Add ckp_dir and tokenizer path for train_dpo.py
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44f30197ac
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5d3f3449ed
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@ -131,10 +131,10 @@ def train_epoch(epoch, wandb):
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def init_model(lm_config):
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def init_model(lm_config):
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tokenizer = AutoTokenizer.from_pretrained('./model/minimind_tokenizer')
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tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_path)
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model = MiniMindLM(lm_config)
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model = MiniMindLM(lm_config)
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moe_path = '_moe' if lm_config.use_moe else ''
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moe_path = '_moe' if lm_config.use_moe else ''
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ckp = f'./out/full_sft_{lm_config.dim}{moe_path}.pth'
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ckp = f'{args.ckp_dir}/full_sft_{lm_config.dim}{moe_path}.pth'
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state_dict = torch.load(ckp, map_location=args.device)
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state_dict = torch.load(ckp, map_location=args.device)
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model.load_state_dict(state_dict, strict=False)
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model.load_state_dict(state_dict, strict=False)
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# 初始化参考模型
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# 初始化参考模型
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@ -165,6 +165,8 @@ def init_distributed_mode():
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if __name__ == "__main__":
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="MiniMind RLHF")
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parser = argparse.ArgumentParser(description="MiniMind RLHF")
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parser.add_argument("--out_dir", type=str, default="out")
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parser.add_argument("--out_dir", type=str, default="out")
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parser.add_argument("--ckp_dir", type=str, default="out", help="Directory to save/load checkpoints")
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parser.add_argument("--tokenizer_path", type=str, default="./model/minimind_tokenizer", help="Path to the tokenizer")
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parser.add_argument("--epochs", type=int, default=2)
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parser.add_argument("--epochs", type=int, default=2)
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parser.add_argument("--batch_size", type=int, default=8)
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parser.add_argument("--batch_size", type=int, default=8)
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# sft阶段学习率为 「5e-6」->「5e-7」长度512,建议离线正负样本「概率」偏好对齐阶段lr <=「1e-8」长度3000,否则很容易遗忘训坏
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# sft阶段学习率为 「5e-6」->「5e-7」长度512,建议离线正负样本「概率」偏好对齐阶段lr <=「1e-8」长度3000,否则很容易遗忘训坏
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