Update Model Card to match exact FP8-AdaRound and standard INT8 repo filenames

README.md

@@ -55,13 +55,13 @@ In the field, plant species can be difficult to distinguish from a single photo.
 ### A. Mobile Edge & Embedded Models (Dense ViT-Base)
 *Optimized for Samsung Galaxy S24 (Exynos 2400 / Snapdragon 8 Gen 3), iOS, Windows DirectML, and Raspberry Pi.*
 
-| Model File | Model Type | Runtime Target | Quantization Method | File Size | Top-1 (1-View) | Top-1 (4-View) | Target Hardware |
+| Model File | Model Type | Runtime Target | Format / Precision | File Size | Top-1 (1-View) | Top-1 (4-View) | Target Hardware |
 | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
 | [**`PlantDetect-Dense-INT8.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-Dense-INT8.safetensors) | Single-Shot | PyTorch / Python | **Standard W8A16 INT8** | **`93.3 MB`** | 83.06% | 97.35% | Ultra-compact Python edge |
-| [**`PlantDetect-Dense-FP8.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-Dense-FP8.safetensors) | Single-Shot | PyTorch / Python | **AdaRound FP8 (E4M3)** | **`92.7 MB`** | 83.06% | 97.35% | GPU / Python lightweight |
+| [**`PlantDetect-Dense-FP8-AdaRound.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-Dense-FP8-AdaRound.safetensors) | Single-Shot | PyTorch / Python | **AdaRound FP8 (E4M3)** | **`92.7 MB`** | 83.06% | 97.35% | GPU / Python lightweight |
 | [**`PlantDetect-Dense-BF16.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-Dense-BF16.safetensors) | Single-Shot | PyTorch / Python | **Full `bfloat16`** | **`181.5 MB`** | 83.06% | 97.35% | Dense PyTorch reference |
 | [**`PlantDetect-Dense-4View-INT8.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-Dense-4View-INT8.safetensors) | **4-View Distilled** | PyTorch / Python | **Standard W8A16 INT8** | **`93.3 MB`** | 82.13% | **97.60%** | Dedicated multi-photo mobile edge |
-| [**`PlantDetect-Dense-4View-FP8.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-Dense-4View-FP8.safetensors) | **4-View Distilled** | PyTorch / Python | **AdaRound FP8 (E4M3)** | **`92.7 MB`** | 82.13% | **97.60%** | Dedicated multi-photo GPU edge |
+| [**`PlantDetect-Dense-4View-FP8-AdaRound.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-Dense-4View-FP8-AdaRound.safetensors) | **4-View Distilled** | PyTorch / Python | **AdaRound FP8 (E4M3)** | **`92.7 MB`** | 82.13% | **97.60%** | Dedicated multi-photo GPU edge |
 | [**`PlantDetect-Dense-4View-BF16.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-Dense-4View-BF16.safetensors) | **4-View Distilled** | PyTorch / Python | **Full `bfloat16`** | **`181.5 MB`** | 82.13% | **97.60%** | Lossless 4-view dense reference |
 | [**`onnx/PlantDetect-Dense-INT8.onnx`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/onnx/PlantDetect-Dense-INT8.onnx) | Single-Shot | ONNX Runtime | **Mixed-Precision INT8** | **`106.3 MB`** | 83.06% | 97.35% | Android, iOS CoreML, Windows |
 | [**`onnx/PlantDetect-Dense-FP32.onnx`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/onnx/PlantDetect-Dense-FP32.onnx) | Single-Shot | ONNX Runtime | **Full FP32** | `363.3 MB` | 83.06% | 97.35% | Standard reference ONNX |
@@ -76,16 +76,16 @@ In the field, plant species can be difficult to distinguish from a single photo.
 
 ### B. Server / Desktop GPU Models (Mixture-of-Experts)
 
-| Model File | Model Type | Architecture | Quantization Method | Size | Top-1 (1-View) | Top-1 (4-View) | Primary Use Case |
+| Model File | Model Type | Architecture | Format | Size | Top-1 (1-View) | Top-1 (4-View) | Primary Use Case |
 | :--- | :--- | :--- | :--- | :--- | :--- | :--- | :--- |
-| [**`PlantDetect-FP8.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-FP8.safetensors) | Single-Shot | MoE++ (16 FFNs) | **AdaRound FP8 (E4M3)** | **`104.3 MB`** | **89.21%** | **96.40%** | **Fastest, ultra-compact GPU server inference** |
+| [**`PlantDetect-FP8-AdaRound.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-FP8-AdaRound.safetensors) | Single-Shot | MoE++ (16 FFNs) | **AdaRound FP8 (E4M3)** | **`104.3 MB`** | **89.21%** | **96.40%** | **Fastest, ultra-compact GPU server inference** |
 | [**`PlantDetect-BF16.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-BF16.safetensors) | Single-Shot | MoE++ (16 FFNs) | **Full `bfloat16`** | **`205.2 MB`** | **89.21%** | **96.40%** | Lossless baseline reference |
-| [**`PlantDetect-4View-FP8.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-4View-FP8.safetensors) | **4-View Distilled** | MoE++ 4-View | **AdaRound FP8 (E4M3)** | **`104.3 MB`** | 85.06% | **99.20%** | Dedicated multi-photo teacher (FP8) |
+| [**`PlantDetect-4View-FP8-AdaRound.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-4View-FP8-AdaRound.safetensors) | **4-View Distilled** | MoE++ 4-View | **AdaRound FP8 (E4M3)** | **`104.3 MB`** | 85.06% | **99.20%** | Dedicated multi-photo teacher (FP8) |
 | [**`PlantDetect-4View-BF16.safetensors`**](https://huggingface.co/thenukegun10x/wa-plant-identifier/resolve/main/PlantDetect-4View-BF16.safetensors) | **4-View Distilled** | MoE++ 4-View | **Full `bfloat16`** | **`205.2 MB`** | 85.06% | **99.20%** | Lossless multi-photo baseline |
 
 > **Quantization Architecture Details:**
-> * **FP8 Models:** Quantized using **AdaRound FP8** with quadratic activation reconstruction loss $\min \|Wx - \tilde{W}(V)x\|_2^2$ over non-uniform E4M3 discrete grids (100% Top-1 FP32 match).
-> * **INT8 Models:** Quantized using **Symmetric Per-Channel W8A16 Dynamic Range Quantization**, delivering **`43.81 dB` SQNR** and **`0.999985` cosine similarity**.
+> * **FP8 Models (`*-FP8-AdaRound.safetensors`):** Quantized using **AdaRound FP8** with quadratic activation reconstruction loss $\min \|Wx - \tilde{W}(V)x\|_2^2$ over non-uniform E4M3 discrete grids (100% Top-1 FP32 match).
+> * **INT8 Models (`*-INT8.safetensors` / `.onnx` / `.tflite`):** Quantized using **Symmetric Per-Channel W8A16 Dynamic Range Quantization**, delivering **`43.81 dB` SQNR** and **`0.999985` cosine similarity**.
 
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