From ee218402cd62d4c58a22f073acba23d870f75657 Mon Sep 17 00:00:00 2001
From: gongjy <2474590974@qq.com>
Date: Fri, 20 Sep 2024 17:04:16 +0800
Subject: [PATCH] update some explain of the code
---
1-pretrain.py | 153 +-
model/LMConfig.py | 90 +-
model/dataset.py | 82 +-
model/model.py | 333 +-
中文逐行注释/1-pretrain.py | 197 +
中文逐行注释/README.md | 6 +
中文逐行注释/model/LMConfig.py | 58 +
中文逐行注释/model/dataset.py | 130 +
.../model/minimind_tokenizer/merges.txt | 6142 ++++++++
.../model/minimind_tokenizer/tokenizer.json | 12603 ++++++++++++++++
.../model/minimind_tokenizer/tokenizer_config.json | 44 +
.../model/minimind_tokenizer/vocab.json | 1 +
中文逐行注释/model/model.py | 431 +
13 files changed, 19932 insertions(+), 338 deletions(-)
create mode 100644 中文逐行注释/1-pretrain.py
create mode 100644 中文逐行注释/README.md
create mode 100644 中文逐行注释/model/LMConfig.py
create mode 100644 中文逐行注释/model/dataset.py
create mode 100644 中文逐行注释/model/minimind_tokenizer/merges.txt
create mode 100644 中文逐行注释/model/minimind_tokenizer/tokenizer.json
create mode 100644 中文逐行注释/model/minimind_tokenizer/tokenizer_config.json
create mode 100644 中文逐行注释/model/minimind_tokenizer/vocab.json
create mode 100644 中文逐行注释/model/model.py
diff --git a/1-pretrain.py b/1-pretrain.py
index ed70c4d..fabb445 100644
--- a/1-pretrain.py
+++ b/1-pretrain.py
@@ -14,62 +14,56 @@ from model.model import Transformer
from model.LMConfig import LMConfig
from model.dataset import PretrainDataset
-# 忽略警告信息
warnings.filterwarnings('ignore')
-# 定义日志打印函数，仅在主进程（rank 0）打印日志信息
+
def Logger(content):
if not ddp or dist.get_rank() == 0:
print(content)
-# 定义学习率调度函数，根据当前迭代次数计算学习率，采用余弦退火策略
-def get_lr(it, all):
- warmup_iters = 0 # 预热迭代次数
- lr_decay_iters = all # 学习率衰减的总迭代次数
- min_lr = learning_rate / 10 # 最小学习率
- # 如果当前迭代次数小于预热迭代次数，使用线性预热策略
+def get_lr(it, all):
+ warmup_iters = 0
+ lr_decay_iters = all
+ min_lr = learning_rate / 10
+
if it < warmup_iters:
return learning_rate * it / warmup_iters
- # 如果当前迭代次数大于衰减迭代次数，返回最小学习率
if it > lr_decay_iters:
return min_lr
- # 计算衰减系数，使用余弦退火策略
decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
assert 0 <= decay_ratio <= 1
coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
return min_lr + coeff * (learning_rate - min_lr)
-# 定义训练 epoch 的函数
+
def train_epoch(epoch, accumulation_steps=8):
- start_time = time.time() # 记录开始时间
- for step, (X, Y) in enumerate(train_loader): # 遍历数据加载器
- X = X.to(device) # 将输入数据移动到设备上
- Y = Y.to(device) # 将目标数据移动到设备上
+ start_time = time.time()
+ for step, (X, Y) in enumerate(train_loader):
+ X = X.to(device)
+ Y = Y.to(device)
- lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch) # 计算当前学习率
+ lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch)
for param_group in optimizer.param_groups:
- param_group['lr'] = lr # 设置优化器的学习率
+ param_group['lr'] = lr
- with ctx: # 使用混合精度训练（如果设备是 GPU）
- out = model(X, Y) # 前向传播，计算输出
- loss = out.last_loss / accumulation_steps # 计算损失，并进行梯度累积
+ with ctx:
+ out = model(X, Y)
+ loss = out.last_loss / accumulation_steps
- scaler.scale(loss).backward() # 反向传播，计算梯度
+ scaler.scale(loss).backward()
- # 每 accumulation_steps 步进行一次梯度更新
if (step + 1) % accumulation_steps == 0:
- scaler.unscale_(optimizer) # 反缩放梯度
- torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # 梯度裁剪
+ scaler.unscale_(optimizer)
+ torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
- scaler.step(optimizer) # 更新模型参数
- scaler.update() # 更新缩放器
+ scaler.step(optimizer)
+ scaler.update()
- optimizer.zero_grad(set_to_none=True) # 清空梯度
+ optimizer.zero_grad(set_to_none=True)
- # 每 100 步打印一次训练信息
if step % 100 == 0:
- spend_time = time.time() - start_time # 计算已用时间
+ spend_time = time.time() - start_time
Logger(
'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
epoch,
@@ -80,27 +74,26 @@ def train_epoch(epoch, accumulation_steps=8):
optimizer.param_groups[-1]['lr'],
spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
- # 每 1000 步保存一次模型
if (step + 1) % 1000 == 0 and (not ddp or dist.get_rank() == 0):
- model.eval() # 切换到评估模式
+ model.eval()
# torch.save(model.state_dict(), '{}/iter_{}.pth'.format(save_dir, int(step + epoch * iter_per_epoch)))
- moe_path = '_moe' if lm_config.use_moe else '' # 根据是否使用 MoE 设置保存路径
+ moe_path = '_moe' if lm_config.use_moe else ''
ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'
if isinstance(model, torch.nn.parallel.DistributedDataParallel):
- state_dict = model.module.state_dict() # 获取模型状态字典
+ state_dict = model.module.state_dict()
else:
state_dict = model.state_dict()
- torch.save(state_dict, ckp) # 保存模型
- model.train() # 切换回训练模式
+ torch.save(state_dict, ckp)
+ model.train()
+
-# 定义初始化模型的函数
def init_model():
def count_parameters(model):
- return sum(p.numel() for p in model.parameters() if p.requires_grad) # 计算模型可训练参数的数量
+ return sum(p.numel() for p in model.parameters() if p.requires_grad)
- # 初始化模型
+ # model init
model = Transformer(lm_config).to(device)
moe_path = '_moe' if lm_config.use_moe else ''
# ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'
@@ -112,57 +105,57 @@ def init_model():
# state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
# model.load_state_dict(state_dict, strict=False)
- Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万') # 打印模型总参数量
+ Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万')
return model
-# 定义初始化分布式训练环境的函数
+
def init_distributed_mode():
if not ddp: return
global ddp_local_rank, DEVICE
- dist.init_process_group(backend="nccl") # 初始化分布式进程组，使用 NCCL 后端
- ddp_rank = int(os.environ["RANK"]) # 获取当前进程的 rank
- ddp_local_rank = int(os.environ["LOCAL_RANK"]) # 获取当前进程的本地 rank
- ddp_world_size = int(os.environ["WORLD_SIZE"]) # 获取分布式训练的总进程数
- DEVICE = f"cuda:{ddp_local_rank}" # 设置当前设备的 CUDA 设备
- torch.cuda.set_device(DEVICE) # 设置当前设备的 CUDA 设备
+ dist.init_process_group(backend="nccl")
+ ddp_rank = int(os.environ["RANK"])
+ ddp_local_rank = int(os.environ["LOCAL_RANK"])
+ ddp_world_size = int(os.environ["WORLD_SIZE"])
+ DEVICE = f"cuda:{ddp_local_rank}"
+ torch.cuda.set_device(DEVICE)
# torchrun --nproc_per_node 2 1-pretrain.py
# I/O
if __name__ == "__main__":
# -----------------------------------------------------------------------------
- lm_config = LMConfig() # 加载配置文件
- max_seq_len = lm_config.max_seq_len # 获取最大序列长度
- out_dir = 'out' # 设置输出目录
- epochs = 20 # 设置训练 epoch 数
- batch_size = 64 # 设置批量大小
- learning_rate = 2e-4 # 设置初始学习率
- device = 'cuda:0' # 设置设备为 CUDA:0
- dtype = 'bfloat16' # 设置数据类型为 bfloat16
- save_dir = os.path.join(out_dir) # 设置模型保存目录
- os.makedirs(save_dir, exist_ok=True) # 创建模型保存目录
- os.makedirs(out_dir, exist_ok=True) # 创建输出目录
- tokens_per_iter = batch_size * max_seq_len # 计算每个迭代处理的 token 数量
- torch.manual_seed(1337) # 设置随机种子
- device_type = device if "cuda" in device else "cpu" # 设置设备类型
+ lm_config = LMConfig()
+ max_seq_len = lm_config.max_seq_len
+ out_dir = 'out'
+ epochs = 20
+ batch_size = 64
+ learning_rate = 2e-4
+ device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
+ dtype = 'bfloat16'
+ save_dir = os.path.join(out_dir)
+ os.makedirs(save_dir, exist_ok=True)
+ os.makedirs(out_dir, exist_ok=True)
+ tokens_per_iter = batch_size * max_seq_len
+ torch.manual_seed(1337)
+ device_type = device if "cuda" in device else "cpu"
ctx = (
- nullcontext() # 如果设备是 CPU，使用 nullcontext
+ nullcontext()
if device_type == "cpu"
- else torch.cuda.amp.autocast() # 如果设备是 GPU，使用混合精度训练
+ else torch.cuda.amp.autocast()
)
- ddp = int(os.environ.get("RANK", -1)) != -1 # 判断是否为分布式训练
- ddp_local_rank, DEVICE = 0, "cuda:0" # 初始化分布式训练的本地 rank 和设备
+ ddp = int(os.environ.get("RANK", -1)) != -1 # is this a ddp run?
+ ddp_local_rank, DEVICE = 0, "cuda:0"
if ddp:
- init_distributed_mode() # 初始化分布式训练环境
- device = torch.device(DEVICE) # 设置设备
+ init_distributed_mode()
+ device = torch.device(DEVICE)
# -----------------------------------------------------------------------------
# -----init dataloader------
- data_path_list = ['./dataset/pretrain_data.bin'] # 设置数据路径
- train_ds = PretrainDataset(data_path_list, max_length=max_seq_len, memmap=True) # 初始化数据集
- train_sampler = DistributedSampler(train_ds) if ddp else None # 如果是分布式训练，使用分布式采样器
- num_workers = 8 # 设置数据加载器的 num_workers
+ data_path_list = ['./dataset/pretrain_data.bin']
+ train_ds = PretrainDataset(data_path_list, max_length=max_seq_len, memmap=True)
+ train_sampler = DistributedSampler(train_ds) if ddp else None
+ num_workers = 8 # 可以根据系统的 CPU 核心数来调整
train_loader = DataLoader(
train_ds,
batch_size=batch_size,
@@ -171,27 +164,27 @@ if __name__ == "__main__":
shuffle=False,
num_workers=num_workers,
sampler=train_sampler
- ) # 初始化数据加载器
+ )
# init model
- model = init_model() # 初始化模型
+ model = init_model()
- scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype)) # 初始化梯度缩放器
+ scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype))
# optimizer
- optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 初始化优化器
+ optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# compile the model
if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2:
Logger("compiling the model... (takes a ~minute)")
unoptimized_model = model
- model = torch.compile(model) # 编译模型（如果条件满足）
+ model = torch.compile(model)
if ddp:
# Ignore the freqs_cis buffer so that DDP does not broadcast it at
# construction time since NCCL does not support ComplexFloat
- model._ddp_params_and_buffers_to_ignore = {"pos_cis"} # 设置 DDP 忽略的参数和缓冲区
- model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) # 使用 DDP 包装模型
+ model._ddp_params_and_buffers_to_ignore = {"pos_cis"}
+ model = DistributedDataParallel(model, device_ids=[ddp_local_rank])
# training loop
- iter_per_epoch = len(train_loader) # 计算每个 epoch 的迭代次数
- for epoch in range(epochs): # 遍历每个 epoch
- train_epoch(epoch) # 训练一个 epoch
\ No newline at end of file
+ iter_per_epoch = len(train_loader)
+ for epoch in range(epochs):
+ train_epoch(epoch)
diff --git a/model/LMConfig.py b/model/LMConfig.py
index f216b48..7069dc2 100644
--- a/model/LMConfig.py
+++ b/model/LMConfig.py
@@ -1,58 +1,58 @@
from transformers import PretrainedConfig
from typing import List
-# 定义 LMConfig 类，继承自 PretrainedConfig
+
class LMConfig(PretrainedConfig):
- model_type = "minimind" # 设置模型类型为 "minimind"
+ model_type = "minimind"
def __init__(
self,
- dim: int = 512, # 模型维度，默认为 512
- n_layers: int = 8, # Transformer 层数，默认为 8
- n_heads: int = 16, # 注意力头数，默认为 16
- n_kv_heads: int = 8, # KV 头数，默认为 8
- vocab_size: int = 6400, # 词汇表大小，默认为 6400
- hidden_dim: int = None, # 隐藏层维度，默认为 None
- multiple_of: int = 64, # 隐藏层维度的倍数，默认为 64
- norm_eps: float = 1e-5, # 归一化层的 epsilon 值，默认为 1e-5
- max_seq_len: int = 512, # 最大序列长度，默认为 512
- dropout: float = 0.0, # Dropout 概率，默认为 0.0
- flash_attn: bool = True, # 是否使用 Flash Attention，默认为 True
+ dim: int = 768,
+ n_layers: int = 16,
+ n_heads: int = 16,
+ n_kv_heads: int = 8,
+ vocab_size: int = 6400,
+ hidden_dim: int = None,
+ multiple_of: int = 64,
+ norm_eps: float = 1e-5,
+ max_seq_len: int = 512,
+ dropout: float = 0.0,
+ flash_attn: bool = True,
####################################################
- # 以下是 MOE（Mixture of Experts）的特定配置
- # 当 use_moe 为 False 时，以下配置无效
+ # Here are the specific configurations of MOE
+ # When use_moe is false, the following is invalid
####################################################
- use_moe: bool = False, # 是否使用 MOE，默认为 False
- num_experts_per_tok=2, # 每个 token 选择的专家数量，默认为 2
- n_routed_experts=4, # 总的专家数量，默认为 4
- n_shared_experts: bool = True, # 是否使用共享专家，默认为 True
- scoring_func='softmax', # 评分函数，默认为 'softmax'
- aux_loss_alpha=0.01, # 辅助损失的 alpha 参数，默认为 0.01
- seq_aux=True, # 是否在序列级别上计算辅助损失，默认为 True
- norm_topk_prob=True, # 是否标准化 top-k 概率，默认为 True
+ use_moe: bool = False,
+ num_experts_per_tok=2,
+ n_routed_experts=4,
+ n_shared_experts: bool = True,
+ scoring_func='softmax',
+ aux_loss_alpha=0.01,
+ seq_aux=True,
+ norm_topk_prob=True,
**kwargs,
):
- self.dim = dim # 设置模型维度
- self.n_layers = n_layers # 设置 Transformer 层数
- self.n_heads = n_heads # 设置注意力头数
- self.n_kv_heads = n_kv_heads # 设置 KV 头数
- self.vocab_size = vocab_size # 设置词汇表大小
- self.hidden_dim = hidden_dim # 设置隐藏层维度
- self.multiple_of = multiple_of # 设置隐藏层维度的倍数
- self.norm_eps = norm_eps # 设置归一化层的 epsilon 值
- self.max_seq_len = max_seq_len # 设置最大序列长度
- self.dropout = dropout # 设置 Dropout 概率
- self.flash_attn = flash_attn # 设置是否使用 Flash Attention
+ self.dim = dim
+ self.n_layers = n_layers
+ self.n_heads = n_heads
+ self.n_kv_heads = n_kv_heads
+ self.vocab_size = vocab_size
+ self.hidden_dim = hidden_dim
+ self.multiple_of = multiple_of
+ self.norm_eps = norm_eps
+ self.max_seq_len = max_seq_len
+ self.dropout = dropout
+ self.flash_attn = flash_attn
####################################################
- # 以下是 MOE（Mixture of Experts）的特定配置
- # 当 use_moe 为 False 时，以下配置无效
+ # Here are the specific configurations of MOE
+ # When use_moe is false, the following is invalid
####################################################
- self.use_moe = use_moe # 设置是否使用 MOE
- self.num_experts_per_tok = num_experts_per_tok # 设置每个 token 选择的专家数量
- self.n_routed_experts = n_routed_experts # 设置总的专家数量
- self.n_shared_experts = n_shared_experts # 设置是否使用共享专家
- self.scoring_func = scoring_func # 设置评分函数
- self.aux_loss_alpha = aux_loss_alpha # 设置辅助损失的 alpha 参数
- self.seq_aux = seq_aux # 设置是否在序列级别上计算辅助损失
- self.norm_topk_prob = norm_topk_prob # 设置是否标准化 top-k 概率
- super().__init__(**kwargs) # 调用父类 PretrainedConfig 的初始化方法
\ No newline at end of file
+ self.use_moe = use_moe
+ self.num_experts_per_tok = num_experts_per_tok # 每个token选择的专家数量
+ self.n_routed_experts = n_routed_experts # 总的专家数量
+ self.n_shared_experts = n_shared_experts # 共享专家
+ self.scoring_func = scoring_func # 评分函数，默认为'softmax'
+ self.aux_loss_alpha = aux_loss_alpha # 辅助损失的alpha参数
+ self.seq_aux = seq_aux # 是否在序列级别上计算辅助损失
+ self.norm_topk_prob = norm_topk_prob # 是否标准化top-k概率
+ super().__init__(**kwargs)
diff --git a/model/dataset.py b/model/dataset.py
index 82c9be8..ef58956 100644
--- a/model/dataset.py
+++ b/model/dataset.py
@@ -9,79 +9,79 @@ import torch
from sklearn.model_selection import train_test_split
import os
-os.environ["TOKENIZERS_PARALLELISM"] = "false" # 禁用 tokenizer 的并行处理
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
-# 定义 PretrainDataset 类，继承自 Dataset
class PretrainDataset(Dataset):
def __init__(self, data_path_lst, max_length=512, memmap=False):
super().__init__()
- # 如果使用内存映射（memmap）
+ #
if memmap:
with open(data_path_lst[0], 'r') as f:
- nbytes = f.seek(0, 2) # 获取文件总字节数
- flen = f.tell() // np.dtype('uint16').itemsize # 计算文件长度
- self.data = np.memmap(data_path_lst[0], dtype=np.dtype('uint16'), shape=(flen // max_length, max_length)) # 使用内存映射加载数据
+ nbytes = f.seek(0, 2)
+ flen = f.tell() // np.dtype('uint16').itemsize
+ self.data = np.memmap(data_path_lst[0], dtype=np.dtype('uint16'), shape=(flen // max_length, max_length))
else:
data_lst = []
for data_path in data_path_lst:
with open(data_path, 'rb') as f:
- data = np.fromfile(f, dtype=np.uint16) # 从文件中读取数据
+ data = np.fromfile(f, dtype=np.uint16)
data_lst.append(data)
- data = np.concatenate(data_lst) # 合并所有数据
- data = data[:max_length * int(len(data) / max_length)] # 截取数据
- # np.random.shuffle(data) # 打乱数据（注释掉了）
- self.data = data.reshape(-1, max_length) # 将数据重塑为 (样本数, 最大长度) 的形状
- # 打印数据形状
+ data = np.concatenate(data_lst)
+ data = data[:max_length * int(len(data) / max_length)]
+ # np.random.shuffle(data)
+ self.data = data.reshape(-1, max_length)
+ #
print("memmap:{} train data.shape:{}".format(memmap, self.data.shape))
print("downloading finished.....")
def __len__(self):
- return self.data.shape[0] # 返回数据集的长度
+ return self.data.shape[0]
def __getitem__(self, index: int):
- # 获取指定索引的样本
+ #
sample = self.data[index]
- X = np.array(sample[:-1]).astype(np.int64) # 输入数据（去掉最后一个 token）
- Y = np.array(sample[1:]).astype(np.int64) # 目标数据（去掉第一个 token）
+ X = np.array(sample[:-1]).astype(np.int64)
+ Y = np.array(sample[1:]).astype(np.int64)
+
+ return torch.from_numpy(X), torch.from_numpy(Y)
- return torch.from_numpy(X), torch.from_numpy(Y) # 返回 PyTorch 张量
-# 定义 SFTDataset 类，继承自 Dataset
class SFTDataset(Dataset):
def __init__(self, df, tokenizer, max_length=1024, prompt_max_len=512, answer_max_len=256):
super().__init__()
- self.df = df # 数据框
- self.max_length = max_length # 最大序列长度
- self.prompt_max_len = prompt_max_len # 提示的最大长度
- self.answer_max_len = answer_max_len # 回答的最大长度
+ self.df = df
+ self.max_length = max_length
+ self.prompt_max_len = prompt_max_len
+ self.answer_max_len = answer_max_len
#
- self.tokenizer = tokenizer # 分词器
- self.padding = 0 # 填充 token ID
- self.bos_id = self.tokenizer('<s>assistant').data['input_ids'] # 开始 token ID
+ self.tokenizer = tokenizer
+ self.padding = 0 # self.tokenizer.special_tokens['<pad>']
+ self.bos_id = self.tokenizer('<s>assistant').data['input_ids']
def __len__(self):
- return self.df.shape[0] # 返回数据集的长度
+ return self.df.shape[0]
def find_sublist_index(self, main_list, sub_list) -> int:
last_index = -1
for i in range(len(main_list) - len(sub_list) + 1):
if main_list[i:i + len(sub_list)] == sub_list:
last_index = i
- return last_index # 查找子列表在主列表中的最后一个索引
+ return last_index
def safe_eval(self, s):
try:
res = eval(s)
except Exception as e:
return []
- return res # 安全地执行 eval 函数
+ return res
def __getitem__(self, index: int):
- # 获取指定索引的样本
+ #
sample = self.df.iloc[index]
- history = self.safe_eval(sample['history']) # 获取历史对话
- q = str(sample['q']) # 获取问题
- a = str(sample['a']) # 获取回答
+ history = self.safe_eval(sample['history'])
+ q = str(sample['q'])
+ a = str(sample['a'])
messages = []
for history_message in history:
@@ -102,29 +102,29 @@ class SFTDataset(Dataset):
messages,
tokenize=False,
add_generation_prompt=True
- ) # 生成新的提示
- input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length] # 分词并截取
+ )
+ input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length]
# 实际长度
question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id)
# 没满最大长度的剩余部分
padding_len = self.max_length - len(input_id)
- input_id = input_id + [self.padding] * padding_len # 填充到最大长度
+ input_id = input_id + [self.padding] * padding_len
mask_len = len(input_id) - question_length - padding_len
# 0表示不计算损失
loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len
input_id = np.array(input_id)
- X = np.array(input_id[:-1]).astype(np.int64) # 输入数据（去掉最后一个 token）
- Y = np.array(input_id[1:]).astype(np.int64) # 目标数据（去掉第一个 token）
- loss_mask = np.array(loss_mask[1:]).astype(np.int64) # 损失掩码
+ X = np.array(input_id[:-1]).astype(np.int64)
+ Y = np.array(input_id[1:]).astype(np.int64)
+ loss_mask = np.array(loss_mask[1:]).astype(np.int64)
X_tensor = torch.from_numpy(X)
Y_tensor = torch.from_numpy(Y)
loss_mask_tensor = torch.from_numpy(loss_mask)
- return X_tensor, Y_tensor, loss_mask_tensor # 返回 PyTorch 张量
+ return X_tensor, Y_tensor, loss_mask_tensor
+
-# 主函数
if __name__ == "__main__":
- pass
\ No newline at end of file
+ pass
diff --git a/model/model.py b/model/model.py
index 9716a87..4901bd7 100644
--- a/model/model.py
+++ b/model/model.py
@@ -10,29 +10,29 @@ from torch import nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast
-# 定义 RMSNorm 类，实现一种归一化方法，类似于 LayerNorm，但计算方式不同
+
class RMSNorm(torch.nn.Module):
def __init__(self, dim: int, eps: float):
super().__init__()
- self.eps = eps # 设置 epsilon，防止除零错误
- self.weight = nn.Parameter(torch.ones(dim)) # 初始化权重参数
+ self.eps = eps
+ self.weight = nn.Parameter(torch.ones(dim))
def _norm(self, x):
- return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) # 计算 RMSNorm
+ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
def forward(self, x):
- output = self._norm(x.float()).type_as(x) # 应用 RMSNorm
- return output * self.weight # 乘以权重参数
+ output = self._norm(x.float()).type_as(x)
+ return output * self.weight
+
-# 定义 precompute_pos_cis 函数，用于预计算位置编码的复数形式
def precompute_pos_cis(dim: int, end: int, theta: float = 10000.0):
- freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # 计算频率
- t = torch.arange(end, device=freqs.device) # 生成时间序列
- freqs = torch.outer(t, freqs).float() # 计算外积
- pos_cis = torch.polar(torch.ones_like(freqs), freqs) # 计算复数形式的位置编码
+ freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
+ t = torch.arange(end, device=freqs.device) # type: ignore
+ freqs = torch.outer(t, freqs).float() # type: ignore
+ pos_cis = torch.polar(torch.ones_like(freqs), freqs) # complex64
return pos_cis
-# 定义 apply_rotary_emb 函数，用于应用旋转位置编码
+
def apply_rotary_emb(xq, xk, pos_cis):
def unite_shape(pos_cis, x):
ndim = x.ndim
@@ -41,14 +41,14 @@ def apply_rotary_emb(xq, xk, pos_cis):
shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
return pos_cis.view(*shape)
- xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # 将 xq 转换为复数形式
- xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # 将 xk 转换为复数形式
- pos_cis = unite_shape(pos_cis, xq_) # 调整 pos_cis 的形状
- xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3) # 应用旋转位置编码
- xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3) # 应用旋转位置编码
- return xq_out.type_as(xq), xk_out.type_as(xk) # 返回结果
+ xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
+ xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
+ pos_cis = unite_shape(pos_cis, xq_)
+ xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
+ xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3)
+ return xq_out.type_as(xq), xk_out.type_as(xk)
+
-# 定义 repeat_kv 函数，用于重复 KV 头的值
def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
"""torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
bs, slen, n_kv_heads, head_dim = x.shape
@@ -60,130 +60,130 @@ def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
.reshape(bs, slen, n_kv_heads * n_rep, head_dim)
)
-# 定义 Attention 类，实现自注意力机制
+
class Attention(nn.Module):
def __init__(self, args: LMConfig):
super().__init__()
- self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads # 设置 KV 头的数量
- assert args.n_heads % self.n_kv_heads == 0 # 确保 KV 头的数量是总头数的因数
- self.n_local_heads = args.n_heads # 设置本地头的数量
- self.n_local_kv_heads = self.n_kv_heads # 设置本地 KV 头的数量
- self.n_rep = self.n_local_heads // self.n_local_kv_heads # 计算重复次数
- self.head_dim = args.dim // args.n_heads # 计算每个头的维度
- self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False) # 初始化 Q 矩阵
- self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) # 初始化 K 矩阵
- self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) # 初始化 V 矩阵
- self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False) # 初始化输出矩阵
- self.k_cache, self.v_cache = None, None # 初始化 KV 缓存
- self.attn_dropout = nn.Dropout(args.dropout) # 初始化注意力 dropout
- self.resid_dropout = nn.Dropout(args.dropout) # 初始化残差 dropout
- self.dropout = args.dropout # 设置 dropout 概率
- self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn # 判断是否使用 Flash Attention
+ self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
+ assert args.n_heads % self.n_kv_heads == 0
+ self.n_local_heads = args.n_heads
+ self.n_local_kv_heads = self.n_kv_heads
+ self.n_rep = self.n_local_heads // self.n_local_kv_heads
+ self.head_dim = args.dim // args.n_heads
+ self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
+ self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+ self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
+ self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
+ self.k_cache, self.v_cache = None, None
+ self.attn_dropout = nn.Dropout(args.dropout)
+ self.resid_dropout = nn.Dropout(args.dropout)
+ self.dropout = args.dropout
+ self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn
if not self.flash:
# print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
- mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf")) # 初始化掩码
- mask = torch.triu(mask, diagonal=1) # 生成上三角掩码
- self.register_buffer("mask", mask) # 注册掩码
+ mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
+ mask = torch.triu(mask, diagonal=1)
+ self.register_buffer("mask", mask)
def forward(self, x: torch.Tensor, pos_cis: torch.Tensor, use_kv_cache=False):
bsz, seqlen, _ = x.shape
- if use_kv_cache and self.eval(): # 如果使用 KV 缓存且在评估模式下
+ if use_kv_cache and self.eval():
if self.k_cache is None or self.k_cache.shape[1] != x.shape[1] - 1:
- xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) # 计算 Q, K, V
+ xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
else:
- token = x[:, -1:, :] # 获取最后一个 token
- xq = torch.cat((torch.zeros_like(x[:, :-1, :]), self.wq(token)), dim=1) # 更新 Q
- xk = torch.cat((self.k_cache, self.wk(token)), dim=1) # 更新 K
- xv = torch.cat((self.v_cache, self.wv(token)), dim=1) # 更新 V
+ token = x[:, -1:, :]
+ xq = torch.cat((torch.zeros_like(x[:, :-1, :]), self.wq(token)), dim=1)
+ xk = torch.cat((self.k_cache, self.wk(token)), dim=1)
+ xv = torch.cat((self.v_cache, self.wv(token)), dim=1)
- self.k_cache, self.v_cache = xk, xv # 更新 KV 缓存
+ self.k_cache, self.v_cache = xk, xv
else:
- xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) # 计算 Q, K, V
+ xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
- xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) # 调整 Q 的形状
- xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) # 调整 K 的形状
- xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) # 调整 V 的形状
+ xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
+ xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
+ xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
- xq, xk = apply_rotary_emb(xq, xk, pos_cis) # 应用旋转位置编码
+ xq, xk = apply_rotary_emb(xq, xk, pos_cis)
- xk = repeat_kv(xk, self.n_rep) # 重复 K 的值
- xv = repeat_kv(xv, self.n_rep) # 重复 V 的值
+ xk = repeat_kv(xk, self.n_rep) # (bs, seqlen, n_local_heads, head_dim)
+ xv = repeat_kv(xv, self.n_rep) # (bs, seqlen, n_local_heads, head_dim)
- xq = xq.transpose(1, 2) # 调整 Q 的形状
- xk = xk.transpose(1, 2) # 调整 K 的形状
- xv = xv.transpose(1, 2) # 调整 V 的形状
+ xq = xq.transpose(1, 2)
+ xk = xk.transpose(1, 2)
+ xv = xv.transpose(1, 2)
if self.flash:
output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None,
dropout_p=self.dropout if self.training else 0.0,
- is_causal=True) # 使用 Flash Attention
+ is_causal=True)
else:
- scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim) # 计算注意力分数
+ scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
assert hasattr(self, 'mask')
- scores = scores + self.mask[:, :, :seqlen, :seqlen] # 应用掩码
- scores = F.softmax(scores.float(), dim=-1).type_as(xq) # 计算 softmax
- scores = self.attn_dropout(scores) # 应用注意力 dropout
- output = torch.matmul(scores, xv) # 计算输出
+ scores = scores + self.mask[:, :, :seqlen, :seqlen] # (bs, n_local_heads, seqlen, cache_len + seqlen)
+ scores = F.softmax(scores.float(), dim=-1).type_as(xq)
+ scores = self.attn_dropout(scores)
+ output = torch.matmul(scores, xv) # (bs, n_local_heads, seqlen, head_dim)
- output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1) # 调整输出的形状
+ output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
+
+ output = self.wo(output)
+ output = self.resid_dropout(output)
+ return output
- output = self.wo(output) # 应用输出矩阵
- output = self.resid_dropout(output) # 应用残差 dropout
- return output # 返回输出
-# 定义 FeedForward 类，实现前馈神经网络
class FeedForward(nn.Module):
def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
super().__init__()
if hidden_dim is None:
- hidden_dim = 4 * dim # 设置隐藏层维度
- hidden_dim = int(2 * hidden_dim / 3) # 调整隐藏层维度
- hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) # 调整隐藏层维度
- self.w1 = nn.Linear(dim, hidden_dim, bias=False) # 初始化第一层线性变换
- self.w2 = nn.Linear(hidden_dim, dim, bias=False) # 初始化第二层线性变换
- self.w3 = nn.Linear(dim, hidden_dim, bias=False) # 初始化第三层线性变换
- self.dropout = nn.Dropout(dropout) # 初始化 dropout
+ hidden_dim = 4 * dim
+ hidden_dim = int(2 * hidden_dim / 3)
+ hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
+ self.w1 = nn.Linear(dim, hidden_dim, bias=False)
+ self.w2 = nn.Linear(hidden_dim, dim, bias=False)
+ self.w3 = nn.Linear(dim, hidden_dim, bias=False)
+ self.dropout = nn.Dropout(dropout)
def forward(self, x):
- return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x))) # 前向传播
+ return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))
+
-# 定义 MoEGate 类，实现专家混合（MoE）的门控机制
class MoEGate(nn.Module):
def __init__(self, config: LMConfig):
super().__init__()
self.config = config
- self.top_k = config.num_experts_per_tok # 设置每个 token 选择的专家数量
- self.n_routed_experts = config.n_routed_experts # 设置路由专家的数量
+ self.top_k = config.num_experts_per_tok
+ self.n_routed_experts = config.n_routed_experts
- self.scoring_func = config.scoring_func # 设置评分函数
- self.alpha = config.aux_loss_alpha # 设置辅助损失的权重
- self.seq_aux = config.seq_aux # 设置序列辅助损失
+ self.scoring_func = config.scoring_func
+ self.alpha = config.aux_loss_alpha
+ self.seq_aux = config.seq_aux
- self.norm_topk_prob = config.norm_topk_prob # 设置是否归一化 top-k 概率
- self.gating_dim = config.dim # 设置门控维度
- self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) # 初始化权重参数
- self.reset_parameters() # 重置参数
+ self.norm_topk_prob = config.norm_topk_prob
+ self.gating_dim = config.dim
+ self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
+ self.reset_parameters()
def reset_parameters(self) -> None:
import torch.nn.init as init
- init.kaiming_uniform_(self.weight, a=math.sqrt(5)) # 使用 Kaiming 初始化权重
+ init.kaiming_uniform_(self.weight, a=math.sqrt(5))
def forward(self, hidden_states):
bsz, seq_len, h = hidden_states.shape
- hidden_states = hidden_states.view(-1, h) # 调整隐藏状态的形状
- logits = F.linear(hidden_states, self.weight, None) # 计算 logits
+ hidden_states = hidden_states.view(-1, h)
+ logits = F.linear(hidden_states, self.weight, None)
if self.scoring_func == 'softmax':
- scores = logits.softmax(dim=-1) # 计算 softmax 评分
+ scores = logits.softmax(dim=-1)
else:
raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
- topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) # 选择 top-k 专家
+ topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
if self.top_k > 1 and self.norm_topk_prob:
- denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 # 计算归一化分母
- topk_weight = topk_weight / denominator # 归一化 top-k 概率
+ denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
+ topk_weight = topk_weight / denominator
if self.training and self.alpha > 0.0:
scores_for_aux = scores
@@ -204,9 +204,9 @@ class MoEGate(nn.Module):
aux_loss = (Pi * fi).sum() * self.alpha
else:
aux_loss = None
- return topk_idx, topk_weight, aux_loss # 返回 top-k 专家索引、权重和辅助损失
+ return topk_idx, topk_weight, aux_loss
+
-# 定义 MOEFeedForward 类，实现专家混合（MoE）的前馈神经网络
class MOEFeedForward(nn.Module):
def __init__(self, config: LMConfig):
super().__init__()
@@ -219,16 +219,16 @@ class MOEFeedForward(nn.Module):
dropout=config.dropout,
)
for _ in range(config.n_routed_experts)
- ]) # 初始化专家列表
+ ])
- self.gate = MoEGate(config) # 初始化门控机制
+ self.gate = MoEGate(config)
if config.n_shared_experts is not None:
self.shared_experts = FeedForward(
dim=config.dim,
hidden_dim=config.hidden_dim,
multiple_of=config.multiple_of,
dropout=config.dropout,
- ) # 初始化共享专家
+ )
def forward(self, x):
identity = x
@@ -281,46 +281,35 @@ class MOEFeedForward(nn.Module):
return expert_cache
-# 定义 TransformerBlock 类，实现 Transformer 的一个块，包括自注意力和前馈神经网络
+
class TransformerBlock(nn.Module):
def __init__(self, layer_id: int, args: LMConfig):
super().__init__()
self.n_heads = args.n_heads
self.dim = args.dim
self.head_dim = args.dim // args.n_heads
- self.attention = Attention(args) # 初始化自注意力机制
+ self.attention = Attention(args)
self.layer_id = layer_id
- self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps) # 初始化注意力归一化
- self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps) # 初始化前馈神经网络归一化
+ self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
+ self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)
if args.use_moe:
- self.feed_forward = MOEFeedForward(args) # 初始化专家混合前馈神经网络
+ self.feed_forward = MOEFeedForward(args)
else:
self.feed_forward = FeedForward(
dim=args.dim,
hidden_dim=args.hidden_dim,
multiple_of=args.multiple_of,
dropout=args.dropout,
- ) # 初始化前馈神经网络
+ )
def forward(self, x, pos_cis, use_kv_cache=False):
- h = x + self.attention(self.attention_norm(x), pos_cis, use_kv_cache) # 计算自注意力
- out = h + self.feed_forward(self.ffn_norm(h)) # 计算前馈神经网络
- return out # 返回输出
+ h = x + self.attention(self.attention_norm(x), pos_cis, use_kv_cache)
+ out = h + self.feed_forward(self.ffn_norm(h))
+ return out
-# 定义 Transformer 类，实现整个 Transformer 模型
-class Transformer(PreTrainedModel):
- config_class = LMConfig
- last_loss: Optional[torch.Tensor]
- def __init__(self, params: LMConfig = None):
- super().__init__(params)
- if not params:
- params = LMConfig()
- self.params = params
- self.vocab_size = params.vocab_size
- self.n_layers = params.n_layers
class Transformer(PreTrainedModel):
config_class = LMConfig
last_loss: Optional[torch.Tensor]
@@ -333,99 +322,99 @@ class Transformer(PreTrainedModel):
self.vocab_size = params.vocab_size
self.n_layers = params.n_layers
- self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim) # 初始化词嵌入层
- self.dropout = nn.Dropout(params.dropout) # 初始化 dropout 层
- self.layers = torch.nn.ModuleList() # 初始化 Transformer 块列表
+ self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
+ self.dropout = nn.Dropout(params.dropout)
+ self.layers = torch.nn.ModuleList()
for layer_id in range(self.n_layers):
- self.layers.append(TransformerBlock(layer_id, params)) # 添加 Transformer 块
- self.norm = RMSNorm(params.dim, eps=params.norm_eps) # 初始化归一化层
- self.output = nn.Linear(params.dim, params.vocab_size, bias=False) # 初始化输出层
- self.tok_embeddings.weight = self.output.weight # 共享词嵌入和输出层的权重
- pos_cis = precompute_pos_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len) # 预计算位置编码
- self.register_buffer("pos_cis", pos_cis, persistent=False) # 注册位置编码缓冲区
+ self.layers.append(TransformerBlock(layer_id, params))
+ self.norm = RMSNorm(params.dim, eps=params.norm_eps)
+ self.output = nn.Linear(params.dim, params.vocab_size, bias=False)
+ self.tok_embeddings.weight = self.output.weight
+ pos_cis = precompute_pos_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len)
+ self.register_buffer("pos_cis", pos_cis, persistent=False)
- self.apply(self._init_weights) # 初始化模型权重
+ self.apply(self._init_weights)
for pn, p in self.named_parameters():
if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
- torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * params.n_layers)) # 对特定权重进行初始化
+ torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * params.n_layers))
- self.last_loss = None # 初始化最后一个损失
- self.OUT = CausalLMOutputWithPast() # 初始化输出对象
+ self.last_loss = None
+ self.OUT = CausalLMOutputWithPast()
def _init_weights(self, module):
if isinstance(module, nn.Linear):
- torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) # 初始化线性层的权重
+ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
if module.bias is not None:
- torch.nn.init.zeros_(module.bias) # 初始化线性层的偏置
+ torch.nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
- torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) # 初始化嵌入层的权重
+ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
def forward(self, tokens: Optional[torch.Tensor] = None, targets: Optional[torch.Tensor] = None,
use_kv_cache=False, **keyargs):
if 'input_ids' in keyargs:
- tokens = keyargs['input_ids'] # 如果传入了 input_ids，则使用 input_ids
+ tokens = keyargs['input_ids']
if 'attention_mask' in keyargs:
- targets = keyargs['attention_mask'] # 如果传入了 attention_mask，则使用 attention_mask
+ targets = keyargs['attention_mask']
- _bsz, seqlen = tokens.shape # 获取批量大小和序列长度
- h = self.tok_embeddings(tokens) # 获取词嵌入
- h = self.dropout(h) # 应用 dropout
- pos_cis = self.pos_cis[:seqlen] # 获取对应序列长度的位置编码
+ _bsz, seqlen = tokens.shape
+ h = self.tok_embeddings(tokens)
+ h = self.dropout(h)
+ pos_cis = self.pos_cis[:seqlen]
for idx, layer in enumerate(self.layers):
- h = layer(h, pos_cis, use_kv_cache) # 逐层应用 Transformer 块
+ h = layer(h, pos_cis, use_kv_cache)
- h = self.norm(h) # 应用归一化
+ h = self.norm(h)
if targets is not None:
- logits = self.output(h) # 计算 logits
- self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1) # 计算交叉熵损失
+ logits = self.output(h)
+ self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
else:
- logits = self.output(h[:, [-1], :]) # 计算最后一个 token 的 logits
- self.last_loss = None # 没有目标时，损失为 None
+ logits = self.output(h[:, [-1], :])
+ self.last_loss = None
- self.OUT.__setitem__('logits', logits) # 设置输出对象的 logits
- self.OUT.__setitem__('last_loss', self.last_loss) # 设置输出对象的 last_loss
+ self.OUT.__setitem__('logits', logits)
+ self.OUT.__setitem__('last_loss', self.last_loss)
- return self.OUT # 返回输出对象
+ return self.OUT
- @torch.inference_mode() # 推理模式
+ @torch.inference_mode()
def generate(self, idx, eos, max_new_tokens, temperature=0.7, top_k=None, stream=True, repetition_penalty=1.,
use_kv_cache=True):
- index = idx.shape[1] # 获取当前序列长度
- while idx.shape[1] < max_new_tokens - 1: # 当生成的 token 数量小于最大数量时
- inference_res = self(idx, use_kv_cache=use_kv_cache) # 进行前向传播
- logits = inference_res.logits # 获取 logits
- logits = logits[:, -1, :] # 获取最后一个 token 的 logits
+ index = idx.shape[1]
+ while idx.shape[1] < max_new_tokens - 1:
+ inference_res = self(idx, use_kv_cache=use_kv_cache)
+ logits = inference_res.logits
+ logits = logits[:, -1, :]
- for token in set(idx.tolist()[0]): # 对重复 token 进行惩罚
+ for token in set(idx.tolist()[0]):
logits[:, token] /= repetition_penalty
- if temperature == 0.0: # 如果温度为 0，直接选择概率最高的 token
+ if temperature == 0.0:
_, idx_next = torch.topk(logits, k=1, dim=-1)
else:
- logits = logits / temperature # 调整 logits
- if top_k is not None: # 如果设置了 top-k 采样
+ logits = logits / temperature
+ if top_k is not None:
v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
- logits[logits < v[:, [-1]]] = -float('Inf') # 将小于 top-k 的 logits 设为负无穷
+ logits[logits < v[:, [-1]]] = -float('Inf')
- probs = F.softmax(logits, dim=-1) # 计算概率
- idx_next = torch.multinomial(probs, num_samples=1, generator=None) # 采样下一个 token
+ probs = F.softmax(logits, dim=-1)
+ idx_next = torch.multinomial(probs, num_samples=1, generator=None)
- if idx_next == eos: # 如果生成的 token 是结束符，停止生成
+ if idx_next == eos:
break
- idx = torch.cat((idx, idx_next), dim=1) # 将生成的 token 添加到序列中
- if stream: # 如果需要流式输出
- yield idx[:, index:] # 返回生成的 token
+ idx = torch.cat((idx, idx_next), dim=1)
+ if stream:
+ yield idx[:, index:]
- if not stream: # 如果不需要流式输出
- yield idx[:, index:] # 返回生成的 token
+ if not stream:
+ yield idx[:, index:]
- @torch.inference_mode() # 推理模式
+ @torch.inference_mode()
def eval_answer(self, idx):
- idx_cond = idx if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:] # 截取序列
- inference_res = self(idx_cond) # 进行前向传播
- logits = inference_res.logits # 获取 logits
- logits = logits[:, -1, :] # 获取最后一个 token 的 logits
- return logits # 返回 logits
\ No newline at end of file
+ idx_cond = idx if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:]
+ inference_res = self(idx_cond)
+ logits = inference_res.logits
+ logits = logits[:, -1, :]
+ return logits
diff --git a/中文逐行注释/1-pretrain.py b/中文逐行注释/1-pretrain.py
new file mode 100644
index 0000000..ed70c4d
--- /dev/null
+++ b/中文逐行注释/1-pretrain.py
@@ -0,0 +1,197 @@
+import os
+import platform
+import time
+import math
+import warnings
+import torch
+import torch.distributed as dist
+from torch import optim
+from torch.nn.parallel import DistributedDataParallel
+from torch.optim.lr_scheduler import CosineAnnealingLR
+from torch.utils.data import DataLoader, DistributedSampler
+from contextlib import nullcontext
+from model.model import Transformer
+from model.LMConfig import LMConfig
+from model.dataset import PretrainDataset
+
+# 忽略警告信息
+warnings.filterwarnings('ignore')
+
+# 定义日志打印函数，仅在主进程（rank 0）打印日志信息
+def Logger(content):
+ if not ddp or dist.get_rank() == 0:
+ print(content)
+
+# 定义学习率调度函数，根据当前迭代次数计算学习率，采用余弦退火策略
+def get_lr(it, all):
+ warmup_iters = 0 # 预热迭代次数
+ lr_decay_iters = all # 学习率衰减的总迭代次数
+ min_lr = learning_rate / 10 # 最小学习率
+
+ # 如果当前迭代次数小于预热迭代次数，使用线性预热策略
+ if it < warmup_iters:
+ return learning_rate * it / warmup_iters
+ # 如果当前迭代次数大于衰减迭代次数，返回最小学习率
+ if it > lr_decay_iters:
+ return min_lr
+ # 计算衰减系数，使用余弦退火策略
+ decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
+ assert 0 <= decay_ratio <= 1
+ coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
+ return min_lr + coeff * (learning_rate - min_lr)
+
+# 定义训练 epoch 的函数
+def train_epoch(epoch, accumulation_steps=8):
+ start_time = time.time() # 记录开始时间
+ for step, (X, Y) in enumerate(train_loader): # 遍历数据加载器
+ X = X.to(device) # 将输入数据移动到设备上
+ Y = Y.to(device) # 将目标数据移动到设备上
+
+ lr = get_lr(epoch * iter_per_epoch + step, epochs * iter_per_epoch) # 计算当前学习率
+ for param_group in optimizer.param_groups:
+ param_group['lr'] = lr # 设置优化器的学习率
+
+ with ctx: # 使用混合精度训练（如果设备是 GPU）
+ out = model(X, Y) # 前向传播，计算输出
+ loss = out.last_loss / accumulation_steps # 计算损失，并进行梯度累积
+
+ scaler.scale(loss).backward() # 反向传播，计算梯度
+
+ # 每 accumulation_steps 步进行一次梯度更新
+ if (step + 1) % accumulation_steps == 0:
+ scaler.unscale_(optimizer) # 反缩放梯度
+ torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0) # 梯度裁剪
+
+ scaler.step(optimizer) # 更新模型参数
+ scaler.update() # 更新缩放器
+
+ optimizer.zero_grad(set_to_none=True) # 清空梯度
+
+ # 每 100 步打印一次训练信息
+ if step % 100 == 0:
+ spend_time = time.time() - start_time # 计算已用时间
+ Logger(
+ 'Epoch:[{}/{}]({}/{}) loss:{:.3f} lr:{:.7f} epoch_Time:{}min:'.format(
+ epoch,
+ epochs,
+ step,
+ iter_per_epoch,
+ loss.item() * accumulation_steps,
+ optimizer.param_groups[-1]['lr'],
+ spend_time / (step + 1) * iter_per_epoch // 60 - spend_time // 60))
+
+ # 每 1000 步保存一次模型
+ if (step + 1) % 1000 == 0 and (not ddp or dist.get_rank() == 0):
+ model.eval() # 切换到评估模式
+ # torch.save(model.state_dict(), '{}/iter_{}.pth'.format(save_dir, int(step + epoch * iter_per_epoch)))
+ moe_path = '_moe' if lm_config.use_moe else '' # 根据是否使用 MoE 设置保存路径
+ ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'
+
+ if isinstance(model, torch.nn.parallel.DistributedDataParallel):
+ state_dict = model.module.state_dict() # 获取模型状态字典
+ else:
+ state_dict = model.state_dict()
+
+ torch.save(state_dict, ckp) # 保存模型
+ model.train() # 切换回训练模式
+
+# 定义初始化模型的函数
+def init_model():
+ def count_parameters(model):
+ return sum(p.numel() for p in model.parameters() if p.requires_grad) # 计算模型可训练参数的数量
+
+ # 初始化模型
+ model = Transformer(lm_config).to(device)
+ moe_path = '_moe' if lm_config.use_moe else ''
+ # ckp = f'{save_dir}/pretrain_{lm_config.dim}{moe_path}.pth'
+ #
+ # state_dict = torch.load(ckp, map_location=device)
+ # unwanted_prefix = '_orig_mod.'
+ # for k, v in list(state_dict.items()):
+ # if k.startswith(unwanted_prefix):
+ # state_dict[k[len(unwanted_prefix):]] = state_dict.pop(k)
+ # model.load_state_dict(state_dict, strict=False)
+
+ Logger(f'LLM总参数量：{count_parameters(model) / 1e6:.3f} 百万') # 打印模型总参数量
+ return model
+
+# 定义初始化分布式训练环境的函数
+def init_distributed_mode():
+ if not ddp: return
+ global ddp_local_rank, DEVICE
+
+ dist.init_process_group(backend="nccl") # 初始化分布式进程组，使用 NCCL 后端
+ ddp_rank = int(os.environ["RANK"]) # 获取当前进程的 rank
+ ddp_local_rank = int(os.environ["LOCAL_RANK"]) # 获取当前进程的本地 rank
+ ddp_world_size = int(os.environ["WORLD_SIZE"]) # 获取分布式训练的总进程数
+ DEVICE = f"cuda:{ddp_local_rank}" # 设置当前设备的 CUDA 设备
+ torch.cuda.set_device(DEVICE) # 设置当前设备的 CUDA 设备
+
+
+# torchrun --nproc_per_node 2 1-pretrain.py
+# I/O
+if __name__ == "__main__":
+ # -----------------------------------------------------------------------------
+ lm_config = LMConfig() # 加载配置文件
+ max_seq_len = lm_config.max_seq_len # 获取最大序列长度
+ out_dir = 'out' # 设置输出目录
+ epochs = 20 # 设置训练 epoch 数
+ batch_size = 64 # 设置批量大小
+ learning_rate = 2e-4 # 设置初始学习率
+ device = 'cuda:0' # 设置设备为 CUDA:0
+ dtype = 'bfloat16' # 设置数据类型为 bfloat16
+ save_dir = os.path.join(out_dir) # 设置模型保存目录
+ os.makedirs(save_dir, exist_ok=True) # 创建模型保存目录
+ os.makedirs(out_dir, exist_ok=True) # 创建输出目录
+ tokens_per_iter = batch_size * max_seq_len # 计算每个迭代处理的 token 数量
+ torch.manual_seed(1337) # 设置随机种子
+ device_type = device if "cuda" in device else "cpu" # 设置设备类型
+ ctx = (
+ nullcontext() # 如果设备是 CPU，使用 nullcontext
+ if device_type == "cpu"
+ else torch.cuda.amp.autocast() # 如果设备是 GPU，使用混合精度训练
+ )
+ ddp = int(os.environ.get("RANK", -1)) != -1 # 判断是否为分布式训练
+ ddp_local_rank, DEVICE = 0, "cuda:0" # 初始化分布式训练的本地 rank 和设备
+ if ddp:
+ init_distributed_mode() # 初始化分布式训练环境
+ device = torch.device(DEVICE) # 设置设备
+ # -----------------------------------------------------------------------------
+
+ # -----init dataloader------
+ data_path_list = ['./dataset/pretrain_data.bin'] # 设置数据路径
+ train_ds = PretrainDataset(data_path_list, max_length=max_seq_len, memmap=True) # 初始化数据集
+ train_sampler = DistributedSampler(train_ds) if ddp else None # 如果是分布式训练，使用分布式采样器
+ num_workers = 8 # 设置数据加载器的 num_workers
+ train_loader = DataLoader(
+ train_ds,
+ batch_size=batch_size,
+ pin_memory=True,
+ drop_last=False,
+ shuffle=False,
+ num_workers=num_workers,
+ sampler=train_sampler
+ ) # 初始化数据加载器
+
+ # init model
+ model = init_model() # 初始化模型
+
+ scaler = torch.cuda.amp.GradScaler(enabled=(dtype == dtype)) # 初始化梯度缩放器
+ # optimizer
+ optimizer = optim.Adam(model.parameters(), lr=learning_rate) # 初始化优化器
+ # compile the model
+ if False and platform.system() != 'Windows' and float(torch.__version__.split('.')[0]) >= 2:
+ Logger("compiling the model... (takes a ~minute)")
+ unoptimized_model = model
+ model = torch.compile(model) # 编译模型（如果条件满足）
+
+ if ddp:
+ # Ignore the freqs_cis buffer so that DDP does not broadcast it at
+ # construction time since NCCL does not support ComplexFloat
+ model._ddp_params_and_buffers_to_ignore = {"pos_cis"} # 设置 DDP 忽略的参数和缓冲区
+ model = DistributedDataParallel(model, device_ids=[ddp_local_rank]) # 使用 DDP 包装模型
+
+ # training loop
+ iter_per_epoch = len(train_loader) # 计算每个 epoch 的迭代次数
+ for epoch in range(epochs): # 遍历每个 epoch
+ train_epoch(epoch) # 训练一个 epoch
\ No newline at end of file
diff --git a/中文逐行注释/README.md b/中文逐行注释/README.md
new file mode 100644
index 0000000..6f70d7c
--- /dev/null
+++ b/中文逐行注释/README.md
@@ -0,0 +1,6 @@
+此目录对[minimind(截至2024-09-20版)](https://github.com/jingyaogong/minimind/tree/c28664dac8cc7eec83dcf6c03decc0ec40ded44d)
+重要的代码进行了逐行注释
+
+感谢[@chuanzhubin](https://github.com/chuanzhubin)贡献此部分内容
+
+很大程度方便了学习者快速理解！
\ No newline at end of file
diff --git a/中文逐行注释/model/LMConfig.py b/中文逐行注释/model/LMConfig.py
new file mode 100644
index 0000000..f216b48
--- /dev/null
+++ b/中文逐行注释/model/LMConfig.py
@@ -0,0 +1,58 @@
+from transformers import PretrainedConfig
+from typing import List
+
+# 定义 LMConfig 类，继承自 PretrainedConfig
+class LMConfig(PretrainedConfig):
+ model_type = "minimind" # 设置模型类型为 "minimind"
+
+ def __init__(
+ self,
+ dim: int = 512, # 模型维度，默认为 512
+ n_layers: int = 8, # Transformer 层数，默认为 8
+ n_heads: int = 16, # 注意力头数，默认为 16
+ n_kv_heads: int = 8, # KV 头数，默认为 8
+ vocab_size: int = 6400, # 词汇表大小，默认为 6400
+ hidden_dim: int = None, # 隐藏层维度，默认为 None
+ multiple_of: int = 64, # 隐藏层维度的倍数，默认为 64
+ norm_eps: float = 1e-5, # 归一化层的 epsilon 值，默认为 1e-5
+ max_seq_len: int = 512, # 最大序列长度，默认为 512
+ dropout: float = 0.0, # Dropout 概率，默认为 0.0
+ flash_attn: bool = True, # 是否使用 Flash Attention，默认为 True
+ ####################################################
+ # 以下是 MOE（Mixture of Experts）的特定配置
+ # 当 use_moe 为 False 时，以下配置无效
+ ####################################################
+ use_moe: bool = False, # 是否使用 MOE，默认为 False
+ num_experts_per_tok=2, # 每个 token 选择的专家数量，默认为 2
+ n_routed_experts=4, # 总的专家数量，默认为 4
+ n_shared_experts: bool = True, # 是否使用共享专家，默认为 True
+ scoring_func='softmax', # 评分函数，默认为 'softmax'
+ aux_loss_alpha=0.01, # 辅助损失的 alpha 参数，默认为 0.01
+ seq_aux=True, # 是否在序列级别上计算辅助损失，默认为 True
+ norm_topk_prob=True, # 是否标准化 top-k 概率，默认为 True
+ **kwargs,
+ ):
+ self.dim = dim # 设置模型维度
+ self.n_layers = n_layers # 设置 Transformer 层数
+ self.n_heads = n_heads # 设置注意力头数
+ self.n_kv_heads = n_kv_heads # 设置 KV 头数
+ self.vocab_size = vocab_size # 设置词汇表大小
+ self.hidden_dim = hidden_dim # 设置隐藏层维度
+ self.multiple_of = multiple_of # 设置隐藏层维度的倍数
+ self.norm_eps = norm_eps # 设置归一化层的 epsilon 值
+ self.max_seq_len = max_seq_len # 设置最大序列长度
+ self.dropout = dropout # 设置 Dropout 概率
+ self.flash_attn = flash_attn # 设置是否使用 Flash Attention
+ ####################################################
+ # 以下是 MOE（Mixture of Experts）的特定配置
+ # 当 use_moe 为 False 时，以下配置无效
+ ####################################################
+ self.use_moe = use_moe # 设置是否使用 MOE
+ self.num_experts_per_tok = num_experts_per_tok # 设置每个 token 选择的专家数量
+ self.n_routed_experts = n_routed_experts # 设置总的专家数量
+ self.n_shared_experts = n_shared_experts # 设置是否使用共享专家
+ self.scoring_func = scoring_func # 设置评分函数
+ self.aux_loss_alpha = aux_loss_alpha # 设置辅助损失的 alpha 参数
+ self.seq_aux = seq_aux # 设置是否在序列级别上计算辅助损失
+ self.norm_topk_prob = norm_topk_prob # 设置是否标准化 top-k 概率
+ super().__init__(**kwargs) # 调用父类 PretrainedConfig 的初始化方法
\ No newline at end of file
diff --git a/中文逐行注释/model/dataset.py b/中文逐行注释/model/dataset.py
new file mode 100644
index 0000000..82c9be8
--- /dev/null
+++ b/中文逐行注释/model/dataset.py
@@ -0,0 +1,130 @@
+import json
+import random
+import re
+
+import pandas as pd
+import numpy as np
+from torch.utils.data import Dataset, DataLoader
+import torch
+from sklearn.model_selection import train_test_split
+import os
+
+os.environ["TOKENIZERS_PARALLELISM"] = "false" # 禁用 tokenizer 的并行处理
+
+# 定义 PretrainDataset 类，继承自 Dataset
+class PretrainDataset(Dataset):
+ def __init__(self, data_path_lst, max_length=512, memmap=False):
+ super().__init__()
+ # 如果使用内存映射（memmap）
+ if memmap:
+ with open(data_path_lst[0], 'r') as f:
+ nbytes = f.seek(0, 2) # 获取文件总字节数
+ flen = f.tell() // np.dtype('uint16').itemsize # 计算文件长度
+ self.data = np.memmap(data_path_lst[0], dtype=np.dtype('uint16'), shape=(flen // max_length, max_length)) # 使用内存映射加载数据
+ else:
+ data_lst = []
+ for data_path in data_path_lst:
+ with open(data_path, 'rb') as f:
+ data = np.fromfile(f, dtype=np.uint16) # 从文件中读取数据
+ data_lst.append(data)
+ data = np.concatenate(data_lst) # 合并所有数据
+ data = data[:max_length * int(len(data) / max_length)] # 截取数据
+ # np.random.shuffle(data) # 打乱数据（注释掉了）
+ self.data = data.reshape(-1, max_length) # 将数据重塑为 (样本数, 最大长度) 的形状
+ # 打印数据形状
+ print("memmap:{} train data.shape:{}".format(memmap, self.data.shape))
+ print("downloading finished.....")
