方法1：观察attention中的线性层

import numpy as np
import pandas as pd
from peft import PeftModel
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel, BitsAndBytesConfig
from typing import List
from tqdm.auto import tqdm
from sentence_transformers import SentenceTransformer
import os
os.environ['CUDA_VISIBLE_DEVICES']='1,2'
os.environ["TOKENIZERS_PARALLELISM"] = "false"model_path ="/home/jovyan/codes/llms/Qwen2.5-14B-Instruct"
base_model = AutoModel.from_pretrained(model_path, device_map='cuda:0',trust_remote_code=True)

打印attention模型层的名字

for name, module in base_model.named_modules():if 'attn' in name or 'attention' in name:  # Common attention module namesprint(name)for sub_name, sub_module in module.named_modules():  # Check sub-modules within attentionprint(f"  - {sub_name}")

方法2：通过bitsandbytes量化查找线性层

import bitsandbytes as bnb
def find_all_linear_names(model):lora_module_names = set()for name, module in model.named_modules():if isinstance(module, bnb.nn.Linear4bit):names = name.split(".")# model-specificlora_module_names.add(names[0] if len(names) == 1 else names[-1])if "lm_head" in lora_module_names:  # needed for 16-bitlora_module_names.remove("lm_head")return list(lora_module_names)

加载模型

bnb_config = BitsAndBytesConfig(load_in_4bit=True,bnb_4bit_use_double_quant=True,bnb_4bit_quant_type="nf4",bnb_4bit_compute_dtype=torch.bfloat16)
base_model = AutoModel.from_pretrained(model_path,quantization_config=bnb_config,device_map="auto")

查找Lora的目标层

find_all_linear_names(base_model)

还有个函数，一样的原理

def find_target_modules(model):# Initialize a Set to Store Unique Layersunique_layers = set()# Iterate Over All Named Modules in the Modelfor name, module in model.named_modules():# Check if the Module Type Contains 'Linear4bit'if "Linear4bit" in str(type(module)):# Extract the Type of the Layerlayer_type = name.split('.')[-1]# Add the Layer Type to the Set of Unique Layersunique_layers.add(layer_type)# Return the Set of Unique Layers Converted to a Listreturn list(unique_layers)find_target_modules(base_model)

