Rename configuration_longcat_flash.py to configuration_spectra.py

configuration_longcat_flash.py → configuration_spectra.py

@@ -1,5 +1,5 @@
 
-"""LongcatFlash model configuration"""
+"""Spectra model configuration"""
 
 from transformers.configuration_utils import PretrainedConfig
 from transformers.modeling_rope_utils import rope_config_validation
@@ -8,11 +8,11 @@ from transformers.modeling_rope_utils import rope_config_validation
 LONGCAT_PRETRAINED_CONFIG_ARCHIVE_MAP = {}
 
 
-class LongcatFlashConfig(PretrainedConfig):
+class SpectraConfig(PretrainedConfig):
     r"""
-    This is the configuration class to store the configuration of a [`LongcatFlashModel`]. It is used to instantiate an LongcatFlash
+    This is the configuration class to store the configuration of a [`SpectraModel`]. It is used to instantiate an Spectra
     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 LongcatFlash.
+    defaults will yield a similar configuration to that of the Spectra.
     Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
     documentation from [`PretrainedConfig`] for more information.
 
@@ -20,7 +20,7 @@ class LongcatFlashConfig(PretrainedConfig):
     Args:
         vocab_size (`int`, *optional*, defaults to 131072):
             Vocabulary size of the Deep model. Defines the number of different tokens that can be represented by the
-            `inputs_ids` passed when calling [`LongcatFlashModel`]
+            `inputs_ids` passed when calling [`SpectraModel`]
         hidden_size (`int`, *optional*, defaults to 7168):
             Dimension of the hidden representations.
         ffn_hidden_size (`int`, *optional*, defaults to 18432):
@@ -90,16 +90,16 @@ class LongcatFlashConfig(PretrainedConfig):
             The type of zero expert to use.
 
     ```python
-    >>> from transformers import LongcatFlashModel, LongcatFlashConfig
+    >>> from transformers import SpectraModel, SpectraConfig
 
-    >>> # Initializing a LongcatFlash style configuration
-    >>> configuration = LongcatFlashConfig()
+    >>> # Initializing a Spectra style configuration
+    >>> configuration = SpectraConfig()
 
     >>> # Accessing the model configuration
     >>> configuration = model.config
     ```"""
 
-    model_type = "longcat_flash"
+    model_type = "spectra"
     keys_to_ignore_at_inference = ["past_key_values"]
     base_model_tp_plan = {
         "layers.*.self_attn.k_proj": "colwise",
@@ -213,4 +213,4 @@ class LongcatFlashConfig(PretrainedConfig):
         return self.num_layers
 
 
-__all__ = ["LongcatFlashConfig"]
+__all__ = ["SpectraConfig"]