From a792e224c535d1666ea842673ae37289a50d655b Mon Sep 17 00:00:00 2001 From: xxl <505279206@qq.com> Date: Tue, 31 Dec 2024 19:05:07 +0800 Subject: [PATCH] first commit --- README.md | 201 +- model-00002-of-00007.safetensors | 3 + model-00003-of-00007.safetensors | 3 + model-00004-of-00007.safetensors | 3 + model-00005-of-00007.safetensors | 3 + model-00006-of-00007.safetensors | 3 + model-00007-of-00007.safetensors | 3 + model.safetensors.index.json | 298 + modeling_exaone.py | 1394 + special_tokens_map.json | 30 + tokenizer.json | 207491 ++++++++++++++++++++++++++++ tokenizer_config.json | 3221 + vocab.json | 1 + 13 files changed, 212652 insertions(+), 2 deletions(-) create mode 100644 model-00002-of-00007.safetensors create mode 100644 model-00003-of-00007.safetensors create mode 100644 model-00004-of-00007.safetensors create mode 100644 model-00005-of-00007.safetensors create mode 100644 model-00006-of-00007.safetensors create mode 100644 model-00007-of-00007.safetensors create mode 100644 model.safetensors.index.json create mode 100644 modeling_exaone.py create mode 100644 special_tokens_map.json create mode 100644 tokenizer.json create mode 100644 tokenizer_config.json create mode 100644 vocab.json diff --git a/README.md b/README.md index 9bf1fe2..2ad9ab0 100644 --- a/README.md +++ b/README.md @@ -1,3 +1,200 @@ -# EXAONE-3.5-7.8B-Instruct_a14125166997008384632002 +--- +license: other +license_name: exaone +license_link: LICENSE +language: +- en +- ko +tags: +- lg-ai +- exaone +- exaone-3.5 +pipeline_tag: text-generation +library_name: transformers +--- -EXAONE-3.5-7.8B-Instruct \ No newline at end of file +

+ +
+ +# EXAONE-3.5-7.8B-Instruct + +## Introduction + +We introduce EXAONE 3.5, a collection of instruction-tuned bilingual (English and Korean) generative models ranging from 2.4B to 32B parameters, developed and released by LG AI Research. EXAONE 3.5 language models include: 1) **2.4B model** optimized for deployment on small or resource-constrained devices, 2) **7.8B model** matching the size of its predecessor but offering improved performance, and 3) **32B model** delivering powerful performance. All models support long-context processing of up to 32K tokens. Each model demonstrates state-of-the-art performance in real-world use cases and long-context understanding, while remaining competitive in general domains compared to recently released models of similar sizes. + +For more details, please refer to our [technical report](https://arxiv.org/abs/2412.04862), [blog](https://www.lgresearch.ai/blog/view?seq=507) and [GitHub](https://github.com/LG-AI-EXAONE/EXAONE-3.5). + +This repository contains the instruction-tuned 7.8B language model with the following features: + +- Number of Parameters (without embeddings): 6.98B +- Number of Layers: 32 +- Number of Attention Heads: GQA with 32 Q-heads and 8 KV-heads +- Vocab Size: 102,400 +- Context Length: 32,768 tokens + +## Quickstart + +We recommend to use `transformers` v4.43 or later. + +Here is the code snippet to run conversational inference with the model: + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer + +model_name = "LGAI-EXAONE/EXAONE-3.5-7.8B-Instruct" + +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=torch.bfloat16, + trust_remote_code=True, + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) + +# Choose your prompt +prompt = "Explain how wonderful you are" # English example +prompt = "스스로를 자랑해 봐" # Korean example + +messages = [ + {"role": "system", + "content": "You are EXAONE model from LG AI Research, a helpful assistant."}, + {"role": "user", "content": prompt} +] +input_ids = tokenizer.apply_chat_template( + messages, + tokenize=True, + add_generation_prompt=True, + return_tensors="pt" +) + +output = model.generate( + input_ids.to("cuda"), + eos_token_id=tokenizer.eos_token_id, + max_new_tokens=128, + do_sample=False, +) +print(tokenizer.decode(output[0])) +``` + +> ### Note +> The EXAONE 3.5 instruction-tuned language models were trained to utilize the system prompt, +> so we highly recommend using the system prompts provided in the code snippet above. + +## Evaluation + +The following table shows the evaluation results of real-world use cases. The full evaluation results can be found in the [technical report](https://arxiv.org/abs/2412.04862). + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelsMT-BenchLiveBenchArena-HardAlpacaEvalIFEvalKoMT-Bench[1]LogicKor
EXAONE 3.5 7.8B8.2939.868.754.278.97.969.08
Qwen 2.5 7B6.4835.648.931.772.55.196.38
Llama 3.1 8B7.5928.327.725.774.54.855.99
Gemma 2 9B7.6432.143.647.354.77.108.05
Phi 3 small (7B)7.6327.926.829.259.53.223.99
+ +- [1] KoMT-Bench is a dataset created by translating MT-Bench into Korean; see [README](https://github.com/LG-AI-EXAONE/KoMT-Bench) for more details. + +## Deployment + +EXAONE 3.5 models can be inferred in the various frameworks, such as: +- `TensorRT-LLM` +- `vLLM` +- `SGLang` +- `llama.cpp` +- `Ollama` + +Please refer to our [EXAONE 3.5 GitHub](https://github.com/LG-AI-EXAONE/EXAONE-3.5) for more details about the inference frameworks. + +## Quantization + +We provide the pre-quantized EXAONE 3.5 models with **AWQ** and several quantization types in **GGUF** format. +Please refer to our [EXAONE 3.5 collection](https://huggingface.co/collections/LGAI-EXAONE/exaone-35-674d0e1bb3dcd2ab6f39dbb4) to find corresponding quantized models. + +## Limitation + +The EXAONE language model has certain limitations and may occasionally generate inappropriate responses. The language model generates responses based on the output probability of tokens, and it is determined during learning from training data. While we have made every effort to exclude personal, harmful, and biased information from the training data, some problematic content may still be included, potentially leading to undesirable responses. Please note that the text generated by EXAONE language model does not reflects the views of LG AI Research. + +- Inappropriate answers may be generated, which contain personal, harmful or other inappropriate information. +- Biased responses may be generated, which are associated with age, gender, race, and so on. +- The generated responses rely heavily on statistics from the training data, which can result in the generation of +semantically or syntactically incorrect sentences. +- Since the model does not reflect the latest information, the responses may be false or contradictory. + +LG AI Research strives to reduce potential risks that may arise from EXAONE language models. Users are not allowed +to engage in any malicious activities (e.g., keying in illegal information) that may induce the creation of inappropriate +outputs violating LG AI’s ethical principles when using EXAONE language models. + +## License + +The model is licensed under [EXAONE AI Model License Agreement 1.1 - NC](./LICENSE) + +## Citation + +``` +@article{exaone-3.5, + title={EXAONE 3.5: Series of Large Language Models for Real-world Use Cases}, + author={LG AI Research}, + journal={arXiv preprint arXiv:https://arxiv.org/abs/2412.04862}, + year={2024} +} +``` + +## Contact +LG AI Research Technical Support: contact_us@lgresearch.ai diff --git a/model-00002-of-00007.safetensors b/model-00002-of-00007.safetensors new file mode 100644 index 0000000..afe7d84 --- /dev/null +++ b/model-00002-of-00007.safetensors @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:8ef9bd31e3c04d770649d702911a5012d09c4724d2a2f53263ecb13f8e509ea9 +size 4999813040 diff --git 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All rights reserved. +# +# This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX +# and OPT implementations in this library. It has been modified from its +# original forms to accommodate minor architectural differences compared +# to GPT-NeoX and OPT used by the Meta AI team that trained the model. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +"""LG AI Research EXAONE Lab""" + +import math +from typing import Optional, Tuple, Union + +import torch +import torch.utils.checkpoint +from packaging import version +from torch import nn +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from transformers.activations import ACT2FN +from transformers.cache_utils import Cache, DynamicCache, StaticCache +from transformers.generation import GenerationMixin +from transformers.modeling_attn_mask_utils import AttentionMaskConverter +from transformers.modeling_flash_attention_utils import _flash_attention_forward +from transformers.modeling_outputs import ( + BaseModelOutputWithPast, + BaseModelOutputWithPastAndCrossAttentions, + CausalLMOutputWithPast, + QuestionAnsweringModelOutput, + SequenceClassifierOutputWithPast, +) +from transformers.modeling_rope_utils import ROPE_INIT_FUNCTIONS +from transformers.modeling_utils import PreTrainedModel +from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS +from transformers.utils import ( + add_code_sample_docstrings, + add_start_docstrings, + add_start_docstrings_to_model_forward, + is_flash_attn_2_available, + logging, +) +from .configuration_exaone import ExaoneConfig + + +if is_flash_attn_2_available(): + try: + import flash_attn + + if version.parse(flash_attn.__version__) > version.parse("2.4.2"): + from flash_attn.ops.triton.layer_norm import rms_norm_fn + else: + from flash_attn.ops.triton.layernorm import rms_norm_fn + except ImportError: + pass + + +logger = logging.get_logger(__name__) + +_CHECKPOINT_FOR_DOC = "exaone" +_CONFIG_FOR_DOC = "ExaoneConfig" + +EXAONE_PRETRAINED_MODEL_ARCHIVE_LIST = [ + "exaone", +] + + +@torch.jit.script +def repeat_kv(hidden_states: torch.Tensor, n_rep: int) -> torch.Tensor: + """ + This is the equivalent of torch.repeat_interleave(x, dim=1, repeats=n_rep). The hidden states go from (batch, + num_key_value_heads, seqlen, head_dim) to (batch, num_attention_heads, seqlen, head_dim) + """ + batch, num_key_value_heads, slen, head_dim = hidden_states.shape + if n_rep == 1: + return hidden_states + hidden_states = hidden_states[:, :, None, :, :].expand(batch, num_key_value_heads, n_rep, slen, head_dim) + return hidden_states.reshape(batch, num_key_value_heads * n_rep, slen, head_dim) + + +def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1): + """Applies Rotary Position Embedding to the query and key tensors. + + Args: + q (`torch.Tensor`): The query tensor. + k (`torch.Tensor`): The key tensor. + cos (`torch.Tensor`): The cosine part of the rotary embedding. + sin (`torch.Tensor`): The sine part of the rotary embedding. + unsqueeze_dim (`int`, *optional*, defaults to 1): + The 'unsqueeze_dim' argument specifies the dimension along which to unsqueeze cos[position_ids] and + sin[position_ids] so that they can be properly broadcasted to the dimensions of q and k. For example, note + that cos[position_ids] and sin[position_ids] have the shape [batch_size, seq_len, head_dim]. Then, if q and + k have the shape [batch_size, heads, seq_len, head_dim], then setting unsqueeze_dim=1 makes + cos[position_ids] and sin[position_ids] broadcastable to the shapes of q and k. Similarly, if q and k have + the shape [batch_size, seq_len, heads, head_dim], then set unsqueeze_dim=2. + Returns: + `tuple(torch.Tensor)` comprising of the query and key tensors rotated using the Rotary Position Embedding. + """ + cos = cos.unsqueeze(unsqueeze_dim) + sin = sin.unsqueeze(unsqueeze_dim) + q_embed = (q * cos) + (rotate_half(q) * sin) + k_embed = (k * cos) + (rotate_half(k) * sin) + return q_embed, k_embed + + +def rotate_half(x): + """Rotates half the hidden dims of the input.""" + x1 = x[..., : x.shape[-1] // 2] + x2 = x[..., x.shape[-1] // 2 :] + return torch.cat((-x2, x1), dim=-1) + + +def _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask: torch.Tensor, + sequence_length: int, + target_length: int, + dtype: torch.dtype, + device: torch.device, + min_dtype: float, + cache_position: torch.Tensor, + batch_size: int, +): + """ + Creates a causal 4D mask of shape `(batch_size, 1, query_length, key_value_length)` from a 2D mask of shape + `(batch_size, key_value_length)`, or if the input `attention_mask` is already 4D, do nothing. + + Args: + attention_mask (`torch.Tensor`): + A 2D attention mask of shape `(batch_size, key_value_length)` or a 4D attention mask of shape `(batch_size, 1, query_length, key_value_length)`. + sequence_length (`int`): + The sequence length being processed. + target_length (`int`): + The target length: when generating with static cache, the mask should be as long as the static cache, to account for the 0 padding, the part of the cache that is not filled yet. + dtype (`torch.dtype`): + The dtype to use for the 4D attention mask. + device (`torch.device`): + The device to plcae the 4D attention mask on. + min_dtype (`float`): + The minimum value representable with the dtype `dtype`. + cache_position (`torch.Tensor`): + Indices depicting the position of the input sequence tokens in the sequence. + batch_size (`torch.Tensor`): + Batch size. + """ + if attention_mask is not None and attention_mask.dim() == 4: + # In this case we assume that the mask comes already in inverted form and requires no inversion or slicing. + causal_mask = attention_mask + else: + causal_mask = torch.full((sequence_length, target_length), fill_value=min_dtype, dtype=dtype, device=device) + if sequence_length != 1: + causal_mask = torch.triu(causal_mask, diagonal=1) + causal_mask *= torch.arange(target_length, device=device) > cache_position.reshape(-1, 1) + causal_mask = causal_mask[None, None, :, :].expand(batch_size, 1, -1, -1) + if attention_mask is not None: + causal_mask = causal_mask.clone() # copy to contiguous memory for in-place edit + mask_length = attention_mask.shape[-1] + padding_mask = causal_mask[:, :, :, :mask_length] + attention_mask[:, None, None, :] + padding_mask = padding_mask == 0 + causal_mask[:, :, :, :mask_length] = causal_mask[:, :, :, :mask_length].masked_fill( + padding_mask, min_dtype + ) + + return causal_mask + + +class ExaoneRMSNorm(torch.nn.Module): + def __init__(self, hidden_size, eps=1e-6): + super().