+
+ def __len__(self):
+ return self.data.shape[0] # 返回数据集的长度
+
+ def __getitem__(self, index: int):
+ # 获取指定索引的样本
+ sample = self.data[index]
+ X = np.array(sample[:-1]).astype(np.int64) # 输入数据（去掉最后一个 token）
+ Y = np.array(sample[1:]).astype(np.int64) # 目标数据（去掉第一个 token）
+
+ return torch.from_numpy(X), torch.from_numpy(Y) # 返回 PyTorch 张量
+
+# 定义 SFTDataset 类，继承自 Dataset
+class SFTDataset(Dataset):
+ def __init__(self, df, tokenizer, max_length=1024, prompt_max_len=512, answer_max_len=256):
+ super().__init__()
+ self.df = df # 数据框
+ self.max_length = max_length # 最大序列长度
+ self.prompt_max_len = prompt_max_len # 提示的最大长度
+ self.answer_max_len = answer_max_len # 回答的最大长度
+ #
+ self.tokenizer = tokenizer # 分词器
+ self.padding = 0 # 填充 token ID
+ self.bos_id = self.tokenizer('<s>assistant').data['input_ids'] # 开始 token ID
+
+ def __len__(self):
+ return self.df.shape[0] # 返回数据集的长度
+
+ def find_sublist_index(self, main_list, sub_list) -> int:
+ last_index = -1
+ for i in range(len(main_list) - len(sub_list) + 1):
+ if main_list[i:i + len(sub_list)] == sub_list:
+ last_index = i
+ return last_index # 查找子列表在主列表中的最后一个索引
+
+ def safe_eval(self, s):
+ try:
+ res = eval(s)
+ except Exception as e:
+ return []
+ return res # 安全地执行 eval 函数
+
+ def __getitem__(self, index: int):
+ # 获取指定索引的样本
+ sample = self.df.iloc[index]
+ history = self.safe_eval(sample['history']) # 获取历史对话
+ q = str(sample['q']) # 获取问题
+ a = str(sample['a']) # 获取回答
+
+ messages = []
+ for history_message in history:
+ if len(history_message) <= 1:
+ continue
+ messages.append(
+ {"role": 'user', "content": str(history_message[0])[:self.max_length // 2]}
+ )
+ messages.append(
+ {"role": 'assistant', "content": str(history_message[1])[:self.max_length // 2]}
+ )
+
+ messages += [
+ {"role": "user", "content": q},
+ {"role": "assistant", "content": a},
+ ]
+ new_prompt = self.tokenizer.apply_chat_template(
+ messages,
+ tokenize=False,
+ add_generation_prompt=True
+ ) # 生成新的提示
+ input_id = self.tokenizer(new_prompt).data['input_ids'][:self.max_length] # 分词并截取
+
+ # 实际长度
+ question_length = self.find_sublist_index(input_id, self.bos_id) + len(self.bos_id)
+ # 没满最大长度的剩余部分
+ padding_len = self.max_length - len(input_id)
+ input_id = input_id + [self.padding] * padding_len # 填充到最大长度
+ mask_len = len(input_id) - question_length - padding_len
+ # 0表示不计算损失
+ loss_mask = [0] * question_length + [1] * (mask_len) + [0] * padding_len
+
+ input_id = np.array(input_id)
+ X = np.array(input_id[:-1]).astype(np.int64) # 输入数据（去掉最后一个 token）
+ Y = np.array(input_id[1:]).astype(np.int64) # 目标数据（去掉第一个 token）
+ loss_mask = np.array(loss_mask[1:]).astype(np.int64) # 损失掩码
+
+ X_tensor = torch.from_numpy(X)
+ Y_tensor = torch.from_numpy(Y)
+ loss_mask_tensor = torch.from_numpy(loss_mask)
+
+ return X_tensor, Y_tensor, loss_mask_tensor # 返回 PyTorch 张量
+
+# 主函数
+if __name__ == "__main__":
+ pass
\ No newline at end of file
diff --git a/中文逐行注释/model/minimind_tokenizer/merges.txt b/中文逐行注释/model/minimind_tokenizer/merges.txt
new file mode 100644
index 0000000..767c649
--- /dev/null
+++ b/中文逐行注释/model/minimind_tokenizer/merges.txt
@@ -0,0 +1,6142 @@
+#version: 0.2
+Ġ t
+Ġ a
+i n
+h e
+r e
+ï ¼
+ä ¸
+o n
+a t
+ç ļ
+çļ Ħ
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+Ġ s
+Ġ c
+n d
+ã Ģ
+e r
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+e s
+e n
+o r
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+Ġa nd
+in g
+Ġ p
+i t
+a l
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+Ġ o
+Ġ w
+ä »
+Ġt o
+i s
+o u
+Ġ m
+ä º
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+Ġ b
+e d
+i on
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+i c
+Ġ d
+Ġo f
+l e
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+å ħ
+en t
+æ ľ
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+å Ĵ
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+ä ½
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+æ Ī
+å ®
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+Ġ h
+Ġt h
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+c t
+ç Ķ
+o m
+å ľ
+å ¤
+æ ĺ
+å Ĭ
+å IJ
+ä¸ Ģ
+i m
+è ¯
+æ ĸ
+at ion
+l o
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+Ġb e
+ãĢ ģ
+i d
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+ä ¹
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+ä» ¥
+c h
+Ġ y
+c e
+ï¼ ļ
+o t
+er s
+Ġ n
+é Ģ
+r a
+å °
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+Ġy ou
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+åľ ¨
+l y
+Ġ is
+ä¸ ª
+Ġ l
+u r
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+u t
+Ġ he
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+ä ¼
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+å ¹
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+e l
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+å ¸
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+æ Ŀ
+æĪ ij
+Ġ on
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+æ Ĺ
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+è¦ ģ
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+æ ķ
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+ä¸ Ń
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+ĠĠ ĠĠ
+å Ľ
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new file mode 100644
index 0000000..dcfabc6
--- /dev/null
+++ b/中文逐行注释/model/minimind_tokenizer/tokenizer.json
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+ "ĠI t",
+ "æł ¼",
+ "Ġa r",
+ "æľ ¯",
+ "éĿ ŀ",
+ "Ġd o",
+ "Ġm ay",
+ "æĭ ©",
+ "u e",
+ "éĢī æĭ©",
+ "r y",
+ "é ĥ",
+ "Ġl ike",
+ "on g",
+ "è ģ",
+ "` `",
+ "i le",
+ "æ± Ĥ",
+ "Ġne w",
+ "i ent",
+ "Ġimp act",
+ "è¿ ĺ",
+ "æ³ ¨",
+ "ä¹ Ī",
+ "çĽ ®",
+ "âĢ ľ",
+ "âĢ Ŀ",
+ "e f",
+ "ä¾ ĭ",
+ "Ġpot ential",
+ "o k",
+ "åı¯ èĥ½",
+ "Ġtr ans",
+ "Ġa ct",
+ "ï¼ ī",
+ "Ġspe c",
+ "æ ¶",
+ "Ġw ill",
+ "äº ¤",
+ "iz e",
+ "ç¾ İ",
+ "å¸ Ĥ",
+ "Ġst ud",
+ "p on",
+ "è º",
+ "ä¸į åIJĮ",
+ "on e",
+ "å¾ Ī",
+ "åı Ĭ",
+ "å¦Ĥ æŀľ",
+ "çIJ ĥ",
+ "an ge",
+ "Ġne ed",
+ "å¤ ĸ",
+ "et y",
+ "ak ing",
+ "è¯ ·",
+ "at er",
+ "Ġpers on",
+ "id ent",
+ "Ġs o",
+ "Ġm ake",
+ "å¹ ³",
+ "å¤ Ł",
+ "èº «",
+ "ï¼ Ī",
+ "Ġin form",
+ "æ ¡",
+ "äº ĭ",
+ "åı Ĺ",
+ "as ed",
+ "il d",
+ "Ġof f",
+ "Ġthe re",
+ "c is",
+ "è ¢",
+ "éĥ ¨",
+ "æ¯ ı",
+ "ra ct",
+ "as s",
+ "Ġlearn ing",
+ "å ĸ",
+ "å½ ¢",
+ "i re",
+ "ä» İ",
+ "bot s",
+ "è Ļ",
+ "å¸ ®",
+ "Ġd es",
+ "ĠI n",
+ "c ess",
+ "Ġp e",
+ "if y",
+ "Ġwh o",
+ "ä¹ ł",
+ "æľ Ł",
+ "Ġexp eri",
+ "é Ĥ",
+ "Ġs c",
+ "e p",
+ "ä½ ķ",
+ "Ġt ime",
+ "éĿŀ 常",
+ "æĭ ¬",
+ "å ķ",
+ "以 ä¸ĭ",
+ "éģ ĵ",
+ "Ġcomm un",
+ "Ġc ould",
+ "a p",
+ "è IJ",
+ "è° ĥ",
+ "l ic",
+ "du ct",
+ "Ġit s",
+ "c y",
+ "è¯ ´",
+ "Ġm ed",
+ "Ġc ol",
+ "ul ar",
+ "éĩį è¦ģ",
+ "Ġs p",
+ "åĪ ©",
+ "èµ ·",
+ "Ġprov id",
+ "ic es",
+ "å Ļ",
+ "æĸ Ļ",
+ "Ġimp ort",
+ "ur al",
+ "åŃ Ĺ",
+ "Ġu nd",
+ "in t",
+ "Ġo ver",
+ "åı ¸",
+ "æł ¹",
+ "é ¥",
+ "pl es",
+ "ä»ĸ 们",
+ "g ra",
+ "ur ing",
+ "n ow",
+ "åį ķ",
+ "è¿Ļ äºĽ",
+ "åī į",
+ "å® ī",
+ "Ġp r",
+ "åĮħ æĭ¬",
+ "ç» Ļ",
+ "T he",
+ "ä½ į",
+ "å §",
+ "ç´ ł",
+ "åij ĺ",
+ "Ġ ident",
+ "åŀ ĭ",
+ "Ġad d",
+ "å¼ º",
+ "æĺ¯ ä¸Ģ",
+ "i p",
+ "g or",
+ "Ġsu pport",
+ "n e",
+ "Ġdiff ere",
+ "åħ ĥ",
+ "Ġas s",
+ "åĨ ³",
+ "é Ľ",
+ "åIJ į",
+ "Ġg o",
+ "Ġtechnolo gy",
+ "æĢ »",
+ "è® ®",
+ "Ġin ter",
+ "Ġin v",
+ "Ġo ur",
+ "æķ Ī",
+ "ust om",
+ "Ġre l",
+ "if e",
+ "åĻ ¨",
+ "ing s",
+ "ä» ·",
+ "Ġp art",
+ "è¢ «",
+ "æī ĭ",
+ "ar y",
+ "Ġres pon",
+ "Ċ ĠĠĠ",
+ "好 çļĦ",
+ "at ive",
+ "帮 åĬ©",
+ "ç» Ł",
+ "æĶ ¾",
+ "ĠH ere",
+ "ç ģ",
+ "Ġb ut",
+ "æģ ¯",
+ "æŃ £",
+ "ar k",
+ "åħ¬ åı¸",
+ "or y",
+ "å¢ ĥ",
+ "le ct",
+ "é Ł",
+ "æĥ ³",
+ "é£ İ",
+ "at ing",
+ "Ġa m",
+ "it s",
+ "æ »",
+ "gor ith",
+ "åĵ į",
+ "ure s",
+ "Ġeff ect",
+ "Ġsh ould",
+ "Ġp er",
+ "è ±",
+ "ç ²",
+ "ic t",
+ "Ġal gorith",
+ "u c",
+ "rou gh",
+ "ä» »",
+ "ä» ¶",
+ "Ġbe t",
+ "i a",
+ "Ġanaly z",
+ "æł¹ æį®",
+ "iz ed",
+ "æµ ģ",
+ "è§ Ĥ",
+ "è £",
+ "æł ĩ",
+ "ir on",
+ "Ġc ustom",
+ "Ġre g",
+ "Ġperson al",
+ "èĥ½ å¤Ł",
+ "ic s",
+ "iv id",
+ "ç Ī",
+ "èµ Ħ",
+ "æŃ ¥",
+ "å® ¹",
+ "åĪ Ľ",
+ "è Ī",
+ "ä¹ IJ",
+ "å¯ ¼",
+ "g an",
+ "èĬ Ĥ",
+ "Ġal l",
+ "en s",
+ "am e",
+ "n ess",
+ "Ġu p",
+ "Ġ U",
+ "èĢ ĥ",
+ "el f",
+ "åĢ ¼",
+ "å° ij",
+ "æľ į",
+ "ar i",
+ "th ical",
+ "v iron",
+ "è ĥ",
+ "or d",
+ "Ġs ign",
+ "éĩ Į",
+ "ou nd",
+ "o ple",
+ "åŁ º",
+ "Ġinform ation",
+ "Ġident ify",
+ "åĽ ŀ",
+ "Ġc re",
+ "éŁ ³",
+ "ib le",
+ "u b",
+ "è¿ IJ",
+ "Ġle ad",
+ "æ¸ ¸",
+ "æ¬ ¡",
+ "åĨ Ļ",
+ "éĤ £",
+ "g et",
+ "è į",
+ "Ġexam ple",
+ "ä¼ ĺ",
+ "å½± åĵį",
+ "is h",
+ "x t",
+ "æ º",
+ "éª Į",
+ "o b",
+ "å® ¢",
+ "å¤ ĩ",
+ "åģ ¥",
+ "è½ ¦",
+ "ç¤ ¾",
+ "ivid ual",
+ "ere d",
+ "l es",
+ "Ġen viron",
+ "Ġpe ople",
+ "æĺ Ł",
+ "ç ĸ",
+ "ç ĭ",
+ "Ġd et",
+ "æĹ ł",
+ "Ġ if",
+ "o se",
+ "it e",
+ "å¢ ŀ",
+ "é Ĵ",
+ "åIJĮ æŶ",
+ "è¿ °",
+ "æĸ¹ å¼ı",
+ "åĽ ½",
+ "é »",
+ "å¤ Ħ",
+ "Ġexam ples",
+ "æ ®",
+ "Ġint o",
+ "æĮ ĩ",
+ "Ġhum an",
+ "åIJ ij",
+ "ç¤ º",
+ "æķ° æį®",
+ "Ġ 3",
+ "Ġ J",
+ "è ı",
+ "çݯ å¢ĥ",
+ "al s",
+ "ers t",
+ "Ġe thical",
+ "ç» Ħ",
+ "ä¼ ł",
+ "Ġdiffere nt",
+ "Ġk now",
+ "åº ı",
+ "Ġind ividual",
+ "æıIJ é«ĺ",
+ "rou nd",
+ "å° ±",
+ "åı ĸ",
+ "åŃ ĺ",
+ "ä¸ ¤",
+ "çŁ ¥",
+ "our ces",
+ "c k",
+ "å £",
+ "in es",
+ "è¾ ¾",
+ "Ġman y",
+ "æķ ´",
+ "æł ·",
+ "dit ional",
+ "om m",
+ "çĶ ±",
+ "éĢ ł",
+ "å®ĥ 们",
+ "u es",
+ "Ġm ent",
+ "Ġimport ant",
+ "Ġo pt",
+ "Ġlo c",
+ "p h",
+ "Ġpro cess",
+ "Ġalgorith ms",
+ "设 计",
+ "Ġsoc ial",
+ "ver y",
+ "åĪ Ļ",
+ "ä¾ĭ å¦Ĥ",
+ "è® ¤",
+ "Ġa ut",
+ "Ġs erv",
+ "g g",
+ "产 åĵģ",
+ "è§ Ħ",
+ "çľ ĭ",
+ "ve l",
+ "æĸ¹ æ³ķ",
+ "Ġb en",
+ "åĽł æѤ",
+ "c are",
+ "p er",
+ "åĬ Ł",
+ "建 议",
+ "Ġp os",
+ "æ ¤",
+ "w e",
+ "åĮ º",
+ "i qu",
+ "Ġre al",
+ "æĹ ¥",
+ "Ġredu ce",
+ "a f",
+ "ang u",
+ "Ġs k",
+ "Ġ ed",
+ "erst and",
+ "åĨ µ",
+ "m ot",
+ "åħ Ī",
+ "ç ¥",
+ "åºĶ 该",
+ "Ġth rough",
+ "Ġcon c",
+ "åıij å±ķ",
+ "è¯ ķ",
+ "æ¡ Ī",
+ "Ġenviron ment",
+ "åı £",
+ "Ġad v",
+ "åĪ «",
+ "Ġben ef",
+ "æ¸ ħ",
+ "åij ³",
+ "åħ ī",
+ "Ġdevelop ment",
+ "en g",
+ "å¦Ĥ ä½ķ",
+ "ç® ¡",
+ "iv ers",
+ "åIJ Ħ",
+ "Ġr is",
+ "ro w",
+ "er gy",
+ "计 ç®Ĺ",
+ "ä¿¡ æģ¯",
+ "Ġpro duct",
+ "è¾ ĥ",
+ "è® º",
+ "èĩªå·± çļĦ",
+ "æĬ ¤",
+ "åı į",
+ "åħ¶ ä»ĸ",
+ "åĪ Ĺ",
+ "ç» Ĩ",
+ "ç© º",
+ "Ġg reat",
+ "e ar",
+ "æº IJ",
+ "j ect",
+ "çĶŁ æ´»",
+ "ä¸Ń çļĦ",
+ "Ġund erstand",
+ "è ĭ",
+ "h at",
+ "Ġpro gra",
+ "ç Ĭ",
+ "éĩ ij",
+ "Ġinclud ing",
+ "Ġacc ess",
+ "ĠĠĠĠ ĠĠĠ",
+ "è¯ Ĩ",
+ "ç ¦",
+ "o g",
+ "è£ ħ",
+ "Ġar t",
+ "Ġw rit",
+ "Ġinc re",
+ "Ġp h",
+ "æĸ¹ éĿ¢",
+ "Ġp ract",
+ "Ġus ing",
+ "é¡ ¹",
+ "æİ ¥",
+ "Ġway s",
+ "Ġl angu",
+ "æĶ ¯",
+ "Ġch all",
+ "åİ »",
+ "__ __",
+ "im ate",
+ "æĸ Ń",
+ "è ¨",
+ "Ġw ell",
+ "l l",
+ "Ġp ol",
+ "æĢ ģ",
+ "Ġ ra",
+ "C an",
+ "åİ Ł",
+ "b er",
+ "è¨ Ģ",
+ "ç« ĭ",
+ "Ġg en",
+ "éħ į",
+ "æ· ±",
+ "t e",
+ "ä¸ ī",
+ "ç§ ij",
+ "ĠF or",
+ "çº ¿",
+ "ç ħ",
+ "æ ¼",
+ "åķ Ĩ",
+ "æĿ IJ",
+ "Ġsign ific",
+ "Ġg u",
+ "Ġde cis",
+ "Ġtra in",
+ "Ġa g",
+ "Ġc reat",
+ "å® Į",
+ "æŶ éĹ´",
+ "Ġon e",
+ "è Ħ",
+ "Ġn at",
+ "åѦ ä¹ł",
+ "çļĦæ ķ",
+ "c ed",
+ "Ġwhe n",
+ "Ġb i",
+ "è İ",
+ "æĽ´ åĬł",
+ "iv es",
+ "p ort",
+ "å·¥ ä½ľ",
+ "v ing",
+ "Ġbe en",
+ "æĻ º",
+ "Ġl ife",
+ "å¼ ķ",
+ "ar m",
+ "çİ ĩ",
+ "çĶ¨ æĪ·",
+ "ä¹ ī",
+ "ä» ½",
+ "è¯ Ŀ",
+ "in ess",
+ "c om",
+ "åº ·",
+ "åĩ ı",
+ "ä» Ģ",
+ "è¾ ĵ",
+ "Ġv ari",
+ "c on",
+ "Ġmo d",
+ "ä»Ģ ä¹Ī",
+ "Ġen ergy",
+ "æĬĢ æľ¯",
+ "ert ain",
+ "m m",
+ "ver all",
+ "åĪ Ĵ",
+ "Ġro bots",
+ "Ġor gan",
+ "æİ ¨",
+ "ant s",
+ "åĩ Ĩ",
+ "d s",
+ "æŀ ģ",
+ "ç Ļ",
+ "Ġre qu",
+ "Ġ ess",
+ "ç® Ģ",
+ "ust ain",
+ "æ ¨",
+ "Ġst r",
+ "c ing",
+ "ab ility",
+ "re e",
+ "Ġed uc",
+ "åİ Ĩ",
+ "Ġcre ate",
+ "åģ¥ 康",
+ "Ġdes ign",
+ "i ps",
+ "åģ ļ",
+ "èĬ ±",
+ "in k",
+ "èı ľ",
+ "æī ¾",
+ "æ® µ",
+ "æµ ĭ",
+ "Ġ V",
+ "ĠB y",
+ "å Ķ",
+ "é¦ ĸ",
+ "è¯ į",
+ "Ġwhe re",
+ "Ġdis c",
+ "äºĨ 解",
+ "r ic",
+ "ä¸ Ķ",
+ "è¶ ³",
+ "æĺ¯ ä¸Ģ个",
+ "ar ch",
+ "ç§ ¯",
+ "å¸ ¦",
+ "Ġwh ile",
+ "Ġsignific ant",
+ "çł ģ",
+ "æĪ ¿",
+ "Ġbe ing",
+ "Ġlangu age",
+ "it ive",
+ "2 0",
+ "Ġanalyz e",
+ "æĻ ¯",
+ "è Į",
+ "ri b",
+ "æ¨ ¡",
+ "ĠS t",
+ "è´ ¹",
+ "' t",
+ "Ġhealth care",
+ "Ġexperi ence",
+ "Ġ 5",
+ "个 人",
+ "ay s",
+ "è± ¡",
+ "p lo",
+ "Ġw ould",
+ "èĻ ij",
+ "æĶ ¶",
+ "é¢ Ħ",
+ "é¢ Ĩ",
+ "ä¿Ŀ æĮģ",
+ "en ces",
+ "åı ª",
+ "èĩ ´",
+ "æĪ ı",
+ "Ġment al",
+ "Ġfe w",
+ "at es",
+ "è¿ĩ ç¨ĭ",
+ "å®ī åħ¨",
+ "Ġs ustain",
+ "Ġw ere",
+ "å¤ ª",
+ "ç Į",
+ "Ġspec ific",
+ "Ġwor ld",
+ "çŃ Ķ",
+ "`` `",
+ "Ġt ake",
+ "åħ »",
+ "éĢ Ł",
+ "e ver",
+ "S S",
+ "éĶ Ģ",
+ "Ġb o",
+ "he s",
+ "Ġm us",
+ "æľį åĬ¡",
+ "è§ Ĵ",
+ "t en",
+ "æŀ IJ",
+ "p ow",
+ "d ict",
+ "v ent",
+ "1 0",
+ "çļĦæ Ĺ",
+ "ĸ çķ",
+ "Ġpro t",
+ "ç½ ®",
+ "Ġh igh",
+ "Ġb us",
+ "Ġind ust",
+ "åIJ ¦",
+ "c ial",
+ "人 们",
+ "ĠA s",
+ "åij Ĭ",
+ "ad e",
+ "æĶ ¹",
+ "ç Ĺ",
+ "Ġh ad",
+ "Ġhe r",
+ "Ġj ust",
+ "ï¼ Ľ",
+ "è´ Ń",
+ "ç¬ ¬",
+ "é ĵ",
+ "Ġw ater",
+ "Ġf ood",
+ "éĺ Ł",
+ "a us",
+ "Ġchall eng",
+ "åħ į",
+ "æĸĩ åĮĸ",
+ "Ġmo st",
+ "é ¸",
+ "ç½ ij",
+ "çĽ ´",
+ "Ġs m",
+ "Ġact iv",
+ "plo y",
+ "O verall",
+ "å¿ «",
+ "ru ct",
+ "Ġindividual s",
+ "å§ ĭ",
+ "g ies",
+ "æŁ ¥",
+ "çĪ ±",
+ "i ety",
+ "I n",
+ "åĪĨ æŀIJ",
+ "è§ Ĩ",
+ "æ¸ ©",
+ "ç» ´",
+ "ol ut",
+ "åŁ Ł",
+ "omm end",
+ "Ġcom ple",
+ "æķ Ļ",
+ "Ġb u",
+ "Ġeduc ation",
+ "at her",
+ "Ġ 4",
+ "t ing",
+ "Ġf ind",
+ "æ² ¡",
+ "Ġh is",
+ "ä¹ĭ éĹ´",
+ "Ġeffect ive",
+ "Ġat t",
+ "Ġre se",
+ "èĥ½ åĬĽ",
+ "åŁ İ",
+ "Ġal low",
+ "Ġa v",
+ "Ġpro mot",
+ "æĻº èĥ½",
+ "æ» ¡",
+ "åħ ±",
+ "ie w",
+ "c ome",
+ "ç³» 绣",
+ "Ġrespon s",
+ "äº Ĵ",
+ "Ġc ult",
+ "pow ered",
+ "Ġrec ommend",
+ "èIJ ¥",
+ "O SS",
+ "Ġch ange",
+ "è¯ ģ",
+ "v ed",
+ "æİ Ĵ",
+ "解 åĨ³",
+ "ic i",
+ "ĠH ow",
+ "Ġfe el",
+ "æľ Ī",
+ "Ġwh at",
+ "以 åıĬ",
+ "Ġse e",
+ "åŃ ©",
+ "b s",
+ "Ġs ur",
+ "æ £",
+ "al ity",
+ "Ġv is",
+ "ç¡® ä¿Ŀ",
+ "p ect",
+ "å®ŀ çİ°",
+ "Ġc are",
+ "å¹ ¿",
+ "ill s",
+ "åº Ń",
+ "as es",
+ "å¤ į",
+ "åºĶ çĶ¨",
+ "çļĦæ ĥ",
+ "ard s",
+ "Ġadd ress",
+ "Ġcomp an",
+ "Ġinv ol",
+ "Ġcustom er",
+ "åĽł 为",
+ "Ġstud ents",
+ "Ġin s",
+ "注 æĦı",
+ "æŀ Ħ",
+ "æ¬ ¢",
+ "æµ ·",
+ "åı Ĥ",
+ "èĩª çĦ¶",
+ "é ©",
+ "ĠThe se",
+ "w n",
+ "æĺ ĵ",
+ "çĬ ¶",
+ "re n",
+ "Ġt reat",
+ "Ġbenef its",
+ "Ċ ĠĠĠĠĠĠĠ",
+ "对 äºİ",
+ "æĢ Ŀ",
+ "id er",
+ "ĠY es",
+ "Ġ K",
+ "åĸ ľ",
+ "Ġ ke",
+ "Ġen g",
+ "Ġpo p",
+ "o st",
+ "p are",
+ "Ġm on",
+ "æ¬ ¾",
+ "ĠM OSS",
+ "Ġem ot",
+ "Ġa c",
+ "ç¼ ĸ",
+ "f ore",
+ "åı ¥",
+ "Ġv al",
+ "il y",
+ "Ġis s",
+ "èĤ ī",
+ "èĩ ³",
+ "游 æĪı",
+ "we en",
+ "Ġinclud e",
+ "Ġprot ect",
+ "åħ³ ç³»",
+ "éĻ ©",
+ "Ġse ver",
+ "Ġth an",
+ "éľĢ æ±Ĥ",
+ "ç» ĥ",
+ "ĠThe y",
+ "is s",
+ "y s",
+ "Ġj ob",
+ "éĺ ³",
+ "æ IJ",
+ "Ġbet ween",
+ "Ġm ach",
+ "---- ----",
+ "èĢĥ èĻij",
+ "è´¨ éĩı",
+ "Ġbus iness",
+ "w or",
+ "ic k",
+ "e g",
+ "åħ ħ",
+ "ç ¯",
+ "æĿ ¡",
+ "n er",
+ "a pt",
+ "Ġapp ro",
+ "Ġpl ay",
+ "没 æľī",
+ "¤ IJ",
