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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-16B-mono) | 350M |
| [Salesforce/codegen-2B-mono](https://huggingface.co/Salesforce/codegen-16B-mono) | 2.7B |
| [Salesforce/codegen-6B-mono](https://huggingface.co/Salesforce/codegen-16B-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)
``` |