__init__() + self.eps = eps + self.weight = torch.nn.Parameter(torch.ones(hidden_size)) + + def forward(self, hidden_states): + input_dtype = hidden_states.dtype + hidden_states = hidden_states.to(torch.float32) + variance = hidden_states.pow(2).mean(-1, keepdim=True) + hidden_states = hidden_states * torch.rsqrt(variance + self.eps) + return self.weight * hidden_states.to(input_dtype) + + +class ExaoneTritonRMSNorm(torch.nn.Module): + def __init__( + self, + hidden_size: int = 0, + eps: float = 1e-5, + ): + super().__init__() + self.eps = eps + self.drop = None + self.weight = torch.nn.Parameter(torch.empty(hidden_size)) + self.register_parameter("bias", None) + self.reset_parameters() + + def reset_parameters(self): + torch.nn.init.ones_(self.weight) + + def forward(self, x, residual=None, prenorm=False, residual_in_fp32=False): + return rms_norm_fn( + x, + self.weight, + self.bias, + residual=residual, + eps=self.eps, + dropout_p=self.drop.p if self.drop is not None and self.training else 0.0, + prenorm=prenorm, + residual_in_fp32=residual_in_fp32, + ) + + +ALL_LAYERNORM_LAYERS.append(ExaoneRMSNorm) +ALL_LAYERNORM_LAYERS.append(ExaoneTritonRMSNorm) + + +class ExaoneRotaryEmbedding(nn.Module): + def __init__(self, config: ExaoneConfig, device=None): + super().__init__() + if config.rope_scaling is not None: + self.rope_type = config.rope_scaling.get("rope_type", config.rope_scaling.get("type")) + else: + self.rope_type = "default" + self.rope_theta = config.rope_theta + self.max_seq_len = config.max_position_embeddings + self.original_max_seq_len = config.max_position_embeddings + + self.config = config + if self.rope_type not in ROPE_INIT_FUNCTIONS: + raise KeyError(f"The EXAONE model does not support RoPE type: {self.rope_type}") + self.rope_init_fn = ROPE_INIT_FUNCTIONS[self.rope_type] + + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.original_inv_freq = self.inv_freq + + def _update_freq(self, position_ids, device): + """ + dynamic RoPE layers should recompute `inv_freq` in the following situations: + 1 - growing beyond the cached sequence length (allow scaling) + 2 - the current sequence length is in the original scale (avoid losing precision with small sequences) + """ + seq_len = torch.max(position_ids) + 1 + if seq_len > self.max_seq_len: # expand to seq_len + inv_freq, self.attention_scaling = self.rope_init_fn(self.config, device, seq_len=seq_len) + self.register_buffer("inv_freq", inv_freq, persistent=False) + self.max_seq_len = seq_len + + if seq_len < self.original_max_seq_len and self.max_seq_len > self.original_max_seq_len: # reset to original + self.register_buffer("inv_freq", self.original_inv_freq, persistent=False) + self.max_seq_len = self.original_max_seq_len + + @torch.no_grad() + def forward(self, x, position_ids): + if "dynamic" in self.rope_type: + self._update_freq(position_ids, device=x.device) + + inv_freq_expanded = self.inv_freq[None, :, None].float().expand(position_ids.shape[0], -1, 1) + position_ids_expanded = position_ids[:, None, :].float() + + device_type = x.device.type + device_type = device_type if isinstance(device_type, str) and device_type != "mps" else "cpu" + with torch.autocast(device_type=device_type, enabled=False): + freqs = (inv_freq_expanded @ position_ids_expanded).transpose(1, 2) + emb = torch.cat((freqs, freqs), dim=-1) + cos, sin = emb.cos(), emb.sin() + + cos, sin = cos * self.attention_scaling, sin * self.attention_scaling + return cos.to(x.dtype), sin.to(x.dtype) + + +class ExaoneSelfAttention(nn.Module): + def __init__(self, config: ExaoneConfig, layer_idx: Optional[int] = None): + super().__init__() + self.config = config + self.layer_idx = layer_idx + self.embed_dim = config.hidden_size + self.num_heads = config.num_attention_heads + self.head_dim = self.embed_dim // self.num_heads + self.num_key_value_heads = config.num_key_value_heads + self.num_key_value_groups = self.num_heads // self.num_key_value_heads + self.attention_dropout_rate = config.attention_dropout + + if self.head_dim * self.num_heads != self.embed_dim: + raise ValueError( + f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim} and `num_heads`: {self.num_heads})." + ) + + self.rotary = ExaoneRotaryEmbedding(config) + + self.k_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim, bias=False) + self.v_proj = nn.Linear(self.embed_dim, self.num_key_value_heads * self.head_dim, bias=False) + self.q_proj = nn.Linear(self.embed_dim, self.num_heads * self.head_dim, bias=False) + self.out_proj = nn.Linear(self.embed_dim, self.embed_dim, bias=False) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + bsz, q_len, _ = hidden_states.size() + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if position_embeddings is None: + cos, sin = self.rotary(value_states, position_ids=position_ids) + else: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + attn_weights = torch.matmul(query_states, key_states.transpose(2, 3)) / math.sqrt(self.head_dim) + + if attention_mask is not None: + causal_mask = attention_mask[:, :, :, : key_states.shape[-2]] + attn_weights = attn_weights + causal_mask + + attn_weights = nn.functional.softmax(attn_weights, dim=-1, dtype=torch.float32).to(query_states.dtype) + attn_weights = nn.functional.dropout(attn_weights, p=self.attention_dropout_rate, training=self.training) + attn_output = torch.matmul(attn_weights, value_states) + + if attn_output.size() != (bsz, self.num_heads, q_len, self.head_dim): + raise ValueError( + f"Attention outputs should be of size {(bsz, self.num_heads, q_len, self.head_dim)}, but is" + f" {attn_output.size()}" + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous() + + attn_output = self.out_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class ExaoneFlashAttention(ExaoneSelfAttention): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def _shape(self, tensor: torch.Tensor, seq_len: int, bsz: int): + return tensor.view(bsz, seq_len, self.num_heads, self.head_dim).transpose(1, 2).contiguous() + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if isinstance(past_key_value, StaticCache): + raise ValueError( + "`static` cache implementation is not compatible with `attn_implementation==flash_attention_2` " + "make sure to use `sdpa` in the mean time, and open an issue at https://github.com/huggingface/transformers" + ) + + output_attentions = False + + bsz, q_len, h_size = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if position_embeddings is None: + cos, sin = self.rotary(value_states, position_ids=position_ids) + else: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + # Only update cache as shape of [bsz, n_head, q_len, head_dim] + # TODO: need to be fixed when transformers' KV cache layout is changed + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + query_states = query_states.transpose(1, 2) + key_states = key_states.transpose(1, 2) + value_states = value_states.transpose(1, 2) + + # In PEFT, usually we cast the layer norms in float32 for training stability reasons + # therefore the input hidden states gets silently casted in float32. Hence, we need + # cast them back in the correct dtype just to be sure everything works as expected. + input_dtype = query_states.dtype + if input_dtype == torch.float32: + if torch.is_autocast_enabled(): + target_dtype = torch.get_autocast_gpu_dtype() + # Handle the case where the model is quantized + elif hasattr(self.config, "_pre_quantization_dtype"): + target_dtype = self.config._pre_quantization_dtype + else: + target_dtype = self.q_proj.weight.dtype + + logger.warning_once( + f"The input hidden states seems to be silently casted in float32, this might be related to" + f" the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in" + f" {target_dtype}." + ) + + query_states = query_states.to(target_dtype) + key_states = key_states.to(target_dtype) + value_states = value_states.to(target_dtype) + + dropout_rate = self.attention_dropout_rate if self.training else 0.0 + + attn_output = _flash_attention_forward( + query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate, is_causal=True + ) + + attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous() + attn_output = self.out_proj(attn_output) + + if not output_attentions: + attn_weights = None + + return attn_output, attn_weights, past_key_value + + +class ExaoneSdpaAttention(ExaoneSelfAttention): + def __init__(self, *args, **kwargs): + super().__init__(*args, **kwargs) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + if output_attentions: + logger.warning_once( + "ExaoneModel is using ExaoneSdpaAttention, but `torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to the manual attention implementation, " + 'but specifying the manual implementation will be required from Transformers version v5.0.0 onwards. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.' + ) + return super().forward( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + + bsz, q_len, _ = hidden_states.size() + + query_states = self.q_proj(hidden_states) + key_states = self.k_proj(hidden_states) + value_states = self.v_proj(hidden_states) + + query_states = query_states.view(bsz, q_len, self.num_heads, self.head_dim).transpose(1, 2) + key_states = key_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + value_states = value_states.view(bsz, q_len, self.num_key_value_heads, self.head_dim).transpose(1, 2) + + if position_embeddings is None: + cos, sin = self.rotary(value_states, position_ids=position_ids) + else: + cos, sin = position_embeddings + query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin) + + if past_key_value is not None: + # sin and cos are specific to RoPE models; cache_position needed for the static cache + cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position} + key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs) + + key_states = repeat_kv(key_states, self.num_key_value_groups) + value_states = repeat_kv(value_states, self.num_key_value_groups) + + causal_mask = attention_mask + if attention_mask is not None: + causal_mask = causal_mask[:, :, :, : key_states.shape[-2]] + + # SDPA with memory-efficient backend is currently (torch==2.1.2) bugged with non-contiguous inputs with custom attn_mask, + # Reference: https://github.com/pytorch/pytorch/issues/112577. + if query_states.device.type == "cuda" and causal_mask is not None: + query_states = query_states.contiguous() + key_states = key_states.contiguous() + value_states = value_states.contiguous() + + # We dispatch to SDPA's Flash Attention or Efficient kernels via this `is_causal` if statement instead of an inline conditional assignment + # in SDPA to support both torch.compile's dynamic shapes and full graph options. An inline conditional prevents dynamic shapes from compiling. + is_causal = True if causal_mask is None and q_len > 1 else False + + attn_output = torch.nn.functional.scaled_dot_product_attention( + query_states, + key_states, + value_states, + attn_mask=causal_mask, + dropout_p=self.attention_dropout_rate if self.training else 0.0, + is_causal=is_causal, + ) + + attn_output = attn_output.transpose(1, 2).contiguous() + attn_output = attn_output.reshape(bsz, q_len, self.embed_dim).contiguous() + + attn_output = self.out_proj(attn_output) + + return attn_output, None, past_key_value + + +class ExaoneAttention(nn.Module): + def __init__(self, config, layer_id=0): + super().__init__() + self.layer_id = layer_id + if "flash" in config._attn_implementation: + self.attention = ExaoneFlashAttention(config, self.layer_id) + elif "sdpa" in config._attn_implementation: + self.attention = ExaoneSdpaAttention(config, self.layer_id) + else: + self.attention = ExaoneSelfAttention(config, self.layer_id) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + **kwargs, + ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]: + return self.attention( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + + +class ExaoneGatedMLP(nn.Module): + def __init__(self, intermediate_size, config): + super().