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language:
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Quantizations for [KoboldAI/LLaMA2-13B-Tiefighter](https://huggingface.co/KoboldAI/LLaMA2-13B-Tiefighter) in the [EXL2 format](https://github.com/turboderp/exllamav2)

Quant|VRAM estimate|Additional
---|---|---
[4k_hb8_b8](https://huggingface.co/Beinsezii/Tiefighter-L2-13B-EXL2/tree/4k_hb8_b8)|18GB|Recommended!
[4k_hb6_b6](https://huggingface.co/Beinsezii/Tiefighter-L2-13B-EXL2/tree/4k_hb6_b6)|15GB|
[4k_hb6_b5](https://huggingface.co/Beinsezii/Tiefighter-L2-13B-EXL2/tree/4k_hb6_b5)|13GB|Should fit in 12GB cards with 2k context


Breaking down the names:
 - **4k** is calibrated with 4096 context @ 82 rows (maximum for wikitext) as opposed to the default 2048 context @ 100 rows.
 - **hb8** is a header depth of 8 bits
 - **b8** is a model weight average of 8.0 bits

All quantizations were calibrated with [wikitext-2](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test)

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.

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.

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.
