在 不同LoRA 适配器之间切换？

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Lightning-AI/lit-llama#193

有没有办法在 LoRA 适配器之间切换？即为不同的任务加载几个不同的适配器，并在它们之间快速切换以执行不同的任务？尽可能使用peft库。

@totaltube我认为我们不支持这一点。你能举个例子吗？请注意，LoRA 和适配器是两个不同的东西.

例如， 要从 LoRA 切换到适配器微调， 您必须 1.删除上下文管理器 2。将模型类替换为 和 3。更改为 。with lora()lit_llama.model.LLaMAlit_llama.adapter.LLaMAmark_only_lora_as_trainablemark_mark_only_adapter_as_trainable(model)

我认为目标不是从 lora 切换到适配器， 而是在同一基本模型之上切换不同的 lora 权重

正确。
这是另一个库中的示例：https://github.com/huggingface/peft/blob/main/examples/multi_adapter_examples/PEFT_Multi_LoRA_Inference.ipynb

目标是 - 我可以独立地为不同的任务微调模型并快速切换权重。无需在扩展数据集上重新训练，例如，扩展数据集包括另一个任务。

关于如何实现@awaelchli所说的内容的示例类：

class FinetunedAdapter:
from lit_llama.adapter import LLaMA, LLaMAConfig

def __init__(
    self,
    adapter_path: Optional[Path] = None,
    pretrained_path: Optional[Path] = None,
    tokenizer_path: Optional[Path] = None,
    quantize: Optional[str] = None,
) -> None:
    if not adapter_path:
        adapter_path = Path("out/adapter/alpaca/lit-llama-adapter-finetuned.pth")
    if not pretrained_path:
        pretrained_path = Path("./checkpoints/lit-llama/7B/lit-llama.pth")
    if not tokenizer_path:
        tokenizer_path = Path("./checkpoints/lit-llama/tokenizer.model")

    assert adapter_path.is_file()
    assert pretrained_path.is_file()
    assert tokenizer_path.is_file()

    self.fabric = L.Fabric(devices=1)
    dtype = (
        torch.bfloat16
        if self.fabric.device.type == "cuda" and torch.cuda.is_bf16_supported()
        else torch.float32
    )

    with EmptyInitOnDevice(
        device=self.fabric.device, dtype=dtype, quantization_mode=quantize
    ):
        self.model = self.LLaMA(self.LLaMAConfig())

    # 1. Load the pretrained weights
    pretrained_checkpoint = lazy_load(pretrained_path)
    self.model.load_state_dict(pretrained_checkpoint, strict=False)

    # 2. Load the fine-tuned adapter weights
    adapter_checkpoint = lazy_load(adapter_path)
    self.model.load_state_dict(adapter_checkpoint, strict=False)

    self.model.eval()
    self.model = self.fabric.setup_module(self.model)

    self.tokenizer = Tokenizer(tokenizer_path)

def load_adapter(self, adapter_path: Path):
    assert adapter_path.is_file()

    adapter_checkpoint = lazy_load(adapter_path)
    self.model.load_state_dict(adapter_checkpoint, strict=False)

def generate(
    self,
    instruction: str = "",
    input_text: str = "",
    max_new_tokens: int = 100,
    top_k: int = 200,
    temperature: float = 0.8,
    use_instruction: bool = True,
):
    if use_instruction:
        sample = {"instruction": instruction, "input": input_text}
        prompt = generate_prompt(sample)
    else:
        assert input_text, "input_text must be provided if use_prompt is False."
        assert (
            len(instruction) == 0
        ), "instruction must be empty if use_prompt is False."
        prompt = generate_no_prompt(input_text)

    encoded = self.tokenizer.encode(
        prompt, bos=True, eos=False, device=self.model.device
    )

    output = generate(
        self.model,
        idx=encoded,
        max_seq_length=max_new_tokens,
        max_new_tokens=max_new_tokens,
        temperature=temperature,
        top_k=top_k,
        eos_id=self.tokenizer.eos_id,
    )

    output = self.tokenizer.decode(output)
    output = output.split("### Response:")[1].strip()
    return output

然后：

adapter = FinetunedAdapter(adapter_path=adapther_path)
adapter.load_adapter(checkpoint)