__init__() + self.config = config + embed_dim = config.hidden_size + self.c_fc_0 = nn.Linear(embed_dim, intermediate_size, bias=False) + self.c_fc_1 = nn.Linear(embed_dim, intermediate_size, bias=False) + self.c_proj = nn.Linear(intermediate_size, embed_dim, bias=False) + self.act = ACT2FN[config.activation_function] + + def forward(self, hidden_states): + output_proj = self.c_proj(self.act(self.c_fc_0(hidden_states)) * self.c_fc_1(hidden_states)) + return output_proj + + +class ExaoneBlock(nn.Module): + def __init__(self, config, layer_id): + super().__init__() + self.config = config + hidden_size = config.hidden_size + inner_dim = config.intermediate_size if config.intermediate_size is not None else 4 * hidden_size + self.ln_1 = ExaoneRMSNorm(hidden_size=hidden_size, eps=config.layer_norm_epsilon) + self.attn = ExaoneAttention(config, layer_id) + self.ln_2 = ExaoneRMSNorm(hidden_size=hidden_size, eps=config.layer_norm_epsilon) + self.mlp = ExaoneGatedMLP(inner_dim, config) + + def forward( + self, + hidden_states: torch.Tensor, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_value: Optional[Cache] = None, + output_attentions: Optional[bool] = False, + use_cache: Optional[bool] = False, + cache_position: Optional[torch.LongTensor] = None, + position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, + **kwargs, + ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]: + residual = hidden_states + hidden_states = self.ln_1(hidden_states) + + hidden_states, self_attn_weights, present_key_value = self.attn( + hidden_states=hidden_states, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_value=past_key_value, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + **kwargs, + ) + # residual connection + hidden_states = residual + hidden_states + + residual = hidden_states + hidden_states = self.ln_2(hidden_states) + hidden_states = self.mlp(hidden_states) + + hidden_states = residual + hidden_states + + outputs = (hidden_states,) + + if output_attentions: + outputs += (self_attn_weights,) + + if use_cache: + outputs += (present_key_value,) + + return outputs + + +class ExaonePreTrainedModel(PreTrainedModel): + """ + An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained + models. + """ + + config_class = ExaoneConfig + base_model_prefix = "transformer" + supports_gradient_checkpointing = True + _no_split_modules = ["ExaoneBlock"] + _skip_keys_device_placement = "past_key_values" + _supports_flash_attn_2 = True + _supports_sdpa = True + _supports_cache_class = True + + def __init__(self, *inputs, **kwargs): + super().__init__(*inputs, **kwargs) + + def _init_weights(self, module): + """Initialize the weights.""" + if isinstance(module, (nn.Linear,)): + # Slightly different from the TF version which uses truncated_normal for initialization + # cf https://github.com/pytorch/pytorch/pull/5617 + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.bias is not None: + module.bias.data.zero_() + elif isinstance(module, nn.Embedding): + module.weight.data.normal_(mean=0.0, std=self.config.initializer_range) + if module.padding_idx is not None: + module.weight.data[module.padding_idx].zero_() + elif isinstance(module, ExaoneRMSNorm): + module.weight.data.fill_(1.0) + + +EXAONE_START_DOCSTRING = r""" + + This model inherits from [`PreTrainedModel`]. Check the superclass documentation for the generic methods the + library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads + etc.) + + This model is also a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. + Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage + and behavior. + + Parameters: + config ([`ExaoneConfig`]): Model configuration class with all the parameters of the model. + Initializing with a config file does not load the weights associated with the model, only the + configuration. Check out the `PreTrainedModel.from_pretrained` method to load the model weights. +""" + +EXAONE_INPUTS_DOCSTRING = r""" + Args: + input_ids (`torch.LongTensor` of shape `(batch_size, input_ids_length)`, *optional*): + `input_ids_length` = `sequence_length` if `past_key_values` is `None` else + `past_key_values.get_seq_length()` (`sequence_length` of input past key value states). Indices of input + sequence tokens in the vocabulary. + + If `past_key_values` is used, only `input_ids` that do not have their past calculated should be + passed as `input_ids`. + + `What are input IDs? <../glossary.html#input-ids>`__ + attention_mask (`torch.FloatTensor` of shape `(batch_size, sequence_length)`, *optional*): + Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: + + - 1 for tokens that are **not masked**, + - 0 for tokens that are **masked**. + + `What are attention masks? <../glossary.html#attention-mask>`__ + position_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Indices of positions of each input sequence tokens in the position embeddings. Selected in the range `[0, + config.max_position_embeddings - 1]`. + + `What are position IDs? <../glossary.html#position-ids>`_ + past_key_values (`Cache`, *optional*): + Contains precomputed hidden-states (key and values in the attention blocks) as computed by the model (see + `past_key_values` output below). Can be used to speed up sequential decoding. This typically consists + in the `past_key_values` returned by the model at a previous stage of decoding, when `use_cache=True` or + `config.use_cache=True`. + inputs_embeds (`torch.FloatTensor` of shape `(batch_size, sequence_length, hidden_size)`, *optional*): + Optionally, instead of passing `input_ids` you can choose to directly pass an embedded representation. + This is useful if you want more control over how to convert `input_ids` indices into associated + vectors than the model's internal embedding lookup matrix. + + If `past_key_values` is used, optionally only the last `inputs_embeds` have to be input (see + `past_key_values`). + use_cache (`bool`, *optional*): + If set to `True`, `past_key_values` key value states are returned and can be used to speed up + decoding (see `past_key_values`). + output_attentions (`bool`, *optional*): + Whether or not to return the attentions tensors of all attention layers. See ``attentions`` under returned + tensors for more detail. + output_hidden_states (`bool`, *optional*): + Whether or not to return the hidden states of all layers. See ``hidden_states`` under returned tensors for + more detail. + return_dict (`bool`, *optional*): + Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. + cache_position (`torch.LongTensor` of shape `(sequence_length)`, *optional*): + Indices depicting the position of the input sequence tokens in the sequence. Contrarily to `position_ids`, + this tensor is not affected by padding. It is used to update the cache in the correct position and to infer + the complete sequence length. +""" + + +@add_start_docstrings( + "The bare EXAONE Model transformer outputting raw hidden-states without any specific head on top.", + EXAONE_START_DOCSTRING, +) +class ExaoneModel(ExaonePreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.config = config + self.embed_dim = config.hidden_size + self.wte = nn.Embedding(config.vocab_size, self.embed_dim, self.config.pad_token_id) + self.drop = nn.Dropout(float(config.embed_dropout)) + self.h = nn.ModuleList([ExaoneBlock(config, layer_id=i) for i in range(config.num_layers)]) + self.ln_f = ExaoneRMSNorm(hidden_size=self.embed_dim, eps=config.layer_norm_epsilon) + self.rotary = ExaoneRotaryEmbedding(config) + self.gradient_checkpointing = False + # Initialize weights and apply final processing + self.post_init() + + def get_input_embeddings(self): + return self.wte + + def set_input_embeddings(self, new_embeddings): + self.wte = new_embeddings + + @add_start_docstrings_to_model_forward(EXAONE_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPastAndCrossAttentions, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPast]: + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + use_cache = use_cache if use_cache is not None else self.config.use_cache + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + if self.gradient_checkpointing and self.training: + if use_cache: + logger.warning_once( + "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`..." + ) + use_cache = False + + if input_ids is not None and inputs_embeds is not None: + raise ValueError("You cannot specify both input_ids and inputs_embeds at the same time") + elif input_ids is not None: + batch_size, seq_length = input_ids.shape[:2] + elif inputs_embeds is not None: + batch_size, seq_length = inputs_embeds.shape[:2] + else: + raise ValueError("You have to specify either input_ids or inputs_embeds") + + return_legacy_cache = False + if ( + use_cache and not isinstance(past_key_values, Cache) and not self.training + ): # kept for BC (non `Cache` `past_key_values` inputs) + return_legacy_cache = True + past_key_values = DynamicCache.from_legacy_cache(past_key_values) + logger.warning_once( + "We detected that you are passing `past_key_values` as a tuple and this is deprecated and will be removed in v4.43. " + "Please use an appropriate `Cache` class (https://huggingface.co/docs/transformers/v4.41.3/en/internal/generation_utils#transformers.Cache)" + ) + + if inputs_embeds is None: + inputs_embeds = self.wte(input_ids) + + if cache_position is None: + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + cache_position = torch.arange( + past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device + ) + if position_ids is None: + position_ids = cache_position.unsqueeze(0) + + causal_mask = self._update_causal_mask( + attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions + ) + + hidden_states = inputs_embeds + hidden_states = self.drop(hidden_states) + + position_embeddings = self.rotary(hidden_states, position_ids) + + all_hidden_states = () if output_hidden_states else None + all_self_attns = () if output_attentions else None + next_decoder_cache = None + + for block in self.h: + if output_hidden_states: + all_hidden_states = all_hidden_states + (hidden_states,) + + if self.gradient_checkpointing and self.training: + outputs = self._gradient_checkpointing_func( + block.__call__, + hidden_states, + causal_mask, + position_ids, + past_key_values, + output_attentions, + use_cache, + cache_position, + position_embeddings, + ) + else: + outputs = block( + hidden_states, + attention_mask=causal_mask, + position_ids=position_ids, + past_key_value=past_key_values, + output_attentions=output_attentions, + use_cache=use_cache, + cache_position=cache_position, + position_embeddings=position_embeddings, + ) + + hidden_states = outputs[0] + if use_cache: + next_decoder_cache = outputs[2 if output_attentions else 1] + + if output_attentions: + all_self_attns += (outputs[1],) + + hidden_states = self.ln_f(hidden_states) + # Add last hidden state + if output_hidden_states: + all_hidden_states += (hidden_states,) + + next_cache = None + if use_cache: + next_cache = next_decoder_cache.to_legacy_cache() if return_legacy_cache else next_decoder_cache + if not return_dict: + return tuple(v for v in [hidden_states, next_cache, all_hidden_states, all_self_attns] if v is not None) + + return BaseModelOutputWithPast( + last_hidden_state=hidden_states, + past_key_values=next_cache, + hidden_states=all_hidden_states, + attentions=all_self_attns, + ) + + def _update_causal_mask( + self, + attention_mask: torch.Tensor, + input_tensor: torch.Tensor, + cache_position: torch.Tensor, + past_key_values: Cache, + output_attentions: bool, + ): + # TODO: As of torch==2.2.0, the `attention_mask` passed to the model in `generate` is 2D and of dynamic length even when the static + # KV cache is used. This is an issue for torch.compile which then recaptures cudagraphs at each decode steps due to the dynamic shapes. + # (`recording cudagraph tree for symint key 13`, etc.), which is VERY slow. A workaround is `@torch.compiler.disable`, but this prevents using + # `fullgraph=True`. See more context in https://github.com/huggingface/transformers/pull/29114 + + if self.config._attn_implementation == "flash_attention_2": + if attention_mask is not None and 0.0 in attention_mask: + return attention_mask + return None + + # For SDPA, when possible, we will rely on its `is_causal` argument instead of its `attn_mask` argument, in + # order to dispatch on Flash Attention 2. This feature is not compatible with static cache, as SDPA will fail + # to infer the attention mask. + past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0 + using_static_cache = isinstance(past_key_values, StaticCache) + + # When output attentions is True, sdpa implementation's forward method calls the eager implementation's forward + if self.config._attn_implementation == "sdpa" and not using_static_cache and not output_attentions: + if AttentionMaskConverter._ignore_causal_mask_sdpa( + attention_mask, + inputs_embeds=input_tensor, + past_key_values_length=past_seen_tokens, + is_training=self.training, + ): + return None + + dtype, device = input_tensor.dtype, input_tensor.device + min_dtype = torch.finfo(dtype).min + sequence_length = input_tensor.shape[1] + if using_static_cache: + target_length = past_key_values.get_max_length() + else: + target_length = ( + attention_mask.shape[-1] + if isinstance(attention_mask, torch.Tensor) + else past_seen_tokens + sequence_length + 1 + ) + + # In case the provided `attention` mask is 2D, we generate a causal mask here (4D). + causal_mask = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=target_length, + dtype=dtype, + device=device, + min_dtype=min_dtype, + cache_position=cache_position, + batch_size=input_tensor.shape[0], + ) + + if ( + self.config._attn_implementation == "sdpa" + and attention_mask is not None + and attention_mask.device.type == "cuda" + and not output_attentions + ): + # Attend to all tokens in fully masked rows in the causal_mask, for example the relevant first rows when + # using left padding. This is required by F.scaled_dot_product_attention memory-efficient attention path. + # Details: https://github.com/pytorch/pytorch/issues/110213 + causal_mask = AttentionMaskConverter._unmask_unattended(causal_mask, min_dtype) + + return causal_mask + + +@add_start_docstrings( + """ + The EXAONE Model transformer with a language modeling head on top (linear layer with weights tied to the input + embeddings). + """, + EXAONE_START_DOCSTRING, +) +class ExaoneForCausalLM(ExaonePreTrainedModel, GenerationMixin): + _tied_weights_keys = ["lm_head.weight"] + + def __init__(self, config): + super().