Create README.md

README.md

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+---
+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.
+---