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README.md

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+
+---
+
+license: apache-2.0
+datasets:
+- Epiculous/SynthRP-Gens-v1-Filtered-n-Cleaned
+- Epiculous/Synthstruct-Gens-v1-Filtered-n-Cleaned
+language:
+- en
+- fr
+- de
+- es
+- it
+- pt
+- ru
+- zh
+- ja
+pipeline_tag: text-generation
+
+---
+
+![](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)
+
+# QuantFactory/Azure_Dusk-v0.1-GGUF
+This is quantized version of [Epiculous/Azure_Dusk-v0.1](https://huggingface.co/Epiculous/Azure_Dusk-v0.1) created using llama.cpp
+
+# Original Model Card
+
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64adfd277b5ff762771e4571/l0b889iXmZy-dz-Yg8e1i.png)
+
+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.
+
+# Quants!
+<strong>full</strong> / [exl2](https://huggingface.co/Epiculous/Azure_Dusk-v0.1-Exl2) / [gguf](https://huggingface.co/Epiculous/Azure_Dusk-v0.1-GGUF)
+
+## Prompting
+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.
+
+```
+"<s>[INST] Prompt goes here [/INST]<\s>"
+```
+### Context and Instruct
+[Magnum-123B-Context.json](https://files.catbox.moe/rkyqwg.json) <br/>
+[Magnum-123B-Instruct.json](https://files.catbox.moe/obb5oe.json) <br/>
+*** NOTE *** <br/>
+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.
+If you are using GGUF I strongly advise using ChatML, for some reason that quantization performs better using ChatML.
+### Current Top Sampler Settings
+[Violet_Twilight-Nitral-Special](https://files.catbox.moe/ot54u3.json)- Considered the best settings! <br/>
+[Crimson_Dawn-Nitral-Special](https://files.catbox.moe/8xjxht.json) <br/>
+[Crimson_Dawn-Magnum-Style](https://files.catbox.moe/lc59dn.json) 
+
+### Tokenizer
+If you are using SillyTavern, please set the tokenizer to API (WebUI/ koboldcpp)
+
+## Training
+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.
+
+[<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)
+
+## Special Thanks
+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.
+Also want to thank my friends in The Chaotic Neutrals for their friendship, support, and guidance.