+ "æľ ª",
+ "æĪ ĺ",
+ "家 åºŃ",
+ "ãĢ ĭ",
+ "en cy",
+ "ĠC h",
+ "ãĢ Ĭ",
+ "Ġprovid ing",
+ "Ġres ources",
+ "âĢ Ļ",
+ "Ġass ist",
+ "Ġnat ural",
+ "è¯ Ħ",
+ "ä¾ ¿",
+ "Ġs af",
+ "åħ· æľī",
+ "è° ¢",
+ "çĥ Ń",
+ "s s",
+ "et h",
+ "ol d",
+ "Ġper form",
+ "Ġsever al",
+ "é ¤IJ",
+ "Ġe ach",
+ "è½ ¬",
+ "c i",
+ "Ġt y",
+ "Ġp ub",
+ "æ´» åĬ¨",
+ "oc us",
+ "çī Į",
+ "è¶ Ĭ",
+ "åĽ ¢",
+ "è½ »",
+ "è¯Ń è¨Ģ",
+ "Ġare as",
+ "éĩ ĩ",
+ "f t",
+ "ri end",
+ "å· ²",
+ "å¸Ĥ åľº",
+ "it ion",
+ "i ents",
+ "管 çIJĨ",
+ "è® ¸",
+ "人 类",
+ "身 ä½ĵ",
+ "iqu e",
+ "Ġpart ic",
+ "ç» Ń",
+ "age ment",
+ "v es",
+ "ç¬ ¦",
+ "l ine",
+ "çº ¢",
+ "åIJ ¸",
+ "Ġpat ter",
+ "00 0",
+ "社 ä¼ļ",
+ "åĨħ 容",
+ "Ġorgan iz",
+ "ou gh",
+ "Ġ ve",
+ "åŃ© åŃIJ",
+ "æĸ ½",
+ "æ¤ į",
+ "åĩ ł",
+ "ä½Ĩ æĺ¯",
+ "Ġa ff",
+ "Ġn um",
+ "le ment",
+ "èī º",
+ "è ij",
+ "Ġc ar",
+ "ag es",
+ "ab or",
+ "æĺ¯ä¸Ģ ç§į",
+ "Ġin st",
+ "è Ľ",
+ "ä¹ĭ ä¸Ģ",
+ "è· ¯",
+ "åį ³",
+ "Ġm ain",
+ "éļ ı",
+ "H ow",
+ "å¿ ħ",
+ "ç¨ĭ åºı",
+ "éŁ³ ä¹IJ",
+ "re d",
+ "æ² ¹",
+ "Ġoff er",
+ "et s",
+ "ç ¢",
+ "Ġd uring",
+ "çļĦ 人",
+ "æĽ´ å¤ļ",
+ "Ġd i",
+ "代 çłģ",
+ "èİ ·",
+ "åħ ĭ",
+ "Ġgu id",
+ "主 è¦ģ",
+ "Ġf am",
+ "æİ §",
+ "éĢļ 常",
+ "ĠA d",
+ "å¤Ħ çIJĨ",
+ "ur n",
+ "ow er",
+ "åij ½",
+ "æı ı",
+ "Ġsk ills",
+ "Ġto ol",
+ "w are",
+ "æĸĩ æľ¬",
+ "Ġpatter ns",
+ "缮 æłĩ",
+ "ac y",
+ "æī ĵ",
+ "åŁİ å¸Ĥ",
+ "Ġe very",
+ "r ies",
+ "è¯ »",
+ "éģ ¿",
+ "çĻ ½",
+ "éĢĤ åIJĪ",
+ "Ġpat ient",
+ "çľ Ł",
+ "ot h",
+ "å¥ ¹",
+ "åĶ ®",
+ "ä¸Ģ ç§į",
+ "Ġm ade",
+ "ä½ İ",
+ "is e",
+ "Ġre m",
+ "æ¶ Ī",
+ "åIJ «",
+ "a ir",
+ "Ġgen er",
+ "o y",
+ "ç² ¾",
+ "æĥħ åĨµ",
+ "ight s",
+ "Ġexp l",
+ "è§ ģ",
+ "Ġpre dict",
+ "ç± ³",
+ "æĽ´ 好",
+ "ä¿ ®",
+ "Ġcl imate",
+ "Ġf ocus",
+ "Ġg row",
+ "客 æĪ·",
+ "ä¸į æĸŃ",
+ "it or",
+ "ĠE n",
+ "çº ¦",
+ "æĺ¯ åIJ¦",
+ "ä» ħ",
+ "æĪij们 çļĦ",
+ "æľ Ľ",
+ "o p",
+ "Ġm aking",
+ "y th",
+ "cc ess",
+ "Ġo wn",
+ "gg est",
+ "Ġt as",
+ "ut ure",
+ "Ġmod el",
+ "p ut",
+ "Ġrese arch",
+ "ere st",
+ "éļ ¾",
+ "Ġ [",
+ "i el",
+ "ation al",
+ "Ġcommun ic",
+ "ç¥ ŀ",
+ "ç© ¶",
+ "Ġre st",
+ "æĪIJ 为",
+ "k ing",
+ "p r",
+ "åĮ »",
+ "c ur",
+ "èĤ ²",
+ "Ġ '",
+ "è¿Ļ ç§į",
+ "ç¯ ĩ",
+ "Ġc he",
+ "ow n",
+ "éĻ ħ",
+ "Ġf in",
+ "åĪ¶ ä½ľ",
+ "Ġsu ggest",
+ "å¢ŀ åĬł",
+ "Ġmed ia",
+ "rib ut",
+ "çļĦæĥ ħ",
+ "åĬł åħ¥",
+ "Ġc le",
+ "åij ¨",
+ "ç« ł",
+ "Ġth ink",
+ "Ġloc al",
+ "pport un",
+ "ĠY ou",
+ "Ġpl an",
+ "Ġev en",
+ "éĽ Ĩ",
+ "å· §",
+ "a x",
+ "Ġchalleng es",
+ "Ġpro f",
+ "ĠC an",
+ "Ġconc er",
+ "Ġf uture",
+ "åĬ ¿",
+ "Ġre f",
+ "èģ Ķ",
+ "Ġs elf",
+ "æĪĸ èĢħ",
+ "b le",
+ "åĽ ´",
+ "è¿IJ åĬ¨",
+ "Ġin f",
+ "éĩ Ĭ",
+ "Ġsustain able",
+ "Ġte xt",
+ "Ġg ra",
+ "äº Į",
+ "åĵģ çīĮ",
+ "ä¸įåIJĮ çļĦ",
+ "l ed",
+ "çĭ ¬",
+ "Ġo pportun",
+ "Ġcont in",
+ "y m",
+ "Ġg et",
+ "å¯ Ĩ",
+ "éĻ ¤",
+ "æ ħ",
+ "éģ¿ åħį",
+ "Ġ +",
+ "è§ ī",
+ "Ġre t",
+ "å¸ ĥ",
+ "Ġint erest",
+ "Ġsoc iety",
+ "ç»ĵ æŀľ",
+ "åIJ ¬",
+ "é¦ĸ åħĪ",
+ "Ġb re",
+ "Ġ2 0",
+ "ĠHow ever",
+ "è® °",
+ "on s",
+ "è¿ ij",
+ "å¼Ģ å§ĭ",
+ "Ġbu ild",
+ "Ġbe h",
+ "' m",
+ "v ers",
+ "Ġg ood",
+ "çIJĨ 解",
+ "res ent",
+ "ç¦ »",
+ "åĬŁ èĥ½",
+ "Ġeff ort",
+ "l abor",
+ "é» ij",
+ "Ġbet ter",
+ "Ġre ad",
+ "å¾ ĭ",
+ "èĽ ĭ",
+ "he d",
+ "ä¹ °",
+ "导 èĩ´",
+ "Ġimp lement",
+ "ç ¿",
+ "äº «",
+ "å¤ ´",
+ "en se",
+ "Ġl ong",
+ "ot her",
+ "é¥ ®",
+ "åŃĺ åľ¨",
+ "çļĦæ Ħ",
+ "ä¸Ģ 份",
+ "yth on",
+ "n ing",
+ "åĩı å°ij",
+ "åĢ Ļ",
+ "ä¸ ĵ",
+ "åIJĦ ç§į",
+ "è ħ",
+ "å° ½",
+ "åį ĩ",
+ "æĬ ¥",
+ "Ġpub lic",
+ "Ġl ar",
+ "ä½ł çļĦ",
+ "a ut",
+ "é¢Ĩ åŁŁ",
+ "æ ļ",
+ "ol low",
+ "èģ Į",
+ "Ġch ang",
+ "Ġb est",
+ "h ip",
+ "åĨ į",
+ "ak es",
+ "Ġch at",
+ "it ed",
+ "Ġp ower",
+ "ä¿Ŀ æĬ¤",
+ "ä¹ ¦",
+ "计 åĪĴ",
+ "éĩįè¦ģ çļĦ",
+ "åıĺ åĮĸ",
+ "il ities",
+ "Ġcons ider",
+ "æĪij们 åı¯ä»¥",
+ "éĤ£ ä¹Ī",
+ "Ġ ide",
+ "æ¼ Ķ",
+ "ag ing",
+ "Ġb ased",
+ "å® Ŀ",
+ "Ġr ange",
+ "Ġres ult",
+ "Ġm em",
+ "çħ §",
+ "Ġle vel",
+ "c ou",
+ "Ġb r",
+ "T h",
+ "ä¼ ģ",
+ "建 ç«ĭ",
+ "Ġun ique",
+ "è® Ń",
+ "Ġm ark",
+ "许 å¤ļ",
+ "è¡Į 为",
+ "Ķ 究",
+ "çļĦæ Ĭ",
+ "Ġs et",
+ "éª ¤",
+ "t s",
+ "Ġh ist",
+ "Ġa round",
+ "Ġre v",
+ "åħ¶ ä¸Ń",
+ "ï¼ ģ",
+ "æıı è¿°",
+ "æľĢ åIJİ",
+ "Ġs im",
+ "n ect",
+ "åĽŀ çŃĶ",
+ "éĺ ²",
+ "èī ¯",
+ "åĪ° äºĨ",
+ "ä¸ ĸçķ",
+ "æĸ¹ æ¡Ī",
+ "æĿIJ æĸĻ",
+ "ä¸ĸçķ Į",
+ "æĽ´å¥½ åľ°",
+ "两 个",
+ "Ġem ploy",
+ "Ġtr y",
+ "æ ĵ",
+ "Ġb ack",
+ "åĪ ĩ",
+ "Ġsu ccess",
+ "Ġdecis ions",
+ "Ġth ose",
+ "å¯ Į",
+ "Ġf act",
+ "æİ ¢",
+ "è¶ £",
+ "Ġpract ices",
+ "åIJ Ĺ",
+ "æī į",
+ "çİ ©",
+ "pt ion",
+ "æĸĩ ç«ł",
+ "Ġfe at",
+ "Ġpre vent",
+ "Ġwrit ing",
+ "çļĦæ Ģ",
+ "Ġn o",
+ "ä» ĭ",
+ "éĹ ¨",
+ "Ġd el",
+ "æ Ĵ",
+ "Ġopt im",
+ "in ation",
+ "Ġ Ċ",
+ "us ion",
+ "Ġacc ount",
+ "l ing",
+ "Ġd ivers",
+ ". \"",
+ "at h",
+ "èĭ ±",
+ "ä¼ģ ä¸ļ",
+ "Ġg rou",
+ "åľ° çIJĥ",
+ "å¤ ±",
+ "Ġpersonal ized",
+ "ĠH e",
+ "表 达",
+ "cur ity",
+ "Ġf ollow",
+ "产 çĶŁ",
+ "Ġe ar",
+ "åİ ĭ",
+ "ver n",
+ "Ġiss ues",
+ "åĿ ĩ",
+ "é ²",
+ "Ġd r",
+ "iv ing",
+ "Ġtrain ing",
+ "Ġris k",
+ "åĩ ½",
+ "åı ²",
+ "æ ij",
+ "çļĦæĹ ¶",
+ "og n",
+ "Ġrequ ire",
+ "Ġenvironment al",
+ "b ack",
+ "éĶ ®",
+ "çĸ Ĺ",
+ "Ġinter act",
+ "åĽ¢ éĺŁ",
+ "æ¯ı 个",
+ "çĦ¶ åIJİ",
+ "Ġd ist",
+ "çĶ¨ äºİ",
+ "认 为",
+ "åĩ½ æķ°",
+ "Ġs ent",
+ "Ċ ĠĠĠĠĠĠĠĠ",
+ "Ġredu cing",
+ "å¹ ²",
+ "Ġre p",
+ "Ġc aus",
+ "Ġmus ic",
+ "ç ª",
+ "Ġmon itor",
+ "Ġfor m",
+ "é¢ ľ",
+ "çĹ ħ",
+ "é¦ Ļ",
+ "Ġof ten",
+ "åı¯èĥ½ ä¼ļ",
+ "åijĺ å·¥",
+ "Ġha nd",
+ "æĬ ķ",
+ "Ġneed s",
+ "æѤ å¤ĸ",
+ "åı ĭ",
+ "iv ity",
+ "Ġactiv ities",
+ "åĸľ 欢",
+ "Ġp ur",
+ "i an",
+ "s elf",
+ "åĬ¨ çī©",
+ "com es",
+ "å ©",
+ "Ġpr iv",
+ "a z",
+ "Ġrel ations",
+ "Ġmach ine",
+ "çļĦæ °",
+ "ä»· æł¼",
+ "ä»· åĢ¼",
+ "ç´ ¢",
+ "Ġfe ed",
+ "ä¸Ģ ä¸ĭ",
+ "Ġte am",
+ "Ġindust ry",
+ "è´ ¢",
+ "ĠP ro",
+ "Ġw ant",
+ "ç§ °",
+ "Ġcl ass",
+ "Ġlo ve",
+ "åħ³ äºİ",
+ "è¾ĵ åħ¥",
+ "Ġtrans port",
+ "Ġcomple x",
+ "Ġy ear",
+ "éĶĢ åĶ®",
+ "å¯ »",
+ "i ence",
+ "ist s",
+ "æĶ¯ æĮģ",
+ "Ġm ind",
+ "Ġf un",
+ "Ġch ar",
+ "æĮ ī",
+ "Ġconcer ns",
+ "con om",
+ "ç®Ģ åįķ",
+ "以ä¸ĭ æĺ¯",
+ "Ġst art",
+ "并 ä¸Ķ",
+ "av i",
+ "ä¸Ń åĽ½",
+ "åħĥ ç´ł",
+ "Ġcon f",
+ "Ġpos itive",
+ "Ġc ur",
+ "Ġc ount",
+ "er y",
+ "å ¡",
+ "å® ¤",
+ "Ġco st",
+ "Ġe qu",
+ "Ġpol ic",
+ "ast e",
+ "a w",
+ "éħ Ĵ",
+ "cou ra",
+ "iv en",
+ "pl ace",
+ "ch ie",
+ "çļĦæķ °",
+ "åĽł ç´ł",
+ "Ġf l",
+ "is m",
+ "Ġmed ical",
+ "Ġhum ans",
+ "Ġaut om",
+ "ertain ly",
+ "Ġ 0",
+ "Ġoff ers",
+ "Ġdet ect",
+ "Ġ 6",
+ "é£İ æł¼",
+ "Ġsh ow",
+ "çģ «",
+ "Ġan im",
+ "é¢ľ èī²",
+ "le ase",
+ "a ve",
+ "åĵ ª",
+ "ĠThe re",
+ "以 ä¸Ĭ",
+ "æľª æĿ¥",
+ "X X",
+ "çī ĩ",
+ "u ch",
+ "Ġtas ks",
+ "åħ· ä½ĵ",
+ "æ¤į çī©",
+ "Ġm in",
+ "èīº æľ¯",
+ "ic ult",
+ "Ġexperi ences",
+ "æݧ åĪ¶",
+ "b e",
+ "Ġpat ients",
+ "å ²",
+ "ĠW e",
+ "Ġrec ogn",
+ "çĥ ¤",
+ "Ġsm all",
+ "åĿ Ĺ",
+ "å Ħ",
+ "太 éĺ³",
+ "ct ion",
+ "Ġ ent",
+ "æį ¢",
+ "Ġbe fore",
+ "Ġbe come",
+ "å·² ç»ı",
+ "表 çİ°",
+ "Ġexp lo",
+ "Ġa chie",
+ "ä»» åĬ¡",
+ "大 çļĦ",
+ "Ġd ay",
+ "Ġf ound",
+ "å± ±",
+ "on d",
+ "Ġtreat ment",
+ "pe nd",
+ "he n",
+ "Ġcon dit",
+ "ç¡® å®ļ",
+ "Ġbusiness es",
+ "ĠW h",
+ "æīĢ æľī",
+ "Ġdevelop ed",
+ "ç» Ī",
+ "æŃ¥ 骤",
+ "Ġdiff icult",
+ "åı ·",
+ "ĠR e",
+ "éĶ Ļ",
+ "Ġch o",
+ "Ġqu est",
+ "Ġtrans pare",
+ "Ġpro ject",
+ "Ġcommun ity",
+ "o v",
+ "å¸ Ī",
+ "å¼ ł",
+ "åĪĨ ç±»",
+ "人 çļĦ",
+ "s is",
+ "çĽ Ĭ",
+ "o id",
+ "ĠA n",
+ "w ays",
+ "Ġe as",
+ "Ġaff ect",
+ "Ġother s",
+ "Ġreg ul",
+ "æĢ§ åĴĮ",
+ "åĸ Ħ",
+ "ag n",
+ "ä½ľ 为",
+ "åı¯ä»¥ 帮åĬ©",
+ "åĦ ¿",
+ "Ġorganiz ations",
+ "é¸ ¡",
+ "åħ ´",
+ "Ġf riend",
+ "Ġ $",
+ "Ġdet ail",
+ "Ġtra ditional",
+ "Ġdesign ed",
+ "è´Ń ä¹°",
+ "ä½ĵ éªĮ",
+ "ç» į",
+ "er m",
+ "Ġcon nect",
+ "è¿Ļ æł·",
+ "Ġrecommend ations",
+ "Ġb oth",
+ "Ł éĢļ",
+ "æ¯ į",
+ "Ġs it",
+ "ä½ľ çĶ¨",
+ "ä»ĭ ç»į",
+ "Ġst e",
+ "ĠS ure",
+ "åı °",
+ "æĤ¨ çļĦ",
+ "Ġs he",
+ "Ġman agement",
+ "j oy",
+ "è´ Ł",
+ "Ġpromot e",
+ "Ġvari ous",
+ "( \"",
+ "p or",
+ "Ġs ens",
+ "Ġess ential",
+ "get her",
+ "ular ly",
+ "äº ī",
+ "ir st",
+ "Ġo p",
+ "Ġspec ies",
+ "çİ° åľ¨",
+ "ch o",
+ "Ġbeh avi",
+ "çŃ ij",
+ "å¥ ³",
+ "Ġqu ality",
+ "Ġex t",
+ "è ¥",
+ "å®Į æĪIJ",
+ "æĢ» ä¹ĭ",
+ "éĥ¨ åĪĨ",
+ "ä»İ èĢĮ",
+ "åĽ ¾",
+ "Ġty p",
+ "Ġstr ate",
+ "è¥ ¿",
+ "Ġhe re",
+ "ar s",
+ "å¸ Į",
+ "çļĦæ Ŀ",
+ "å° Ŀ",
+ "e e",
+ "i er",
+ "Ġe c",
+ "ical ly",
+ "er ing",
+ "å¿ µ",
+ "ĠD e",
+ "Ġne g",
+ "建 çŃij",
+ "Ġserv ices",
+ "Ġab le",
+ "im es",
+ "Ġopt ions",
+ "缸 åħ³",
+ "Ġsu b",
+ "Ġdecis ion",
+ "ĠC ertainly",
+ "Ġ åľ¨",
+ "æ ¢",
+ "Ġserv ice",
+ ") :",
+ "带 æĿ¥",
+ "Ġch ild",
+ "解 éĩĬ",
+ "ir t",
+ "ç Ĩ",
+ "ä¸į ä»ħ",
+ "æĿ ¾",
+ "积 æŀģ",
+ "r on",
+ "åı ¤",
+ "çł Ķ究",
+ "ç² ī",
+ "h or",
+ "Ġprof ess",
+ "çļĦ éĹ®é¢ĺ",
+ "Ġopportun ities",
+ "åİĨ åı²",
+ "Ġde f",
+ "ĠA m",
+ "Ġg r",
+ "a ur",
+ "å± Ĥ",
+ "çŃ ĸ",
+ "Ġpop ular",
+ "æ´ ģ",
+ "åıij çİ°",
+ "Ġpo em",
+ "èµ Ľ",
+ "Ġo b",
+ "Ġd on",
+ "Ġs ound",
+ "Ġtransport ation",
+ "i ous",
+ "åı ¦",
+ "Ġro le",
+ "Ġf iel",
+ "ç§ij åѦ",
+ "èĢ ģ",
+ "re en",
+ "æľī æķĪ",
+ "Ġc or",
+ "Ġfeed back",
+ "Ġtechnolo gies",
+ "交 éĢļ",
+ "Ġad apt",
+ "' re",
+ "erv ation",
+ "Ġcommun ities",
+ "çİ° 代",
+ "Ġlo ok",
+ "Ġf ac",
+ "çĶµ å½±",
+ "Ġcol lect",
+ "å¾Ĺ åĪ°",
+ "h ips",
+ "Ġav ail",
+ "ere n",
+ "ä¸Ģ èµ·",
+ "çī Ľ",
+ "Ġpos s",
+ "Ġwe ather",
+ "Ġeffort s",
+ "¿ Ģ",
+ "æĹ ħ",
+ "o h",
+ "Ġcol labor",
+ "æĭ ¥",
+ "æĪIJ åĬŁ",
+ "èİ· å¾Ĺ",
+ "å± ħ",
+ "Ġt re",
+ "Ġs ources",
+ "Ġstud y",
+ "Ġprogra ms",
+ "éĻ IJ",
+ "Ġt ips",
+ "Ġmark et",
+ "al ly",
+ "å® ³",
+ "w ards",
+ "æ£ Ģ",
+ "ä¸Ģ ç¯ĩ",
+ "ri or",
+ "Ġto p",
+ "Ġe nd",
+ "å ĭ",
+ "Ġlar ge",
+ "ici ency",
+ "Ġde c",
+ "å®ļ çļĦ",
+ "ic ient",
+ "è¿ĩç¨ĭ ä¸Ń",
+ "lic ations",
+ "ç¼ º",
+ "Ġto ur",
+ "Ġto gether",
+ "人 工",
+ "Ġtool s",
+ "æĸ ¯",
+ "æ° ij",
+ "æĬ Ĭ",
+ "ä¹ĭéĹ´ çļĦ",
+ "çī¹ çĤ¹",
+ "Ġbe l",
+ "ditional ly",
+ "åĪ© çĶ¨",
+ "è¾ ¹",
+ "éĻ į",
+ "ĠI f",
+ "é¢ Ŀ",
+ "åį ı",
+ "å¾ Ģ",
+ "l ish",
+ "è¯ ī",
+ "in s",
+ "å¥ ¶",
+ "Ġe conom",
+ "Ġinv est",
+ "ĠD o",
+ "t ain",
+ "åĩº çİ°",
+ "çļĦ å½±åĵį",
+ "ater ial",
+ "Ġs ure",
+ "Ġp ass",
+ "çĶ »",
+ "è´ £",
+ "ç»ĵ æŀĦ",
+ "æķ ħ",
+ "æĥħ æĦŁ",
+ "æ ¿Ģ",
+ "ell ig",
+ "ä¼ Ĺ",
+ "æ¯Ķ è¾ĥ",
+ "ter n",
+ "Ġout comes",
+ "u p",
+ "Ġbe aut",
+ "re ad",
+ "çĶŁ æĪIJ",
+ "æķ° åŃĹ",
+ "Ġde m",
+ "i res",
+ "åı¯ä»¥ éĢļè¿ĩ",
+ "æĸ° çļĦ",
+ "Ġde ep",
+ "å ¨",
+ "çĭ Ĺ",
+ "åħ³ 注",
+ "çĶŁ åij½",
+ "ä¼ł 绣",
+ "Ġst ay",
+ "æŃ Į",
+ "åħ³ éĶ®",
+ "Ġpl ace",
+ "主 é¢ĺ",
+ "å¾Ī å¤ļ",
+ "èĪ Ĵ",
+ "Ġprofess ional",
+ "y le",
+ "æĽ ²",
+ "1 9",
+ "Ġess ay",
+ "Ġg ive",
+ "ç³ ĸ",
+ "Ġon ly",
+ "æŁ IJ",
+ "Ġph ys",
+ "对 è¯Ŀ",
+ "Ġcont ro",
+ "Ġam ount",
+ "ce pt",
+ "iz ation",
+ "ç¼ĸ åĨĻ",
+ "åıĹ åĪ°",
+ "Ġal ways",
+ "æ¯Ķ å¦Ĥ",
+ "Ġpriv acy",
+ "a u",
+ "____ ____",
+ "Ġrespons ible",
+ "( )",
+ "çŃī çŃī",
+ "Ġm aterial",
+ "Ġon line",
+ "é ¼",
+ "æĶ ¿",
+ "åĽ Ľ",
+ "Ġen joy",
+ "åľ Ł",
+ "Ġsaf ety",
+ "Ġt w",
+ "Ġcommunic ation",
+ "ä¸ ½",
+ "æĺ ¾",
+ "olut ion",
+ "er g",
+ "į ä½ľ",
+ "Ġus er",
+ "Ġemot ional",
+ "t ime",
+ "é ¾",
+ "Ġse curity",
+ "Ġs ense",
+ "el ines",
+ "åĬ ±",
+ "çī© è´¨",
+ "u ra",
+ "Ġsh are",
+ "Ġanalyz ing",
+ "it al",
+ "é ±",
+ "irt ual",
+ "Ġvis it",
+ "b ers",
+ "Ġc our",
+ "Ġpro ble",
+ "设 å¤ĩ",
+ "at ch",
+ "l and",
+ "é± ¼",
+ "æĪij们 éľĢè¦ģ",
+ "ç¨ ³",
+ "ib ility",
+ "Ġeff iciency",
+ "å£ °",
+ "è Ĵ",
+ "æľº åĻ¨",
+ "Ġcle ar",
+ "åĪ¶ å®ļ",
+ "iz ing",
+ "Ġcondit ions",
+ "l usion",
+ "Ġlo w",
+ "Ġl im",
+ "her s",
+ "Ġris ks",
+ "ç¿ »",
+ "Ġle t",
+ "åĴ ĸ",
+ "å¿ĥ çIJĨ",
+ "è¿ ľ",
+ "pr int",
+ "Ġchang es",
+ "Ġme as",
+ "Ġimpro ving",
+ "Ġc rit",
+ "5 0",
+ "å¸Į æľĽ",
+ "Ġa ud",
+ "åį Ĺ",
+ "æĹł æ³ķ",
+ "Ġneg ative",
+ "项 缮",
+ "u nd",
+ "at s",
+ "Ġcompan ies",
+ "æī¾ åĪ°",
+ "Ġcont ribut",
+ "æŃ£ ç¡®",
+ "é» Ħ",
+ "å± ŀ",
+ "Ġunderstand ing",
+ "Ġm ult",
+ "Ġc lo",
+ "å¾ ģ",
+ "Ġp rior",
+ "r im",
+ "人工 æĻºèĥ½",
+ "Ġvari ety",
+ "Ġt aking",
+ "å Ĥ",
+ "as ter",
+ "od y",
+ "Ġ {",
+ "çļĦ éĩįè¦ģ",
+ "Ġf ore",
+ "èµĦ æºIJ",
+ "è¦ģ æ±Ĥ",
+ "Ġfeat ures",
+ "èį ī",
+ "m e",
+ "èĮ ĥ",
+ "Ġo per",
+ "çº §",
+ "é² ľ",
+ "æĬĢ å·§",
+ "ij æĪĺ",
+ "ç±» åŀĭ",
+ "æĿ ¿",
+ "è½ ¯",
+ "e w",
+ "Ġrest aur",
+ "Ġwith out",
+ "ruct ure",
+ "çļĦ æĺ¯",
+ "ç ı",
+ "Ġl ist",
+ "ur ate",
+ "Ġbo ok",
+ "äº ²",
+ "åº Ĺ",
+ "ä¹Ł æĺ¯",
+ "ä»» ä½ķ",
+ "Ġc am",
+ "ĠB e",
+ "Ġgo vern",
+ "Ġbehavi or",
+ "è®Ń ç»ĥ",
+ "Ġfam ily",
+ "æĿ Ĥ",
+ "Ġc ity",
+ "Ġappro ach",
+ "Ġacc urate",
+ "Ġs om",
+ "Ġe l",
+ "èĪ ŀ",
+ "è ŀ",
+ "åŁº æľ¬",
+ "Ġdis e",
+ "Ġen coura",
+ "ĠW hat",
+ "å ĥ",
+ "è¯ ¦",
+ "¦ Ĥ",
+ "å·¥ åħ·",
+ "åķ ¡",
+ "Ġst ill",
+ "cho ol",
+ "æĦŁ åĪ°",
+ "çĶŁ çī©",
+ "åĴĸ åķ¡",
+ "åĩĨ å¤ĩ",
+ "Ġw aste",
+ "Ġev ents",
+ "æķĻ èĤ²",
+ "Ġ 8",
+ "Ġm ust",
+ "i ed",
+ "as ing",
+ "å½¢ æĪIJ",
+ "Ġproduct s",
+ "åħ ¸",
+ "è® ²",
+ "f ter",
+ "å· ®",
+ "l ess",
+ "Ġc ro",
+ "Ġfin an",
+ "åıį åºĶ",
+ "åĪĽ éĢł",
+ "Ġguid elines",
+ "åĪ ¤",
+ "ä½ľ åĵģ",
+ "表 示",
+ "å¼ Ĥ",
+ "Ġknow n",
+ "Ġt est",
+ "è¯ ¯",
+ "o pe",
+ "Ġus ers",
+ "A I",
+ "å¾ ·",
+ "ne w",
+ "è¿ ½",
+ "iqu es",
+ "模 åŀĭ",
+ "åĬĽ åĴĮ",
+ "Ġhist ory",
+ "ĠA l",
+ "æĬķ èµĦ",
+ "å°Ŀ è¯ķ",
+ "an k",
+ "Ġh ome",
+ "éĴ Ł",
+ "ä¸ °",
+ "èĪĴ éĢĤ",
+ "Ġincre ase",
+ "Ġh ab",
+ "åĪ »",
+ "è¾ĵ åĩº",
+ "Ġlead ing",
+ "Ġ 7",
+ "é£İ éĻ©",
+ "Ġperform ance",
+ "Ġha pp",
+ "åŃ £",
+ "Ġst and",
+ "t y",
+ "ç¦ ı",
+ "Ġcustom ers",
+ "åį İ",
+ "Ġbel ie",
+ "Ġcompan y",
+ "å½ ķ",
+ "é£Ł çī©",
+ "ĠU n",
+ "Ġsu mm",
+ "re nt",
+ "ĠC on",
+ "éĢĤ éĩı",
+ "an ced",
+ "Ġ i",
+ "Ġl ight",
+ "Ġanaly sis",
+ "å° Ĭ",
+ "ĠU se",
+ "ou se",
+ "t ed",
+ "Ġchar act",
+ "Ġ #",
+ "t o",
+ "ç» ľ",
+ "ä¸į æĺ¯",
+ "Ġdevelop ing",
+ "åŁ ¹",
+ "Ġstrate gies",
+ "Ġm ight",
+ "çŁ Ń",
+ "çļĦæ İ",
+ "Ġf irst",
+ "èĥ Į",
+ "çĮ «",
+ "Ġinclud es",
+ "åĽ Ń",
+ "Ġdi agn",
+ "Ġgrow th",
+ "ä¸ĵ ä¸ļ",
+ "Ġdo es",
+ "1 2",
+ "ç» ¿",
+ "Ġke ep",
+ "详 ç»Ĩ",
+ "åĥ ı",
+ "åıij çĶŁ",
+ "f act",
+ "åı¯ä»¥ åľ¨",
+ "ç« Ļ",
+ "æĭ ī",
+ "æµ İ",
+ "Ġchat bots",
+ "Ġbre ak",
+ "è¡ ¡",
+ "çŁ ³",
+ "æĮģ ç»Ń",
+ "l ife",
+ "Ġ1 0",
+ "æ´ Ĺ",
+ "ĠAd ditionally",
+ "å£ «",
+ "em ber",
+ "Ġgo als",
+ "å¾ ®",
+ "Ġv iew",
+ "Â ·",
+ "o ve",
+ "åŁº ç¡",
+ "Ġoptim ize",
+ "Ġt em",
+ "Ġd own",
+ "åŁºç¡ Ģ",
+ "è¶ ħ",
+ "er cis",
+ "Ġl ess",
+ "e es",
+ "æĿ ĥ",
+ "Ġke y",
+ "Ġwor ks",
+ "è® ¨",
+ "åı¥ åŃIJ",
+ "Ġro bot",
+ "us s",
+ "åħ¨ çIJĥ",
+ "ç»ı æµİ",
+ "æīį èĥ½",
+ "eg r",
+ "ä»ĸ们 çļĦ",
+ "äº Ķ",
+ "èµ· æĿ¥",
+ "ç ĵ",
+ "Ġfact ors",
+ "Ġcult ural",
+ "æľ ¨",
+ "Ġwork ing",
+ "ä¼ ¼",
+ "èIJ ½",
+ "éĢŁ 度",
+ "ä½ ı",
+ "Ġeffect s",
+ "å© ļ",
+ "b r",
+ "åİ ħ",
+ "ra in",
+ "\" )",
+ "åѦ çĶŁ",
+ "\" ,",
+ "Ġp ar",
+ "at form",
+ "Ġens uring",
+ "çĶ± äºİ",
+ "Ġm uch",
+ "Ġwor ds",
+ "Ġm ar",
+ "ç»ı éªĮ",
+ "为 äºĨ",
+ "åIJĪ ä½ľ",
+ "v en",
+ "Ġ /",
+ "Ġfinan cial",
+ "wor k",
+ "or ies",
+ "æ² »",
+ "Ġtechn iques",
+ "æĭ¥ æľī",
+ "ra p",
+ "å° Ķ",
+ "Ġ est",
+ "Ġavail able",
+ "Ġl it",
+ "æ ¹",
+ "Ġeff icient",
+ "el s",
+ "o ver",
+ "Ġl and",
