v0.59.0

See https://github.com/qualcomm/ai-hub-models/releases/v0.59.0 for changelog.

README.md

@@ -16,7 +16,7 @@ pipeline_tag: text-generation
 Phi-4-mini-instruct is a lightweight open model built upon synthetic data and filtered publicly available websites - with a focus on high-quality, reasoning dense data.
 
 This is based on the implementation of Phi-4-Mini-Instruct found [here](https://huggingface.co/microsoft/Phi-4-mini-instruct).
-This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/phi_4_mini_instruct) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
+This repository contains pre-exported model files optimized for Qualcomm® devices. You can use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/phi_4_mini_instruct) library to export with custom configurations. More details on model performance across various devices, can be found [here](#performance-summary).
 
 Qualcomm AI Hub Models uses [Qualcomm AI Hub Workbench](https://workbench.aihub.qualcomm.com) to compile, profile, and evaluate this model. [Sign up](https://myaccount.qualcomm.com/signup) to run these models on a hosted Qualcomm® device.
 
@@ -40,14 +40,14 @@ For more device-specific assets and performance metrics, visit **[Phi-4-Mini-Ins
 
 ### Option 2: Export with Custom Configurations
 
-Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/phi_4_mini_instruct) Python library to compile and export the model with your own:
+Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/phi_4_mini_instruct) Python library to compile and export the model with your own:
 - Custom weights (e.g., fine-tuned checkpoints)
 - Custom input shapes
 - Target device and runtime configurations
 
 This option is ideal if you need to customize the model beyond the default configuration provided here.
 
-See our repository for [Phi-4-Mini-Instruct on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.58.0/src/qai_hub_models/models/phi_4_mini_instruct) for usage instructions.
+See our repository for [Phi-4-Mini-Instruct on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.59.0/src/qai_hub_models/models/phi_4_mini_instruct) for usage instructions.
 
 ## Model Details
 
@@ -85,9 +85,21 @@ See our repository for [Phi-4-Mini-Instruct on GitHub](https://github.com/qualco
 | Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X Elite | 4096 | 15.617096 | 0.913791375 - 29.241324
 | Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X Elite | 4096 | 16.000366 | 0.9590768749999999 - 30.690459999999998
 | Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Snapdragon® X Elite | 4096 | 9.673267 | 0.41174053125 - 13.175697
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ IQ-X7181 | 512 | 27.988389 | 0.40832025 - 1.633281
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ IQ-X7181 | 512 | 27.143556 | 0.46132475 - 1.845299
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ IQ-X7181 | 512 | 16.042448 | 0.25184725 - 1.007389
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ Q-8750 | 512 | 24.055461 | 0.9468802500000001 - 3.7875210000000004
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ Q-8750 | 512 | 23.939526 | 0.95809125 - 3.832365
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ Q-8750 | 512 | 19.557942 | 0.1830165 - 0.732066
 | Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ IQ-9075 | 4096 | 13.1 | 1.9971918880000001 - 63.91014
 | Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ IQ-9075 | 4096 | 3.0 | 2.471042471 - 79.073359
 | Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ IQ-9075 | 4096 | 10.0 | 0.44137930999999997 - 14.124138
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ IQ-X7181 | 4096 | 15.617096 | 0.913791375 - 29.241324
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ IQ-X7181 | 4096 | 16.000366 | 0.9590768749999999 - 30.690459999999998
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ IQ-X7181 | 4096 | 9.673267 | 0.41174053125 - 13.175697
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ Q-8750 | 4096 | 13.965758 | 1.94027484375 - 62.088795
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ Q-8750 | 4096 | 8.177678 | 2.3551633124999998 - 75.36522599999999
+| Phi-4-Mini-Instruct | GENIEX_LLAMACPP | q4_0 | Qualcomm® Dragonwing™ Q-8750 | 4096 | 13.116693 | 0.35286768749999997 - 11.291765999999999
 
 ## License
 * The license for the original implementation of Phi-4-Mini-Instruct can be found

release_assets.json

@@ -1,5 +1,5 @@
 {
-    "version": "0.58.0",
+    "version": "0.59.0",
     "precisions": {
         "q4_0": {
             "universal_assets": {