Update Lemonade SDK links and pypi package

example/llm/lemonade/README.md

@@ -1,6 +1,6 @@
 # Ryzen AI LLM Lemonade Examples
 
-The following table contains a curated list of LLMs that have been validated with the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) on Ryzen AI hybrid execution mode, along with CPU implementations of those same checkpoints. 
+The following table contains a curated list of LLMs that have been validated with the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade) on Ryzen AI hybrid execution mode, along with CPU implementations of those same checkpoints. 
 
 The hybrid examples are built on top of OnnxRuntime GenAI (OGA), while the CPU baseline is built on top of Hugging Face (HF) ``transformers``. Validation is defined as running all commands in the example page successfully.
 

example/llm/lemonade/cpu/CodeLlama_7b_Instruct_hf.md

@@ -4,15 +4,15 @@ This guide contains all of the instructions necessary to get started with the mo
 
 The CPU implementation in this guide is designed to run on most PCs. However, for optimal performance on Ryzen AI 300-series PCs, try the [hybrid execution mode](../hybrid/CodeLlama_7b_Instruct_hf.md).
 
-The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
+The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
 
 # Checkpoint
 
 The Hugging Face CPU implementation of [`meta-llama/CodeLlama-7b-Instruct-hf`](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf) uses the original safetensors weights provided in the Hugging Face checkpoint.
 
 # Setup
 
-To get started with the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) in a Python environment, follow these instructions.
+To get started with the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade) in a Python environment, follow these instructions.
 
 ### System-level pre-requisites
 
@@ -35,9 +35,9 @@ To create and set up an environment, run these commands in your terminal:
     conda activate ryzenai-llm
     ```
 
-3. Install ONNX TurnkeyML to get access to the LLM tools and APIs.
+3. Install the Lemonade SDK to get access to the LLM tools and APIs.
     ```bash
-    pip install turnkeyml[llm]
+    pip install lemonade-sdk[llm]
     ```
 
 # Validation Tools
@@ -62,7 +62,7 @@ lemonade -i meta-llama/CodeLlama-7b-Instruct-hf huggingface-load --device cpu --
 
 ## Task Performance
 
-To measure the model's accuracy on the [MMLU test](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/mmlu_accuracy.md) `management` subject, run:
+To measure the model's accuracy on the [MMLU test](https://github.com/lemonade-sdk/lemonade/blob/main/docs/mmlu_accuracy.md) `management` subject, run:
 
 ```bash
 lemonade -i meta-llama/CodeLlama-7b-Instruct-hf huggingface-load --device cpu --dtype bfloat16 accuracy-mmlu --tests management
@@ -138,7 +138,7 @@ This guide provided instructions for testing and deploying an LLM on a target de
 
 - Visit the [Lemonade LLM examples table](../README.md) to learn how to do this for any of the supported combinations of LLM and device.
 - Visit the [overall Ryzen AI LLM documentation](https://ryzenai.docs.amd.com/en/latest/llm/overview.html#) to learn about other deployment options, such as native C++ libraries.
-- Visit the [Lemonade SDK repository](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) to learn about more tools and features.
+- Visit the [Lemonade SDK repository](https://github.com/lemonade-sdk/lemonade) to learn about more tools and features.
 
 # Copyright
 

example/llm/lemonade/cpu/DeepSeek_R1_Distill_Llama_8B.md

@@ -4,15 +4,15 @@ This guide contains all of the instructions necessary to get started with the mo
 
 The CPU implementation in this guide is designed to run on most PCs. However, for optimal performance on Ryzen AI 300-series PCs, try the [hybrid execution mode](../hybrid/DeepSeek_R1_Distill_Llama_8B.md).
 
-The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
+The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
 
 # Checkpoint
 
 The Hugging Face CPU implementation of [`deepseek-ai/DeepSeek-R1-Distill-Llama-8B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Llama-8B) uses the original safetensors weights provided in the Hugging Face checkpoint.
 
 # Setup
 
-To get started with the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) in a Python environment, follow these instructions.
+To get started with the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade) in a Python environment, follow these instructions.
 
 ### System-level pre-requisites
 
@@ -35,9 +35,9 @@ To create and set up an environment, run these commands in your terminal:
     conda activate ryzenai-llm
     ```
 
-3. Install ONNX TurnkeyML to get access to the LLM tools and APIs.
+3. Install the Lemonade SDK to get access to the LLM tools and APIs.
     ```bash
-    pip install turnkeyml[llm]
+    pip install lemonade-sdk[llm]
     ```
 
 # Validation Tools
@@ -62,7 +62,7 @@ lemonade -i deepseek-ai/DeepSeek-R1-Distill-Llama-8B huggingface-load --device c
 
 ## Task Performance
 
-To measure the model's accuracy on the [MMLU test](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/mmlu_accuracy.md) `management` subject, run:
+To measure the model's accuracy on the [MMLU test](https://github.com/lemonade-sdk/lemonade/blob/main/docs/mmlu_accuracy.md) `management` subject, run:
 
