transformers safetensors mistral text-generation merge license:cc-by-nc-4.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: cc-by-nc-4.0
tags:
- merge
---

## original readme below, this is only an exl version of it
original: https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE-GGUF

## original readme below, this is only an exl version of it
original: https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE-GGUF

## original readme below, this is only an exl version of it
original: https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE-GGUF

## original readme below, this is only an exl version of it
original: https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE-GGUF


![image/png](https://huggingface.co/SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE/resolve/main/bruins-maid.png)

<!-- description start -->
## Description

This repository hosts FP16 files for **Loyal-Toppy-Bruins-Maid-7B**, a 7B model aimed at having engaging RP with solid character card adherence and being a smart cookie at the same time.

Its foundation is [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha), notable for its performance in the LMSYS Chatbot Arena, even surpassing GPT-3.5-Turbo-1106. The model incorporates [rwitz/go-bruins-v2](https://huggingface.co/rwitz/go-bruins-v2), a [Q-bert/MetaMath-Cybertron-Starling](https://huggingface.co/Q-bert/MetaMath-Cybertron-Starling) derivative with Alpaca RP data tuning.

The other foundational model is [chargoddard/loyal-piano-m7](https://huggingface.co/chargoddard/loyal-piano-m7), chosen for its strong RP performance and Alpaca format training, with a diverse dataset including PIPPA, rpbuild, and LimaRP.

[Undi95/Toppy-M-7B](https://huggingface.co/Undi95/Toppy-M-7B), known for its creativity, brings in useful RP data from various sources. It ranks first among 7B models on [OpenRouter](https://openrouter.ai/rankings) for a good reason.

[NeverSleep/Noromaid-7b-v0.1.1](https://huggingface.co/NeverSleep/Noromaid-7b-v0.1.1), a Mistral finetune with unique RP data not present in other models, was also added for bringing in a unique RP dataset and being a well-regarded RP model.

The models were merged using the DARE ties method, with a targeted 1.2 absolute weight and high density (0.5-0.6), as discussed in the [MergeKit GitHub Repo](https://github.com/cg123/mergekit/issues/26).

Currently, this model ranks at the top of my personal RP unit test benchmark and scored a very solid 20 on [lilblam's LLM Logic Test](https://docs.google.com/spreadsheets/d/1NgHDxbVWJFolq8bLvLkuPWKC7i_R6I6W/edit#gid=1278290632). My first impressions of it for RPing are very good but, admittedly, this model came out of the oven today so I haven't played it with it too much 😊

### The sauce
```
models: # Top-Loyal-Bruins-Maid-DARE-7B_v2
  - model: mistralai/Mistral-7B-v0.1
    # no parameters necessary for base model
  - model: rwitz/go-bruins-v2 # MetamathCybertronStarling base
    parameters:
      weight: 0.5
      density: 0.6
  - model: chargoddard/loyal-piano-m7 # Pull in some PIPPA/LimaRP/Orca/rpguild
    parameters:
      weight: 0.5
      density: 0.6
  - model: Undi95/Toppy-M-7B
    parameters:
      weight: 0.1
      density: 0.5
  - model: NeverSleep/Noromaid-7b-v0.1.1
    parameters:
      weight: 0.1
      density: 0.5
merge_method: dare_ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  normalize: false
  int8_mask: true
dtype: bfloat16
```

<!-- description end -->
<!-- prompt-template start -->
## Prompt template: Custom format, or Alpaca

### Custom format:
I found the best SillyTavern results from using the Noromaid template.

SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).

Otherwise, I tried to ensure that all of the underlying merged models were Alpaca favored.

### Alpaca:
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

```