__init__(config) + self.transformer = ExaoneModel(config) + self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False) + self.config = config + # Initialize weights and apply final processing + self.post_init() + + def get_output_embeddings(self): + return self.lm_head + + def set_output_embeddings(self, new_embeddings): + self.lm_head = new_embeddings + + @add_start_docstrings_to_model_forward(EXAONE_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=BaseModelOutputWithPast, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + cache_position: Optional[torch.LongTensor] = None, + ) -> Union[Tuple[torch.Tensor], BaseModelOutputWithPast]: + r""" + Args: + labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*): + Labels for language modeling. Note that the labels **are shifted** inside the model, i.e. you can set + `labels = input_ids` Indices are selected in `[-100, 0, ..., config.vocab_size]` All labels set to `-100` + are ignored (masked), the loss is only computed for labels in `[0, ..., config.vocab_size]` + + Example: + + ```python + >>> from transformers import AutoModelForCausalLM, AutoTokenizer + + >>> model = AutoModelForCausalLM.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct", + trust_remote_code=True) + >>> tokenizer = AutoTokenizer.from_pretrained("LGAI-EXAONE/EXAONE-3.0-7.8B-Instruct") + + >>> prompt = "Explain how wonderful you are" + >>> messages = [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": prompt} + ] + >>> input_ids = tokenizer.apply_chat_template( + messages, + tokenize=True, + add_generation_prompt=True, + return_tensors="pt" + ) + + >>> output = model.generate(input_ids, max_new_tokens=128) + >>> tokenizer.decode(output[0], skip_special_tokens=True) + "[|system|]You are a helpful assistant.\n[|user|]Explain how wonderful you are\n[|assistant|]Thank you for your kind words! I'm here to assist you with information, answer questions, and help you in any way I can. My goal is to provide accurate, helpful, and timely responses. Whether you need help with a specific task, want to learn something new, or just need someone to talk to, I'm here for you. How can I assist you today?" + ``` + """ + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + transformer_outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + past_key_values=past_key_values, + position_ids=position_ids, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + cache_position=cache_position, + ) + hidden_states = transformer_outputs[0] + lm_logits = self.lm_head(hidden_states) + lm_logits = lm_logits.float() + loss = None + if labels is not None: + lm_logits = lm_logits.to(torch.float32) + + # Shift so that tokens < n predict n + shift_logits = lm_logits[..., :-1, :].contiguous() + shift_labels = labels[..., 1:].contiguous() + # Flatten the tokens + loss_fct = CrossEntropyLoss() + loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1)) + + lm_logits = lm_logits.to(hidden_states.dtype) + loss = loss.to(hidden_states.dtype) + + if not return_dict: + output = (lm_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return CausalLMOutputWithPast( + loss=loss, + logits=lm_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + def prepare_inputs_for_generation( + self, + input_ids, + past_key_values=None, + attention_mask=None, + inputs_embeds=None, + cache_position=None, + position_ids=None, + use_cache=True, + **kwargs, + ): + # If we have cache: let's slice `input_ids` through `cache_position`, to keep only the unprocessed tokens + # Exception 1: when passing input_embeds, input_ids may be missing entries + # Exception 2: some generation methods do special slicing of input_ids, so we don't need to do it here + if past_key_values is not None: + if inputs_embeds is not None: # Exception 1 + input_ids = input_ids[:, -cache_position.shape[0] :] + elif input_ids.shape[1] != cache_position.shape[0]: # Default case (the "else", a no op, is Exception 2) + input_ids = input_ids[:, cache_position] + + if attention_mask is not None and position_ids is None: + # create position_ids on the fly for batch generation + position_ids = attention_mask.long().cumsum(-1) - 1 + position_ids.masked_fill_(attention_mask == 0, 1) + if past_key_values: + position_ids = position_ids[:, -input_ids.shape[1] :] + + # This `clone` call is needed to avoid recapturing cuda graphs with `torch.compile`'s `mode="reduce-overhead`, as otherwise the input `position_ids` would have various stride during the decoding. Here, simply using `.contiguous()` is not sufficient as in the batch size = 1 case, `position_ids` is already contiguous but with varying stride which retriggers a capture. + position_ids = position_ids.clone(memory_format=torch.contiguous_format) + + # if `inputs_embeds` are passed, we only want to use them in the 1st generation step + if inputs_embeds is not None and cache_position[0] == 0: + model_inputs = {"inputs_embeds": inputs_embeds, "input_ids": None} + else: + model_inputs = {"input_ids": input_ids, "inputs_embeds": None} + + if isinstance(past_key_values, StaticCache) and attention_mask.ndim == 2: + if inputs_embeds is not None: + batch_size, sequence_length, _ = inputs_embeds.shape + device = inputs_embeds.device + else: + batch_size, sequence_length = input_ids.shape + device = input_ids.device + + dtype = self.lm_head.weight.dtype + min_dtype = torch.finfo(dtype).min + + attention_mask = _prepare_4d_causal_attention_mask_with_cache_position( + attention_mask, + sequence_length=sequence_length, + target_length=past_key_values.get_max_length(), + dtype=dtype, + device=device, + min_dtype=min_dtype, + cache_position=cache_position, + batch_size=batch_size, + ) + + model_inputs.update( + { + "position_ids": position_ids, + "cache_position": cache_position, + "past_key_values": past_key_values, + "use_cache": use_cache, + "attention_mask": attention_mask, + } + ) + return model_inputs + + +@add_start_docstrings( + """ + The EXAONE Model transformer with a sequence classification head on top (linear layer). + + [`ExaoneForSequenceClassification`] uses the last token in order to do the classification, as + other causal models (e.g. GPT-1) do. + + Since it does classification on the last token, it requires to know the position of the last token. If a + `pad_token_id` is defined in the configuration, it finds the last token that is not a padding token in each + row. If no `pad_token_id` is defined, it simply takes the last value in each row of the batch. Since it cannot + guess the padding tokens when `inputs_embeds` are passed instead of `input_ids`, it does the same (take + the last value in each row of the batch). + """, + EXAONE_START_DOCSTRING, +) +class ExaoneForSequenceClassification(ExaonePreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.transformer = ExaoneModel(config) + self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False) + + # Initialize weights and apply final processing + self.post_init() + + @add_start_docstrings_to_model_forward(EXAONE_INPUTS_DOCSTRING) + @add_code_sample_docstrings( + checkpoint=_CHECKPOINT_FOR_DOC, + output_type=SequenceClassifierOutputWithPast, + config_class=_CONFIG_FOR_DOC, + ) + def forward( + self, + input_ids: Optional[torch.Tensor] = None, + attention_mask: Optional[torch.Tensor] = None, + position_ids: Optional[torch.Tensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.Tensor] = None, + labels: Optional[torch.Tensor] = None, + use_cache: Optional[bool] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], SequenceClassifierOutputWithPast]: + r""" + labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for computing the sequence classification/regression loss. Indices should be in `[0, ..., + config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If + `config.num_labels > 1` a classification loss is computed (Cross-Entropy). + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + transformer_outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + use_cache=use_cache, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = transformer_outputs[0] + logits = self.score(hidden_states) + + if input_ids is not None: + batch_size, sequence_length = input_ids.shape[:2] + else: + batch_size, sequence_length = inputs_embeds.shape[:2] + + if self.config.pad_token_id is None and batch_size != 1: + raise ValueError("Cannot handle batch sizes > 1 if no padding token is defined.") + if self.config.pad_token_id is None: + sequence_lengths = -1 + else: + if input_ids is not None: + # if no pad token found, use modulo instead of reverse indexing for ONNX compatibility + sequence_lengths = torch.ne(input_ids, self.config.pad_token_id).sum(-1) - 1 + sequence_lengths = sequence_lengths % input_ids.shape[-1] + sequence_lengths = sequence_lengths.to(logits.device) + else: + sequence_lengths = -1 + logger.warning( + f"{self.__class__.__name__} will not detect padding tokens in `inputs_embeds`. Results may be " + "unexpected if using padding tokens in conjunction with `inputs_embeds.`" + ) + + pooled_logits = logits[torch.arange(batch_size, device=logits.device), sequence_lengths] + + loss = None + if labels is not None: + labels = labels.to(logits.device) + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + if self.num_labels == 1: + loss = loss_fct(pooled_logits.squeeze(), labels.squeeze()) + else: + loss = loss_fct(pooled_logits, labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(pooled_logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(pooled_logits, labels) + if not return_dict: + output = (pooled_logits,) + transformer_outputs[1:] + return ((loss,) + output) if loss is not None else output + + return SequenceClassifierOutputWithPast( + loss=loss, + logits=pooled_logits, + past_key_values=transformer_outputs.past_key_values, + hidden_states=transformer_outputs.hidden_states, + attentions=transformer_outputs.attentions, + ) + + +@add_start_docstrings( + """ + The EXAONE Model transformer with a span classification head on top for extractive question-answering tasks like + SQuAD (a linear layers on top of the hidden-states output to compute `span start logits` and `span end logits`). + """, + EXAONE_START_DOCSTRING, +) +class ExaoneForQuestionAnswering(ExaonePreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.transformer = ExaoneModel(config) + self.qa_outputs = nn.Linear(config.hidden_size, config.num_labels) + + # Model parallel + self.model_parallel = False + self.device_map = None + + # Initialize weights and apply final processing + self.post_init() + + def forward( + self, + input_ids: Optional[torch.LongTensor] = None, + attention_mask: Optional[torch.FloatTensor] = None, + position_ids: Optional[torch.LongTensor] = None, + past_key_values: Optional[Cache] = None, + inputs_embeds: Optional[torch.FloatTensor] = None, + start_positions: Optional[torch.LongTensor] = None, + end_positions: Optional[torch.LongTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + ) -> Union[Tuple[torch.Tensor], QuestionAnsweringModelOutput]: + r""" + start_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the start of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the + sequence are not taken into account for computing the loss. + end_positions (`torch.LongTensor` of shape `(batch_size,)`, *optional*): + Labels for position (index) of the end of the labelled