41 lines
1.5 KiB
Python
41 lines
1.5 KiB
Python
import torch
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import warnings
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from model.LMConfig import LMConfig
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from model.model import Transformer
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warnings.filterwarnings('ignore', category=UserWarning)
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def count_parameters(model):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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def export_transformers_model():
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tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer',
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trust_remote_code=True, use_fast=False)
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LMConfig.register_for_auto_class()
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Transformer.register_for_auto_class("AutoModelForCausalLM")
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lm_config = LMConfig()
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lm_model = Transformer(lm_config)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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moe_path = '_moe' if lm_config.use_moe else ''
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ckpt_path = f'./out/{lm_config.dim}{moe_path}_vlm_sft.pth'
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state_dict = torch.load(ckpt_path, map_location=device)
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unwanted_prefix = '_orig_mod.'
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for k, v in list(state_dict.items()):
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if k.startswith(unwanted_prefix):
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state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
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lm_model.load_state_dict(state_dict, strict=False)
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print(f'模型参数: {count_parameters(lm_model) / 1e6} 百万 = {count_parameters(lm_model) / 1e9} B (Billion)')
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lm_model.save_pretrained("minimind-v-v1-small", safe_serialization=False)
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tokenizer.save_pretrained("minimind-v-v1-small")
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if __name__ == '__main__':
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# 1
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export_transformers_model()
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