README.md

---
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](https://huggingface.co/Epiculous/Violet_Twilight-v0.1) / <strong>exl2</strong> / [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.

```
"<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)

## 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

```