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Nondzu/zephyr-7b-beta-pl-exl2
zephyr-7b-beta-pl-exl2 is a machine learning model from Nondzu. Use it for the machine learning task on the model card, and read the license before you ship it in a product. The card lists the license as mit.
Quantized version exllamav2 https://github.com/turboderp/exllamav2/
Downloads · 30 days
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.json3.3 MB · 100%
From the Hugging Face model README
Quantized version exllamav2 https://github.com/turboderp/exllamav2/
Original model: https://huggingface.co/Nondzu/zephyr-7b-beta-pl
<a href="https://huggingface.co/Nondzu/zephyr-7b-beta-pl-exl2/tree/2.5">2.5 bits per weight</a>
<a href="https://huggingface.co/Nondzu/zephyr-7b-beta-pl-exl2/tree/3.0">3.0 bits per weight</a>
<a href="https://huggingface.co/Nondzu/zephyr-7b-beta-pl-exl2/tree/3.5">3.5 bits per weight</a>
<a href="https://huggingface.co/Nondzu/zephyr-7b-beta-pl-exl2/tree/4.0">4.0 bits per weight</a>
<a href="https://huggingface.co/Nondzu/zephyr-7b-beta-pl-exl2/tree/5.0">5.0 bits per weight</a>
<a href="https://huggingface.co/Nondzu/zephyr-7b-beta-pl-exl2/tree/6.0">6.0 bits per weight</a>
<a href="https://huggingface.co/Nondzu/zephyr-7b-beta-pl-exl2/tree/7.0">7.0 bits per weight</a>
<a href="https://huggingface.co/Nondzu/zephyr-7b-beta-pl-exl2/tree/8.0">8.0 bits per weight</a>
With git:
git clone --single-branch --branch 4.0 https://huggingface.co/Nondzu/zephyr-7b-beta-pl-exl2
With huggingface hub (credit to TheBloke for instructions):
pip3 install huggingface-hub
To download the main (only useful if you only care about measurement.json) branch to a folder called zephyr-7b-beta-pl-exl2:
mkdir zephyr-7b-beta-pl-exl2
huggingface-cli download Nondzu/zephyr-7b-beta-pl-exl2 --local-dir zephyr-7b-beta-pl-exl2 --local-dir-use-symlinks False
To download from a different branch, add the --revision parameter:
mkdir zephyr-7b-beta-pl-exl2
huggingface-cli download Nondzu/zephyr-7b-beta-pl-exl2 --revision 8.0 --local-dir zephyr-7b-beta-pl-exl2 --local-dir-use-symlinks False
I trained the model using 3xRTX 3090 for 163 hours.

As this is an experimental model, users should be aware of the following:
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:

User feedback is crucial during this testing phase. We encourage users to provide feedback on model performance, issues encountered, and any suggestions for improvements. Contributions in terms of shared test results, datasets, or code improvements are also welcome.
Disclaimer: This experimental model is provided 'as is', without warranty of any kind. Users should use the model at their own risk. The creators or maintainers of the model are not responsible for any consequences arising from its use.
