| inference: | |
| greedy: False # Whether or not to use sampling ; use greedy decoding otherwise | |
| top_k: 0 # The number of highest probability vocabulary tokens to keep for top-k-filtering. | |
| top_p: 0.9 # If set to float < 1, only the most probable tokens with probabilities that add up to top_p or higher are kept for generation. | |
| temperature: 1.0 # sampling temperature | |
| add_BOS: True # add the bos token at the begining of the prompt | |
| tokens_to_generate: 30 # The minimum length of the sequence to be generated. | |
| all_probs: False # whether return the log prob for all the tokens in vocab | |
| repetition_penalty: 1.2 # The parameter for repetition penalty. 1.0 means no penalty. | |
| min_tokens_to_generate: 0 # The minimum length of the sequence to be generated. | |
| compute_logprob: False # a flag used to compute logprob of all the input text, a very special case of running inference, default False | |
| batch_size: 1 | |
| trainer: | |
| devices: 1 | |
| num_nodes: 1 | |
| accelerator: gpu | |
| logger: False # logger provided by exp_manager | |
| precision: 16 # 16, 32, or bf16 | |
| tensor_model_parallel_size: 1 | |
| pipeline_model_parallel_size: 1 | |
| gpt_model_file: null # GPT nemo file path | |
| virtual_prompt_model_file: ??? # path to a MegatronGPTPromptLearningModel model if you want to use soft prompts | |
| pred_file_path: ??? # Path will model predictions will be written | |
| data_paths: # paths to .jsonl files you want to perform inference on | |
| num_workers: 8 | |