Saving yolov6n crashes
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- [X] I have searched the Ultralytics YOLO [issues](https://github.com/ultralytics/ultralytics/issues) and found no similar bug report.


### Ultralytics YOLO Component

Export

### Bug

Crash on "yolov6n.yaml" save.

```
New https://pypi.org/project/ultralytics/8.3.49 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.3.44 🚀 Python-3.10.12 torch-2.5.1+cu124 CPU (AMD Ryzen 3 3200G with Radeon Vega Graphics)
engine/trainer: task=detect, mode=train, model=yolov6n.yaml, data=coco8.yaml, epochs=1, time=None, patience=100, batch=16, imgsz=640, save=True, save_period=-1, cache=False, device=None, workers=8, project=None, name=train11, exist_ok=False, pretrained=True, optimizer=auto, verbose=True, seed=0, deterministic=True, single_cls=False, rect=False, cos_lr=False, close_mosaic=10, resume=False, amp=True, fraction=1.0, profile=False, freeze=None, multi_scale=False, overlap_mask=True, mask_ratio=4, dropout=0.0, val=True, split=val, save_json=False, save_hybrid=False, conf=None, iou=0.7, max_det=300, half=False, dnn=False, plots=True, source=None, vid_stride=1, stream_buffer=False, visualize=False, augment=False, agnostic_nms=False, classes=None, retina_masks=False, embed=None, show=False, save_frames=False, save_txt=False, save_conf=False, save_crop=False, show_labels=True, show_conf=True, show_boxes=True, line_width=None, format=torchscript, keras=False, optimize=False, int8=False, dynamic=False, simplify=True, opset=None, workspace=None, nms=False, lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=7.5, cls=0.5, dfl=1.5, pose=12.0, kobj=1.0, nbs=64, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, bgr=0.0, mosaic=1.0, mixup=0.0, copy_paste=0.0, copy_paste_mode=flip, auto_augment=randaugment, erasing=0.4, crop_fraction=1.0, cfg=None, tracker=botsort.yaml, save_dir=runs/detect/train11
activation: nn.ReLU()

                   from  n    params  module                                       arguments                     
  0                  -1  1       464  ultralytics.nn.modules.conv.Conv             [3, 16, 3, 2]                 
  1                  -1  1      4672  ultralytics.nn.modules.conv.Conv             [16, 32, 3, 2]                
  2                  -1  2     18560  ultralytics.nn.modules.conv.Conv             [32, 32, 3, 1]                
  3                  -1  1     18560  ultralytics.nn.modules.conv.Conv             [32, 64, 3, 2]                
  4                  -1  4    147968  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 1]                
  5                  -1  1     73984  ultralytics.nn.modules.conv.Conv             [64, 128, 3, 2]               
  6                  -1  6    886272  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 1]              
  7                  -1  1    295424  ultralytics.nn.modules.conv.Conv             [128, 256, 3, 2]              
  8                  -1  2   1180672  ultralytics.nn.modules.conv.Conv             [256, 256, 3, 1]              
  9                  -1  1    164608  ultralytics.nn.modules.block.SPPF            [256, 256, 5]                 
 10                  -1  1     16512  ultralytics.nn.modules.conv.Conv             [256, 64, 1, 1]               
 11                  -1  1     16448  torch.nn.modules.conv.ConvTranspose2d        [64, 64, 2, 2, 0]             
 12             [-1, 6]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 13                  -1  1    110720  ultralytics.nn.modules.conv.Conv             [192, 64, 3, 1]               
 14                  -1  3    110976  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 1]                
 15                  -1  1      2112  ultralytics.nn.modules.conv.Conv             [64, 32, 1, 1]                
 16                  -1  1      4128  torch.nn.modules.conv.ConvTranspose2d        [32, 32, 2, 2, 0]             
 17             [-1, 4]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 18                  -1  1     27712  ultralytics.nn.modules.conv.Conv             [96, 32, 3, 1]                
 19                  -1  3     27840  ultralytics.nn.modules.conv.Conv             [32, 32, 3, 1]                
 20                  -1  1      9280  ultralytics.nn.modules.conv.Conv             [32, 32, 3, 2]                
 21            [-1, 15]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 22                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 1]                
 23                  -1  3    110976  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 1]                
 24                  -1  1     36992  ultralytics.nn.modules.conv.Conv             [64, 64, 3, 2]                
 25            [-1, 10]  1         0  ultralytics.nn.modules.conv.Concat           [1]                           
 26                  -1  1    147712  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 1]              
 27                  -1  3    443136  ultralytics.nn.modules.conv.Conv             [128, 128, 3, 1]              
 28        [19, 23, 27]  1    607360  ultralytics.nn.modules.head.Detect           [80, [32, 64, 128]]           
YOLOv6n summary: 195 layers, 4,500,080 parameters, 4,500,064 gradients, 13.1 GFLOPs

TensorBoard: Start with 'tensorboard --logdir runs/detect/train11', view at http://localhost:6006/
Freezing layer 'model.28.dfl.conv.weight'
train: Scanning datasets/coco8/labels/train.cache... 4 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4/4 [00:00<?, ?it/s]
val: Scanning datasets/coco8/labels/val.cache... 4 images, 0 backgrounds, 0 corrupt: 100%|██████████| 4/4 [00:00<?, ?it/s]
Plotting labels to runs/detect/train11/labels.jpg... 
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically... 
optimizer: AdamW(lr=0.000119, momentum=0.9) with parameter groups 53 weight(decay=0.0), 62 weight(decay=0.0005), 61 bias(decay=0.0)
TensorBoard: model graph visualization added ✅
Image sizes 640 train, 640 val
Using 0 dataloader workers
Logging results to runs/detect/train11
Starting training for 1 epochs...

      Epoch    GPU_mem   box_loss   cls_loss   dfl_loss  Instances       Size
        1/1         0G      3.483      5.686      4.311         22        640: 100%|██████████| 1/1 [00:05<00:00,  5.91s/it]
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:02<00:00,  2.16s/it]
                   all          4         17          0          0          0          0

1 epochs completed in 0.006 hours.
Optimizer stripped from runs/detect/train11/weights/last.pt, 9.2MB
Optimizer stripped from runs/detect/train11/weights/best.pt, 9.2MB

Validating runs/detect/train11/weights/best.pt...
WARNING ⚠️ validating an untrained model YAML will result in 0 mAP.
Ultralytics 8.3.44 🚀 Python-3.10.12 torch-2.5.1+cu124 CPU (AMD Ryzen 3 3200G with Radeon Vega Graphics)
YOLOv6n summary (fused): 142 layers, 4,495,392 parameters, 0 gradients, 13.0 GFLOPs
                 Class     Images  Instances      Box(P          R      mAP50  mAP50-95): 100%|██████████| 1/1 [00:01<00:00,  1.31s/it]
                   all          4         17          0          0          0          0
Speed: 4.7ms preprocess, 314.3ms inference, 0.0ms loss, 2.3ms postprocess per image
Results saved to runs/detect/train11
Traceback (most recent call last):
  File "main.py", line 91, in <module>
    detectImageWithYolo()
  File "main.py", line 70, in detectImageWithYolo
    model.save("yolov6coco.pt")
  File ".venv/lib/python3.10/site-packages/ultralytics/engine/model.py", line 414, in save
    torch.save({**self.ckpt, **updates}, filename)
TypeError: 'NoneType' object is not a mapping
```

