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language: |
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- en |
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Quantizations for [PygmalionAI/mythalion-13b](https://huggingface.co/PygmalionAI/mythalion-13b) in the [EXL2 format](https://github.com/turboderp/exllamav2) |
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[4k_hb8_b8](https://huggingface.co/Beinsezii/Mythalion-13b-EXL2/tree/4k_hb8_b8)|18GB|Recommended! |
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[4k_hb6_b6](https://huggingface.co/Beinsezii/Mythalion-13b-EXL2/tree/4k_hb6_b6)|15GB| |
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[4k_hb6_b5](https://huggingface.co/Beinsezii/Mythalion-13b-EXL2/tree/4k_hb6_b5)|13GB|Should fit in 12GB cards with 2k context |
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Breaking down the names: |
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- **4k** is calibrated with 4096 context @ 82 rows (maximum for wikitext) as opposed to the default 2048 context @ 100 rows. |
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- **hb8** is a header depth of 8 bits |
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- **b8** is a model weight average of 8.0 bits |
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All quantizations were calibrated with [wikitext-2](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) |
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You can run a model calibrated at 2k with a 4k context or vice versa. The actual difference between 2k and 4k calibrations appears to be very small. |
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VRAM estimates are performed with an extremely long chatlog in [oobabooga webui](https://github.com/oobabooga/text-generation-webui) on a 7900 XTX using [nvtop](https://github.com/Syllo/nvtop) to monitor **pytorch usage only**, rounded up. Systems with lots of extra background processes may use more. Additionally, NVIDIA based systems with [flash attention 2](https://github.com/Dao-AILab/flash-attention) **will use less VRAM** than otherwise estimated. |
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The measurement files are provided in the main branch so you can [make your own quants](https://github.com/turboderp/exllamav2/blob/master/doc/convert.md) at other bit depths without going through the 2-3 hours of measuring. |
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