span for computing the token classification loss. + Positions are clamped to the length of the sequence (`sequence_length`). Position outside of the + sequence are not taken into account for computing the loss. + """ + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + outputs = self.transformer( + input_ids, + attention_mask=attention_mask, + position_ids=position_ids, + past_key_values=past_key_values, + inputs_embeds=inputs_embeds, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + sequence_output = outputs[0] + + logits = self.qa_outputs(sequence_output) + start_logits, end_logits = logits.split(1, dim=-1) + start_logits = start_logits.squeeze(-1).contiguous() + end_logits = end_logits.squeeze(-1).contiguous() + + total_loss = None + if start_positions is not None and end_positions is not None: + # If we are on multi-GPU, split add a dimension + if len(start_positions.size()) > 1: + start_positions = start_positions.squeeze(-1).to(start_logits.device) + if len(end_positions.size()) > 1: + end_positions = end_positions.squeeze(-1).to(end_logits.device) + # sometimes the start/end positions are outside our model inputs, we ignore these terms + ignored_index = start_logits.size(1) + start_positions = start_positions.clamp(0, ignored_index) + end_positions = end_positions.clamp(0, ignored_index) + + loss_fct = CrossEntropyLoss(ignore_index=ignored_index) + start_loss = loss_fct(start_logits, start_positions) + end_loss = loss_fct(end_logits, end_positions) + total_loss = (start_loss + end_loss) / 2 + + if not return_dict: + output = (start_logits, end_logits) + outputs[2:] + return ((total_loss,) + output) if total_loss is not None else output + + return QuestionAnsweringModelOutput( + loss=total_loss, + start_logits=start_logits, + end_logits=end_logits, + hidden_states=outputs.hidden_states, + attentions=outputs.attentions, + ) diff --git a/special_tokens_map.json b/special_tokens_map.json new file mode 100644 index 0000000..b652595 --- /dev/null +++ b/special_tokens_map.json @@ -0,0 +1,30 @@ +{ + "bos_token": { + "content": "[BOS]", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "eos_token": { + "content": "[|endofturn|]", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "pad_token": { + "content": "[PAD]", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + }, + "unk_token": { + "content": "[UNK]", + "lstrip": false, + "normalized": false, + "rstrip": false, + "single_word": false + } +} diff --git a/tokenizer.json b/tokenizer.json new file mode 100644 index 0000000..25afac7 --- /dev/null +++ b/tokenizer.json @@ -0,0 +1,207491 @@ +{ + "version": "1.0", + "truncation": null, + "padding": null, + "added_tokens": [ + { + "id": 0, + "content": "[PAD]", + "single_word": false, + "lstrip": false, + "rstrip": false, + "normalized": false, + "special": true + }, + { + "id": 1, + "content": "[BOS]", + "single_word": 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"ĠìķĦíĶĦ": 6846, + "================": 6847, + "ĠMS": 6848, + "ĠIT": 6849, + "Ġëĵ±ë¡Ŀ": 6850, + "Ġëĭ¤íĸī": 6851, + "Ġtas": 6852, + "ille": 6853, + "Ġê´Ģê´ij": 6854, + "Ġìľ¼ëĤĺ": 6855, + "Ġdro": 6856, + "ìłĦíŀĪ": 6857, + "Ġappropriate": 6858, + "Ġëĵľëł¸": 6859, + "oma": 6860, + "Ġæĺ": 6861, + "ĠíĿij": 6862, + "co": 6863, + "Ġëĭ¤ìĿ´ìĸ´íĬ¸": 6864, + "MS": 6865, + "gn": 6866, + "Ġwave": 6867, + "ĠëķIJ": 6868, + "Ġpsych": 6869, + "Ġparent": 6870, + "ĠOf": 6871, + "Ġprocessing": 6872, + "ĠëıĻìĭľ": 6873, + "Ġcoord": 6874, + "Ġdescription": 6875, + "íĺĪ": 6876, + "Ġíģ¬ê¸°": 6877, + "ìĬ¤íĦ°": 6878, + "Ġeveryone": 6879, + "Ġcool": 6880, + "Ġ무ë£Į": 6881, + "Ġhighest": 6882, + "ĠëĶ°ëĿ¼ìĦľ": 6883, + "Ġplease": 6884, + "Ġ꾸ì¤Ģ": 6885, + "ëĶ±": 6886, + "ĠìĿ´ìģĺ": 6887, + "ĠíĺĦìĭ¤": 6888, + "rench": 6889, + "ĠíĮ©": 6890, + "íħIJ": 6891, + "Ġë²Ħ본": 6892, + "ĠìĿ¸ìĥģ": 6893, + "ott": 6894, + "uge": 6895, + "ani": 6896, + "king": 6897, + "istrict": 6898, + "íĿ¥": 6899, + "ĠÃij": 6900, + "ãĥ¼ãĥ": 6901, + 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"Ġprincip": 7127, + "Ġproceed": 7128, + "tau": 7129, + "ept": 7130, + "Ġsche": 7131, + "undred": 7132, + "ĠPresident": 7133, + "Ġmodern": 7134, + "Ġê°ģê°ģ": 7135, + "sel": 7136, + "ĠíķĦìĪĺ": 7137, + "Ġ\"\"": 7138, + "Ġ\"}).": 7139, + "Ġë¶Ģ르": 7140, + "Ġíݼ": 7141, + "Ġsolutions": 7142, + "Ġinternational": 7143, + "Ġles": 7144, + "ëĭµëĭĪëĭ¤": 7145, + "Ġcandid": 7146, + "ëłĢ": 7147, + "ĠåŃ": 7148, + "===": 7149, + "Ġsafe": 7150, + "ĠêµIJì²´": 7151, + "zz": 7152, + "Ġdog": 7153, + "ĠìĪĺìĿµ": 7154, + "Ġì²´íģ¬": 7155, + "Ġproduce": 7156, + "Ġìŀ¥ê´Ģ": 7157, + "Ġë²łìĿ´": 7158, + "Ġexists": 7159, + "Ġrou": 7160, + "oti": 7161, + "rol": 7162, + "ì¨": 7163, + "ĠìłĪëĮĢ": 7164, + "Ġsup": 7165, + "Ġê±´ëį°": 7166, + "Ġìľłë¦¬": 7167, + "Ġìĺ¥": 7168, + "Ġìĥ·": 7169, + "Ġreasons": 7170, + "UR": 7171, + "ìŀł": 7172, + "Ġdb": 7173, + "Ġready": 7174, + "íĩ´": 7175, + "Ġsupported": 7176, + "itter": 7177, + "Ġìļ": 7178, + "ìļ´ëĵľ": 7179, + "Ġtransport": 7180, + "unk": 7181, + "ham": 7182, + "enty": 7183, + "Ġ짱": 7184, + "ëĶĶìĺ¤": 7185, + "ĠìĬ¤íĬ¸ëłĪìĬ¤": 7186, + "Ġfight": 7187, + "arr": 7188, + "ĠâĻ¥": 7189, + "Ġ공기": 7190, + "ĠMus": 7191, + "Ġtau": 7192, + "Ġvirt": 7193, + "ĠSection": 7194, + "ibly": 7195, + "Ġbey": 7196, + "ware": 7197, + "ĠCir": 7198, + ">>": 7199, + "Pl": 7200, + "Ġconcept": 7201, + "Ġliqu": 7202, + "Ġìł¤": 7203, + "ĠìĺĪìłĦ": 7204, + "Ġëĵ¤ìĸ´ìĺ¤": 7205, + "Cont": 7206, + "Ġnamed": 7207, + "Ġcarry": 7208, + "Ġsometimes": 7209, + "ê°ĢëĿ½": 7210, + "Ġactions": 7211, + "Ġì¹ĺíĤ¨": 7212, + "Ġsrc": 7213, + "Ġ**(": 7214, + "ĠìĦłìłķ": 7215, + "ìľ¤": 7216, + "Ġbeyond": 7217, + "tian": 7218, + "Ġspectr": 7219, + "Ġtor": 7220, + "ĠDon": 7221, + "Ġquickly": 7222, + "Ġgenes": 7223, + "ĠTex": 7224, + "ĠìłIJìłIJ": 7225, + "ĠDistrict": 7226, + "Ġimg": 7227, + "Ġ겪": 7228, + "OP": 7229, + "Ġvariety": 7230, + "ayer": 7231, + "Ġconstruction": 7232, + "Ġ맥주": 7233, + "elle": 7234, + "Ġvisual": 7235, + "bre": 7236, + "ëĭ¤ìĭľ": 7237, + "ĠUp": 7238, + "Ġuns": 7239, + "Ġåį": 7240, + "Ġêµ³": 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"Ġíݸì§Ģ": 12405, + "Ġshr": 12406, + "Ġapplicable": 12407, + "Ġresidents": 12408, + "Ġæľī": 12409, + "ĠíĿIJ르": 12410, + "Ġedges": 12411, + "ĠÏĦ": 12412, + "grade": 12413, + "ĠIslam": 12414, + "ĠObama": 12415, + "ĠìłķìĦ±": 12416, + "ocytes": 12417, + "ĠìĿĺíĺ¹": 12418, + "ĠVe": 12419, + "Ġ묶": 12420, + "Ġëıķ": 12421, + "Ġmedian": 12422, + "Ġsounds": 12423, + "ername": 12424, + "Ġë©ĶìĿ´íģ¬ìĹħ": 12425, + "Ġhabit": 12426, + "Ġschedule": 12427, + "Ġì¹´íĨ¡": 12428, + "Ġhelping": 12429, + "Ġ주ìľĦ": 12430, + "Ġê³¼ìŀIJ": 12431, + "Ġcircle": 12432, + "Ġë¶ĢíĴĪ": 12433, + "ĠMain": 12434, + "coming": 12435, + "era": 12436, + "ĠMB": 12437, + "ëł¤ê³ł": 12438, + "Ġìłķ기": 12439, + "Ġ}}^": 12440, + "Ġê±°ì§ĵ": 12441, + "Ġarchitecture": 12442, + "ĠRichard": 12443, + ",\\": 12444, + "Ġattacks": 12445, + "ictor": 12446, + "Ġê°ĸì¶Ķ": 12447, + "Ġëħ¸ì¶ľ": 12448, + "Ġpackages": 12449, + "ìİ": 12450, + "ĠAdmin": 12451, + "ìĦ¸ê³Ħ": 12452, + "pan": 12453, + "ĠìĿ¸êµ¬": 12454, + "ials": 12455, + "Ġsought": 12456, + 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"Ġì§ľì¦Ŀ": 12508, + "Ġê°Ģë¡ľ": 12509, + "ε": 12510, + "Ġìłijì¢ħ": 12511, + "ëŃĶ": 12512, + "Ġsteel": 12513, + "Ġnod": 12514, + "Ġclar": 12515, + "Ġë´IJìĦľ": 12516, + "Ġ무ë¦İ": 12517, + "Ġëį§ë¶ĻìĺĢ": 12518, + "ĠíĶĦëĿ¼": 12519, + "ĠìĪĺë§İ": 12520, + "Ġunion": 12521, + "Ġ먼ì§Ģ": 12522, + "Ġreliable": 12523, + "Ġíķľê°Ģ": 12524, + "Ġconstructed": 12525, + "ĠRule": 12526, + "ĠìĿ¼ìŀIJ리": 12527, + "Ġì²´ê²°": 12528, + "Ġк": 12529, + "Ġhang": 12530, + "ĠLand": 12531, + "Ġì¶ĺ": 12532, + "먼ì§Ģ": 12533, + "Dis": 12534, + "Ġë²Ĺìĸ´ëĤĺ": 12535, + "Ġì£ĦìĨ¡": 12536, + "aft": 12537, + "Ġforming": 12538, + "Ġprincipal": 12539, + "Ġsugar": 12540, + "like": 12541, + "Ġnegoti": 12542, + "ĠJohnson": 12543, + "ĠìĿ´ê¸°": 12544, + "Ġìłķë¹Ħ": 12545, + "Ġdrawn": 12546, + "Ġconcerning": 12547, + "Ġinfer": 12548, + "udd": 12549, + "Ġê²°ë¡ł": 12550, + "ĠìĬĪíį¼": 12551, + "Ġfellow": 12552, + "ART": 12553, + "ìĿ¼ë¦¬": 12554, + "Ġkin": 12555, + "Ġ***": 12556, + "ĠíĮĮíĬ¸": 12557, + "ìķĦë²Ħì§Ģ": 12558, + "iest": 12559, + 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"Log": 12767, + "ĠMedic": 12768, + "ëŀĢíĬ¸": 12769, + "Ġcars": 12770, + "plify": 12771, + "######": 12772, + "ĠìĿ´ë»IJ": 12773, + "EX": 12774, + "ëĭĪìķĦ": 12775, + "ĠëĮĢìĦł": 12776, + "ĠìħĪ": 12777, + "ñ": 12778, + "Ġrightarrow": 12779, + "ĠRock": 12780, + "Ġilleg": 12781, + "걸ìĿ´": 12782, + "Ġìĭ¬ì§Ģìĸ´": 12783, + "커íĶ¼": 12784, + "ĠíĶ¼ë¡ľ": 12785, + "Ġ멤ë²Ħ": 12786, + "Ġìĥ¤ìĽĮ": 12787, + "Ġperiods": 12788, + "Ġë¶ĵ": 12789, + "Ġdimensions": 12790, + "Ġlogic": 12791, + "Over": 12792, + "str": 12793, + "Ġloan": 12794, + "Ġì¢ħì¢ħ": 12795, + "ë°ĶëŀĮ": 12796, + "Ġchapter": 12797, + "åı": 12798, + "Ġextensive": 12799, + "ĠíķĺëĤĺíķĺëĤĺ": 12800, + "Ġsituations": 12801, + "ĠLake": 12802, + "ĠManagement": 12803, + "Ġ주ìĤ¬": 12804, + "Ġdetermining": 12805, + "missions": 12806, + "ĠìķĦíĶĪ": 12807, + "ittle": 12808, + "Ġexperts": 12809, + "Ġ-(": 12810, + "Ġæł": 12811, + "ĠCup": 12812, + "ĠíĺĦê¸Ī": 12813, + "ĠëĤľëĭ¤": 12814, + "ĠëıħìĦľ": 12815, + "icate": 12816, + "Ġwalls": 12817, + "Ġdedicated": 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12924, + "Ġpreced": 12925, + "ìĺĪìłĦ": 12926, + "Ġا": 12927, + "zyme": 12928, + "Ġdebt": 12929, + "Ġì§ijëĭ¨": 12930, + "Ġê³łê³ł": 12931, + "Ġ«": 12932, + "Ġmapping": 12933, + "Ġrg": 12934, + "ìĿ´ëŁ¬": 12935, + "kr": 12936, + "Ġedit": 12937, + "Ġforget": 12938, + "Ġë°©ìĹŃ": 12939, + "Ġinjection": 12940, + "Ġputting": 12941, + "Hz": 12942, + "???": 12943, + "Ġìĭ¤íĸī": 12944, + "ì°¾": 12945, + "Ġenabled": 12946, + "ĠíĺķìĭĿ": 12947, + "Ġsomewhat": 12948, + "Ġcellular": 12949, + "à®": 12950, + "Ġforest": 12951, + "ele": 12952, + "ĠCreate": 12953, + "Ġìľłë°ľ": 12954, + "ĠìĨĮê³łê¸°": 12955, + "Ġ*(-": 12956, + "ĠMake": 12957, + "Ġbuildings": 12958, + "ĠìĬ¤íħĮìĿ´íģ¬": 12959, + "ì¦IJ": 12960, + "ĠLook": 12961, + "ì§ķìĸ´": 12962, + "Ġë¡ľìį¨": 12963, + "Ġhonest": 12964, + "Ġinters": 12965, + "icts": 12966, + "External": 12967, + "Ġnam": 12968, + "Ġconstraints": 12969, + "unicip": 12970, + "Ġíģ¬ë¡ľ": 12971, + "Ġpig": 12972, + "ĠMont": 12973, + "Ġê»į": 12974, + "Ġê³łë§Ļ": 12975, + "Ġkinds": 12976, + 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"Ġchecked": 13135, + "Ġexecution": 13136, + "Ġìłķëĭ¹": 13137, + "Ġfurn": 13138, + "ëĵľë¦¬": 13139, + "Ġì´Ŀ리": 13140, + "ëĵľëĶĶìĸ´": 13141, + "짱": 13142, + "Ġgradient": 13143, + "Ġì¸ł": 13144, + "Ġrational": 13145, + "uters": 13146, + "미êµŃ": 13147, + "Ġarrow": 13148, + "Ġdialog": 13149, + "ë±": 13150, + "ĠìĨĮ주": 13151, + "ãĥ³ãĤ": 13152, + "car": 13153, + "Ġcoupling": 13154, + "ĠëıĻë°ĺ": 13155, + "Ġë¹Ħë¹Ķ": 13156, + "ĠLambda": 13157, + "ĠìĽIJì¹Ļ": 13158, + "Ġstem": 13159, + "ĠYOU": 13160, + "ĠTen": 13161, + "ĠíĬľ": 13162, + "ĠInte": 13163, + "ĠFL": 13164, + "ĠCard": 13165, + "Ġutils": 13166, + "íķĺíķĺ": 13167, + "ìĪŃ": 13168, + "ĠíĮĮíģ¬": 13169, + "More": 13170, + "Ġê±į": 13171, + "Ġlegisl": 13172, + "$,": 13173, + "ĠChristmas": 13174, + "Ġspending": 13175, + "Ġprivacy": 13176, + "ĠìĦ¼ìĬ¤": 13177, + "Ġacquired": 13178, + "ircraft": 13179, + "Ġ극복": 13180, + "Ġfacility": 13181, + "Ġfro": 13182, + "entials": 13183, + "Ġwealth": 13184, + "ĠíļŁ": 13185, + "ĠìłĦ주": 13186, + "ĠìĤ¬ìĹħìŀIJ": 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16729, + "Ġstake": 16730, + "Ġtel": 16731, + "Ġê³µì§Ģ": 16732, + "tack": 16733, + "Ġbehalf": 16734, + "Ġcomplications": 16735, + "Ġlateral": 16736, + "gage": 16737, + "ployment": 16738, + "ĠFrancisco": 16739, + "Ġlattice": 16740, + "ubb": 16741, + "Ġkil": 16742, + "ĠìĿ¼ìĤ°": 16743, + "Ġinsight": 16744, + "Ġhell": 16745, + "Ġì´Īì½ľë¦¿": 16746, + "sen": 16747, + "Ġwherein": 16748, + "ailable": 16749, + "Ġdy": 16750, + "ĠHo": 16751, + "Ġplates": 16752, + "Ġkeeps": 16753, + "Ġíģ´ëłĮ": 16754, + "ĠProt": 16755, + "ìŀ¡ìĿ´": 16756, + "ĠìºłíĶĦ": 16757, + "ĠíĥĪì¶ľ": 16758, + "Ġaims": 16759, + "ĠìĭĿíĥģ": 16760, + "Ġì±Į": 16761, + "eff": 16762, + "Ġíķľê¸Ģ": 16763, + "ĠException": 16764, + "ĠìĥĮëĵľìľĦì¹ĺ": 16765, + ">>>": 16766, + "ĠLiving": 16767, + "Ġgear": 16768, + "ĠìĥĿìĦ±": 16769, + "ĠDonald": 16770, + "Ġpipe": 16771, + "Ġsurprised": 16772, + "Ġstreets": 16773, + "una": 16774, + "æĹ": 16775, + "Ġenjoyed": 16776, + "Ġè©": 16777, + "Ġgauge": 16778, + "ante": 16779, + "ĠTreat": 16780, + "Ġarter": 16781, + "aret": 16782, + "ĠMichigan": 16783, + "ĠìĦ¸ìĿ¼": 16784, + "Ġìĺģíĺ¼": 16785, + "×ķ": 16786, + "Ġê·¸ëŁ¬ëĭĪê¹Į": 16787, + "Ġpad": 16788, + "ĠGET": 16789, + "Ġìĸµìłľ": 16790, + "ILL": 16791, + "Ġìłķê·ľ": 16792, + "Ġbonus": 16793, + "tm": 16794, + "Ġoccurrence": 16795, + "Ġ¤": 16796, + "ĠíĬ¸ëłĮ": 16797, + "Ġenroll": 16798, + "Ġoutputs": 16799, + "Ġ긴ê¸ī": 16800, + "áĦĭ": 16801, + "advant": 16802, + "Ġdisclosed": 16803, + "Ġgrain": 16804, + "ë¬¸ìłľ": 16805, + "ĠTaylor": 16806, + "Ġì¢ĮìĦĿ": 16807, + "ìĬ¤íĮħ": 16808, + "ĠObs": 16809, + "Ġ구ìĽIJ": 16810, + "ìļ´ìłĦ": 16811, + "Ġ为": 16812, + "Ġë¶Ģì±Ħ": 16813, + "Ġinfections": 16814, + "ìļ°ëĵľ": 16815, + "ĠInput": 16816, + "ê·¼íĺľ": 16817, + "Ġë¸Į리": 