Update README.md

README.md

@@ -8,8 +8,14 @@ datasets:
 
 This is an instruction fine-tuned llama-2 model, using synthetic instructions generated by [airoboros](https://github.com/jondurbin/airoboros)
 
-The 2.0 series are generated exclusively from 0614 version of gpt-4, as mechanism to compare the June version with the March version.
-The m2.0 series have the 1.4.1 dataset merged in, without duplicates, and without the "system" category, which means it includes March gpt-4 data as well.
+- The 2.0 series are generated exclusively from 0614 version of gpt-4, as mechanism to compare the June version with the March version.
+- The m2.0 series have the 1.4.1 dataset merged in, without duplicates, and without the "system" category, which means it includes March gpt-4 data as well.
+- 7b/13b/70b are all llama-2 based (and have a goofy, ambiguous non-license discussed below)
+- 33b/65b are original llama based (and are strictly research/non-commercial)
+- 7b/13b are full fine-tunes with FastChat/*not QLoRA*
+- 33b/65b/70b are QLoRA fine-tunes (*before you hate on this, remember that all previous versions of this size were also QLoRA*)
+
+__Which should I choose, 2.0 or m2.0?__ I have no idea, try them both and see which is better.  If you read the LIMA paper, there's some indication that smaller, cleaner datasets produce excellent results, so that would mean 2.0 is probably a better choice.  If you really enjoyed 1.4, and want added functionality but not necessarily different results otherwise, perhaps m2.0.
 
 ### Prompt format