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Model: Epiculous/Azure_Dusk-v0.1-GGUF Source: Original Platform
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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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---
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Flipping the training process that created Crimson Dawn on it's head, I present to you, Azure Dusk! While both models are built using [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407); Azure Dusk's training methodology was instruct first, then RP dataset applied after, however, the end goal reamains the same AI should not be a boring bland generic assistant, but something that you can connect with on a more personal level. Something that can be interesting in a Roleplay, but useful as an assistant too.
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# Quants!
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[full](https://huggingface.co/Epiculous/Azure_Dusk-v0.1) / [exl2](https://huggingface.co/Epiculous/Azure_Dusk-v0.1-Exl2) / <strong>gguf</strong>
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## Prompting
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Azure Dusk was 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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[Violet_Twilight-Nitral-Special](https://files.catbox.moe/ot54u3.json)- Considered the best settings! <br/>
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[Crimson_Dawn-Nitral-Special](https://files.catbox.moe/8xjxht.json) <br/>
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[Crimson_Dawn-Magnum-Style](https://files.catbox.moe/lc59dn.json)
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### Tokenizer
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If you are using SillyTavern, please set the tokenizer to API (WebUI/ koboldcpp)
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## Training
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Training was done twice over 2 epochs each on two 2x [NVIDIA A6000 GPUs](https://www.nvidia.com/en-us/design-visualization/rtx-a6000/) using LoRA. A two-phased approach was used in which the base model was trained 2 epochs on Instruct data, the LoRA was then applied to base. Finally, the new modified base was trained 2 epochs on RP, and the new RP LoRA was applied to the modified base, resulting in what you see here.
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[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
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## Special Thanks
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Special thanks to my friends over at Anthracite! Without their help and Kalomaze starting the synthetic data script, none of this would have been possible.
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Also want to thank my friends in The Chaotic Neutrals for their friendship, support, and guidance.
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