Clarify architecture-only yamls and historical framing on model pages (#24233)

Co-authored-by: Jing Qiu <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Onuralp SEZER <onuralp@ultralytics.com>

docs/en/models/rtdetr.md

@@ -88,6 +88,10 @@ This table presents the model types, the specific pretrained weights, the tasks
 | RT-DETR Large       | [rtdetr-l.pt](https://github.com/ultralytics/assets/releases/download/v8.4.0/rtdetr-l.pt) | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ✅       | ✅     |
 | RT-DETR Extra-Large | [rtdetr-x.pt](https://github.com/ultralytics/assets/releases/download/v8.4.0/rtdetr-x.pt) | [Object Detection](../tasks/detect.md) | ✅        | ✅         | ✅       | ✅     |
 
+!!! note "Architecture-only variants"
+
+    [`rtdetr-resnet50.yaml`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/rt-detr/rtdetr-resnet50.yaml) and [`rtdetr-resnet101.yaml`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/rt-detr/rtdetr-resnet101.yaml) are shipped as YAML architectures only. Ultralytics releases pretrained weights only for `rtdetr-l` and `rtdetr-x`. Instantiate the ResNet variants from YAML (for example, `RTDETR("rtdetr-resnet50.yaml")`) and train or fine-tune them as needed.
+
 ## Ideal Use Cases
 
 RT-DETR is particularly well-suited for applications requiring both high accuracy and real-time performance:

docs/en/models/yolo26.md

@@ -69,6 +69,10 @@ YOLO26 builds upon the versatile model range established by earlier Ultralytics
 
 This unified framework ensures YOLO26 is applicable across real-time detection, segmentation, classification, pose estimation, and oriented object detection — all with training, validation, inference, and export support.
 
+!!! note "Architecture-only variants"
+
+    [`yolo26-p2.yaml`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/26/yolo26-p2.yaml) and [`yolo26-p6.yaml`](https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/models/26/yolo26-p6.yaml) add a P2 (small-object) or P6 (large-input) detection head and are shipped as YAML architectures only. No scale-specific `yolo26*-p2.pt` or `yolo26*-p6.pt` weights are released. Instantiate a scaled config from YAML (for example, `YOLO("yolo26n-p6.yaml")`) and train or fine-tune it as needed.
+
 ---
 
 ## Performance Metrics

docs/en/models/yolov4.md

@@ -32,7 +32,7 @@ When compared to other models in the YOLO family, such as [YOLOv5](https://docs.
 
 ## Usage Examples
 
-As of the time of writing, Ultralytics does not currently support YOLOv4 models. Therefore, any users interested in using YOLOv4 will need to refer directly to the YOLOv4 GitHub repository for installation and usage instructions.
+YOLOv4 is a Darknet-based model and is **not natively supported** by the Ultralytics Python package: there are no `yolov4.pt` pretrained weights published on [ultralytics/assets](https://github.com/ultralytics/assets/releases) and no `ultralytics/cfg/models/v4/` YAMLs. This page is kept as an architectural reference. Users interested in running YOLOv4 should refer directly to the YOLOv4 GitHub repository for installation and usage instructions.
 
 Here is a brief overview of the typical steps you might take to use YOLOv4:
 
@@ -44,7 +44,7 @@ Here is a brief overview of the typical steps you might take to use YOLOv4:
 
 Please note that the specific steps may vary depending on your specific use case and the current state of the YOLOv4 repository. Therefore, it is strongly recommended to refer directly to the instructions provided in the YOLOv4 GitHub repository.
 
-We regret any inconvenience this may cause and will strive to update this document with usage examples for Ultralytics once support for YOLOv4 is implemented.
+For training and inference within the Ultralytics framework, see [YOLO11](yolo11.md) or [YOLO26](yolo26.md).
 
 ## Conclusion
 

docs/en/models/yolov7.md

@@ -91,7 +91,7 @@ YOLOv7 introduces several key features:
 
 ## Usage Examples
 
-As of the time of writing, Ultralytics only supports ONNX and TensorRT inference for YOLOv7.
+Ultralytics does not publish `yolov7.pt` pretrained weights or `ultralytics/cfg/models/v7/` YAMLs, and native PyTorch training and inference for YOLOv7 are **not supported** by the Ultralytics Python package. However, you can bring a YOLOv7 checkpoint trained in the [upstream YOLOv7 repository](https://github.com/WongKinYiu/yolov7) into Ultralytics by exporting it to ONNX or TensorRT, as shown below.
 
 ### ONNX Export