transformers pytorch llama text-generation license:other autotrain_compatible text-generation-inference endpoints_compatible region:us
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license: other
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[WizardLM-Uncensored-SuperCOT-StoryTelling-30b](https://huggingface.co/Monero/WizardLM-Uncensored-SuperCOT-StoryTelling-30b) merged with kaiokendev's [33b SuperHOT 8k LoRA](https://huggingface.co/kaiokendev/superhot-30b-8k-no-rlhf-test), quantized at 4 bit.

It was created with GPTQ-for-LLaMA with group size 32 and act order true as parameters, to get the maximum perplexity vs FP16 model. 

I HIGHLY suggest to use exllama, to evade some VRAM issues.

Use compress_pos_emb = 4 for any context up to 8192 context.

If you have 2x24 GB VRAM GPUs cards, to not get Out of Memory errors at 8192 context, use:

gpu_split: 9,21