+ "Ġare a",
+ "Ġint ellig",
+ "Ġpre f",
+ "at ure",
+ "çŁ¥ è¯Ĩ",
+ "æĵ įä½ľ",
+ "å¾ ħ",
+ "ig ate",
+ "çļĦæ Ķ",
+ "Ġme an",
+ "b o",
+ "Ġcontro l",
+ "éĩĩ çĶ¨",
+ "ric ult",
+ "Ġprogra mm",
+ "Ġto wards",
+ "th ing",
+ "ä¸į è¦ģ",
+ "Ġth ough",
+ "å½ ©",
+ "Ġc ertain",
+ "Ġw ild",
+ "ä» Ĭ",
+ "Ġcons ervation",
+ "çŁ¥ éģĵ",
+ "Ġreal ly",
+ "çļĦ åľ°",
+ "i o",
+ "é¥ °",
+ "Ġf ul",
+ "çݯ ä¿Ŀ",
+ "Ġexplo re",
+ "çļĦæ ¸",
+ "Ġdivers e",
+ "åĬł 强",
+ "çļ ®",
+ "Ġemot ions",
+ "Ġav oid",
+ "' ll",
+ "çļĦæ ī",
+ "åį ¡",
+ "Ġpl atform",
+ "an ces",
+ "Ġsit u",
+ "ä» ĺ",
+ "ä½į ç½®",
+ "or ing",
+ "çĽ IJ",
+ "ä¸ ĩ",
+ "Ġde v",
+ "n ov",
+ "as h",
+ "Ġtw o",
+ "å® ł",
+ "b on",
+ "èµ °",
+ "åĪĹ è¡¨",
+ "Ġc y",
+ "èį IJ",
+ "ĠS ome",
+ "Ġexpl ain",
+ "Ġa ware",
+ "社 交",
+ "d ay",
+ "åı Į",
+ "æ² ŁéĢļ",
+ "æ° §",
+ "å¼Ģ åıij",
+ "åħ¬åı¸ çļĦ",
+ "Ġa ir",
+ "åĩ »",
+ "ar ing",
+ "éĥ½ æĺ¯",
+ "Ġlevel s",
+ "od s",
+ "Ġste ps",
+ "Ġc ap",
+ "æ´ ŀ",
+ "é© ¬",
+ "Ġret urn",
+ "Ġm et",
+ "çĶŁ æĢģ",
+ "丰 å¯Į",
+ "æŁ ĵ",
+ "æīĢ 以",
+ "é¡ »",
+ "Ġ er",
+ "Ġf ra",
+ "3 0",
+ "è ĵ",
+ "âĢ Ķ",
+ "Ġ å½ĵ",
+ "a h",
+ "ä¿ ĥ",
+ "Ġlike ly",
+ "ĠĠĠĠĠĠĠĠ ĠĠĠĠĠĠĠĠ",
+ "åĪ Ŀ",
+ "Ġcreat ing",
+ "Ġf arm",
+ "Ġb al",
+ "Ġl ives",
+ "å®ĥ çļĦ",
+ "Ġab ility",
+ "ä¸Ĭ çļĦ",
+ "Ġsent ence",
+ "åĤ ¨",
+ "Ġr out",
+ "Ġprovid es",
+ "Ġag ain",
+ "å®ł çī©",
+ "éĢ IJ",
+ "Ġyear s",
+ "èŀ į",
+ "Ġphys ical",
+ "P ython",
+ "ĠE x",
+ "it ing",
+ "è°ĥ æķ´",
+ "ç½ij 绾",
+ "æħ ¢",
+ "空 éĹ´",
+ "åĽ °",
+ "è± Ĩ",
+ "æĽ´å¤ļ çļĦ",
+ "ĠA r",
+ "Ġmain tain",
+ "å®ŀ éĻħ",
+ "Ġtra vel",
+ "Ġs at",
+ "p ro",
+ "çĶµ åŃIJ",
+ "æ± ½",
+ "e x",
+ "åģ ĩ",
+ "æIJ Ń",
+ "éļı çĿĢ",
+ "è¿ĺ æľī",
+ "ç¤ ¼",
+ "al e",
+ "Ġcons um",
+ "Ċ Ġ",
+ "n cy",
+ "Ġquest ions",
+ "f ort",
+ "m aking",
+ "Ġdes c",
+ "1 5",
+ "Ġinvol ves",
+ "Ġst ress",
+ "åŃŠ符",
+ "he re",
+ "Ġimpact s",
+ "Ġex ercis",
+ "åĿ ļ",
+ "led ge",
+ "ç§ij æĬĢ",
+ "oc i",
+ "Ġeffective ly",
+ "æ¶Ī è´¹",
+ "Ġconc lusion",
+ "éĺ ħ",
+ "Ġst re",
+ "iss ions",
+ "æ· »",
+ "I t",
+ "éĿ Ļ",
+ "Ġv irtual",
+ "è¡ £",
+ "Ġachie ve",
+ "our ce",
+ "è¿ ŀ",
+ "ac ks",
+ "表 æł¼",
+ "Ġimport ance",
+ "èĩª æĪij",
+ "The se",
+ "n um",
+ "çļĦæ ł",
+ "Ġrelations hips",
+ "Ġwork ers",
+ "g ical",
+ "or por",
+ "ers on",
+ "åij ¢",
+ "nd s",
+ "æݨ èįIJ",
+ "oh n",
+ "å¿ħ é¡»",
+ "容 æĺĵ",
+ "ĠG o",
+ "Ġt ell",
+ "ĠR es",
+ "on om",
+ "Ġbe c",
+ "æ³ Ľ",
+ "p os",
+ "Ġmo ve",
+ "Ġst ory",
+ "æŃ ¢",
+ "Ġprior it",
+ "Ġindust ries",
+ "è ľ",
+ "Ġposs ible",
+ "ĠM an",
+ "Ġexp ress",
+ "ab ilities",
+ "Ġint egr",
+ "代 表",
+ "Ġrespon d",
+ "åĪĨ éĴŁ",
+ "æľº ä¼ļ",
+ "Ġth ings",
+ "交 æµģ",
+ "Ġm eth",
+ "ur ther",
+ "Ġw ide",
+ "èij Ĺ",
+ "æĪij çļĦ",
+ "ĸçķ ¥",
+ "id es",
+ "eth ing",
+ "ĠWh ile",
+ "p an",
+ "çŃ ĸçķ¥",
+ "Ġc ent",
+ "Ġp lease",
+ "olo gy",
+ "ura cy",
+ "å¾ ª",
+ "w ard",
+ "n ce",
+ "Ġthe n",
+ "çª ģ",
+ "å¥ ĩ",
+ "Ġb lo",
+ "a i",
+ "æŀ Ĺ",
+ "ç®Ĺ æ³ķ",
+ "ç» ¼",
+ "Ġpr int",
+ "ac es",
+ "l u",
+ "ª æĸ½",
+ "p re",
+ "çļĦæĦ ı",
+ "Ġs ol",
+ "Ġover all",
+ "h old",
+ "Ġ es",
+ "çļĦ ä¸Ģ",
+ "éģ ĩ",
+ "Ġpop ul",
+ "å°ı 说",
+ "æ³ ¢",
+ "åį ģ",
+ "ä¹Ł åı¯ä»¥",
+ "é£Ł åĵģ",
+ "Ġcont ent",
+ "å° Ħ",
+ "Ġrequ ires",
+ "æ£Ģ æŁ¥",
+ "ĊĠĠĠĠĠĠĠĠ ĠĠĠ",
+ "Ġgrou ps",
+ "Ġf air",
+ "Ġb l",
+ "å®ŀ éªĮ",
+ "æĮī çħ§",
+ "os p",
+ "st r",
+ "ä¸į èĥ½",
+ "Ġh arm",
+ "Ġpro du",
+ "çļĦæĬ Ģ",
+ "ç ĩ",
+ "t le",
+ "Ġanim als",
+ "è§Ĵ èī²",
+ "le v",
+ "æ¸ IJ",
+ "å¤į æĿĤ",
+ "Ġde pend",
+ "æĮ ijæĪĺ",
+ "åĮħ åIJ«",
+ "Ġhelp s",
+ "Ġop en",
+ "Ġn et",
+ "ĠĠĠĠ Ġ",
+ "Ġstr ong",
+ "Ġj our",
+ "广 æ³Ľ",
+ "æķ´ 个",
+ "Ġe lect",
+ "Ġrespon se",
+ "åįķ è¯į",
+ "æľ ĭ",
+ "Ġ <",
+ "åĮĸ åѦ",
+ "éĴ Ī",
+ "Ġqu ick",
+ "ual ly",
+ "Ġsom ething",
+ "Ġtra ck",
+ "度 åĴĮ",
+ "eren ces",
+ "æł ij",
+ "Ġacc uracy",
+ "Ġex c",
+ "é£ ŀ",
+ "Ġfiel d",
+ "寻 æī¾",
+ "éħ ¸",
+ "Ġh ope",
+ "ç ij",
+ "Ġin nov",
+ "ç» ª",
+ "al k",
+ "Ġtyp es",
+ "Ġd id",
+ "åĬ ª",
+ "Ġc all",
+ "è¯ Ĺ",
+ "Ġear ly",
+ "ĠO ne",
+ "a pp",
+ "Ġcomm on",
+ "æľĢ ç»Ī",
+ "Ġche ck",
+ "Ġs ym",
+ "çĤ Ĵ",
+ "æĬĢ èĥ½",
+ "Ġen h",
+ "Ġag ricult",
+ "Ġim m",
+ "ç» ĩ",
+ "满 足",
+ "Ġs chool",
+ "b al",
+ "Ġfollow ing",
+ "b ased",
+ "Ġwe bs",
+ "Ġcult ure",
+ "ĠC om",
+ "w ay",
+ "ä¸Ģ å®ļ",
+ "åķĨ åĵģ",
+ "ud e",
+ "çļĦ åıijå±ķ",
+ "çĶŁ 产",
+ "os ystem",
+ "Ġpl ant",
+ "åı ¶",
+ "åIJ ĥ",
+ "ä»ĸ çļĦ",
+ "d er",
+ "è¯ ¢",
+ "家 åħ·",
+ "Ġf ree",
+ "ç§ »",
+ "æİ Į",
+ "Ġb ody",
+ "Ġp resent",
+ "Ġpartic ularly",
+ "Ġchild ren",
+ "Ġstud ent",
+ ") .",
+ "çī¹ å¾ģ",
+ "è Ķ",
+ "éĺħ 读",
+ "æķĪ çİĩ",
+ "Ġprogra m",
+ "éħ ±",
+ "åıĺ å¾Ĺ",
+ "i x",
+ "Ġcom e",
+ "çļĦæ ²",
+ "ĠT e",
+ "ĠT o",
+ "åħ± åIJĮ",
+ "Ġemploy ees",
+ "说 æĺİ",
+ "Ġhe art",
+ "Ġm ot",
+ "æľĭ åıĭ",
+ "er ic",
+ "è¯ ij",
+ "Ġcur rent",
+ "æĪIJ æľ¬",
+ "Ġto o",
+ "çİ© 家",
+ "åĪĽ æĸ°",
+ "Ġec osystem",
+ "常 è§ģ",
+ "ä¸Ģ æŃ¥",
+ "Ġp res",
+ "Ġmult i",
+ "åijĬ è¯ī",
+ "ä¸ ¥",
+ "Ġm it",
+ "Ġact ion",
+ "çĨ Ł",
+ "Ġhab it",
+ "åı£ æĦŁ",
+ "ç® ±",
+ "Ġus es",
+ "å¢ŀ 强",
+ "ç»Ļ åĩº",
+ "Ġ 9",
+ "Ġde p",
+ "Ġeconom ic",
+ "æĢ§ çļĦ",
+ "1 8",
+ "åĨ °",
+ "Ġhelp ed",
+ "åIJ¸ å¼ķ",
+ "çİ ĭ",
+ "Ġdiagn os",
+ "å ł",
+ "èģĶ ç³»",
+ "ç¾ ¤",
+ "ç»ĥ ä¹ł",
+ "æĪIJ éķ¿",
+ "Ġpo int",
+ "å®ļ æľŁ",
+ "åij ¼",
+ "èį ¯",
+ "æĿ ¯",
+ "æ¤ Ĵ",
+ "æķĪ æŀľ",
+ "Ġspec ial",
+ "æ· ·",
+ "åĩł 个",
+ "aus e",
+ "é Ĩ",
+ "æ¯Ķ èµĽ",
+ "è· Ŀ",
+ "W hat",
+ "Ġt imes",
+ "ic les",
+ "Ġ *",
+ "ç´ §",
+ "å¦Ĥæŀľ ä½ł",
+ "çĭ¬ çī¹",
+ "çģ µ",
+ "ç¨ İ",
+ "Ġcar bon",
+ "Ġbi as",
+ "åĬ© äºİ",
+ "Ġcon st",
+ "èĩª çĶ±",
+ "æĿ¥ 说",
+ "å°± æĺ¯",
+ "åį °",
+ "Ġme et",
+ "è§Ħ åĪĴ",
+ "çļĦç ¾",
+ "èIJ¥ åħ»",
+ "at ors",
+ "稳 å®ļ",
+ "od e",
+ "çħ ®",
+ "Ġass oci",
+ "å¿ Ĺ",
+ "è¡Į æĺŁ",
+ "æĿ İ",
+ "Ġrev iew",
+ "åĩ Ģ",
+ "ĠR o",
+ "Ġknow ledge",
+ "以 便",
+ "æµĭ è¯ķ",
+ "åIJĪ éĢĤ",
+ "s c",
+ "å½¢ å¼ı",
+ "Ġfriend s",
+ "Ġnat ure",
+ "Ġcrit ical",
+ "æ´ ĭ",
+ "Ġa fter",
+ "er ve",
+ "Ġre ce",
+ "çļĦæ Ń",
+ "汽 车",
+ "çķ Į",
+ "Ġlo ss",
+ "Ġapp lications",
+ "å¤ļ ç§į",
+ "éĶ ħ",
+ "ä¸ ²",
+ "Ġins p",
+ "-- -",
+ "ĠS h",
+ "Ġv ol",
+ "l ut",
+ "o ks",
+ "se qu",
+ "Ġb ir",
+ "åIJĪ çIJĨ",
+ "Ġne cess",
+ "æĪij æĥ³",
+ "çŃī æĸ¹éĿ¢",
+ "é¼ ĵ",
+ "Ġso ft",
+ "Ġl ive",
+ "å°ı æĺİ",
+ "ĠI nd",
+ "Ġbr ing",
+ "æĺ¯ æĮĩ",
+ "Ġso il",
+ "il ar",
+ "ä¸ ľ",
+ "æĿ¡ 件",
+ "Ġt ri",
+ "äº ®",
+ "Ġm om",
+ "æı ¡",
+ "ä¼ °",
+ "ŀ äºī",
+ "çĽ ij",
+ "èĤ ¤",
+ "è´¢ åĬ¡",
+ "æ·» åĬł",
+ "饮 é£Ł",
+ "Ġallow ing",
+ "åº ķ",
+ "Ġr ight",
+ "Ġexp ert",
+ "Ġsu pp",
+ "Ġin it",
+ "çļĦæ µ",
+ "ar get",
+ "Ġexp ect",
+ "Ġ1 9",
+ "Ġmeas ures",
+ "olut ions",
+ "j ust",
+ "ar c",
+ "å° ļ",
+ "Ġpract ice",
+ "æľī åĬ©äºİ",
+ "大 éĩı",
+ "' ,",
+ "im ent",
+ "Ġcontin ue",
+ "Ġdisc uss",
+ "1 00",
+ "éļ ľ",
+ "çļĦæĦ Ł",
+ "Ġref lect",
+ "it ation",
+ "åį «",
+ "äºĨ ä¸Ģ",
+ "ne y",
+ "ĠL e",
+ "is ed",
+ "è¶ ĭ",
+ "äºĨ ä¸Ģ个",
+ "Ġincre asing",
+ "çļĦæ Į",
+ "Ġst ru",
+ "æĢ» ç»ĵ",
+ "e ly",
+ "å® ĩ",
+ "Ġaut hor",
+ "表 éĿ¢",
+ "Ġ x",
+ "æķħ äºĭ",
+ "em ic",
+ "Ġrep resent",
+ "g er",
+ "Ġincre ased",
+ "on es",
+ "ain s",
+ "Ġtrain ed",
+ "Ġf ish",
+ "Ġst ate",
+ "åĨ ·",
+ "çĶŁ éķ¿",
+ "Ġre new",
+ "ord ing",
+ "åĮ Ĺ",
+ "æİ ªæĸ½",
+ "å¹³ è¡¡",
+ "Ġsuccess ful",
+ "ä¸ĭ éĿ¢",
+ "Ġactiv ity",
+ "èĮ ¶",
+ "éĢĤ åºĶ",
+ "èĦ ij",
+ "æİ¢ ç´¢",
+ "ff ic",
+ "ç»Ħ æĪIJ",
+ "at ives",
+ "äº ļ",
+ "Ġsc en",
+ "æ² Ļ",
+ "g ress",
+ "使 å¾Ĺ",
+ "æī ¿",
+ "Ġdisc rim",
+ "Ġassist ants",
+ "Ġex ist",
+ "çķ Ļ",
+ "Ġsp ace",
+ "æľĢ è¿ij",
+ "Ġide as",
+ "éĩĩ åıĸ",
+ "l ight",
+ "注 éĩį",
+ "çļĦæŶ éĹ´",
+ "è¿ İ",
+ "Ġcom b",
+ "éĢĤ å½ĵ",
+ "Ġyour self",
+ "rit e",
+ "as on",
+ "åĮ Ģ",
+ "åı¯ä»¥ 使çĶ¨",
+ "åħħ 满",
+ "Ġval ues",
+ "æ ½",
+ "Ġbi ases",
+ "ä¿ĥ è¿Ľ",
+ "åľº æĻ¯",
+ "ro ss",
+ "åį³ åı¯",
+ "Ġc ru",
+ "Ġnum ber",
+ "Ġty pe",
+ "r ast",
+ "åĩĨ ç¡®",
+ "Th is",
+ "Ġp ast",
+ "çģ ¯",
+ "å®ļ ä¹ī",
+ "Ġs olutions",
+ "Ġt er",
+ "ä¿Ŀ è¯ģ",
+ "èĶ ¬",
+ "å¹ ¸",
+ "åī §",
+ "åħ´ 趣",
+ "å ª",
+ "ent ion",
+ "av or",
+ "Ġsc ient",
+ "åĬª åĬĽ",
+ "Ġprovid ers",
+ "Ġpolic ies",
+ "al u",
+ "ĠI m",
+ "Ġallow s",
+ "Ġintellig ence",
+ "çļĦ æĸ¹æ³ķ",
+ "è¿Ļ æĺ¯",
+ "Ġ `",
+ "Ġem issions",
+ "Ġ å°Ĩ",
+ "Ġmean ing",
+ "Ġst yle",
+ "åİŁ åĽł",
+ "Ġstru gg",
+ "çļĦç¾ İ",
+ "if ul",
+ "dit ion",
+ "éĥ½ æľī",
+ "空 æ°Ķ",
+ "å®ĥ们 çļĦ",
+ "ä¼ĺ åĮĸ",
+ "Ġinf lu",
+ "åŁº äºİ",
+ "Ġdetail s",
+ "Ġtranspare ncy",
+ "Ġm ess",
+ "ĠC l",
+ "Ġg ame",
+ "p ri",
+ "è¶ĭ åĬ¿",
+ "å½ Ĵ",
+ "ç¿» è¯ij",
+ "æķ £",
+ "B y",
+ "é Ń",
+ "ĠAm eric",
+ "Ġproduct ion",
+ "Ġinc orpor",
+ "æĻ ļ",
+ "Ġinvol ve",
+ "Ġh ot",
+ "æĻ ®",
+ "b y",
+ "Ġf low",
+ "Ġem erg",
+ "åº §",
+ "Ġide a",
+ "åİĭ åĬĽ",
+ "éĿ Ĵ",
+ "om s",
+ "èģĮ ä¸ļ",
+ "Ġre port",
+ "Ġp ap",
+ "Ġthe rap",
+ "Ġs al",
+ "åıĤ ä¸İ",
+ "æĸĩ åѦ",
+ "æIJŃ éħį",
+ "o ot",
+ ") ,",
+ "Ġc r",
+ "Ġprocess es",
+ "g in",
+ "å¹³ åı°",
+ "å¯ Ł",
+ "Ġpromot ing",
+ "æļ ĸ",
+ "ake hold",
+ "ç» §",
+ "iv er",
+ "æ ¦Ĥ",
+ "Ġmodel s",
+ "Ġd ra",
+ "è ĸ",
+ "Ġgrou p",
+ "足 å¤Ł",
+ "Ġg reen",
+ "Ġhealth y",
+ "Ġcom fort",
+ "Ġad ditional",
+ "ä¸Ģ 次",
+ "é¤IJ åİħ",
+ "Ġmaterial s",
+ "Ġman age",
+ "çļĦæ ¯",
+ "ä¼ ¤",
+ "åıĬ æŶ",
+ "Ġg lo",
+ "Ġst at",
+ "å¿« éĢŁ",
+ "Ġmonitor ing",
+ "ail y",
+ "ra nd",
+ "o ice",
+ "res h",
+ "ç»Ħ ç»ĩ",
+ "Ġund er",
+ "Ġnecess ary",
+ "Ġhelp ful",
+ "ĠC ol",
+ "é»ij æ´ŀ",
+ "åģļ åĩº",
+ "Ġcour se",
+ "Ġm at",
+ "Ġle g",
+ "Ġf ace",
+ "ä» ¤",
+ "èī¯ 好çļĦ",
+ "oc k",
+ "åĮ» çĸĹ",
+ "çĽ ĸ",
+ "id ence",
+ "Ġassoci ated",
+ "Ġpro gress",
+ "åľ Ĩ",
+ "Ġevery one",
+ "ç¼ ĵ",
+ "ĠEn g",
+ "w ord",
+ "èĵ Ŀ",
+ "天 æ°Ķ",
+ "Ġact ions",
+ "em s",
+ "ĠP l",
+ "å® Ļ",
+ "us h",
+ "é¡ ¾",
+ "Ġcost s",
+ "at or",
+ "ç© ¿",
+ "Ġamount s",
+ "èĶ¬ èıľ",
+ ". .",
+ "Ġman ner",
+ "Ġcon sequ",
+ "æ°Ķ åĢĻ",
+ "Ġins ights",
+ "be ing",
+ "at ory",
+ "en er",
+ "le x",
+ "Ġme ans",
+ "Ġcollabor ation",
+ "Ġpers pect",
+ "or m",
+ "pri ate",
+ "å°Ĭ éĩį",
+ "Ġt arget",
+ "è®° å½ķ",
+ "åĢ Ĵ",
+ "Ġrenew able",
+ "æĦ ¿",
+ "èĥ½ æºIJ",
+ "Ġin put",
+ "å®ĩ å®Ļ",
+ "a pe",
+ "Ġad just",
+ "er ies",
+ "Ġd ire",
+ "ä¾ Ŀ",
+ "ust r",
+ "f ect",
+ "Ġbeaut iful",
+ "Ġd ue",
+ "re ci",
+ "çĮ ®",
+ "èĥĮ æĻ¯",
+ "èĤ ¡",
+ "Ġd am",
+ "i k",
+ "Ġadv anced",
+ "缸 对",
+ "åIJį 称",
+ "Ġsh ort",
+ "Ġob ject",
+ "è¿Ļ éĩĮ",
+ "éĢł æĪIJ",
+ "èIJ¥ éĶĢ",
+ "çļĦæĥħ æĦŁ",
+ "ç¥ ¨",
+ "Ġcount ries",
+ "in ing",
+ "ist ic",
+ "Ġpl ans",
+ "è´£ ä»»",
+ "Ġst akehold",
+ "t he",
+ "Ġass ess",
+ "æĢĿ èĢĥ",
+ "e ch",
+ "æĪIJ åijĺ",
+ "2 1",
+ "Ġd aily",
+ "Ġcomp ut",
+ "çļĦæĥħ åĨµ",
+ "æıIJ åĩº",
+ "Ġ âĢľ",
+ "åª Ĵ",
+ "ä¸Ń å¿ĥ",
+ "is hed",
+ "ĠS e",
+ "onom ous",
+ "er n",
+ "ç»´ æĬ¤",
+ "am es",
+ "Ġpriorit ize",
+ "çº ¸",
+ "èĤ ¥",
+ "Ġtem per",
+ "æ¸ħ æ´ģ",
+ "us e",
+ "æ± ¡",
+ "Ġmin im",
+ "æĺ¯ åľ¨",
+ "大 å°ı",
+ "åĵª äºĽ",
+ "Ġapp reci",
+ "ren g",
+ "Ġregul ations",
+ "Ġ Z",
+ "éĶĻ 误",
+ "r ans",
+ "èĢĮ ä¸Ķ",
+ "èĪ ¬",
+ "èij ±",
+ "è Ĩ",
+ "æ°´ å¹³",
+ "è´Ń çī©",
+ "åŃĹ符 串",
+ "对 æĸ¹",
+ "Ġh im",
+ "Ġconsequ ences",
+ "å· ´",
+ "é¼ĵ åĬ±",
+ "Ġf il",
+ "人 åijĺ",
+ "è·Ŀ 离",
+ "ĠW hen",
+ "çļĦæ° ´",
+ "çī© çIJĨ",
+ "åIJĮæŶ ä¹Ł",
+ "åľ¨ è¿Ļ个",
+ "åħ¶ 次",
+ ", \"",
+ "æ¶ ²",
+ "çĶ ·",
+ "iv al",
+ "åı¯ä»¥ 让",
+ "æĥ ¯",
+ "Ġadv ance",
+ "Ġve h",
+ "å¦Ĥæŀľ æĤ¨",
+ "Ġest ab",
+ "ri pt",
+ "ç« ¯",
+ "ä¸į ä¼ļ",
+ "Ġtranspare nt",
+ "æķ° éĩı",
+ "çĽ ĺ",
+ "Ġspe ak",
+ "Ġp ark",
+ "Ġstakehold ers",
+ "é º",
+ "Ġev ent",
+ "çļĦæķ° æį®",
+ "èĩª åĬ¨",
+ "ç»Ĩ èĬĤ",
+ "è¯Ħ ä¼°",
+ "æ¶ ¦",
+ "Ġpref erences",
+ "Ġve get",
+ "æį Ł",
+ "e qu",
+ "Ġg l",
+ "Ġp ain",
+ "o gra",
+ "Ġtra ffic",
+ "Ġo ce",
+ "ä¹ ĺ",
+ "e xt",
+ "âĢĿ ï¼Į",
+ "Ġan other",
+ "å¤ļ å°ij",
+ "Ġagain st",
+ "ç»ı åİĨ",
+ "计ç®Ĺ æľº",
+ "èĢ IJ",
+ "软 件",
+ "ĠP re",
+ "Ġpl ants",
+ "缸 äºĴ",
+ "é¢ ij",
+ "\\ _",
+ "Ġs ame",
+ "ru g",
+ "Ġval u",
+ "Ġo cc",
+ "çļĦç ¤",
+ "Ġsustain ability",
+ "ĠS he",
+ "d e",
+ "ot e",
+ "Ġd ig",
+ "N A",
+ "Ġcru cial",
+ "æī §",
+ "å± Ģ",
+ "æĭ Ł",
+ "æĭ Į",
+ "Ġn on",
+ "Ġeng aging",
+ "Ġinter n",
+ "L P",
+ "温 度",
+ "æł ¸",
+ "æĬ¥ åijĬ",
+ "æĿ¥ è¶Ĭ",
+ "h ood",
+ "ä¸ī 个",
+ "å¦Ĥ ä¸ĭ",
+ "çī© ä½ĵ",
+ "for ce",
+ "Ġneed ed",
+ "Ġim ages",
+ "Ġbuild ing",
+ "ici ous",
+ "Ġ æĪij",
+ "è¶Ĭ æĿ¥è¶Ĭ",
+ "æĶ¾ åħ¥",
+ "g o",
+ "éĻį ä½İ",
+ "å½ĵ åľ°",
+ "æ¶Īè´¹ èĢħ",
+ "ç £",
+ "ivers ity",
+ "é¢Ħ ç®Ĺ",
+ "ic le",
+ "æ·· åIJĪ",
+ "Ġpartic ip",
+ "Ġdis hes",
+ "Ġthrough out",
+ "Ġwith in",
+ "åı ³",
+ "é«ĺ çļĦ",
+ "Ġph ot",
+ "Ġtr ust",
+ "æĦı è¯Ĩ",
+ "以 ç¡®ä¿Ŀ",
+ "çĬ¶ æĢģ",
+ "Ġautom ation",
+ "1 1",
+ "Ġpo st",
+ "æīĭ æľº",
+ "wor ks",
+ "éĢ ı",
+ "åº ĵ",
+ "Ġw ind",
+ "Ġ= =",
+ "Ġprocess ing",
+ "èĮĥ åĽ´",
+ "æĦı ä¹ī",
+ "追 æ±Ĥ",
+ "Ã ©",
+ "å¾ Ħ",
+ "éĿ ł",
+ "ä¸ ĸ",
+ "èĻ ½",
+ "ç« ŀäºī",
+ "Ġappro priate",
+ "æĽ´ 好çļĦ",
+ "Ġcharact er",
+ "c l",
+ "ç§ ĺ",
+ "it ude",
+ "Ġte ac",
+ "le ep",
+ "ĠDe velop",
+ "in ce",
+ "å· ¦",
+ "g round",
+ "è¡Į ä¸ļ",
+ "éĴĪ 对",
+ "å¿ħ è¦ģ",
+ "Ġdet erm",
+ "-------- --------",
+ "Ġst reng",
+ "d o",
+ "Ġchalleng ing",
+ "or k",
+ "Ġan x",
+ "èī² çļĦ",
+ "Ġh ard",
+ "æĺİ ç¡®",
+ "åĪĨ 享",
+ "æĶ¹ åıĺ",
+ "ä½ ³",
+ "åıª æľī",
+ "å±ķ 示",
+ "Ġcam p",
+ "çº ³",
+ "a j",
+ "et ic",
+ "u ment",
+ "ä½ł åı¯ä»¥",
+ "Ġpol lut",
+ "Ġh ig",
+ "pp ing",
+ "e ad",
+ "çĦ¶ èĢĮ",
+ "第 äºĮ",
+ "é¸ Ł",
+ "çī© åĵģ",
+ "ä¸ ¾",
+ "Ġencoura ge",
+ "pe cial",
+ "Ġac ross",
+ "el ves",
+ "äºĭ 件",
+ "c le",
+ "æ ©",
+ "åªĴ ä½ĵ",
+ "n ers",
+ "Ġc al",
+ "èĻ½ çĦ¶",
+ "åĽ º",
+ "ä¹ł æĥ¯",
+ "Ġsaf e",
+ "èĥ½ éĩı",
+ "ist ics",
+ "ä¹ĭ åīį",
+ "Ġiss ue",
+ "å¤ļ 个",
+ "åĨ³ çŃĸ",
+ "è¾¾ åĪ°",
+ "æĹ ©",
+ "ä¸į åı¯",
+ "ä¸Ģ 缴",
+ "å· ¨",
+ "æĦŁ è°¢",
+ "ĠN ew",
+ "ä¸Ģ 段",
+ "Ġmach ines",
+ "å°Ĩ åħ¶",
+ "继 ç»Ń",
+ "Ġwor d",
+ "çī¹ åĪ«",
+ "Ġagricult ure",
+ "æĢ İ",
+ "éĢIJ æ¸IJ",
+ "éĵ ¾",
+ "è¯ ¾",
+ "Ġk ind",
+ "å¢ Ļ",
+ "è°¢ è°¢",
+ "Ġalgorith m",
+ "è£ħ 饰",
+ "Ġal ong",
+ "Ġeas y",
+ "äº ij",
+ "解åĨ³ æĸ¹æ¡Ī",
+ "Ġaware ness",
+ "' ve",
+ "æĸ¹ åIJij",
+ "Ġne ver",
+ "Ġquick ly",
+ "Ġres pect",
+ "çļĦæ Ļ",
+ "Ġam ong",
+ "Ġaccount ability",
+ "Ġl aw",
+ "en ing",
+ "Ġdef in",
+ "Ġsur round",
+ "éĵ ģ",
+ "Ġpower ful",
+ "A n",
+ "Ġcaus e",
+ "æ ¥",
+ "æİĮ æı¡",
+ "è¿ĺ æĺ¯",
+ "Ġcreat ive",
+ "è¡ Ģ",
+ "Ġloc ated",
+ "un ning",
+ "åľ° åĮº",
+ "éĿ¢ 积",
+ "éĽ ¨",
+ "Ġne ar",
+ "Ġinit i",
+ "ress ion",
+ "ä¸ĭ æĿ¥",
+ "2 5",
+ "é© ¶",
+ "¾ çĹħ",
+ "ab les",
+ "æľī 趣",
+ "循 çݯ",
+ "çŃĶ æ¡Ī",
+ "çł ´",
+ "ic ation",
+ "éĻ ¢",
+ "æ²» çĸĹ",
+ "Ġad dition",
+ "äºĭ æĥħ",
+ "Ġbec ause",
+ "åı Ī",
+ "èĤ Į",
+ "çº ª",
+ "s ide",
+ "æĭ ħ",
+ "æ¹ ¿",
+ "åį Ĭ",
+ "é¡ º",
+ "ĠA nd",
+ "Ġrestaur ant",
+ "Ġv ide",
+ "Ġproble m",
+ "az ing",
+ "Ġmem bers",
+ "Ġn ut",
+ "Ġc ou",
+ "æµ ª",
+ "Ġ è¿Ļ",
+ "Ġhelp ing",
+ "ĠI s",
+ "æıIJ åįĩ",
+ "ĠĠĠĠ ĠĠ",
+ "Ġsh o",
+ "Ġre lev",
+ "Ġar g",
+ "Ġbal ance",
+ "ill ed",
+ "æĺ¯ ä»Ģä¹Ī",
+ "åĬĽ éĩı",
+ "ire d",
+ "å¤ ľ",
+ "åı¯ æĮģç»Ń",
+ "Ġper fect",
+ "* *",
+ "ific ation",
+ "æ¶ ī",
+ "Ġwild life",
+ "an e",
+ "Ġrel ated",
+ "室 åĨħ",
+ "åº ľ",
+ "享 åıĹ",
+ "our s",
+ "è· ij",
+ "åķĨ ä¸ļ",
+ "ach ing",
+ "Ġsu n",
+ "Ġrecogn ition",
+ "el t",
+ "Ġor der",
+ "å¹³ åĿĩ",
+ "g ing",
