Update scenario6.mdx

data/hackathon/scenario6.mdx

@@ -20,43 +20,43 @@ Your challenge is to
 6) Train the student model to integrate the new knowledge - this will take approx 20 minutes
 7) Verify that the new knowledge is present
 
-## 6.0 - 
+## 6.0 - Be in the Know...
 Documentation you may find helpful is:
 - https://github.com/instructlab/instructlab
 - https://shonpaz.medium.com/rewiring-the-way-we-think-on-ai-part-1-model-fine-tuning-using-instructlab-ebba7017e5d5
+- In case you want to build your RHEL AI image yourself later: https://github.com/RedHatOfficial/rhelai-dev-preview
 
-## 6.1 - 
-- Go to demo.redhat.com and start your teams' InstructLab RHEL VM (Nvidia/CUDA) - or build an equivalent RHEL AI dev preview instance with NVIDIA drivers yourself following the instructions at https://github.com/RedHatOfficial/rhelai-dev-preview (not recommended as results may vary)
-- Create and/or activate the virtual python environment
+## 6.1 - Setting Up
+- Go to demo.redhat.com and order your teams' InstructLab RHEL VM (Nvidia/CUDA) environment
 - Install the instruct lab command line tooling
 - Serve the Model
 
 ## 6.2 - 
-- Chat with the model and test its knowledge about Instruct Lab by asking 'What is the Instructlab project?'. 
-If you find the answers somewhat peculiar, your mission is to fix that - should you accept it. And no, this message will not self-destruct. Should you be happy with the answer you can select a different knowledge area to improve. Things to test out are edge computing, the movie back to the future, or the 2024 European soccer championship results.
+- Chat with the model and test its knowledge about Instruct Lab 
+If you find the answers somewhat peculiar, your mission is to fix that - should you accept it. And no, this message will not self-destruct. Should you be happy with the answer you can select a different knowledge area to improve.
 
 ## 6.3 -
-- Clone the open source InstructLab taxomoy tree onto your machine
+- Acquire the InstructLab taxonomy 
 - Add new knowledge. 
 - Verify that the taxonomy tree is A-OK.
 
 ## 6.4 -
-- Generate new synthetic data with a teacher model of your choice.
-- Does generate need a model being served? Why or Why not?
+- Generate new synthetic data with a teacher model 
+- Does synthetic data generation need a model being served? Why/Why not?
 
 ## 6.5 -
 - Verify the synthetic data generation via the critic model output
 - Discuss: Does the critic model _need_ to be a different model compared to the student or teacher model?
-- Create a screenshot and show the files generated via the generate phase and the discarded data from the critic model
+- Create a screenshot showing the files generated via the generate phase and the discarded data from the critic model and post it into the slack channel.
 
 ## 6.6 -
 - Does training require a model being served? Why or Why not?
 - Train the model
 - When would you / should you use quantisation?
 
 ## 6.7
-- Chat with the newly trained model and verify if it has additional knowledge.
-- Create screenshot and post it in the slack channel.
+- Chat with the newly trained model and verify if it has the additional knowledge you added.
+- Create a screenshot and post it in the slack channel.
 
 
 # HINTS