16818, + "Start": 16819, + "Ġalert": 16820, + "Ġå£": 16821, + "ãģį": 16822, + "Ġvertex": 16823, + "agnetic": 16824, + "Ġvend": 16825, + "ĠìĿĮìĦ±": 16826, + "ĠABC": 16827, + "Ġdesktop": 16828, + "points": 16829, + "owed": 16830, + "Ġsmoking": 16831, + "Ġoriented": 16832, + "apor": 16833, + 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"ĠìĤ¬íı¬": 46854, + "Ġãģ§ãģĤãģ£ãģŁ": 46855, + "Ġ문경": 46856, + "oooo": 46857, + "Ġëĭ¤ëĭ¤": 46858, + "riteria": 46859, + "Ġ'-'": 46860, + "Ġìĭ¸ìĽł": 46861, + "ĠFiction": 46862, + "ìĬĪíį¼": 46863, + "Ġruined": 46864, + "jn": 46865, + "ĠSic": 46866, + "cond": 46867, + "ĠìķĦë°ĺëĸ¼": 46868, + "Cross": 46869, + "íķľìŀħ": 46870, + "Ġinterconnected": 46871, + "Ġshipment": 46872, + "Ġelucidate": 46873, + "Ġ기ë¦Ħì§Ħ": 46874, + "Ġê¶ĮìĿµ": 46875, + "Ġnullable": 46876, + "Ġdrained": 46877, + "omeric": 46878, + "Ġhandic": 46879, + "ĠìĦ±ìĭ¬": 46880, + "Ġhonors": 46881, + "terson": 46882, + "ĠBurns": 46883, + "ĠçĮ": 46884, + "lance": 46885, + "Experimental": 46886, + "Ġå¯Ĩ": 46887, + "ĠKumar": 46888, + "ĠìĺģìľĦ": 46889, + "ĠìķĪ목": 46890, + "ĠTHREE": 46891, + "ĠëĤĻê´Ģ": 46892, + "ĠAuthentication": 46893, + "Ġgraduating": 46894, + "Ġjerk": 46895, + "Ġ..;;;": 46896, + "Ġì¡°ìĭ¬íŀĪ": 46897, + "Ġwd": 46898, + "ĠíĻĶëĤ´": 46899, + "Ġimperfect": 46900, + "Ġexpands": 46901, + "ë³ijëĮĢ": 46902, + "Ġ걸ëłĪ": 46903, + "Ġëĭ¥ì¹ĺ": 46904, + "Ġamplify": 46905, + "Ġnoteworthy": 46906, + "ĠSandy": 46907, + "ëŀĺ기": 46908, + "Ġconceded": 46909, + "Ġìĺ¬ë¦°ëĭ¤": 46910, + "ĠìłĢì¡°": 46911, + "ê°¯": 46912, + "Ġspars": 46913, + "Attr": 46914, + "Camp": 46915, + "ĠViv": 46916, + "ĠãĥĻ": 46917, + "Ġnude": 46918, + "otomy": 46919, + "ĠSomal": 46920, + "ulla": 46921, + "ĠDoor": 46922, + "otting": 46923, + "Ġåijĺ": 46924, + "ĠMyers": 46925, + "ĠLatino": 46926, + "udal": 46927, + "Ġë©įíķĺëĭĪ": 46928, + "ĠìĨĮëªħ": 46929, + "ĠëĤĺìļ°": 46930, + "Ġmodelled": 46931, + "ĠGuinea": 46932, + "Ġplanetary": 46933, + "Ġ조문": 46934, + "Ġcot": 46935, + "Ġrebut": 46936, + "Chain": 46937, + "ìĪĺëıĻ": 46938, + "Ġê²ĢìĿĢìĥī": 46939, + "Ġíģ¼ì§Ģë§ī": 46940, + "Ġëį°ëł¤ìĻĶ": 46941, + "ĠìķĮë¡ľìĹIJ": 46942, + "ê³łìĨįëıĦë¡ľ": 46943, + "mand": 46944, + "ĠíĻĶëįķ": 46945, + "íıŃíĴį": 46946, + "ìĭ¬íĶĮ": 46947, + "ëī´ìĸ¼": 46948, + "XY": 46949, + "Ġë§ĺëĮĢë¡ľ": 46950, + "ĠPlayStation": 46951, + "ĠçĹħ": 46952, + "Ġconnectors": 46953, + "ĠJoel": 46954, 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+ "Ġexceptionally": 47005, + "operations": 47006, + "Ġëĸ³": 47007, + "Ġvou": 47008, + "ĠSTRING": 47009, + "Ġdispens": 47010, + "Ġ미ìĽĮ": 47011, + "HB": 47012, + "Ġponder": 47013, + "ĠìłķìłĦ": 47014, + "Ġepoxy": 47015, + "colm": 47016, + "Ġ쪼ê¸Ī": 47017, + "ì¶ľë°ľ": 47018, + "DN": 47019, + "Hence": 47020, + "kinson": 47021, + "ĠHonda": 47022, + "idable": 47023, + "LowerCase": 47024, + "ê±°íĴĪ": 47025, + "ĠæĻ®": 47026, + "Ġentails": 47027, + "Ġð": 47028, + "ç¦": 47029, + "Ġê²½ìĹ°": 47030, + "fac": 47031, + "ìĭłë£Į": 47032, + "Ġë°Ģë©´": 47033, + "etting": 47034, + "Sources": 47035, + "Ġãģ¨ãģ®": 47036, + "ĠJosé": 47037, + "Ġconfrontation": 47038, + "Ãŀ": 47039, + "ĠìĽIJìĨĮ": 47040, + "Ġì¢ħ족": 47041, + "Ġëĸ¨ë¦¼": 47042, + "ĠTriple": 47043, + "ĠROC": 47044, + "ĠPSA": 47045, + "ĠBehind": 47046, + "ĠDocumentation": 47047, + "ìĤ¬íĤ¤": 47048, + "riad": 47049, + "Ġancestor": 47050, + "Ġìĺ¤ë¥´ë§ī": 47051, + "Ġcorticoster": 47052, + "ĠíĸIJ": 47053, + "awi": 47054, + "Ġëĸłìĺ¤ë¥¸": 47055, + "Ġhomosexual": 47056, + "Ġjudging": 47057, + "Ġoligonucle": 47058, + "ĠíķŃìķĦ리": 47059, + "Ġê·Ģì¤ij": 47060, + "ĠìłĢê°IJ": 47061, + "ĠëĦĺìĸ´ìĦł": 47062, + "anya": 47063, + "ĠìĤ°ëĭ¤ëĬĶ": 47064, + "ĠFacility": 47065, + "Ġ=,": 47066, + "ĠDiff": 47067, + "Ġìĺ¤ë¥¸ëĭ¤": 47068, + "Ġbak": 47069, + "Ġdisrupted": 47070, + "uga": 47071, + "Ġìĸ´ëĶĺì§Ģ": 47072, + "ê³Ħíļį": 47073, + "Ġpersuade": 47074, + "Ġëıħ립ìļ´ëıĻ": 47075, + "onate": 47076, + "ĠëıĻë°ĺìŀIJ": 47077, + "Ġrebel": 47078, + "ĠíĿIJ볤": 47079, + "asitic": 47080, + "ĠInvestigation": 47081, + "Ġhappier": 47082, + "ĠëıĻê·¸ëĿ¼ë¯¸": 47083, + "Ġ맨íĪ¬ë§¨": 47084, + "Ġabdomen": 47085, + "ĠìĪ¨ìī¬": 47086, + "ĠqPCR": 47087, + "ëĭ¤ìĦ¯": 47088, + "ĠMorrison": 47089, + "example": 47090, + "Ġìķħìĸ´": 47091, + "Ġ그리ìĬ¤ëıĦìĿ¸": 47092, + "Ġdeeds": 47093, + "Ġê¸Īê³ł": 47094, + "ĠëĪĦëĿ½": 47095, + "Ġpotenti": 47096, + "Ws": 47097, + "ĠíĨłëĭ¥": 47098, + "ãĥ¼ãĤ·": 47099, + "product": 47100, + "ĠìĦ±ìĦľ": 47101, + "Ġ}.\\": 47102, + "íħĮ르": 47103, + "ĠìĥĿê°ģëĤľëĭ¤": 47104, + "ĠTheme": 47105, + "ĠVietnamese": 47106, + "Validator": 47107, + "ìĪĺìķĦ": 47108, + "Ġbols": 47109, + "Ġë°°ëŀĢ": 47110, + "ĠMJ": 47111, + "tituted": 47112, + "Ġë³Ģíķł": 47113, + "Ġlearnt": 47114, + "Ġsofa": 47115, + "Ġê°Ģìļ´": 47116, + "Ġplaylist": 47117, + "ĠìĽĮíģ¬ìĪį": 47118, + "ĠíĹĮíĺĪ": 47119, + "resource": 47120, + "Ġunbound": 47121, + "cache": 47122, + "Ġê²½ìłĦ": 47123, + "Ġeyel": 47124, + "ĠMcN": 47125, + "Ġincur": 47126, + "ĠìĿ´ìĤ¿ì§IJ": 47127, + "ìĪĺëĬ¥": 47128, + "리그": 47129, + "ĠëģĿëĤ¸": 47130, + "ĠSob": 47131, + "Ġforecasting": 47132, + "Ġç±»": 47133, + "idic": 47134, + "ĠBle": 47135, + "Ġìłķ립": 47136, + "ìĸ´ì©ľ": 47137, + "ĠíıŃìĭĿ": 47138, + "Ġ침ëŀµ": 47139, + "Ġìķ½ì¹¨": 47140, + "ĠìĿµëªħ": 47141, + "Ġtumours": 47142, + "Ġ**|": 47143, + "ìĿ¼ìłķ": 47144, + "ìĮĮ": 47145, + "Ġëľ»ê¹Ĭ": 47146, + "Ġcurb": 47147, + "Ġåıª": 47148, + "Ġìŀ¥ë³ij": 47149, + "Ġìķħ몽": 47150, + "Ġê³µëĮĢ": 47151, + "Ġecology": 47152, + "Pad": 47153, + "Ġlonge": 47154, + "ĠíĬķ": 47155, + "Ġhistone": 47156, + "Ġfacie": 47157, + "ĠOG": 47158, + "Ġmacrophage": 47159, + "Ġgd": 47160, + "ocity": 47161, + "--------------": 47162, + "ĠIsn": 47163, + "ĠìĹ°ìķ½": 47164, + "Ġéĵ": 47165, + "Ġresearching": 47166, + "ĠXV": 47167, + "pit": 47168, + "Ġlively": 47169, + "ĠVilla": 47170, + "Ġë³´íĺ¸ë²ķ": 47171, + "Ġleaked": 47172, + "Ġvalence": 47173, + "ĠíİĺëĦIJ": 47174, + "olan": 47175, + "Ġ###": 47176, + "Ġ-{\\": 47177, + "ija": 47178, + "ìĪĺíķĻ": 47179, + "ĠëıĦ무ì§Ģ": 47180, + "ĠãĥĢ": 47181, + "Ġpoets": 47182, + "Ġoxides": 47183, + "Ġfiduciary": 47184, + "Ġsta": 47185, + "ĠìķĪì°©": 47186, + "risoned": 47187, + "모ìĸij": 47188, + "ĠìĦľìļĶ": 47189, + "ĠAppellees": 47190, + "Ġíķľë°ľ": 47191, + "Ġdepended": 47192, + "Ġaffiliation": 47193, + "ĠìĭŃìĭľ": 47194, + "Ġ벽걸ìĿ´": 47195, + "Ġmanga": 47196, + "Ġ.??": 47197, + "usement": 47198, + "Ġ립ëĭĪ": 47199, + "íĿĶíŀĪ": 47200, + "ĠìŀIJë°Ķ": 47201, + "Enum": 47202, + "Ġmkdir": 47203, + "ĠComing": 47204, + "ĠSTEM": 47205, + "ĠëĤ¨ìĽIJ": 47206, + "Ġì²ĺëħĢ": 47207, + "Ġlockdown": 47208, + "Ym": 47209, + "ĠCache": 47210, + "ëį±ìĬ¤": 47211, + "ĠíĶĦ린íĮħ": 47212, + "ĠìĹ°ëĭ¬": 47213, + "ĠìĹ´ìĦł": 47214, + "Ġê³¼ê°IJíŀĪ": 47215, + "Ġuterine": 47216, + "Ġíļ¨ìĦ±": 47217, + "ĠValentine": 47218, + "Ġìĥ¤ìĿ¸": 47219, + "ĠìĤ°ìłIJ": 47220, + "Ġë°ķíĥĪ": 47221, + "ĠMining": 47222, + "zier": 47223, + "aaS": 47224, + "íģ¬ëĿ¼ìĿ´ëĤĺ": 47225, + "Ġtemples": 47226, + "Environment": 47227, + "Ġauc": 47228, + "astructure": 47229, + "ĠGlu": 47230, + "Ġbanana": 47231, + "Ġmaritime": 47232, + "Ġrecalls": 47233, + "Ġadrenal": 47234, + "Ġê³łìĤ¬ë¦¬": 47235, + "ĠëĿ¼ìĿ´ëįĶ": 47236, + "ĠEurop": 47237, + "Ġëĭ´ê¶Ī": 47238, + "anza": 47239, + "Ġpathophys": 47240, + "ĠìĻĶëĭ¤ê°Ķëĭ¤": 47241, + "Ġ|_{": 47242, + "tructured": 47243, + "ĠSIR": 47244, + "Ġê°Ģëĭ´": 47245, + "ĠBone": 47246, + "Ġhomeowners": 47247, + "Ġposters": 47248, + "Ġsecretly": 47249, + "ëĭ¤ìŀĦ": 47250, + "Ġì¹ĺìķĪ": 47251, + "Ġcorrecting": 47252, + "Ġembodied": 47253, + "ĠMID": 47254, + "ĠASD": 47255, + "ĠìĪĺ첩": 47256, + "Ġfeather": 47257, + "ĠêµIJë¶Ģ": 47258, + "Ġfrightened": 47259, + "Ġmanifested": 47260, + "Ġì²Ń주ìĭľ": 47261, + "chor": 47262, + "Ġtails": 47263, + "ERC": 47264, + "Ġspectrometer": 47265, + "Ġtrapping": 47266, + "ĠBurke": 47267, + "Ġì¡°ê·¸ë§Ī": 47268, + "ĠìĦ¸ìħĺ": 47269, + "Getting": 47270, + "Ġìį»": 47271, + "ĠWool": 47272, + "ĠShannon": 47273, + "íĤ¤ìķĦ": 47274, + "unda": 47275, + "Ġlactate": 47276, + "Ġgoss": 47277, + "Ġcentrifuged": 47278, + "ĠEllen": 47279, + "Ġdpkg": 47280, + "Ġchor": 47281, + "Ġ*[": 47282, + "UPDATE": 47283, + "Ġfiller": 47284, + "Ġcountryside": 47285, + "Ġnavy": 47286, + "Ġcytotoxicity": 47287, + "edit": 47288, + "Ġlaughs": 47289, + "ĠHBV": 47290, + "Ġë°©ìĦĿ": 47291, + "Ø°": 47292, + "Ġèģĸ": 47293, + "Ġsoybean": 47294, + "Ġpeg": 47295, + "Speaking": 47296, + "Ġdiph": 47297, + "ĠCod": 47298, + "Saturday": 47299, + "ια": 47300, + "ĠERP": 47301, + "Ġíļ¨ìŀIJ": 47302, + "ĠTroy": 47303, + "ĠDual": 47304, + "Ġë³¼íİľ": 47305, + "Ġìĥģê³ł": 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47354, + "Ġarticulated": 47355, + "ĠLynn": 47356, + "ĠSchwarz": 47357, + "社": 47358, + "NET": 47359, + "ĠPLA": 47360, + "forth": 47361, + "Ġdentist": 47362, + "Ġम": 47363, + "Ġë§ĪìĿ¼ë¦¬": 47364, + "ĠRugby": 47365, + "ĠEthiopia": 47366, + "Ġê°Īì¦Ŀ": 47367, + "Ġperoxide": 47368, + "DEF": 47369, + "Ġìĩ¼íĬ¸": 47370, + "Ġamazed": 47371, + "꺽": 47372, + "Ġ릴ëłĪìĿ´": 47373, + "ĠìĿ´ëª¨íĭ°ì½ĺ": 47374, + "Ġfurthermore": 47375, + "Ġë¹ĪíĭĪ": 47376, + "rated": 47377, + "Ġrbrack": 47378, + "Connect": 47379, + "ĠìĤ´ë¦°": 47380, + "ocalyp": 47381, + "Ġìĺ®ê¸¸": 47382, + "ĠCLE": 47383, + "ĠComputing": 47384, + "Ġ기체": 47385, + "ëıĻê°ķ": 47386, + "iferase": 47387, + "Ġì²Ńíĥģ": 47388, + "ĠInform": 47389, + "Ġteammates": 47390, + "viations": 47391, + "Ïİ": 47392, + "Ġë¿Į린": 47393, + "Mz": 47394, + "ид": 47395, + "ĠHenri": 47396, + "ìŬëĵľë¦Ħ": 47397, + "Ġíĺ¸íĪ¬": 47398, + "Ġoutreach": 47399, + "ĠìĿ´ìŀIJì¹´ìķ¼": 47400, + "ĠSach": 47401, + "ãģĭãģĹ": 47402, + "Ġ説": 47403, + "Ġacrylic": 47404, + "Ġ^__": 47405, + 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"ĠëļĿëļĿ": 47656, + "Ġinfantry": 47657, + "Brit": 47658, + "Ġgreet": 47659, + "ĠNavigation": 47660, + "ĠëģĮìĸ´ìķĪ": 47661, + "ĠAtlas": 47662, + "Ġadipose": 47663, + "ĠInfantry": 47664, + "íĺ¸ì£¼": 47665, + "Ġstumbled": 47666, + "Ø·": 47667, + "ĠGardens": 47668, + "Ġcaffeine": 47669, + "Ġê°ķëıĻ구": 47670, + "ãģ§ãģĤãĤĭ": 47671, + "Ġ\\;\\": 47672, + "Ġshortcomings": 47673, + "Ġshortcut": 47674, + "Ġarrog": 47675, + "ideos": 47676, + "Ġê°ĦìĨĮ": 47677, + "ĠParen": 47678, + "ĠmA": 47679, + "ĠTimothy": 47680, + "Ġ문건": 47681, + "æŀľ": 47682, + "merged": 47683, + "Ġgentlemen": 47684, + "Ġbalances": 47685, + "ìĦ¸ì¢ħ": 47686, + "ĠSuff": 47687, + "ĠìĿ´ë¤Ħì§ĢëĬĶ": 47688, + "ĠEisen": 47689, + "Ġvartheta": 47690, + "Ġëľ¨ê±°ìĽĮ": 47691, + "ç®": 47692, + "ìĭľë¦¬": 47693, + "Ġtrademarks": 47694, + "Ġmening": 47695, + "ĠFK": 47696, + "Ġply": 47697, + "ĠìĬ¹ë¥ł": 47698, + "ĠãĥĿ": 47699, + "Ġê²Ģìĥīìĸ´": 47700, + "Ġlabs": 47701, + "biased": 47702, + "Ġspeculate": 47703, + "archar": 47704, + "ĠKeys": 47705, + "ĠìĽIJì¹ĺ": 47706, + "ĠìĤŃê°IJ": 47707, + "ì¢ħìĿ¼": 47708, + "ĠAcqu": 47709, + "Ġliabilities": 47710, + "ĠrRNA": 47711, + "Ġç§ģ": 47712, + "ĠíĽĦìŀIJ": 47713, + "Ġestoppel": 47714, + "Ġsadness": 47715, + "ç±": 47716, + "ĠParents": 47717, + "ĠCharter": 47718, + "ĠCron": 47719, + "Ġhunter": 47720, + "ĠìĦłëľ»": 47721, + "ymers": 47722, + "ght": 47723, + "Ġìłķì°¨": 47724, + "Ġinformational": 47725, + "Ġìĺ¬ìĿ¸ìĽIJ": 47726, + "Ġbinder": 47727, + "Poly": 47728, + "ullah": 47729, + "Ġshuttle": 47730, + "Ġì§Ģëıħ": 47731, + "Ġëĭ¬íĮ½ìĿ´": 47732, + "ĠCanadians": 47733, + "기ì´Ī": 47734, + "Ġë³¼ìĪĺë¡Ŀ": 47735, + "ĠêµŃ기": 47736, + "Ġ믿기": 47737, + "ìľłì§Ģ": 47738, + "Lock": 47739, + "ĠPrix": 47740, + "ĠCertified": 47741, + "ĠNGC": 47742, + "ableness": 47743, + "ĠìļĶ구르íĬ¸": 47744, + "íķľíĻĶ": 47745, + "Ġwy": 47746, + "Ġìĸ´ìļ¸ëł¤ìĦľ": 47747, + "ĠDSM": 47748, + "Ġ\\_{\\\\": 47749, + "ĠìĬ¤íİĢ": 47750, + "ĠKick": 47751, + "Ġutmost": 47752, + "ĠMZ": 47753, + "Ġë¹Ħëijĺ기": 47754, + "ë¶Īíݸ": 47755, + 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"Ġìŀħìŀ¥ê¶Į": 47805, + "ĠìłĢìĦľ": 47806, + "Ġjuror": 47807, + "ÏĨ": 47808, + "모ëłĪ": 47809, + "ĠëĿ¼ë²¤ëįĶ": 47810, + "ĠShift": 47811, + "ĠëģĿíĮIJ": 47812, + "ĠëĤ´ëł¤ìĻĢìĦľ": 47813, + "Ġåħµ": 47814, + "Ġåºĥ": 47815, + "Ġrv": 47816, + "Ġë¶Ļëĵ¤": 47817, + "Ġê³µìĤ°ëĭ¹": 47818, + "ĠTrace": 47819, + "Ġjunctions": 47820, + "Board": 47821, + "ĠBrien": 47822, + "Ġâ¬": 47823, + "Ġë¸ĮëłĪ": 47824, + "CV": 47825, + "Ġ}',": 47826, + "Ġwaved": 47827, + "ĠìĬ¤íĬ¸ë¦¬": 47828, + "Ġ^^...": 47829, + "Ġpituitary": 47830, + "ĠSouthwest": 47831, + "Ca": 47832, + "ĠBened": 47833, + "ĠìĽIJìĭľ": 47834, + "ĠíķĻê¸ī": 47835, + "Ġcollaps": 47836, + "ĠMadd": 47837, + "Ġlumen": 47838, + "desc": 47839, + "Ġils": 47840, + "ê³µì¦Ŀ": 47841, + "Ġmuff": 47842, + "Ġ주ìĭĿíļĮìĤ¬": 47843, + "Ġbriefing": 47844, + "íĶĦëĿ¼ìĿ´ì¦Ī": 47845, + "ĠLQ": 47846, + "Ġnicotine": 47847, + "íĻĶ볤": 47848, + "ì¦Ŀë§ŀ": 47849, + "---------------------": 47850, + "Ġìľ¼ìħ¨ìĸ´ìļĶ": 47851, + "Ġarte": 47852, + "Ġë°ĺìŀħ": 47853, + "Ġä¼Ĭ": 47854, + "zie": 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"ĠìĪĺë³Ģ": 60992, + "Ġê¹Ģíķľ": 60993, + "Ġë³µì§ģ": 60994, + "hon": 60995, + "ĠTir": 60996, + "Ġworkaround": 60997, + "Ġyrs": 60998, + "Ġ}}\\,": 60999, + "Direction": 61000, + "ĠëıĮë°ľ": 61001, + "Ġcron": 61002, + "ë©´ìĹŃëł¥": 61003, + "ìĬ¤ë¯¼": 61004, + "Ġë¶Ī매": 61005, + "ORAND": 61006, + "Ġltr": 61007, + "Ġhypothesize": 61008, + "asic": 61009, + "Bind": 61010, + "Ġ']);": 61011, + "Ġsummaries": 61012, + "ĠMembership": 61013, + "pZ": 