Create README.md

README.md

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+---
+language:
+- en
+---
+
+# Input files for generating the Importance Matrix
+
+## How to quantize with an imatrix in llama.cpp
+
+1. Get one of the input files collected here, or eleswhere.
+2. Convert or download the model you want to quantise, in fp16 GGUF format.
+3. Generate an imatrix file specific to the model you want to quantise
+```
+cd <llama.cpp directory>
+./imatrix -m <model_path>/ggml-model-f16.gguf -f <matrix_training_path>/<plain_text_matrix_file> -o <output_binary_file.matrix> -t 12 -ngl 144 --chunks 100 -b 512 -c 512
+
+# -ngl    : layers offloaded to gpu (recommended to use number of layers the model contains)
+# -t 12   : number of threads (should probably match no of cpu)
+# -c 512  : context size, testing seems to show 512 is recommended (default=512, 0=loaded from model)
+# -b 200  : batch size (default=512)
+# --chunks 100 (recommended)
+# --mlock : keep model in ram (only use if you had sufficient RAM for the whole fp16)
+```
+4. Use the generated binary matrix file to quantise the model
+```
+./quantize <model_path>/ggml-model-f16.gguf -matrix <matrix_file> <output_model_path>/ggml-model-IQ4_XS.gguf IQ4_XS
+```
+Note: normal quantisation also benefits from using a matrix file. It also seem that a larger input data is
+better for higher quantisation.