| inference: | |
| greedy: True # 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 | |
| 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 | |
| pipeline_model_parallel_split_rank: 0 # used for encoder and decoder model | |
| gpt_model_file: ??? # GPT nemo file path # used when starting from a .nemo file | |
| adapter_model_file: ??? # .nemo file saved during training (using megatron_gpt_adapter_tuning.py) | |
| pred_file_path: null # save predictions to this file | |
| checkpoint_dir: null # checkpoint file dir. This is used to load the PTL checkpoint generated during the GPT training | |
| checkpoint_name: null # PTL checkpoint file name, only used for PTL checkpoint loading | |
| hparams_file: null # model configuration file, only used for PTL checkpoint loading | |
| data_paths: ??? # prompts for GPT inference | |
| server: False # whether launch the inference server | |
| port: 5555 # the port number for the inference server | |
| batch_size: 8 | |
| num_workers: 8 | |