v0.61.0

See https://github.com/qualcomm/ai-hub-models/releases/v0.61.0 for changelog.

README.md

@@ -1,6 +1,6 @@
 ---
 library_name: pytorch
-license: other
+license: llama3
 tags:
 - llm
 - generative_ai
@@ -16,7 +16,7 @@ pipeline_tag: text-generation
 Llama 3 is a family of LLMs. The model is quantized to w4a16 (4-bit weights and 16-bit activations) and part of the model is quantized to w8a16 (8-bit weights and 16-bit activations) making it suitable for on-device deployment. For Prompt and output length specified below, the time to first token is Llama-PromptProcessor-Quantized's latency and average time per addition token is Llama-TokenGenerator-Quantized's latency.
 
 This is based on the implementation of Llama-v3-8B-Instruct found [here](https://huggingface.co/meta-llama/Meta-Llama-3-8B-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/v0.60.0/src/qai_hub_models/models/llama_v3_8b_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/v0.61.0/src/qai_hub_models/models/llama_v3_8b_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.
 
@@ -30,12 +30,12 @@ See the [LLM-on-Genie](https://github.com/qualcomm/ai-hub-apps/tree/main/tutoria
 
 ## Getting Started
 Due to licensing restrictions, we cannot distribute pre-exported model assets for this model.
-Use the [Qualcomm® AI Hub Models](https://github.com/qualcomm/ai-hub-models/blob/v0.60.0/src/qai_hub_models/models/llama_v3_8b_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/v0.61.0/src/qai_hub_models/models/llama_v3_8b_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
 
-See our repository for [Llama-v3-8B-Instruct on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.60.0/src/qai_hub_models/models/llama_v3_8b_instruct) for usage instructions.
+See our repository for [Llama-v3-8B-Instruct on GitHub](https://github.com/qualcomm/ai-hub-models/blob/v0.61.0/src/qai_hub_models/models/llama_v3_8b_instruct) for usage instructions.
 
 
 ## Model Details
@@ -43,9 +43,9 @@ See our repository for [Llama-v3-8B-Instruct on GitHub](https://github.com/qualc
 **Model Type:** Model_use_case.text_generation
 
 **Model Stats:**
+- Response Rate: Rate of response generation after the first response token.
 - Supported languages: English.
 - TTFT: Time To First Token is the time it takes to generate the first response token. This is expressed as a range because it varies based on the length of the prompt. The lower bound is for a short prompt (up to 128 tokens, i.e., one iteration of the prompt processor) and the upper bound is for a prompt using the full context length (4096 tokens).
-- Response Rate: Rate of response generation after the first response token.
 
 ## Performance Summary
 | Model | Runtime | Precision | Chipset | Context Length | Response Rate (tokens per second) | Time To First Token (range, seconds)
@@ -53,17 +53,18 @@ See our repository for [Llama-v3-8B-Instruct on GitHub](https://github.com/qualc
 | Llama-v3-8B-Instruct | GENIE | w4a16 | Snapdragon® 8 Elite Gen 5 Mobile | 4096 | 16.36505870819092 | 0.0993634 - 3.1796288
 | Llama-v3-8B-Instruct | GENIE | w4a16 | Snapdragon® 8 Elite Mobile | 4096 | 15.001426887512206 | 0.1370342 - 4.3850944
 | Llama-v3-8B-Instruct | GENIE | w4a16 | Snapdragon® X2 Elite | 4096 | 19.47 | 0.147975 - 4.7352
-| Llama-v3-8B-Instruct | GENIE | w4a16 | Snapdragon® X Elite | 4096 | 8.732598209381104 | 0.2160252 - 6.9128064
+| Llama-v3-8B-Instruct | GENIE | w4a16 | Snapdragon® X Elite | 4096 | 5.120432376861572 | 0.206663 - 6.613216
 | Llama-v3-8B-Instruct | GENIE | w4a16 | Qualcomm® Dragonwing™ IQ-9075 | 4096 | 10.764844226837159 | 0.18326900000000002 - 5.8646080000000005
-| Llama-v3-8B-Instruct | GENIE | w4a16 | Qualcomm® Dragonwing™ IQ-X7181 | 4096 | 8.732598209381104 | 0.2160252 - 6.9128064
+| Llama-v3-8B-Instruct | GENIE | w4a16 | Qualcomm® Dragonwing™ IQ-X7181 | 4096 | 5.120432376861572 | 0.206663 - 6.613216
 | Llama-v3-8B-Instruct | GENIE | w4a16 | Qualcomm® Dragonwing™ Q-8750 | 4096 | 15.001426887512206 | 0.1370342 - 4.3850944
-| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® 8 Elite Gen 5 Mobile | 4096 | 16.572434 | 0.1359 - 4.3488
-| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® 8 Elite Mobile | 4096 | 14.305629 | 0.174756 - 5.592192
-| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® X2 Elite | 4096 | 23.133006 | 0.10394 - 3.32608
-| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® X Elite | 4096 | 11.437278 | 0.228314 - 7.306048
-| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ IQ-9075 | 4096 | 10.249869 | 0.223498 - 7.151936
-| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ IQ-X7181 | 4096 | 11.437278 | 0.228314 - 7.306048
-| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ Q-8750 | 4096 | 14.305629 | 0.174756 - 5.592192
+| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® 8 Elite Gen 5 Mobile | 4096 | 16.504283 | 0.135585 - 4.33872
+| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® 8 Elite Mobile | 4096 | 14.052707 | 0.182029 - 5.824928
+| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® X2 Elite | 4096 | 23.024586 | 0.104363 - 3.339616
+| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Snapdragon® X Elite | 4096 | 11.330596 | 0.22880099999999998 - 7.321631999999999
+| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ IQ-8275 | 4096 | 9.875264 | 0.220166 - 7.045312
+| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ IQ-9075 | 4096 | 10.341933 | 0.222752 - 7.128064
+| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ IQ-X7181 | 4096 | 11.330596 | 0.22880099999999998 - 7.321631999999999
+| Llama-v3-8B-Instruct | GENIEX_QAIRT | w4a16 | Qualcomm® Dragonwing™ Q-8750 | 4096 | 14.052707 | 0.182029 - 5.824928
 
 ## License
 * The license for the original implementation of Llama-v3-8B-Instruct can be found