> ## Documentation Index
> Fetch the complete documentation index at: https://mintlify.com/ikawrakow/ik_llama.cpp/llms.txt
> Use this file to discover all available pages before exploring further.

# Importance matrix (imatrix)

> Generate and use importance matrices to improve quantization quality

An importance matrix (imatrix) is calibration data derived by running a model over a representative text corpus. It records which weights matter most for accurate predictions, and the quantizer uses this information to allocate precision where it reduces the most loss.

imatrix is supported for all quant types **except bitnet**. Its effect is most significant at lower bit levels — for quants below `Q6_0`, using an imatrix is strongly recommended.

## Generating an imatrix

<Steps>
  <Step title="Prepare a calibration dataset">
    You need a plain-text file containing representative text. A common choice is a deduplicated sample from the same domain the model will be used on.
  </Step>

  <Step title="Run llama-imatrix">
    ```bash theme={null}
    llama-imatrix \
      -m model-bf16.gguf \
      -f calibration_data.txt \
      -o model.imatrix
    ```

    Run from a BF16 or F16 source model for the most accurate importance values.
  </Step>
</Steps>

## Options

### --layer-similarity

Collect cosine-similarity statistics that measure how much each layer changes its activations:

```bash theme={null}
llama-imatrix \
  -m model-bf16.gguf \
  -f calibration_data.txt \
  -o model.imatrix \
  --layer-similarity
```

This produces additional diagnostic output useful for understanding which layers are most sensitive to quantization.

### --hide-imatrix

Obscure the imatrix provenance in the output file. When this flag is set, `llama-imatrix` stores `top_secret` in the data file name and calibration dataset fields, and writes zeros for the batch size and number of chunks:

```bash theme={null}
llama-imatrix \
  -m model-bf16.gguf \
  -f calibration_data.txt \
  -o model.imatrix \
  --hide-imatrix
```

## Using an imatrix when quantizing

Pass `--imatrix` to `llama-quantize`:

```bash theme={null}
llama-quantize \
  --imatrix model.imatrix \
  model-bf16.gguf \
  output.gguf \
  IQ4_KS
```

Always include `--imatrix` for best quality results. Omitting it on quants below `Q6_0` will noticeably degrade output quality.

## Converting GGUF imatrix files

Some imatrix files are distributed in the newer GGUF format rather than the `.dat` format used by ik\_llama.cpp. Convert them with the included script:

```bash theme={null}
python3 convert_imatrix_gguf_to_dat.py
```

## Verifying imatrix usage

To confirm that a downloaded GGUF was quantized with an imatrix, inspect its metadata. Look for fields with the `quantize.imatrix.*` prefix — their presence confirms an imatrix was applied during quantization.

You can view metadata with `gguf_dump.py`:

```bash theme={null}
python3 gguf-py/scripts/gguf_dump.py model.gguf
```

<Tip>
  When loading a model, the server logs show the quant types per tensor. Combined with the imatrix metadata check, this confirms both what was quantized and how.
</Tip>