 ```bash
 lemonade -i deepseek-ai/DeepSeek-R1-Distill-Llama-8B huggingface-load --device cpu --dtype bfloat16 accuracy-mmlu --tests management
@@ -138,7 +138,7 @@ This guide provided instructions for testing and deploying an LLM on a target de
 
 - Visit the [Lemonade LLM examples table](../README.md) to learn how to do this for any of the supported combinations of LLM and device.
 - Visit the [overall Ryzen AI LLM documentation](https://ryzenai.docs.amd.com/en/latest/llm/overview.html#) to learn about other deployment options, such as native C++ libraries.
-- Visit the [Lemonade SDK repository](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) to learn about more tools and features.
+- Visit the [Lemonade SDK repository](https://github.com/lemonade-sdk/lemonade) to learn about more tools and features.
 
 # Copyright
 

example/llm/lemonade/cpu/DeepSeek_R1_Distill_Qwen_1_5B.md

@@ -4,15 +4,15 @@ This guide contains all of the instructions necessary to get started with the mo
 
 The CPU implementation in this guide is designed to run on most PCs. However, for optimal performance on Ryzen AI 300-series PCs, try the [hybrid execution mode](../hybrid/DeepSeek_R1_Distill_Qwen_1_5B.md).
 
-The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
+The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
 
 # Checkpoint
 
 The Hugging Face CPU implementation of [`deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B) uses the original safetensors weights provided in the Hugging Face checkpoint.
 
 # Setup
 
-To get started with the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) in a Python environment, follow these instructions.
+To get started with the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade) in a Python environment, follow these instructions.
 
 ### System-level pre-requisites
 
@@ -35,9 +35,9 @@ To create and set up an environment, run these commands in your terminal:
     conda activate ryzenai-llm
     ```
 
-3. Install ONNX TurnkeyML to get access to the LLM tools and APIs.
+3. Install the Lemonade SDK to get access to the LLM tools and APIs.
     ```bash
-    pip install turnkeyml[llm]
+    pip install lemonade-sdk[llm]
     ```
 
 # Validation Tools
@@ -62,7 +62,7 @@ lemonade -i deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B huggingface-load --device
 
