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  This repo contains exl2 (2.55bpw, h6) files of Dawn-70B, a merge I have done with the new [layer shuffle](https://github.com/cg123/mergekit/blob/main/mergekit/scripts/layershuffle.py) method from mergekit.
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  [UtopiaXL](https://huggingface.co/Undi95/UtopiaXL-13B) was a huge success for me, I really liked it, so I took the same path to do this 70B: A good base, some psychologic data, some medical data, a little bit of this, of that, and LimaRP at the end as always.
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  NOTE: This repo contain the file [measurement.json](https://huggingface.co/Undi95/Dawn-v2-70B-2.4bpw-h6-exl2/blob/main/measurement.json) needed to do your own exl2 quant (I use [wikitext](https://huggingface.co/datasets/wikitext/resolve/refs%2Fconvert%2Fparquet/wikitext-2-raw-v1/train/0000.parquet)).
 
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  This repo contains exl2 (2.55bpw, h6) files of Dawn-70B, a merge I have done with the new [layer shuffle](https://github.com/cg123/mergekit/blob/main/mergekit/scripts/layershuffle.py) method from mergekit.
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+ 2.55bpw seems a little hard to make working, but it can be sorted out, just try to find good setting. Other quant available [here](https://huggingface.co/AzureBlack/Dawn-v2-70B-exl2) thanks to [AzureBlack](https://huggingface.co/AzureBlack/), also, a big thanks to [TheBloke](https://huggingface.co/TheBloke) for the compute power that let me test different GPU for the EXL2 quant.
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  [UtopiaXL](https://huggingface.co/Undi95/UtopiaXL-13B) was a huge success for me, I really liked it, so I took the same path to do this 70B: A good base, some psychologic data, some medical data, a little bit of this, of that, and LimaRP at the end as always.
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  NOTE: This repo contain the file [measurement.json](https://huggingface.co/Undi95/Dawn-v2-70B-2.4bpw-h6-exl2/blob/main/measurement.json) needed to do your own exl2 quant (I use [wikitext](https://huggingface.co/datasets/wikitext/resolve/refs%2Fconvert%2Fparquet/wikitext-2-raw-v1/train/0000.parquet)).