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37 lines
1.1 KiB
37 lines
1.1 KiB
2 years ago
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from typing import Optional
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import torch.nn as nn
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from transformers import LlamaConfig, LlamaForCausalLM, LlamaModel
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from ..base import RewardModel
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class LlamaRM(RewardModel):
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"""
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Llama Reward model.
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Args:
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pretrained (str): Pretrained model name or path.
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config (LlamaConfig): Model config.
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lora_rank (int): LoRA rank.
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lora_train_bias (str): LoRA bias training mode.
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"""
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def __init__(self,
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pretrained: Optional[str] = None,
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config: Optional[LlamaConfig] = None,
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lora_rank: int = 0,
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lora_train_bias: str = 'none') -> None:
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if pretrained is not None:
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model = LlamaModel.from_pretrained(pretrained)
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elif config is not None:
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model = LlamaModel(config)
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else:
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model = LlamaModel(LlamaConfig())
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value_head = nn.Linear(model.config.hidden_size, 1)
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value_head.weight.data.normal_(mean=0.0, std=1 / (model.config.hidden_size + 1))
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super().__init__(model, value_head, lora_rank, lora_train_bias)
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