Create README.md

README.md

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+---
+language:
+- en
+---
+## Information
+
+This is a Exl2 quantized version of [MN-12B-Lyra-v1](https://huggingface.co/Sao10K/MN-12B-Lyra-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-12B-Lyra-v1-exl2 -r 6bpw -p MN-12B-Lyra-v1-exl2-6bpw
+```
+
+
+With HuggingFace hub (`pip install huggingface_hub`)
+
+```shell
+huggingface-cli download royallab/MN-12B-Lyra-v1-exl2 --revision 6bpw --local-dir MN-12B-Lyra-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-12B-Lyra-v1-exl2-6bpw`
+   
+   1. You can also use an argument `--model_name MN-12B-Lyra-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.
+---