Update README.md

README.md

@@ -5,8 +5,7 @@ Test network using differential attention instead of classical attention (using
 - `test_train.py` runs with the exact configurations used to train this model and is the reproduction script. Data is assumed to be in JSONL format with `"text":"example text", "text":"..."`
 
 # Notes:
-Compared to the control model of Smollm2, this is bordering on incoherent. Potentially this model size is too small to correctly leverage differential attention. It's clearly picked up on some ideas in language, but is generally worse than the control model using GQA in terms of human output.
-
+Appears to be very competent, learned significantly faster than the GQA control. Achieved as slightly better min loss.
 
 # Training Metrics