[Idefics -> BaseMultiModalModel] [Vilt => BaseMultiModalModel]

swarms/models/idefics.py

@@ -102,9 +102,13 @@ def __init__(
             model_name, torch_dtype=torch_dtype, *args, **kwargs
         ).to(self.device)
 
-        self.processor = AutoProcessor.from_pretrained(model_name)
+        self.processor = AutoProcessor.from_pretrained(
+            model_name, *args, **kwargs
+        )
 
-    def run(self, task: str, *args, **kwargs) -> str:
+    def run(
+        self, task: str = None, img: str = None, *args, **kwargs
+    ) -> str:
         """
         Generates text based on the provided prompts.
 

swarms/models/openai_tts.py

@@ -61,6 +61,8 @@ def __init__(
         chunk_size=1024 * 1024,
         autosave: bool = False,
         saved_filepath: str = None,
+        *args,
+        **kwargs,
     ):
         super().__init__()
         self.model_name = model_name

swarms/models/vilt.py

@@ -1,9 +1,11 @@
-from transformers import ViltProcessor, ViltForQuestionAnswering
 import requests
 from PIL import Image
+from transformers import ViltForQuestionAnswering, ViltProcessor
 
+from swarms.models.base_multimodal_model import BaseMultiModalModel
 
-class Vilt:
+
+class Vilt(BaseMultiModalModel):
     """
     Vision-and-Language Transformer (ViLT) model fine-tuned on VQAv2.
     It was introduced in the paper ViLT: Vision-and-Language Transformer Without
@@ -21,15 +23,21 @@ class Vilt:
 
     """
 
-    def __init__(self):
+    def __init__(
+        self,
+        model_name: str = "dandelin/vilt-b32-finetuned-vqa",
+        *args,
+        **kwargs,
+    ):
+        super().__init__(model_name, *args, **kwargs)
         self.processor = ViltProcessor.from_pretrained(
-            "dandelin/vilt-b32-finetuned-vqa"
+            model_name, *args, **kwargs
         )
         self.model = ViltForQuestionAnswering.from_pretrained(
-            "dandelin/vilt-b32-finetuned-vqa"
+            model_name, *args, **kwargs
         )
 
-    def __call__(self, text: str, image_url: str):
+    def run(self, task: str = None, img: str = None, *args, **kwargs):
         """
         Run the model
 
@@ -38,9 +46,9 @@ def __call__(self, text: str, image_url: str):
 
         """
         # Download the image
-        image = Image.open(requests.get(image_url, stream=True).raw)
+        image = Image.open(requests.get(img, stream=True).raw)
 
-        encoding = self.processor(image, text, return_tensors="pt")
+        encoding = self.processor(image, task, return_tensors="pt")
 
         # Forward pass
         outputs = self.model(**encoding)

swarms/structs/base.py

@@ -15,6 +15,52 @@
 
 
 class BaseStructure(ABC):
+    """Base structure.
+
+
+    Attributes:
+        name (Optional[str]): _description_
+        description (Optional[str]): _description_
+        save_metadata (bool): _description_
+        save_artifact_path (Optional[str]): _description_
+        save_metadata_path (Optional[str]): _description_
+        save_error_path (Optional[str]): _description_
+
+    Methods:
+        run: _description_
+        save_to_file: _description_
+        load_from_file: _description_
+        save_metadata: _description_
+        load_metadata: _description_
+        log_error: _description_
+        save_artifact: _description_
+        load_artifact: _description_
+        log_event: _description_
+        run_async: _description_
+        save_metadata_async: _description_
+        load_metadata_async: _description_
+        log_error_async: _description_
+        save_artifact_async: _description_
+        load_artifact_async: _description_
+        log_event_async: _description_
+        asave_to_file: _description_
+        aload_from_file: _description_
+        run_in_thread: _description_
+        save_metadata_in_thread: _description_
+        run_concurrent: _description_
+        compress_data: _description_
+        decompres_data: _description_
+        run_batched: _description_
+        load_config: _description_
+        backup_data: _description_
+        monitor_resources: _description_
+        run_with_resources: _description_
+        run_with_resources_batched: _description_
+
+    Examples:
+
+    """
+
     def __init__(
         self,
         name: Optional[str] = None,