salesforce/CodeGen

CodeGen

Official release for the CodeGen1 and CodeGen2 models (350M, 1B, 3B, 7B 16B) for Program Synthesis by Salesforce AI Research.

News

July 2023

CodeGen2.5 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
Erik Nijkamp*, Bo Pang*, Hiroaki Hayashi*, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong
ICLR, 2023

CodeGen2: Lessons for Training LLMs on Programming and Natural Languages
Erik Nijkamp*, Hiroaki Hayashi*, Caiming Xiong, Silvio Savarese, and Yingbo Zhou
ICLR, 2023

Usage

The models are available on the Hugging Face Hub.

CodeGen1.0

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

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

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:

@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 and AI AUP.