61014, + "ĠAmended": 61015, + "Ġíı¬ë§·": 61016, + "ìĿ´ìĸ´ìĦľ": 61017, + "ĠëĮĢìĿĺ": 61018, + "ÑģÑĤи": 61019, + "llll": 61020, + "Beyond": 61021, + "GIS": 61022, + "ĠìĨĮìĿ¸": 61023, + "Ġabnormality": 61024, + "ìķĦíĶĦ리카": 61025, + "ĠEntreprene": 61026, + "Ġdifferentiating": 61027, + "Obviously": 61028, + "Ġ$\\|": 61029, + "ì§ľìŀ¥": 61030, + "starz": 61031, + "íĶĮë¡ľ": 61032, + "Ġàº": 61033, + "ĠRESP": 61034, + "ì¤ijê³łì°¨": 61035, + "Ġpartitioned": 61036, + "ĠPediatric": 61037, + "ìłĦë©´": 61038, + "Ġ꾸며ì§Ħ": 61039, + "Ġ쪼ëģĶ": 61040, + "ĠíĻĶíķ©ë¬¼": 61041, + 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"ĠìĤ¬ìĹħëĭ¨": 61092, + "ìĺ¤íĨł": 61093, + "Ġdisagreed": 61094, + "breviations": 61095, + "Ġë§ĪìħĶëıĦ": 61096, + "ê°Ģë²¼ìļ´": 61097, + "mass": 61098, + "Ġthumbs": 61099, + "ëĤ¨ë¯¸": 61100, + "ĠSOM": 61101, + "ĠATL": 61102, + "tidine": 61103, + "Ġtris": 61104, + "Ġë²Ħíħ¼": 61105, + "Ġskewed": 61106, + "ĠëĨĢëŀĮ": 61107, + "Ġëĭ¬ìķĦëĤĺ": 61108, + "ë¡ľìļ¸": 61109, + "ionate": 61110, + "ocol": 61111, + "ĠìŀIJìĿĺ": 61112, + "irsch": 61113, + "ìĽĢì§ģ": 61114, + "Ġë°ĶëĢIJëĭ¤": 61115, + "Ġrepairing": 61116, + "Ġzyga": 61117, + "Ġì°½ìĦ¤": 61118, + "Ġë§Ī리ìĺ¤": 61119, + "Ġê³łê°Ī": 61120, + "ĠMUS": 61121, + "Ġcentroid": 61122, + "Ġ몸ì§ĵ": 61123, + "ĠHag": 61124, + "ĠMarcel": 61125, + "ĠMajority": 61126, + "Ġcondemnation": 61127, + "apl": 61128, + "Ġinplace": 61129, + "ĠëĿ¼ìĺ¨": 61130, + "ãģĦãģŁ": 61131, + "Ġìħī": 61132, + "ĠìłĢì¶ľìĤ°": 61133, + "ĠNewsletter": 61134, + "Ġç§į": 61135, + "ĠíķĦëĿ¼ëį¸íĶ¼ìķĦ": 61136, + "ĠìĨįì¶ľ": 61137, + "Ġì°®": 61138, + "Ġacuity": 61139, + "ĠDrama": 61140, + "Ġ모ë¹ĦìĬ¤": 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61192, + "Ġenlisted": 61193, + "Ġê·¼ë§ī": 61194, + "oxyl": 61195, + "ĠìĿ¼ìĤ¼": 61196, + "Ġíĭ°ìłĢ": 61197, + "ĠLuxemb": 61198, + "Ġsketches": 61199, + "ĠìķĦíĮĮíķĺ": 61200, + "Ġ리ìļ°": 61201, + "ĠìĹĩëĭ¤": 61202, + "Ġhorrific": 61203, + "swith": 61204, + "ĠMyth": 61205, + "ĠìĭľíĤ¨ëĭ¤ëĬĶ": 61206, + "ĠFAT": 61207, + "ĠèĪĪ": 61208, + "ĠLaugh": 61209, + "Federal": 61210, + "Ġкон": 61211, + "ëĤĺê°Ī": 61212, + "Ġê²½ìĥģëıĦ": 61213, + "Ġìĸ´ìķ¼ì£ł": 61214, + "Ġë½Ī": 61215, + "Ġíĺ¹ìŬëĤĺ": 61216, + "Ġcybersecurity": 61217, + "Sof": 61218, + "ĠPreston": 61219, + "Ġfollicles": 61220, + "ê°ľê³¨": 61221, + "ĠInj": 61222, + "Ġìĺ¨ëĤľ": 61223, + "ĠENA": 61224, + "ĠFilipino": 61225, + "Ġdeserted": 61226, + "Ġdebtors": 61227, + "Ġä¸ĢèĪ¬": 61228, + "Ox": 61229, + "ĠTriton": 61230, + "vat": 61231, + "ĠìłĢìļ¸": 61232, + "его": 61233, + "ĠWhatsApp": 61234, + "ëĭ¨ë°±ì§Ī": 61235, + "ĠëĶ°ê°ij": 61236, + "Ġedged": 61237, + "Ġíį¼íĬ¸": 61238, + "ê°ijìŀij": 61239, + "íķ´íĶ¼": 61240, + "Identity": 61241, + "Ġcallable": 61242, + "ìī¬ìļ´": 61243, + "ĠMCP": 61244, + "selected": 61245, + "Ġkur": 61246, + "ëĬĶëį°ëıĦ": 61247, + "Ġ}}{{\\": 61248, + "Ġroofs": 61249, + "acio": 61250, + "ĠDud": 61251, + "ORANDUM": 61252, + "Ġì§Ģì¹ł": 61253, + "ĠíķĺíļĮ": 61254, + "omez": 61255, + "ĠTrends": 61256, + "ĠìĿĢë°Ģ": 61257, + "Ġì§ĢíıIJ": 61258, + "Ġamphib": 61259, + "Ġ거머": 61260, + "Ġsciss": 61261, + "Ġintegrin": 61262, + "ĠOre": 61263, + "Ġportfolios": 61264, + "Ġ\"&": 61265, + "ĠGMP": 61266, + "ĠBSD": 61267, + "Ġmornings": 61268, + "ĠëĮĢê¶Į": 61269, + "Ġnada": 61270, + "Ġëħ¸ìĿ´ì¦Ī": 61271, + "ĠTopics": 61272, + "Ġstron": 61273, + "ĠIsab": 61274, + "Ġpedagog": 61275, + "Ġmisunderstanding": 61276, + "Ġnucleon": 61277, + "Ġë¶ĢëŁ¬ìĽł": 61278, + "Ay": 61279, + "ĠPitch": 61280, + "Ġith": 61281, + "Ġeloqu": 61282, + "ĠFn": 61283, + "Ġtracts": 61284, + "맸": 61285, + "Ġìĭľë¬´": 61286, + "Into": 61287, + "Ġmysteries": 61288, + "ĠgetValue": 61289, + "Ġìŀ¬ìĦł": 61290, + "Ġprobed": 61291, + "ĠëŀĺìĬ¤": 61292, + "Ġë¸Įëłī": 61293, 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"ĠHast": 61343, + "GFR": 61344, + "ĠCurry": 61345, + "Ġbumps": 61346, + "gently": 61347, + "Ġtect": 61348, + "Ġë°©ìĤ°": 61349, + "Ġíģ¬ë¦°": 61350, + "ĠLig": 61351, + "Ġjm": 61352, + "kie": 61353, + "ë²Ħìĭľ": 61354, + "ëį°ìĿ¼ë¦¬": 61355, + "ĠìĤ¬íķĺ": 61356, + "Ġì¹ĺ른ëĭ¤": 61357, + "Ġenumerated": 61358, + "ĠTumor": 61359, + "Ġpeninsula": 61360, + "ulfide": 61361, + "Nov": 61362, + "ĠPAD": 61363, + "secured": 61364, + "Ġimitation": 61365, + "á»ij": 61366, + "Ġslogan": 61367, + "Ġyourselves": 61368, + "ĠRech": 61369, + "sy": 61370, + "pet": 61371, + "목ìļķ": 61372, + "KD": 61373, + "Ġpharmaceuticals": 61374, + "Ġcasts": 61375, + "Ġ뾰루ì§Ģ": 61376, + "èĹ": 61377, + "akr": 61378, + "Too": 61379, + "Ġë¸Įëŀľ": 61380, + "Ġê¼Ī": 61381, + "Ġmeson": 61382, + "Ġannounces": 61383, + "ĠSPD": 61384, + "äm": 61385, + "Ġsymb": 61386, + "ĠìķĮëłīìĬ¤": 61387, + "íij¸ì§IJ": 61388, + "ë¹Ħìĭ¸": 61389, + "Ġibid": 61390, + "Ġ무ëªħ": 61391, + "Ġperennial": 61392, + "ĠCONCLUSION": 61393, + "ìłķíĻķíŀĪ": 61394, + "Ġattainment": 61395, + "Ġë²Ķíķĺ": 61396, + "è½": 61397, + "ì¡°ìĭĿ": 61398, + "ĠOffense": 61399, + "Ġë°Ķìģł": 61400, + "ĠRoof": 61401, + "ĠìľłëijIJ": 61402, + "Ġë°Ģìĸ´ë¶Ļ": 61403, + "izzard": 61404, + "Ġprevailed": 61405, + "Ġdiversification": 61406, + "ĠUnix": 61407, + "ĠPhysRev": 61408, + "Ġyoutu": 61409, + "ĠMarathon": 61410, + "Span": 61411, + "ĠëĨĢìĿ´ë°©": 61412, + "Ġì°¨ë¹Ħ": 61413, + "aler": 61414, + "Ġbeliever": 61415, + "lé": 61416, + "Ġê½ĥë°Ķ구ëĭĪ": 61417, + "Ġintraper": 61418, + "ĠìľĦì´ī": 61419, + "Ġreusable": 61420, + "ĠìĮĵìķĺ": 61421, + "Ġê·¸ë¡ľ": 61422, + "ĠHmm": 61423, + "ocellular": 61424, + "Soft": 61425, + "Ġê³łìķĪ": 61426, + "ĠKod": 61427, + "ĠAsst": 61428, + "ĠPaso": 61429, + "Ġdove": 61430, + "Ġwrath": 61431, + "ĠFIX": 61432, + "$-": 61433, + "ç¹": 61434, + "Ġlush": 61435, + "Ġíķ´ì§Ģì§Ģ": 61436, + "ê¹Ģì¢ħ": 61437, + "ĠâĶģ": 61438, + "Ġì·¨ìĹħìŀIJ": 61439, + "ETS": 61440, + "IQ": 61441, + "kb": 61442, + "Ġë³Ħìŀ¥": 61443, + "YOU": 61444, + "ĠIrving": 61445, + "zilla": 61446, + "Ġascribed": 61447, + "ĠAfricans": 61448, + "Ġmethoxy": 61449, + "ĠHonorable": 61450, + "Ever": 61451, + "ĠìķĦì¿łìķĦ리ìĽĢ": 61452, + "Written": 61453, + "ë©ĶëŀĦëĵľ": 61454, + "((": 61455, + "+-": 61456, + "Ġíĸīë°©": 61457, + "Ġìĭłê°Ģ": 61458, + "ueb": 61459, + "hom": 61460, + "ĠìĤ¬ë¹Ħ": 61461, + "Ġredsh": 61462, + "ëįĺíĬ¸": 61463, + "ìĬ¤ìľĦ": 61464, + "ĠJal": 61465, + "ìĿ´ë¶Ī": 61466, + "ìĸ´ìĬ¤": 61467, + "Ġpsychopath": 61468, + "das": 61469, + "ĠåĬŁ": 61470, + "Ġ리카": 61471, + "ĠConstraints": 61472, + "Ġinterviewing": 61473, + "ĠPokémon": 61474, + "Ġå¿ħè¦ģ": 61475, + "ë§ĮìĦ±": 61476, + "Ġ리ë©Ķ": 61477, + "Ġpatter": 61478, + "Ġjoys": 61479, + "ê°ķíĻĶ": 61480, + "iddles": 61481, + "Ġcatastrophe": 61482, + "ĠSaxon": 61483, + "Japanese": 61484, + "ĠìĿ´ëį°": 61485, + "ĠPhe": 61486, + "Ġrichest": 61487, + "aryng": 61488, + "ĠScri": 61489, + "\"-": 61490, + "Ġedits": 61491, + "ĠìĦľìĹ°": 61492, + "Ġ민ì§Ģ": 61493, + "ĠíĮł": 61494, + "chro": 61495, + "ì¤ijíķĻêµIJ": 61496, + "Pair": 61497, 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61547, + "Ġfas": 61548, + "Ġê²½ê°ģ": 61549, + "Ġëĭ¤ëĿ¼": 61550, + "Ġreaff": 61551, + "ĠæıIJä¾Ľ": 61552, + "Ġhides": 61553, + "Ġ}{|": 61554, + "Ġëĵ±ë¶Ħ": 61555, + "Ġhasta": 61556, + "Ġgoalk": 61557, + "Ġresisting": 61558, + "Ġë¸ĮëĿ¼ìļ°ìłĢ": 61559, + "Ġdifferentiable": 61560, + "ĠmAb": 61561, + "Ġç½Ĺ": 61562, + "KG": 61563, + "Ġå¾Ģ": 61564, + "Ġ\\**": 61565, + "reading": 61566, + "ìĨĮë°©": 61567, + "Ġzw": 61568, + "ĠACA": 61569, + "Ġswell": 61570, + "ĠShane": 61571, + "Ġê³łíĿIJ": 61572, + "Ġresurrection": 61573, + "ĠPDA": 61574, + "Ġìĭ¤ìŀ¬": 61575, + "Ġlia": 61576, + "Ġìŀ¡ê³¡": 61577, + "UMP": 61578, + "Ġwards": 61579, + "Ġ})},": 61580, + "ĠTelesc": 61581, + "íĤ¨ëĭ¤": 61582, + "Ġmicrog": 61583, + "Ġpetty": 61584, + "ĠICA": 61585, + "ëijIJ콩": 61586, + "curl": 61587, + "Ġ모ìĿĦ": 61588, + "abine": 61589, + "Ġcommanders": 61590, + "ĠJW": 61591, + "Ġ리ì½ĶíĥĢ": 61592, + "inence": 61593, + "Ġãģ¶": 61594, + "ĠmAh": 61595, + "ĠìĸµìĥĪ": 61596, + "Ġnoodles": 61597, + "ĠìķĪê²½íħĮ": 61598, + "Ġ깨ìĽĮ": 61599, + "Ġabscess": 61600, + "Ġëĭ´ê·¼": 61601, + "Ġì±ĦìĽĮìł¸": 61602, + "Ġmultiplicative": 61603, + "Ġëħ¸ê³¨": 61604, + "ĠDismiss": 61605, + "oyer": 61606, + "Ġë°ľì§Ŀ": 61607, + "Ġexposes": 61608, + "ĠnÃ¥": 61609, + "ĠìŀIJìĹ°ìĤ°": 61610, + "ĠëĿ¼ìĺ¤": 61611, + "Ġ観": 61612, + "Ġreminding": 61613, + "ĠAlg": 61614, + "Ġ주íĥĿê°Ģ": 61615, + "ìĸ´ì§IJ": 61616, + "Ġë§Īìļ´íĬ¸": 61617, + "ĠìķĦ리ìķĦ": 61618, + "Ġimpending": 61619, + "Ġì²ĺì§Ħ": 61620, + "Ġcrafting": 61621, + "valence": 61622, + ">>>>": 61623, + "Oct": 61624, + "abo": 61625, + "Ġìĸijìľ¡ë¹Ħ": 61626, + "Ġcontradicts": 61627, + "ĠÑĸ": 61628, + "ĠVerizon": 61629, + "Ġworsh": 61630, + "Ġclosures": 61631, + "Ġì¹´ìĦ¸": 61632, + "Ġê½ĥ길": 61633, + "Ġê´´ë¡Ńíŀĺ": 61634, + "Unless": 61635, + "Ġê²īìĺ·": 61636, + "ĠJong": 61637, + "Ġìłģìŀĸ": 61638, + "Ġê°ĢíĮĮ르": 61639, + "ĠAudit": 61640, + "Machine": 61641, + "Ġexponents": 61642, + "Ġ뾬ëĭ¤": 61643, + "ĠBid": 61644, + "Ġíĺ¸ìĿ¼": 61645, + "Ġqs": 61646, + "ĠSisters": 61647, + "Ġexchanging": 61648, + "onc": 61649, + "ĠPrecision": 61650, + "Ġê±´ëĦĪê°Ģ": 61651, + "Ġ')[": 61652, + "ĠRefuge": 61653, + "Ġconceivable": 61654, + "à³į": 61655, + "ĠUni": 61656, + "ìĬ¤íĥł": 61657, + "Ġíĺ¸ì¹ĺ": 61658, + "ĠëıĮì§Ħ": 61659, + "íı¬ìĬ¤": 61660, + "ediatric": 61661, + "à·": 61662, + "Ġfilenames": 61663, + "ĠëĮĢìĽħ": 61664, + "Ġêµ°ìŀIJ": 61665, + "zers": 61666, + "ëĭµëĭµ": 61667, + "Ġgatherings": 61668, + "Cookie": 61669, + "Ġharmonics": 61670, + "BMW": 61671, + "ĠNaples": 61672, + "Ġect": 61673, + "ëıĻìĤ¬": 61674, + "ĠæĪĺ": 61675, + "Transition": 61676, + "ë¹ĦëĭIJ": 61677, + "Ġ골격ê³Ħ": 61678, + "Ġrés": 61679, + "ì²ŃëħĦ": 61680, + "ĠADV": 61681, + "ĠìľłìĦľ": 61682, + "Ġ伯": 61683, + "ĠTales": 61684, + "design": 61685, + "Ġcomplains": 61686, + "Charl": 61687, + "ĠìŀIJê·¸ë§Ī": 61688, + "Ġmouths": 61689, + "Ġê³µìĺģ주": 61690, + "Ġëªħíĥľ": 61691, + "Ġìĺ¬ëĿ¼ê°ijëĭĪëĭ¤": 61692, + "ĠíħIJëıĻ": 61693, + "Ġê½ĥë§IJ": 61694, + "ĠíĮĮëĿ¼ëĭ¤ìĿ´ìĬ¤": 61695, + "ë³´ìĤ´": 61696, + "ĠìĹ°ë©´ìłģ": 61697, + "Ġë°ľëĮĢìĭĿ": 61698, + "ĠNurse": 61699, + "nets": 61700, + "Ġalarms": 61701, + "Ġeminent": 61702, + "Ġkidding": 61703, + "ĠSED": 61704, + "ĠRoland": 61705, + "íķĦë¦Ħ": 61706, + "Ġjsfiddle": 61707, + "Ġbeasts": 61708, + "????": 61709, + "ĠéĬĢ": 61710, + "Ġfeb": 61711, + "ĠFixes": 61712, + "Ġìľ¡ì§Ī": 61713, + "íĥĢëĬĦ": 61714, + "Ġdoctrines": 61715, + "Ġê·¸ëŁ´ê¹Į": 61716, + "oust": 61717, + "ĠLeaders": 61718, + "Ġmisrepresentation": 61719, + "focus": 61720, + "LM": 61721, + "Ġnexus": 61722, + "ĠPresbyter": 61723, + "ĠTheodore": 61724, + "ĠUltr": 61725, + "ãģĦãĤĭ": 61726, + "asionally": 61727, + "Ġzebrafish": 61728, + "ĠLibraries": 61729, + "Ġê²½ì¾Į": 61730, + "ĠSolo": 61731, + "Ġ커íĮħ": 61732, + "ĠìĿ¸ë°©": 61733, + "ĠVikings": 61734, + "ĠTm": 61735, + "Ġê°Ģ림": 61736, + "Ġcheerful": 61737, + "Ġmounts": 61738, + "ĠMorton": 61739, + "쬬": 61740, + "Ġwf": 61741, + "Film": 61742, + "Ġ.~": 61743, + "Ġactionable": 61744, + "ĠëĤ´ëĨĶ": 61745, + "Ġì°¬ë°ĺ": 61746, + "Ġëĭ¹íĺ¹": 61747, + "Ġoverlaps": 61748, + "Ġëĵ¤ëĿ½": 61749, + "orno": 61750, + "Ġë°°ë¶Ī룬": 61751, + "Ġë©ĶìĿ¸ë³´ëĵľ": 61752, + "Ġìĺ¤ëª©": 61753, + "Ġskillet": 61754, + "models": 61755, + "ĠUndefined": 61756, + "ĠINV": 61757, + "Ġquir": 61758, + "Ġeditable": 61759, + "ĠëĴ¤ì²Ļ": 61760, + "Ġfoliage": 61761, + "Ġbiting": 61762, + "Ġlocalities": 61763, + "ê±°ìĬ¤": 61764, + "Ġfigs": 61765, + "ĠìĿĺìĿ¸": 61766, + "Ġì¹´ëĿ¼ë°ĺ": 61767, + "Ele": 61768, + "Ġeats": 61769, + "ĠIPTV": 61770, + "íĮĮ주": 61771, + "ĠDiversity": 61772, + "entru": 61773, + "Ġsculptures": 61774, + "gm": 61775, + "theus": 61776, + "ĠCITY": 61777, + "ır": 61778, + "Ġcounterfe": 61779, + "Ġìļ°ë¦´": 61780, + "ĠìĬ¤íı°ìĦľ": 61781, + "ĠìłĦ문ëĮĢ": 61782, + "ĠNarr": 61783, + "ancip": 61784, + "ĠëıĻìĽĶ": 61785, + "Ġbloc": 61786, + "ìĬĪíĥĢìĿ¸": 61787, + "ĠìIJIJ": 61788, + "Ġgallons": 61789, + "ĠGOD": 61790, + "ĠSchul": 61791, + "Ġprecedence": 61792, + "zek": 61793, + "reech": 61794, + "Ġì§ĪìĨĮ": 61795, + "Ġconcentrating": 61796, + "Ġë§ĮëħĦ": 61797, + "Ġweekday": 61798, + "Ġìĭľì±ħ": 61799, + "Ġshutting": 61800, + "Ġ무ì§Ģíķĺ": 61801, + "ĠBUILD": 61802, + "Ġìŀ¬ìĭ¬": 61803, + "ĠìķłëıĦ": 61804, + "Ġë¶Ģì¹¨ê°ľ": 61805, + "ĠOwens": 61806, + "Ġìŀ¥íĥĢ": 61807, + "ĠLOS": 61808, + "ĠíķľëĮĢìļĶ": 61809, + "ĠKJ": 61810, + "Ġcaul": 61811, + "akan": 61812, + "Ġhandmade": 61813, + "ĠTau": 61814, + "íķ´ìĤ°ë¬¼": 61815, + "à¤Ĺ": 61816, + "Ġìĵ°ëŁ¬ì¡Į": 61817, + "ej": 61818, + "ĠTHERE": 61819, + "OTO": 61820, + "ĠPASSWORD": 61821, + "rypted": 61822, + "Ġë¶Ī룬ëıĦ": 61823, + "ĠíĽĦêµ°": 61824, + "Ġë°ķìĿ¸": 61825, + "ĠGujar": 61826, + "Ġìĸ´ì§Ģê°Ħ": 61827, + "ê´Ģ리본ë¶Ģ": 61828, + "çĶ°": 61829, + "ì¶ľíĺĪ": 61830, + "ĠBEL": 61831, + "ĠTou": 61832, + "ĠìĦ±ë¶ģ구": 61833, + "ĠTODAY": 61834, + "Ġserializers": 61835, + "bil": 61836, + "Ġprolific": 61837, + "ĠNj": 61838, + "stellar": 61839, + "Ġcaves": 61840, + "ĠTarg": 61841, + "ĠíĥĢìĿ´ê±°ì¦Ī": 61842, + "ëįĶë¸Ķ": 61843, + "Ġíĺ¼ëıĻ": 61844, + "ĠëıĦìĻĶ": 61845, + "ĠìłķíĺĦ": 61846, + "Ġenvisioned": 61847, + "Ġìºłíķijì¹´": 61848, + "ĠHubbard": 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61898, + "Ġisotopic": 61899, + "Ġç¾ħ": 61900, + "Ġjudicata": 61901, + "Ġconverse": 61902, + "Ġjihad": 61903, + "Ġhomosexuality": 61904, + "ĠRefs": 61905, + "Ġprototypes": 61906, + "ĠìłĦ매": 