The CodeGen architecture follows a standard transformer decoder with left-to-right causal masking. With rotary position embedding for the positional encoding [(Su et al., 2021)](https://arxiv.org/abs/2104.09864), and a context length of 2048. CodeGen models are trained in various sizes. 

<div align="center">

|Model | # parameters | 
|   -   |   -  | 
| [Salesforce/codegen-350m-mono](https://huggingface.co/Salesforce/codegen-350-mono) | 350M |
| [Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-2B-mono) | 2.7B |
| [Salesforce/codegen-6B-mono](https://huggingface.co/Salesforce/codegen-6B-mono) | 6.1B |
| [Salesforce/codegen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono) | 16.1B |

</div>


You can load the model and tokenizer directly from 🤗 [`transformers`](https://huggingface.co/docs/transformers/index):

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained('Salesforce/codegen-16B-mono')
model = AutoModelForCausalLM.from_pretrained('Salesforce/codegen-16B-mono')

inputs = tokenizer("def hello_world():", return_tensors="pt")
outputs = model(**inputs)
```