 ## Task Performance
 
-To measure the model's accuracy on the [MMLU test](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/mmlu_accuracy.md) `management` subject, run:
+To measure the model's accuracy on the [MMLU test](https://github.com/lemonade-sdk/lemonade/blob/main/docs/mmlu_accuracy.md) `management` subject, run:
 
 ```bash
 lemonade -i deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B huggingface-load --device cpu --dtype bfloat16 accuracy-mmlu --tests management
@@ -138,7 +138,7 @@ This guide provided instructions for testing and deploying an LLM on a target de
 
 - Visit the [Lemonade LLM examples table](../README.md) to learn how to do this for any of the supported combinations of LLM and device.
 - Visit the [overall Ryzen AI LLM documentation](https://ryzenai.docs.amd.com/en/latest/llm/overview.html#) to learn about other deployment options, such as native C++ libraries.
-- Visit the [Lemonade SDK repository](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) to learn about more tools and features.
+- Visit the [Lemonade SDK repository](https://github.com/lemonade-sdk/lemonade) to learn about more tools and features.
 
 # Copyright
 

example/llm/lemonade/cpu/DeepSeek_R1_Distill_Qwen_7B.md

@@ -4,15 +4,15 @@ This guide contains all of the instructions necessary to get started with the mo
 
 The CPU implementation in this guide is designed to run on most PCs. However, for optimal performance on Ryzen AI 300-series PCs, try the [hybrid execution mode](../hybrid/DeepSeek_R1_Distill_Qwen_7B.md).
 
-The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
+The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
 
 # Checkpoint
 
 The Hugging Face CPU implementation of [`deepseek-ai/DeepSeek-R1-Distill-Qwen-7B`](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) uses the original safetensors weights provided in the Hugging Face checkpoint.
 
 # Setup
 
-To get started with the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) in a Python environment, follow these instructions.
+To get started with the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade) in a Python environment, follow these instructions.
 
 ### System-level pre-requisites
 
@@ -35,9 +35,9 @@ To create and set up an environment, run these commands in your terminal:
     conda activate ryzenai-llm
     ```
 
-3. Install ONNX TurnkeyML to get access to the LLM tools and APIs.
+3. Install the Lemonade SDK to get access to the LLM tools and APIs.
     ```bash
-    pip install turnkeyml[llm]
+    pip install lemonade-sdk[llm]
     ```
 
 # Validation Tools
@@ -62,7 +62,7 @@ lemonade -i deepseek-ai/DeepSeek-R1-Distill-Qwen-7B huggingface-load --device cp
 
 ## Task Performance
 
-To measure the model's accuracy on the [MMLU test](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/mmlu_accuracy.md) `management` subject, run:
+To measure the model's accuracy on the [MMLU test](https://github.com/lemonade-sdk/lemonade/blob/main/docs/mmlu_accuracy.md) `management` subject, run:
 
 ```bash
 lemonade -i deepseek-ai/DeepSeek-R1-Distill-Qwen-7B huggingface-load --device cpu --dtype bfloat16 accuracy-mmlu --tests management
@@ -138,7 +138,7 @@ This guide provided instructions for testing and deploying an LLM on a target de
 
 - Visit the [Lemonade LLM examples table](../README.md) to learn how to do this for any of the supported combinations of LLM and device.
 - Visit the [overall Ryzen AI LLM documentation](https://ryzenai.docs.amd.com/en/latest/llm/overview.html#) to learn about other deployment options, such as native C++ libraries.
-- Visit the [Lemonade SDK repository](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) to learn about more tools and features.
+- Visit the [Lemonade SDK repository](https://github.com/lemonade-sdk/lemonade) to learn about more tools and features.
 
 # Copyright
 

example/llm/lemonade/cpu/Llama_2_7b_chat_hf.md

@@ -4,15 +4,15 @@ This guide contains all of the instructions necessary to get started with the mo
 
 The CPU implementation in this guide is designed to run on most PCs. However, for optimal performance on Ryzen AI 300-series PCs, try the [hybrid execution mode](../hybrid/Llama_2_7b_chat_hf.md).
 
-The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
+The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
 
 # Checkpoint
 
 The Hugging Face CPU implementation of [`meta-llama/Llama-2-7b-chat-hf`](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) uses the original safetensors weights provided in the Hugging Face checkpoint.
 
 # Setup
 
-To get started with the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) in a Python environment, follow these instructions.
+To get started with the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade) in a Python environment, follow these instructions.
 
