metadata

license: apache-2.0
base_model_relation: quantized
quantized_by: Quant-Cartel
base_model: rAIfle/SorcererLM-8x22b-bf16
pipeline_tag: text-generation
tags:
  - chat

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PROUDLY PRESENTS

SorcererLM-8x22b-exl2-longcal

Quantized using 115 rows of 8192 tokens from the default ExLlamav2-calibration dataset.

Branches:

main -- measurement.json
8b8h -- 8bpw, 8bit lm_head
6b6h -- 6bpw, 6bit lm_head
5b6h -- 5bpw, 6bit lm_head
4.5b6h -- 4.5bpw, 6bit lm_head
4b6h -- 4bpw, 6bit lm_head
3b6h -- 3bpw, 6bit lm_head
2.25b6h -- 2.25bpw, 6bit lm_head

Original model link: rAIfle/SorcererLM-8x22b-bf16

Original model README below.

SorcererLM-8x22b-bf16

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

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