diff --git a/config.json b/config.json
new file mode 100644
index 0000000000000000000000000000000000000000..fd81c02a600b51187a93565cc4524779538310c0
--- /dev/null
+++ b/config.json
@@ -0,0 +1,72 @@
+{
+  "_name_or_path": "/fs/archive/share/yulan/data/aa_mini/output/minicpm-2B-final-stage19/checkpoint-184750",
+  "architectures": [
+    "MiniCPMForCausalLM"
+  ],
+"auto_map": {
+"AutoConfig": "configuration_minicpm.MiniCPMConfig",
+"AutoModel": "modeling_minicpm.MiniCPMModel",
+"AutoModelForCausalLM": "modeling_minicpm.MiniCPMForCausalLM",
+"AutoModelForSeq2SeqLM": "modeling_minicpm.MiniCPMForCausalLM",
+"AutoModelForSequenceClassification": "modeling_minicpm.MiniCPMForSequenceClassification"
+},
+  "attention_bias": true,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dim_model_base": 1920,
+  "dim_model_base_attn": 64,
+  "dim_model_base_init": null,
+  "dim_model_base_lmh": 1,
+  "dim_model_base_logits": 1920.0,
+  "dim_model_base_lr": 256.0,
+  "down_proj_alpha": 0.03450327796711771,
+  "embed_tokens_alpha": 1,
+  "embedding_ln": false,
+  "embedding_rmsln": false,
+  "eos_token_id": 2,
+  "gate_up_proj_alpha": 0.3651483716701107,
+  "gradient_checkpointing_step": 11,
+  "hidden_act": "silu",
+  "hidden_size": 1920,
+  "hidden_states_shrink": 0.18708286933869706,
+  "init_scale_o": 1,
+  "initializer_range": 5e-05,
+  "input_layernorm_alpha": 1.0,
+  "intermediate_size": 4800,
+  "k_proj_alpha": 0.3651483716701107,
+  "layer_norm_eps": 1e-06,
+  "lm_head_alpha": 1.0,
+  "ln_scale": 1,
+  "max_position_embeddings": 4096,
+  "model_reproduce": "transformer",
+  "model_type": "minicpm",
+  "norm_alpha": 1.0,
+  "num_attention_heads": 30,
+  "num_epochs_trained_before_this_epoch": 18.0,
+  "num_hidden_layers": 56,
+  "num_key_value_heads": 6,
+  "num_steps_trained_before_this_epoch": 175608,
+  "o_proj_alpha": 0.03450327796711771,
+  "post_attention_layernorm_alpha": 1.0,
+  "q_proj_alpha": 0.3651483716701107,
+  "qk_layernorm": false,
+  "rms_norm_eps": 1e-06,
+  "rms_type": "llama",
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "scale_emb": 10.0,
+  "shrink_alpha": 1,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.44.2",
+  "use_cache": true,
+  "use_emb_alpha": true,
+  "use_liger": true,
+  "use_norm_alpha": false,
+  "use_sliding_window": false,
+  "v_proj_alpha": 0.3651483716701107,
+  "vocab_size": 99000,
+  "wesar_weights": false,
+  "z_loss": 0.0001
+}
diff --git a/configuration_minicpm.py b/configuration_minicpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..21eb158572ad0cfbfdf51072106d5018c2ff8ca5
--- /dev/null
+++ b/configuration_minicpm.py
@@ -0,0 +1,202 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. 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.
+""" MiniCPM model configuration"""
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+MINICPM_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
+
+
+class MiniCPMConfig(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MiniCPMModel`]. It is used to instantiate an MiniCPM
+    model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
+    defaults will yield a similar configuration to that of the MiniCPM-7B.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+
+    Args:
+        vocab_size (`int`, *optional*, defaults to 32000):
+            Vocabulary size of the MiniCPM model. Defines the number of different tokens that can be represented by the
+            `inputs_ids` passed when calling [`MiniCPMModel`]
+        hidden_size (`int`, *optional*, defaults to 4096):
+            Dimension of the hidden representations.
+        intermediate_size (`int`, *optional*, defaults to 11008):
+            Dimension of the MLP representations.
+        num_hidden_layers (`int`, *optional*, defaults to 32):
+            Number of hidden layers in the Transformer decoder.
+        num_attention_heads (`int`, *optional*, defaults to 32):
+            Number of attention heads for each attention layer in the Transformer decoder.
+        num_key_value_heads (`int`, *optional*):
+            This is the number of key_value heads that should be used to implement Grouped Query Attention. If
+            `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
+            `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
+            converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
+            by meanpooling all the original heads within that group. For more details checkout [this
+            paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
+            `num_attention_heads`.
+        hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
+            The non-linear activation function (function or string) in the decoder.
+        max_position_embeddings (`int`, *optional*, defaults to 2048):
+            The maximum sequence length that this model might ever be used with. MiniCPM 1 supports up to 2048 tokens,
+            MiniCPM 2 up to 4096, CodeMiniCPM up to 16384.
+        initializer_range (`float`, *optional*, defaults to 0.02):
+            The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
+        rms_norm_eps (`float`, *optional*, defaults to 1e-06):
+            The epsilon used by the rms normalization layers.
+        use_cache (`bool`, *optional*, defaults to `True`):
+            Whether or not the model should return the last key/values attentions (not used by all models). Only
+            relevant if `config.is_decoder=True`.
+        pad_token_id (`int`, *optional*):
+            Padding token id.
+        bos_token_id (`int`, *optional*, defaults to 1):
+            Beginning of stream token id.
+        eos_token_id (`int`, *optional*, defaults to 2):
+            End of stream token id.
+        pretraining_tp (`int`, *optional*, defaults to 1):
+            Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
+            document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
+            necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
+            issue](https://github.com/pytorch/pytorch/issues/76232).
+        tie_word_embeddings (`bool`, *optional*, defaults to `False`):
+            Whether to tie weight embeddings
+        rope_theta (`float`, *optional*, defaults to 10000.0):
+            The base period of the RoPE embeddings.
+        rope_scaling (`Dict`, *optional*):
+            Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
+            strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
+            `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
+            `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
+            these scaling strategies behave:
+            https://www.reddit.com/r/LocalMiniCPM/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
+            experimental feature, subject to breaking API changes in future versions.
+        attention_bias (`bool`, defaults to `False`, *optional*, defaults to `False`):
+            Whether to use a bias in the query, key, value and output projection layers during self-attention.
+        attention_dropout (`float`, *optional*, defaults to 0.0):
+            The dropout ratio for the attention probabilities.
+
+    ```python
+    >>> from transformers import MiniCPMModel, MiniCPMConfig
+
+    >>> # Initializing a MiniCPM minicpm-7b style configuration
+    >>> configuration = MiniCPMConfig()
+
+    >>> # Initializing a model from the minicpm-7b style configuration
+    >>> model = MiniCPMModel(configuration)
+
+    >>> # Accessing the model configuration
+    >>> configuration = model.config
+    ```"""
+
+    model_type = "minicpm"
+    keys_to_ignore_at_inference = ["past_key_values"]
+
+    def __init__(
+        self,
+        vocab_size=32000,
+        hidden_size=4096,
+        intermediate_size=11008,
+        num_hidden_layers=32,
+        num_attention_heads=32,
+        num_key_value_heads=None,
+        hidden_act="silu",
+        max_position_embeddings=2048,
+        initializer_range=0.02,
+        rms_norm_eps=1e-6,
+        use_cache=True,
+        pad_token_id=None,
+        bos_token_id=1,
+        eos_token_id=2,
+        pretraining_tp=1,
+        tie_word_embeddings=True,
+        rope_theta=10000.0,
+        rope_scaling=None,
+        attention_bias=False,
+        attention_dropout=0.0,
+        scale_emb=1,
+        dim_model_base=1,
+        scale_depth=1,
+        **kwargs,
+    ):
+        self.vocab_size = vocab_size
+        self.max_position_embeddings = max_position_embeddings
+        self.hidden_size = hidden_size
+        self.intermediate_size = intermediate_size
+        self.num_hidden_layers = num_hidden_layers
+        self.num_attention_heads = num_attention_heads
+
+        # for backward compatibility
+        if num_key_value_heads is None:
+            num_key_value_heads = num_attention_heads
+
+        self.num_key_value_heads = num_key_value_heads
+        self.hidden_act = hidden_act
+        self.initializer_range = initializer_range
+        self.rms_norm_eps = rms_norm_eps
+        self.pretraining_tp = pretraining_tp
+        self.use_cache = use_cache
+        self.rope_theta = rope_theta
+        self.rope_scaling = rope_scaling
+        self._rope_scaling_validation()
+        self.attention_bias = attention_bias
+        self.attention_dropout = attention_dropout
+        self.scale_emb = scale_emb
+        self.dim_model_base = dim_model_base
+        self.scale_depth = scale_depth
+
+        super().__init__(
+            pad_token_id=pad_token_id,
+            bos_token_id=bos_token_id,
+            eos_token_id=eos_token_id,
+            tie_word_embeddings=tie_word_embeddings,
+            **kwargs,
+        )
+        try:
+            import flash_attn
+            self._attn_implementation = "flash_attention_2"
+        except:
+            pass
+
+    def _rope_scaling_validation(self):
+        """
+        Validate the `rope_scaling` configuration.
+        """
+        if self.rope_scaling is None:
+            return
+
+        if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
+            raise ValueError(
+                "`rope_scaling` must be a dictionary with with two fields, `type` and `factor`, "
+                f"got {self.rope_scaling}"
+            )
+        rope_scaling_type = self.rope_scaling.get("type", None)
+        rope_scaling_factor = self.rope_scaling.get("factor", None)
+        if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic"]:
+            raise ValueError(
+                f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], got {rope_scaling_type}"
+            )
+        if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
+            raise ValueError(f"`rope_scaling`'s factor field must be a float > 1, got {rope_scaling_factor}")
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diff --git a/modeling_minicpm.py b/modeling_minicpm.py
new file mode 100644
index 0000000000000000000000000000000000000000..5336996eb0221a3d7f9dee84c067799e4b12f4b0
--- /dev/null
+++ b/modeling_minicpm.py
@@ -0,0 +1,1453 @@
+# coding=utf-8
+# Copyright 2022 EleutherAI and the HuggingFace Inc. team. 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.
+""" PyTorch MiniCPM model."""
+import math
+import warnings
+from typing import List, Optional, Tuple, Union, Dict
+
+import torch
+import torch.nn.functional as F
+import torch.utils.checkpoint
+from torch import nn
+from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
+
+from transformers.activations import ACT2FN
+from transformers.cache_utils import Cache, DynamicCache
+from transformers.modeling_attn_mask_utils import (
+    AttentionMaskConverter,
+    _prepare_4d_attention_mask,
+    _prepare_4d_causal_attention_mask,
+    _prepare_4d_causal_attention_mask_for_sdpa,
+)
+from transformers.modeling_outputs import BaseModelOutputWithPast, CausalLMOutputWithPast, SequenceClassifierOutputWithPast
+from transformers.modeling_utils import PreTrainedModel
+from transformers.pytorch_utils import ALL_LAYERNORM_LAYERS, is_torch_greater_or_equal_than_1_13
+from transformers.utils import (
+    add_start_docstrings,
+    add_start_docstrings_to_model_forward,
+    is_flash_attn_2_available,
+    is_flash_attn_greater_or_equal_2_10,
+    logging,
+    replace_return_docstrings,
+)
+from transformers.utils.import_utils import is_torch_fx_available
+from .configuration_minicpm import MiniCPMConfig
+import re
+
+try:
+    from flash_attn import flash_attn_func, flash_attn_varlen_func
+    from flash_attn.bert_padding import index_first_axis, pad_input, unpad_input  # noqa
+except:
+    pass
+
+
+# This makes `_prepare_4d_causal_attention_mask` a leaf function in the FX graph.
+# It means that the function will not be traced through and simply appear as a node in the graph.
+if is_torch_fx_available():
+    if not is_torch_greater_or_equal_than_1_13:
+        import torch.fx
+
+    _prepare_4d_causal_attention_mask = torch.fx.wrap(_prepare_4d_causal_attention_mask)
+
+
+logger = logging.get_logger(__name__)
+
+_CONFIG_FOR_DOC = "MiniCPMConfig"
+
+
+def _get_unpad_data(attention_mask):
+    seqlens_in_batch = attention_mask.sum(dim=-1, dtype=torch.int32)
+    indices = torch.nonzero(attention_mask.flatten(), as_tuple=False).flatten()
+    max_seqlen_in_batch = seqlens_in_batch.max().item()
+    cu_seqlens = F.pad(torch.cumsum(seqlens_in_batch, dim=0, dtype=torch.torch.int32), (1, 0))
+    return (
+        indices,
+        cu_seqlens,
+        max_seqlen_in_batch,
+    )
+
+
+def _expand_mask(mask: torch.Tensor, dtype: torch.dtype, tgt_len: Optional[int] = None):
+    warnings.warn(
+        "Calling `transformers.models.minicpm.modeling_minicpm._prepare_4d_attention_mask` is deprecated and will be removed in v4.37. Use `transformers.modeling_attn_mask_utils._prepare_4d_attention_mask"
+    )
+    return _prepare_4d_attention_mask(mask=mask, dtype=dtype, tgt_len=tgt_len)
+
+
+def _make_causal_mask(
+    input_ids_shape: torch.Size, dtype: torch.dtype, device: torch.device, past_key_values_length: int = 0
+):
+    warnings.warn(
+        "Calling `transformers.models.minicpm.modeling_minicpm._make_causal_mask` is deprecated and will be removed in v4.37. Use `transformers.models.minicpm.modeling_minicpm.AttentionMaskConverter._make_causal_mask"
+    )
+    return AttentionMaskConverter._make_causal_mask(
+        input_ids_shape=input_ids_shape, dtype=dtype, device=device, past_key_values_length=past_key_values_length
+    )
+
+# @torch.jit.script  # type: ignore
+def rms_layernorm(hidden: torch.Tensor, weight: torch.Tensor, eps: float):
+    old_dtype = hidden.dtype
+    variance = hidden.to(torch.float32).pow(2).mean(dim=-1, keepdim=True)
+    hidden = (hidden * torch.rsqrt(variance + eps)).to(old_dtype)
+    return hidden * weight
+
+
+class MiniCPMRMSNorm(nn.Module):
+    def __init__(self, hidden_size, eps=1e-6):
+        """
+        MiniCPMRMSNorm is equivalent to T5LayerNorm
+        """
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(hidden_size))
+        self.variance_epsilon = eps
+
+    def forward(self, hidden_states):
+        return rms_layernorm(hidden_states, self.weight, self.variance_epsilon)
+
+
+ALL_LAYERNORM_LAYERS.append(MiniCPMRMSNorm)
+
+
+class MiniCPMRotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None):
+        super().__init__()
+
+        self.dim = dim
+        self.max_position_embeddings = max_position_embeddings
+        self.base = base
+        inv_freq = 1.0 / (self.base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+        self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        # Build here to make `torch.jit.trace` work.
+        self._set_cos_sin_cache(
+            # seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.get_default_dtype()
+            seq_len=max_position_embeddings, device=self.inv_freq.device, dtype=torch.float32
+        )
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+    def forward(self, x, seq_len=None):
+        # x: [bs, num_attention_heads, seq_len, head_size]
+        if seq_len > self.max_seq_len_cached:
+            self._set_cos_sin_cache(seq_len=seq_len, device=x.device, dtype=x.dtype)
+
+        return (
+            self.cos_cached[:seq_len].to(dtype=x.dtype),
+            self.sin_cached[:seq_len].to(dtype=x.dtype),
+        )
+
+
+class MiniCPMLinearScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
+    """MiniCPMRotaryEmbedding extended with linear scaling. Credits to the Reddit user /u/kaiokendev"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+        t = t / self.scaling_factor
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+class MiniCPMDynamicNTKScalingRotaryEmbedding(MiniCPMRotaryEmbedding):
+    """MiniCPMRotaryEmbedding extended with Dynamic NTK scaling. Credits to the Reddit users /u/bloc97 and /u/emozilla"""
+
+    def __init__(self, dim, max_position_embeddings=2048, base=10000, device=None, scaling_factor=1.0):
+        self.scaling_factor = scaling_factor
+        super().__init__(dim, max_position_embeddings, base, device)
+
+    def _set_cos_sin_cache(self, seq_len, device, dtype):
+        self.max_seq_len_cached = seq_len
+
+        if seq_len > self.max_position_embeddings:
+            base = self.base * (
+                (self.scaling_factor * seq_len / self.max_position_embeddings) - (self.scaling_factor - 1)
+            ) ** (self.dim / (self.dim - 2))
+            inv_freq = 1.0 / (base ** (torch.arange(0, self.dim, 2).float().to(device) / self.dim))
+            self.register_buffer("inv_freq", inv_freq, persistent=False)
+
+        t = torch.arange(self.max_seq_len_cached, device=device, dtype=self.inv_freq.dtype)
+
+        freqs = torch.outer(t, self.inv_freq)
+        # Different from paper, but it uses a different permutation in order to obtain the same calculation
+        emb = torch.cat((freqs, freqs), dim=-1)
+
+        self.register_buffer("cos_cached", emb.cos().to(dtype), persistent=False)
+        self.register_buffer("sin_cached", emb.sin().to(dtype), persistent=False)
+
+
+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 apply_rotary_pos_emb(q, k, cos, sin, position_ids, 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.
+        position_ids (`torch.Tensor`):
+            The position indices of the tokens corresponding to the query and key tensors. For example, this can be
+            used to pass offsetted position ids when working with a KV-cache.
+        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[position_ids].unsqueeze(unsqueeze_dim)
+    # sin = sin[position_ids].unsqueeze(unsqueeze_dim)
+    # q_embed = (q * cos) + (rotate_half(q) * sin)
+    # k_embed = (k * cos) + (rotate_half(k) * sin)
+    orig_dtype = k.dtype
+    cos = cos[position_ids].unsqueeze(unsqueeze_dim)  # [bs, 1, seq_len, dim]
+    sin = sin[position_ids].unsqueeze(unsqueeze_dim)  # [bs, 1, seq_len, dim]
+    q_fp32 = q.to(dtype=torch.float32, device=q.device)
+    k_fp32 = k.to(dtype=torch.float32, device=k.device)
+    q_embed = (q_fp32 * cos) + (rotate_half(q_fp32) * sin)
+    k_embed = (k_fp32 * cos) + (rotate_half(k_fp32) * sin)
+    return q_embed.to(dtype=orig_dtype), k_embed.to(dtype=orig_dtype)
+
+class MiniCPMMLP(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+        self.hidden_size = config.hidden_size
+        self.intermediate_size = config.intermediate_size
+        self.gate_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.up_proj = nn.Linear(self.hidden_size, self.intermediate_size, bias=False)
+        self.down_proj = nn.Linear(self.intermediate_size, self.hidden_size, bias=False)
+        self.act_fn = ACT2FN[config.hidden_act]
+
+    def forward(self, x):
+        if self.config.pretraining_tp > 1:
+            slice = self.intermediate_size // self.config.pretraining_tp
+            gate_proj_slices = self.gate_proj.weight.split(slice, dim=0)
+            up_proj_slices = self.up_proj.weight.split(slice, dim=0)
+            down_proj_slices = self.down_proj.weight.split(slice, dim=1)
+
+            gate_proj = torch.cat(
+                [F.linear(x, gate_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1
+            )
+            up_proj = torch.cat([F.linear(x, up_proj_slices[i]) for i in range(self.config.pretraining_tp)], dim=-1)
+
+            intermediate_states = (self.act_fn(gate_proj) * up_proj).split(slice, dim=2)
+            down_proj = [
+                F.linear(intermediate_states[i], down_proj_slices[i]) for i in range(self.config.pretraining_tp)
+            ]
+            down_proj = sum(down_proj)
+        else:
+            down_proj = self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+        return down_proj
+
+
+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)
+
+
+
+class MiniCPMAttention(nn.Module):
+    """Multi-headed attention from 'Attention Is All You Need' paper"""
+
+    def __init__(self, config: MiniCPMConfig, layer_idx: Optional[int] = None):
+        super().__init__()
+        self.config = config
+        self.layer_idx = layer_idx
+        if layer_idx is None:
+            logger.warning_once(
+                f"Instantiating {self.__class__.__name__} without passing `layer_idx` is not recommended and will "
+                "to errors during the forward call, if caching is used. Please make sure to provide a `layer_idx` "
+                "when creating this class."
+            )
+
+        self.attention_dropout = config.attention_dropout
+        self.hidden_size = config.hidden_size
+        self.num_heads = config.num_attention_heads
+        self.head_dim = self.hidden_size // 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.max_position_embeddings = config.max_position_embeddings
+        self.rope_theta = config.rope_theta
+        self.is_causal = True
+
+        if (self.head_dim * self.num_heads) != self.hidden_size:
+            raise ValueError(
+                f"hidden_size must be divisible by num_heads (got `hidden_size`: {self.hidden_size}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        self.q_proj = nn.Linear(self.hidden_size, self.num_heads * self.head_dim, bias=config.attention_bias)
+        self.k_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.v_proj = nn.Linear(self.hidden_size, self.num_key_value_heads * self.head_dim, bias=config.attention_bias)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, self.hidden_size, bias=config.attention_bias)
+        self._init_rope()
+
+    def _init_rope(self):
+        if self.config.rope_scaling is None:
+            self.rotary_emb = MiniCPMRotaryEmbedding(
+                self.head_dim,
+                max_position_embeddings=self.max_position_embeddings,
+                base=self.rope_theta,
+            )
+        else:
+            scaling_type = self.config.rope_scaling["type"]
+            scaling_factor = self.config.rope_scaling["factor"]
+            if scaling_type == "linear":
+                self.rotary_emb = MiniCPMLinearScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            elif scaling_type == "dynamic":
+                self.rotary_emb = MiniCPMDynamicNTKScalingRotaryEmbedding(
+                    self.head_dim,
+                    max_position_embeddings=self.max_position_embeddings,
+                    scaling_factor=scaling_factor,
+                    base=self.rope_theta,
+                )
+            else:
+                raise ValueError(f"Unknown RoPE scaling type {scaling_type}")
+
+    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: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        bsz, q_len, _ = hidden_states.size()
+
+        if self.config.pretraining_tp > 1:
+            key_value_slicing = (self.num_key_value_heads * self.head_dim) // self.config.pretraining_tp
+            query_slices = self.q_proj.weight.split(
+                (self.num_heads * self.head_dim) // self.config.pretraining_tp, dim=0
+            )
+            key_slices = self.k_proj.weight.split(key_value_slicing, dim=0)
+            value_slices = self.v_proj.weight.split(key_value_slicing, dim=0)
+
+            query_states = [F.linear(hidden_states, query_slices[i]) for i in range(self.config.pretraining_tp)]
+            query_states = torch.cat(query_states, dim=-1)
+
+            key_states = [F.linear(hidden_states, key_slices[i]) for i in range(self.config.pretraining_tp)]
+            key_states = torch.cat(key_states, dim=-1)
+
+            value_states = [F.linear(hidden_states, value_slices[i]) for i in range(self.config.pretraining_tp)]
+            value_states = torch.cat(value_states, dim=-1)
+
+        else:
+            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)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            if self.layer_idx is None:
+                raise ValueError(
+                    f"The cache structure has changed since version v4.36. If you are using {self.__class__.__name__} "
+                    "for auto-regressive decoding with k/v caching, please make sure to initialize the attention class "
+                    "with a layer index."
+                )
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            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 attn_weights.size() != (bsz, self.num_heads, q_len, kv_seq_len):
+            raise ValueError(
+                f"Attention weights should be of size {(bsz, self.num_heads, q_len, kv_seq_len)}, but is"
+                f" {attn_weights.size()}"
+            )
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+            attn_weights = attn_weights + attention_mask
+
+        # upcast attention to fp32
+        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, 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"`attn_output` 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.hidden_size)
+
+        if self.config.pretraining_tp > 1:
+            attn_output = attn_output.split(self.hidden_size // self.config.pretraining_tp, dim=2)
+            o_proj_slices = self.o_proj.weight.split(self.hidden_size // self.config.pretraining_tp, dim=1)
+            attn_output = sum([F.linear(attn_output[i], o_proj_slices[i]) for i in range(self.config.pretraining_tp)])
+        else:
+            attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+
+class MiniCPMFlashAttention2(MiniCPMAttention):
+    """
+    MiniCPM flash attention module. This module inherits from `MiniCPMAttention` as the weights of the module stays
+    untouched. The only required change would be on the forward pass where it needs to correctly call the public API of
+    flash attention and deal with padding tokens in case the input contains any of them.