方法3：通过分析开源框架的源码swift

代码地址

from collections import OrderedDict
from dataclasses import dataclass, field
from typing import List, Union@dataclass
class ModelKeys:model_type: str = Nonemodule_list: str = Noneembedding: str = Nonemlp: str = Nonedown_proj: str = Noneattention: str = Noneo_proj: str = Noneq_proj: str = Nonek_proj: str = Nonev_proj: str = Noneqkv_proj: str = Noneqk_proj: str = Noneqa_proj: str = Noneqb_proj: str = Nonekva_proj: str = Nonekvb_proj: str = Noneoutput: str = None@dataclass
class MultiModelKeys(ModelKeys):language_model: Union[List[str], str] = field(default_factory=list)connector: Union[List[str], str] = field(default_factory=list)vision_tower: Union[List[str], str] = field(default_factory=list)generator: Union[List[str], str] = field(default_factory=list)def __post_init__(self):# compatfor key in ['language_model', 'connector', 'vision_tower', 'generator']:v = getattr(self, key)if isinstance(v, str):setattr(self, key, [v])if v is None:setattr(self, key, [])LLAMA_KEYS = ModelKeys(module_list='model.layers',mlp='model.layers.{}.mlp',down_proj='model.layers.{}.mlp.down_proj',attention='model.layers.{}.self_attn',o_proj='model.layers.{}.self_attn.o_proj',q_proj='model.layers.{}.self_attn.q_proj',k_proj='model.layers.{}.self_attn.k_proj',v_proj='model.layers.{}.self_attn.v_proj',embedding='model.embed_tokens',output='lm_head',
)INTERNLM2_KEYS = ModelKeys(module_list='model.layers',mlp='model.layers.{}.feed_forward',down_proj='model.layers.{}.feed_forward.w2',attention='model.layers.{}.attention',o_proj='model.layers.{}.attention.wo',qkv_proj='model.layers.{}.attention.wqkv',embedding='model.tok_embeddings',output='output',
)CHATGLM_KEYS = ModelKeys(module_list='transformer.encoder.layers',mlp='transformer.encoder.layers.{}.mlp',down_proj='transformer.encoder.layers.{}.mlp.dense_4h_to_h',attention='transformer.encoder.layers.{}.self_attention',o_proj='transformer.encoder.layers.{}.self_attention.dense',qkv_proj='transformer.encoder.layers.{}.self_attention.query_key_value',embedding='transformer.embedding',output='transformer.output_layer',
)BAICHUAN_KEYS = ModelKeys(module_list='model.layers',mlp='model.layers.{}.mlp',down_proj='model.layers.{}.mlp.down_proj',attention='model.layers.{}.self_attn',qkv_proj='model.layers.{}.self_attn.W_pack',embedding='model.embed_tokens',output='lm_head',
)YUAN_KEYS = ModelKeys(module_list='model.layers',mlp='model.layers.{}.mlp',down_proj='model.layers.{}.mlp.down_proj',attention='model.layers.{}.self_attn',qk_proj='model.layers.{}.self_attn.qk_proj',o_proj='model.layers.{}.self_attn.o_proj',q_proj='model.layers.{}.self_attn.q_proj',k_proj='model.layers.{}.self_attn.k_proj',v_proj='model.layers.{}.self_attn.v_proj',embedding='model.embed_tokens',output='lm_head',
)CODEFUSE_KEYS = ModelKeys(module_list='gpt_neox.layers',mlp='gpt_neox.layers.{}.mlp',down_proj='gpt_neox.layers.{}.mlp.dense_4h_to_h',attention='gpt_neox.layers.{}.attention',o_proj='gpt_neox.layers.{}.attention.dense',qkv_proj='gpt_neox.layers.{}.attention.query_key_value',embedding='gpt_neox.embed_in',output='gpt_neox.embed_out',
)PHI2_KEYS = ModelKeys(module_list='transformer.h',mlp='transformer.h.{}.mlp',down_proj='transformer.h.{}.mlp.c_proj',attention='transformer.h.{}.mixer',o_proj='transformer.h.{}.mixer.out_proj',qkv_proj='transformer.h.{}.mixer.Wqkv',embedding='transformer.embd',output='lm_head',
)QWEN_KEYS = ModelKeys(module_list='transformer.h',mlp='transformer.h.{}.mlp',down_proj='transformer.h.{}.mlp.c_proj',attention='transformer.h.{}.attn',o_proj='transformer.h.{}.attn.c_proj',qkv_proj='transformer.h.{}.attn.c_attn',embedding='transformer.wte',output='lm_head',
)PHI3_KEYS = ModelKeys(module_list='model.layers',mlp='model.layers.{}.mlp',down_proj='model.layers.{}.mlp.down_proj',attention='model.layers.{}.self_attn',o_proj='model.layers.{}.self_attn.o_proj',qkv_proj='model.layers.{}.self_attn.qkv_proj',embedding='model.embed_tokens',output='lm_head',
)PHI3_SMALL_KEYS = ModelKeys(module_list='model.layers',mlp='model.layers.{}.mlp',down_proj='model.layers.{}.mlp.down_proj',attention='model.layers.{}.self_attn',o_proj='model.layers.{}.self_attn.dense',qkv_proj='model.layers.{}.self_attn.query_key_value',embedding='model.embed_tokens',output='lm_head',
)DEEPSEEK_V2_KEYS = ModelKeys(module_list='model.layers',mlp='model.layers.{}.mlp',down_proj='model.layers.{}.mlp.down_proj',attention='model.layers.{}.self_attn',o_proj='model.layers.{}.self_attn.o_proj',qa_proj='model.layers.{}.self_attn.q_a_proj',qb_proj='model.layers.{}.self_attn.q_b_proj',kva_proj='model.layers.{}.self_attn.kv_a_proj_with_mqa',kvb_proj='model.layers.{}.self_attn.kv_b_proj',embedding='model.embed_tokens',output='lm_head',
)

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