--- 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 tags: - merge --- ![image/png](https://cdn-uploads.huggingface.co/production/uploads/64adfd277b5ff762771e4571/oTCM28M_Hu6lM4DmOp7tC.png) Now for something a bit different, Violet_Twilight! This model is a SLERP merge of Azure_Dusk and Crimson_Dawn! # Quants! full / [exl2](https://huggingface.co/Epiculous/Violet_Twilight-v0.1-Exl2) / [gguf](https://huggingface.co/Epiculous/Violet_Twilight-v0.1-GGUF) ## Prompting Violet_Twilight's models were trained with the Mistral Instruct template, therefore it should be prompted in a similar way that you would prompt any other mistral based model. ``` "[INST] Prompt goes here [/INST]<\s>" ``` ### Context and Instruct [Magnum-123B-Context.json](https://files.catbox.moe/rkyqwg.json)
[Magnum-123B-Instruct.json](https://files.catbox.moe/obb5oe.json)
*** NOTE ***
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 [Crimson_Dawn-Nitral-Special](https://files.catbox.moe/8xjxht.json) - Considered the best settings!
[Crimson_Dawn-Magnum-Style](https://files.catbox.moe/lc59dn.json) ## Merging The following config was used to merge Azure Dusk and Crimson Dawn ```yaml slices: - sources: - model: Epiculous/Azure_Dusk-v0.1 layer_range: [0, 40] - model: Epiculous/Crimson_Dawn-V0.1 layer_range: [0, 40] merge_method: slerp base_model: Epiculous/Azure_Dusk-v0.1 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 # fallback for rest of tensors dtype: bfloat16 ```