README.md

---
license: cc-by-nc-4.0
inference: false
pipeline_tag: text-generation
tags:
- gguf
- quantized
- text-generation-inference
---

> [!TIP]
> **Credits:** <br>
> Made with love by [**@Lewdiculous**](https://huggingface.co/Lewdiculous). <br>
> *If this proves useful for you, feel free to credit and share the repository and authors.*

> [!WARNING]
> **Warning:** <br>
> Not intended to handle Llama at the moment.

Pull Requests with your own features and improvements to this script are always welcome.

# GGUF-IQ-Imatrix-Quantization-Script:

![image/png](https://cdn-uploads.huggingface.co/production/uploads/65ddabb9bbffb280f4b45d8e/vwlPdqxrSdILCHM24n_M2.png)

Simple python script (`gguf-imat.py`) to generate various GGUF-IQ-Imatrix quantizations from a Hugging Face `author/model` input, for Windows and NVIDIA hardware.

This is setup for a Windows machine with 8GB of VRAM, assuming use with an NVIDIA GPU. If you want to change the `-ngl` (number of GPU layers) amount, you can do so at [**line 135**](https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script/blob/main/gguf-imat.py#L135). This is only relevant during the `--imatrix` data generation. If you don't have enough VRAM you can decrease the `-ngl` amount or set it to 0 to only use your System RAM instead for all layers, this will make the imatrix data generation take longer, so it's a good idea to find the number that gives your own machine the best results.

Your `imatrix.txt` is expected to be located inside the `imatrix` folder. I have already included a file that is considered a good starting option, [this discussion](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) is where it came from. If you have suggestions or other imatrix data to recommend, please do so.

Adjust `quantization_options` in [**line 153**](https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script/blob/main/gguf-imat.py#L153).

> [!NOTE]  
> Models downloaded to be used for quantization are cached at `C:\Users\{{User}}\.cache\huggingface\hub`. You can delete these files manually as needed after you're done with your quantizations, you can do it directly from your Terminal if you prefer with the `rmdir "C:\Users\{{User}}\.cache\huggingface\hub"` command. You can put it into another script or alias it to a convenient command if you prefer. 


**Hardware:**

- NVIDIA GPU with 8GB of VRAM.
- 32GB of system RAM.

**Software Requirements:**
- Git
- Python 3.11
  - `pip install huggingface_hub`
 
**Usage:**
```
python .\gguf-imat.py 
```
Quantizations will be output into the created `models\{model-name}-GGUF` folder.
<br><br>