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

This is a Exl2 quantized version of [Meta-Llama-3-8B-Instruct-abliterated-v3](https://huggingface.co/failspy/Meta-Llama-3-8B-Instruct-abliterated-v3)

Please refer to the original creator for more information.

Calibration dataset: Exllamav2 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).
- Please ask for more bpws in the community tab if necessary.

## 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)

If you don't have huggingface-cli, please run `pip install huggingface_hub`.

To run this model, follow these steps:

1. Make a directory inside your models folder called `L3-8B-Instruct-abliterated-v3-exl2`

2. Open a terminal inside your models folder

3. Run `huggingface-cli download royallab/L3-8B-Instruct-abliterated-v3-exl2 --revision 4bpw --local-dir L3-8B-Instruct-abliterated-v3-exl2`
   
   1. The `--revision` flag corresponds to the branch name on the model repo. Please select the appropriate bpw branch for your system.

4. Inside TabbyAPI's config.yml, set `model_name` to `L3-8B-Instruct-abliterated-v3-exl2` or you can use the `/model/load` endpoint after launching.

5. Launch TabbyAPI inside your python env by running `python main.py`

## 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.
---