+    """
+
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+
+        # TODO: Should be removed once Flash Attention for RoCm is bumped to 2.1.
+        # flash_attn<2.1 generates top-left aligned causal mask, while what is needed here is bottom-right alignement, that was made default for flash_attn>=2.1. This attribute is used to handle this difference. Reference: https://github.com/Dao-AILab/flash-attention/releases/tag/v2.1.0.
+        # Beware that with flash_attn<2.1, using q_seqlen != k_seqlen (except for the case q_seqlen == 1) produces a wrong mask (top-left).
+        self._flash_attn_uses_top_left_mask = not is_flash_attn_greater_or_equal_2_10()
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.LongTensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Cache] = None,
+        output_attentions: bool = False,
+        use_cache: bool = False,
+        **kwargs,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        # MiniCPMFlashAttention2 attention does not support output_attentions
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+            # overwrite attention_mask with padding_mask
+            attention_mask = kwargs.pop("padding_mask")
+
+        output_attentions = False
+
+        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)
+
+        # Flash attention requires the input to have the shape
+        # batch_size x seq_length x head_dim x hidden_dim
+        # therefore we just need to keep the original shape
+        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)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states.to(torch.float32), seq_len=kv_seq_len)
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
+
+        # TODO: These transpose are quite inefficient but Flash Attention requires the layout [batch_size, sequence_length, num_heads, head_dim]. We would need to refactor the KV cache
+        # to be able to avoid many of these transpose/reshape/view.
+        query_states = query_states.transpose(1, 2)
+        key_states = key_states.transpose(1, 2)
+        value_states = value_states.transpose(1, 2)
+
+        dropout_rate = self.attention_dropout if self.training else 0.0
+
+        # 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.
+        # This might slowdown training & inference so it is recommended to not cast the LayerNorms
+        # in fp32. (MiniCPMRMSNorm handles it correctly)
+
+        input_dtype = query_states.dtype
+        if input_dtype == torch.float32:
+            # Handle the case where the model is quantized
+            if 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)
+
+        attn_output = self._flash_attention_forward(
+            query_states, key_states, value_states, attention_mask, q_len, dropout=dropout_rate
+        )
+
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size).contiguous()
+        attn_output = self.o_proj(attn_output)
+
+        if not output_attentions:
+            attn_weights = None
+
+        return attn_output, attn_weights, past_key_value
+
+    def _flash_attention_forward(
+        self, query_states, key_states, value_states, attention_mask, query_length, dropout=0.0, softmax_scale=None
+    ):
+        """
+        Calls the forward method of Flash Attention - if the input hidden states contain at least one padding token
+        first unpad the input, then computes the attention scores and pad the final attention scores.
+
+        Args:
+            query_states (`torch.Tensor`):
+                Input query states to be passed to Flash Attention API
+            key_states (`torch.Tensor`):
+                Input key states to be passed to Flash Attention API
+            value_states (`torch.Tensor`):
+                Input value states to be passed to Flash Attention API
+            attention_mask (`torch.Tensor`):
+                The padding mask - corresponds to a tensor of size `(batch_size, seq_len)` where 0 stands for the
+                position of padding tokens and 1 for the position of non-padding tokens.
+            dropout (`int`, *optional*):
+                Attention dropout
+            softmax_scale (`float`, *optional*):
+                The scaling of QK^T before applying softmax. Default to 1 / sqrt(head_dim)
+        """
+        if not self._flash_attn_uses_top_left_mask:
+            causal = self.is_causal
+        else:
+            # TODO: Remove the `query_length != 1` check once Flash Attention for RoCm is bumped to 2.1. For details, please see the comment in MiniCPMFlashAttention2 __init__.
+            causal = self.is_causal and query_length != 1
+        # Contains at least one padding token in the sequence
+        if attention_mask is not None:
+            batch_size = query_states.shape[0]
+            query_states, key_states, value_states, indices_q, cu_seq_lens, max_seq_lens = self._upad_input(
+                query_states, key_states, value_states, attention_mask, query_length
+            )
+
+            cu_seqlens_q, cu_seqlens_k = cu_seq_lens
+            max_seqlen_in_batch_q, max_seqlen_in_batch_k = max_seq_lens
+            attn_output_unpad = flash_attn_varlen_func(
+                query_states,
+                key_states,
+                value_states,
+                cu_seqlens_q=cu_seqlens_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_q=max_seqlen_in_batch_q,
+                max_seqlen_k=max_seqlen_in_batch_k,
+                dropout_p=dropout,
+                softmax_scale=softmax_scale,
+                causal=causal,
+            )
+
+            attn_output = pad_input(attn_output_unpad, indices_q, batch_size, query_length)
+        else:
+            attn_output = flash_attn_func(
+                query_states, key_states, value_states, dropout, softmax_scale=softmax_scale, causal=causal
+            )
+
+        return attn_output
+
+    def _upad_input(self, query_layer, key_layer, value_layer, attention_mask, query_length):
+        indices_k, cu_seqlens_k, max_seqlen_in_batch_k = _get_unpad_data(attention_mask)
+        batch_size, kv_seq_len, num_key_value_heads, head_dim = key_layer.shape
+
+        key_layer = index_first_axis(
+            key_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        value_layer = index_first_axis(
+            value_layer.reshape(batch_size * kv_seq_len, num_key_value_heads, head_dim), indices_k
+        )
+        if query_length == kv_seq_len:
+            query_layer = index_first_axis(
+                query_layer.reshape(batch_size * kv_seq_len, self.num_heads, head_dim), indices_k
+            )
+            cu_seqlens_q = cu_seqlens_k
+            max_seqlen_in_batch_q = max_seqlen_in_batch_k
+            indices_q = indices_k
+        elif query_length == 1:
+            max_seqlen_in_batch_q = 1
+            cu_seqlens_q = torch.arange(
+                batch_size + 1, dtype=torch.int32, device=query_layer.device
+            )  # There is a memcpy here, that is very bad.
+            indices_q = cu_seqlens_q[:-1]
+            query_layer = query_layer.squeeze(1)
+        else:
+            # The -q_len: slice assumes left padding.
+            attention_mask = attention_mask[:, -query_length:]
+            query_layer, indices_q, cu_seqlens_q, max_seqlen_in_batch_q = unpad_input(query_layer, attention_mask)
+
+        return (
+            query_layer,
+            key_layer,
+            value_layer,
+            indices_q,
+            (cu_seqlens_q, cu_seqlens_k),
+            (max_seqlen_in_batch_q, max_seqlen_in_batch_k),
+        )
+
+
+class MiniCPMSdpaAttention(MiniCPMAttention):
+    """
+    MiniCPM attention module using torch.nn.functional.scaled_dot_product_attention. This module inherits from
+    `MiniCPMAttention` as the weights of the module stays untouched. The only changes are on the forward pass to adapt to
+    SDPA API.
+    """
+
+    # Adapted from MiniCPMAttention.forward
+    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: bool = False,
+        use_cache: bool = False,
+    ) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
+        if output_attentions:
+            # TODO: Improve this warning with e.g. `model.config.attn_implementation = "manual"` once this is implemented.
+            logger.warning_once(
+                "MiniCPMModel is using MiniCPMSdpaAttention, 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,
+            )
+
+        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)
+
+        kv_seq_len = key_states.shape[-2]
+        if past_key_value is not None:
+            kv_seq_len += past_key_value.get_usable_length(kv_seq_len, self.layer_idx)
+        cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len)
+
+        query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin, position_ids)
+
+        if past_key_value is not None:
+            cache_kwargs = {"sin": sin, "cos": cos}  # Specific to RoPE models
+            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)
+
+        if attention_mask is not None:
+            if attention_mask.size() != (bsz, 1, q_len, kv_seq_len):
+                raise ValueError(
+                    f"Attention mask should be of size {(bsz, 1, q_len, kv_seq_len)}, but is {attention_mask.size()}"
+                )
+
+        # 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 attention_mask is not None:
+            query_states = query_states.contiguous()
+            key_states = key_states.contiguous()
+            value_states = value_states.contiguous()
+
+        attn_output = torch.nn.functional.scaled_dot_product_attention(
+            query_states,
+            key_states,
+            value_states,
+            attn_mask=attention_mask,
+            dropout_p=self.attention_dropout if self.training else 0.0,
+            # The q_len > 1 is necessary to match with AttentionMaskConverter.to_causal_4d that does not create a causal mask in case q_len == 1.
+            is_causal=self.is_causal and attention_mask is None and q_len > 1,
+        )
+
+        attn_output = attn_output.transpose(1, 2).contiguous()
+        attn_output = attn_output.reshape(bsz, q_len, self.hidden_size)
+
+        attn_output = self.o_proj(attn_output)
+
+        return attn_output, None, past_key_value
+
+
+MINICPM_ATTENTION_CLASSES = {
+    "eager": MiniCPMAttention,
+    "flash_attention_2": MiniCPMFlashAttention2,
+    "sdpa": MiniCPMSdpaAttention,
+}
+
+
+class MiniCPMDecoderLayer(nn.Module):
+    def __init__(self, config: MiniCPMConfig, layer_idx: int):
+        super().__init__()
+        self.hidden_size = config.hidden_size
+        self.self_attn = MINICPM_ATTENTION_CLASSES[config._attn_implementation](config=config, layer_idx=layer_idx)
+
+        self.mlp = MiniCPMMLP(config)
+        self.input_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+        self.post_attention_layernorm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.scale_depth = config.scale_depth
+        self.num_hidden_layers = config.num_hidden_layers
+
+    def forward(
+        self,
+        hidden_states: torch.Tensor,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_value: Optional[Tuple[torch.Tensor]] = None,
+        output_attentions: Optional[bool] = False,
+        use_cache: Optional[bool] = False,
+        **kwargs,
+    ) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
+        """
+        Args:
+            hidden_states (`torch.FloatTensor`): input to the layer of shape `(batch, seq_len, embed_dim)`
+            attention_mask (`torch.FloatTensor`, *optional*):
+                attention mask of size `(batch_size, sequence_length)` if flash attention is used or `(batch_size, 1,
+                query_sequence_length, key_sequence_length)` if default attention is used.
+            output_attentions (`bool`, *optional*):
+                Whether or not to return the attentions tensors of all attention layers. See `attentions` under
+                returned tensors for more detail.
+            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`).
+            past_key_value (`Tuple(torch.FloatTensor)`, *optional*): cached past key and value projection states
+        """
+        if "padding_mask" in kwargs:
+            warnings.warn(
+                "Passing `padding_mask` is deprecated and will be removed in v4.37. Please make sure use `attention_mask` instead.`"
+            )
+
+        residual = hidden_states
+        hidden_states = self.input_layernorm(hidden_states)
+        # Self Attention
+        hidden_states, self_attn_weights, present_key_value = self.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,
+            **kwargs,
+        )
+
+        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+
+        # Fully Connected
+        residual = hidden_states
+        hidden_states = self.post_attention_layernorm(hidden_states)
+
+        hidden_states = self.mlp(hidden_states)
+        hidden_states = residual + hidden_states * (self.scale_depth / math.sqrt(self.num_hidden_layers))
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (self_attn_weights,)
+
+        if use_cache:
+            outputs += (present_key_value,)
+
+        return outputs
+
+
+MINICPM_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 ([`MiniCPMConfig`]):
+            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.
+"""
+
+
+@add_start_docstrings(
+    "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
+    MINICPM_START_DOCSTRING,
+)
+class MiniCPMPreTrainedModel(PreTrainedModel):
+    config_class = MiniCPMConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+    _no_split_modules = ["MiniCPMDecoderLayer"]
+    _skip_keys_device_placement = "past_key_values"
+    _supports_flash_attn_2 = True
+    _supports_sdpa = True
+    _supports_cache_class = True
+
+    def _init_weights(self, module):
+        std = self.config.initializer_range
+        if isinstance(module, nn.Linear):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.Embedding):
+            module.weight.data.normal_(mean=0.0, std=std)
+            if module.padding_idx is not None:
+                module.weight.data[module.padding_idx].zero_()
+
+
+MINICPM_INPUTS_DOCSTRING = r"""
+    Args:
+        input_ids (`torch.LongTensor` of shape `(batch_size, sequence_length)`):
+            Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
+            it.
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            [What are input IDs?](../glossary#input-ids)
+        attention_mask (`torch.Tensor` 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#attention-mask)
+
+            Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and
+            [`PreTrainedTokenizer.__call__`] for details.
+
+            If `past_key_values` is used, optionally only the last `input_ids` have to be input (see
+            `past_key_values`).
+
+            If you want to change padding behavior, you should read [`modeling_opt._prepare_decoder_attention_mask`]
+            and modify to your needs. See diagram 1 in [the paper](https://arxiv.org/abs/1910.13461) for more
+            information on the default strategy.
+
+            - 1 indicates the head is **not masked**,
+            - 0 indicates the head is **masked**.
+        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.n_positions - 1]`.
+
+            [What are position IDs?](../glossary#position-ids)
+        past_key_values (`Cache` or `tuple(tuple(torch.FloatTensor))`, *optional*):
+            Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
+            blocks) that 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`.
+
+            Two formats are allowed:
+            - a [`~cache_utils.Cache`] instance;
+            - Tuple of `tuple(torch.FloatTensor)` of length `config.n_layers`, with each tuple having 2 tensors of
+            shape `(batch_size, num_heads, sequence_length, embed_size_per_head)`). This is also known as the legacy
+            cache format.
+
+            The model will output the same cache format that is fed as input. If no `past_key_values` are passed, the
+            legacy cache format will be returned.
+
+            If `past_key_values` are used, the user can optionally input only the last `input_ids` (those that don't
+            have their past key value states given to this model) of shape `(batch_size, 1)` instead of all `input_ids`
+            of shape `(batch_size, sequence_length)`.
+        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.
+        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.
+"""
+
+
+@add_start_docstrings(
+    "The bare MiniCPM Model outputting raw hidden-states without any specific head on top.",
+    MINICPM_START_DOCSTRING,
+)
+class MiniCPMModel(MiniCPMPreTrainedModel):
+    """
+    Transformer decoder consisting of *config.num_hidden_layers* layers. Each layer is a [`MiniCPMDecoderLayer`]
+
+    Args:
+        config: MiniCPMConfig
+    """
+
+    def __init__(self, config: MiniCPMConfig):
+        super().__init__(config)
+        self.padding_idx = config.pad_token_id
+        self.vocab_size = config.vocab_size
+
+        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
+        self.layers = nn.ModuleList(
+            [MiniCPMDecoderLayer(config, layer_idx) for layer_idx in range(config.num_hidden_layers)]
+        )
+        self._use_sdpa = config._attn_implementation == "sdpa"
+        self._use_flash_attention_2 = config._attn_implementation == "flash_attention_2"
+
+        self.norm = MiniCPMRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
+
+        self.gradient_checkpointing = False
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, 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
+
+        # retrieve input_ids and inputs_embeds
+        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")
+
+        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
+
+        past_key_values_length = 0
+        if use_cache:
+            use_legacy_cache = not isinstance(past_key_values, Cache)
+            if use_legacy_cache:
+                past_key_values = DynamicCache.from_legacy_cache(past_key_values)
+            past_key_values_length = past_key_values.get_usable_length(seq_length)
+
+        if position_ids is None:
+            device = input_ids.device if input_ids is not None else inputs_embeds.device
+            position_ids = torch.arange(
+                past_key_values_length, seq_length + past_key_values_length, dtype=torch.long, device=device
+            )
+            position_ids = position_ids.unsqueeze(0)
+
+        if inputs_embeds is None:
+            inputs_embeds = self.embed_tokens(input_ids) * self.config.scale_emb
+
+        if self._use_flash_attention_2:
+            # 2d mask is passed through the layers
+            attention_mask = attention_mask if (attention_mask is not None and 0 in attention_mask) else None
+        elif self._use_sdpa and not output_attentions:
+            # output_attentions=True can not be supported when using SDPA, and we fall back on
+            # the manual implementation that requires a 4D causal mask in all cases.
+            attention_mask = _prepare_4d_causal_attention_mask_for_sdpa(
+                attention_mask,
+                (batch_size, seq_length),
+                inputs_embeds,
+                past_key_values_length,
+            )
+        else:
+            # 4d mask is passed through the layers
+            attention_mask = _prepare_4d_causal_attention_mask(
+                attention_mask, (batch_size, seq_length), inputs_embeds, past_key_values_length
+            )
+
+        # embed positions
+        hidden_states = inputs_embeds
+
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        next_decoder_cache = None
+
+        for decoder_layer in self.layers:
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+            if self.gradient_checkpointing and self.training:
+                layer_outputs = self._gradient_checkpointing_func(
+                    decoder_layer.__call__,
+                    hidden_states,
+                    attention_mask,
+                    position_ids,
+                    past_key_values,
+                    output_attentions,
+                    use_cache,
+                )
+            else:
+                layer_outputs = decoder_layer(
+                    hidden_states,
+                    attention_mask=attention_mask,
+                    position_ids=position_ids,
+                    past_key_value=past_key_values,
+                    output_attentions=output_attentions,
+                    use_cache=use_cache,
+                )
+
+            hidden_states = layer_outputs[0]
+
+            if use_cache:
+                next_decoder_cache = layer_outputs[2 if output_attentions else 1]
+
+            if output_attentions:
+                all_self_attns += (layer_outputs[1],)
+
+        hidden_states = self.norm(hidden_states)
+
+        # add hidden states from the last decoder layer
+        if output_hidden_states:
+            all_hidden_states += (hidden_states,)
+
+        next_cache = None
+        if use_cache:
+            next_cache = next_decoder_cache.to_legacy_cache() if use_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,
+        )
+
+
+class MiniCPMForCausalLM(MiniCPMPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MiniCPMModel(config)
+        self.vocab_size = config.vocab_size
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    def get_output_embeddings(self):
+        return self.lm_head
+
+    def set_output_embeddings(self, new_embeddings):
+        self.lm_head = new_embeddings
+
+    def set_decoder(self, decoder):
+        self.model = decoder
+
+    def get_decoder(self):
+        return self.model
+
+    @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
+    @replace_return_docstrings(output_type=CausalLMOutputWithPast, config_class=_CONFIG_FOR_DOC)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, CausalLMOutputWithPast]:
+        r"""
+        Args:
+            labels (`torch.LongTensor` of shape `(batch_size, sequence_length)`, *optional*):
+                Labels for computing the masked language modeling loss. Indices should either be in `[0, ...,
+                config.vocab_size]` or -100 (see `input_ids` docstring). Tokens with indices set to `-100` are ignored
+                (masked), the loss is only computed for the tokens with labels in `[0, ..., config.vocab_size]`.
+
+        Returns:
+
+        Example:
+
+        ```python
+        >>> from transformers import AutoTokenizer, MiniCPMForCausalLM
+
+        >>> model = MiniCPMForCausalLM.from_pretrained(PATH_TO_CONVERTED_WEIGHTS)
+        >>> tokenizer = AutoTokenizer.from_pretrained(PATH_TO_CONVERTED_TOKENIZER)
+
+        >>> prompt = "Hey, are you conscious? Can you talk to me?"
+        >>> inputs = tokenizer(prompt, return_tensors="pt")
+
+        >>> # Generate
+        >>> generate_ids = model.generate(inputs.input_ids, max_length=30)
+        >>> tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+        "Hey, are you conscious? Can you talk to me?\nI'm not conscious, but I can talk to you."
+        ```"""
+        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
+
+        # decoder outputs consists of (dec_features, layer_state, dec_hidden, dec_attn)
+        outputs = self.model(
+            input_ids=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 = outputs[0]
+        if self.config.pretraining_tp > 1:
+            lm_head_slices = self.lm_head.weight.split(self.vocab_size // self.config.pretraining_tp, dim=0)
+            logits = [F.linear(hidden_states, lm_head_slices[i]) for i in range(self.config.pretraining_tp)]
+            logits = torch.cat(logits, dim=-1)
+        else:
+            logits = self.lm_head(hidden_states / (self.config.hidden_size / self.config.dim_model_base))
+        logits = logits.float()
+
+        loss = None
+        if labels is not None:
+            # Shift so that tokens < n predict n
+            shift_logits = logits[..., :-1, :].contiguous()
+            shift_labels = labels[..., 1:].contiguous()
+            # Flatten the tokens
+            loss_fct = CrossEntropyLoss()
+            shift_logits = shift_logits.view(-1, self.config.vocab_size)
+            shift_labels = shift_labels.view(-1)
+            # Enable model parallelism
+            shift_labels = shift_labels.to(shift_logits.device)
+            loss = loss_fct(shift_logits, shift_labels)
+
+        if not return_dict:
+            output = (logits,) + outputs[1:]
+            return (loss,) + output if loss is not None else output
+
+        return CausalLMOutputWithPast(
+            loss=loss,
+            logits=logits,
+            past_key_values=outputs.past_key_values,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+    def prepare_inputs_for_generation(
+        self, input_ids, past_key_values=None, attention_mask=None, inputs_embeds=None, **kwargs
+    ):
+        if past_key_values is not None:
+            if isinstance(past_key_values, Cache):
+                cache_length = past_key_values.get_seq_length()
+                past_length = past_key_values.seen_tokens
+                max_cache_length = past_key_values.get_max_length()
+            else:
+                cache_length = past_length = past_key_values[0][0].shape[2]
+                max_cache_length = None
+
+            # Keep only the unprocessed tokens:
+            # 1 - If the length of the attention_mask exceeds the length of input_ids, then we are in a setting where
+            # some of the inputs are exclusivelly passed as part of the cache (e.g. when passing input_embeds as
+            # input)
+            if attention_mask is not None and attention_mask.shape[1] > input_ids.shape[1]:
+                input_ids = input_ids[:, -(attention_mask.shape[1] - past_length) :]
+            # 2 - If the past_length is smaller than input_ids', then input_ids holds all input tokens. We can discard
+            # input_ids based on the past_length.
+            elif past_length < input_ids.shape[1]:
+                input_ids = input_ids[:, past_length:]
+            # 3 - Otherwise (past_length >= input_ids.shape[1]), let's assume input_ids only has unprocessed tokens.
+
+            # If we are about to go beyond the maximum cache length, we need to crop the input attention mask.
+            if (
+                max_cache_length is not None
+                and attention_mask is not None
+                and cache_length + input_ids.shape[1] > max_cache_length
+            ):
+                attention_mask = attention_mask[:, -max_cache_length:]
+
+        position_ids = kwargs.get("position_ids", None)
+        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] :]
+
+        # if `inputs_embeds` are passed, we only want to use them in the 1st generation step
+        if inputs_embeds is not None and past_key_values is None:
+            model_inputs = {"inputs_embeds": inputs_embeds}
+        else:
+            model_inputs = {"input_ids": input_ids}
+
+        model_inputs.update(
+            {
+                "position_ids": position_ids,
+                "past_key_values": past_key_values,
+                "use_cache": kwargs.get("use_cache"),
+                "attention_mask": attention_mask,
+            }
+        )
+        return model_inputs
+
+    @staticmethod
+    def _reorder_cache(past_key_values, beam_idx):
+        reordered_past = ()
+        for layer_past in past_key_values:
+            reordered_past += (
+                tuple(past_state.index_select(0, beam_idx.to(past_state.device)) for past_state in layer_past),
+            )
+        return reordered_past
+
+    @torch.inference_mode()
+    def chat(self, tokenizer, query: str, history: List[Dict] = None, role: str = "user",
+             max_length: int = 4096, num_beams=1, do_sample=True, top_p=0.8, temperature=0.3, logits_processor=None,
+             **kwargs):
+        if history is None:
+            history = []
+        if logits_processor:
+            gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
+                        "temperature": temperature, "logits_processor": logits_processor, **kwargs}
+        else:
+            gen_kwargs = {"max_length": max_length, "num_beams": num_beams, "do_sample": do_sample, "top_p": top_p,
+                        "temperature": temperature, "logits_processor": logits_processor, **kwargs}
+
+        history.append({"role": role, "content": query})
+        history_str = tokenizer.apply_chat_template(history, tokenize=False, add_generation_prompt=False)
+        inputs = tokenizer(history_str, return_tensors='pt').to(self.device)
+        outputs = self.generate(**inputs, **gen_kwargs)
+        outputs = outputs.tolist()[0][len(inputs["input_ids"][0]):-1]
+        response = tokenizer.decode(outputs)
+        pattern = re.compile(r".*?(?=<AI>|<用户>)", re.DOTALL)
+        matches = pattern.findall(response)
+        if len(matches) > 0:
+            response = matches[0]
+        history.append({"role": "assistant", "content": response})
+        return response, history
+
+
+@add_start_docstrings(
+    """
+    The MiniCPM Model transformer with a sequence classification head on top (linear layer).
