Update README.md

README.md

@@ -12,6 +12,38 @@ This requires a CUDA GPU, expect a 50GB model to take around 3-4 hours to quanti
 
 I prefer to llama-quantize to 8_0, then to generate a imatrix from that 8_0, and finally to quantize the 8_0 file to Q2K.
 
+# Lattice Repair
+
+This is NOT yet included in the quantizers automated pipeline but DOES function.
+
+It is invoked by doing the following:
+
+python3 lattice_repair.py \
+        --source BF16GGUF.gguf \
+        --quant QUANTGGUF.gguf \
+        --output OUTPUT.gguf \
+        --force \
+        --thresh 1.0 \
+        --fix-frames \
+        --dc-lambda 5.0 \
+        --ulp-radius 8 \
+        --refit-batch 512 \
+        --catalog ./lr_moved.csv
+      Or wherever you want to point the weight dumps.
+
+Output looks like this:
+
+  blk.11.attn_output.weight [Q2_K] outliers 60829/31457280 moved 26504 (blocks 23737/122880) frame-limited 34410 in 28363 blocks, frames fixed 13822 | rmse 4.2478e-03->4.1842e-03 max|e| 5.834e-01->3.314e-01 mean|DC| 1.380e-03->4.602e-03
+
+Numbers to pay attention to: rmse 4.2478e-03->4.1842e-03          max|e| 5.834e-01->3.314e-01.
+
+RMSE ends up cut a little, Max Error is absolutely shattered and cut in half recovering critical function, at the cost of DC.
+
+DC is my heuristic to assemble the LLM with a initial pass, then Lattice Repair polishes things off.
+
+Q2K and Q3K don't really have a lot of space to move, so the weights are mostly recalculated with fix-frame to bring them closer to the source GGUF.
+
+
 # HPC-Quantize V2
 
 ## Holographic Phase Contraction, Geometry-Preserving Weight Analogs, and Algebraic Coupled Bilinear Factorization for Ultra-Low-Bit (`Q2_K`) LLM Quantization