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- ---
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- license: apache-2.0
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- ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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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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+
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+ ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64adfd277b5ff762771e4571/AEWJsybnM6wILRxgwlmrU.png)
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+
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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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+
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+ # Quants!
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+ [full](https://huggingface.co/Epiculous/Crimson_Dawn-v0.2) / [exl2](https://huggingface.co/Epiculous/Crimson_Dawn-v0.2-exl2) / <strong>gguf</strong>
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+
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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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+
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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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+
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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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+
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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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+
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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)