+
+    [`MiniCPMForSequenceClassification`] uses the last token in order to do the classification, as other causal models
+    (e.g. GPT-2) 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).
+    """,
+    MINICPM_START_DOCSTRING,
+)
+class MiniCPMForSequenceClassification(MiniCPMPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.num_labels = config.num_labels
+        self.model = MiniCPMModel(config)
+        self.score = nn.Linear(config.hidden_size, self.num_labels, bias=False)
+
+        # Initialize weights and apply final processing
+        self.post_init()
+
+    def get_input_embeddings(self):
+        return self.model.embed_tokens
+
+    def set_input_embeddings(self, value):
+        self.model.embed_tokens = value
+
+    @add_start_docstrings_to_model_forward(MINICPM_INPUTS_DOCSTRING)
+    def forward(
+        self,
+        input_ids: torch.LongTensor = None,
+        attention_mask: Optional[torch.Tensor] = None,
+        position_ids: Optional[torch.LongTensor] = None,
+        past_key_values: Optional[List[torch.FloatTensor]] = None,
+        inputs_embeds: Optional[torch.FloatTensor] = None,
+        labels: Optional[torch.LongTensor] = None,
+        use_cache: Optional[bool] = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+    ) -> Union[Tuple, 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.model(
+            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 = input_ids.shape[0]
+        else:
+            batch_size = inputs_embeds.shape[0]
+
+        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:
+                sequence_lengths = (torch.eq(input_ids, self.config.pad_token_id).int().argmax(-1) - 1).to(
+                    logits.device
+                )
+            else:
+                sequence_lengths = -1
+
+        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,
+        )
diff --git a/special_tokens_map.json b/special_tokens_map.json
new file mode 100644
index 0000000000000000000000000000000000000000..a782b2f1cdab4d0bacb2dc0f85d02c4b1e31f0bd
--- /dev/null
+++ b/special_tokens_map.json
@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "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 0000000000000000000000000000000000000000..93efa0d862564bfac68cec7e18d2b451fa56f1aa
--- /dev/null
+++ b/tokenizer.json
@@ -0,0 +1,226270 @@
+{
+  "version": "1.0",
+  "truncation": null,
+  "padding": null,
+  "added_tokens": [
+    {
+      "id": 0,
+      "content": "<unk>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 1,
+      "content": "<s>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 2,
+      "content": "</s>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    },
+    {
+      "id": 102,
+      "content": "<pad>",
+      "single_word": false,
+      "lstrip": false,
+      "rstrip": false,
+      "normalized": false,
+      "special": true
+    }
+  ],
+  "normalizer": {
+    "type": "Sequence",
+    "normalizers": [
+      {
+        "type": "Prepend",
+        "prepend": "▁"
+      },
+      {
+        "type": "Replace",
+        "pattern": {
+          "String": " "
+        },
+        "content": "▁"
+      },
+      {
+        "type": "Replace",
+        "pattern": {
+          "String": "　"
+        },
+        "content": "▁"
+      }
+    ]
+  },
+  "pre_tokenizer": null,
+  "post_processor": {
+    "type": "TemplateProcessing",
+    "single": [
+      {
+        "SpecialToken": {
+          "id": "<s>",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      }
+    ],
+    "pair": [
+      {
+        "SpecialToken": {
+          "id": "<s>",
+          "type_id": 0
+        }
+      },
+      {
+        "Sequence": {
+          "id": "A",
+          "type_id": 0
+        }
+      },
+      {
+        "SpecialToken": {
+          "id": "<s>",
+          "type_id": 1
+        }
+      },
+      {
+        "Sequence": {
+          "id": "B",
+          "type_id": 1
+        }
+      }
+    ],
+    "special_tokens": {
+      "<s>": {
+        "id": "<s>",
+        "ids": [
+          1
+        ],
+        "tokens": [
+          "<s>"
+        ]
+      }
+    }
+  },
+  "decoder": {
+    "type": "Sequence",
+    "decoders": [
+      {
+        "type": "Replace",
+        "pattern": {
+          "String": "▁"
+        },
+        "content": " "
+      },
+      {
+        "type": "ByteFallback"
+      },
+      {
+        "type": "Fuse"
+      },
+      {
+        "type": "Strip",
+        "content": " ",
+        "start": 1,
+        "stop": 0
+      }
+    ]
+  },
+  "model": {
+    "type": "BPE",
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+    "continuing_subword_prefix": null,
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+    "byte_fallback": true,
+    "ignore_merges": false,
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+      "您所选定的客房": 53386,
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+      "请仔细分析这些选项": 59542,
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+      "中国移动": 62105,
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+      "起诉必须符合下列条件": 62149,
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+      "分别求出": 62185,
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+      "▁Strength": 62200,
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+      "▁indispens": 62202,
+      "请您务必仔细地阅读选项": 62203,
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+      "均以": 68338,
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+      "大约是": 68341,
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+      "认罪态度较好": 68345,
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+      "高新技术产业开发区": 68355,
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+      "鳞片": 68361,
+      "▁XV": 68362,
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+      "培养基": 68364,
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+      "jira": 68368,
+      "▁awe": 68369,
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+      "然后就是": 68431,
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+      "由于其": 68442,
+      "msdn": 68443,
+      "为了保证": 68444,
+      "ANNEL": 68445,
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+      "°,": 68450,
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+      "▁noct": 68453,
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+      "▁relent": 68455,
+      "未发现被执行人有": 68456,
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+      "达成执行和解协议": 68651,
+      "###############\\": 68652,
+      "全套": 68653,
+      "南站": 68654,
+      "红烧": 68655,
+      "配以": 68656,
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+      "▁raft": 68659,
+      "此题的关键": 68660,
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+      "戴尔": 68671,
+      "NPC": 68672,
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+      "的图片": 68675,
+      "有良好的": 68676,
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+      "疯狂的": 68683,
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+      "请从给出的选项中": 68691,
+      "nFeatures": 68692,
+      "宁夏回族自治区银川市": 68693,
+      "选出你认为正确的答案": 68694,
+      "▁glucocorticoid": 68695,
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+      "最起码": 68802,
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+      "我将给您": 68837,
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+      "请从所提供的": 68849,
+      "选项中做出最佳": 68850,
+      "一道复杂的选择题": 68851,
+      "方面的知识来找出": 68852,
+      "要求您综合运用多": 68853,
+      "▁Sporting": 68854,
+      "我将给您一道复杂的选择题": 68855,
+      "方面的知识来找出正确的答案": 68856,
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+      "花粉": 68858,
+      "衣食": 68859,
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+      "▁Drivers": 68900,
+      "本院经审理认定事实如下": 68901,
+      "第十七": 68902,
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+      "湿疹": 68949,
+      "就是不": 68950,
+      "技术及": 68951,
+      "的运用": 68952,
+      "Rain": 68953,
+      "请你帮我": 68954,
+      "▁Funds": 68955,
+      "▁Monaco": 68956,
+      "▁knight": 68957,
+      "▁Vatican": 68958,
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+      "菏泽市": 68965,
+      "▁Kok": 68966,
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+      "是宋代": 68971,
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+      "培根": 68981,
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+      "特勒": 68983,
+      "▁AUC": 68984,
+      "▁lil": 68985,
+      "综艺节目": 68986,
+      "nMatt": 68987,
+      "▁GOODS": 68988,
+      "▁incap": 68989,
+      "▁tapes": 68990,
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+      "▁noodles": 68993,
+      "有名": 68994,
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+      "原告人": 68997,
+      "和解析": 68998,
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+      "平均亩产": 69003,
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+      "听觉": 69013,
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+      "▁$$[": 69017,
+      "▁Rak": 69018,
+      "▁mah": 69019,
+      "选项正确": 69020,
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+      "▁garnered": 69022,
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+      "伦贝尔": 69025,
+      "engo": 69026,
+      "minent": 69027,
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+      "▁acyl": 69037,
+      "▁Influ": 69038,
+      "查封被执行人": 69039,
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+      "▁Flooring": 69041,
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+      "▁tearDown": 69043,
+      "丧尸": 69044,
+      "名思义": 69045,
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+      "老太太": 69064,
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+      "子叶植物纲": 69066,
+      "▁Camden": 69067,
+      "olitical": 69068,
+      "▁Cellular": 69069,
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+      "机场等接": 69073,
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+      "Sunday": 69075,
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+      "accharomyces": 69078,
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+      "▁psychologist": 69080,
+      "宝玉": 69081,
+      "度为": 69082,
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+      "▁Glu": 69085,
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+      "▁religions": 69090,
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+      "▁paediatric": 69092,
+      "▁warranties": 69093,
+      "浮生": 69094,
+      "连同": 69095,
+      "不得已": 69096,
+      "往往会": 69097,
+      "PLUS": 69098,
+      "世界第一": 69099,
+      "为代表的": 69100,
+      "▁TRAN": 69101,
+      "▁fecal": 69102,
+      "并给出正确的答案": 69103,
+      "▁adventurous": 69104,
+      "不振": 69105,
+      "成就感": 69106,
+      "网络通信": 69107,
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+      "ledon": 69109,
+      "▁mats": 69110,
+      "olfram": 69111,
+      "▁boobs": 69112,
+      "请分析下面这道题": 69113,
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+      "大队长": 69117,
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+      "▁Vest": 69120,
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+      "一道选择题": 69122,
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+      "▁Cros": 69131,
+      "▁toug": 69132,
+      "Recomm": 69133,
+      "▁decou": 69134,
+      "▁Carmen": 69135,
+      "cylinder": 69136,
+      "▁buckets": 69137,
+      "▁recursively": 69138,
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+      "而得名": 69140,
+      "还不能": 69141,
+      "所用到的": 69142,
+      "(\"\")\\": 69143,
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+      "▁Lymph": 69145,
+      "▁Nikol": 69146,
+      "▁Beatles": 69147,
+      "▁Falcons": 69148,
+      "▁foreach": 69149,
+      "▁informing": 69150,
+      "▁treadmill": 69151,
+      "▁milestones": 69152,
+      "实干": 69153,
+      "萨拉": 69154,
+      "被除": 69155,
+      "AVI": 69156,
+      "及技术": 69157,
+      "手机版": 69158,
+      "nPDF": 69159,
+      "效力球队": 69160,
+      "的影响力": 69161,
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+      "▁Norwich": 69165,
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+      "首款": 69185,
+      "请立即": 69186,
+      "队员们": 69187,
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+      "国际标准": 69189,
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+      "代表国家队": 69192,
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+      "▁fractal": 69194,
+      "公布失信被执行人名单": 69195,
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+      "多利亚": 69197,
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+      "可酌情从轻处罚": 69203,
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+      "▁河北": 69209,
+      "行驶证": 69210,
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+      "生产总值": 69224,
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+      "过敏性": 69234,
+      "▁Neon": 69235,
+      "方可开展经营活动": 69236,
+      "剧团": 69237,
+      "置疑": 69238,
+      "对该案": 69239,
+      "一个小时": 69240,
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+      "othes": 69242,
+      "▁Lans": 69243,
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+      "▁Nagar": 69245,
+      "RUPTION": 69246,
+      "欠缺": 69247,
+      "▁'\\": 69248,
+      "东北部": 69249,
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+      "▁Uzbek": 69251,
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+      "▁Aside": 69260,
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+      "吸水": 69264,
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+      "Asc": 69269,
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+      "▁Nd": 69276,
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+      "缓期二年执行": 69279,
+      "▁Cookies": 69280,
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+      "nExperience": 69283,
+      "不比": 69284,
+      "写上": 69285,
+      "是否为": 69286,
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+      "进行处理": 69288,
+      "▁sway": 69289,
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+      "▁Abuse": 69291,
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+      "▁视频": 69295,
+      "某某诉称": 69296,
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+      "▁hydroxyl": 69320,
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+      "nnp": 69325,
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+      "▁Mae": 69327,
+      "元为基数": 69328,
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+      "▁universally": 69332,
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+      "第八条": 69338,
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+      "▁synapse": 69342,
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+      "▁polyethylene": 69344,
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+      "PHA": 69347,
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+      "▁Dept": 69349,
+      "自然保护区": 69350,
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+      "▁CY": 69356,
+      "六安市": 69357,
+      "方式是": 69358,
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+      "▁Lean": 69360,
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+      "二个": 69364,
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+      "印象深刻": 69366,
+      "▁conqu": 69367,
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+      "▁blindness": 69369,
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+      "判别式": 69374,
+      "大小的": 69375,
+      "Season": 69376,
+      "▁Beaver": 69377,
+      "▁feared": 69378,
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+      "觉悟": 69380,
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+      "▁[%": 69383,
+      "一起吃": 69384,
+      "当天的": 69385,
+      "▁STO": 69386,
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+      "▁scars": 69388,
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+      "▁dataframe": 69393,
+      "▁ribosomal": 69394,
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+      "暴躁": 69397,
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+      "的天空": 69399,
+      "的路上": 69400,
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+      "▁motives": 69404,
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+      "▁stranded": 69406,
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+      "▁prominence": 69409,
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+      "下周": 69412,
+      "害虫": 69413,
+      "建民": 69414,
+      "放着": 69415,
+      "很高兴": 69416,
+      "金字塔": 69417,
+      "Known": 69418,
+      "▁Toll": 69419,
+      "Victor": 69420,
+      "作出正确判断": 69421,
+      "▁shooters": 69422,
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+      "洛夫": 69425,
+      "与文化": 69426,
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+      "paul": 69428,
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+      "刑罚执行机关": 69431,
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+      "TypeName": 69433,
+      "没有约定或者约定": 69434,
+      "▁Genetics": 69435,
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+      "▁uncontrolled": 69438,
+      "张志": 69439,
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+      "第六届": 69441,
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+      "▁Mp": 69454,
+      "在网络": 69455,
+      "望远镜": 69456,
+      "不是因为": 69457,
+      "▁Founded": 69458,
+      "▁Technique": 69459,
+      "东城": 69460,
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+      "聚氨酯": 69467,
+      "让用户": 69468,
+      "yout": 69469,
+      "▁DFT": 69470,
+      "您将面临": 69471,
+      "文学作品": 69472,
+      "检验检疫": 69473,
+      "淋浴设施": 69474,
+      "ensing": 69475,
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+      "三生": 69477,
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+      "尽责": 69479,
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+      "两次以上的": 69485,
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+      "nSeeds": 69488,
+      "▁unbeat": 69489,
+      "▁arginine": 69490,
+      "沦为": 69491,
+      "骑兵": 69492,
+      "在英国": 69493,
+      "葫芦岛": 69494,
+      "amus": 69495,
+      "▁Zar": 69496,
+      "▁nth": 69497,
+      "ignty": 69498,
+      "▁合作关系": 69499,
+      "▁mailbox": 69500,
+      "▁Supplier": 69501,
+      "epithelial": 69502,
+      "哈密": 69503,
+      "憔悴": 69504,
+      "熟的": 69505,
+      "而他": 69506,
+      "到自己": 69507,
+      "未提出": 69508,
+      "▁Cra": 69509,
+      "不断扩大": 69510,
+      "给你一段": 69511,
+      "最佳的答案": 69512,
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+      "typeparam": 69514,
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+      "直接用": 69516,
+      "bron": 69517,
+      "回答一道": 69518,
+      "大家都是": 69519,
+      "这个事情": 69520,
+      "这方面的": 69521,
+      "Japan": 69522,
+      "Terms": 69523,
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+      "▁mage": 69538,
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+      "evil": 69548,
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+      "元的事实": 69550,
+      "▁geom": 69551,
+      "▁proj": 69552,
+      "需在入住时": 69553,
+      "计算在总价中": 69554,
+      "AppData": 69555,
+      "▁faucet": 69556,
+      "附加费用不会自动": 69557,
+      "需在入住时另行支付": 69558,
+      "setObjectName": 69559,
+      "附加费用不会自动计算在总价中": 69560,
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+      "▁Rash": 70150,
+      "并从提供的": 70151,
+      "▁Saxony": 70152,
+      "一段文字描述和": 70153,
+      "请思考以下有关": 70154,
+      "▁apology": 70155,
+      "下面是一个选择题": 70156,
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+      "仔细预测问题的答案": 70158,
+      "备选答案中选择一个": 70159,
+      "专业领域知识的选择题": 70160,
+      "▁univariate": 70161,
+      "我会给你一段文字描述和": 70162,
+      "▁mindfulness": 70163,
+      "备选答案中选择一个正确的选项": 70164,
+      "我会给你一段文字描述和对应的选项": 70165,
+      "请你从选项中选出你认为正确的答案": 70166,
+      "万分": 70167,
+      "乔丹": 70168,
+      "住所地在": 70169,
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+      "麻烦帮我做一道题": 70174,
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+      "▁wingspan": 70176,
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+      "从选项中选出正确的选项": 70178,
+      "▁imprisonment": 70179,
+      "储藏": 70180,
+      "卑微": 70181,
+      "护卫": 70182,
+      "ABB": 70183,
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+      "我不能": 70185,
+      "设立的": 70186,
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+      "工作温度": 70188,
+      "▁Kell": 70189,
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+      "▁gt": 70197,
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+      "一个好的": 70221,
+      "英雄合击": 70222,
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+      "申请再审称": 70225,
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+      "网游之": 70246,
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+      "▁Maven": 70292,
+      "申请执行人同意": 70293,
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+      "机械工业": 70307,
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+      "▁Wheat": 70363,
+      "这道题需要您": 70364,
+      "icially": 70365,
+      "接下来的选择题": 70366,
+      "FILENAME": 70367,
+      "nDisplay": 70368,
+      "以便作出正确判断": 70369,
+      "并从所给选项中挑": 70370,
+      "运用扎实的知识和": 70371,
+      "▁Packages": 70372,
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+      "请仔细研究接下来的选择题": 70374,
+      "运用扎实的知识和仔细的思考": 70375,
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+      "当下的": 70766,
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+      "冻结被告": 70782,
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+      "▁sided": 70786,
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+      "逆向": 70791,
+      "▁医院": 70792,
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+      "乐山市": 70794,
+      "打不过": 70795,
+      "▁Vod": 70796,
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+      "邢台市": 70819,
+      "▁Lor": 70820,
+      "是单选题": 70821,
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+      "足协": 70854,
+      "题名": 70855,
+      "lei": 70856,
+      "sinA": 70857,
+      "▁${{\\\\": 70858,
+      "悔改表现突出": 70859,
+      "patible": 70860,
+      "▁bassist": 70861,
+      "▁å": 70862,
+      "网易云音乐": 70863,
+      "▁Volvo": 70864,
+      "nMobile": 70865,
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+      "何在": 70868,
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+      "是哪": 70889,
+      "神经元": 70890,
+      "Chief": 70891,
+      "centos": 70892,
+      "这道题是单选题": 70893,
+      "请你做一个选择题": 70894,
+      "请你选出正确的选项": 70895,
+      "▁shipments": 70896,
+      "问题和选项如下文所示": 70897,
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+      "这是一个涉及": 70932,
