---
language:
- en
---

# Input files for generating the Importance Matrix


## Which file to use for generating the importance matrix

Not all importance matrices are equal. The best results are obtained when using a source file similar to the
training data. Size also matters: the bigger the model (eg: 70b vs 13b) and the higher the quant (eg: q6k_ vs iq3_xs),
the bigger the source file needs to be to make an impact. Multiple input files can be combined if needed; 
for example:
```
cat technical.txt multilingual.txt wiki.txt >custom.txt
```

You will find below descriptions for the various input files provided, to help you choose the correct one.

## Community provided files

### 8k_random_data

### 20k_random_data

### groups_merged

### group_10_merged

### ptb.train

## exllamav2 calibration data

https://github.com/turboderp/exllamav2/tree/master/conversion/standard_cal_data

### c4

### code

Programming

### multilingual

English, Arabic, Chinese, French, German, Japanese, Polish, Russian, Spanish, Swedish, Turkish, Hebrew,
Macedonian, Norwegian, Lithuanian, Greek, Italian, Afrikaans, Dutch, Danish. 

### technical

Technical writing.

### tiny

Very short stories.

### wiki

Wikipedia dump.

## 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.