### Environment

Ultralytics 8.3.44 🚀 Python-3.10.12 torch-2.5.1+cu124 CPU (AMD Ryzen 3 3200G with Radeon Vega Graphics)
Setup complete ✅ (4 CPUs, 23.3 GB RAM, 2769.5/3185.4 GB disk)
OS                  Linux-6.8.0-49-generic-x86_64-with-glibc2.35
Environment         Linux
Python              3.10.12
Install             pip
RAM                 23.35 GB
Disk                2769.5/3185.4 GB
CPU                 AMD Ryzen 3 3200G with Radeon Vega Graphics
CPU count           4
GPU                 None
GPU count           None
CUDA                None
numpy               ✅ 2.0.2>=1.23.0
numpy               ✅ 2.0.2<2.0.0; sys_platform == "darwin"
matplotlib          ✅ 3.9.2>=3.3.0
opencv-python       ✅ 4.10.0.84>=4.6.0
pillow              ✅ 11.0.0>=7.1.2
pyyaml              ✅ 6.0.2>=5.3.1
requests            ✅ 2.32.3>=2.23.0
scipy               ✅ 1.14.1>=1.4.1
torch               ✅ 2.5.1>=1.8.0
torch               ✅ 2.5.1!=2.4.0,>=1.8.0; sys_platform == "win32"
torchvision         ✅ 0.20.1>=0.9.0
tqdm                ✅ 4.67.1>=4.64.0
psutil              ✅ 6.1.0
py-cpuinfo          ✅ 9.0.0
pandas              ✅ 2.2.3>=1.1.4
seaborn             ✅ 0.13.2>=0.11.0
ultralytics-thop    ✅ 2.0.12>=2.0.0
{'OS': 'Linux-6.8.0-49-generic-x86_64-with-glibc2.35', 'Environment': 'Linux', 'Python': '3.10.12', 'Install': 'pip', 'RAM': '23.35 GB', 'Disk': '2769.5/3185.4 GB', 'CPU': 'AMD Ryzen 3 3200G with Radeon Vega Graphics', 'CPU count': 4, 'GPU': None, 'GPU count': None, 'CUDA': None, 'Package Info': {'numpy': '✅ 2.0.2<2.0.0; sys_platform == "darwin"', 'matplotlib': '✅ 3.9.2>=3.3.0', 'opencv-python': '✅ 4.10.0.84>=4.6.0', 'pillow': '✅ 11.0.0>=7.1.2', 'pyyaml': '✅ 6.0.2>=5.3.1', 'requests': '✅ 2.32.3>=2.23.0', 'scipy': '✅ 1.14.1>=1.4.1', 'torch': '✅ 2.5.1!=2.4.0,>=1.8.0; sys_platform == "win32"', 'torchvision': '✅ 0.20.1>=0.9.0', 'tqdm': '✅ 4.67.1>=4.64.0', 'psutil': '✅ 6.1.0', 'py-cpuinfo': '✅ 9.0.0', 'pandas': '✅ 2.2.3>=1.1.4', 'seaborn': '✅ 0.13.2>=0.11.0', 'ultralytics-thop': '✅ 2.0.12>=2.0.0'}}


### Minimal Reproducible Example

```
model = YOLO("yolov6n.yaml")
model.train(data="coco8.yaml", epochs=1, imgsz=640)
model.save("yolov6coco.pt")
```

### Additional

_No response_

### Are you willing to submit a PR?

- [ ] Yes I'd like to help by submitting a PR!