Rename Metadata.yaml to Download.py

Download.py

@@ -0,0 +1,26 @@
+filter: [
+		{
+			bool: {
+				/// Include documents that match at least one of the following rules
+				should: [
+					/// Downloaded from diffusers lib
+					{
+						term: { path: "model_index.json" },
+					},
+					/// Direct downloads (LoRa, Auto1111 and others)
+					/// Filter out nested safetensors and pickle weights to avoid double counting downloads from the diffusers lib
+					{
+						regexp: { path: "[^/]*\\.safetensors" },
+					},
+					{
+						regexp: { path: "[^/]*\\.ckpt" },
+					},
+					{
+						regexp: { path: "[^/]*\\.bin" },
+					},
+				],
+				minimum_should_match: 1,
+			},
+		},
+	]
+}

Metadata.yaml

@@ -1,69 +0,0 @@
----
-model-index:
-- name: Agentic Unified Mind UANN
-  results:
-    - task:
-        type: text-classification
-      dataset:
-        name: imdb
-        type: huggingface
-        split: train[:10%]
-    - task:
-        type: image-classification
-      dataset:
-        name: cifar10
-        type: huggingface
-        split: train[:10%]
-    - task:
-        type: structured-data
-      dataset:
-        name: titanic
-        type: huggingface
-        split: train
-model_description: |
-  The Agentic Unified Mind UANN integrates text, image, and structured data processing using advanced neural network architectures and reinforcement learning. This multi-modal AI model combines BERT for text, ResNet50 for images, and dense neural networks for structured data.
-model_type: multi-modal
-languages:
-  - en
-library_name: tensorflow
-tags:
-  - multi-modal
-  - reinforcement-learning
-  - text-classification
-  - image-classification
-  - structured-data
-license: apache-2.0
-datasets:
-  - imdb
-  - cifar10
-  - titanic
-metrics:
-  - accuracy
-  - loss
----
-
-# Agentic Unified Mind UANN
-
-## Model Description
-
-The Agentic Unified Mind UANN integrates:
-- Text processing using BERT.
-- Image processing using ResNet50.
-- Structured data processing with dense neural networks.
-- Reinforcement learning for autonomous decision-making.
-
-## Features
-
-- **Multi-modal Inputs:** Handles text, images, and structured data.
-- **Advanced Neural Network Architectures:** Uses BERT for text, ResNet50 for images, and dense layers for structured data.
-- **Unified Cognitive Framework:** Combines information from multiple modalities for better decision-making.
-- **Reinforcement Learning:** Enhances the model's ability to learn and adapt from interactions.
-
-## Setup
-
-### Installation
-
-Install the required dependencies:
-
-```bash
-pip install -r requirements.txt