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+      "了很久": 70934,
+      "将达到": 70935,
+      "STAR": 70936,
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+      "就是我们": 70944,
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+      "Esc": 70951,
+      "我都会": 70952,
+      "lain": 70953,
+      "分析并从": 70954,
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+      "管理水平": 70980,
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+      "▁CMake": 70983,
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+      "▁Sodium": 70986,
+      "四章": 70987,
+      "泰兴": 70988,
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+      ":],": 70990,
+      "年上海": 70991,
+      "示意图": 70992,
+      "等其他": 70993,
+      "结构与": 70994,
+      "信息的选择题": 70995,
+      "给定的选项中": 70996,
+      "▁Tucson": 70997,
+      "您需要分析并从": 70998,
+      "▁QString": 70999,
+      "请仔细研究以下问题": 71000,
+      "综合运用各种信息的选择题": 71001,
+      "您需要分析并从给定的选项中": 71002,
+      "▁'$": 71003,
+      "与我们": 71004,
+      "UBLE": 71005,
+      "▁bye": 71006,
+      "候选答案": 71007,
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+      "进一步的": 71010,
+      "▁Khal": 71011,
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+      "▁tossed": 71013,
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+      "preferences": 71015,
+      "▁cybersecurity": 71016,
+      "鸭子": 71017,
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+      "nMaking": 71020,
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+      "▁HY": 71024,
+      "压力大": 71025,
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+      "伟大复兴": 71027,
+      "感兴趣的": 71028,
+      "抽样调查": 71029,
+      "▁bakery": 71030,
+      "nAlgebra": 71031,
+      "TypeError": 71032,
+      "▁WebSocket": 71033,
+      "▁embracing": 71034,
+      "物业管理有限责任公司": 71035,
+      "▁counterexample": 71036,
+      "苏丹": 71037,
+      "Sig": 71038,
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+      "▁名称": 71040,
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+      "交通工具": 71042,
+      "目前没有": 71043,
+      "ifera": 71044,
+      "often": 71045,
+      "下面我会给你": 71046,
+      "的撤诉申请符合法律规定": 71047,
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+      "防潮": 71050,
+      "GSM": 71051,
+      "只是个": 71052,
+      "的方案": 71053,
+      "urse": 71054,
+      "中山大学": 71055,
+      "whole": 71056,
+      "请你仔细分析": 71057,
+      "irected": 71058,
+      "▁Icelandic": 71059,
+      "微小": 71060,
+      "筹集": 71061,
+      "▁最新": 71062,
+      "如果想要": 71063,
+      "▁golfers": 71064,
+      "以及几个候选答案": 71065,
+      "▁overdose": 71066,
+      "下面我会给你一道题": 71067,
+      "然后选出正确的选项": 71068,
+      "▁counselor": 71069,
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+      "打发": 71071,
+      "贪心": 71072,
+      "输血": 71073,
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+      "可以给": 71078,
+      "衢州市": 71079,
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+      "农资": 71090,
+      "断层": 71091,
+      "▁UM": 71092,
+      "到什么": 71093,
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+      "城市规划": 71096,
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+      "▁validating": 71100,
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+      "▁pedig": 71109,
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+      "▁dances": 71111,
+      "信息查询结果单": 71112,
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+      "nuniform": 71114,
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+      "▁periodont": 71117,
+      "▁ultrasonography": 71118,
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+      "▁gib": 71120,
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+      "▁cler": 71122,
+      "#,": 71123,
+      "七天": 71124,
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+      "官司": 71188,
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+      "▁Ez": 71190,
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+      "日产": 71200,
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+      "HAL": 71227,
+      "也同样": 71228,
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+      "nKevin": 71231,
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+      "Dockerfile": 71233,
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+      "▁siRNA": 71242,
+      "法定代表人杨": 71243,
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+      "八首": 71326,
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+      "单职业": 71341,
+      "喜欢看": 71342,
+      "数字是": 71343,
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+      "Sah": 71354,
+      "太大了": 71355,
+      "可以利用": 71356,
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+      "▁macroscopic": 71362,
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+      "作品展": 71369,
+      "▁parathyroid": 71370,
+      "壮观": 71371,
+      "散户": 71372,
+      "沧州": 71373,
+      "痛风": 71374,
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+      "▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁",
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+      "Stat ion",
+      "é r",
+      "全 方位",
+      "Li mit",
+      "L imit",
+      "ul ence",
+      "ule nce",
+      "▁p orts",
+      "▁port s",
+      "▁por ts",
+      "▁ ports",
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+      "▁c ursor",
+      "▁ cursor",
+      "▁Con dition",
+      "▁Cond ition",
+      "▁ Condition",
+      "毕业 后",
+      "经济 损失",
+      "十 足",
+      "ge ts",
+      "get s",
+      "g ets",
+      "▁M all",
+      "▁Mal l",
+      "▁Ma ll",
+      "如果 不",
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+      "▁p hp",
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+      "▁Day s",
+      "▁Da ys",
+      "▁ Days",
+      "善 于",
+      "▁antib iotics",
+      "▁antibiotic s",
+      "▁consult ing",
+      "▁Arch ive",
+      "▁Arc hive",
+      "▁ Archive",
+      "及 利息",
+      "” )",
+      "就 把",
+      "刑期 从",
+      "▁f uzzy",
+      "▁G B",
+      "▁ GB",
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+      "最 新的",
+      "▁f lip",
+      "▁fl ip",
+      "▁ flip",
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+      "等 腰",
+      "▁stim ulus",
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+      "一 句话",
+      "▁compr ised",
+      "▁comprise d",
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+      "infect ed",
+      "有的 人",
+      "有 的人",
+      "▁margin al",
+      "▁marg inal",
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+      "▁商 品",
+      "▁ 商品",
+      "▁cent res",
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+      "▁critical ly",
+      "▁critic ally",
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+      "▁c hips",
+      "▁ch ips",
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+      "▁chi ps",
+      "会 导致",
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+      "▁Dro p",
+      "▁ Drop",
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+      "b etween",
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+      "所 有人",
+      "▁K az",
+      "▁Ka z",
+      "可以 使用",
+      "可以使 用",
+      "▁Every thing",
+      "▁ Everything",
+      "三 维",
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+      "▁Mad rid",
+      "受 益",
+      "▁h unting",
+      "▁hun ting",
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+      "n Sp",
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+      "之 一的",
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+      "▁re sp",
+      "▁r esp",
+      "▁res p",
+      "▁ resp",
+      "在服刑期间 确有悔改表现",
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+      "▁assist ed",
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+      "▁k er",
+      "▁ke r",
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+      "或 查封",
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+      "▁bee f",
+      "▁c ure",
+      "▁cur e",
+      "▁cu re",
+      "ut enant",
+      "ute nant",
+      "uten ant",
+      "u tenant",
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+      "▁Corp s",
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+      "▁instant ly",
+      "▁hipp ocamp",
+      "极 端",
+      "▁r ivers",
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+      "▁ri vers",
+      "▁riv ers",
+      "▁ rivers",
+      "ar ms",
+      "arm s",
+      "跑 步",
+      "总 监",
+      "G G",
+      "申请 人的",
+      "申请人 的",
+      "▁inter pol",
+      "▁ interpol",
+      "第一百四十五 条第一款的规定",
+      "第一百四十五条第一款 的规定",
+      "he ading",
+      "head ing",
+      "hea ding",
+      "自 学",
+      "脾 气",
+      "▁B P",
+      "▁ BP",
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+      "▁ti es",
+      "▁ ties",
+      "质 保",
+      "ri el",
+      "rie l",
+      "r iel",
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+      "▁com pass",
+      "▁comp ass",
+      "▁ compass",
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+      "▁Circ le",
+      "▁Cir cle",
+      "▁ Circle",
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+      "▁garden s",
+      "▁gard ens",
+      "▁l adies",
+      "▁la dies",
+      "斯 科",
+      "no s",
+      "n os",
+      "▁ad ren",
+      "▁ adren",
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+      "ram e",
+      "r ame",
+      "▁K ay",
+      "▁Ka y",
+      "▁propos als",
+      "▁proposal s",
+      "普通 人",
+      "根据 您的",
+      "第七 十九条",
+      "第七十九 条",
+      "有 力",
+      "▁B ruce",
+      "▁Br uce",
+      "▁Bru ce",
+      "Com ponents",
+      "Component s",
+      "防 腐",
+      "▁is ot",
+      "▁iso t",
+      "▁pro found",
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+      "染 色",
+      "此 类",
+      "职 权",
+      "▁K ir",
+      "▁Ki r",
+      "▁H ERE",
+      "▁HE RE",
+      "▁HER E",
+      "▁ HERE",
+      "东莞 市",
+      "▁f ounding",
+      "▁found ing",
+      "▁fo unding",
+      "▁dead line",
+      "的 各种",
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+      "▁stimulate d",
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+      "h p",
+      "调 料",
+      "bum s",
+      "bu ms",
+      "b ums",
+      "裁定书送达之日起 十日内",
+      "▁horm ones",
+      "▁hormone s",
+      "▁sustain ability",
+      "user Id",
+      "ex tr",
+      "ext r",
+      "▁Th ompson",
+      "▁Thom pson",
+      "帮 你",
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+      "a pon",
+      "▁reserv oir",
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+      "resolve d",
+      "▁h older",
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+      "▁hol der",
+      "▁ holder",
+      "▁Col on",
+      "▁Co lon",
+      "dump s",
+      "d umps",
+      "很 重要",
+      "这道 题",
+      "这 道题",
+      "le tt",
+      "let t",
+      "l ett",
+      "▁De cor",
+      "▁Dec or",
+      "▁ Decor",
+      "一 身",
+      "▁▁ ▁▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁ ▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁ ▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁▁ ▁",
+      "▁▁▁▁▁▁ ▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁ ▁▁▁",
+      "▁▁▁ ▁▁▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁ ▁▁▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁ ▁▁▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁ ▁▁▁▁",
+      "▁▁▁▁▁▁▁▁▁▁▁ ▁▁",
+      "▁ ▁▁▁▁▁▁▁▁▁▁▁▁",
+      "诱 导",
+      "▁n ov",
+      "▁no v",
+      "▁ nov",
+      "▁Per fect",
+      "ia h",
+      "i ah",
+      "IS H",
+      "I SH",
+      "特殊 的",
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+      "㈰ ㈰",
+      "果 然",
+      "am ping",
+      "amp ing",
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+      "▁purch ases",
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+      "m ma",
+      "给定 的",
+      "给 定的",
+      "拯 救",
+      "nW ho",
+      "nWh o",
+      "n Who",
+      "族自治 县",
+      "cor rect",
+      "c orrect",
+      "Bi nd",
+      "Bin d",
+      "B ind",
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+      "CS S",
+      "C SS",
+      "nA uthor",
+      "nAut hor",
+      "n Author",
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+      "演 绎",
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+      "loc ks",
+      "lock s",
+      "l ocks",
+      "▁F riends",
+      "▁Friend s",
+      "▁Fri ends",
+      "▁st ochastic",
+      "起 码",
+      "os omes",
+      "osome s",
+      "oso mes",
+      "} &",
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+      ": “",
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+      "nRe turn",
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+      "春 秋",
+      "▁B ug",
+      "▁Bu g",
+      "▁ Bug",
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+      "▁pair ed",
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+      "blo cks",
+      "b locks",
+      "▁recruit ed",
+      "▁recru ited",
+      "▁U buntu",
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+      "▁inv esting",
+      "▁invest ing",
+      "▁G PS",
+      "▁GP S",
+      "▁ GPS",
+      "▁Ass essment",
+      "▁Assess ment",
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+      "▁u nfortunately",
+      "▁unf ortunately",
+      "▁unfort unately",
+      "▁unfortunate ly",
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+      "阻 止",
+      "|| |",
+      "| ||",
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+      "u cer",
+      "即可 求出",
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+      "▁rhyth m",
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+      "向 人民法院",
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+      "▁Cre ative",
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+      "▁ rotate",
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+      "▁fu ndra",
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+      "▁Elect ron",
+      "▁Electro n",
+      "▁J ess",
+      "▁Jes s",
+      "▁Je ss",
+      "RE FIX",
+      "REF IX",
+      "▁contract ors",
+      "▁contr actors",
+      "▁contractor s",
+      "oc ent",
+      "o cent",
+      "▁S creen",
+      "▁Sc reen",
+      "▁Scre en",
+      "▁Scr een",
+      "▁ Screen",
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+      "谷 歌",
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+      "select ion",
+      "sel ection",
+      "s election",
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+      "▁Ar c",
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+      "▁AI DS",
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+      "▁ 主演",
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+      "的 人物",
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+      "免 费的",
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+      "一 个个",
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+      "c ond",
+      "亲 戚",
+      "的 同学",
+      "▁z inc",
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+      "s mart",
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+      "器 官",
+      "▁M Y",
+      "▁ MY",
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+      "▁Express ion",
+      "▁Exp ression",
+      "▁ Expression",
+      "▁interpret ed",
+      "立案 执行",
+      "▁dial ogue",
+      "▁dialog ue",
+      "▁t s",
+      "▁ ts",
+      "的重要 性",
+      "的 重要性",
+      "File Name",
+      "世 间",
+      "童 话",
+      "续 航",
+      "才 知道",
+      "nt emplate",
+      "n template",
+      "CU R",
+      "C UR",
+      "ole sale",
+      "oles ale",
+      "▁p g",
+      "▁ pg",
+      "转 载",
+      "被执行人 张",
+      "Sh ared",
+      "Share d",
+      "Sha red",
+      "乘 客",
+      "▁F alls",
+      "▁Fall s",
+      "▁Fal ls",
+      "▁perspect ives",
+      "▁perspective s",
+      "▁Element ary",
+      "Ab s",
+      "A bs",
+      "St ats",
+      "Stat s",
+      "▁W ard",
+      "▁War d",
+      "▁Wa rd",
+      "▁autom ation",
+      "数 十",
+      "秉 承",
+      "花 了",
+      "赛 车",
+      "▁excell ence",
+      "or neys",
+      "orney s",
+      "orne ys",
+      "▁C ass",
+      "▁Ca ss",
+      "▁Cas s",
+      "上 门",
+      "ki ll",
+      "kil l",
+      "k ill",
+      "oph age",
+      "ophag e",
+      "高 清",
+      "▁ch apters",
+      "▁chapter s",
+      "▁chap ters",
+      "芜 湖",
+      "一 笑",
+      "im mer",
+      "imm er",
+      "ie mann",
+      "iem ann",
+      "i emann",
+      "▁m ad",
+      "▁ma d",
+      "▁ mad",
+      "所以 我",
+      "pro tein",
+      "prot ein",
+      "prote in",
+      "大家 的",
+      "大 家的",
+      "▁visual ization",
+      "▁m p",
+      "▁ mp",
+      "▁M Ps",
+      "▁MP s",
+      "us ive",
+      "▁Mon itor",
+      "▁ Monitor",
+      "▁L em",
+      "▁Le m",
+      "▁att enu",
+      "▁atten u",
+      "np rivate",
+      "npr ivate",
+      "n private",
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+      "cent ral",
+      "c entral",
+      "▁G las",
+      "▁Gl as",
+      "ec ycle",
+      "e cycle",
+      "▁P eters",
+      "▁Pet ers",
+      "▁Pe ters",
+      "▁Peter s",
+      "▁Pete rs",
+      "ER Y",
+      "E RY",
+      "ze ns",
+      "zen s",
+      "z ens",
+      "Da vid",
+      "D avid",
+      "▁C emetery",
+      "图 文",
+      "▁I BM",
+      "▁IB M",
+      "▁ IBM",
+      "▁dr aws",
+      "▁draw s",
+      "对 中国",
+      "▁out line",
+      "▁ou tline",
+      "▁ outline",
+      "Pr es",
+      "Pre s",
+      "P res",
+      "▁P ET",
+      "▁PE T",
+      "▁ PET",
+      "▁s orts",
+      "▁sort s",
+      "▁sor ts",
+      "nP erson",
+      "nPer son",
+      "n Person",
+      "主 观",
+      "P B",
+      "ab olic",
+      "abol ic",
+      "abo lic",
+      "a bolic",
+      "羁押一日 折抵刑期一日",
+      "为主 要",
+      "为 主要",
+      "ro st",
+      "ros t",
+      "r ost",
+      "vi c",
+      "v ic",
+      "▁ex ceptions",
+      "▁except ions",
+      "▁exception s",
+      "▁ exceptions",
+      "> _",
+      "▁air way",
+      "el ection",
+      "ele ction",
+      "elect ion",
+      "e lection",
+      "▁t ends",
+      "▁ten ds",
+      "▁tend s",
+      "▁Te X",
+      "▁ TeX",
+      "修 订",
+      "▁re nown",
+      "目的 探讨",
+      "ze ros",
+      "zer os",
+      "zero s",
+      "z eros",
+      "▁M ode",
+      "▁Mod e",
+      "▁Mo de",
+      "▁ Mode",
+      "▁F ather",
+      "▁Fa ther",
+      "▁Fat her",
+      "default s",
+      "过 后",
+      "▁det ecting",
+      "▁detect ing",
+      "in ian",
+      "ini an",
+      "inia n",
+      "agram s",
+      "agr ams",
+      "agra ms",
+      "a grams",
+      "▁C roat",
+      "▁Cro at",
+      "少 了",
+      "IC S",
+      "I CS",
+      "▁am ateur",
+      "区 委",
+      "▁aband oned",
+      "▁abandon ed",
+      "星 星",
+      "na ments",
+      "nam ents",
+      "nament s",
+      "n aments",
+      "年中 国",
+      "年 中国",
+      "um bai",
+      "umb ai",
+      "umba i",
+      "▁n eph",
+      "▁ne ph",
+      "▁nep h",
+      "ur ally",
+      "ural ly",
+      "▁ 剧",
+      "水 库",
+      "oc ese",
+      "▁作品 目录",
+      "▁conflic ts",
+      "▁conflict s",
+      "预 警",
+      "it ects",
+      "ite cts",
+      "itect s",
+      "Sc roll",
+      "▁st rat",
+      "▁str at",
+      "▁col ored",
+      "▁color ed",
+      "▁ colored",
+      "▁log arith",
+      "ococc us",
+      "ococ cus",
+      "权利 人",
+      "▁网络 小说",
+      "▁ 网络小说",
+      "▁h elic",
+      "▁he lic",
+      "▁hel ic",
+      "Pre sent",
+      "Pres ent",
+      "P resent",
+      "т а",
+      "果 实",
+      "淋 巴",
+      "让 孩子",
+      "▁trem end",
+      "率 先",
+      "▁w elfare",
+      "▁wel fare",
+      "ke ley",
+      "kel ey",
+      "M obile",
+      "▁insp ire",
+      "▁inspir e",
+      "▁attract ed",
+      "▁attr acted",
+      "av an",
+      "ava n",
+      "a van",
+      "on gue",
+      "ong ue",
+      "▁G ram",
+      "▁Gr am",
+      "▁Gra m",
+      "道 士",
+      "Se lection",
+      "Select ion",
+      "S election",
+      "乌 克兰",
+      "▁acc idents",
+      "▁accident s",
+      "▁f leet",
+      "▁fle et",
+      "▁flee t",
+      "▁O t",
+      "IF A",
+      "I FA",
+      "▁K ap",
+      "▁Ka p",
+      "st o",
+      "s to",
+      "hed ules",
+      "hedul es",
+      "hedule s",
+      "▁kin etics",
+      "▁kinetic s",
+      "高新 区",
+      "高 新区",
+      "▁pl ots",
+      "▁plot s",
+      "▁ plots",
+      "时 机",
+      "▁Re pair",
+      "▁Rep air",
+      "间 隔",
+      "▁m d",
+      "▁ md",
+      "▁d as",
+      "▁da s",
+      "▁ das",
+      "▁T ob",
+      "▁To b",
+      "代 价",
+      "▁insert ed",
+      "素 材",
+      "般 的",
+      "▁J uan",
+      "▁Ju an",
+      "nS ongs",
+      "nSong s",
+      "size of",
+      "▁v endor",
+      "▁ven dor",
+      "▁ vendor",
+      "华 人",
+      "▁A bb",
+      "▁Ab b",
+      "新 开",
+      "▁B irth",
+      "▁Bir th",
+      "▁ Birth",
+      "▁gr ants",
+      "▁grant s",
+      "▁gran ts",
+      "畜 牧",
+      "▁im ported",
+      "▁import ed",
+      "▁imp orted",
+      "▁exp ecting",
+      "▁expect ing",
+      "▁o nClick",
+      "▁on Click",
+      "▁ onClick",
+      "电子 产品",
+      "var epsilon",
+      "vare psilon",
+      "原 文",
+      "▁jewel ry",
+      "▁s oup",
+      "▁so up",
+      "▁sou p",
+      "▁ soup",
+      "an ners",
+      "ann ers",
+      "anner s",
+      "anne rs",
+      "▁manip ulation",
+      "奥运 会",
+      "奥 运会",
+      "的 地",
+      "un ittest",
+      "unit test",
+      "uni ttest",
+      "合肥 市",
+      "ats by",
+      "▁M edit",
+      "▁Med it",
+      "▁Me dit",
+      "zi ng",
+      "z ing",
+      "sh ore",
+      "综合 运用",
+      "▁B etter",
+      "▁Bet ter",
+      "▁Fr eder",
+      "▁Fre der",
+      "▁Fred er",
+      "点 点",
+      "▁foot age",
+      "▁foo tage",
+      "gr ades",
+      "grad es",
+      "grade s",
+      "gra des",
+      "▁d fs",
+      "▁df s",
+      "▁ dfs",
+      "▁se venth",
+      "▁seven th",
+      "▁sevent h",
+      "▁enc ryption",
+      "▁encrypt ion",
+      "MAG ES",
+      "MAGE S",
+      "M AGES",
+      "q quad",
+      "na re",
+      "nar e",
+      "n are",
+      "月 的",
+      "等 着",
+      "▁S um",
+      "▁Su m",
+      "▁ Sum",
+      "▁trans itions",
+      "▁transition s",
+      "▁transit ions",
+      "异 性",
+      "enn ial",
+      "▁R y",
+      "应当 在",
+      "nin clude",
+      "n include",
+      "天 涯",
+      "▁D ir",
+      "▁Di r",
+      "▁ Dir",
+      "ab lab",
+      "abl ab",
+      "nM oths",
+      "客 运",
+      "▁t ick",
+      "▁ti ck",
+      "▁ tick",
+      "本院 不予支持",
+      "本院不予 支持",
