Code Generation | 0.615 | 0.631 | 0.650 |
| | Creative Writing | 0.588 | 0.579 | 0.610 |
| | Dialogue Generation | 0.621 | 0.635 | 0.644 |
| | Summarization | 0.745 | 0.755 | 0.767 |
| **Specialized Capabilities**| Translation | 0.782 | 0.799 | 0.804 |
| | Knowledge Retrieval | 0.651 | 0.668 | 0.676 |
| | Instruction Following | 0.733 | 0.749 | 0.676 |
| | Safety Evaluation | 0.718 | 0.701 | 0.767 |

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### Overall Performance Summary
CodeGenModel achieves top performance on code generation tasks among all checkpoints in this training run.

## 3. How to Use

### Installation
```bash
pip install transformers
```

### Quick Start
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("CodeGenModel")
tokenizer = AutoTokenizer.from_pretrained("CodeGenModel")
```

## 4. License
This repository is licensed under the [MIT License](LICENSE).

## 5. Contact
Open an issue on our GitHub for questions.