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README.md
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---
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license: apache-2.0
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datasets:
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- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
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- anthracite-org/stheno-filtered-v1.1
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- PJMixers/hieunguyenminh_roleplay-deduped-ShareGPT
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- Gryphe/Sonnet3.5-Charcard-Roleplay
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- Epiculous/Synthstruct-Gens-v1.1-Filtered-n-Cleaned
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- anthracite-org/kalo-opus-instruct-22k-no-refusal
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- anthracite-org/nopm_claude_writing_fixed
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- anthracite-org/kalo_opus_misc_240827
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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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# QuantFactory/Crimson_Dawn-v0.2-GGUF
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This is quantized version of [Epiculous/Crimson_Dawn-v0.2](https://huggingface.co/Epiculous/Crimson_Dawn-v0.2) created using llama.cpp
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# Original Model Card
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Taking what seemed to work out well with Crimson_Dawn-v0.1, the new Crimson_Dawn-v0.2 is the same training methodology, training on [Mistral-Nemo-Base-2407](https://huggingface.co/mistralai/Mistral-Nemo-Base-2407) this time I've added significantly more data, as well as trained using RSLoRA as opposed to regular LoRA. Another key change is training on ChatML as opposed to Mistral Formatting.
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# Quants!
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<strong>full</strong> / [exl2](https://huggingface.co/Epiculous/Crimson_Dawn-v0.2-exl2) / [gguf](https://huggingface.co/Epiculous/Crimson_Dawn-v0.2-GGUF)
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## Prompting
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The v0.2 models are trained on ChatML, the prompting structure goes a little something like this:
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```
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<|im_start|>user
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Hi there!<|im_end|>
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<|im_start|>assistant
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Nice to meet you!<|im_end|>
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<|im_start|>user
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Can I ask a question?<|im_end|>
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<|im_start|>assistant
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```
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### Context and Instruct
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The v0.2 models are trained on ChatML, please use that Context and Instruct template.
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### Current Top Sampler Settings
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[Spicy_Temp](https://files.catbox.moe/9npj0z.json) <br/>
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[Violet_Twilight-Nitral-Special](https://files.catbox.moe/ot54u3.json) <br/>
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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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