Update config.json

config.json

@@ -1,44 +1,74 @@
 {
-  "model_type": "custom",
-  "custom_model": {
-    "module": "path.to.your.custom_module",
-    "class": "CSUMLMModel",
-    "config": {
-      "text_encoder": {
-        "model_name": "bert-base-uncased",
-        "pretrained_weights": "path/to/bert-base-uncased"
-      },
-      "image_encoder": {
-        "model_name": "resnet50",
-        "pretrained_weights": "path/to/resnet50"
-      },
-      "audio_encoder": {
-        "model_name": "wav2vec2-base",
-        "pretrained_weights": "path/to/wav2vec2-base"
-      },
-      "fusion_layer": {
-        "type": "concatenate",
-        "output_dim": 1024
-      },
-      "output_layer": {
-        "type": "dense",
-        "units": 1,
-        "activation": "sigmoid"
-      },
-      "attention_mechanism": {
-        "traditional_attention": {
-          "type": "bahdanau"
-        },
-        "self_attention": {
-          "type": "scaled_dot_product"
-        },
-        "linear_attention": {
-          "type": "linear"
-        }
-      },
-      "belief_desire_intent_tree": {
-        "type": "recursive_neural_network"
-      }
+  "model_type": "unified_multimodal_language_model",
+  "model_name": "CognoSphere/CSUMLM",
+  "model_description": "CognoSphere Unified Multimodal Language Model (CSUMLM) is an advanced AI model capable of processing and generating text, images, and audio data. It combines transfer learning, deep learning, self-supervised learning, meta-learning, deep meta-learning, reinforcement learning, and cross-domain analogy extraction to achieve state-of-the-art performance in multimodal tasks.",
+  "model_architecture": {
+    "text_encoder": {
+      "type": "transformer",
+      "num_layers": 12,
+      "hidden_size": 768,
+      "num_attention_heads": 12,
+      "intermediate_size": 3072
+    },
+    "image_encoder": {
+      "type": "convolutional",
+      "num_layers": 5,
+      "kernel_sizes": [3, 3, 3, 3, 3],
+      "channels": [64, 128, 256, 512, 512]
+    },
+    "audio_encoder": {
+      "type": "recurrent",
+      "num_layers": 3,
+      "hidden_size": 512,
+      "bidirectional": true
+    },
+    "multimodal_fusion": {
+      "type": "transformer",
+      "num_layers": 6,
+      "hidden_size": 1024,
+      "num_attention_heads": 16,
+      "intermediate_size": 4096
+    },
+    "decoder": {
+      "type": "transformer",
+      "num_layers": 12,
+      "hidden_size": 768,
+      "num_attention_heads": 12,
+      "intermediate_size": 3072
     }
-  }
-}
+  },
+  "training_data": {
+    "text": [
+      "path/to/text/data/file1.txt",
+      "path/to/text/data/file2.txt",
+      "..."
+    ],
+    "images": [
+      "path/to/image/data/image1.jpg",
+      "path/to/image/data/image2.png",
+      "..."
+    ],
+    "audio": [
+      "path/to/audio/data/audio1.wav",
+      "path/to/audio/data/audio2.mp3",
+      "..."
+    ]
+  },
+  "tokenizer": {
+    "type": "byte-level-bpe",
+    "vocab_size": 50000,
+    "merge_file": "path/to/bpe/merge_file.txt"
+  },
+  "optimizer": {
+    "type": "adamw",
+    "learning_rate": 5e-5,
+    "weight_decay": 0.01
+  },
+  "loss_function": "cross_entropy",
+  "evaluation_metrics": [
+    "bleu",
+    "meteor",
+    "rouge",
+    "cider"
+  ]
+    }