SorcererLM-8x22b-iMat-GGUF

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PROUDLY PRESENTS         

SorcererLM-8x22b-iMat-GGUF

Quantized with love from fp16 using the alpha=32 version.

Original model author: rAIfle

• Importance Matrix calculated using groups_merged.txt in 105 chunks, n_ctx=512, and fp16 precision weights

Original model README here and below:

SorcererLM-8x22b-bf16

<img src="https://files.catbox.moe/1kohx8.png" width="400"/>

<audio controls src="https://cdn-uploads.huggingface.co/production/uploads/6569a4ed2419be6072890cf8/L_uGojVkNUsK6QHvWgs9o.mpga"></audio>

Oh boy, here we go. Low-rank (r=16, alpha=32) 16bit-LoRA on top of WizardLM-2-8x22B, trained on 2 epochs of (cleaned & deduped) c2-logs. As far as I can tell, this is an upgrade from WizardLM-2-8x22B for RP purposes.

Alongside this ready-to-use release I'm also releasing the LoRA itself as well as the earlier epoch1-checkpoint of the LoRA.

Why A LoRA?

The choice was fully intentional. I briefly considered a FFT but for this particular use-case a LoRA seemed a better fit. WizardLM-2-8x22B is smart by itself but its used vocabulary leaves much to be desired when it comes to RP. By training a low-rank LoRA on top of it to teach it some of Claude's writing style, we remedy that.

Prompting

• Use the templates in Quant-Cartel/Recommended-Settings under the SorcererLM-folder.
• Or Vicuna 1.1 and a sane context template. It's somewhat sensitive to samplers, I'd recommend Temperature 1, MinP 0.05 and a dash of DRY but YMMV. Shorter prompts seem to work better, too.

Quantized Versions

• iMat GGUFs
• longcal exl2s

Acknowledgments

The main shoutout I want to make is to my Cartel bros, Envoid and particularly I^2, for being amazing. I count this as a team effort, so they deserve kudos too if you like this.

Training

Trained using qlora-pipe. Configs included in the train-subfolder.

Safety

... n/a