Update README.md

README.md

@@ -120,10 +120,10 @@ We did not considered it for our score, but "if" considered those extra 5 questi
 ### TIME Comparison Table with models of Bigger size
 
 
-### <font color=red>Nanbeige/Nanbeige4.1-3B </font> (4 billion model, "marketed" as 3B billions), with 92% HumanEval accuracy in Python
+### <font color=red>Nanbeige/Nanbeige4.1-3B </font> (4 billion model, "marketed" as 3B billions), with 92% HumanEval accuracy in Python (FP16)
 - Time to finish 160 questions from HumanEval: 22263.80s --- <b><u>MORE THAN SIX HOURS</b></u> !!! (in a RTX 5060 ti 16gb)
 
-### <font color=red>Nerdsking/nerdsking-python-coder-3B-i</font> (real 3 billion model) with 88,41% HumanEval accuracy in Python
+### <font color=red>Nerdsking/nerdsking-python-coder-3B-i</font> (real 3 billion model) with 88,41% HumanEval accuracy in Python (FP16)
 - Time to finish 160 questions from HumanEval: 210.51s --- <b><u>THREE MINUTES AND HALF</b></u> !!! (in a RTX 5060 ti 16gb)
 
 <b><i><font color=red>Lesson: Anyone can create a bigger model with an "eternal loop" to solve problems by randomly trying all possible variables. That is not "intelligence", that's