Update README.md

README.md

@@ -51,7 +51,7 @@ NEO dataset improves overall performance.
 
 CODER dataset is specifically for coding performance.
 
-DUEL ("DI")-> Separate Imatrix datasets (generated separately per model) are co-joined to create a new Imatrix dataset, which is then applied to the quants.
+DUEL ("DI")-> Separate Imatrix datasets ("NEO" and "CODER" - generated separately per model) are co-joined to create a new Imatrix dataset, which is then applied to the quants.
 
 Model also passed "hard" coding test too (4 experts); no issues (IQ4_NL).
 
@@ -66,9 +66,9 @@ There are TWO "IQ4_NL" quants:
 - OpenAI-20B-NEO-CODE-DIMAT-2-IQ4_NL.gguf : DI Imatrix applied, including output tensor (also imatrixed), and embed tensor at IQ4_NL.
 
 There are THREE NEO MXFP4_MOE quants:
-- OpenAI-20B-NEO-CODE-DIMAT-MXFP4_MOE2.gguf : Output tensor Q5_1 (NEO Imatrix)
-- OpenAI-20B-NEO-CODE-DIMAT-MXFP4_MOE3.gguf : Output tensor IQ4_NL (NEO Imatrix)
-- OpenAI-20B-NEO-CODE-DIMAT-MXFP4_MOE4.gguf : Output tensor IQ4_NL (NEO Imatrix) AND Embed at IQ4_NL - this makes this quant the smallest version.
+- OpenAI-20B-NEO-CODE-DIMAT-MXFP4_MOE2.gguf : Output tensor Q5_1 (DI Imatrix applied)
+- OpenAI-20B-NEO-CODE-DIMAT-MXFP4_MOE3.gguf : Output tensor IQ4_NL (DI Imatrix applied)
+- OpenAI-20B-NEO-CODE-DIMAT-MXFP4_MOE4.gguf : Output tensor IQ4_NL (DI Imatrix applied) AND Embed at IQ4_NL - this makes this quant the smallest version.
 
 MXFP4_MOE quants vastly outperform (at the moment) all other quants, except IQ4_NL, Q5_1 and Q8_0 due to odd
 issues compressing OpenAI's 20B model due to odd "tensor" dimensions.