# CodeGen

**Repository Path**: mirrors_salesforce/CodeGen

## Basic Information

- **Project Name**: CodeGen
- **Description**: CodeGen is a family of open-source model for program synthesis. Trained on TPU-v4. Competitive with OpenAI Codex.
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-03-31
- **Last Updated**: 2026-01-10

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

<p align="center">
  <img src="assets/codegen_logo.png" width="25%">
</p>

# CodeGen
Official release for the **CodeGen1** and **CodeGen2** models (`350M`, `1B`, `3B`, `7B` `16B`) for **Program Synthesis** by [Salesforce AI Research](https://www.salesforceairesearch.com/).

<p align="center">
  <img src="assets/two.gif" width="60%">
</p>

## News

**July 2023**

[**CodeGen2.5**](https://github.com/salesforce/CodeGen/tree/main/codegen25) released outperforming 16B parameter models with only 7B.

**May 2023**

**CodeGen2.0** released with strong infill sampling capability.

**March 2022**

**CodeGen1.0** released on par with OpenAI Codex at the time.

## Publications

[CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis](https://arxiv.org/abs/2203.13474)  
[Erik Nijkamp](https://enijkamp.github.io/)\*, [Bo Pang](https://scholar.google.com/citations?user=s9fNEVEAAAAJ&hl=en)\*, [Hiroaki Hayashi](https://hiroakih.me/)\*, [Lifu Tu](https://home.ttic.edu/~lifu/), [Huan Wang](https://scholar.google.com/citations?user=7NpTttkAAAAJ&hl=en), [Yingbo Zhou](https://scholar.google.com/citations?user=H_6RQ7oAAAAJ&hl=en), [Silvio Savarese](https://scholar.google.com/citations?user=ImpbxLsAAAAJ&hl=en), and [Caiming Xiong](https://scholar.google.com/citations?user=vaSdahkAAAAJ&hl=en)   
ICLR, 2023

[CodeGen2: Lessons for Training LLMs on Programming and Natural Languages](https://arxiv.org/abs/2305.02309)   
[Erik Nijkamp](https://enijkamp.github.io/)\*, [Hiroaki Hayashi](https://hiroakih.me/)\*, [Caiming Xiong](https://scholar.google.com/citations?user=vaSdahkAAAAJ&hl=en), [Silvio Savarese](https://scholar.google.com/citations?user=ImpbxLsAAAAJ&hl=en), and [Yingbo Zhou](https://scholar.google.com/citations?user=H_6RQ7oAAAAJ&hl=en)  
ICLR, 2023

## Usage

The models are available on the [Hugging Face Hub](https://huggingface.co/models?search=salesforce+codegen).

**CodeGen1.0**

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-2B-mono")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-2B-mono")
inputs = tokenizer("# this function prints hello world", return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0], truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"]))
```

**CodeGen2.0**

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen2-7B")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen2-7B", trust_remote_code=True, revision="main")
inputs = tokenizer("# this function prints hello world", return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0], truncate_before_pattern=[r"\n\n^#", "^'''", "\n\n\n"]))
```

**CodeGen2.5**

```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-mono", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen25-7b-mono")
inputs = tokenizer("# this function prints hello world", return_tensors="pt")
sample = model.generate(**inputs, max_length=128)
print(tokenizer.decode(sample[0]))
```

## Training

The Jaxformer library for data pre-processing, training and fine-tuning the CodeGen models can be found here:

https://github.com/salesforce/jaxformer

## Citation
If you find our code or paper useful, please cite the paper:
```bibtex
@article{nijkamp2022codegen,
  title={CodeGen: An Open Large Language Model for Code with Multi-Turn Program Synthesis},
  author={Nijkamp, Erik and Pang, Bo and Hayashi, Hiroaki and Tu, Lifu and Wang, Huan and Zhou, Yingbo and Savarese, Silvio and Xiong, Caiming},
  journal={ICLR},
  year={2023}
}

@article{nijkamp2023codegen2,
  title={CodeGen2: Lessons for Training LLMs on Programming and Natural Languages},
  author={Nijkamp, Erik and Hayashi, Hiroaki and Xiong, Caiming and Savarese, Silvio and Zhou, Yingbo},
  journal={ICLR},
  year={2023}
}
```

## Ethics disclaimer for Salesforce AI models, data, code

This release is for research purposes only in support of an academic
paper. Our models, datasets, and code are not specifically designed or
evaluated for all downstream purposes. We strongly recommend users
evaluate and address potential concerns related to accuracy, safety, and
fairness before deploying this model. We encourage users to consider the
common limitations of AI, comply with applicable laws, and leverage best
practices when selecting use cases, particularly for high-risk scenarios
where errors or misuse could significantly impact people’s lives, rights,
or safety. For further guidance on use cases, refer to our standard
[AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ExternalFacing_Services_Policy.pdf)
and [AI AUP](https://www.salesforce.com/content/dam/web/en_us/www/documents/legal/Agreements/policies/ai-acceptable-use-policy.pdf).