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+ # coding=utf-8
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+ # Modification Copyright 2023 Dawei Zhu
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+ # Copyright 2022 EleutherAI and the HuggingFace Inc. team. All rights reserved.
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+ #
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+ # This code is based on EleutherAI's GPT-NeoX library and the GPT-NeoX
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+ # and OPT implementations in this library. It has been modified from its
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+ # original forms to accommodate minor architectural differences compared
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+ # to GPT-NeoX and OPT used by the Meta AI team that trained the model.
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+ #
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+ # Licensed under the Apache License, Version 2.0 (the "License");
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+ # you may not use this file except in compliance with the License.
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+ # You may obtain a copy of the License at
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+ #
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+ # http://www.apache.org/licenses/LICENSE-2.0
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+ #
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+ # Unless required by applicable law or agreed to in writing, software
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+ # distributed under the License is distributed on an "AS IS" BASIS,
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+ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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+ # See the License for the specific language governing permissions and
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+ # limitations under the License.
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+ """ LLaMA model configuration"""
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+
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+ # from ...configuration_utils import PretrainedConfig
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+ # from ...utils import logging
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+ from transformers.configuration_utils import PretrainedConfig
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+ from transformers.utils import logging
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+
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+
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+ logger = logging.get_logger(__name__)
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+
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+ LLAMA_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
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+
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+
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+ class LlamaConfig(PretrainedConfig):
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+ r"""
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+ This is the configuration class to store the configuration of a [`LlamaModel`]. It is used to instantiate an LLaMA
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+ model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
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+ defaults will yield a similar configuration to that of the LLaMA-7B.
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+
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+ Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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+ documentation from [`PretrainedConfig`] for more information.
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+
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+
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+ Args:
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+ vocab_size (`int`, *optional*, defaults to 32000):
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+ Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
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+ `inputs_ids` passed when calling [`LlamaModel`]
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+ hidden_size (`int`, *optional*, defaults to 4096):
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+ Dimension of the hidden representations.
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+ intermediate_size (`int`, *optional*, defaults to 11008):
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+ Dimension of the MLP representations.
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+ num_hidden_layers (`int`, *optional*, defaults to 32):
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+ Number of hidden layers in the Transformer encoder.
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+ num_attention_heads (`int`, *optional*, defaults to 32):
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+ Number of attention heads for each attention layer in the Transformer encoder.
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+ num_key_value_heads (`int`, *optional*):
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+ This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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+ `num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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+ `num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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+ converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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+ by meanpooling all the original heads within that group. For more details checkout [this
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+ paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to
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+ `num_attention_heads`.
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+ pretraining_tp (`int`, *optional*, defaults to `1`):
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+ Experimental feature. Tensor parallelism rank used during pretraining. Please refer to [this
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+ document](https://huggingface.co/docs/transformers/parallelism) to understand more about it. This value is
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+ necessary to ensure exact reproducibility of the pretraining results. Please refer to [this
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+ issue](https://github.com/pytorch/pytorch/issues/76232).
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+ hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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+ The non-linear activation function (function or string) in the decoder.
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+ max_position_embeddings (`int`, *optional*, defaults to 2048):
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+ The maximum sequence length that this model might ever be used with. Typically set this to something large
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+ just in case (e.g., 512 or 1024 or 2048).
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+ initializer_range (`float`, *optional*, defaults to 0.02):
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+ The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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+ rms_norm_eps (`float`, *optional*, defaults to 1e-12):
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+ The epsilon used by the rms normalization layers.
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+ use_cache (`bool`, *optional*, defaults to `True`):
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+ Whether or not the model should return the last key/values attentions (not used by all models). Only
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+ relevant if `config.is_decoder=True`.
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+ tie_word_embeddings(`bool`, *optional*, defaults to `False`):
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+ Whether to tie weight embeddings
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+ rope_scaling (`Dict`, *optional*):
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+ Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
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+ strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
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+ is `{"type": strategy name, "factor": scaling factor}`. When using this flag, don't update
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+ `max_position_embeddings` to the expected new maximum. See the following thread for more information on how
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+ these scaling strategies behave:
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+ https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/. This is an
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+ experimental feature, subject to breaking API changes in future versions.
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+
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+ Example:
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+
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+ ```python
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+ >>> from transformers import LlamaModel, LlamaConfig
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+
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+ >>> # Initializing a LLaMA llama-7b style configuration
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+ >>> configuration = LlamaConfig()
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+
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+ >>> # Initializing a model from the llama-7b style configuration
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+ >>> model = LlamaModel(configuration)
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+
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+ >>> # Accessing the model configuration
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+ >>> configuration = model.config
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+ ```"""
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+ model_type = "llama"
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+ keys_to_ignore_at_inference = ["past_key_values"]
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+
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+ def __init__(
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+ self,
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+ vocab_size=32000,
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+ hidden_size=4096,
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+ intermediate_size=11008,
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+ num_hidden_layers=32,
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+ num_attention_heads=32,
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+ num_key_value_heads=None,
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+ hidden_act="silu",
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+ max_position_embeddings=2048,
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+ initializer_range=0.02,
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+ rms_norm_eps=1e-6,
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+ use_cache=True,
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+ pad_token_id=0,
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+ bos_token_id=1,
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+ eos_token_id=2,
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+ pretraining_tp=1,
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+ tie_word_embeddings=False,
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+ rope_scaling=None,
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+ **kwargs,
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+ ):
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+ self.vocab_size = vocab_size
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+ self.max_position_embeddings = max_position_embeddings
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+ self.hidden_size = hidden_size
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+ self.intermediate_size = intermediate_size
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+ self.num_hidden_layers = num_hidden_layers
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+ self.num_attention_heads = num_attention_heads
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+
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+ # for backward compatibility
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+ if num_key_value_heads is None:
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+ num_key_value_heads = num_attention_heads
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+
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+ self.num_key_value_heads = num_key_value_heads
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+ self.hidden_act = hidden_act
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+ self.initializer_range = initializer_range
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+ self.rms_norm_eps = rms_norm_eps
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+ self.pretraining_tp = pretraining_tp
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+ self.use_cache = use_cache
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+ self.rope_scaling = rope_scaling
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+ self._rope_scaling_validation()
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+
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+ super().__init__(
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+ pad_token_id=pad_token_id,
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+ bos_token_id=bos_token_id,
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+ eos_token_id=eos_token_id,
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+ tie_word_embeddings=tie_word_embeddings,
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+ **kwargs,
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+ )
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+
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+ def _rope_scaling_validation(self):
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+ """
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+ Validate the `rope_scaling` configuration.
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+ """
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+ if self.rope_scaling is None:
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+ return
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+
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+ if not isinstance(self.rope_scaling, dict):
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+ raise ValueError(
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+ "`rope_scaling` must be a dictionary with with two fields, `name` and `factor`, "
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+ f"got {self.rope_scaling}"
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+ )
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+ rope_scaling_type = self.rope_scaling.get("type", None)
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+ rope_scaling_factor = self.rope_scaling.get("factor", None)
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+ if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "vanilla_ntk", "yarn"]:
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+ raise ValueError(
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+ f"`rope_scaling`'s name field must be one of ['linear', 'dynamic', 'vanilla_ntk', 'yarn'], got {rope_scaling_type}"
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+ )
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+ if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
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+ raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")