+ "ä¸ ´",
+ "çĤ ¼",
+ "Ġgo ing",
+ "åij¼ åIJ¸",
+ "Ġsoft ware",
+ "Ġre mot",
+ "èijĹ åIJį",
+ "幸 ç¦ı",
+ "Ġenh ance",
+ "èĻ ļ",
+ "Ġn ow",
+ "Ġth reat",
+ "Ġd est",
+ "åĿĩ åĮĢ",
+ "Ġac ad",
+ "åºĶ 对",
+ "çľĭ åĪ°",
+ "c ast",
+ "è¾ Ĩ",
+ "ific ial",
+ "Ġ very",
+ "o ok",
+ "åĮº åŁŁ",
+ "¹ ģ",
+ "æĪ¿ éĹ´",
+ "æıIJä¾Ľ äºĨ",
+ "Ġmot iv",
+ "Ġaccess ible",
+ "åĨ³ å®ļ",
+ "Ġh y",
+ "å® Ī",
+ "Ġf lo",
+ "u g",
+ "Ġinform ed",
+ "åĵģ è´¨",
+ "çļĦç Ł",
+ "av es",
+ "ar r",
+ "ĠW ith",
+ "le t",
+ "è§Ĥ çĤ¹",
+ "en ge",
+ "è¡Į åĬ¨",
+ "f riend",
+ "ç³ ķ",
+ "Ġf urther",
+ "ĠE ns",
+ "ç§ ģ",
+ "Ġad o",
+ "Ġcle an",
+ "缸 åºĶ",
+ "Ġf re",
+ "pecial ly",
+ "è Ĺ",
+ "Ġc apt",
+ "çļĦç ľ",
+ "Ġsome one",
+ "Ġc ell",
+ "æĶ¾ åľ¨",
+ "欢 è¿İ",
+ "Ġ âĢ",
+ "Ġdev ices",
+ "çļĦ æĸ¹å¼ı",
+ "Ġjob s",
+ "au gh",
+ "n ot",
+ "æľī äºĽ",
+ "åħ¬ åħ±",
+ "g est",
+ "çļĦ çĶŁæ´»",
+ "çľ ¼",
+ "çļĦ ä¿¡æģ¯",
+ "ĠC ons",
+ "æİĴ åºı",
+ "Ġbenef it",
+ "re ct",
+ "å¤ ı",
+ "un te",
+ "符 åIJĪ",
+ "ä¸Ģ ä½į",
+ "åĨħ éĥ¨",
+ "Ġlook ing",
+ "d ing",
+ "æĬ ĺ",
+ "è¾ ij",
+ "è¿Ļ个 éĹ®é¢ĺ",
+ "Ġes pecially",
+ "çľ ł",
+ "âĢĿ ãĢĤ",
+ "å¥ ı",
+ "ra y",
+ "è¿ĺ åı¯ä»¥",
+ "åĪĽ ä½ľ",
+ "com ing",
+ "Ġmulti ple",
+ "éļ IJ",
+ "æ³ ¡",
+ "æłĩ åĩĨ",
+ "Ġm il",
+ "éľĢè¦ģ 注æĦı",
+ "Ġanx iety",
+ "æĶ¹ è¿Ľ",
+ "å± ĭ",
+ "污 æŁĵ",
+ "ç¼ĸ ç¨ĭ",
+ "è´¹ çĶ¨",
+ "Ġev alu",
+ "imate ly",
+ "Ġlit er",
+ "ogra ph",
+ "Ġse arch",
+ "1 6",
+ "en ced",
+ "Ġmeth ods",
+ "çĥ Ī",
+ "模 å¼ı",
+ "çĬ¶ åĨµ",
+ "æĶ¹ åĸĦ",
+ "å¤ļ æł·",
+ "c er",
+ "å¥ ĸ",
+ "Ġsat is",
+ "Ġwebs ite",
+ "åĬ ŀ",
+ "åģ¥ 身",
+ "Ġglo bal",
+ "Ġas k",
+ "Ġplatform s",
+ "Ġdise ases",
+ "çİ° 象",
+ "t ics",
+ "æ± ģ",
+ "åĪ¤ æĸŃ",
+ "Ġcon vers",
+ "Ġrelations hip",
+ "设 置",
+ "æ³ķ å¾ĭ",
+ "Ġmind ful",
+ "é¢Ħ æµĭ",
+ "o very",
+ "åģ ľ",
+ "çĶµ è§Ĩ",
+ "è§Ħ åĪĻ",
+ "ak en",
+ "Ġimplement ing",
+ "is ing",
+ "åıĤ åĬł",
+ "æĥħ 绪",
+ "Ġprovid ed",
+ "æ·± åħ¥",
+ "Ġprogramm ed",
+ "Ġrelev ant",
+ "çļĦç ĥ",
+ "çĸ ¾çĹħ",
+ "åĮ» çĶŁ",
+ "åĪĽ 建",
+ "Ġgener ate",
+ "æĶ¶ åħ¥",
+ "ä¼ ij",
+ "iz es",
+ "Ġtrans form",
+ "éģ µ",
+ "ast ic",
+ "åij Ī",
+ "æ¯ı 个人",
+ "è¿ Ķ",
+ "i et",
+ "Ġv oice",
+ "éĢ Ķ",
+ "æĶ¾ æĿ¾",
+ "åį ´",
+ "èĥ ľ",
+ "Ġst ructure",
+ "æŶ å°ļ",
+ "Ġ Q",
+ "Ġel se",
+ "du c",
+ "Ġem p",
+ "èģ ļ",
+ "è´ §",
+ "ac hes",
+ "ç§ Ģ",
+ "an ks",
+ "Ġn ight",
+ "Ġprofessional s",
+ "Ġb as",
+ "è´ µ",
+ "e c",
+ "Ġdivers ity",
+ "it es",
+ "d r",
+ "åĽ° éļ¾",
+ "ĥ åľ",
+ "åŀ ĥåľ",
+ "åŀĥåľ ¾",
+ "Ġd rug",
+ "ç¢ ³",
+ "Ġn ame",
+ "åĮĸ çļĦ",
+ "a id",
+ "æľĢ 大",
+ "æij Ħ",
+ "ç®Ģåįķ çļĦ",
+ "Ġw arm",
+ "Ġd one",
+ "Ġfun ction",
+ "as c",
+ "强 è°ĥ",
+ "Ġdem and",
+ "Ġvis ual",
+ "Ġup d",
+ "æŃ£ åľ¨",
+ "Ġsim ilar",
+ "éĢ Ĵ",
+ "æ¯ Ľ",
+ "éĶ »",
+ "ent ly",
+ "Ġvalu able",
+ "Ġdis aster",
+ "ä¸Ģ èĪ¬",
+ "æ´ ²",
+ "ĠR eg",
+ "Ġdiscrim ination",
+ "åĨĻ ä¸Ģç¯ĩ",
+ "Ġgovern ment",
+ "Ġ 好çļĦ",
+ "5 00",
+ "ly ing",
+ "Ġpre v",
+ "Ġpre pare",
+ "Ġproble ms",
+ "è· ³",
+ "Ġpro m",
+ "åĨ ²",
+ "å®ī è£ħ",
+ "éĶ» çĤ¼",
+ "æµ ĵ",
+ "è ¹",
+ "åºĶçĶ¨ ç¨ĭåºı",
+ "n g",
+ "Ġcomp et",
+ "åĪĨ åĪ«",
+ "olo gical",
+ "å® ¡",
+ "Ġtrans l",
+ "Ġdire ct",
+ "åī Ĥ",
+ "Ġsuggest ions",
+ "Ġpap er",
+ "Ġrecogn ize",
+ "t on",
+ "Ġmit igate",
+ "讨 论",
+ "äºĴ åĬ¨",
+ "ĠE ar",
+ "Ġam azing",
+ "c re",
+ "é¦ Ī",
+ "Ġinvol ved",
+ "f ace",
+ "æľī åħ³",
+ ") )",
+ "Ġex ce",
+ "Ġproduct ivity",
+ "è Ń",
+ "é¦ Ĩ",
+ "Ġsound s",
+ "Ġidentify ing",
+ "] ,",
+ "é¾ Ļ",
+ "Ġf it",
+ "Ġcontribut e",
+ "th s",
+ "friend ly",
+ "e le",
+ "if ied",
+ "iven ess",
+ "ite ly",
+ "Ġ X",
+ "Ġl ed",
+ "åĿ ı",
+ "Ġhist or",
+ "Ġd at",
+ "Ġjour ney",
+ "Ġ }",
+ "Ġse lect",
+ "æ¼ «",
+ "Ġcon duct",
+ "è¿Ľ ä¸ĢæŃ¥",
+ "ç»Ļ æĪij",
+ "Ġl if",
+ "è£ħ ä¿®",
+ "为 ä»Ģä¹Ī",
+ "äº ¬",
+ "Ġn av",
+ "Ġwho le",
+ "ç ¹ģ",
+ "åĨ ľ",
+ "æĶ »",
+ "Ġb reat",
+ "Ġm iss",
+ "é¾ Ħ",
+ "t t",
+ "s w",
+ "Ġb ar",
+ "请 éĹ®",
+ "èģĶ ç½ij",
+ "Ġatt ract",
+ "æĤ¨ åı¯ä»¥",
+ "O ne",
+ "åħħ åĪĨ",
+ "r ing",
+ "Ġå½ĵ çĦ¶",
+ "re am",
+ "Ġev ol",
+ "Ġs n",
+ "ĠE m",
+ "m osp",
+ "Ġcho ose",
+ "v iew",
+ "Ġar r",
+ "Ġs leep",
+ "end ed",
+ "æŀ ¶",
+ "Ġveh icles",
+ "Ġf resh",
+ "Ġorganiz ation",
+ "è¿Ļ 段",
+ "æ± ¤",
+ "ĠI nt",
+ "Ġcont ext",
+ "åı¦ å¤ĸ",
+ "Ġoce an",
+ "æĦŁ åıĹ",
+ "Ġpollut ion",
+ "ur b",
+ "æī§ è¡Į",
+ "erson al",
+ "ĠHe alth",
+ "ä¼ĺ çĤ¹",
+ "Ġatt ention",
+ "æľī çĿĢ",
+ "é£Ł æĿIJ",
+ "Ġer r",
+ "çļĦæĿ ¥",
+ "çļĦç Ī",
+ "èŃ ¦",
+ "è· Ł",
+ "æĹħ è¡Į",
+ "èĴ ľ",
+ "çļĦæĢ Ŀ",
+ "Ġchat bot",
+ "çļĦ éľĢæ±Ĥ",
+ "çķ ¥",
+ "Ġfeel ing",
+ "Ġimplement ed",
+ "社 åĮº",
+ "çļĦ 建议",
+ "æIJ ħ",
+ "éĹ »",
+ "åıį é¦Ī",
+ "缴 æİ¥",
+ "æĺ ¥",
+ "it able",
+ "æĪij ä¼ļ",
+ "åį ±",
+ "èī¯ 好",
+ "Ġl iving",
+ "åıĺ éĩı",
+ "ĠB ut",
+ "Ġcomple te",
+ "Ġtre nds",
+ "Ġm akes",
+ "ä»Ĭ 天",
+ "Ġdist ribut",
+ "Ġcomm it",
+ "Ġat mosp",
+ "ä¼ ´",
+ "Ġsens ors",
+ "Ġs w",
+ "æĹł 论",
+ "om en",
+ "æĶ¿ åºľ",
+ "Ġchall enge",
+ "Ġt urn",
+ "çIJĨ 论",
+ "p ar",
+ "Ġwrit e",
+ "ç»ı åħ¸",
+ "em ember",
+ "é¥ Ń",
+ "æĸ¹ 便",
+ "Ġc u",
+ "Ġval ue",
+ "Ġf und",
+ "p ose",
+ "è°ĥ æŁ¥",
+ "çĿ ¡",
+ "Ġcommunic ate",
+ "Ġdise ase",
+ "Ġrese arc",
+ "Ġl ack",
+ "arn ing",
+ "ĠP ark",
+ "çĦ ¦",
+ "é«ĺ 度",
+ "Ġr ather",
+ "å® £",
+ "çĪ ¶",
+ "éĺ ¶",
+ "è® ¢",
+ "çĥ §",
+ "Ġhig her",
+ "Ġsumm ary",
+ "ĠA ut",
+ "çļĦæ ³",
+ "Ġe le",
+ "is ms",
+ "Ġrel i",
+ "ä¹Ł ä¼ļ",
+ "f ra",
+ "åijĬè¯ī æĪij",
+ "æĬ ½",
+ "Ġsitu ations",
+ "Ġmar ine",
+ "æĥ³ è¦ģ",
+ "in ci",
+ "in al",
+ "Ġg ain",
+ "Ġdiffere nce",
+ "æľºåĻ¨ 人",
+ "æµģ ç¨ĭ",
+ "ĠC hat",
+ "ç½ij ç«Ļ",
+ "æľ «",
+ "Ġcol or",
+ "Ġas pect",
+ "ç½ Ĺ",
+ "ĠE duc",
+ "Ġde ploy",
+ "Ġbeaut y",
+ "æĤ £",
+ "ruct ion",
+ "it ut",
+ "æĿ Ł",
+ "让 æĪij们",
+ "éķ¿ 度",
+ "ul es",
+ "æ¶ī åıĬ",
+ "Ġdig ital",
+ "Ġexist ing",
+ "ĠO r",
+ "\\_ \\_",
+ "Ġback ground",
+ "çĹ ĩ",
+ "æ¯ı 天",
+ "p ython",
+ "Ġfarm ers",
+ "Ġcontin u",
+ "\" :",
+ "Ġg iven",
+ "å°ı æŶ",
+ "Ġmom ent",
+ "2 00",
+ "J ohn",
+ "éĿ¢ 对",
+ "Ġint ro",
+ "Ġtherap y",
+ "è¿Ķ åĽŀ",
+ "并 åľ¨",
+ "Ġ z",
+ "Ġaff ord",
+ "ä¸ Ŀ",
+ "å® ½",
+ "Ġ Ã",
+ "ĠN ational",
+ "èĥ ¡",
+ "Ġexercis e",
+ "æIJħ æĭĮ",
+ "æĶ¯ ä»ĺ",
+ "éĺ³ åħī",
+ "è¯ ļ",
+ "Ġs ect",
+ "ĠS u",
+ "å¢ŀ éķ¿",
+ "ç¾İ 丽",
+ "Ġw a",
+ "以ä¸ĭæĺ¯ ä¸ĢäºĽ",
+ "èĽĭ ç³ķ",
+ "Ġ ill",
+ "æ¸ħ æĻ",
+ "et ry",
+ "æ¢ ¦",
+ "ç¾İ åĽ½",
+ "ä» į",
+ "one y",
+ "Ġecosystem s",
+ "æĮĩ 导",
+ "d ef",
+ "9 9",
+ "æŁ Ķ",
+ "pp ed",
+ "Ġlim it",
+ "çİ ī",
+ "Ġacad emic",
+ "Ġrestaur ants",
+ "Ġhe ad",
+ "ä¿¡ ä»»",
+ "ast ers",
+ "å² ģ",
+ "ak ers",
+ "1 4",
+ "A s",
+ "æł ¡",
+ "é«ĺ æķĪ",
+ "ph as",
+ "y n",
+ "ç¨ĭ 度",
+ "è¾ £",
+ "ä¸Ĭ éĿ¢",
+ "家 å±ħ",
+ "ter m",
+ "ç¾İ é£Ł",
+ "Ġo vers",
+ "å® ĺ",
+ "Ġind ic",
+ "ĠY our",
+ "S t",
+ "形 象",
+ "è´ ¡",
+ "åº Ĭ",
+ "ĠS c",
+ "ag ra",
+ "羣 æŃ£",
+ "o int",
+ "id s",
+ "are nt",
+ "éĵ ¶",
+ "èģ Ĭ",
+ "Ġreg ular",
+ "ä¼ĺ ç§Ģ",
+ "Ġcol le",
+ "çĸ ij",
+ "Ġsub ject",
+ "Ġgreat er",
+ "Ġst ore",
+ "åŁ¹ è®Ń",
+ "Ġim ag",
+ "Ġan sw",
+ "ä½ Ļ",
+ "Ġsp ot",
+ "åĪĨ åŃIJ",
+ "Ġaud ience",
+ "p et",
+ "Ġv ers",
+ "Ġtra il",
+ "åĭ ĩ",
+ "er ous",
+ "Ġguid ance",
+ "Ġspe ech",
+ "åĵ ²",
+ "æĺ¯ çĶ±",
+ "è´¡ çĮ®",
+ "åIJĪéĢĤ çļĦ",
+ "设 æĸ½",
+ "ä»ĸ 人",
+ "ens ive",
+ "åĢ ¾",
+ "al ing",
+ "Ġproject s",
+ "å ³",
+ "Ġt akes",
+ "ç» ©",
+ "T hat",
+ "Ġb ro",
+ "iv ed",
+ "Ġ &",
+ "åĿ IJ",
+ "place ment",
+ "è¿ŀ æİ¥",
+ "çļĦç¤ ¾",
+ "ĠT ra",
+ "Ġrel ax",
+ "u fact",
+ "éģ į",
+ "Ġsur v",
+ "åı£ åij³",
+ "Ġcreat ivity",
+ "o f",
+ "å¨ ģ",
+ "çļĦç ł",
+ "Ġbreat h",
+ "Ġpl aces",
+ "Ġdesc rib",
+ "èĭ± è¯Ń",
+ "Ġdam age",
+ "or ation",
+ "为 æĤ¨",
+ "if t",
+ "Ġc ase",
+ "å¹´ é¾Ħ",
+ "Ġp ress",
+ "çĶ ľ",
+ "éĩ İ",
+ "æĹħ 游",
+ "Ġt aken",
+ "in ed",
+ "Ġcon cept",
+ "æĴ Ń",
+ "Ġinterest ing",
+ "è· µ",
+ "Ġse a",
+ "6 0",
+ "Ġf oot",
+ "ĠN ame",
+ "Ġresearc hers",
+ "éĢ ģ",
+ "Ġwe e",
+ ") ;",
+ "çļĦ åħ³éĶ®",
+ "ä¼ ½",
+ "ele br",
+ "å¡ ij",
+ "W e",
+ "ç»ı 常",
+ "Ġpopul ations",
+ "åħ¬ å¼ı",
+ "or n",
+ "çĩ ĥ",
+ "人 çĶŁ",
+ "1 7",
+ "æİ¥ åıĹ",
+ "Ġloc ation",
+ "Ġin equ",
+ "Ġinter vent",
+ "Ġinterest ed",
+ "Ġdefin itely",
+ "Ġassist ance",
+ "è¿Ļ ä¸Ģ",
+ "åIJĪ åIJĮ",
+ "ä¼ĺ åĬ¿",
+ "çļĦ å·¥ä½ľ",
+ "Ġ1 2",
+ "Ġmo v",
+ "åģ ı",
+ "åŃĺ åĤ¨",
+ "us ive",
+ "æĹ ı",
+ "ï¼ī ï¼Į",
+ "Ġg as",
+ "Ġinterest s",
+ "æ¸ħæĻ °",
+ "Ġg ard",
+ "çĸ «",
+ "Ġs ay",
+ "å¤ «",
+ "g es",
+ "èIJ ¨",
+ "ä¸ļ åĬ¡",
+ "个 æĢ§",
+ "åIJ ¯",
+ "Ġeng agement",
+ "Ġb ig",
+ "éľĢè¦ģ èĢĥèĻij",
+ "Ġpr inci",
+ "åij¨ åĽ´",
+ "Ġopportun ity",
+ "çģ ¾",
+ "èĹ ı",
+ "re l",
+ "缺 çĤ¹",
+ "Ġhapp y",
+ "åĴĮ åħ¶ä»ĸ",
+ "av a",
+ "Ġestab lish",
+ "鸡 èĽĭ",
+ "i king",
+ "ĠT rans",
+ "rast ructure",
+ "fore st",
+ "èİ· åıĸ",
+ "èĦ ļ",
+ "in ally",
+ "èµ ı",
+ "Ġdel icious",
+ "Ġresult s",
+ "è§Ĥ å¯Ł",
+ "å®ŀ è·µ",
+ "Ġl ast",
+ "Ġpol it",
+ "æĢ§ èĥ½",
+ "F or",
+ "b i",
+ "缸 ä¿¡",
+ "ff ee",
+ "Ġph r",
+ "Ġfore st",
+ "ell ing",
+ "æµģ è¡Į",
+ "at ic",
+ "大 家",
+ "ĠIn st",
+ "æķ° åѦ",
+ "æī ©",
+ "å®Į åħ¨",
+ "å¼ķ èµ·",
+ "es e",
+ "转 æį¢",
+ "Ġaffect ed",
+ "Ġrobot ics",
+ "综 ä¸Ĭ",
+ "Ġpro p",
+ "让 人",
+ "æ² ³",
+ "ä¸Ń æľĢ",
+ "Ġaut onomous",
+ "Ġha ving",
+ "Ġtri p",
+ "ur y",
+ "Ġbi ased",
+ "Ġconsider ations",
+ "Ġpartic ular",
+ "åį ł",
+ "æݨ 广",
+ "Ġiniti atives",
+ "ial s",
+ "åij³ éģĵ",
+ "Ġtreat ments",
+ "Ġem phas",
+ "çĭ¬çī¹ çļĦ",
+ "Ġl ay",
+ "æĶ¿ çŃĸ",
+ "æĢİ ä¹Ī",
+ "ron ic",
+ "pl ay",
+ "Ġco ok",
+ "è¿Ľ åħ¥",
+ "è½ ®",
+ "Ġvol unte",
+ "Ġra in",
+ "ĠM on",
+ "Ġconsum ption",
+ "èĽĭ çĻ½",
+ "ĠS oc",
+ "å£ ¤",
+ "Ġrout ine",
+ "Ġimpro ved",
+ "T o",
+ "人 çī©",
+ "读 èĢħ",
+ "Ġgo al",
+ "广 åijĬ",
+ "éķ¿ æľŁ",
+ "Ġe y",
+ "H e",
+ "Ġout do",
+ "Ġcu is",
+ "Ġa way",
+ "Ġbo oks",
+ "Ġtop ic",
+ "大 åĪ©",
+ "h ouse",
+ "Ġon es",
+ "ç§ Ł",
+ "' :",
+ "æĪ¿ å±ĭ",
+ "移 åĬ¨",
+ "Ġdis asters",
+ "est s",
+ "ill ing",
+ "绿 èī²",
+ "åĵ² åѦ",
+ "æĪIJ åĪĨ",
+ "Ġocc ur",
+ "ľ ä¼½",
+ "åľŁ 壤",
+ "çļĦ 主è¦ģ",
+ "çİ° å®ŀ",
+ "Ġanim al",
+ "é¢Ĩ 导",
+ "Ġview s",
+ "éĤ ®",
+ "æ°§ åĮĸ",
+ "ath y",
+ "éģĵ å¾·",
+ "社交 åªĴä½ĵ",
+ "ĠP ersonal",
+ "Ľ åĽ´",
+ "Ġpur ch",
+ "Ġcount ry",
+ "Ġrem ind",
+ "å¯ ¸",
+ "Ġr ights",
+ "çļĦ çݯå¢ĥ",
+ "ĠP r",
+ "Ġl ine",
+ "ib r",
+ "é© ¾",
+ "Ġm aj",
+ "Ġover come",
+ "Ġne xt",
+ "æīĢ è¿°",
+ "è§Ħ å®ļ",
+ "Ġinteract ions",
+ "Ġconf lic",
+ "Ġwh y",
+ "ç³» åĪĹ",
+ "å° ¼",
+ "ib ly",
+ "çīĽ 奶",
+ "Ġrespons es",
+ "s es",
+ "åѦ ä¼ļ",
+ "b ol",
+ "Ġstand ards",
+ "ul ner",
+ "对è¯Ŀ åĨħ容",
+ "l ished",
+ "çļĦæĢ §",
+ "çĶŁæĢģ ç³»ç»Ł",
+ "an n",
+ "æĥħåĨµ ä¸ĭ",
+ "寻 æ±Ĥ",
+ "Ġh old",
+ "d en",
+ "åį ĥ",
+ "Ġment ion",
+ "ĠMan y",
+ "缴 åĪ°",
+ "éģ Ĺ",
+ "he l",
+ "Ġbelie ve",
+ "ar ies",
+ "æľī ä¸Ģ个",
+ "1 3",
+ "Ġatmosp here",
+ "Ġm or",
+ "æĹ¥ æľŁ",
+ "ä¹ ħ",
+ "ä½ł 好",
+ "Ġaddress ing",
+ "ĠâĢ ĵ",
+ "çļĦåľ° æĸ¹",
+ "m ing",
+ "Ġcan not",
+ "Ġman ufact",
+ "Ġp ie",
+ "ic ing",
+ "Ġstud ies",
+ "ç¾İ åij³",
+ "ĠAmeric an",
+ "ĠN LP",
+ "Ġacc ording",
+ "ms elves",
+ "èĦ Ĥ",
+ "èĩª ä¿¡",
+ "æīĢ éľĢ",
+ "Ġthe mselves",
+ "Ġremot e",
+ "åŁ¹ åħ»",
+ "å®ī æİĴ",
+ "ä½ł éľĢè¦ģ",
+ "Ġreg ard",
+ "ir ing",
+ "è¯Ĩ åĪ«",
+ "Ġart icle",
+ "æģ Ĵ",
+ "æĢ» çļĦæĿ¥",
+ "Ġal ign",
+ "æ± ł",
+ "ten ance",
+ "fact ion",
+ "åĬ¨ ä½ľ",
+ "çļĦç ©",
+ "ç¼ ©",
+ "æĢ ¥",
+ "Ġ1 00",
+ "Ġtest ing",
+ "åŃĹ æ¯į",
+ "å¹´ è½»",
+ "åĪ¶ éĢł",
+ "Ġs we",
+ "å° º",
+ "he ns",
+ "æ°´ æŀľ",
+ "Ġinf rastructure",
+ "èī² 彩",
+ "æĢ»çļĦæĿ¥ 说",
+ "æľī ä»Ģä¹Ī",
+ "te xt",
+ "车 è¾Ĩ",
+ "Ġp ay",
+ "ro p",
+ "Ċ ĠĠ",
+ "Ġcaus ed",
+ "Ġcor rect",
+ "Ġ ì",
+ "èĥ ŀ",
+ "ĠM ed",
+ "ç²¾ ç¥ŀ",
+ "æ°ĶåĢĻ åıĺåĮĸ",
+ "ĠR ed",
+ "äºĴ èģĶç½ij",
+ "Ġeng age",
+ "åĪĨ 为",
+ "ĠD ata",
+ "Ġful l",
+ "en c",
+ "éĩį æĸ°",
+ "æŃ£ç¡® çļĦ",
+ "çļĦæ° Ķ",
+ "åıĮ æĸ¹",
+ "Ġcom es",
+ "åı¤ 代",
+ "æŁIJ äºĽ",
+ "åijĪ çİ°",
+ "Ġto day",
+ "ag ed",
+ "æĪij åı¯ä»¥",
+ "æĹ¥ 常",
+ "æ» ij",
+ "Ġcl in",
+ "Ġ \\",
+ "Ġo bs",
+ "Ġart ificial",
+ "Ġexce ll",
+ "çļĦç ¬",
+ "all s",
+ "Ġprodu ce",
+ "ĠD es",
+ "os s",
+ "è¹ Ī",
+ "Ġdra w",
+ "Ġlet ter",
+ "Ġadv ice",
+ "Ġhigh ly",
+ "çĬ ¯",
+ "综ä¸Ĭ æīĢè¿°",
+ "满 æĦı",
+ "Ġprinci ples",
+ "èĮ Ħ",
+ "Ġfeel ings",
+ "çļĦæ ´",
+ "Ġh om",
+ "Ġf ail",
+ "Ġcro p",
+ "å§ ľ",
+ "Ġquest ion",
+ "Ġdis abilities",
+ "èĪŀ è¹Ī",
+ "Ġimp lications",
+ "r al",
+ "Ġs ing",
+ "4 0",
+ "Ġfam il",
+ "Ġgovern ments",
+ "Ġrec ord",
+ "å½¢ çĬ¶",
+ "Ġbe gin",
+ "is es",
+ "çļĦæĥ ³",
+ "ach ine",
+ "è° ±",
+ "Ġv ulner",
+ "Ġpro per",
+ "Ġovers ight",
+ "è´Ł éĿ¢",
+ "Ġem ail",
+ "Ġnew s",
+ "Ġexpl oring",
+ "Ġf avor",
+ "æ¥ ¼",
+ "å® ľ",
+ "Ġun ivers",
+ "å·® å¼Ĥ",
+ "ï¼ī ãĢĤ",
+ "解åĨ³ éĹ®é¢ĺ",
+ "Ġfam ous",
+ "g n",
+ "Ġmess age",
+ "at itude",
+ "Ġc ra",
+ "Ġco ver",
+ "æ·± åĪ»",
+ "åı¯ä»¥ éĢīæĭ©",
+ "çĶŁæ´» ä¸Ń",
+ "ç§į ç±»",
+ "Ġsm art",
+ "on str",
+ "ve y",
+ "çĶ ²",
+ "Ġreg ularly",
+ "ĠS m",
+ "æĦŁ è§ī",
+ "Ġthough t",
+ "Ġex h",
+ "c ure",
+ "ç» ĺ",
+ "认 è¯Ĩ",
+ "Ġo ld",
+ "æĦ ī",
+ "称 为",
+ "Ġfiel ds",
+ "Ġcons ist",
+ "ã ģ",
+ "ç»Ĩ èĥŀ",
+ "Ġh ours",
+ "8 0",
+ "al king",
+ "è§ī å¾Ĺ",
+ "ç» Ŀ",
+ "ä½ł 们",
+ "ĠEng lish",
+ "Ġsignificant ly",
+ "Ġs ource",
+ "Ġan t",
+ "Ġeducation al",
+ "Ġtas k",
+ "Ġhand le",
+ "æIJ ľ",
+ "ĠS p",
+ "Ġcall ed",
+ "Ġter ms",
+ "æ² ī",
+ "Ġw in",
+ "duct ion",
+ "Ġmod ern",
+ "Ġcuis ine",
+ "å¥ Ĺ",
+ "è§ ¦",
+ "olut ely",
+ "ç« ¥",
+ "p ite",
+ "Ġf elt",
+ "Ġcomp re",
+ "Ġw ond",
+ "è¿IJ è¡Į",
+ "Ġres il",
+ "缸 ä¼¼",
+ "éĩij èŀį",
+ "çĪ± æĥħ",
+ "ç¬ Ķ",
+ "èĪ ª",
+ "è° Ī",
+ "åĬĽ çļĦ",
+ "æľī æīĢ",
+ "æ½ ľ",
+ "ul ate",
+ "Ġdetect ion",
+ "宣 ä¼ł",
+ "Ġmat ter",
+ "éĩı åŃIJ",
+ "W rite",
+ "ç»ĵ åIJĪ",
+ "ç»ı è¿ĩ",
+ "Ġdevelop ers",
+ "è ª",
+ "Ġ ---",
+ "人 éĻħ",
+ "çŃ ¾",
+ "ï¼ļ âĢľ",
+ "Ġinnov ative",
+ "ãĢĤ âĢĿ",
+ "å½ ¼",
+ "é¥ ¼",
+ "è¿ĩ 度",
+ "Ġplan et",
+ "åħ °",
+ "å¸ ģ",
+ "æķ ¬",
+ "Ġleg al",
+ "Ġlo t",
+ "æĪIJ为 äºĨ",
+ "i ate",
+ "Ġm is",
+ "åģĩ 设",
+ "çļĦ æĸĩç«ł",
+ "ĠCom pan",
+ "Ġd oc",
+ "Ġcare ful",
+ "Ġe ver",
+ "æĪij们 å°Ĩ",
+ "ä¾ĭ åŃIJ",
+ "ä¹ ³",
+ "ä½ľ èĢħ",
+ "åIJ §",
+ "æļ ´",
+ "Ġrem ember",
+ "缮 çļĦ",
+ "Ġp ut",
+ "常è§ģ çļĦ",
+ "Ġf est",
+ "建 设",
+ "å®ŀ çĶ¨",
+ "Ġact ive",
+ "çª Ĺ",
+ "ou th",
+ "åİŁ çIJĨ",
+ "Ġtry ing",
+ "è¿ ·",
+ "缸 åIJĮ",
+ "éħĴ åºĹ",
+ "An other",
+ "æľĢ ä½³",
+ "Ġanaly tics",
+ "Ġper pet",
+ "ip ment",
+ "Ġ å¦Ĥæŀľ",
+ "è§Ĥ ä¼Ĺ",
+ "Ġc elebr",
+ "Ġhe av",
+ "Ġmed itation",
+ "大 æ°Ķ",
+ "A nd",
+ "ä¸į éĶĻ",
+ "Ġwhe ther",
+ "s et",
+ "Ġdem onstr",
+ "ä¸Ģ 款",
+ "æĶ¶ éĽĨ",
+ "éĻIJ åĪ¶",
+ "Ġ ing",
+ "Ġrev olution",
+ "çľ ģ",
+ "Ġsc ience",
+ "缮 åīį",
+ "Ġthink ing",
+ "± ä¹IJ",
+ "课 ç¨ĭ",
+ "Ġp ack",
+ "Ġim age",
+ "lo c",
+ "Ġst ories",
+ "uc k",
+ "Ġsatis faction",
+ "Ġcollect ion",
+ "h o",
+ "èµ ŀ",
+ "éĿ¢ 临",
+ "Ġl a",
+ "Ġsym bol",
+ "Ġem b",
+ "Ġhabit ats",
+ "Ġlow er",
+ "Ġcontin ues",
+ "éľ ĩ",
+ "åĵ Ī",
+ "ĠT ake",
+ "Ġenviron ments",
+ "Ġth ree",
+ "Ġen c",
+ "ĠA cc",
+ "æĦı åij³",
+ "åİ ¨",
+ "ch an",
+ "ĠH um",
+ "Ġtr ue",
+ "åĪĩ æĪIJ",
+ "s ing",
+ "âĢĶ âĢĶ",
+ "åĩº æĿ¥",
+ "Ġreg ion",
+ "Ġinter pre",
+ "Ġdiagnos is",
+ "é ŀ",
+ "Ġdo ing",
+ "Ġr un",
+ "Ġco ffee",
+ "Ġmaj or",
+ "Ġmindful ness",
+ "Ġafford able",
+ "çĻ ¾",
+ "Ġdetail ed",
+ "éĿŀ常 éĩįè¦ģçļĦ",
+ "çļĦæ² ŁéĢļ",
+ "çļĦæķ ħ",
+ "åĢĴ åħ¥",
