Update README.md

README.md

@@ -48,36 +48,36 @@ https://huggingface.co/datasets/wikitext
 or "good articles".\
 https://huggingface.co/datasets/asi/wikitext_fr
 
-## exllamav2 calibration data
+**c4** (exllamav2)\
+Constructed from news articles?
 
-https://github.com/turboderp/exllamav2/tree/master/conversion/standard_cal_data
-
-**c4**\
-
-**code**\
+**code** (exllamav2)\
 Programming
 
-**multilingual**\
+**multilingual** (exllamav2)\
 English, Arabic, Chinese, French, German, Japanese, Polish, Russian, Spanish, Swedish, Turkish, Hebrew,
 Macedonian, Norwegian, Lithuanian, Greek, Italian, Afrikaans, Dutch, Danish. 
 
-**technical**\
+**technical** (exllamav2)\
 Technical writing.
 
-**tiny**\
-Very short stories.
+**tiny** (exllamav2)\
+Very short stories. Be mindful of the prevalence of _"Once upon a time"_ and _"<|end_of_text|>"_.
 
-**wiki**\
-Wikipedia dump.
+**wiki** (exllamav2)\
+Small Wikipedia dump. Unclean, contains many unwanted tags.
+
+exllamav2 calibration data taken from:\
+https://github.com/turboderp/exllamav2/tree/master/conversion/standard_cal_data
 
-## How to quantize with an imatrix in llama.cpp
+## How to quantize using an imatrix, with llama.cpp
 
-1. Get one of the input files collected here, or eleswhere.
+1. Get one of the input files collected here, or elsewhere.
 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
+./imatrix -m <model_path>/ggml-model-f16.gguf -f <plain_text_matrix_file> -o <output.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)
@@ -86,9 +86,9 @@ cd <llama.cpp directory>
 # --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
+4. Use the generated 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
+./quantize --matrix <output.matrix> <model_path>/ggml-model-f16.gguf <quantisation_level, eg:IQ4_XS>
 ```
-Note: normal quantisation also benefits from using a matrix file. It also seem that a larger input data is
+Note: normal quantisation also benefits from using a matrix file. It also seem that a bigger input matrix is
 better for higher quantisation.