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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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