+ "Ġthem es",
+ "Ġnet work",
+ "ï¼ī ï¼ļ",
+ "ĠUn ited",
+ "çļĦæĮ ĩ",
+ "ort s",
+ "åį« çĶŁ",
+ "Ġplan ning",
+ "æĥ ł",
+ "åī ª",
+ "ĠPro v",
+ "çļĦ åºĶçĶ¨",
+ "Ġp eri",
+ "Ġaccount able",
+ "çī Ļ",
+ "çļĦç ģ",
+ "Ġcho ice",
+ "ĠC omm",
+ "id ents",
+ "çļĦ å®īåħ¨",
+ "并 ä¸į",
+ "太éĺ³ ç³»",
+ "Ġrece ive",
+ "Ġclo se",
+ "çļĦæŶ åĢĻ",
+ "Ġchang ing",
+ "ä»·åĢ¼ è§Ĥ",
+ "Ġperpet u",
+ "Ġse ason",
+ "Ġm en",
+ "Ġlearn ed",
+ "Ġsitu ation",
+ "Ġre place",
+ "he ad",
+ "让 æĪij",
+ "åľ¨ ä¸Ģèµ·",
+ "çļĦç© º",
+ "éľ ²",
+ "Ġen ough",
+ "å±ķ çİ°",
+ "Ġlead ers",
+ "an cing",
+ "Ġtemper ature",
+ "åı «",
+ "Ġ3 0",
+ "æĦıåij³ çĿĢ",
+ "æ± ĩ",
+ "ĠGo vern",
+ "Ġfocus ed",
+ "u ro",
+ "Ġsim ple",
+ "Ġh iking",
+ "æ¯ Ĵ",
+ "Ġcompre hens",
+ "äº Ī",
+ "Ġcreat ed",
+ "con d",
+ "é¡ µ",
+ "ĠW or",
+ "è¯ģ æį®",
+ "Ġwork place",
+ "Ġcharact ers",
+ "çļĦ 设计",
+ "Ġme chan",
+ "ĠD is",
+ "ç¥ŀ ç§ĺ",
+ "å· ŀ",
+ "ĠO n",
+ "< /",
+ "ç§į æ¤į",
+ "Ġpat h",
+ "Ġlim ited",
+ "Ġsol ar",
+ "çļĦæ ı",
+ "2 2",
+ "Ġappreci ate",
+ "å¿« ä¹IJ",
+ "æĦŁ åıĹåĪ°",
+ "èĢ Ĺ",
+ "m ed",
+ "ic ine",
+ "Ġnot e",
+ "å½ĵ åīį",
+ "æĪij们 åºĶ该",
+ "Ġse en",
+ "ä¸Ģ åIJį",
+ "å°½ åı¯èĥ½",
+ "è¿IJ ç®Ĺ",
+ "è§Ĵ 度",
+ "Ġequ ipment",
+ "Ġsp read",
+ "è ¸",
+ "è® ¿",
+ "åı¥ è¯Ŀ",
+ "æĮ ¥",
+ "Ġpur pose",
+ "请 ä½ł",
+ "Y our",
+ "ari an",
+ "ä» ª",
+ "Ġperspect ives",
+ "åĩº äºĨ",
+ "å©ļ 礼",
+ "Ġexcell ent",
+ "ĠEns uring",
+ "Ġre ach",
+ "éĺ¶ æ®µ",
+ "ä¿Ŀ éļľ",
+ "Ġemp athy",
+ "ĠM y",
+ "çij ľä¼½",
+ "Ġ ver",
+ "ab el",
+ "ĠPre dict",
+ "Ġmain tenance",
+ "è¯Ħ ä»·",
+ "Ġ ult",
+ "åĴ ¨",
+ "o x",
+ "åĴ¨ 询",
+ "Ġshare d",
+ "in a",
+ "l ist",
+ "Ġoutdo or",
+ "Ġthough ts",
+ "in ating",
+ "éĴ ±",
+ "Ġfra me",
+ "éĺ ¿",
+ "åĪ© 润",
+ "çļĦæİ ¨",
+ "åį ļ",
+ "Ġrec ent",
+ "Ġal tern",
+ "are d",
+ "= =",
+ "Ġro ad",
+ "äºĭ 项",
+ "g ed",
+ "y nt",
+ "Ġspe nd",
+ "ç½ ª",
+ "åıĸ å¾Ĺ",
+ "é ¹",
+ "l i",
+ "æŶ æľŁ",
+ "严 éĩį",
+ "å¿ Ĩ",
+ "å© ´",
+ "æİ¥ ä¸ĭæĿ¥",
+ "ĠEar th",
+ "ĠChat bots",
+ "Ġset ting",
+ "ç¥ Ŀ",
+ "éĶĢåĶ® é¢Ŀ",
+ "ä¼ ¦",
+ "Ġread ing",
+ "æİ¢ 讨",
+ "a ign",
+ "éŀ ĭ",
+ "Ġyou ng",
+ "Ġcare er",
+ "Ġteac hers",
+ "çļĦ è´¨éĩı",
+ "å±ŀ äºİ",
+ "Ġeas ier",
+ "Ġscient ific",
+ "ç¾İ åħĥ",
+ "Ġsp ir",
+ "åĬ ³",
+ "çļĦæĶ ¯",
+ "r ist",
+ "èµĦ 产",
+ "çĶŁ åŃĺ",
+ "èĩ³ å°ij",
+ "å§ ¿",
+ "Ġvide o",
+ "Ġa im",
+ "å®Ŀ å®Ŀ",
+ "çĪ¶ æ¯į",
+ "________ ________",
+ "al ities",
+ "Ġb ud",
+ "Ġstre et",
+ "Ġ æĺ¯",
+ "æĸ¹ ç¨ĭ",
+ "ä¸ĸ 纪",
+ "c hes",
+ "ear ch",
+ "æĴ °",
+ "Ġeng ine",
+ "Ġdis placement",
+ "ĠRo bots",
+ "erv ised",
+ "é¡ ¶",
+ "ou d",
+ "Ġw alk",
+ "Ġemerg ency",
+ "èģ ĺ",
+ "n al",
+ "Ġdat as",
+ "åĢ º",
+ "åIJİ çļĦ",
+ "å¾Ī 好",
+ "Ġmy self",
+ "çļĦæī ĭ",
+ "Ġus age",
+ "Ġsh own",
+ "æ® Ĭ",
+ "Ġtyp ically",
+ "u ly",
+ "æĸ° éĹ»",
+ "æĽ ¿",
+ "Ġor ig",
+ "è½» æĿ¾",
+ "æĺ¾ ç¤º",
+ "Ġado pt",
+ "èĤ¡ 票",
+ "Ġp arent",
+ "a ps",
+ "æĢĿ æĥ³",
+ "Ġmarket ing",
+ "èĻ «",
+ "éĥ¨ éŨ",
+ "çļĦæķ Ī",
+ "Ġcomfort able",
+ "åŃ¦ä¹ł åĴĮ",
+ "Ġfore cast",
+ "ict ion",
+ "Ġget ting",
+ "Ġtre es",
+ "av ing",
+ "çļĦ åŁºç¡Ģ",
+ "read y",
+ "æĸ° é²ľ",
+ "go ing",
+ "¹ é¥",
+ "Ġev idence",
+ "¹é¥ ª",
+ "ç§ ĭ",
+ "æľī å¾Īå¤ļ",
+ "éĿ¢ è¯ķ",
+ "éģĩ åĪ°",
+ "ç»Ļ å®ļ",
+ "ir c",
+ "åı¯ä»¥ æł¹æį®",
+ "驾 驶",
+ "å·§ åħĭ",
+ "Ġst unning",
+ "çļĦæ ¦Ĥ",
+ "æ¡ Į",
+ "ĠJ ohn",
+ "ul ation",
+ "åıĤ èĢĥ",
+ "Ġf lex",
+ "çĦ¦ èĻij",
+ "ym akers",
+ "Ġfor ms",
+ "s h",
+ "v al",
+ "ĠS o",
+ "c o",
+ "æݨ åĬ¨",
+ "èħ ¿",
+ "çī¹ æ®Ĭ",
+ "Ġen ab",
+ "å°Ĩ ä¼ļ",
+ "æĶ¯ åĩº",
+ "åĿļ æĮģ",
+ "红 èī²",
+ "Ġopt ion",
+ "Ġstart ed",
+ "r ation",
+ "Ġpo etry",
+ "Ġp ort",
+ "g en",
+ "èª ī",
+ "Ġdel iv",
+ "çĶ ļ",
+ "éĢ »",
+ "éĢī 项",
+ "Ġg round",
+ "å½¼ æѤ",
+ "an a",
+ "çļĦæĹ ¥",
+ "åľ¨ 线",
+ "Ġse cure",
+ "Ġ æł¹æį®",
+ "饮 æĸĻ",
+ "Ġgr atitude",
+ "第 ä¸ī",
+ "Ġs ong",
+ "Ġpoint s",
+ "Ġal ready",
+ "çļĦçĪ ±",
+ "ĠTe chn",
+ "Ġreal ity",
+ "çı Ń",
+ "Ġs ince",
+ "Ġpopul ation",
+ "y ond",
+ "b or",
+ "ĠSoc ial",
+ "æıIJ åıĸ",
+ "å·¥ ç¨ĭ",
+ "a ff",
+ "交 æĺĵ",
+ "Ġwor th",
+ "å¡ «",
+ "å¨ ±ä¹IJ",
+ "Ġdo g",
+ "ĠAr t",
+ "ç¡ ¬",
+ "æµ· æ´ĭ",
+ "åĨ Ĵ",
+ "çī Ī",
+ "Ġprogramm ing",
+ "ĠAs s",
+ "ĠM achine",
+ "åĢ¼ å¾Ĺ",
+ "请 è¾ĵåħ¥",
+ "声 éŁ³",
+ "Ġexercis es",
+ "åħī 线",
+ "æ³ķ åĴĮ",
+ "Ġfeat ure",
+ "e ff",
+ "è¿Ľ æŃ¥",
+ "女 æĢ§",
+ "Ġefficient ly",
+ "çļĦæĬĢ æľ¯",
+ "Ġgen etic",
+ "令 人",
+ "è´ ¦",
+ "çļĦ 产åĵģ",
+ "åİ ļ",
+ "åĴĮ æĸĩåĮĸ",
+ "éĻ Ħ",
+ "Ġmo b",
+ "综 åIJĪ",
+ "t ers",
+ "æľī ä¸Ģ",
+ "å¦ Ĩ",
+ "åį Ī",
+ "Ġout side",
+ "Ġprop ert",
+ "éĤ® 件",
+ "主 ä¹ī",
+ "Ġpolic y",
+ "èĩª 身",
+ "Ġnav igate",
+ "Ġst y",
+ "çĶµ èĦij",
+ "Ġab ilities",
+ "Ġfac ed",
+ "çļĦç ¼",
+ "çļĦ å°ı",
+ "è ķ",
+ "Ġt one",
+ "ig ation",
+ "åıĤ æķ°",
+ "èĽĭçĻ½ è´¨",
+ "ä½ Ľ",
+ "çĶļ èĩ³",
+ "Ġsk in",
+ "èĴ ¸",
+ "æĭ Ľ",
+ "éŃ Ķ",
+ "ash ion",
+ "Ġing red",
+ "æĹ ĭ",
+ "Ġcamp aign",
+ "Ġm ount",
+ "Ġcons id",
+ "Ġmus e",
+ "n ter",
+ "w ater",
+ "ä¼ļ è®®",
+ "Ġprotect ion",
+ "ä¿Ŀ éĻ©",
+ "Ġcro ps",
+ "og le",
+ "éļı æŶ",
+ "æļ Ĺ",
+ "i um",
+ "ä¹ ı",
+ "Ġdi et",
+ "l ies",
+ "çĶ¨ æĿ¥",
+ "ĠEn coura",
+ "æĬ Ĺ",
+ "ap an",
+ "éĺ² æŃ¢",
+ "W ow",
+ "çļĦ åŁºæľ¬",
+ "å¹³ æĸ¹",
+ "Ġst ep",
+ "åı¯ éĿł",
+ "表 æĺİ",
+ "Ġpredict ions",
+ "Ġsym pt",
+ "Ġdiagnos es",
+ "åħ¬ åĽŃ",
+ "Ġsupp ly",
+ "Ġprev ious",
+ "ç»Ħ åIJĪ",
+ ". ,",
+ "çļĦ è¿ĩç¨ĭ",
+ "æķ ı",
+ "s u",
+ "ar is",
+ "çķ ħ",
+ "oc ol",
+ "æIJľ ç´¢",
+ "it le",
+ "éĨ Ĵ",
+ "顾 客",
+ "éĢ» è¾ij",
+ "éĿŀ常 éĩįè¦ģ",
+ "ĠB i",
+ "å·¦ åı³",
+ "am m",
+ "Ġevery thing",
+ "æĺ ł",
+ "Ġincre d",
+ "Ġpe ace",
+ "èľ ľ",
+ "Ġmuse um",
+ "çĭ¬ ç«ĭ",
+ "Ġcomprehens ive",
+ "Ġr ates",
+ "/ /",
+ "Ġra d",
+ "åĦ¿ ç«¥",
+ "çī¹ èī²",
+ "ĠPredict ive",
+ "å¼ķ åĬĽ",
+ "l er",
+ "å° ¤",
+ "ic ro",
+ "è¡ ¥",
+ "Ġdeterm ine",
+ "çļĦ åĨħ容",
+ "Ġcom pl",
+ "Ġgreen house",
+ "èħ IJ",
+ "Ġhigh light",
+ "Ġpart ners",
+ "Ġdo ct",
+ "çļĦ 使çĶ¨",
+ "æŃĮ æĽ²",
+ "æĮĩ åįĹ",
+ "ĠA f",
+ "æľº æŀĦ",
+ "éĢ Ģ",
+ "Ġpoem s",
+ "å¿ĥ åĴĮ",
+ "Ġatt end",
+ "çļĦæ¸ ¸",
+ "Ġs ide",
+ "al es",
+ "Ġmention ed",
+ "ĠA bs",
+ "Ġhistor ical",
+ "Ġle ft",
+ "以ä¸ĭ åĩłä¸ª",
+ "åıĹ æ¬¢è¿İ",
+ "èıľ åĵģ",
+ "Ġrem ain",
+ "æ ĩ",
+ "Ġtour s",
+ "ł éģĵ",
+ "Ġerr ors",
+ "æľº åĪ¶",
+ "æ ¦",
+ "æĤ£ èĢħ",
+ "m ore",
+ "Ġexpert s",
+ "çļĦçł Ķ究",
+ "ç»ĵ æĿŁ",
+ "Ġwrit ten",
+ "çł Ķ",
+ "Ġe t",
+ "in put",
+ "æ°Ķ ä½ĵ",
+ "è ļ",
+ "æĥ Ĭ",
+ "Ġa ge",
+ "éĩį å¤į",
+ "å¼ ¹",
+ "åŃ ¤",
+ "Ġsympt oms",
+ "Ġbelie f",
+ "' d",
+ "i ol",
+ "Ġ1 8",
+ "åħħ 足",
+ "çı į",
+ "force ment",
+ "æĸ Ĺ",
+ "ª èĮĦ",
+ "Ġ1 5",
+ "ä¸Ģ个 人",
+ "Ġapp lic",
+ "è´ ¥",
+ "ä½į äºİ",
+ "éĻ¤ äºĨ",
+ "= \"",
+ "ä¸ī è§Ĵ",
+ "æĢĿ ç»´",
+ "åį ·",
+ "Ġf ru",
+ "ĠCol labor",
+ "Ġpr im",
+ "Ġrequire d",
+ "Ġw atch",
+ "è°ĥ åij³",
+ "ç»ĵ 论",
+ "on y",
+ "Ġgu ide",
+ "Ġm ax",
+ "ĠC ould",
+ "Ġadv ent",
+ "ĠO verall",
+ "çļĦæĬ ķ",
+ "Ġexp er",
+ "å ĺ",
+ "ic ial",
+ "ost er",
+ "çļĦ é¢ľèī²",
+ "Ġoper ations",
+ "éĥ ģ",
+ "Ġm oney",
+ "le y",
+ "c ling",
+ "Ġo il",
+ "çļ® èĤ¤",
+ "Ġg e",
+ "Ġb at",
+ "ĠP h",
+ "Ġsc he",
+ "Ġelect ric",
+ "v est",
+ "Ġch ain",
+ "Ġcap abilities",
+ "ir d",
+ "è¯ģ æĺİ",
+ "æľĢ 好",
+ "iv il",
+ "Ġdepend ing",
+ "Ġs ave",
+ "Ġpract ical",
+ "Ġcult ures",
+ "缸åºĶ çļĦ",
+ "s y",
+ "çļĦç ²",
+ "Ġbeh ind",
+ "æŶéĹ´ åĴĮ",
+ "å¹ ħ",
+ "ĠA g",
+ "Ġeffect iveness",
+ "A d",
+ "ĠO f",
+ "Ġany thing",
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+ "æ£ ®",
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+ "æ¬ §",
+ "Ġspe ed",
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+ "åĨĻ ä½ľ",
+ "Th anks",
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+ "Ġh ouse",
+ "认 çŁ¥",
+ "Ġsurround ing",
+ "Ġredu ced",
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+ "Ġbe yond",
+ "ĠC o",
+ "ro om",
+ "è¯Ĺ æŃĮ",
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+ "Ġlit tle",
+ "Ġg ames",
+ "ä¹ĭ åIJİ",
+ "éĥ½ ä¼ļ",
+ "è¯Ń éŁ³",
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+ "çī¹ å®ļ",
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+ "Ġdep ression",
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+ "ç¼ĸåĨĻ ä¸Ģ个",
+ "Ġintern ational",
+ "Ġcan cer",
+ "åѦ èĢħ",
+ "Ġdisc over",
+ "he t",
+ "Ġcomp os",
+ "Ġrec y",
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+ "åIJ« æľī",
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+ "ç¼ĵ 解",
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+ "çĶ ³",
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+ "Ġun t",
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+ "Ġop in",
+ "ĠGovern ments",
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+ "ä¸Ĭ æľĢ",
+ "ä»į çĦ¶",
+ "l ier",
+ "æ³ ³",
+ "äºĴ 缸",
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+ "az on",
+ "Ġar ch",
+ "Ġche m",
+ "çļĦ èĥ½åĬĽ",
+ "çļĦ ä¸Ģ个",
+ "Ġa p",
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+ "Ġw omen",
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+ "å§ »",
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+ "对 象",
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+ "Ġconsid ered",
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+ "æĦı 大åĪ©",
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+ "Ġco gn",
+ "ä» °",
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+ "iz z",
+ "çļĦæĿ IJ",
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+ "éĤ£ äºĽ",
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+ "éļIJ ç§ģ",
+ "Ġpolic ymakers",
+ "è¿ĺ éľĢè¦ģ",
+ "并 æıIJä¾Ľ",
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+ "Ċ ĉ",
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+ "ä½ł èĥ½",
+ "è¶³å¤Ł çļĦ",
+ "è§Ĩ è§ī",
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+ "æ¦Ĥ 念",
+ "é£Ł çĶ¨",
+ "èĢ ³",
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+ "æĿ¥ èĩª",
+ "èĤĮ èĤī",
+ "Ġd ays",
+ "Ġass ign",
+ "Ġadv ant",
+ "Ġteam s",
+ "é¢ Ĺ",
+ "now n",
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+ "it ions",
+ "Ġeas ily",
+ "ĠB l",
+ "n ame",
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+ "Ġsens itive",
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+ "Ġoffer ing",
+ "åįģ åĪĨ",
+ "èĢĮ ä¸įæĺ¯",
+ "Th ank",
+ "æĽ ¾",
+ "Ġele ments",
+ "ç² Ĵ",
+ "Ġcour ses",
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+ "agra ph",
+ "åŁº åĽł",
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+ "åı¦ ä¸Ģ个",
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+ "å° ģ",
+ "Ġconnect ion",
+ "åľ¨ ä¸Ģ个",
+ "Ġuse ful",
+ "è¯Ń åı¥",
+ "åĪĨ å¸ĥ",
+ "表 æ¼Ķ",
+ "æľī æŶ",
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+ "çļĦæĢ »",
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+ "èĭ ¦",
+ "è¦ģ 注æĦı",
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+ "åĴĮ æľįåĬ¡",
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+ "ers e",
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+ "åŃ©åŃIJ çļĦ",
+ "ä» Ķ",
+ "çĻ »",
+ "ĠA re",
+ "Ġco ol",
+ "Ġcogn itive",
+ "åIJĦ 个",
+ "l ike",
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+ "å¹ »",
+ "ron t",
+ "家 éķ¿",
+ "缺 ä¹ı",
+ "Ġcy ber",
+ "il t",
+ "Ġcapt ure",
+ "å Ĺ",
+ "åľ¨ äºİ",
+ "Ġthreat s",
+ "åĴĮ 社ä¼ļ",
+ "Ġcell s",
+ "æ¸ħ åįķ",
+ "ĠV is",
+ "æİ ī",
+ "Ġh ol",
+ "åŃIJ çļĦ",
+ "C h",
+ "è Ŀ",
+ "Ġs aid",
+ "Ġd ream",
+ "un ch",
+ "un e",
+ "ĠD on",
+ "家 人",
+ "ç± į",
+ "æĦŁ åĴĮ",
+ "Ġexperi enced",
+ "çļĦéĩįè¦ģ æĢ§",
+ "å¼ ĥ",
+ "um p",
+ "éĺ IJ",
+ "Ġhabit at",
+ "è¢ ĭ",
+ "Ġj o",
+ "ç®Ģ æ´ģ",
+ "Ġb ur",
+ "Ġvisit ors",
+ "éĽ ħ",
+ "çļĦçŁ ¥",
+ "Ġent ire",
+ "讲 述",
+ "äºĨ ä¸ĢäºĽ",
+ "åįı ä½ľ",
+ "ĠB us",
+ "å° ¾",
+ "çļĦæķ Ļ",
+ "olo g",
+ "Ġsign s",
+ "Ġspeak er",
+ "çļĦ éŁ³ä¹IJ",
+ "Ġno vel",
+ "å±ħ æ°ij",
+ "çļĦ åıĺåĮĸ",
+ "å°½ éĩı",
+ "Ġspir it",
+ "å®Į ç¾İ",
+ "è´ ·",
+ "å¿ħè¦ģ çļĦ",
+ "ie f",
+ "示 ä¾ĭ",
+ "Ġd iv",
+ "æķ´ æķ°",
+ "Ġeconom y",
+ "Ġethical ly",
+ "éĻ Ī",
+ "Ġschool s",
+ "Ġnet works"
+ ]
+ }
+}
\ No newline at end of file
diff --git a/中文逐行注释/model/minimind_tokenizer/tokenizer_config.json b/中文逐行注释/model/minimind_tokenizer/tokenizer_config.json
new file mode 100644
index 0000000..5f3fa2b
--- /dev/null
+++ b/中文逐行注释/model/minimind_tokenizer/tokenizer_config.json
@@ -0,0 +1,44 @@
+{
+ "add_bos_token": false,
+ "add_eos_token": false,
+ "add_prefix_space": true,
+ "added_tokens_decoder": {
+ "0": {
+ "content": "<unk>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "1": {
+ "content": "<s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ },
+ "2": {
+ "content": "</s>",
+ "lstrip": false,
+ "normalized": false,
+ "rstrip": false,
+ "single_word": false,
+ "special": true
+ }
+ },
+ "additional_special_tokens": [],
+ "bos_token": "<s>",
+ "clean_up_tokenization_spaces": false,
+ "eos_token": "</s>",
+ "legacy": true,
+ "model_max_length": 1000000000000000019884624838656,
+ "pad_token": null,
+ "sp_model_kwargs": {},
+ "spaces_between_special_tokens": false,
+ "tokenizer_class": "PreTrainedTokenizerFast",
+ "unk_token": "<unk>",
+ "use_default_system_prompt": false,
+ "chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ system_message }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<s>user\\n' + content + '</s>\\n<s>assistant\\n' }}{% elif message['role'] == 'assistant' %}{{ content + '</s>' + '\\n' }}{% endif %}{% endfor %}"
+}
\ No newline at end of file
diff --git a/中文逐行注释/model/minimind_tokenizer/vocab.json b/中文逐行注释/model/minimind_tokenizer/vocab.json
new file mode 100644
index 0000000..e1318cc
--- /dev/null
+++ b/中文逐行注释/model/minimind_tokenizer/vocab.json
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diff --git a/中文逐行注释/model/model.py b/中文逐行注释/model/model.py
new file mode 100644
index 0000000..9716a87
--- /dev/null
+++ b/中文逐行注释/model/model.py
@@ -0,0 +1,431 @@
+import math
+import struct
+import inspect
+from .LMConfig import LMConfig
+from typing import Any, Optional, Tuple
+import numpy as np
+import torch
+import torch.nn.functional as F
+from torch import nn
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+
+# 定义 RMSNorm 类，实现一种归一化方法，类似于 LayerNorm，但计算方式不同
+class RMSNorm(torch.nn.Module):
+ def __init__(self, dim: int, eps: float):
+ super().__init__()
+ self.eps = eps # 设置 epsilon，防止除零错误
+ self.weight = nn.Parameter(torch.ones(dim)) # 初始化权重参数
+
+ def _norm(self, x):
+ return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) # 计算 RMSNorm
+
+ def forward(self, x):
+ output = self._norm(x.float()).type_as(x) # 应用 RMSNorm
+ return output * self.weight # 乘以权重参数
+
+# 定义 precompute_pos_cis 函数，用于预计算位置编码的复数形式
+def precompute_pos_cis(dim: int, end: int, theta: float = 10000.0):
+ freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)) # 计算频率
+ t = torch.arange(end, device=freqs.device) # 生成时间序列
+ freqs = torch.outer(t, freqs).float() # 计算外积
+ pos_cis = torch.polar(torch.ones_like(freqs), freqs) # 计算复数形式的位置编码
+ return pos_cis
+
+# 定义 apply_rotary_emb 函数，用于应用旋转位置编码
+def apply_rotary_emb(xq, xk, pos_cis):
+ def unite_shape(pos_cis, x):
+ ndim = x.ndim
+ assert 0 <= 1 < ndim
+ assert pos_cis.shape == (x.shape[1], x.shape[-1])
+ shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
