Update index

Author: danieloh30

content/modules/ROOT/pages/index.adoc

@@ -12,11 +12,11 @@ Although we use the insurance industry in this workshop the principles are widel
 
 Throughout this workshop, you as a developer will be interfacing with a variety of LLMs deployed on *Red Hat OpenShift AI* and designed for different use cases. Some of these have been fine-tuned with Parasol's private corporate data to demonstrate how organizations like Parasol can achieve business value with AI without giving away their secrets. The models you will use:
 
-* https://huggingface.co/instructlab/merlinite-7b-lab[Merlinite 7B^] - This model is derived from a https://huggingface.co/mistralai[Mistral model^], and trained with the https://arxiv.org/abs/2403.01081[LAB methodology^] and a https://github.com/instructlab/taxonomy[community taxonomy] of data. It has not been tuned with Parasol-specific data.
+* https://huggingface.co/ibm-granite/granite-3.0-8b-instruct[IBM Granite-3.0-8B-Instruct^] - This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. It has not been tuned with Parasol-specific data.
 
-* **parasol-chat** - This model is derived from the https://huggingface.co/instructlab/granite-7b-lab[InstructLab's Granite model^] and has been fine-tuned using the https://github.com/instructlab/community[InstructLAB project] with a https://github.com/rh-rad-ai-roadshow/parasol-taxonomy[Parasol-specific dataset^]. The name _chat_ implies that this is primarily useful for conversations, and can respond with knowledge of Parasol's private data.
+* **parasol-chat** - This model is derived from the https://huggingface.co/ibm-granite/granite-3.0-8b-instruct[IBM Granite-3.0-8B-Instruct^] and has been fine-tuned using the https://github.com/instructlab/community[InstructLAB project] with a https://github.com/rh-rad-ai-roadshow/parasol-taxonomy[Parasol-specific dataset^]. The name _chat_ implies that this is primarily useful for conversations, and can respond with knowledge of Parasol's private data.
 
-* **parasol-instruct** - This model is derived from https://huggingface.co/ibm-granite/granite-7b-instruct[IBM Granite Instruct], and also fine-tuned as above with Parasol-specific data. The name _instruct_ implies that this is primarily good at following instructions to complete a task. This is useful when you want the model to follow explicit instructions, such as "Respond only with JSON with the following schema: {...}".
+* **parasol-instruct** - This model is derived from https://huggingface.co/ibm-granite/granite-3.0-8b-instruct[IBM Granite-3.0-8B-Instruct^], and also fine-tuned as above with Parasol-specific data. The name _instruct_ implies that this is primarily good at following instructions to complete a task. This is useful when you want the model to follow explicit instructions, such as "Respond only with JSON with the following schema: {...}".
 
 * **parasol-tools** - This model is based on the https://huggingface.co/meta-llama/Meta-Llama-3.1-8B-Instruct[Meta Llama Instruct^] model(s) and has specifically been trained to use agents (also called tools) to complete tasks that you as a developer instruct give it. This is used for "Agentic" applications that provide agents that can do specific tasks like send an email, calculate math problems, and more.