| Usage example | |
| thon | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| checkpoint = "Salesforce/codegen-350M-mono" | |
| model = AutoModelForCausalLM.from_pretrained(checkpoint) | |
| tokenizer = AutoTokenizer.from_pretrained(checkpoint) | |
| text = "def hello_world():" | |
| completion = model.generate(**tokenizer(text, return_tensors="pt")) | |
| print(tokenizer.decode(completion[0])) | |
| def hello_world(): | |
| print("Hello World") | |
| hello_world() | |
| Resources | |
| Causal language modeling task guide | |
| CodeGenConfig | |
| [[autodoc]] CodeGenConfig | |
| - all | |
| CodeGenTokenizer | |
| [[autodoc]] CodeGenTokenizer | |
| - save_vocabulary | |
| CodeGenTokenizerFast | |
| [[autodoc]] CodeGenTokenizerFast | |
| CodeGenModel | |
| [[autodoc]] CodeGenModel | |
| - forward | |
| CodeGenForCausalLM | |
| [[autodoc]] CodeGenForCausalLM | |
| - forward |