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README.md
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---
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license: apache-2.0
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---
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license: apache-2.0
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datasets:
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- Epiculous/SynthRP-Gens-v1-Filtered-n-Cleaned
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- Epiculous/Synthstruct-Gens-v1-Filtered-n-Cleaned
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language:
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- en
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- fr
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- de
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- es
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- it
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- pt
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- ru
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- zh
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- ja
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pipeline_tag: text-generation
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tags:
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- merge
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---
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![image/png](https://cdn-uploads.huggingface.co/production/uploads/64adfd277b5ff762771e4571/oTCM28M_Hu6lM4DmOp7tC.png)
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Now for something a bit different, Violet_Twilight! This model is a SLERP merge of Azure_Dusk and Crimson_Dawn!
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# Quants!
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<strong>full</strong> / [exl2](https://huggingface.co/Epiculous/Azure_Dusk-v0.1-Exl2) / [gguf](https://huggingface.co/Epiculous/Azure_Dusk-v0.1-GGUF)
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## Prompting
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Violet_Twilight's models were trained with the Mistral Instruct template, therefore it should be prompted in a similar way that you would prompt any other mistral based model.
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```
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"<s>[INST] Prompt goes here [/INST]<\s>"
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```
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### Context and Instruct
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[Magnum-123B-Context.json](https://files.catbox.moe/rkyqwg.json) <br/>
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[Magnum-123B-Instruct.json](https://files.catbox.moe/obb5oe.json) <br/>
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*** NOTE *** <br/>
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There have been reports of the quantized model misbehaving with the mistral prompt, if you are seeing issues it may be worth trying ChatML Context and Instruct templates.
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If you are using GGUF I strongly advise using ChatML, for some reason that quantization performs better using ChatML.
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### Current Top Sampler Settings
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[Crimson_Dawn-Nitral-Special](https://files.catbox.moe/8xjxht.json) - Considered the best settings! <br/>
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[Crimson_Dawn-Magnum-Style](https://files.catbox.moe/lc59dn.json)
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## Merging
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The following config was used to merge Azure Dusk and Crimson Dawn
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```yaml
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slices:
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- sources:
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- model: Epiculous/Azure_Dusk-v0.1
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layer_range: [0, 40]
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- model: Epiculous/Crimson_Dawn-V0.1
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layer_range: [0, 40]
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merge_method: slerp
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base_model: Epiculous/Azure_Dusk-v0.1
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parameters:
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t:
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- filter: self_attn
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value: [0, 0.5, 0.3, 0.7, 1]
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- filter: mlp
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value: [1, 0.5, 0.7, 0.3, 0]
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- value: 0.5 # fallback for rest of tensors
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dtype: bfloat16
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```
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