+      "he ld",
+      "hel d",
+      "h eld",
+      "▁r uled",
+      "▁rule d",
+      "▁rul ed",
+      "▁ru led",
+      "但 他",
+      "▁laws uit",
+      "▁antib iotic",
+      "毫 升",
+      "▁bar rel",
+      "▁barr el",
+      "End point",
+      "副 教授",
+      "案 发",
+      "▁P rim",
+      "▁Pr im",
+      "▁Pri m",
+      "▁ Prim",
+      "何 时",
+      "严 肃",
+      "的女 人",
+      "的 女人",
+      "▁B loom",
+      "▁Bl oom",
+      "▁Blo om",
+      "▁metast atic",
+      "▁meta static",
+      "但是 在",
+      "但 是在",
+      "▁T ower",
+      "▁To wer",
+      "▁Tow er",
+      "涉及 到",
+      "▁over night",
+      "nH on",
+      "温 州",
+      "援 助",
+      "▁C ash",
+      "▁Ca sh",
+      "▁Cas h",
+      "▁ Cash",
+      "倡 导",
+      "对 他",
+      "酒 吧",
+      "os al",
+      "osa l",
+      "o sal",
+      "▁Ad ams",
+      "▁Adam s",
+      "▁Ada ms",
+      "oo ns",
+      "oon s",
+      "o ons",
+      "* ,",
+      "大 佬",
+      "我 没",
+      "board s",
+      "bo ards",
+      "▁com prom",
+      "▁comp rom",
+      "▁compr om",
+      "▁dem ographic",
+      "▁demo graphic",
+      "被告 的",
+      "Wh ile",
+      "() ));\\",
+      "()) );\\",
+      "())) ;\\",
+      "( )));\\",
+      "执行 期间",
+      "▁A y",
+      "▁spect acular",
+      "▁A bd",
+      "▁Ab d",
+      "te l",
+      "t el",
+      "▁P ool",
+      "▁Po ol",
+      "▁ Pool",
+      "▁C lose",
+      "▁Cl ose",
+      "▁Clo se",
+      "▁ Close",
+      "▁intern ationally",
+      "▁international ly",
+      "▁M etal",
+      "▁Me tal",
+      "▁Met al",
+      "▁Meta l",
+      "比赛 中",
+      "比 赛中",
+      "▁consider ably",
+      "今天 的",
+      "今 天的",
+      "▁ex cluded",
+      "▁exclude d",
+      "妹 子",
+      "Sc anner",
+      "Scan ner",
+      "An imation",
+      "Anim ation",
+      "三 人",
+      "暖 气",
+      "▁l ys",
+      "▁ly s",
+      "▁ lys",
+      "▁la bour",
+      "▁lab our",
+      "关 怀",
+      "主 导",
+      "曾 在",
+      "玩 的",
+      "▁it eration",
+      "▁iter ation",
+      "▁ iteration",
+      "at able",
+      "ata ble",
+      "a table",
+      "怀 特",
+      "▁sh ear",
+      "▁she ar",
+      "work flow",
+      "▁S hel",
+      "▁Sh el",
+      "▁She l",
+      "有 害",
+      "IE W",
+      "I EW",
+      "为 中国",
+      "体现 了",
+      "▁L ot",
+      "▁Lo t",
+      "▁repeated ly",
+      "▁repeat edly",
+      "肇 事",
+      "▁K EY",
+      "▁KE Y",
+      "▁ KEY",
+      "▁f ingers",
+      "▁fin gers",
+      "▁finger s",
+      "▁fing ers",
+      "大 使",
+      "▁N elson",
+      "的 文化",
+      "math frak",
+      "▁b ilateral",
+      "▁bil ateral",
+      "▁inc idents",
+      "▁incident s",
+      "见 证",
+      "▁sk etch",
+      "承 载",
+      "广 阔",
+      "下落 不明",
+      "▁introdu ces",
+      "▁introduce s",
+      "ng inx",
+      "n ginx",
+      "足球 运动员",
+      "▁s eal",
+      "▁se al",
+      "▁sea l",
+      "▁prom ises",
+      "▁promise s",
+      "▁v ine",
+      "▁vi ne",
+      "▁ vine",
+      "im ension",
+      "imens ion",
+      "▁sports people",
+      "▁dev ast",
+      "锅 炉",
+      "▁Fac ulty",
+      "趣 味",
+      "▁prevent ed",
+      "▁prev ented",
+      "▁HE ALTH",
+      "您的 选择",
+      "您 的选择",
+      "▁Con stitution",
+      "▁Const itution",
+      "▁my th",
+      "▁c argo",
+      "▁car go",
+      "▁ cargo",
+      "AB A",
+      "A BA",
+      "党组 书记",
+      "备 注",
+      "br ief",
+      "b rief",
+      "nex cept",
+      "n except",
+      "type name",
+      "typ ename",
+      "值 的",
+      "判断 对错",
+      "▁r ival",
+      "▁ri val",
+      "▁riv al",
+      "▁ rival",
+      "▁b row",
+      "▁br ow",
+      "▁bro w",
+      "反 思",
+      "la yers",
+      "lay ers",
+      "layer s",
+      "l ayers",
+      "▁int ens",
+      "▁b er",
+      "▁be r",
+      "▁ ber",
+      "真实 的",
+      "真 实的",
+      "rit eria",
+      "rite ria",
+      "riter ia",
+      "动 员",
+      "▁R eferences",
+      "▁Re ferences",
+      "▁Reference s",
+      "▁Refer ences",
+      "▁ References",
+      "▁veter an",
+      "▁p ets",
+      "▁pe ts",
+      "▁pet s",
+      "大 奖",
+      "nD id",
+      "nDi d",
+      "n Did",
+      "▁Den ver",
+      "um bing",
+      "umb ing",
+      "制作 的",
+      "制 作的",
+      "▁F urn",
+      "▁Fu rn",
+      "▁Fur n",
+      "Reg ion",
+      "ang hai",
+      "▁h andy",
+      "▁hand y",
+      "▁han dy",
+      "▁extra ordinary",
+      "▁ ”",
+      "▁nation wide",
+      "▁T B",
+      "▁ TB",
+      "/ :",
+      "轨 迹",
+      "小 红",
+      "轮 胎",
+      "▁c riter",
+      "▁cr iter",
+      "▁crit er",
+      "▁H A",
+      "▁ HA",
+      "an cers",
+      "ance rs",
+      "anc ers",
+      "ancer s",
+      "▁trans genic",
+      "▁b rowse",
+      "▁brow se",
+      "▁ browse",
+      "杨 某",
+      "▁m elt",
+      "▁me lt",
+      "▁mel t",
+      "en berg",
+      "me sh",
+      "mes h",
+      "m esh",
+      "id entity",
+      "ident ity",
+      "表现 为",
+      "表 现为",
+      "▁ac res",
+      "▁acre s",
+      "▁Dou glas",
+      "▁Doug las",
+      "收 缩",
+      "UL E",
+      "U LE",
+      "Sh op",
+      "S hop",
+      "▁B oys",
+      "▁Bo ys",
+      "▁Boy s",
+      "乌 鲁",
+      "合适 的答案",
+      "合适的 答案",
+      "阅读 选项",
+      "qu eous",
+      "que ous",
+      "▁icon ic",
+      "▁ic onic",
+      "▁reason ing",
+      "▁commission ed",
+      "噪 音",
+      "该 案",
+      "▁de serve",
+      "▁des erve",
+      "W H",
+      "军 人",
+      "在 中",
+      "man y",
+      "ma ny",
+      "m any",
+      "科 长",
+      "这里 的",
+      "这 里的",
+      "▁M AT",
+      "▁MA T",
+      "▁ MAT",
+      "热 的",
+      "年 至",
+      "lo ra",
+      "lor a",
+      "l ora",
+      "▁W elcome",
+      "▁Wel come",
+      "▁ Welcome",
+      "▁propag ation",
+      "pr il",
+      "pri l",
+      "p ril",
+      "ri ents",
+      "rient s",
+      "rien ts",
+      "r ients",
+      "▁author ized",
+      "▁ authorized",
+      "伊 朗",
+      "发展 和",
+      "▁e pic",
+      "▁ep ic",
+      "▁t une",
+      "▁tun e",
+      "▁tu ne",
+      "最高 的",
+      "最 高的",
+      "sh ields",
+      "shield s",
+      "▁s ingers",
+      "▁sing ers",
+      "▁sin gers",
+      "▁singer s",
+      "无 聊",
+      "战 术",
+      "超 出",
+      "北 路",
+      "nc an",
+      "n can",
+      "集团 公司",
+      "▁Scient ific",
+      "ablab la",
+      "▁pr ayer",
+      "▁pra yer",
+      "▁pray er",
+      "▁mys tery",
+      "▁myst ery",
+      "▁myster y",
+      "▁An dy",
+      "▁And y",
+      "cook ie",
+      "c ookie",
+      "▁coll apse",
+      "▁ collapse",
+      "iO S",
+      "i OS",
+      "▁different iate",
+      "Ce rt",
+      "C ert",
+      "py test",
+      "导 入",
+      "▁hor ror",
+      "▁ho rror",
+      "▁horr or",
+      "hu m",
+      "h um",
+      "的一 次",
+      "的 一次",
+      "> /",
+      "De velopment",
+      "Develop ment",
+      "je n",
+      "j en",
+      "nP lot",
+      "nPl ot",
+      "n Plot",
+      "▁D ig",
+      "▁Di g",
+      "▁ Dig",
+      "Col um",
+      "C olum",
+      "▁sl ides",
+      "▁slide s",
+      "▁ slides",
+      "要求 的",
+      "▁tor que",
+      "nM an",
+      "nMa n",
+      "n Man",
+      "SER VER",
+      "ja min",
+      "jam in",
+      "j amin",
+      "姿 态",
+      "▁alleg edly",
+      "▁alleged ly",
+      "大 楼",
+      "ir ates",
+      "ira tes",
+      "i rates",
+      "orect al",
+      "▁mur ine",
+      "▁mu rine",
+      "▁l imb",
+      "▁li mb",
+      "▁lim b",
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+      "n Could",
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+      "光 伏",
+      "媳 妇",
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+      "的 申请符合法律规定",
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+      "这才 是",
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+      "ru nch",
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+      "ther efore",
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+      "土 家族",
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+      "先 生的",
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+      "bon d",
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+      "发电 机",
+      "发 电机",
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+      "你 了",
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+      "t ron",
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+      "▁da r",
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+      "S ci",
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+      "▁Ga z",
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+      "平 台上",
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+      "二〇一六年十一 月",
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+      "第一百一 十九条",
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+      "ro cyte",
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+      "▁Object s",
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+      "a pur",
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+      "▁ret aining",
+      "▁retain ing",
+      "▁ 北",
+      "▁O C",
+      "▁ OC",
+      "是不 会",
+      "是 不会",
+      "Br idge",
+      "B ridge",
+      "▁d ownt",
+      "▁down t",
+      "▁dow nt",
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+      "▁infil tration",
+      "▁infiltr ation",
+      "▁@ @",
+      "▁ @@",
+      "las thandoff",
+      "能 使",
+      "采访 时",
+      "建筑 工程有限公司",
+      "建筑工程 有限公司",
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+      "英文 名",
+      "英 文名",
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+      "ur ate",
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+      "u rate",
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+      "ectin g",
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+      "n Product",
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+      "▁w his",
+      "▁wh is",
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+      "bal anced",
+      "balance d",
+      "▁res iding",
+      "▁resid ing",
+      "第二百六 十四条",
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+      "▁pr eterm",
+      "▁pret erm",
+      "浓 缩",
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+      "gev ity",
+      "sh aring",
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+      "红色 的",
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+      "拍 的",
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+      "中 性",
+      "ka l",
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+      "▁that s",
+      "▁re name",
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+      "▁differ ed",
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+      "▁b rom",
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+      "检验 报告",
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+      "▁Andre a",
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+      "工商银行 股份有限公司",
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+      "实录加西亚 延长赛落败",
+      "融券筹码 不断累加深天马",
+      "融券筹码不断累加深天马 重组案再遭",
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+      "却依然 在向",
+      "扬子 晚饭团",
+      "明明 废弃了",
+      "一直在 关注我",
+      "却被物管 私吞",
+      "并将 你的遭遇",
+      "我坚信 有球队",
+      "我知道 有球队",
+      "所以我很 刻苦",
+      "而且 这个城市",
+      "怀特 非常高兴能",
+      "我现在 来到了一",
+      "说出 你的遭遇吧",
+      "赢得胜利的 球队",
+      "业主收取 公摊水费",
+      "我非常感激 这一切",
+      "租金 却被物管私吞",
+      "请登录 西祠讨论版",
+      "又有 多少小区的水景",
+      "将自己磨练 得更全面",
+      "我们将予以 追踪报道",
+      "我知道有球队 注意我",
+      "而且这个城市 也很好",
+      "我坚信有球队 会签下我",
+      "一个不断 赢得胜利的球队",
+      "南京还有 多少小区的水景",
+      "怀特非常高兴能 签约火箭",
+      "有几支球队 一直在关注我",
+      "却依然在向 业主收取公摊水费",
+      "又有多少小区的水景 挪作他用",
+      "并将你的遭遇 反映给相关部门",
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+      "▁Z ag",
+      "il ogy",
+      "ilo gy",
+      "ilog y",
+      "i logy",
+      "景点的距离 如下",
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+      "da tab",
+      "dat ab",
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+      "▁ DEBUG",
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+      "▁Yi i",
+      "▁as ylum",
+      "▁r ugged",
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+      "nBase ball",
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+      "▁awa iting",
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+      "▁Filip ino",
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+      "▁advertise d",
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+      "nMar ch",
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+      "d uty",
+      "▁$$ |",
+      "据 新华社",
+      "ch emy",
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+      "c hemy",
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+      "▁l aptops",
+      "▁lap tops",
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+      "人民 卫生",
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+      "▁trademark s",
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+      "洋 洋",
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+      "F lu",
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+      "ins e",
+      "i nse",
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+      "▁Ether net",
+      "{ <",
+      "再 多",
+      "MM P",
+      "M MP",
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+      "教育学 院",
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+      "中国 工商银行股份有限公司",
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+      "用 具",
+      "只要 是",
+      "只 要是",
+      "视网 膜",
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+      "actual ly",
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+      "▁Red irect",
+      "▁ Redirect",
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+      "这 群",
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+      "A RE",
+      "▁Y un",
+      "▁Yu n",
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+      "生活 习惯",
+      "明智 的选择",
+      "甲基 苯丙胺",
+      "▁th under",
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+      "适 中",
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+      "▁rev ert",
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+      "▁I G",
+      "▁ IG",
+      "▁德 国",
+      "▁ 德国",
+      "▁re ck",
+      "▁r eck",
+      "▁rec k",
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+      "▁approxim ations",
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+      "▁H K",
+      "▁ HK",
+      "立方 分米",
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+      "▁fung us",
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+      "▁multi ples",
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+      "▁anti cancer",
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+      "学 分",
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+      "十三条 的规定",
+      "▁her editary",
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+      "▁Cru ise",
+      "▁compl aining",
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+      "上 大学",
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+      "▁Cl early",
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+      "▁Clear ly",
+      "▁L agrangian",
+      "应 邀",
+      "新鲜 的",
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+      "read able",
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+      "笼 罩",
+      "车 库",
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+      "▁Bur ton",
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+      "根本 上",
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+      "小 店",
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+      "依法应予 准许",
+      "▁card io",
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+      "▁quiet ly",
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+      "都 有一个",
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+      "b acked",
+      "▁K indle",
+      "▁Kind le",
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+      "个 月的",
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+      "▁L und",
+      "▁Lu nd",
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+      "的 人类",
+      "▁se cular",
+      "▁sec ular",
+      "▁ter restrial",
+      "▁interpret ing",
+      "佣 金",
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+      "立方 厘米",
+      "▁turb ulence",
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+      "向本院提供 被执行人",
+      "▁Techn iques",
+      "▁Technique s",
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+      "考 评",
+      "牛仔 裤",
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+      "▁Rou gh",
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+      "▁tw entieth",
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+      "提供的 证据",
+      "第一百四十 四条的规定",
+      "第一百四十四 条的规定",
+      "第一百四十四条 的规定",
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+      "\":[ \\",
+      "债权 依法",
+      "在 人民法院",
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+      "▁Prest on",
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+      "▁Equ ity",
+      "▁Eq uity",
+      "ress ions",
+      "ression s",
+      "r essions",
+      "▁Im proved",
+      "▁Improve d",
+      "▁vis ceral",
+      "一 连",
+      "免 税",
+      "冲 锋",
+      "妨 害",
+      "涡 轮",
+      "DA T",
+      "D AT",
+      "^* $",
+      "^ *$",
+      "想象 力",
+      "ac ian",
+      "aci an",
+      "acia n",
+      "▁Ad aptive",
+      "▁Ada ptive",
+      "▁Adapt ive",
+      "▁Pain ting",
+      "▁Paint ing",
+      "▁Pharm aceutical",
+      "永 生",
+      "二次 元",
+      "带 孩子",
+      "pl ug",
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+      "▁kernel s",
+      "▁ker nels",
+      "he on",
+      "h eon",
+      "ens ory",
+      "ensor y",
+      "▁R iley",
+      "▁Ri ley",
+      "▁d rones",
+      "▁dr ones",
+      "▁dro nes",
+      "▁drone s",
+      "本次执行程序 应予终结",
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+      "eb a",
+      "e ba",
+      "yn ch",
+      "ync h",
+      "y nch",
+      "进行 研究",
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+      "▁ witch",
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+      "Player s",
+      "Play ers",
+      "P layers",
+      "bad ges",
+      "badge s",
+      "▁Cal if",
+      "▁Ca lif",
+      "▁condition er",
+      "就 让",
+      "身 旁",
+      "MC s",
+      "M Cs",
+      "俗 话说",
+      "me mo",
+      "mem o",
+      "m emo",
+      "nP rom",
+      "nPro m",
+      "nPr om",
+      "n Prom",
+      "经济 发展的",
+      "经济发展 的",
+      "▁cl utch",
+      "▁on board",
+      "或 在",
+      "看 向",
+      "ra j",
+      "r aj",
+      "好 莱坞",
+      "▁P II",
+      "▁PI I",
+      "▁ PII",
+      "nw ell",
+      "nwe ll",
+      "n well",
+      "tro viral",
+      "▁H ampton",
+      "▁Ham pton",
+      "▁Hamp ton",
+      "▁spec ials",
+      "▁special s",
+      "圣 经",
+      "太 后",
+      "幼 虫",
+      "数据 线",
+      "显示 出",
+      "\\\\ }$$",
+      "\\\\} $$",
+      "\\\\}$ $",
+      "fr eeze",
+      "free ze",
+      "f reeze",
+      "▁oxid ized",
+      "瘙 痒",
+      "追 回",
+      "飞行 员",
+      "▁} }{",
+      "▁}} {",
+      "▁ }}{",
+      "由 审判员",
+      "li ers",
+      "lier s",
+      "lie rs",
+      "l iers",
+      "族自治州 中级人民法院",
+      "哈 萨克",
+      "空气 中",
+      "▁web cam",
+      "▁dipl oma",
+      "▁diplom a",
+      "▁ 试",
+      "俄 国",
+      "的 组合",
+      "主要 内容",
+      "问卷 调查",
+      "pl asty",
+      "p lasty",
+      "▁Mer ge",
+      "▁Merg e",
+      "▁ Merge",
+      "enn ials",
+      "ennial s",
+      "▁det ects",
+      "▁detect s",
+      "劳务 合同纠纷一案",
+      "人 脸",
+      "so on",
+      "s oon",
+      "it ating",
+      "ita ting",
+      "itat ing",
+      "礼 拜",
+      "Mu r",
+      "M ur",
+      "就 够了",
+      "▁pr ince",
+      "▁princ e",
+      "▁pri nce",
+      "▁crit ique",
+      "监 制",
+      "做好 了",
+      "做 好了",
+      "▁ext rav",
+      "▁extra v",
+      "▁extr av",
+      "▁Re porter",
+      "▁Rep orter",
+      "▁Report er",
+      "▁ Reporter",
+      "▁contract ile",
+      "▁unexpected ly",
+      "鼻 炎",
+      "我们 有",
+      "▁d ei",
+      "▁de i",
+      "sc anner",
+      "scan ner",
+      "▁abs urd",
+      "▁ins erts",
+      "▁insert s",
+      "鸟 类",
+      "bul k",
+      "b ulk",
+      "▁S lot",
+      "▁Sl ot",
+      "▁Slo t",
+      "▁ Slot",
+      "▁h eur",
+      "▁he ur",
+      "可供 您选择",
+      "ou ched",
+      "ouch ed",
+      "sec rets",
+      "secret s",
+      "Pr eference",
+      "Pre ference",
+      "P reference",
+      "Compat Activity",
+      "红 茶",
+      "青 椒",
+      "▁{ %",
+      "▁ {%",
+      "中国 画",
+      "中 国画",
+      "是 这个",
+      "的 优点",
+      "st ay",
+      "sta y",
+      "荆 州",
+      "nU N",
+      "n UN",
+      "桂林 市",
+      "▁C ly",
+      "▁Cl y",
+      "▁F unk",
+      "▁Fun k",
+      "▁Fu nk",
+      "▁rad ios",
+      "▁radio s",
+      "▁radi os",
+      "▁tr actor",
+      "▁tra ctor",
+      "▁tract or",
+      "starts With",
+      "▁for bidden",
+      "香 料",
+      "医疗 卫生",
+      "然后 告诉我",
+      "请告诉我 您的选择",
+      "conf irmed",
+      "confirm ed",
+      "日向本院提出 财产保全",
+      "▁narr atives",
+      "▁narrative s",
+      "请仔细阅读 下面这道选择题",
+      "请 仔细阅读下面这道选择题",
+      "文 旅",
+      "中 全会",
+      "的 课程",
+      "某某 负担",
+      "某 某负担",
+      "▁Sal em",
+      "▁Sa lem",
+      "▁Sale m",
+      "▁st igma",
+      "▁phosphory lated",
+      "瓷 器",
+      "长 子",
+      "Sk y",
+      "S ky",
+      "高 精度",
+      "▁a ur",
+      "▁au r",
+      "▁ aur",
+      "▁l ord",
+      "▁lo rd",
+      "▁ lord",
+      "De ploy",
+      "Dep loy",
+      "card ia",
+      "古 装",
+      "备 战",
+      "武 警",
+      "金 字",
+      "▁号 码",
+      "▁ 号码",
+      "去年 的",
+      "去 年的",
+      "▁Tw elve",
+      "▁s prite",
+      "▁sp rite",
+      "▁spr ite",
+      "▁ sprite",
+      "中华人民共和国 土地管理法",
+      "追 捧",
+      "速度 和",
+      "速 度和",
+      "▁J ag",
+      "▁Ja g",
+      "Back up",
+      "▁Be tty",
+      "▁Bet ty",
+      "知识渊博 的人",
+      "▁Bal let",
+      "▁Ball et",
+      "等 诸多",
+      "组织 中",
+      "il in",
+      "ili n",
+      "i lin",
+      "更多 信息",
+      "注册 资金",
+      "▁he ir",
+      "▁p erv",
+      "▁per v",
+      "▁pe rv",
+      "▁pay day",
+      "园 艺",
+      "iz i",
+      "i zi",
+      "多万 元",
+      "多 万元",
+      "添加 了",
+      "添 加了",
+      "您认为 的",
+      "▁ 占地面积",
+      "人民卫生 出版社",
+      "▁anticip ation",
+      "▁antic ipation",
+      "N X",
+      "读 写",
+      "EP S",
+      "E PS",
+      "应 依法",
