Upload config

config.json

@@ -1,15 +1,15 @@
 {
   "architectures": [
-    "MERaLiONForConditionalGeneration"
+    "MERaLiON2ForConditionalGeneration"
   ],
   "auto_map": {
-    "AutoConfig": "configuration_meralion.MERaLiONConfig",
-    "AutoModelForSpeechSeq2Seq": "modeling_meralion.MERaLiONForConditionalGeneration"
+    "AutoConfig": "configuration_meralion2.MERaLiON2Config",
+    "AutoModelForSpeechSeq2Seq": "modeling_meralion2.MERaLiON2ForConditionalGeneration"
   },
   "head_dim": 256,
   "hidden_size": 3584,
   "intermediate_size": 14336,
-  "model_type": "meralion",
+  "model_type": "meralion2",
   "num_attention_heads": 16,
   "num_hidden_layers": 42,
   "num_key_value_heads": 8,

configuration_meralion2.py

@@ -0,0 +1,76 @@
+"""MERaLiON2 model configuration"""
+
+from transformers import Gemma2Config, WhisperConfig
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+
+logger = logging.get_logger(__name__)
+
+
+class MERaLiON2Config(PretrainedConfig):
+    r"""
+    This is the configuration class to store the configuration of a [`MERaLiON2ForConditionalGeneration`]. It is used to instantiate an
+    MERaLiON2 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 MERaLiON2.
+
+    Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
+    documentation from [`PretrainedConfig`] for more information.
+
+    Args:
+        audio_config (`Union[AutoConfig, dict]`,  *optional*, defaults to `CLIPVisionConfig`):
+            The config object or dictionary of the audio backbone.
+        text_config (`Union[AutoConfig, dict]`, *optional*, defaults to `LlamaConfig`):
+            The config object or dictionary of the text backbone.
+        audio_token_index (`int`, *optional*, defaults to 151646):
+            The image token index to encode the image prompt.
+    """
+
+    model_type = "meralion2"
+    is_composition = False
+
+    def __init__(
+        self,
+        speech_config=None,
+        text_config=None,
+        speech_mlp_scale_factor=15,
+        speech_token_index=255999,
+        **kwargs,
+    ):
+        
+        if isinstance(speech_config, dict):
+            speech_config = WhisperConfig(**speech_config)
+        elif speech_config is None:
+            speech_config = WhisperConfig(
+                d_model=1280,
+                encoder_attention_heads=20,
+                encoder_ffn_dim=5120,
+                encoder_layerdrop=0.0,
+                encoder_layers=32,
+                num_mel_bins=128,
+                max_source_positions=1500,
+                scale_embedding=False,
+                activation_function="gelu",
+            )
+
+        self.speech_config = speech_config
+
+        if isinstance(text_config, dict):
+            text_config = Gemma2Config(**text_config)
+        elif text_config is None:
+            text_config = Gemma2Config()
+
+        self.text_config = text_config
+
+        self.speech_mlp_scale_factor = speech_mlp_scale_factor
+        self.speech_token_index = speech_token_index
+        
+        self.sliding_window = self.text_config.sliding_window
+        self.hidden_size = self.text_config.hidden_size
+        self.num_attention_heads = self.text_config.num_attention_heads
+        self.num_hidden_layers = self.text_config.num_hidden_layers
+        self.num_key_value_heads = self.text_config.num_key_value_heads
+        self.head_dim = self.text_config.head_dim
+        self.intermediate_size = self.text_config.intermediate_size
+        
+        super().__init__(**kwargs)