| |
|
| | --- |
| | |
| | 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 |
| |
|
| | --- |
| | |
| |  |
| |
|
| | # 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 |
| | |
| | |
| |  |
| | |
| | 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. |
| | |
