---
license: apache-2.0
base_model_relation: quantized
quantized_by: Quant-Cartel
base_model: rAIfle/SorcererLM-8x22b-bf16
pipeline_tag: text-generation
tags:
- chat
- 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](https://huggingface.co/rAIfle/)
* Importance Matrix calculated using [groups_merged.txt](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384) in 105 chunks, n_ctx=512, and fp16 precision weights
Original model README [here](https://huggingface.co/rAIfle/SorcererLM-8x22b-bf16) and below:
# SorcererLM-8x22b-bf16
Oh boy, here we go. Low-rank (`r=16, alpha=32`) 16bit-LoRA on top of [WizardLM-2-8x22B](https://huggingface.co/alpindale/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](https://huggingface.co/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](https://huggingface.co/Quant-Cartel/SorcererLM-8x22b-iMat-GGUF)
- [longcal exl2s](https://huggingface.co/Quant-Cartel/SorcererLM-8x22b-exl2-longcal)
## Acknowledgments
The main shoutout I want to make is to my [Cartel](https://huggingface.co/Quant-Cartel) bros, [Envoid](https://huggingface.co/Envoid) and particularly [I^2](https://huggingface.co/InferenceIllusionist), for being amazing. I count this as a team effort, so they deserve kudos too if you like this.
## Training
Trained using [qlora-pipe](https://github.com/tdrussell/qlora-pipe). Configs included in the `train`-subfolder.
## Safety
... n/a
Oh boy, here we go. Low-rank (`r=16, alpha=32`) 16bit-LoRA on top of [WizardLM-2-8x22B](https://huggingface.co/alpindale/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](https://huggingface.co/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](https://huggingface.co/Quant-Cartel/SorcererLM-8x22b-iMat-GGUF)
- [longcal exl2s](https://huggingface.co/Quant-Cartel/SorcererLM-8x22b-exl2-longcal)
## Acknowledgments
The main shoutout I want to make is to my [Cartel](https://huggingface.co/Quant-Cartel) bros, [Envoid](https://huggingface.co/Envoid) and particularly [I^2](https://huggingface.co/InferenceIllusionist), for being amazing. I count this as a team effort, so they deserve kudos too if you like this.
## Training
Trained using [qlora-pipe](https://github.com/tdrussell/qlora-pipe). Configs included in the `train`-subfolder.
## Safety
... n/a