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Exllama v2 Quantizations of dolphin-2.6-mistral-7b-dpo-laser

Using turboderp's ExLlamaV2 v0.0.11 for quantization.

Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.

Original model: https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo-laser/

Model Size: 7b

Branch Bits lm_head bits Dataset Size Description
8_0 8.0 8.0 Default 9.8 GB Maximum quality that ExLlamaV2 can produce, near unquantized performance.
6_5 6.5 8.0 Default 8.6 GB Very similar to 8.0, good tradeoff of size vs performance, recommended.
5_0 5.0 6.0 Default 7.4 GB Slightly lower perplexity vs 6.5.
4_0 4.0 6.0 Default 6.5 GB Just under GPTQ equivalent bits per weight.
3_5 3.5 6.0 Default 6.1 GB Lower quality, only use if you have to.

All VRAM requirements estimated from 16k context. For 32k context add ~2 GB.

Download instructions

With git:

git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/dolphin-2.6-mistral-7b-dpo-laser-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 dolphin-2.6-mistral-7b-dpo-laser-exl2:

mkdir dolphin-2.6-mistral-7b-dpo-laser-exl2
huggingface-cli download bartowski/dolphin-2.6-mistral-7b-dpo-laser-exl2 --local-dir dolphin-2.6-mistral-7b-dpo-laser-exl2 --local-dir-use-symlinks False

To download from a different branch, add the --revision parameter:

mkdir dolphin-2.6-mistral-7b-dpo-laser-exl2-6_5
huggingface-cli download bartowski/dolphin-2.6-mistral-7b-dpo-laser-exl2 --revision 6_5 --local-dir dolphin-2.6-mistral-7b-dpo-laser-exl2-6_5 --local-dir-use-symlinks False
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Datasets used to train bartowski/dolphin-2.6-mistral-7b-dpo-laser-exl2