> ## 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.

# Model formats and conversion

> GGUF format, model splits, and HuggingFace conversion

## GGUF format

ik\_llama.cpp uses the **GGUF** (GPT-Generated Unified Format) binary format. Every model file stores tensor data alongside metadata such as the architecture, tokenizer, and quantization type.

Key metadata fields logged at startup include tensor types (`f32`, `q6_K`, etc.) and KV cache sizes. Check these when diagnosing memory or quality issues.

## Converting from HuggingFace

Convert a HuggingFace model to GGUF with the bundled `convert_hf_to_gguf.py` script:

```bash theme={null}
python3 convert_hf_to_gguf.py /path/to/hf-model --outfile model-bf16.gguf
```

The script supports legacy quant conversion schemes. Pass `--help` for the full option list.

## Inspecting a GGUF file

Use `gguf_dump.py` to view all tensor names, shapes, and metadata:

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

You can also open a GGUF file directly in a browser on HuggingFace — scroll to the Tensors table to inspect layer counts and shapes without downloading the file.

## Splitting large models

Split an oversized GGUF into parts for easier storage or upload:

```bash theme={null}
llama-gguf-split --split --split-max-size 1G --no-tensor-first-split \
  /models/model.gguf /models/parts/model.gguf
```

When loading a split model, pass only the **first part** to `--model`. ik\_llama.cpp discovers the remaining parts automatically.

## Checking imatrix metadata

An importance matrix (imatrix) calibrates quantization to reduce perceptual loss. To verify whether a GGUF was quantized with an imatrix, inspect its metadata:

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

Look for `quantize.imatrix.*` fields. Their presence indicates the file was built with imatrix data. For quantization types below `Q6_0`, imatrix use is strongly recommended.

<Note>
  To convert a GGUF imatrix file to the older `.dat` format expected by some
  tools, use `convert_imatrix_gguf_to_dat.py`.
</Note>