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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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Back from the dead! Hoping to make something cool to share with everyone! Introducing Crimson Dawn! Built atop the impressive [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407); Crimson Dawn was built with the idea that 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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[exl2](https://huggingface.co/lucyknada/Epiculous_Crimson_Dawn-V0.1-exl2) / [gguf](https://huggingface.co/mradermacher/Crimson_Dawn-V0.1-GGUF) |
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## Prompting |
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Crimson Dawn 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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If you are using GGUF I strongly advise using ChatML, for some reason that quantization performs better using ChatML. |
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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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~~[Mistral-Custom-Context.json](https://files.catbox.moe/l9w0ry.json)~~<br/> |
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~~[Mistral-Custom-Instruct.json](https://files.catbox.moe/9xiiwb.json)~~ <br/> |
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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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### 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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## 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 RP data, the LoRA was then applied to base. Finally, the new modified base was trained 2 epochs on instruct, and the new instruct 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. |
