202 lines
9.4 KiB
Python
202 lines
9.4 KiB
Python
# coding=utf-8
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# Copyright 2023 the Falcon authors and HuggingFace Inc. team. All rights reserved.
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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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"""Falcon configuration"""
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from ...configuration_utils import PretrainedConfig
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from ...utils import logging
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logger = logging.get_logger(__name__)
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from ..deprecated._archive_maps import FALCON_PRETRAINED_CONFIG_ARCHIVE_MAP # noqa: F401, E402
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class FalconConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`FalconModel`]. It is used to instantiate a Falcon
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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
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[tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) architecture.
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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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Args:
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vocab_size (`int`, *optional*, defaults to 65024):
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Vocabulary size of the Falcon model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`FalconModel`]
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hidden_size (`int`, *optional*, defaults to 4544):
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Dimension of the hidden representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer decoder.
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num_attention_heads (`int`, *optional*, defaults to 71):
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Number of attention heads for each attention layer in the Transformer encoder.
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layer_norm_epsilon (`float`, *optional*, defaults to 1e-05):
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The epsilon used by the layer normalization layers.
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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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use_cache (`bool`, *optional*, defaults to `True`):
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Whether the model should return the last key/values attentions (not used by all models). Only relevant if
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`config.is_decoder=True`.
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hidden_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for MLP layers.
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attention_dropout (`float`, *optional*, defaults to 0.0):
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The dropout probability for attention layers.
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num_kv_heads (`int`, *optional*):
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Number of key-value heads to use per attention layer. If unset, defaults to the same value as
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`num_attention_heads`.
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alibi (`bool`, *optional*, defaults to `False`):
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Whether to use ALiBi positional biases during self-attention.
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new_decoder_architecture (`bool`, *optional*, defaults to `False`):
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Whether to use the new (Falcon-40B) decoder architecture. If `True`, the `multi_query` and `parallel_attn`
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arguments are ignored, as the new decoder always uses parallel attention.
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multi_query (`bool`, *optional*, defaults to `True`):
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Whether to use multi-query attention in the decoder. Ignored when `new_decoder_architecture` is `True`.
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parallel_attn (`bool`, *optional*, defaults to `True`):
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Whether to compute attention in parallel with the feedforward layer. If False, they are consecutive
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instead, as in the original Transformer architecture. Ignored when `new_decoder_architecture` is `True`.
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bias (`bool`, *optional*, defaults to `False`):
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Whether to use bias on Linear layers.
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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, when `alibi` is `False`. Pretrained
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Falcon models with RoPE support up to 2048 tokens.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
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strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
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`{"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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bos_token_id (`int`, *optional*, defaults to 11):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 11):
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The id of the "end-of-sequence" token.
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ffn_hidden_size (`int`, *optional*):
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The hidden size of the feedforward layer in the Transformer decoder.
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defaults to 4x hidden dim
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activation (`str`, *optional*, defaults to `"gelu"`):
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The activation function used in the feedforward layer.
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Example:
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```python
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>>> from transformers import FalconModel, FalconConfig
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>>> # Initializing a small (2-layer) Falcon configuration
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>>> configuration = FalconConfig(num_hidden_layers=2)
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>>> # Initializing a model from the small configuration
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>>> model = FalconModel(configuration)
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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 = "falcon"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=65024,
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hidden_size=4544,
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num_hidden_layers=32,
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num_attention_heads=71,
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layer_norm_epsilon=1e-5,
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initializer_range=0.02,
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use_cache=True,
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hidden_dropout=0.0,
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attention_dropout=0.0,
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num_kv_heads=None,
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alibi=False,
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new_decoder_architecture=False,
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multi_query=True,
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parallel_attn=True,
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bias=False,
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max_position_embeddings=2048,
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rope_theta=10000.0,
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rope_scaling=None,
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bos_token_id=11,
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eos_token_id=11,
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ffn_hidden_size=None,
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activation="gelu",
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**kwargs,
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):
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self.vocab_size = vocab_size
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# Backward compatibility with n_embed kwarg
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n_embed = kwargs.pop("n_embed", None)
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self.hidden_size = hidden_size if n_embed is None else n_embed
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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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self.layer_norm_epsilon = layer_norm_epsilon
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self.initializer_range = initializer_range
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self.use_cache = use_cache
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self.hidden_dropout = hidden_dropout
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self.attention_dropout = attention_dropout
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self.bos_token_id = bos_token_id
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self.eos_token_id = eos_token_id
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self.num_kv_heads = num_attention_heads if num_kv_heads is None else num_kv_heads
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self.alibi = alibi
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self.new_decoder_architecture = new_decoder_architecture
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self.multi_query = multi_query # Ignored when new_decoder_architecture is True
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self.parallel_attn = parallel_attn
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self.bias = bias
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self.max_position_embeddings = max_position_embeddings
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self.rope_theta = rope_theta
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self.rope_scaling = rope_scaling
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self.activation = activation
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if ffn_hidden_size is None:
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self.ffn_hidden_size = hidden_size * 4
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else:
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self.ffn_hidden_size = ffn_hidden_size
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self._rope_scaling_validation()
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super().__init__(bos_token_id=bos_token_id, eos_token_id=eos_token_id, **kwargs)
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@property
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def head_dim(self):
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return self.hidden_size // self.num_attention_heads
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@property
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def rotary(self):
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return not self.alibi
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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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if self.alibi:
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raise ValueError("`rope_scaling` is not supported when `alibi` is `True`.")
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if not isinstance(self.rope_scaling, dict) or len(self.rope_scaling) != 2:
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raise ValueError(
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"`rope_scaling` must be a dictionary with two fields, `type` and `factor`, " 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"]:
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raise ValueError(
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f"`rope_scaling`'s type field must be one of ['linear', 'dynamic'], 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 a float > 1, got {rope_scaling_factor}")
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