 ### System-level pre-requisites
 
@@ -35,9 +35,9 @@ To create and set up an environment, run these commands in your terminal:
     conda activate ryzenai-llm
     ```
 
-3. Install ONNX TurnkeyML to get access to the LLM tools and APIs.
+3. Install the Lemonade SDK to get access to the LLM tools and APIs.
     ```bash
-    pip install turnkeyml[llm]
+    pip install lemonade-sdk[llm]
     ```
 
 # Validation Tools
@@ -62,7 +62,7 @@ lemonade -i meta-llama/Llama-2-7b-chat-hf huggingface-load --device cpu --dtype
 
 ## Task Performance
 
-To measure the model's accuracy on the [MMLU test](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/mmlu_accuracy.md) `management` subject, run:
+To measure the model's accuracy on the [MMLU test](https://github.com/lemonade-sdk/lemonade/blob/main/docs/mmlu_accuracy.md) `management` subject, run:
 
 ```bash
 lemonade -i meta-llama/Llama-2-7b-chat-hf huggingface-load --device cpu --dtype bfloat16 accuracy-mmlu --tests management
@@ -138,7 +138,7 @@ This guide provided instructions for testing and deploying an LLM on a target de
 
 - Visit the [Lemonade LLM examples table](../README.md) to learn how to do this for any of the supported combinations of LLM and device.
 - Visit the [overall Ryzen AI LLM documentation](https://ryzenai.docs.amd.com/en/latest/llm/overview.html#) to learn about other deployment options, such as native C++ libraries.
-- Visit the [Lemonade SDK repository](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) to learn about more tools and features.
+- Visit the [Lemonade SDK repository](https://github.com/lemonade-sdk/lemonade) to learn about more tools and features.
 
 # Copyright
 

example/llm/lemonade/cpu/Llama_2_7b_hf.md

@@ -4,15 +4,15 @@ This guide contains all of the instructions necessary to get started with the mo
 
 The CPU implementation in this guide is designed to run on most PCs. However, for optimal performance on Ryzen AI 300-series PCs, try the [hybrid execution mode](../hybrid/Llama_2_7b_hf.md).
 
-The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
+The commands and scripts in this guide leverage the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade), which provides everything you need to get up and running with LLMs on the OnnxRuntime GenAI (OGA) framework, as well as the support for Hugging Face `transformers` baselines leveraged in this guide.
 
 # Checkpoint
 
 The Hugging Face CPU implementation of [`meta-llama/Llama-2-7b-hf`](https://huggingface.co/meta-llama/Llama-2-7b-hf) uses the original safetensors weights provided in the Hugging Face checkpoint.
 
 # Setup
 
-To get started with the [Lemonade SDK](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) in a Python environment, follow these instructions.
+To get started with the [Lemonade SDK](https://github.com/lemonade-sdk/lemonade) in a Python environment, follow these instructions.
 
 ### System-level pre-requisites
 
@@ -35,9 +35,9 @@ To create and set up an environment, run these commands in your terminal:
     conda activate ryzenai-llm
     ```
 
-3. Install ONNX TurnkeyML to get access to the LLM tools and APIs.
+3. Install the Lemonade SDK to get access to the LLM tools and APIs.
     ```bash
-    pip install turnkeyml[llm]
+    pip install lemonade-sdk[llm]
     ```
 
 # Validation Tools
@@ -62,7 +62,7 @@ lemonade -i meta-llama/Llama-2-7b-hf huggingface-load --device cpu --dtype bfloa
 
 ## Task Performance
 
-To measure the model's accuracy on the [MMLU test](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/mmlu_accuracy.md) `management` subject, run:
+To measure the model's accuracy on the [MMLU test](https://github.com/lemonade-sdk/lemonade/blob/main/docs/mmlu_accuracy.md) `management` subject, run:
 
 ```bash
 lemonade -i meta-llama/Llama-2-7b-hf huggingface-load --device cpu --dtype bfloat16 accuracy-mmlu --tests management
@@ -138,7 +138,7 @@ This guide provided instructions for testing and deploying an LLM on a target de
 
 - Visit the [Lemonade LLM examples table](../README.md) to learn how to do this for any of the supported combinations of LLM and device.
 - Visit the [overall Ryzen AI LLM documentation](https://ryzenai.docs.amd.com/en/latest/llm/overview.html#) to learn about other deployment options, such as native C++ libraries.
-- Visit the [Lemonade SDK repository](https://github.com/onnx/turnkeyml/blob/main/docs/lemonade/README.md) to learn about more tools and features.
+- Visit the [Lemonade SDK repository](https://github.com/lemonade-sdk/lemonade) to learn about more tools and features.
 
 # Copyright