---
language:
- en
---
## Information

This is a Exl2 quantized version of [MN-LooseCannon-12B-v1](https://huggingface.co/GalrionSoftworks/MN-LooseCannon-12B-v1)

Please refer to the original creator for more information.

Calibration dataset: Exl2 default

## Branches:

- main: Measurement files
- 4bpw: 4 bits per weight
- 5bpw: 5 bits per weight
- 6bpw: 6 bits per weight

## Notes

- 6bpw is recommended for the best quality to vram usage ratio (assuming you have enough vram).
- Quants greater than 6bpw will not be created because there is no improvement in using them. If you really want them, ask someone else or make them yourself.


## Download


With [async-hf-downloader](https://github.com/theroyallab/async-hf-downloader): A lightweight and asynchronous huggingface downloader created by me


```shell
./async-hf-downloader royallab/MN-LooseCannon-12B-v1-exl2 -r 6bpw -p MN-LooseCannon-12B-v1-exl2-6bpw
```


With HuggingFace hub (`pip install huggingface_hub`)

```shell
huggingface-cli download royallab/MN-LooseCannon-12B-v1-exl2 --revision 6bpw --local-dir MN-LooseCannon-12B-v1-exl2-6bpw
```

## Run in TabbyAPI

TabbyAPI is a pure exllamav2 FastAPI server developed by us. You can find TabbyAPI's source code here: [https://github.com/theroyallab/TabbyAPI](https://github.com/theroyallab/TabbyAPI)

1. Inside TabbyAPI's config.yml, set `model_name` to `MN-LooseCannon-12B-v1-exl2-6bpw`
   
   1. You can also use an argument `--model_name MN-LooseCannon-12B-v1-exl2-6bpw` on startup or you can use the `/v1/model/load` endpoint

2. Launch TabbyAPI inside your python env by running `./start.bat` or `./start.sh`


## Donate?

All my infrastructure and cloud expenses are paid out of pocket. If you'd like to donate, you can do so here: https://ko-fi.com/kingbri

You should not feel obligated to donate, but if you do, I'd appreciate it.
---