+      "Lo ss",
+      "L oss",
+      "继续 进行",
+      "AC TER",
+      "ACT ER",
+      "▁pl ur",
+      "▁L opez",
+      "▁Lo pez",
+      "re levant",
+      "▁hon esty",
+      "▁honest y",
+      "▁hone sty",
+      "▁individual ized",
+      "白 领",
+      "nK en",
+      "nKe n",
+      "n Ken",
+      "请用 您的",
+      "ar box",
+      "arb ox",
+      "▁M ens",
+      "▁Me ns",
+      "▁Men s",
+      "▁E qual",
+      "▁Equ al",
+      "▁Eq ual",
+      "▁ Equal",
+      "▁ 维",
+      "Ne ver",
+      "N ever",
+      "请您务必 注意",
+      "在回答 以下这道",
+      "问题和选项 的内容",
+      "▁work sheet",
+      "▁works heet",
+      "从选项中 选定正确答案",
+      "具有挑战性的 选择题时",
+      "▁ast rocytes",
+      "▁astr ocytes",
+      "请您务必注意 问题和选项的内容",
+      "CO PY",
+      "C OPY",
+      "pon ge",
+      "po nge",
+      "p onge",
+      "Sec ure",
+      "▁o zone",
+      "▁oz one",
+      "▁L ounge",
+      "▁Lou nge",
+      "▁Lo unge",
+      "%%%% %%%%",
+      "▁David son",
+      "▁in efficient",
+      "交 织",
+      "相比 于",
+      "醉酒 后",
+      "醉 酒后",
+      "▁B ali",
+      "▁Bal i",
+      "▁Ba li",
+      "bra him",
+      "工贸 有限公司",
+      "▁De bian",
+      "▁Deb ian",
+      "▁bat ting",
+      "▁batt ing",
+      "审慎考虑 您的选择",
+      "并告诉我 您认为的",
+      "请用您的 专业知识",
+      "nEvery one",
+      "▁work outs",
+      "▁workout s",
+      "▁summar izes",
+      "▁summarize s",
+      "以下备选答案 可供您选择",
+      "请用您的专业知识 回答以下问题",
+      "南阳 市",
+      "南 阳市",
+      "以下 命令",
+      "▁l aughter",
+      "▁laugh ter",
+      "▁imp ressions",
+      "▁impression s",
+      "▁impress ions",
+      "约 占",
+      "理由 正当",
+      "dor ff",
+      "dorf f",
+      "gu ess",
+      "gue ss",
+      "▁a vid",
+      "▁av id",
+      "▁ avid",
+      "▁Wes ley",
+      "current Time",
+      "混 淆",
+      "迷 人",
+      "▁X Y",
+      "▁ XY",
+      "开放 式",
+      "指出 了",
+      "指 出了",
+      "ox id",
+      "li kes",
+      "like s",
+      "lik es",
+      "l ikes",
+      "▁C hal",
+      "▁Ch al",
+      "▁Cha l",
+      "▁V ERY",
+      "▁VE RY",
+      "▁ VERY",
+      "▁f erm",
+      "▁fe rm",
+      "▁fer m",
+      "nAd min",
+      "n Admin",
+      "▁s arcoma",
+      "▁sar coma",
+      "▁sarc oma",
+      "▁advert ise",
+      "▁advertis e",
+      "长 短",
+      "领 略",
+      "灵敏 度",
+      "▁S eth",
+      "▁Se th",
+      "▁Set h",
+      "open ssl",
+      "opens sl",
+      "XXXX XXXX",
+      "ell aneous",
+      "季 后",
+      "解 散",
+      "▁L t",
+      "的一 段",
+      "的 一段",
+      "起诉 必须",
+      "请选择 你认为",
+      "found land",
+      "д у",
+      "不 归",
+      "此 地",
+      "专 升本",
+      "如果 你想",
+      "如果你 想",
+      "请选择你认为 合适的答案",
+      "人 权",
+      "丫 头",
+      "丽 莎",
+      "营 利",
+      "转 子",
+      "都 喜欢",
+      "森林 公园",
+      "▁J agu",
+      "▁Ja gu",
+      "▁Jag u",
+      "p uppet",
+      "| [",
+      "LE N",
+      "L EN",
+      "fl uent",
+      "flu ent",
+      "f luent",
+      "▁pione ering",
+      "▁pioneer ing",
+      "心 率",
+      "OT H",
+      "O TH",
+      "▁{ /",
+      "▁ {/",
+      "吸血 鬼",
+      "请 审查",
+      "近 距离",
+      "严格 执行",
+      "▁Thom son",
+      "astruct ural",
+      "a structural",
+      "▁file system",
+      "▁files ystem",
+      "▁ filesystem",
+      "▁cent imeters",
+      "准 时",
+      "坏 人",
+      "移 交",
+      "…… [",
+      "fin e",
+      "fi ne",
+      "f ine",
+      "▁h oc",
+      "▁ho c",
+      "▁ hoc",
+      "ne num",
+      "nen um",
+      "n enum",
+      "urt les",
+      "urtle s",
+      "实际应用 领域",
+      "▁r ented",
+      "▁rent ed",
+      "他 俩",
+      "录 入",
+      "面 目",
+      "co rs",
+      "cor s",
+      "c ors",
+      "▁B yz",
+      "▁By z",
+      "▁b lu",
+      "▁bl u",
+      "发展 方向",
+      "人民检察院 以",
+      "▁d oping",
+      "▁do ping",
+      "▁dop ing",
+      "▁pet rol",
+      "最高人民法院关于 执行案件立案",
+      "发 给",
+      "失 控",
+      "次 于",
+      "起 初",
+      "▁ 开发商",
+      "第十 三章",
+      "第十三 章",
+      "第 十三章",
+      "选择 是什么",
+      "▁F loyd",
+      "▁Flo yd",
+      "get Type",
+      "▁cr acked",
+      "▁crack ed",
+      "▁contr asting",
+      "▁contrast ing",
+      "参 见",
+      "博物 院",
+      "在 生活中",
+      "▁Joy ce",
+      "▁str aps",
+      "▁strap s",
+      "▁disadvant age",
+      "刷 牙",
+      "在 同",
+      "提取 物",
+      "nI ce",
+      "随着 我国",
+      "▁t idal",
+      "▁ti dal",
+      "▁tid al",
+      "Drop down",
+      "Ext ended",
+      "▁obs essed",
+      "▁Attribute Error",
+      "随 处",
+      "非常 好的",
+      "非常好 的",
+      "▁stabil ize",
+      "凌 云",
+      "环 形",
+      "竹 林",
+      "乐 意",
+      "Ow n",
+      "O wn",
+      "Draw er",
+      "D rawer",
+      "st ance",
+      "stan ce",
+      "sta nce",
+      "{{' }}(",
+      "▁ch eek",
+      "▁che ek",
+      "cont ained",
+      "▁sandwich es",
+      "▁sandw iches",
+      "他 将",
+      "口 中",
+      "引 言",
+      "水 务",
+      "MP a",
+      "M Pa",
+      "乘 进率",
+      "▁R ise",
+      "▁Ri se",
+      "▁Ris e",
+      "itor ium",
+      "ito rium",
+      "请审查 以下问题",
+      "▁el usive",
+      "▁ens ured",
+      "▁ensure d",
+      "▁St ability",
+      "作为一名 知识渊博的人",
+      "Ow nProperty",
+      "Own Property",
+      "南 岸",
+      "垫 付",
+      "烟 雾",
+      "智能 家居",
+      "an dan",
+      "and an",
+      "anda n",
+      "a ndan",
+      "ef fer",
+      "eff er",
+      "▁h ips",
+      "▁hip s",
+      "▁hi ps",
+      "▁ hips",
+      "On Init",
+      "O nInit",
+      "工业 和信息化",
+      "▁am azon",
+      "▁ amazon",
+      "人 流",
+      "eb o",
+      "e bo",
+      "▁A da",
+      "▁Ad a",
+      "▁S ham",
+      "▁Sh am",
+      "▁Sha m",
+      "Par allel",
+      "▁m ornings",
+      "▁mor nings",
+      "▁morning s",
+      "恐 慌",
+      "nA g",
+      "n Ag",
+      "▁c t",
+      "▁ ct",
+      "有一 条",
+      "有 一条",
+      "ce ls",
+      "cel s",
+      "c els",
+      "只是 为了",
+      "只 是为了",
+      "▁Al leg",
+      "▁All eg",
+      "▁Sc andin",
+      "▁Scan din",
+      "前 瞻",
+      "房 地",
+      "雨 后",
+      "tt o",
+      "t to",
+      "nH ol",
+      "n Hol",
+      "得到 结果",
+      "选项 涉及到",
+      "▁rad iographic",
+      "▁radio graphic",
+      "▁radi ographic",
+      "推 翻",
+      "人口 与",
+      "和 个人",
+      "ep pe",
+      "与 对照组",
+      "▁read iness",
+      "延 边",
+      "疤 痕",
+      "以下 的问题和",
+      "以下的 问题和",
+      "审阅 这道选择题",
+      "ter raform",
+      "以下的问题和 选项涉及到",
+      "并从给出的选项中 选定正确答案",
+      "交 响",
+      "▁p u",
+      "▁ pu",
+      "的最 高",
+      "的 最高",
+      "roll up",
+      "▁Fl uid",
+      "▁Flu id",
+      "math scr",
+      "▁S inger",
+      "▁Sing er",
+      "▁Sin ger",
+      "' _",
+      "▁ 廖",
+      "日 为",
+      "瓦 斯",
+      "跑 去",
+      "迷 信",
+      "特别 好",
+      "▁acc ents",
+      "▁ac cents",
+      "▁accent s",
+      "▁int imid",
+      "▁intim id",
+      "共 分",
+      "有 空",
+      "诅 咒",
+      "你不 能",
+      "你 不能",
+      "不良 影响",
+      "St rict",
+      "Str ict",
+      "▁centr ifug",
+      "▁digital ly",
+      "▁digit ally",
+      "征 地",
+      "飞 扬",
+      "在有 限",
+      "在 有限",
+      "▁G ap",
+      "▁Ga p",
+      "希望 通过",
+      "▁nom bre",
+      "▁ nombre",
+      "▁coinc ide",
+      "塔 尔",
+      "事业 发展",
+      "kr ainian",
+      "▁non invasive",
+      "切 的",
+      "d ream",
+      "▁head phones",
+      "▁re nin",
+      "▁r enin",
+      "Game Object",
+      "▁advert isers",
+      "▁advertis ers",
+      "▁advertise rs",
+      "国有 资产",
+      "▁ev angel",
+      "▁Mad agascar",
+      "▁authent icity",
+      "▁authentic ity",
+      "库 里",
+      "角 质",
+      "饭 后",
+      "at u",
+      "a tu",
+      "这 玩意",
+      "▁Ar ms",
+      "▁Arm s",
+      "▁S han",
+      "▁Sh an",
+      "▁Sha n",
+      "▁neuro logic",
+      "▁neur ologic",
+      "Error Message",
+      "So n",
+      "S on",
+      "吃 起来",
+      "植物 门",
+      "的 现状",
+      "sm tp",
+      "积极 开展",
+      "ass oci",
+      "asso ci",
+      "ment al",
+      "men tal",
+      "m ental",
+      "▁alk yl",
+      "▁b rides",
+      "▁br ides",
+      "▁brid es",
+      "▁bride s",
+      "▁sur plus",
+      "排 污",
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+      "▁gall ons",
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+      "看 不懂",
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+      "▁L R",
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+      "ias t",
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+      "▁L ua",
+      "▁Lu a",
+      "行政 管理",
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+      "并给出 正确答案",
+      "古 巴",
+      "缠 绕",
+      "位数 是",
+      "位 数是",
+      "县委 常委",
+      "县 委常委",
+      "在面对 以下",
+      "请您 认真分析",
+      "▁Dec imal",
+      "▁ Decimal",
+      "并告诉我 您的选择",
+      "在面对以下 选择题时",
+      "▁econom ist",
+      "请仔细浏览 以下备选答案",
+      "请您认真分析 并给出正确答案",
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+      "全 班",
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+      "▁EV ERY",
+      "房地产开发 有限责任公司",
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+      "▁' _",
+      "▁ '_",
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+      "就 是我",
+      "并 表示",
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+      "担 负",
+      "为主 料的",
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+      "▁Par king",
+      "▁Park ing",
+      "▁pl otted",
+      "▁plot ted",
+      "▁br ighter",
+      "▁bright er",
+      "▁dipl omat",
+      "▁diplom at",
+      "▁diploma t",
+      "请仔细 分析这些选项",
+      "然后告诉我 您的最佳选择",
+      "这是一道需 综合运用多方面",
+      "th i",
+      "t hi",
+      "保持 在",
+      "SA TA",
+      "SAT A",
+      "S ATA",
+      "Da ily",
+      "D aily",
+      "内容 判断出",
+      "包含一个 问题和",
+      "内容判断出 您认为",
+      "以下内容 包含一个问题和",
+      "▁Pseud omonas",
+      "依据问题 内容判断出您认为",
+      "以下内容包含一个问题和 若干选项",
+      "前 夕",
+      "感 性",
+      "这 儿",
+      "El l",
+      "E ll",
+      "法律 的",
+      "魔法 师",
+      "魔 法师",
+      "or ax",
+      "ora x",
+      "o rax",
+      "▁A FL",
+      "▁AF L",
+      "▁A gu",
+      "▁Ag u",
+      "▁V oy",
+      "▁Vo y",
+      "fil ls",
+      "fill s",
+      "f ills",
+      "▁M aint",
+      "▁Main t",
+      "▁Ma int",
+      "▁Mai nt",
+      "short cuts",
+      "▁W ik",
+      "▁Wi k",
+      "UM ENT",
+      "U MENT",
+      "in itis",
+      "init is",
+      "ini tis",
+      "le ctic",
+      "lect ic",
+      "lec tic",
+      "▁Alex a",
+      "▁Ale xa",
+      "▁em pathy",
+      "▁emp athy",
+      "▁Est onian",
+      "▁Eston ian",
+      "▁Estonia n",
+      ", {",
+      "▁A ML",
+      "▁AM L",
+      "▁ AML",
+      "周边 配套",
+      "▁D ash",
+      "▁Da sh",
+      "▁Das h",
+      "▁ Dash",
+      "turn ed",
+      "tur ned",
+      "▁f b",
+      "▁ fb",
+      "erial izer",
+      "erialize r",
+      "▁re create",
+      "▁rec reate",
+      "毛 细",
+      "短 片",
+      "cs c",
+      "c sc",
+      "▁H utch",
+      "▁Chap man",
+      "▁None theless",
+      "▁Non etheless",
+      "县 的",
+      "拳 头",
+      "JD K",
+      "J DK",
+      "交流 与",
+      "这个 地图",
+      "▁H elm",
+      "▁He lm",
+      "▁Hel m",
+      "sp here",
+      "s phere",
+      "▁pres umed",
+      "▁presum ed",
+      "依 依",
+      "沙 龙",
+      "营 商",
+      "仍不 能",
+      "仍 不能",
+      "所用 的",
+      "所 用的",
+      "TE MP",
+      "TEM P",
+      "T EMP",
+      "▁AB OUT",
+      "▁ABO UT",
+      "ut ively",
+      "utive ly",
+      "uti vely",
+      "▁Bol lywood",
+      "▁Sett lement",
+      "▁document ing",
+      "▁fashion able",
+      "二 层",
+      "疏 散",
+      "的 还是",
+      "nA mb",
+      "nAm b",
+      "n Amb",
+      "oblast ic",
+      "obl astic",
+      "▁vacc inated",
+      "▁vaccin ated",
+      "烤 肉",
+      "热 衷",
+      "为他 们",
+      "为 他们",
+      "审判 庭",
+      "与被 上诉人",
+      "与 被上诉人",
+      "access or",
+      "▁Stand ing",
+      "▁Stan ding",
+      "▁compass ionate",
+      "▁compassion ate",
+      "机动车交通事故责任 纠纷一案中",
+      "机动车交通事故责任纠纷一案 中",
+      "无 言",
+      "猕 猴",
+      "着 作",
+      "得出 答案",
+      "龙头 企业",
+      "is dir",
+      "on ium",
+      "oni um",
+      "给定 的问题",
+      "给定的 问题",
+      "▁sup erc",
+      "▁super c",
+      "▁non atomic",
+      "▁ nonatomic",
+      "毛 衣",
+      "的 快",
+      "皮 下",
+      "IL Y",
+      "I LY",
+      "▁Qu ad",
+      "▁C ookie",
+      "▁Cook ie",
+      "▁ Cookie",
+      "▁climb ed",
+      "▁clim bed",
+      "▁cli mbed",
+      "领 会",
+      "tc tx",
+      "t ctx",
+      "nj obs",
+      "n jobs",
+      "eh icles",
+      "ehicle s",
+      "lar gest",
+      "large st",
+      "larg est",
+      "▁p aints",
+      "▁pain ts",
+      "▁paint s",
+      "▁pa ints",
+      "| _",
+      "超 额",
+      "在 国外",
+      "给定 问题",
+      "▁H undred",
+      "▁json ify",
+      "▁Ap artment",
+      "▁Apart ment",
+      "D y",
+      "东 市",
+      "孔 雀",
+      "无 毒",
+      "Pa g",
+      "P ag",
+      "ot to",
+      "ott o",
+      "o tto",
+      "这个 行业",
+      "sequ ent",
+      "seq uent",
+      "L y",
+      "嘴 唇",
+      "GR E",
+      "G RE",
+      "付出 的",
+      "付 出的",
+      "▁P lat",
+      "▁Pl at",
+      "现在 我会给你",
+      "依据 给定的问题",
+      "Api Model",
+      "仔细阅读 上面这个问题",
+      "现在我会给你 几个选项",
+      "请你根据 问题进行分析",
+      "从选项中选出 合适的答案",
+      "揭 晓",
+      "新 书",
+      "比 方",
+      "▁C omm",
+      "▁Com m",
+      "▁Co mm",
+      "▁ Comm",
+      "上面 这段话",
+      "▁the ology",
+      "Collect ors",
+      "Coll ectors",
+      "Collector s",
+      "nSimilar ly",
+      "attach ments",
+      "attachment s",
+      "▁cred ential",
+      "▁ φ",
+      "双 面",
+      "eu s",
+      "e us",
+      "ass uming",
+      "att erson",
+      "atter son",
+      "'} \\",
+      "' }\\",
+      "em ap",
+      "ema p",
+      "e map",
+      "mon ds",
+      "mond s",
+      "m onds",
+      "▁o bey",
+      "▁ob ey",
+      "DE VICE",
+      "DEV ICE",
+      "Ir eland",
+      "I reland",
+      "▁Fin ish",
+      "▁ Finish",
+      "▁PE OPLE",
+      "▁sal iva",
+      "▁Dragon s",
+      "▁Drag ons",
+      "已发生法律效力 的",
+      "▁ins ufficiency",
+      "与 传统",
+      "▁catar act",
+      "▁Dist inguished",
+      "N b",
+      "▁ 星",
+      "深 海",
+      "使用 方法",
+      "Version s",
+      "Vers ions",
+      "▁imm obil",
+      "依法适用 普通程序",
+      "操 场",
+      "有 声",
+      "一分 钟",
+      "一 分钟",
+      "至少 要",
+      "是否 可以",
+      "in ium",
+      "ini um",
+      "▁p leasing",
+      "▁ple asing",
+      "▁pleas ing",
+      "▁plea sing",
+      "▁assist ants",
+      "▁assistant s",
+      "二 三",
+      "奋 力",
+      "j Query",
+      "▁p ests",
+      "▁pe sts",
+      "▁pest s",
+      "▁pes ts",
+      "\\\\\": \\\\\"",
+      "▁depict ing",
+      "▁ 来",
+      "近 些",
+      "逃 生",
+      "fe ng",
+      "fen g",
+      "f eng",
+      "▁c aud",
+      "▁ca ud",
+      "opath ology",
+      "▁Rec ording",
+      "▁Record ing",
+      "▁Ge ographic",
+      "▁Geo graphic",
+      "▁interfer ing",
+      "▁interf ering",
+      "刻 录",
+      "ka s",
+      "k as",
+      "nA pr",
+      "nAp r",
+      "n Apr",
+      "▁D isk",
+      "▁Dis k",
+      "▁Di sk",
+      "▁ Disk",
+      "ic ators",
+      "icator s",
+      "superv ised",
+      "▁River side",
+      "▁Rivers ide",
+      "建 工",
+      "显存 容量",
+      "rem oved",
+      "remove d",
+      "▁Eff icient",
+      "波 长",
+      "科 创",
+      "红 颜",
+      "贮 藏",
+      "M ajor",
+      "▁m aze",
+      "▁ma ze",
+      "▁prosec utor",
+      "[] {",
+      "[ ]{",
+      "▁m oi",
+      "▁mo i",
+      "期限届满 前",
+      "nView s",
+      "n Views",
+      "esar ean",
+      "esare an",
+      "▁De ploy",
+      "▁Dep loy",
+      "▁ Deploy",
+      "poss ibly",
+      "亦 是",
+      "低 迷",
+      "写 道",
+      "加 尔",
+      "筹 划",
+      "▁ 创办",
+      "al ist",
+      "ali st",
+      "alis t",
+      "a list",
+      "▁D onna",
+      "▁Don na",
+      "hand led",
+      "handle d",
+      "▁sm oked",
+      "▁smoke d",
+      "▁gran ules",
+      "▁granul es",
+      "▁integr in",
+      "oo oo",
+      "Re try",
+      "Ret ry",
+      "R etry",
+      "▁N ath",
+      "▁Na th",
+      "▁Nat h",
+      "申请 执行费",
+      "申请执行 费",
+      "出具借条 一份",
+      "▁rev ival",
+      "▁t c",
+      "▁ tc",
+      "ngo ing",
+      "n going",
+      "▁Res idential",
+      "小 憩",
+      "都 用",
+      "第一 季",
+      "ix ir",
+      "Sc ott",
+      "S cott",
+      "▁trans itional",
+      "▁transition al",
+      "▁transit ional",
+      "九 天",
+      "欺 诈",
+      "账 单",
+      "这样 就",
+      "ro at",
+      "olph in",
+      "web app",
+      "▁Complex ity",
+      "▁inher ently",
+      "▁inherent ly",
+      "▁carbohyd rates",
+      "▁carbohydrate s",
+      "k V",
+      "校 外",
+      "牙 膏",
+      "配 角",
+      "决策 和",
+      "ca rs",
+      "car s",
+      "c ars",
+      "从而 达到",
+      "▁new com",
+      "▁general ize",
+      "二 胎",
+      "对 战",
+      "数的 定义",
+      "数 的定义",
+      "社区 矫正",
+      "*\\\\ *\\\\",
+      "res ume",
+      "sh util",
+      "shu til",
+      "▁HT TPS",
+      "▁HTTP S",
+      "▁ HTTPS",
+      "▁bl ending",
+      "▁blend ing",
+      "we d",
+      "w ed",
+      "以 达到",
+      "Sl ug",
+      "▁ac upuncture",
+      "生产 能力",
+      "▁V all",
+      "▁Val l",
+      "▁Va ll",
+      "▁Cal vin",
+      "▁hon ours",
+      "▁honour s",
+      "▁refer ee",
+      "▁refere e",
+      "▁t actical",
+      "▁tact ical",
+      "丽 江",
+      "变得 更",
+      "变 得更",
+      "在 现场",
+      "老 干部",
+      "Rub y",
+      "Ru by",
+      "新冠 病毒",
+      "car go",
+      "c argo",
+      "▁Gi ovanni",
+      "▁in compatible",
+      "up p",
+      "u pp",
+      "加 起来",
+      "时间 段",
+      "高级 的",
+      "高 级的",
+      "uc ci",
+      "ucc i",
+      "u cci",
+      "▁R ex",
+      "▁Re x",
+      "▁Y uk",
+      "▁Yu k",
+      "执行 到位",
+      "▁qu as",
+      "\\' ]))\\",
+      "\\'] ))\\",
+      "\\']) )\\",
+      "▁extra pol",
+      "▁extr apol",
+      "eval uation",
+      "evalu ation",
+      "太 守",
+      "慷 慨",
+      "▁R eb",
+      "▁Re b",
+      "▁ Reb",
+      "or set",
+      "ors et",
+      "orse t",
+      "▁P ond",
+      "▁Po nd",
+      "▁Pon d",
+      "▁Tre aty",
+      "▁Treat y",
+      "▁p ushes",
+      "▁push es",
+      "mun ition",
+      "▁Fib onacci",
+      "新 风",
+      "航空 航天",
+      "▁p lex",
+      "▁pl ex",
+      "▁ple x",
+      "▁ plex",
+      "吃 得",
+      "成 套",
+      "泥 土",
+      "号 房屋",
+      "an no",
+      "ann o",
+      "a nno",
+      "im ab",
+      "ima b",
+      "有的 时候",
+      "有 的时候",
+      "正是 因为",
+      "正 是因为",
+      "nV ari",
+      "n Vari",
+      "▁M ing",
+      "▁Min g",
+      "▁Mi ng",
+      "orph ous",
+      "oxy genase",
+      "oxygen ase",
+      "▁sol vents",
+      "▁solvent s",
+      "▁solv ents",
+      "▁Autom otive",
+      "总 能",
+      "cu s",
+      "c us",
+      "Le af",
+      "▁E GF",
+      "▁EG F",
+      "▁C trl",
+      "▁ Ctrl",
+      "▁Fr anco",
+      "▁Franc o",
+      "▁Fra nco",
+      "Phys ical",
+      "▁s ickness",
+      "▁sick ness",
+      "▁volcan ic",
+      "▁hyd ration",
+      "▁zebra fish",
+      "减 压",
+      "橡 皮",
+      "认 清",
+      "贴 合",
+      "ux t",
+      "u xt",
+      "但是 这",
+      "▁] ];",
+      "什么 都不",
+      "什么都 不",
+      "民终 字第",
+      "民 终字第",
+      "刑 二",
+      "售 卖",
+      "洗 脑",
+      "问 了",
+      "tt y",
+      "t ty",
+      "有效 性",
+      "▁E UR",
+      "▁EU R",
+      "▁ EUR",
+      "▁D ensity",
+      "▁aspir in",
+      "斑 点",
+      "也能 够",
+      "也 能够",
+      "奥斯 卡",
+      "▁b abe",
+      "▁bab e",
+      "▁ba be",
+      "Imp act",
+      "block quote",
+      "(\" @",
+      "( \"@",
+      "zo o",
+      "z oo",
+      "观众 的",
+      "▁职务 任免",
+      "受案 登记表",
+      "在一定 程度上",
+      "在 一定程度上",
+      "▁Bar rett",
+      "▁Barr ett",
+      "▁por cine",
+      "▁clar ified",
+      "洞 察",
+      "肉 眼",
+      "闭 幕",
+      "哪怕 是",
+      "▁sy nov",
+      "▁syn ov",
+      "请你 根据题意",
+      "请你根据 题意",
+      "st ories",
+      "sto ries",
+      "stor ies",
+      "▁giant s",
+      "▁gi ants",
+      "▁Ext reme",
+      "▁Extr eme",
+      "▁imm ersion",
+      "▁immers ion",
+      "皆 是",
+      "▁T in",
+      "▁Ti n",
+      "▁bl amed",
+      "▁blame d",
+      "▁C reation",
+      "▁Creat ion",
+      "▁Cre ation",
+      "▁ Creation",
+      "▁in ferred",
+      "▁infer red",
+      "nPublic ation",
+      "层面 的",
+      "层 面的",
+      "ge ns",
+      "gen s",
+      "g ens",
+      "分类 讨论",
+      "nd oes",
+      "ndo es",
+      "n does",
+      "t Assert",
+      "▁K olkata",
+      "nF acebook",
+      "n Facebook",
+      "周 三",
+      "贪 污",
+      "▁T I",
+      "▁ TI",
+      "bi ke",
+      "b ike",
+      "▁war fare",
+      "不 休",
+      "为 大",
+      "奖 章",
+      "小 球",
+      "fr m",
+      "f rm",
+      "层次 的",
+      "层 次的",
+      "消失 了",
+      "设置 的",
+      "▁f oi",
+      "▁fo i",
+      "▁T ours",
+      "▁To urs",
+      "▁Tour s",
+      "▁Tou rs",
+      "又包含了 选项",
+      "既包含了 问题",
+      "Us uario",
+      "上面这段话 既包含了问题",
+      "请你根据题意 选出正确的答案",
+      "书 本",
+      "喝 茶",
+      "患 上",
+      "枕 头",
+      "IZ E",
+      "I ZE",
+      "的 危险",
+      "▁\\' [",
+      "nt het",
+      "nthe t",
+      "nth et",
+      "▁Tr ials",
+      "▁Tri als",
+      "▁Trial s",
+      "▁bure auc",
+      "▁Input Stream",
+      "▁ InputStream",
+      "▁phot ographed",
+      "▁photograph ed",
+      "е й",
+      "▁ 紫",
+      "西 市",
+      "贯 注",
+      "▁A H",
+      "▁ AH",
+      "中文 名",
+      "中 文名",
+      "▁E rd",
+      "▁Er d",
+      "gluc ose",
+      "此题主要 考查了",
+      "▁hand some",
+      "▁hands ome",
+      "▁consult ations",
+      "▁consultation s",
+      "▁instant aneous",
+      "倍数 的",
+      "倍 数的",
+      "临床 资料",
+      "职业 学院",
+      "ail le",
+      "a ille",
+      "▁P itt",
+      "▁Pi tt",
+      "▁Pit t",
+      "▁sl avery",
+      "▁slave ry",
+      "李 世",
+      "漫 漫",
+      "ey ond",
+      "e yond",
+      "▁c hel",
+      "▁ch el",
+      "▁che l",
+      "▁ chel",
+      "格 斯",
+      "分子 的",
+      "分 子的",
+      "zb ek",
+      "Dir ective",
+      "Direct ive",
+      "▁grand parents",
+      "nF ire",
+      "n Fire",
+      "Ch oose",
+      "Cho ose",
+      "ham mer",
+      "▁e clipse",
+      "▁ecl ipse",
+      "▁ eclipse",
+      "some times",
+      "s ometimes",
+      "未发现 可供执行的财产",
+      "▁Univers ities",
+      "不 长",
+      "你说 的",
+      "你 说的",
