This tool takes a GGUF input model file, typically in a high-precision format like F32 or BF16, and converts it to a quantized format.
Quantization reduces the precision of model weights (e.g., from 32-bit floats to 4-bit integers), which shrinks the model's size and can speed up inference.
This process however, may introduce some accuracy loss which is usually measured in [Perplexity](https://huggingface.co/docs/transformers/en/perplexity) (ppl) and/or [Kullback–Leibler Divergence](https://en.wikipedia.org/wiki/Kullback%E2%80%93Leibler_divergence) (kld).
This can be minimized by using a suitable imatrix file.
You can also use the [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space on Hugging Face to build your own quants without any setup.
Note: It is synced from llama.cpp `main` every 6 hours.
*`--allow-requantize` allows requantizing tensors that have already been quantized. Warning: This can severely reduce quality compared to quantizing from 16bit or 32bit
*`--leave-output-tensor` will leave output.weight un(re)quantized. Increases model size but may also increase quality, especially when requantizing
*`--pure` disables k-quant mixtures and quantizes all tensors to the same type
*`--imatrix` uses data in file generated by `llama-imatrix` as importance matrix for quant optimizations (highly recommended)
*`--include-weights` use an importance matrix for tensor(s) in the list. Cannot be used with `--exclude-weights`
*`--exclude-weights` use an importance matrix for tensor(s) in the list. Cannot be used with `--include-weights`
*`--output-tensor-type` use a specific quant type for the output.weight tensor
*`--token-embedding-type` use a specific quant type for the token embeddings tensor
*`--keep-split` will generate the quantized model in the same shards as the input file otherwise it will produce a single quantized file
Advanced options:
*`--tensor-type` quantize specific tensor(s) to specific quant types. Supports regex syntax. May be specified multiple times.
*`--prune-layers` prune (remove) the layers in the list
*`--override-kv` option to override model metadata by key in the quantized model. May be specified multiple times
Examples:
```bash
# naive Q4_K_M quantization using default settings and 8 CPU threads. Output will be "ggml-model-Q4_K_M.gguf"
./llama-quantize input-model-f32.gguf q4_k_m 8
```
```bash
# quantize model enabling re-quantization, leaving the output tensor unquantized and all others quantized at the same level (Q4_K)
# override expert used count metadata to 16, prune layers 20, 21, and 22 without quantizing the model (copy tensors) and use specified name for the output file
When running the larger models, make sure you have enough disk space to store all the intermediate files.
As the models are currently fully loaded into memory, you will need adequate disk space to save them and sufficient RAM to load them. At the moment, memory and disk requirements are the same. For exmaple (Llama 3.1):
| Model | Original size | Quantized size (Q4_K_M) |
