license: bsd-3-clause
CodeGen (CodeGen-Multi 6B)
Model description
CodeGen is a family of autoregressive language models for program synthesis from the paper: A Conversational Paradigm for Program Synthesis by Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, Caiming Xiong. The models are originally released in this repository, under 3 pre-training data variants (NL, Multi, Mono) and 4 model size variants (350M, 2B, 6B, 16B).
The checkpoint included in this repository is denoted as CodeGen-Multi 6B in the paper, where "Multi" means the model is initialized with CodeGen-NL 6B and further pre-trained on a dataset of multiple programming languages, and "6B" refers to the number of trainable parameters.
Training data
This checkpoint (CodeGen-Multi 6B) was firstly initialized with CodeGen-NL 6B, and then pre-trained on BigQuery, a large-scale dataset of multiple programming languages from GitHub repositories. The data consists of 119.2B tokens and includes C, C++, Go, Java, JavaScript, and Python.
Training procedure
CodeGen was trained using cross-entropy loss to maximize the likelihood of sequential inputs. The family of models are trained using multiple TPU-v4-512 by Google, leveraging data and model parallelism. See Section 2.3 of the paper for more details.
Evaluation results
We evaluate our models on two code generation benchmark: HumanEval and MTPB. Please refer to the paper for more details.
Intended Use and Limitations
As an autoregressive language model, CodeGen is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them. However, the model is intended for and best at program synthesis, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.
How to use
This model can be easily loaded using the AutoModelForCausalLM functionality:
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen-6B-multi")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen-6B-multi")
text = "def hello_world():"
input_ids = tokenizer(text, return_tensors="pt").input_ids
generated_ids = model.generate(input_ids, max_length=128)
print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
BibTeX entry and citation info
@article{Nijkamp2022ACP,
title={A Conversational Paradigm for 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={arXiv preprint},
year={2022}
}
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 28.38 |
| ARC (25-shot) | 27.22 |
| HellaSwag (10-shot) | 41.11 |
| MMLU (5-shot) | 25.71 |
| TruthfulQA (0-shot) | 45.65 |
| Winogrande (5-shot) | 53.91 |
| GSM8K (5-shot) | 0.99 |
| DROP (3-shot) | 4.06 |