+      "办理 了",
+      "在各 种",
+      "在 各种",
+      "没 见过",
+      "统计 分析",
+      "App ro",
+      "Ap pro",
+      "od end",
+      "ode nd",
+      "oden d",
+      "红 袖添香网",
+      "▁doctor ate",
+      "▁ampl itudes",
+      "▁amplit udes",
+      "▁amplitude s",
+      "上 司",
+      "的 差",
+      "表 征",
+      "CM V",
+      "C MV",
+      "功能 和",
+      "nc om",
+      "nco m",
+      "n com",
+      "▁S ys",
+      "▁Sy s",
+      "▁ Sys",
+      "▁a pk",
+      "▁ap k",
+      "▁ apk",
+      "▁H ess",
+      "▁He ss",
+      "li mate",
+      "lim ate",
+      "l imate",
+      "闪 耀",
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+      "州 市中级人民法院提出上诉",
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+      "▁Ve s",
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+      "下面是 一个",
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+      "ig ators",
+      "igator s",
+      "sn ippet",
+      "▁h acked",
+      "▁hack ed",
+      "达成 执行和解协议",
+      "############ ###\\",
+      "# ##############\\",
+      "全 套",
+      "南 站",
+      "红 烧",
+      "配 以",
+      "ны е",
+      "Da sh",
+      "D ash",
+      "▁ra ft",
+      "▁ raft",
+      "此题 的关键",
+      "▁bas il",
+      "▁ba sil",
+      "▁S utton",
+      "▁local ize",
+      "李 晓",
+      "▁S UN",
+      "▁SU N",
+      "机电 设备",
+      "wYWNr YWdl",
+      "▁clean sing",
+      "▁cleans ing",
+      "▁prec autions",
+      "▁special ization",
+      "戴 尔",
+      "NP C",
+      "N PC",
+      "nc o",
+      "n co",
+      "但是 却",
+      "的 图片",
+      "有 良好的",
+      "这种 现象",
+      "Th ird",
+      "doc type",
+      "do ctype",
+      "留 着",
+      "穿 了",
+      "道 人",
+      "疯狂 的",
+      "▁b ere",
+      "▁be re",
+      "▁ber e",
+      "法人 或者其他",
+      "▁ex erts",
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+      "▁Cit izen",
+      "▁alias es",
+      "▁ali ases",
+      "▁ aliases",
+      "▁book let",
+      "▁pl ugged",
+      "▁plug ged",
+      "请从 给出的选项中",
+      "nFe atures",
+      "n Features",
+      "宁夏回族自治区 银川市",
+      "选出 你认为正确的答案",
+      "▁glucocortic oid",
+      "救 赎",
+      "电 荷",
+      "之 目的",
+      "Hol d",
+      "Ho ld",
+      "H old",
+      "nG ot",
+      "nGo t",
+      "n Got",
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+      "▁Sl e",
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+      "▁Loc h",
+      "▁la ughed",
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+      "送 礼",
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+      "E NE",
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+      "▁NE C",
+      "人民 调解",
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+      "I ENTS",
+      "fire s",
+      "fi res",
+      "f ires",
+      "Solve r",
+      "Sol ver",
+      "S olver",
+      "Wait ing",
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+      "▁cosm etics",
+      "ж и",
+      "即 有",
+      "家 门",
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+      "b ass",
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+      "休 克",
+      "张 扬",
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+      "▁thy mus",
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+      "▁mono cyte",
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+      "质量管理 体系",
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+      "一 万元",
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+      "nM onday",
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+      "u kes",
+      "我将 给您",
+      "还有 其他",
+      "还 有其他",
+      "▁ch emo",
+      "▁che mo",
+      "▁chem o",
+      "▁Daw son",
+      "▁pot tery",
+      "▁dr agging",
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+      "应 交",
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+      "主体 的",
+      "主 体的",
+      "之外 的",
+      "之 外的",
+      "TA CT",
+      "T ACT",
+      "cr unch",
+      "c runch",
+      "请从 所提供的",
+      "选项中 做出最佳",
+      "一道复杂的 选择题",
+      "方面的知识 来找出",
+      "要求您 综合运用多",
+      "▁Sport ing",
+      "我将给您 一道复杂的选择题",
+      "方面的知识来找出 正确的答案",
+      "请从所提供的 选项中做出最佳",
+      "花 粉",
+      "衣 食",
+      "CM C",
+      "C MC",
+      "▁* (",
+      "▁ *(",
+      "▁U i",
+      "▁ Ui",
+      "▁R PC",
+      "▁RP C",
+      "▁ RPC",
+      "机器 学习",
+      "深入 学习",
+      "▁C hes",
+      "▁Ch es",
+      "▁Che s",
+      "▁L INE",
+      "▁LI NE",
+      "▁LIN E",
+      "▁ LINE",
+      "▁e ats",
+      "▁eat s",
+      "▁pro tr",
+      "▁pr otr",
+      "▁prot r",
+      "▁le aking",
+      "▁leak ing",
+      "▁mand ibular",
+      "▁v iz",
+      "▁vi z",
+      "▁ viz",
+      "▁y um",
+      "▁ yum",
+      "▁H int",
+      "▁Hi nt",
+      "▁ Hint",
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+      "isation al",
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+      "▁const ellation",
+      "▁ 鲁",
+      "ip a",
+      "i pa",
+      "ow ler",
+      "owl er",
+      "Inf luence",
+      "▁Ven tures",
+      "▁Vent ures",
+      "▁umb ilical",
+      "▁young sters",
+      "○ ○",
+      "初 审",
+      "即 墨",
+      "果 肉",
+      "不下 去",
+      "不 下去",
+      "不小 的",
+      "不 小的",
+      "清楚 的",
+      "os arcoma",
+      "▁belong ings",
+      "▁belonging s",
+      "▁default Value",
+      "▁ defaultValue",
+      "▁hypoth alamus",
+      "卵 管",
+      "浮 现",
+      "Sy n",
+      "S yn",
+      "▁D rivers",
+      "▁Dr ivers",
+      "▁Drive rs",
+      "▁Driver s",
+      "本院经审理 认定事实如下",
+      "第十 七",
+      "第 十七",
+      "ci ón",
+      "c ión",
+      "▁El even",
+      "▁Ele ven",
+      "▁Elev en",
+      "▁Gen tle",
+      "▁Gent le",
+      "▁past ry",
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+      "▁pas try",
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+      "▁analys ing",
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+      "得 天",
+      "疙 瘩",
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+      "I SM",
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+      "RES ET",
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+      "▁Bron x",
+      "▁fe eder",
+      "▁feed er",
+      "▁fee der",
+      "喜欢 他",
+      "多大 的",
+      "多 大的",
+      "下面 这道题",
+      "car bons",
+      "carbon s",
+      "▁Out come",
+      "▁Type Script",
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+      "外 币",
+      "大 帝",
+      "我 俩",
+      "轮 流",
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+      "n ih",
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+      "▁Schr o",
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+      "▁enum eration",
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+      "的 运用",
+      "Ra in",
+      "R ain",
+      "请你 帮我",
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+      "▁V atican",
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+      "▁anaest hesia",
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+      "的 创新",
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+      "▁Gl u",
+      "▁ Glu",
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+      "fee ds",
+      "f eeds",
+      "▁br ushes",
+      "▁brush es",
+      "ass essment",
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+      "▁religion s",
+      "▁incre ments",
+      "▁increment s",
+      "▁pa ediatric",
+      "▁warrant ies",
+      "浮 生",
+      "连 同",
+      "不得 已",
+      "往往 会",
+      "PL US",
+      "世界 第一",
+      "为 代表的",
+      "▁T RAN",
+      "▁TR AN",
+      "▁TRA N",
+      "▁ TRAN",
+      "▁f ecal",
+      "▁fe cal",
+      "并给出 正确的答案",
+      "▁advent urous",
+      "不 振",
+      "成就 感",
+      "网络 通信",
+      "at ant",
+      "ata nt",
+      "atan t",
+      "le don",
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+      "▁mat s",
+      "▁ma ts",
+      "ol fram",
+      "olf ram",
+      "▁bo obs",
+      "请分析 下面这道题",
+      "▁sed ation",
+      "文 创",
+      "ya r",
+      "y ar",
+      "大队 长",
+      "大 队长",
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+      "n down",
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+      "▁eye b",
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+      "▁par ishes",
+      "▁parish es",
+      "▁absor bing",
+      "▁absorb ing",
+      "分 队",
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+      "穿 插",
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+      "▁nore pinephrine",
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+      "楼 宇",
+      "ba k",
+      "b ak",
+      "▁ 河北",
+      "行驶 证",
+      "▁G ott",
+      "▁Go tt",
+      "▁Got t",
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+      "冰 冷",
+      "吕 布",
+      "尿 管",
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+      "Ro m",
+      "R om",
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+      "Br oad",
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+      "B road",
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+      "全 职",
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+      "▁Ne on",
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+      "方可 开展经营活动",
+      "剧 团",
+      "置 疑",
+      "对该 案",
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+      "一个 小时",
+      "一个小 时",
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+      "▁V ish",
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+      "▁Vi sh",
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+      "▁wrestler s",
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+      "▁Dep t",
+      "自然 保护区",
+      "重庆 时时彩",
+      "▁ch ords",
+      "▁chord s",
+      "▁chor ds",
+      "▁Cath olics",
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+      "美 式",
+      "▁C Y",
+      "▁ CY",
+      "六 安市",
+      "方式 是",
+      "羽绒 服",
+      "▁L ean",
+      "▁Le an",
+      "▁ Lean",
+      "可以 向人民法院",
+      "可以向 人民法院",
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+      "▁pro j",
+      "▁pr oj",
+      "▁ proj",
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+      "计算在 总价中",
+      "App Data",
+      "▁fauc et",
+      "附加费用 不会自动",
+      "需在入住时 另行支付",
+      "set ObjectName",
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+      "▁ 郝",
+      "不 识",
+      "张 开",
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+      "五 年级",
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+      "▁cr a",
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+      "物业管理有限公司 撤回起诉",
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+      "主要是 因为",
+      "然后从 提供的",
+      "请您 细致分析",
+      "oint ment",
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+      "您将面临 一个挑战",
+      "回答一道 复杂选择题",
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+      "歌曲 歌词",
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+      "给你一段 描述",
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+      "▁fl ipped",
+      "▁flip ped",
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+      "▁Logistic s",
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+      "仔细 预测问题的答案",
+      "备选答案 中选择一个",
+      "专业领域 知识的选择题",
+      "▁un ivariate",
+      "我会给你 一段文字描述和",
+      "▁mind fulness",
+      "备选答案中选择一个 正确的选项",
+      "我会给你一段文字描述和 对应的选项",
+      "请你 从选项中选出你认为正确的答案",
+      "万 分",
+      "乔 丹",
+      "住所 地在",
+      "住所地 在",
+      "额外的 床",
+      "麻烦 帮我做",
+      "con vex",
+      "conv ex",
+      "▁tw ists",
+      "▁twist s",
+      "麻烦帮我做 一道题",
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+      "n instance",
+      "▁wing span",
+      "▁wings pan",
+      "se tdefault",
+      "set default",
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+      "从选项中选出 正确的选项",
+      "▁imprison ment",
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+      "卑 微",
+      "护 卫",
+      "AB B",
+      "A BB",
+      "LT E",
+      "L TE",
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+      "我 不能",
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+      "▁Ke ll",
+      "▁Kel l",
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+      "打 仗",
+      "▁g t",
+      "▁ gt",
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+      "lab elled",
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+      "▁}) ,\\",
+      "▁ }),\\",
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+      "iz u",
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+      "承 德市",
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+      "▁[] )",
+      "▁ [])",
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+      "▁Cro hn",
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+      "▁prior i",
+      "▁pri ori",
+      "▁Mod ular",
+      "▁un locked",
+      "▁unlock ed",
+      "▁合作 两次以上的影人",
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+      "▁ 하",
+      "也 知道",
+      "有效 果",
+      "有 效果",
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+      "▁mar s",
+      "▁ma rs",
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+      "▁s olute",
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+      "Media Type",
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+      "三轮 车",
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+      "▁N CI",
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+      "正 在进行",
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+      "\\') ]\\",
+      "rel ude",
+      "relu de",
+      "▁St arter",
+      "▁Star ter",
+      "▁Start er",
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+      "▁\\\\\\\\\\ \\\\\\",
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+      "▁antidepress ant",
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+      "申请执行人 同意",
+      "▁blot ting",
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+      "▁H olt",
+      "▁Hol t",
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+      "▁C orb",
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+      "履行生效法律文书 所确定的义务",
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+      "月 末",
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+      "均需 获得",
+      "并从 所给",
+      "▁W heat",
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+      "icial ly",
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+      "FILE NAME",
+      "nDis play",
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+      "并从所给 选项中挑",
+      "运用扎实 的知识和",
+      "▁Pack ages",
+      "▁Package s",
+      "▁ Packages",
+      "Pro jection",
+      "Project ion",
+      "请仔细研究 接下来的选择题",
+      "运用扎实的知识和 仔细的思考",
+      "并从所给选项中挑 选出正确的答案",
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+      "不 相信",
+      "商业 街",
+      "地下 室",
+      "资料 来源",
+      "▁W eld",
+      "▁We ld",
+      "▁Wel d",
+      "由题意 可知",
+      "UR EMENT",
+      "URE MENT",
+      "quant um",
+      "in frared",
+      "短 发",
+      "耐 热",
+      "bi r",
+      "b ir",
+      "▁M ö",
+      "▁试 读",
+      "的单 位",
+      "的 单位",
+      "休闲 小憩",
+      "▁M eat",
+      "▁Me at",
+      "中 刑执字第",
+      "▁N inja",
+      "▁Nin ja",
+      "note book",
+      "▁prompt ing",
+      "▁in sensitive",
+      "▁ins ensitive",
+      "从 上",
+      "法 检测",
+      "特点 和",
+      "特 点和",
+      "的人 是",
+      "的 人是",
+      "看到 这个",
+      "这种 情况下",
+      "这种情况 下",
+      "▁Viol ence",
+      "▁tax ation",
+      "▁taxa tion",
+      "南 宫",
+      "娄 底",
+      "比 尔",
+      "灵 异",
+      "里 奥",
+      "执行 逮捕",
+      "ess ie",
+      "occup ied",
+      "▁Not Found",
+      "▁ NotFound",
+      "▁Numer ous",
+      "▁ground breaking",
+      "平 庸",
+      "林 的",
+      "江 海",
+      "点 开",
+      "球 星",
+      "RI C",
+      "R IC",
+      "be h",
+      "b eh",
+      "▁V ie",
+      "▁Vi e",
+      "行政 部门",
+      "▁M alt",
+      "▁Mal t",
+      "▁Ma lt",
+      "nAsk ed",
+      "nDep artment",
+      "n Department",
+      "▁prote olytic",
+      "▁approxim ated",
+      "▁approx imated",
+      "▁approximate d",
+      "更 佳",
+      "西 宁",
+      "aq u",
+      "a qu",
+      "▁C z",
+      "外星 人",
+      "工作 组",
+      "时间 去",
+      "但是 没有",
+      "但 是没有",
+      "在不同 的",
+      "在 不同的",
+      "显着 高于",
+      "服务 器的",
+      "服务器 的",
+      "这组 数据",
+      "这 组数据",
+      "tun signed",
+      "t unsigned",
+      "▁inter oper",
+      "t E",
+      "中 生",
+      "我 更",
+      "普 罗",
+      "涂 装",
+      "喜欢 上",
+      "aut y",
+      "au ty",
+      "a uty",
+      "mer e",
+      "me re",
+      "m ere",
+      "nD ave",
+      "nE ric",
+      "nEr ic",
+      "n Eric",
+      "▁th irst",
+      "▁thi rst",
+      "di methyl",
+      "dim ethyl",
+      "又 好",
+      "吃 掉",
+      "过 失",
+      "▁C p",
+      "Br on",
+      "Bro n",
+      "B ron",
+      "▁X in",
+      "▁Xi n",
+      "▁K ant",
+      "▁Ka nt",
+      "▁Kan t",
+      "如下文 所示",
+      "▁Anth rop",
+      "▁spect ators",
+      "乡 长",
+      "滑 坡",
+      "苍 茫",
+      "▁i ç",
+      "塑料 袋",
+      "定义 在",
+      "管理 等",
+      "nN et",
+      "nNe t",
+      "n Net",
+      "tr ees",
+      "tree s",
+      "tre es",
+      "t rees",
+      "▁caf é",
+      "▁V ault",
+      "▁Va ult",
+      "▁ Vault",
+      "▁r ains",
+      "▁ra ins",
+      "▁rain s",
+      "▁obl ique",
+      "▁Http Servlet",
+      "▁ HttpServlet",
+      "不 以",
+      "之 都",
+      "当下 的",
+      "当 下的",
+      "新 模式",
+      "循序 渐进",
+      "ne mail",
+      "nem ail",
+      "n email",
+      "Make file",
+      "check sum",
+      "checks um",
+      "ere quisites",
+      "▁ 健",
+      "下 颌",
+      "书 生",
+      "分析 仪",
+      "在 欧洲",
+      "委 党校",
+      "LIC K",
+      "LI CK",
+      "L ICK",
+      "▁I PO",
+      "▁IP O",
+      "▁ IPO",
+      "▁Z am",
+      "冻结 被告",
+      "积极 作用",
+      "nB rian",
+      "nBr ian",
+      "▁Ch oir",
+      "▁Cho ir",
+      "▁s ided",
+      "▁side d",
+      "▁si ded",
+      "▁sid ed",
+      "▁ sided",
+      "Object Id",
+      "Token izer",
+      "左 键",
+      "贪 婪",
+      "逆 向",
+      "▁ 医院",
+      "不住 了",
+      "不 住了",
+      "乐 山市",
+      "打 不过",
+      "▁V od",
+      "▁Vo d",
+      "▁p av",
+      "▁pa v",
+      "以为 自己",
+      "以 为自己",
+      "分析 步骤",
+      "等 农作物",
+      "▁s lit",
+      "▁sl it",
+      "做一个 选择题",
+      "pr inter",
+      "print er",
+      "pri nter",
+      "▁ 剑",
+      "上 当",
+      "伴 奏",
+      "抓 取",
+      "十九 年",
+      "十 九年",
+      "bys hev",
+      "ing les",
+      "ingle s",
+      "▁graph ite",
+      "▁clip board",
+      "▁emphas izing",
+      "≤ \\",
+      "岳 麓",
+      "木 工",
+      "CB A",
+      "C BA",
+      "收入 的",
+      "邢台 市",
+      "▁L or",
+      "▁Lo r",
+      "是单 选题",
+      "▁D ust",
+      "▁Du st",
+      "EMPL ARY",
+      "▁fe athers",
+      "▁feather s",
+      "à o",
+      "寄 存",
+      "布 料",
+      "悠 然",
+      "我 行",
+      "河 边",
+      "达 拉",
+      "不对 称",
+      "不 对称",
+      "体积 是",
+      "▁a gr",
+      "▁ag r",
+      "▁ agr",
+      "▁ID EA",
+      "▁IDE A",
+      "nL iter",
+      "n Liter",
+      "▁g ears",
+      "▁ge ars",
+      "▁gear s",
+      "▁H ouses",
+      "▁House s",
+      "▁Ho uses",
+      "▁Hou ses",
+      "▁trim med",
+      "▁stand point",
+      "揭 牌",
+      "fe lt",
+      "f elt",
+      "身体 素质",
+      "▁w hip",
+      "▁wh ip",
+      "ver ses",
+      "vers es",
+      "verse s",
+      "▁sh utter",
+      "▁shut ter",
+      "in tensive",
+      "int ensive",
+      "valid ated",
+      "validate d",
+      "▁w arrants",
+      "▁warrant s",
+      "▁def endants",
+      "▁defend ants",
+      "▁defendant s",
+      "为 谁",
+      "划 线",
+      "妈 咪",
+      "足 协",
+      "题 名",
+      "le i",
+      "l ei",
+      "sin A",
+      "si nA",
+      "▁$ {{\\\\",
+      "▁${ {\\\\",
+      "▁${{ \\\\",
+      "悔改表现 突出",
+      "pat ible",
+      "▁bass ist",
+      "▁ å",
+      "网易云 音乐",
+      "▁Vol vo",
+      "nM obile",
+      "n Mobile",
+      "▁免费 洗浴用品",
+      "ellig ence",
+      "elli gence",
+      "何 在",
+      "毅 力",
+      "畏 惧",
+      "总 承包",
+      "用 除法",
+      "的 局面",
+      "def s",
+      "de fs",
+      "ic om",
+      "ico m",
+      "i com",
+      "nO UT",
+      "n OUT",
+      "no st",
+      "nos t",
+      "n ost",
+      "nthe se",
+      "nth ese",
+      "n these",
+      "right s",
+      "r ights",
+      "▁Cl erk",
+      "▁Cle rk",
+      "nEx ercise",
+      "nSyn opsis",
+      "▁option ally",
+      "▁optional ly",
+      "▁sil houette",
+      "▁recomm ending",
+      "▁recommend ing",
+      "不 回",
+      "心 血",
+      "所 著",
+      "是 哪",
+      "神经 元",
+      "Ch ief",
+      "cent os",
+      "这道题 是单选题",
+      "请你 做一个选择题",
+      "请你 选出正确的选项",
+      "▁ship ments",
+      "▁shipment s",
+      "问题和选项 如下文所示",
+      "▁Ar ithmetic",
+      "悲 惨",
+      "找 寻",
+      "素 的",
+      "虚 空",
+      "pe p",
+      "p ep",
+      "ta nd",
+      "tan d",
+      "t and",
+      "uc kle",
+      "uck le",
+      "Des pite",
+      "all ocate",
+      "alloc ate",
+      "▁optim ally",
+      "▁optimal ly",
+      "▁ver ifying",
+      "▁verify ing",
+      "史 学",
+      "竹 马",
+      "件事 情",
+      "件 事情",
+      "的热 爱",
+      "的 热爱",
+      "\\' \",",
+      "\\'\" ,",
+      "or na",
+      "orn a",
+      "全面 推进",
+      "nW ait",
+      "n Wait",
+      "im ilar",
+      "imi lar",
+      "nM useum",
+      "▁disc iples",
+      "▁discipl es",
+      "▁dem olition",
+      "江 门",
+      "调 速",
+      "▁p its",
+      "▁pi ts",
+      "▁pit s",
+      "ex posure",
+      "exp osure",
+      "▁reimburse ment",
+      "天 蝎",
+      "扫 黑",
+      "遴 选",
+      "▁r ut",
+      "▁ru t",
+      "ns ign",
+      "n sign",
+      "这是一个 涉及",
+      "▁cass ette",
+      "了 很久",
+      "将 达到",
+      "ST AR",
+      "STA R",
+      "ac om",
+      "aco m",
+      "a com",
+      "建立 一个",
+      "艺术 设计",
+      "▁ves icle",
+      "▁POSS IBILITY",
+      "川 市",
+      "nA bs",
+      "nAb s",
+      "n Abs",
+      "就是 我们",
+      "就是我 们",
+      "就 是我们",
+      "▁cal endars",
+      "▁calendar s",
+      "▁butter flies",
+      "伤 寒",
+      "特 价",
+      "眉 头",
+      "骑 车",
+      "Es c",
+      "E sc",
+      "我都 会",
+      "我 都会",
+      "la in",
+      "l ain",
+      "分析 并从",
+      "yz ing",
+      "y zing",
+      "▁L aud",
+      "▁La ud",
+      "▁fant as",
+      "▁fan tas",
+      "user Info",
+      "▁loc ating",
+      "▁descript ors",
+      "▁descriptor s",
+      "▁progen itors",
+      "▁progenitor s",
+      "▁R X",
+      "▁ RX",
+      "赡养 费",
+      "Se nse",
+      "Sen se",
+      "S ense",
+      "故意杀人 罪",
+      "▁kn ives",
+      "bi ological",