61907, + "ĠExposure": 61908, + "Ġenron": 61909, + "Ġíģ´ë¦°íĦ´": 61910, + "ĠìĤ°ëıĻ": 61911, + "changes": 61912, + "Ġpreaching": 61913, + "ĠCBT": 61914, + "Ġdusty": 61915, + "Ġìĸ¸ë¡łìĿ¸": 61916, + "Ġê°ķ림": 61917, + "Ġ구ëªħ": 61918, + "Ġcosine": 61919, + "Ġ맡긴": 61920, + "Ġbacterium": 61921, + "ultz": 61922, + "oblasts": 61923, + "ìĥĮëĵľ": 61924, + "еж": 61925, + "ĠSurgical": 61926, + "Ġê³ģëĵ¤ìĿ´": 61927, + "문ìłIJ": 61928, + "Ġcontinual": 61929, + "Ġvolcano": 61930, + "Ġbask": 61931, + "Ġ.;": 61932, + "Argument": 61933, + "Ġbromide": 61934, + "ĠMunicipality": 61935, + "Ġdab": 61936, + "ĠëĮĢìļ´": 61937, + "Ġmodo": 61938, + "Ġ기ëĭ¤ë¦°ëĭ¤": 61939, + "Ġ-{": 61940, + "tresp": 61941, + "fts": 61942, + "Ġç¯Ģ": 61943, + "ĠDY": 61944, + "ĠDetail": 61945, + "ĠBrom": 61946, + "plastic": 61947, + "ĠëĨįìĦ±": 61948, + 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69227, + "Ġchloroform": 69228, + "ĠAnswers": 69229, + "Ġì½ľë¦°": 69230, + "Ġbrides": 69231, + "ĠLOAD": 69232, + "Ġë°´ì¿łë²Ħ": 69233, + "Ġê³µìłķê±°ëŀĺìľĦ": 69234, + "ĠDistr": 69235, + "ĠArd": 69236, + "쯤": 69237, + "Ġfilmmakers": 69238, + "Ġë¹Ħëĵľ": 69239, + "Ġdolls": 69240, + "Ġì¤ijìŀĦ": 69241, + "ĠãģķãĤĮãģ¦": 69242, + "emiah": 69243, + "Ġrubb": 69244, + "Consequ": 69245, + "Ġìķİ": 69246, + "ĠHelena": 69247, + "ĠTobacco": 69248, + "Ġ={};": 69249, + "象": 69250, + "Ġtwofold": 69251, + "ĠGiovanni": 69252, + "elier": 69253, + "ĠAck": 69254, + "Ġ모ìķ¼": 69255, + "ĠAntib": 69256, + "ĠLawyers": 69257, + "klore": 69258, + "Ġstewards": 69259, + "ĠëĤ´ì§Ħ": 69260, + "QUEST": 69261, + "atore": 69262, + "ĠëĦĪì§Ģ": 69263, + "ĠLecture": 69264, + "ìĭłê³ł": 69265, + "ĠSap": 69266, + "ĠTitans": 69267, + "ĠìĹĺíĶĦ": 69268, + "Ġpoder": 69269, + "ê±°ìłľ": 69270, + "å®Ĺ": 69271, + "ĠgetText": 69272, + "Ġlug": 69273, + "Ġìŀ¡íŀIJ": 69274, + "ĠìĿµíŀIJ": 69275, + "INC": 69276, + "ì½Ķì§Ģ": 69277, + "ëĿ¼ìĿ´ëįĶ": 69278, + "ĠìķĦ모ëłĪ": 69279, + "Ġsignup": 69280, + "ĠìĿ´ìļ©íķľ": 69281, + "ĠSCR": 69282, + "Ġpager": 69283, + "주먹": 69284, + "ĠíĥĦìĭĿ": 69285, + "ĠìķĦíĶĦê°Ħ": 69286, + "Ġãģ©": 69287, + "ĠEnt": 69288, + "ê¸Ģ거리": 69289, + "ĠAberdeen": 69290, + "Ġtoolbox": 69291, + "Ġejected": 69292, + "ĠNEVER": 69293, + "Ġgv": 69294, + "terness": 69295, + "Ġì§ģì±ħ": 69296, + "Ġfavoring": 69297, + "ĠëıĻê³µ": 69298, + "ĠìłIJ주": 69299, + "Ġë°±ëijIJìĤ°": 69300, + "Ġgob": 69301, + "ĠGoldberg": 69302, + "andes": 69303, + "Ġnotebooks": 69304, + "Ġhelicopters": 69305, + "few": 69306, + "iphatic": 69307, + "ĠEvel": 69308, + "ìĪľìĪĺ": 69309, + "Ġdiscontinuity": 69310, + "ë°ĺ죽": 69311, + "ĠProposed": 69312, + "Ġbury": 69313, + "리ìĤ¬": 69314, + "èĦ": 69315, + "Ġcropped": 69316, + "Ġ^âĪĴ/âĪĴ^": 69317, + "Ġë²Īíĺ¸íijľ": 69318, + "ìĹIJìĿ´ì§ķ": 69319, + "Ġboutique": 69320, + "ìĺģìĪĺì¦Ŀ": 69321, + "ĠEmerson": 69322, + "Ġnanocompos": 69323, + "ĠMatters": 69324, + "ĠíĮĮëĤĺ": 69325, + "ĠPrefecture": 69326, + "IRD": 69327, + 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+ "Ġloot": 78827, + "ÑĤа": 78828, + "ĠíĮĮ멸": 78829, + "ĠHeinrich": 78830, + "Ġxrange": 78831, + "ĠMEMS": 78832, + "ĠKann": 78833, + "Ġhairst": 78834, + "quine": 78835, + "ĠìĽIJ리ê¸Ī": 78836, + "ĠDDP": 78837, + "Ġintersp": 78838, + "Ġdor": 78839, + "Ġíݸì§ijìŀIJ": 78840, + "Ġyn": 78841, + "Ġmetaphors": 78842, + "Ġëĭ¨êµ°": 78843, + "ĠRomero": 78844, + "ĠTaipei": 78845, + "ĠFury": 78846, + "ĠåĢį": 78847, + "Ġcomprehensively": 78848, + "enb": 78849, + "Ġstumble": 78850, + "typical": 78851, + "Ġsoot": 78852, + "ĠFellows": 78853, + "ìĹ°ì¶ľ": 78854, + "ĠëĬĺìĸ´ìł¸": 78855, + "ĠLydia": 78856, + "Boolean": 78857, + "ĠElla": 78858, + "Ġíķ´ìķĪê°Ģ": 78859, + "Ġê°ī": 78860, + "ĠJae": 78861, + "Ġfram": 78862, + "ĠNaomi": 78863, + "Ġtoughest": 78864, + "Ġgps": 78865, + "Ġvial": 78866, + "Ġtextsc": 78867, + "Ġë°ĶëŀĮë§īìĿ´": 78868, + "ĠåĿĩ": 78869, + "밸리": 78870, + "Lex": 78871, + "ìĦłë°ľ": 78872, + "ĠCommands": 78873, + "ĠSenegal": 78874, + "Ġë°ķíĺķ": 78875, + "Ġì¡°ëł¥ìŀIJ": 78876, + "Ġë§ŀ춤ë²ķ": 78877, + "Ġanticipating": 78878, + "ĠìĬ´ëıł": 78879, + "Ġinund": 78880, + "Ġyolk": 78881, + "ĠíİłíĬ¸": 78882, + "ĠNish": 78883, + "Ġì§ĦìłĢ": 78884, + "ë¸Ķ린": 78885, + "Ġaccelerometer": 78886, + "Ġoats": 78887, + "ĠëIJĺì§ļ": 78888, + "ĠTiffany": 78889, + "ìĿ´ë§Ī": 78890, + "Ġè·Ŀ": 78891, + "entanyl": 78892, + "Ġairplanes": 78893, + "ĠëĤ±ê°ľ": 78894, + "ì»·ëī´ìĬ¤": 78895, + "Ġdepolarization": 78896, + "ĠDefined": 78897, + "distance": 78898, + "åĽĽ": 78899, + "vall": 78900, + "Ġdde": 78901, + "ì¡´ìŀ¬": 78902, + "Ġê¸īì§Ħ": 78903, + "ophag": 78904, + "Ġìĸ´ëł¤ìĽĮìļĶ": 78905, + "Constant": 78906, + "Ġíķłë¯¸": 78907, + "ĠìĤ°ë°ľ": 78908, + "Ġ==(": 78909, + "Ġytn": 78910, + "Ġë³Ģì¡°": 78911, + "Ġsorption": 78912, + "Captain": 78913, + "Ġ=',": 78914, + "Ġtransactional": 78915, + "Rain": 78916, + "Ġadorned": 78917, + "Ġbisexual": 78918, + "Ġì§ĢìĦł": 78919, + "çĻ¼": 78920, + "ĠìĿ´íĿ¬": 78921, + "Ġ먹구ë¦Ħ": 78922, + "Ġpherom": 78923, + "Produc": 78924, + "ĠCuc": 78925, + "ĠìĿĺë£ĮìĿ¸": 78926, + "ánd": 78927, + "ĠìłĦ기ìŀ¥íĮIJ": 78928, + "ĠìĪĺëıĦê¼Ńì§Ģ": 78929, + "Ġinterchangeable": 78930, + "ĠåĨĨ": 78931, + "Ġê³µìĥģ": 78932, + "ĠCrab": 78933, + "Ġíķľíķĺ": 78934, + "Ġconsultancy": 78935, + "Ġëĭ¤ë°©ë©´": 78936, + "Ġwinged": 78937, + "Ġupro": 78938, + "REEN": 78939, + "ĠëĤ©ìĦ¸": 78940, + "Ġtriaz": 78941, + "Ġdwarfs": 78942, + "Ġìĺ»": 78943, + "Ġ缣": 78944, + "Ġãģłãģij": 78945, + "ĠArbitration": 78946, + "development": 78947, + "ruc": 78948, + "ì¿łëĿ¼": 78949, + "ãĥ©ãĤ¯": 78950, + "Ġpaving": 78951, + "Ġë¶ģëıĭ": 78952, + "çķ¥": 78953, + "ĠTl": 78954, + "Ġshowcases": 78955, + "Spin": 78956, + "åĪĩ": 78957, + "Redirect": 78958, + "Ġê°Ģë¥´ì¹ł": 78959, + "ĠRETURN": 78960, + "ì±ĻìĿ´": 78961, + "ĠAless": 78962, + "Ġcarbs": 78963, + "Ġли": 78964, + "ĠíĨłëĭ¥íĨłëĭ¥": 78965, + "ĠìłĪìĿ¸": 78966, + "Ġ}]$": 78967, + "onite": 78968, + "ĠThrones": 78969, + "ĠìĦ¤ìķħ": 78970, + "Ġnitrite": 78971, + "ĠéĦŃ": 78972, + "Ġionizing": 78973, + "Ġê±°ëľ¬íŀĪ": 78974, + "ĠTis": 78975, + "OCI": 78976, + "ĠBombay": 78977, + "uw": 78978, + "shed": 78979, + "Ġë¹Ļê¸Ģë¹Ļê¸Ģ": 78980, + "Ġ^_": 78981, + "ĠìĽĶê°Ģ": 78982, + "ĠProfessionals": 78983, + "Ġsemigroup": 78984, + "ĠSioux": 78985, + "ĠëĤĺìķĦì§Ħ": 78986, + "piratory": 78987, + "ĠëĤĺ루íĨł": 78988, + "íķĺìĦ±": 78989, + "ĠìłĢëł¥": 78990, + "Ġaffluent": 78991, + "Ġorthonormal": 78992, + "Ġ기ìŀIJìŀ¬": 78993, + "HAM": 78994, + "Ġpeered": 78995, + "ĠFFFFFF": 78996, + "ĠìĥģìķĦ": 78997, + "Ġìłľëªħ": 78998, + "ĠìĿ¼ìĸ´ëĤ©ëĭĪëĭ¤": 78999, + "Ġshepherd": 79000, + "Ġ과민ìĦ±": 79001, + "ĠBayer": 79002, + "Ġtrimethyl": 79003, + "ĠDisaster": 79004, + "ĠìĦŀìŬìĦľ": 79005, + "Ġhelmets": 79006, + "Ġhadronic": 79007, + "Ġmasonry": 79008, + "Wr": 79009, + "Ġ몸ìĭ¸ìĽĢ": 79010, + "Ġë°ijì¤Ħ": 79011, + "Ġbifurc": 79012, + "ĠBoundary": 79013, + "ĠManufacturers": 79014, + "Ġê²ĮìŀĦ기": 79015, + "ĠëĥĦìĥĪëĤĺ": 79016, + "MIT": 79017, + "ĠëĶ§": 79018, + "Ġrehearsal": 79019, + "umas": 79020, + "Ġitalics": 79021, + "Ġìĭľëĭ¬ëł¸": 79022, + "Ġëį°ìĿ´ì§Ģ": 79023, + "urved": 79024, + "ĠScouts": 79025, + "Ġbroccoli": 79026, + "Ġuntrans": 79027, + "충ì¹ĺ": 79028, + "ãĥ¼ãĤºãĥ³": 79029, + "ë³´ê³łìĦľ": 79030, + "Ġëī": 79031, + "Ġcertiorari": 79032, + "ĠNodes": 79033, + "Ġì°©íķ´": 79034, + "Ġescalating": 79035, + "Ġmucho": 79036, + "ĠIsing": 79037, + "ĠScandinavian": 79038, + "PDF": 79039, + "Ġdarn": 79040, + "Ġsupersymmetric": 79041, + "è§Ĵ": 79042, + "along": 79043, + "Ġmammal": 79044, + "Ġmonotonically": 79045, + "Ġwn": 79046, + "ĠìķĦë¡ł": 79047, + "ìĬ¤íĬ¸ë¦¼": 79048, + "Ġenormously": 79049, + "ulata": 79050, + "Ġjsonify": 79051, + "íĥĢëĤĺ": 79052, + "orna": 79053, + "ĠGarage": 79054, + "Ġagro": 79055, + "ĠÑģÑĤÑĢ": 79056, + "Ġundermining": 79057, + "Ġpulsar": 79058, + "Ġunchecked": 79059, + "Ġquerying": 79060, + "ĠëĶ¸ëĤ´ë¯¸": 79061, + "Ġsubclasses": 79062, + "ĠOSS": 79063, + "Ġê°ķ민": 79064, + "Ġflute": 79065, + "Ġ缮çļĦ": 79066, + "scroll": 79067, + "ĠLup": 79068, + "ĠâĹĭâĹĭ": 79069, + "ì¢ħêµIJ": 79070, + "ÑĢÑĭ": 79071, + "ĠŽ": 79072, + "æĤ": 79073, + "ĠíĻĺê¸īê¸Ī": 79074, + "Ġpanoramic": 79075, + "Ġ\\]-": 79076, + "ĠæĬĢè¡ĵ": 79077, + "ë³´ìĮĪ": 79078, + "Ġëĭ¤íĬ¸": 79079, + "Ġ모ëįĶ": 79080, + "ĠìľłíĥĢ": 79081, + "Ġconfers": 79082, + "Ġwaven": 79083, + "Ġ}):": 79084, + "Ġpropyl": 79085, + "Ġssd": 79086, + "rai": 79087, + "Ġì¶ľì¡°": 79088, + "Ġëĵ¤ìĸ´ë§ŀ": 79089, + "ĠlÃŃ": 79090, + "Ġон": 79091, + "ëĵ¤ë¦¬ìĹIJ": 79092, + "Ġdinosaur": 79093, + "Ġjester": 79094, + "ĠìĹĦì§ĢìĨIJê°ĢëĿ½": 79095, + "Fred": 79096, + "íķ´ìķĪ": 79097, + "Ġident": 79098, + "Ġê²ĮìŬ": 79099, + "Ġindemnity": 79100, + "Ġnik": 79101, + "ĠSich": 79102, + "ĠBlocks": 79103, + "ĠRockefeller": 79104, + "Ġpragma": 79105, + "reich": 79106, + "ĠíĸīìĿ¸": 79107, + "ição": 79108, + "ÏĢÏĮ": 79109, + "ĠìĿ´ë¡ľìļ´": 79110, + "ëĶĶì¦ĪëĭĪ": 79111, + "ĠAnnotation": 79112, + "Backend": 79113, + "ĠìĪĺìļ©ìĨĮ": 79114, + "Ġìĸijìľ¡ê¶Į": 79115, + "ĠHazard": 79116, + "Ġãģ¨ãģĻãĤĭ": 79117, + "ĠìĻ¸ê´Ģìĥģ": 79118, + "Mind": 79119, + "Positive": 79120, + "ĠAuss": 79121, + "Ġfalciparum": 79122, + "Ġ\"=>": 79123, + "Ġ리ë¹Ħ": 79124, + "Ġconsul": 79125, + "Ġ조물조물": 79126, + "ĠContinuing": 79127, + "Ġordinances": 79128, + "Ġë°ķì¥IJ": 79129, + "ĠPoverty": 79130, + "Ġpesso": 79131, + "ĠíĿ¡ìĹ°ìŀIJ": 79132, + "ĠíĥĢê°ľ": 79133, + "Ġì²łìĤ¬": 79134, + "ìĦŃì·¨": 79135, + "Ġ..^": 79136, + "Ġê½ĥ구경": 79137, + "ĠGonzales": 79138, + "Ġcoaxial": 79139, + "Ġmultivariable": 79140, + "Ġastronauts": 79141, + "Ġunil": 79142, + "Ġë°¤ë°Ķëĭ¤": 79143, + "Implementation": 79144, + "Ġ기ê²ģ": 79145, + "ĠDok": 79146, + "Ġkafka": 79147, + "Ġministries": 79148, + "odiac": 79149, + "çģ«": 79150, + "Ġì¤Ģìļ°ìĬ¹": 79151, + "ĠRevision": 79152, + "ëĬIJëŁ¬ë¯¸": 79153, + "ylic": 79154, + "ĠSEA": 79155, + "ĠåĭĴ": 79156, + "ĠZeus": 79157, + "Ġ..@": 79158, + "Ġtaxing": 79159, + "Ġmotherboard": 79160, + "ãģĹãĤĩãģĨ": 79161, + "graded": 79162, + "instead": 79163, + "Ġìĥģíķł": 79164, + "Ġdern": 79165, + "Ġrefrigeration": 79166, + "Ġteasing": 79167, + "ê°ľê°ķ": 79168, + "ĠGerard": 79169, + "ĠTHz": 79170, + "Ġcljs": 79171, + "ĠTroub": 79172, + "Ġfools": 79173, + "rehensible": 79174, + "Ġimperfections": 79175, + "ĠìĤ¬ìĿ´íĮIJ": 79176, + "ĠVOD": 79177, + "ç¯ī": 79178, + "Ġtrunks": 79179, + "ĠìĹ°ìĤ¬": 79180, + "ĠìĿĺê²°ê¶Į": 79181, + "ĠìĦ±ê³¼ê¸ī": 79182, + "ĠLSU": 79183, + "ìĤ¬ìĿ´íĬ¸": 79184, + "Ġslashed": 79185, + "ĠíļĮìĿĺìŀ¥": 79186, + "ìļ°ëıĦ": 79187, + "Ġë°ĺëĶ§": 79188, + "Ġëĭ¤ë¶Ħ": 79189, + "ĠLauder": 79190, + "ĠSloan": 79191, + "ĠåĨł": 79192, + "Ġenr": 79193, + "Ġabsorbs": 79194, + "cade": 79195, + "ë²Īì©į": 79196, + "DATE": 79197, + "Ġìµľë©´": 79198, + "ìĿ´ëĶ°": 79199, + "Ġìĺ¤ê¸Ī": 79200, + "Ġmatured": 79201, + "Ġworsened": 79202, + "Ġsquirrel": 79203, + "ĠKeyword": 79204, + "Ġannoyance": 79205, + "ĠìĬ¤íĭ°ë¡ľíı¼": 79206, + "molecular": 79207, + "ĠFDIC": 79208, + "ĠZhong": 79209, + "ĠBAP": 79210, + "Ġump": 79211, + "Ġadaptor": 79212, + "Ġsheltered": 79213, + "Ġëĭ¤ëĭĻëĭĪëĭ¤": 79214, + "Ġvows": 79215, + "ĠUne": 79216, + "music": 79217, + "é£Ł": 79218, + "Ġrethink": 79219, + "äºĶ": 79220, + "ĠHib": 79221, + "Ġquiescent": 79222, + "ucha": 79223, + "Ġ\">,::": 80287, + "Ġfru": 80288, + "ĠOvers": 80289, + "ĠëĤĺê·¸ëĦ¤": 80290, + "ĠPrimer": 80291, + "oxane": 80292, + "Ġmotorists": 80293, + "Ġironically": 80294, + "Ġmathematician": 80295, + "ĠìŀIJê¶ģê²½ë¶ĢìķĶ": 80296, + "ìĹ¬ëŁ¬ëª¨ë¡ľ": 80297, + "Ġobstetric": 80298, + "Ġë¶ĪëģĪ": 80299, + "íĥľìĺģ": 80300, + "Ġstrut": 80301, + "Ġdeepen": 80302, + "BV": 80303, + "ĠFalcons": 80304, + "Ġlobster": 80305, + "theon": 80306, + "Ġrepealed": 80307, + "Ġê³łìĿĦ": 80308, + "ĠìĪĺêµIJ": 80309, + "Ġabrasion": 80310, + "Ġhousekeeping": 80311, + "ëijIJ볤": 80312, + "Ġantidepressants": 80313, + "ëĤ¨ëħĢëħ¸ìĨĮ": 80314, + "yrin": 80315, + "ĠعÙĦÙī": 80316, + "ĠSFR": 80317, + "ĠAmendments": 80318, + "Ġë°°ìļ´ëĭ¤": 80319, + "ĠBallet": 80320, + "Ġflushing": 80321, + "Crypto": 80322, + "ĠìŀIJì¹Ń": 80323, + "ìĭľëłģ": 80324, + "à¸Ī": 80325, + "Ġparameterization": 80326, + "ĠìĽIJëıĦ": 80327, + "ĠëĭĪê»ĺ": 80328, + "ĠINR": 80329, + "ĠOer": 80330, + "Ġvorticity": 80331, + "ĠìķĪìĥī": 80332, + "ĠTeach": 80333, + "ĠçŃĸ": 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80384, + "ë½ij": 80385, + "ĠZionist": 80386, + "Ġtransgress": 80387, + "Ġëĸłìĺ¬ë¦´": 80388, + "ĠHorm": 80389, + "리ì¼Ģ": 80390, + "ĠìĹIJë¸Į리": 80391, + "Ġ}.$": 80392, + "Ġethos": 80393, + "ĠìĪĺìĺģ구": 80394, + "necessary": 80395, + "ĠKickstarter": 80396, + "ĠìłIJì³IJ": 80397, + "ĠìķĦìī¬ìļ°": 80398, + "Ġìľłì¶©": 80399, + "Ġë³´ë¡ľ": 80400, + "Ġë°±ìĬ¤": 80401, + "...,": 80402, + "Ġíķ©ìĦ±ìĸ´": 80403, + "Lists": 80404, + "Ġobtainable": 80405, + "ĠÄIJ": 80406, + "CONCLUSIONS": 80407, + "clip": 80408, + "CLE": 80409, + "Ġhefty": 80410, + "Ġìĭłì¶ķìĦ±": 80411, + "建": 80412, + "Ġrevascular": 80413, + "Ġsurjective": 80414, + "ĠShack": 80415, + "Ġhierarchies": 80416, + "ouvel": 80417, + "Ġ볬": 80418, + "versed": 80419, + "ì§ģìĦł": 80420, + "Ġì¢ĮìĿµìĪĺ": 80421, + "material": 80422, + "Ġpund": 80423, + "íĺĦ미": 80424, + "ĠPreferably": 80425, + "ĠConfeder": 80426, + "ĠíĿ¬ìĨĮìĭĿ": 80427, + "JK": 80428, + "ĠìłĢëĭ¹": 80429, + "Ġremun": 80430, + "Ġnecrotic": 80431, + "Convention": 80432, + "ĠkeyCode": 80433, 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