| import torch |
| from transformers import GPT2TokenizerFast |
| from modeling_perklm import PerkLMConfig, PerkLM |
|
|
|
|
| checkpoint = torch.load('perklm.pt', map_location = 'cpu', weights_only = True) |
| old_config = checkpoint['config'] |
|
|
| hf_config = PerkLMConfig(d_model = old_config['model']['d_model'], |
| n_heads = old_config['model']['n_heads'], |
| n_layers = old_config['model']['n_layers'], |
| d_ff = old_config['model']['d_ff'], |
| maxt = old_config['model']['maxt'], |
| dropout = old_config['model']['dropout'], |
| tokenizer_path = 'tokenizer') |
|
|
| hf_config.auto_map = { |
| "AutoConfig": "modeling_perklm.PerkLMConfig", |
| "AutoModelForCausalLM": "modeling_perklm.PerkLM"} |
|
|
| model = PerkLM(hf_config) |
| model.transformer.load_state_dict(checkpoint['model_state_dict']) |
|
|
| model.save_pretrained('perklm_hf') |
| hf_config.save_pretrained('perklm_hf') |
|
|