+ return pos_cis.view(*shape)
+
+ xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2)) # 将 xq 转换为复数形式
+ xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2)) # 将 xk 转换为复数形式
+ pos_cis = unite_shape(pos_cis, xq_) # 调整 pos_cis 的形状
+ xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3) # 应用旋转位置编码
+ xk_out = torch.view_as_real(xk_ * pos_cis).flatten(3) # 应用旋转位置编码
+ return xq_out.type_as(xq), xk_out.type_as(xk) # 返回结果
+
+# 定义 repeat_kv 函数，用于重复 KV 头的值
+def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
+ """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
+ bs, slen, n_kv_heads, head_dim = x.shape
+ if n_rep == 1:
+ return x
+ return (
+ x[:, :, :, None, :]
+ .expand(bs, slen, n_kv_heads, n_rep, head_dim)
+ .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
+ )
+
+# 定义 Attention 类，实现自注意力机制
+class Attention(nn.Module):
+ def __init__(self, args: LMConfig):
+ super().__init__()
+ self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads # 设置 KV 头的数量
+ assert args.n_heads % self.n_kv_heads == 0 # 确保 KV 头的数量是总头数的因数
+ self.n_local_heads = args.n_heads # 设置本地头的数量
+ self.n_local_kv_heads = self.n_kv_heads # 设置本地 KV 头的数量
+ self.n_rep = self.n_local_heads // self.n_local_kv_heads # 计算重复次数
+ self.head_dim = args.dim // args.n_heads # 计算每个头的维度
+ self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False) # 初始化 Q 矩阵
+ self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) # 初始化 K 矩阵
+ self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False) # 初始化 V 矩阵
+ self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False) # 初始化输出矩阵
+ self.k_cache, self.v_cache = None, None # 初始化 KV 缓存
+ self.attn_dropout = nn.Dropout(args.dropout) # 初始化注意力 dropout
+ self.resid_dropout = nn.Dropout(args.dropout) # 初始化残差 dropout
+ self.dropout = args.dropout # 设置 dropout 概率
+ self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and args.flash_attn # 判断是否使用 Flash Attention
+
+ if not self.flash:
+ # print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
+ mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf")) # 初始化掩码
+ mask = torch.triu(mask, diagonal=1) # 生成上三角掩码
+ self.register_buffer("mask", mask) # 注册掩码
+
+ def forward(self, x: torch.Tensor, pos_cis: torch.Tensor, use_kv_cache=False):
+ bsz, seqlen, _ = x.shape
+ if use_kv_cache and self.eval(): # 如果使用 KV 缓存且在评估模式下
+ if self.k_cache is None or self.k_cache.shape[1] != x.shape[1] - 1:
+ xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) # 计算 Q, K, V
+ else:
+ token = x[:, -1:, :] # 获取最后一个 token
+ xq = torch.cat((torch.zeros_like(x[:, :-1, :]), self.wq(token)), dim=1) # 更新 Q
+ xk = torch.cat((self.k_cache, self.wk(token)), dim=1) # 更新 K
+ xv = torch.cat((self.v_cache, self.wv(token)), dim=1) # 更新 V
+
+ self.k_cache, self.v_cache = xk, xv # 更新 KV 缓存
+ else:
+ xq, xk, xv = self.wq(x), self.wk(x), self.wv(x) # 计算 Q, K, V
+
+ xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim) # 调整 Q 的形状
+ xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) # 调整 K 的形状
+ xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim) # 调整 V 的形状
+
+ xq, xk = apply_rotary_emb(xq, xk, pos_cis) # 应用旋转位置编码
+
+ xk = repeat_kv(xk, self.n_rep) # 重复 K 的值
+ xv = repeat_kv(xv, self.n_rep) # 重复 V 的值
+
+ xq = xq.transpose(1, 2) # 调整 Q 的形状
+ xk = xk.transpose(1, 2) # 调整 K 的形状
+ xv = xv.transpose(1, 2) # 调整 V 的形状
+
+ if self.flash:
+ output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None,
+ dropout_p=self.dropout if self.training else 0.0,
+ is_causal=True) # 使用 Flash Attention
+ else:
+ scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim) # 计算注意力分数
+ assert hasattr(self, 'mask')
+ scores = scores + self.mask[:, :, :seqlen, :seqlen] # 应用掩码
+ scores = F.softmax(scores.float(), dim=-1).type_as(xq) # 计算 softmax
+ scores = self.attn_dropout(scores) # 应用注意力 dropout
+ output = torch.matmul(scores, xv) # 计算输出
+
+ output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1) # 调整输出的形状
+
+ output = self.wo(output) # 应用输出矩阵
+ output = self.resid_dropout(output) # 应用残差 dropout
+ return output # 返回输出
+
+# 定义 FeedForward 类，实现前馈神经网络
+class FeedForward(nn.Module):
+ def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
+ super().__init__()
+ if hidden_dim is None:
+ hidden_dim = 4 * dim # 设置隐藏层维度
+ hidden_dim = int(2 * hidden_dim / 3) # 调整隐藏层维度
+ hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of) # 调整隐藏层维度
+ self.w1 = nn.Linear(dim, hidden_dim, bias=False) # 初始化第一层线性变换
+ self.w2 = nn.Linear(hidden_dim, dim, bias=False) # 初始化第二层线性变换
+ self.w3 = nn.Linear(dim, hidden_dim, bias=False) # 初始化第三层线性变换
+ self.dropout = nn.Dropout(dropout) # 初始化 dropout
+
+ def forward(self, x):
+ return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x))) # 前向传播
+
+# 定义 MoEGate 类，实现专家混合（MoE）的门控机制
+class MoEGate(nn.Module):
+ def __init__(self, config: LMConfig):
+ super().__init__()
+ self.config = config
+ self.top_k = config.num_experts_per_tok # 设置每个 token 选择的专家数量
+ self.n_routed_experts = config.n_routed_experts # 设置路由专家的数量
+
+ self.scoring_func = config.scoring_func # 设置评分函数
+ self.alpha = config.aux_loss_alpha # 设置辅助损失的权重
+ self.seq_aux = config.seq_aux # 设置序列辅助损失
+
+ self.norm_topk_prob = config.norm_topk_prob # 设置是否归一化 top-k 概率
+ self.gating_dim = config.dim # 设置门控维度
+ self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim))) # 初始化权重参数
+ self.reset_parameters() # 重置参数
+
+ def reset_parameters(self) -> None:
+ import torch.nn.init as init
+ init.kaiming_uniform_(self.weight, a=math.sqrt(5)) # 使用 Kaiming 初始化权重
+
+ def forward(self, hidden_states):
+ bsz, seq_len, h = hidden_states.shape
+
+ hidden_states = hidden_states.view(-1, h) # 调整隐藏状态的形状
+ logits = F.linear(hidden_states, self.weight, None) # 计算 logits
+ if self.scoring_func == 'softmax':
+ scores = logits.softmax(dim=-1) # 计算 softmax 评分
+ else:
+ raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')
+
+ topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False) # 选择 top-k 专家
+
+ if self.top_k > 1 and self.norm_topk_prob:
+ denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20 # 计算归一化分母
+ topk_weight = topk_weight / denominator # 归一化 top-k 概率
+
+ if self.training and self.alpha > 0.0:
+ scores_for_aux = scores
+ aux_topk = self.top_k
+ topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
+ if self.seq_aux:
+ scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
+ ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
+ ce.scatter_add_(1, topk_idx_for_aux_loss,
+ torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
+ seq_len * aux_topk / self.n_routed_experts)
+ aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
+ else:
+ mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
+ ce = mask_ce.float().mean(0)
+ Pi = scores_for_aux.mean(0)
+ fi = ce * self.n_routed_experts
+ aux_loss = (Pi * fi).sum() * self.alpha
+ else:
+ aux_loss = None
+ return topk_idx, topk_weight, aux_loss # 返回 top-k 专家索引、权重和辅助损失
+
+# 定义 MOEFeedForward 类，实现专家混合（MoE）的前馈神经网络
+class MOEFeedForward(nn.Module):
+ def __init__(self, config: LMConfig):
+ super().__init__()
+ self.config = config
+ self.experts = nn.ModuleList([
+ FeedForward(
+ dim=config.dim,
+ hidden_dim=config.hidden_dim,
+ multiple_of=config.multiple_of,
+ dropout=config.dropout,
+ )
+ for _ in range(config.n_routed_experts)
+ ]) # 初始化专家列表
+
+ self.gate = MoEGate(config) # 初始化门控机制
+ if config.n_shared_experts is not None:
+ self.shared_experts = FeedForward(
+ dim=config.dim,
+ hidden_dim=config.hidden_dim,
+ multiple_of=config.multiple_of,
+ dropout=config.dropout,
+ ) # 初始化共享专家
+
+ def forward(self, x):
+ identity = x
+ orig_shape = x.shape
+ bsz, seq_len, _ = x.shape
+
+ # 使用门控机制选择专家
+ topk_idx, topk_weight, aux_loss = self.gate(x)
+
+ x = x.view(-1, x.shape[-1])
+ flat_topk_idx = topk_idx.view(-1)
+
+ if self.training:
+ # 训练模式下，重复输入数据
+ x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
+ y = torch.empty_like(x, dtype=torch.float16)
+ for i, expert in enumerate(self.experts):
+ y[flat_topk_idx == i] = expert(x[flat_topk_idx == i])
+ y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
+ y = y.view(*orig_shape)
+ else:
+ # 推理模式下，只选择最优专家
+ y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)
+
+ if self.config.n_shared_experts is not None:
+ y = y + self.shared_experts(identity)
+
+ return y
+
+ @torch.no_grad()
+ def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
+ expert_cache = torch.zeros_like(x)
+ idxs = flat_expert_indices.argsort()
+ tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
+ token_idxs = idxs // self.config.num_experts_per_tok
+ # 例如当tokens_per_expert=[6, 15, 20, 26, 33, 38, 46, 52]
+ # 当token_idxs=[3, 7, 19, 21, 24, 25, 4, 5, 6, 10, 11, 12...]
+ # 意味着当token_idxs[:6] -> [3, 7, 19, 21, 24, 25, 4]位置的token都由专家0处理，token_idxs[6:15]位置的token都由专家1处理......
+ for i, end_idx in enumerate(tokens_per_expert):
+ start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
+ if start_idx == end_idx:
+ continue
+ expert = self.experts[i]
+ exp_token_idx = token_idxs[start_idx:end_idx]
+ expert_tokens = x[exp_token_idx]
+ expert_out = expert(expert_tokens)
+ expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
+ # 使用 scatter_add_ 进行 sum 操作
+ expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)
+
+ return expert_cache
+
+# 定义 TransformerBlock 类，实现 Transformer 的一个块，包括自注意力和前馈神经网络
+class TransformerBlock(nn.Module):
+ def __init__(self, layer_id: int, args: LMConfig):
+ super().__init__()
+ self.n_heads = args.n_heads
+ self.dim = args.dim
+ self.head_dim = args.dim // args.n_heads
+ self.attention = Attention(args) # 初始化自注意力机制
+
+ self.layer_id = layer_id
+ self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps) # 初始化注意力归一化
+ self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps) # 初始化前馈神经网络归一化
+
+ if args.use_moe:
+ self.feed_forward = MOEFeedForward(args) # 初始化专家混合前馈神经网络
+ else:
+ self.feed_forward = FeedForward(
+ dim=args.dim,
+ hidden_dim=args.hidden_dim,
+ multiple_of=args.multiple_of,
+ dropout=args.dropout,
+ ) # 初始化前馈神经网络
+
+ def forward(self, x, pos_cis, use_kv_cache=False):
+ h = x + self.attention(self.attention_norm(x), pos_cis, use_kv_cache) # 计算自注意力
+ out = h + self.feed_forward(self.ffn_norm(h)) # 计算前馈神经网络
+ return out # 返回输出
+
+# 定义 Transformer 类，实现整个 Transformer 模型
+class Transformer(PreTrainedModel):
+ config_class = LMConfig
+ last_loss: Optional[torch.Tensor]
+
+ def __init__(self, params: LMConfig = None):
+ super().__init__(params)
+ if not params:
+ params = LMConfig()
+ self.params = params
+ self.vocab_size = params.vocab_size
+ self.n_layers = params.n_layers
+class Transformer(PreTrainedModel):
+ config_class = LMConfig
+ last_loss: Optional[torch.Tensor]
+
+ def __init__(self, params: LMConfig = None):
+ super().__init__(params)
+ if not params:
+ params = LMConfig()
+ self.params = params
+ self.vocab_size = params.vocab_size
+ self.n_layers = params.n_layers
+
+ self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim) # 初始化词嵌入层
+ self.dropout = nn.Dropout(params.dropout) # 初始化 dropout 层
+ self.layers = torch.nn.ModuleList() # 初始化 Transformer 块列表
+ for layer_id in range(self.n_layers):
+ self.layers.append(TransformerBlock(layer_id, params)) # 添加 Transformer 块
+ self.norm = RMSNorm(params.dim, eps=params.norm_eps) # 初始化归一化层
+ self.output = nn.Linear(params.dim, params.vocab_size, bias=False) # 初始化输出层
+ self.tok_embeddings.weight = self.output.weight # 共享词嵌入和输出层的权重
+ pos_cis = precompute_pos_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len) # 预计算位置编码
+ self.register_buffer("pos_cis", pos_cis, persistent=False) # 注册位置编码缓冲区
+
+ self.apply(self._init_weights) # 初始化模型权重
+
+ for pn, p in self.named_parameters():
+ if pn.endswith('w3.weight') or pn.endswith('wo.weight'):
+ torch.nn.init.normal_(p, mean=0.0, std=0.02 / math.sqrt(2 * params.n_layers)) # 对特定权重进行初始化
+
+ self.last_loss = None # 初始化最后一个损失
+ self.OUT = CausalLMOutputWithPast() # 初始化输出对象
+
+ def _init_weights(self, module):
+ if isinstance(module, nn.Linear):
+ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) # 初始化线性层的权重
+ if module.bias is not None:
+ torch.nn.init.zeros_(module.bias) # 初始化线性层的偏置
+ elif isinstance(module, nn.Embedding):
+ torch.nn.init.normal_(module.weight, mean=0.0, std=0.02) # 初始化嵌入层的权重
+
+ def forward(self, tokens: Optional[torch.Tensor] = None, targets: Optional[torch.Tensor] = None,
+ use_kv_cache=False, **keyargs):
+ if 'input_ids' in keyargs:
+ tokens = keyargs['input_ids'] # 如果传入了 input_ids，则使用 input_ids
+ if 'attention_mask' in keyargs:
+ targets = keyargs['attention_mask'] # 如果传入了 attention_mask，则使用 attention_mask
+
+ _bsz, seqlen = tokens.shape # 获取批量大小和序列长度
+ h = self.tok_embeddings(tokens) # 获取词嵌入
+ h = self.dropout(h) # 应用 dropout
+ pos_cis = self.pos_cis[:seqlen] # 获取对应序列长度的位置编码
+ for idx, layer in enumerate(self.layers):
+ h = layer(h, pos_cis, use_kv_cache) # 逐层应用 Transformer 块
+
+ h = self.norm(h) # 应用归一化
+
+ if targets is not None:
+ logits = self.output(h) # 计算 logits
+ self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1) # 计算交叉熵损失
+ else:
+ logits = self.output(h[:, [-1], :]) # 计算最后一个 token 的 logits
+ self.last_loss = None # 没有目标时，损失为 None
+
+ self.OUT.__setitem__('logits', logits) # 设置输出对象的 logits
+ self.OUT.__setitem__('last_loss', self.last_loss) # 设置输出对象的 last_loss
+
+ return self.OUT # 返回输出对象
+
+ @torch.inference_mode() # 推理模式
+ def generate(self, idx, eos, max_new_tokens, temperature=0.7, top_k=None, stream=True, repetition_penalty=1.,
+ use_kv_cache=True):
+ index = idx.shape[1] # 获取当前序列长度
+ while idx.shape[1] < max_new_tokens - 1: # 当生成的 token 数量小于最大数量时
+ inference_res = self(idx, use_kv_cache=use_kv_cache) # 进行前向传播
+ logits = inference_res.logits # 获取 logits
+ logits = logits[:, -1, :] # 获取最后一个 token 的 logits
+
+ for token in set(idx.tolist()[0]): # 对重复 token 进行惩罚
+ logits[:, token] /= repetition_penalty
+
+ if temperature == 0.0: # 如果温度为 0，直接选择概率最高的 token
+ _, idx_next = torch.topk(logits, k=1, dim=-1)
+ else:
+ logits = logits / temperature # 调整 logits
+ if top_k is not None: # 如果设置了 top-k 采样
+ v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+ logits[logits < v[:, [-1]]] = -float('Inf') # 将小于 top-k 的 logits 设为负无穷
+
+ probs = F.softmax(logits, dim=-1) # 计算概率
+ idx_next = torch.multinomial(probs, num_samples=1, generator=None) # 采样下一个 token
+
+ if idx_next == eos: # 如果生成的 token 是结束符，停止生成
+ break
+
+ idx = torch.cat((idx, idx_next), dim=1) # 将生成的 token 添加到序列中
+ if stream: # 如果需要流式输出
+ yield idx[:, index:] # 返回生成的 token
+
+ if not stream: # 如果不需要流式输出
+ yield idx[:, index:] # 返回生成的 token
+
+ @torch.inference_mode() # 推理模式
+ def eval_answer(self, idx):
+ idx_cond = idx if idx.size(1) <= self.params.max_seq_len else idx[:, -self.params.max_seq_len:] # 截取序列
+ inference_res = self(idx_cond) # 进行前向传播
+ logits = inference_res.logits # 获取 logits
+ logits = logits[:, -1, :] # 获取最后一个 token 的 logits
+ return logits # 返回 logits
\ No newline at end of file