Topic: Batch Size in Deep Learning
When training my neural network, I can get a loss below 4.5 at 10,000 iterations. The latest attempt includes trying batch sizes of 512, 1024, and 2048 while keeping the block size  at a 4. All of this is done in the context of nanoGPT. It's worth noting that when I ran a batch size of 12 and a block size of 1024, I managed to get the loss down to 4.32 after 10,000 iterations. In your opinion and experience, what steps can I take in order to reduce the loss? Please keep in mind that my video card has 12GB of RAM and the vocabulary is made up of 20,000 words. Each sentence is made up of exactly four tokens. Do you have any suggestions how I could improve the neural network, please?

Topic: Batch Size in Deep Learning
Here are the top issues reported for a Scheduling system.  Can you categorize them and report on counts for the most common issues:

Title	ShortResolution
Planner-Loadboard Sync Issue.	Replicated job fixed issue.
Loadboard-Planner Task Sync Issue.	Forecast indicator removed by renaming.
West Allis MLS HDSS Header Update.	Renamed resource replicated next day.
"Daily Task Board Setup"	Duplex task run creation fixed.
"Cancelled jobs tasks remain in LB2"	Characters issue fixed. OM updated.
Missing Task for Press in 3 Hours	Data resent and planner updated.
Loadboard job display error.	Reset Citrix connection.
Presort error for Cafe Sheet batch.	New job number created.
Filter not catching FSC MC.	Added 'contains' operator for search.
Access issues with LB2 & Finishing Toolset shortcuts at PEI-111.	LB2 deployment successful.
Access issues with LB2 workstation.	Resolved LB2 deployment issue.
Loadboard crashes and login issues.	Citrix server resolved, login fix in progress.
LB2 Loadboard Tool Error.	LB2 error resolved, no action taken.
Deployment delays causing downtime	Problem not solved. Presses deploy requested.
Loadboard server error.	Broker switch resolved LB2 issue.
Loadboard Malfunction - Urgent!	Ink jet data corrected; schedule loaded.