+      "bio logical",
+      "b iological",
+      "▁Berks hire",
+      "▁conjug ates",
+      "▁conjugate s",
+      "▁incl ination",
+      "▁sub projects",
+      "▁subproject s",
+      "▁ 春",
+      "扰 乱",
+      "睁 开",
+      "虫 子",
+      "BM P",
+      "B MP",
+      "MA R",
+      "M AR",
+      "人员 和",
+      "文学 奖",
+      "管理 水平",
+      "as ers",
+      "ase rs",
+      "aser s",
+      "ron ics",
+      "ronic s",
+      "▁C Make",
+      "▁CM ake",
+      "▁ CMake",
+      "▁st ark",
+      "▁star k",
+      "▁Cor pus",
+      "▁Corp us",
+      "▁S odium",
+      "四 章",
+      "泰 兴",
+      "纤 细",
+      ":] ,",
+      ": ],",
+      "年 上海",
+      "示 意图",
+      "等 其他",
+      "结构 与",
+      "信息的 选择题",
+      "给定的 选项中",
+      "▁Tuc son",
+      "您需要 分析并从",
+      "▁Q String",
+      "▁ QString",
+      "请仔细研究 以下问题",
+      "综合运用各种 信息的选择题",
+      "您需要分析并从 给定的选项中",
+      "▁' $",
+      "▁ '$",
+      "与我 们",
+      "与 我们",
+      "UB LE",
+      "U BLE",
+      "▁b ye",
+      "▁by e",
+      "▁ bye",
+      "候选 答案",
+      "候 选答案",
+      "基本 参数",
+      "说法 错误",
+      "进一步 的",
+      "▁K hal",
+      "▁Kh al",
+      "▁Gab ri",
+      "▁toss ed",
+      "▁con gest",
+      "▁cong est",
+      "pr eferences",
+      "pre ferences",
+      "prefer ences",
+      "p references",
+      "▁cyber security",
+      "鸭 子",
+      "▁[ /",
+      "▁ [/",
+      "Pro of",
+      "nM aking",
+      "nMa king",
+      "n Making",
+      "set Data",
+      "▁gener osity",
+      "▁integr ative",
+      "▁H Y",
+      "▁ HY",
+      "压力 大",
+      "▁B am",
+      "▁Ba m",
+      "伟大 复兴",
+      "感兴趣 的",
+      "抽样 调查",
+      "▁bake ry",
+      "▁bak ery",
+      "nAl gebra",
+      "n Algebra",
+      "Type Error",
+      "▁Web Socket",
+      "▁ WebSocket",
+      "▁embr acing",
+      "物业管理 有限责任公司",
+      "▁counter example",
+      "苏 丹",
+      "Si g",
+      "S ig",
+      "[{ \"",
+      "[ {\"",
+      "▁名 称",
+      "▁ 名称",
+      "▁E MS",
+      "▁EM S",
+      "交通 工具",
+      "目前 没有",
+      "if era",
+      "ife ra",
+      "ifer a",
+      "of ten",
+      "oft en",
+      "o ften",
+      "下面 我会给你",
+      "的撤诉申请 符合法律规定",
+      "▁perox idation",
+      "匠 心",
+      "防 潮",
+      "GS M",
+      "G SM",
+      "只是 个",
+      "只 是个",
+      "的 方案",
+      "ur se",
+      "urs e",
+      "中山 大学",
+      "wh ole",
+      "who le",
+      "w hole",
+      "请你仔细 分析",
+      "ir ected",
+      "irect ed",
+      "▁Iceland ic",
+      "微 小",
+      "筹 集",
+      "▁ 最新",
+      "如果 想要",
+      "如果想 要",
+      "▁golf ers",
+      "▁gol fers",
+      "以及几个 候选答案",
+      "▁over dose",
+      "下面我会给你 一道题",
+      "然后 选出正确的选项",
+      "▁counsel or",
+      "▁mar riages",
+      "▁marriage s",
+      "打 发",
+      "贪 心",
+      "输 血",
+      "黑 板",
+      "Hy p",
+      "H yp",
+      "bl a",
+      "b la",
+      "rn e",
+      "r ne",
+      "可以 给",
+      "衢 州市",
+      "过来 了",
+      "过 来了",
+      "重生 之",
+      "St rip",
+      "Str ip",
+      "S trip",
+      "ul ton",
+      "ult on",
+      "ys sey",
+      "▁M ons",
+      "▁Mon s",
+      "▁Mo ns",
+      "▁m anic",
+      "▁man ic",
+      "▁ma nic",
+      "uttg art",
+      "▁comorbid ities",
+      "j r",
+      "农 资",
+      "断 层",
+      "▁U M",
+      "▁ UM",
+      "到 什么",
+      "目标 和",
+      "▁S UR",
+      "▁SU R",
+      "城市 规划",
+      "nM ade",
+      "nMa de",
+      "nMad e",
+      "n Made",
+      "▁Ram sey",
+      "▁Rams ey",
+      "▁you ths",
+      "▁youth s",
+      "▁valid ating",
+      "对 本",
+      "放 出",
+      "缔 造",
+      "mv c",
+      "m vc",
+      "的地 步",
+      "行为 规范",
+      "银行 同期",
+      "▁d ean",
+      "▁de an",
+      "▁pe dig",
+      "▁ped ig",
+      "▁ali ens",
+      "▁alien s",
+      "▁d ances",
+      "▁dance s",
+      "▁dan ces",
+      "信息查询 结果单",
+      "Neg ative",
+      "nun iform",
+      "n uniform",
+      "unning ham",
+      "unn ingham",
+      "▁Ware house",
+      "▁period ont",
+      "▁peri odont",
+      "▁ultrason ography",
+      "又 说",
+      "▁g ib",
+      "▁gi b",
+      "ub ern",
+      "ube rn",
+      "uber n",
+      "▁c ler",
+      "▁cl er",
+      "▁cle r",
+      "▁ cler",
+      "# ,",
+      "七 天",
+      "声 中",
+      "大 名",
+      "悲 观",
+      "与 世界",
+      "准 考证",
+      "gp io",
+      "▁W it",
+      "▁Wi t",
+      "th ick",
+      "thi ck",
+      "申请执行人 杨",
+      "违反 法律规定",
+      "real path",
+      "▁royal ty",
+      "▁kin ematic",
+      "栖 霞",
+      "疏 导",
+      "▁c z",
+      "▁ cz",
+      "基本 功",
+      "(\" {}",
+      "(\"{ }",
+      "▁L una",
+      "▁Lu na",
+      "▁thr ombo",
+      "▁throm bo",
+      "▁thromb o",
+      "text width",
+      "tex twidth",
+      "▁d isciplinary",
+      "▁disc iplinary",
+      "▁discipl inary",
+      "▁ disciplinary",
+      "仙 子",
+      "舅 舅",
+      "颤 抖",
+      "IC P",
+      "I CP",
+      "两 方面",
+      "还能 够",
+      "还 能够",
+      "▁w get",
+      "▁ch ili",
+      "▁chi li",
+      "oplas ma",
+      "opl asma",
+      "oplasm a",
+      "▁Se gment",
+      "▁Seg ment",
+      "▁ Segment",
+      "▁fin alist",
+      "▁final ist",
+      "九 九",
+      "固 有",
+      "李 子",
+      "症 的",
+      "变更 为",
+      "变 更为",
+      "赔偿 金",
+      "E con",
+      "▁L OL",
+      "▁LO L",
+      "▁U SC",
+      "▁US C",
+      "高职 高专",
+      "▁S lide",
+      "▁Sl ide",
+      "▁ Slide",
+      "▁t orus",
+      "▁to rus",
+      "▁tor us",
+      "▁Har ley",
+      "▁Willi s",
+      "▁Will is",
+      "▁Wil lis",
+      "ac oustic",
+      "▁emb roidery",
+      "北 辰",
+      "各 区",
+      "妆 容",
+      "截 然",
+      "$_ {",
+      "$ _{",
+      "委 常委",
+      "审查 核实",
+      "▁Cl iff",
+      "▁Clif f",
+      "扣划 被执行人",
+      "▁Buddh a",
+      "▁Budd ha",
+      "▁is lets",
+      "▁islet s",
+      "Enum erator",
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+      "这两 天",
+      "这 两天",
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+      "m uted",
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+      "u dp",
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+      "▁ stalk",
+      "nc urrent",
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+      "▁All ison",
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+      "▁depend ed",
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+      "▁ underline",
+      "伊 始",
+      "和 张",
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+      "C SI",
+      "HA L",
+      "H AL",
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+      "▁si RNA",
+      "法定代表人 杨",
+      "▁war rior",
+      "水 区",
+      "肉 体",
+      "了 不起",
+      "永远 的",
+      "永 远的",
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+      "C han",
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+      "n Ill",
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+      "▁W istar",
+      "▁Wis tar",
+      "▁Wi star",
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+      "▁B N",
+      "▁ BN",
+      "in tl",
+      "int l",
+      "▁P AN",
+      "▁PA N",
+      "两组 患者",
+      "工业 企业",
+      "最大限度 地",
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+      "Client s",
+      "Cli ents",
+      "▁sum mers",
+      "▁summer s",
+      "▁summ ers",
+      "▁a merican",
+      "▁foster ing",
+      "▁fost ering",
+      "nG astropods",
+      "▁councill ors",
+      "养 育",
+      "差 额",
+      "心 痛",
+      "的热 点",
+      "的 热点",
+      "临床 应用",
+      "比较 简单",
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+      "wi red",
+      "w ired",
+      "▁Cr ash",
+      "▁Cra sh",
+      "enn ifer",
+      "▁min ors",
+      "▁minor s",
+      "▁pill ars",
+      "▁Child hood",
+      "一 架",
+      "加 斯",
+      "待 定",
+      "意 气",
+      "中国 梦",
+      "热烈 的",
+      "热 烈的",
+      "▁c han",
+      "▁ch an",
+      "▁cha n",
+      "▁ chan",
+      "ud ding",
+      "udd ing",
+      "▁C oral",
+      "▁Co ral",
+      "▁Cor al",
+      "brand ed",
+      "v matrix",
+      "▁tax onomy",
+      "Measure ment",
+      "▁trans plants",
+      "▁transplant s",
+      "小 便",
+      "不过 在",
+      "为人 民",
+      "为 人民",
+      "▁F rem",
+      "▁Fr em",
+      "▁Fre m",
+      "es ture",
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+      "▁is cha",
+      "Min utes",
+      "other wise",
+      "▁Le besgue",
+      "Australia n",
+      "A ustralian",
+      "oph aryngeal",
+      "trans formed",
+      "transform ed",
+      "▁ 商",
+      "制 宜",
+      "备 课",
+      "nM i",
+      "n Mi",
+      "教学 法",
+      "涉及 的",
+      "ar ie",
+      "ari e",
+      "a rie",
+      "水平的 提高",
+      "符合 条件的",
+      "▁cob alt",
+      "▁Ham mond",
+      "▁Hamm ond",
+      "▁Power ed",
+      "▁Pow ered",
+      "▁let tuce",
+      "▁plas mon",
+      "▁plasm on",
+      "▁safe guard",
+      "▁safegu ard",
+      "í k",
+      "八 首",
+      "ax on",
+      "ha el",
+      "h ael",
+      "\"] );\\",
+      "\"]) ;\\",
+      "\" ]);\\",
+      "▁g ast",
+      "▁gas t",
+      "▁ga st",
+      "olog na",
+      "▁D ancing",
+      "▁Dan cing",
+      "▁t opping",
+      "▁to pping",
+      "▁top ping",
+      "astructure s",
+      "astruct ures",
+      "a structures",
+      "亚 目",
+      "会 和",
+      "抗 病",
+      "缅 怀",
+      "英 才",
+      ">} \\",
+      "> }\\",
+      "单 职业",
+      "喜欢 看",
+      "数字 是",
+      "▁N eb",
+      "▁Ne b",
+      "Sp ect",
+      "Spec t",
+      "Spe ct",
+      "S pect",
+      "ur sed",
+      "urs ed",
+      "urse d",
+      "BIN ARY",
+      "cent ering",
+      "center ing",
+      "Linked List",
+      "墨 水",
+      "抒 情",
+      "省 道",
+      "股 指",
+      "Sa h",
+      "S ah",
+      "太大 了",
+      "太 大了",
+      "可以 利用",
+      "法律 关系",
+      "▁de nim",
+      "▁den im",
+      "▁ou nce",
+      "▁oun ce",
+      "▁ ounce",
+      "▁Vis itors",
+      "▁Visit ors",
+      "▁inoc ulated",
+      "▁macro scopic",
+      "吃 不",
+      "昂 贵",
+      "蒸 馏",
+      "NT s",
+      "N Ts",
+      "收入 来源",
+      "▁K ens",
+      "▁Ke ns",
+      "▁Ken s",
+      "作品 展",
+      "▁par athyroid",
+      "壮 观",
+      "散 户",
+      "沧 州",
+      "痛 风",
+      "书法 家",
+      "剩余 的",
+      "文化 教育",
+      "st rain",
+      "str ain",
+      "stra in",
+      "s train",
+      "ENAB LED",
+      "nTur kish",
+      "▁ball oons",
+      "▁balloon s",
+      "▁taxp ayer",
+      "遵守 法律法规及监规",
+      "▁dilig ence",
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+      "朝 代",
+      "赫 尔",
+      "韵 味",
+      "竞争 中",
+      "▁E ur",
+      "Test er",
+      "Te ster",
+      "T ester",
+      "nP ress",
+      "nPre ss",
+      "nPr ess",
+      "nPres s",
+      "n Press",
+      "▁sk aters",
+      "▁skate rs",
+      "▁Com mands",
+      "▁Command s",
+      "▁Comm ands",
+      "▁ Commands",
+      "▁prof icient",
+      "文 革",
+      "耐 久",
+      "耗 时",
+      "阻 断",
+      "雄 蕊",
+      "碧 桂园",
+      ">\" )\\",
+      "> \")\\",
+      "▁L IN",
+      "▁LI N",
+      "▁ LIN",
+      "住 湖北省",
+      "看到 一个",
+      "看 到一个",
+      "▁N ass",
+      "▁Na ss",
+      "▁Nas s",
+      "ann ual",
+      "工作 领导小组",
+      "ilib ili",
+      "▁get away",
+      "▁electro card",
+      "▁electr ocard",
+      "▁electroc ard",
+      "警 官",
+      "可以 和",
+      "light er",
+      "l ighter",
+      "▁Bo xing",
+      "▁Box ing",
+      "▁r hesus",
+      "▁rh esus",
+      "▁rhe sus",
+      "▁Log ging",
+      "▁ Logging",
+      "声 声",
+      "ST S",
+      "S TS",
+      "原 标题",
+      "应 支付",
+      "▁H ak",
+      "▁Ha k",
+      "sl ave",
+      "共计 人民币",
+      "是不 一样的",
+      "是 不一样的",
+      "▁Card iac",
+      "▁Cardi ac",
+      "▁check sum",
+      "▁checks um",
+      "▁ checksum",
+      "▁chrom ium",
+      "▁ chromium",
+      "经相关部门批准后 方可开展经营活动",
+      "中 点",
+      "乙 酸",
+      "台 账",
+      "在 有",
+      "bs t",
+      "b st",
+      "▁т е",
+      "▁ те",
+      "No me",
+      "Nom e",
+      "N ome",
+      "▁E TF",
+      "▁ET F",
+      "▁ ETF",
+      "欢迎 所有",
+      "环境 优美",
+      "稳产 农田",
+      "并给出 解析",
+      "▁Tool kit",
+      "▁Sup erman",
+      "▁Super man",
+      "▁onc ogenic",
+      "▁re written",
+      "▁trans istors",
+      "▁transistor s",
+      "* $",
+      "M b",
+      "▁ 车",
+      "上 榜",
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+      "连 城",
+      "ary nx",
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+      "▁Fu ck",
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+      "▁Pi ed",
+      "▁Pie d",
+      "▁BU ILD",
+      "▁ BUILD",
+      "se tName",
+      "set Name",
+      "▁d umped",
+      "▁dump ed",
+      "▁gl owing",
+      "▁glo wing",
+      "▁glow ing",
+      "▁hum oral",
+      "▁humor al",
+      "toHave Been",
+      "▁com pressive",
+      "▁comp ressive",
+      "▁compr essive",
+      "▁compress ive",
+      "ä l",
+      "下 有",
+      "切 碎",
+      "钱 财",
+      "雪 梅",
+      "风 起",
+      "生活 和",
+      "_( \\'",
+      "_ (\\'",
+      "▁M VC",
+      "▁MV C",
+      "Mar ie",
+      "Ma rie",
+      "M arie",
+      "ER ENCE",
+      "▁Bo hem",
+      "▁Boh em",
+      "▁CA USED",
+      "▁epit ope",
+      "▁go odies",
+      "▁good ies",
+      "▁follic le",
+      "equ ivalent",
+      "equiv alent",
+      "▁C omposition",
+      "▁Com position",
+      "▁Comp osition",
+      "选出你认为正确的答案 并给出解析",
+      "S X",
+      "乐 曲",
+      "Fe e",
+      "F ee",
+      "是 负数",
+      "顺德 区",
+      "iv ar",
+      "iva r",
+      "i var",
+      "其他 国家",
+      "er eal",
+      "ere al",
+      "e real",
+      "engu ins",
+      "enguin s",
+      "ra ising",
+      "rais ing",
+      "▁course work",
+      "宝 妈",
+      "应 从",
+      "时 限",
+      "统 统",
+      "黄 酮",
+      "西北 部",
+      "西 北部",
+      "bm od",
+      "b mod",
+      "nB al",
+      "n Bal",
+      "▁A gr",
+      "▁Ag r",
+      "Mac ro",
+      "Ma cro",
+      "ag her",
+      "agh er",
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+      "▁special ties",
+      "又 无",
+      "朱 元",
+      "能 与",
+      "锦 江",
+      "的 计划",
+      "个人 观点",
+      "文明 城市",
+      "Order ed",
+      "Ord ered",
+      "▁Alber to",
+      "▁Alb erto",
+      "▁Albert o",
+      "▁Default s",
+      "▁ Defaults",
+      "▁piece wise",
+      "拨 款",
+      "版 块",
+      "只是 想",
+      "只 是想",
+      "pin ion",
+      "▁hal ves",
+      "犯 容留他人吸毒罪",
+      "▁bi ophysical",
+      "▁bio physical",
+      "后 才",
+      "国 泰",
+      "则 这个",
+      "OP PO",
+      "▁s ut",
+      "▁su t",
+      "谈话 笔录",
+      "▁r aster",
+      "▁ra ster",
+      "▁Gr avity",
+      "▁ex iting",
+      "▁exit ing",
+      "县 市",
+      "可 想",
+      "日 将",
+      "某 在",
+      "肥 皂",
+      "蜂 窝",
+      ")} (",
+      ") }(",
+      "社会 上",
+      "社 会上",
+      "财产 的",
+      "财 产的",
+      "es tr",
+      "est r",
+      "e str",
+      "▁E Ps",
+      "▁EP s",
+      "写出 你的",
+      "▁注意 事项",
+      "▁ 注意事项",
+      "Rot ate",
+      "opa edic",
+      "▁ch angelog",
+      "▁chang elog",
+      "▁change log",
+      "▁ changelog",
+      "▁pos itional",
+      "▁position al",
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+      "▁initial State",
+      "拖 动",
+      "hu n",
+      "h un",
+      "▁S v",
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+      "应 该怎么",
+      "nST ATIC",
+      "n STATIC",
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+      "某 天",
+      "及时 的",
+      "及 时的",
+      "都 十分",
+      "▁g an",
+      "▁ga n",
+      "▁ gan",
+      "二〇一六年 四",
+      "im iento",
+      "imi ento",
+      "▁rot ary",
+      "S aturday",
+      "aut omatic",
+      "auto matic",
+      "autom atic",
+      "挂 着",
+      "耕 耘",
+      "锻 造",
+      "ub i",
+      "u bi",
+      "з ов",
+      "▁ 纠错",
+      "的 培养",
+      "RO LE",
+      "ROL E",
+      "▁P LEASE",
+      "充分不 必要条件",
+      "▁In jection",
+      "▁Inject ion",
+      "▁Inj ection",
+      "▁ Injection",
+      "自 闭",
+      "诗 意",
+      "差异 化",
+      "系 主任",
+      "图文 并茂",
+      "▁Rub ber",
+      "▁Mich ele",
+      "▁Michel e",
+      "▁Miche le",
+      "▁den ying",
+      "▁deny ing",
+      "na issance",
+      "号征收社会抚养费 决定书",
+      "▁multi national",
+      "▁multin ational",
+      "▁function alities",
+      "▁functional ities",
+      "痛 经",
+      "yc h",
+      "y ch",
+      "自贡 市",
+      "▁r ho",
+      "▁rh o",
+      "▁ rho",
+      "no vel",
+      "nov el",
+      "n ovel",
+      "Ma king",
+      "M aking",
+      "li able",
+      "lia ble",
+      "l iable",
+      "▁inhal ed",
+      "▁inh aled",
+      "ne ighbors",
+      "▁Wild erness",
+      "▁suppress es",
+      "▁ 同",
+      "剖 宫",
+      "为 公司",
+      "待 今后",
+      "第四 次",
+      "第 四次",
+      "le on",
+      "l eon",
+      "▁C NC",
+      "▁CN C",
+      "ble rs",
+      "bl ers",
+      "bler s",
+      "b lers",
+      "▁h aha",
+      "▁ha ha",
+      "▁作品 原文",
+      "▁pol yt",
+      "▁poly t",
+      "安徽省 合肥市",
+      "▁bo oster",
+      "▁boost er",
+      "▁fascin ated",
+      "▁fasc inated",
+      "▁ Ω",
+      "思 绪",
+      "桥 头",
+      "芭 蕾",
+      "三个 人",
+      "三 个人",
+      "被 发现",
+      "▁P es",
+      "▁Pe s",
+      "▁s aga",
+      "▁sa ga",
+      "▁sag a",
+      "or ylation",
+      "ory lation",
+      "▁allerg en",
+      "▁re distribution",
+      "▁redist ribution",
+      "湛 江",
+      "补 给",
+      "▁r ag",
+      "▁ra g",
+      "▁ rag",
+      "lang s",
+      "lan gs",
+      "l angs",
+      "And rew",
+      "▁Cl air",
+      "▁ign ores",
+      "▁ignore s",
+      "▁ignor es",
+      "▁re factor",
+      "▁ref actor",
+      "▁ad versely",
+      "▁adverse ly",
+      "▁advers ely",
+      "▁end oscopy",
+      "▁ 集",
+      "跳 动",
+      "鹤 壁",
+      "你 分析",
+      "▁orient ations",
+      "▁orientation s",
+      "▁quant ization",
+      "有点 像",
+      "贵 金属",
+      "습 니다",
+      "▁C RP",
+      "▁CR P",
+      "▁ CRP",
+      "▁J ah",
+      "▁Ja h",
+      "▁A min",
+      "▁Am in",
+      "主张 的事实",
+      "ouch ers",
+      "oucher s",
+      "▁Harm ony",
+      "▁spe eding",
+      "▁speed ing",
+      "▁after ward",
+      "卫生 和计划生育委员会",
+      "是连载于起点中文网 的一部",
+      "第一百五十四条第一款第 五",
+      "第一百五十四条第一款 第五",
+      "前 几",
+      "生 怕",
+      "都 与",
+      "休闲 娱乐",
+      "Hy dro",
+      "H ydro",
+      "Wr ong",
+      "W rong",
+      "第八 十四条",
+      "▁pop ul",
+      "▁V ehicles",
+      "▁Vehicle s",
+      "▁ О",
+      "吞 吐",
+      "在 点"
+    ]
+  }
+}
\ No newline at end of file
diff --git a/tokenizer_config.json b/tokenizer_config.json
new file mode 100644
index 0000000000000000000000000000000000000000..1f46debaef91a9fa2ddbd4646878f27693b9404d
--- /dev/null
+++ b/tokenizer_config.json
@@ -0,0 +1,52 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "add_prefix_space": null,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
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diff --git a/training_args.bin b/training_args.bin
new file mode 100644
index 0000000000000000000000000000000000000000..8a53bd0e980eec7abb23165394b5d6a60a49e546
--- /dev/null
+++ b/training_args.bin
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+version https://git-lfs.github.com/spec/v1
+oid sha256:5c6d232e08c6f7ae9d3c0b2f3b7ed888effd26c203d487044bac647ce3ee8814
+size 10808
diff --git a/zero_to_fp32.py b/zero_to_fp32.py
new file mode 100644
index 0000000000000000000000000000000000000000..24cc342e78d1a006c782b3a4cd68d9ce786d8fd8
--- /dev/null
+++ b/zero_to_fp32.py
@@ -0,0 +1,604 @@
+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# example: python zero_to_fp32.py . pytorch_model.bin
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+
+    total_files = len(files)
+    state_dicts = []
+    for f in files:
+        state_dict = torch.load(f, map_location=device)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if not ZERO_STAGE in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    if zero_stage <= 2:
+        fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    elif zero_stage == 3:
+        # if there is more than one param group, there will be multiple flattened tensors - one
+        # flattened tensor per group - for simplicity merge them into a single tensor
+        #
+        # XXX: could make the script more memory efficient for when there are multiple groups - it
+        # will require matching the sub-lists of param_shapes for each param group flattened tensor
+
+        fp32_flat_groups = [
+            torch.cat(state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key], 0) for i in range(len(state_dicts))
+        ]
+
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _has_callable(obj, fn):
+    attr = getattr(obj, fn, None)
+    return callable(attr)
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = fp32_flat_groups[0].numel() * world_size
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    for name, shape in param_shapes.items():
+
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # XXX: memory usage doubles here
+        state_dict[name] = torch.cat(
+            tuple(fp32_flat_groups[i].narrow(0, offset, partitioned_numel) for i in range(world_size)),
+            0).narrow(0, 0, unpartitioned_numel).view(shape)
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag=None, exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+
+    Returns:
+        - pytorch ``state_dict``
+
+    Note: this approach may not work if your application doesn't have sufficient free CPU memory and
+    you may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    return _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir, output_file, tag=None, exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_file``: path to the pytorch fp32 state_dict output file (e.g. path/pytorch_model.bin)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+    """
+
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag, exclude_frozen_parameters)
+    print(f"Saving fp32 state dict to {output_file}")
+    torch.save(state_dict, output_file)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info(f"Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info(f"Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument(
+        "output_file",
+        type=str,
+        help="path to the pytorch fp32 state_dict output file (e.g. path/checkpoint-12/pytorch_model.bin)")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
+                                               args.output_file,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)