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README.md
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license: apache-2.0
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---
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license: apache-2.0
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language:
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- en
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---
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## Information
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This is a Exl2 quantized version of [Magnum-Picaro-0.7-v2-12b](https://huggingface.co/Trappu/Magnum-Picaro-0.7-v2-12b)
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Please refer to the original creator for more information.
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Calibration dataset: Exl2 default
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## Branches:
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- main: Measurement files
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- 4bpw: 4 bits per weight
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- 5bpw: 5 bits per weight
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- 6bpw: 6 bits per weight
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## Notes
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- 6bpw is recommended for the best quality to vram usage ratio (assuming you have enough vram).
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- 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.
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## Download
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With [async-hf-downloader](https://github.com/theroyallab/async-hf-downloader): A lightweight and asynchronous huggingface downloader created by me
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```shell
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./async-hf-downloader royallab/Magnum-Picaro-0.7-v2-12b-exl2 -r 6bpw -p Magnum-Picaro-0.7-v2-12b-exl2-6bpw
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```
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With HuggingFace hub (`pip install huggingface_hub`)
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```shell
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huggingface-cli download royallab/Magnum-Picaro-0.7-v2-12b-exl2 --revision 6bpw --local-dir Magnum-Picaro-0.7-v2-12b-exl2-6bpw
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```
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## Run in TabbyAPI
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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)
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1. Inside TabbyAPI's config.yml, set `model_name` to `Magnum-Picaro-0.7-v2-12b-exl2-6bpw`
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1. You can also use an argument `--model_name Magnum-Picaro-0.7-v2-12b-exl2-6bpw` on startup or you can use the `/v2/model/load` endpoint
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2. Launch TabbyAPI inside your python env by running `./start.bat` or `./start.sh`
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## Donate?
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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
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You should not feel obligated to donate, but if you do, I'd appreciate it.
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