---
license: cc-by-nc-4.0
tags:
- conversational
- mixtral
- merge
- mergekit
---


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```
# TeTO-MS-8x7b-exl2-rpcal

Quantized using 200 samples of 8192 tokens from an RP-oriented [PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) dataset.

Branches:
- `main` -- `measurement.json`
- `8b8h` -- 8bpw, 8bit lm_head
- `6b6h` -- 6bpw, 6bit lm_head
- `4b6h` -- 4bpw, 6bit lm_head
- `3b6h` -- 3bpw, 6bit lm_head
- `2.25b6h` -- 2.25bpw, 6bit lm_head

Original model link: [InferenceIllusionist/TeTO-MS-8x7b](https://huggingface.co/InferenceIllusionist/TeTO-MS-8x7b)

Original model README below.

-----


<img src="https://files.catbox.moe/zdxyzv.png" width="400"/>

## TeTO-MS-8x7b

<u><b>Te</b></u>soro  + <u><b>T</b></u>yphon + <u><b>O</b></u>penGPT

Presenting a Model Stock experiment combining the unique strengths from the following 8x7b Mixtral models:
* Tess-2.0-Mixtral-8x7B-v0.2 / [migtissera](https://huggingface.co/migtissera) / General Purpose 
* Typhon-Mixtral-v1 / [Sao10K](https://huggingface.co/Sao10K) / Creative & Story Completion
* Open_Gpt4_8x7B_v0.2 / [rombodawg](https://huggingface.co/rombodawg) / Conversational

Weighted (iMat) GGUFS: https://huggingface.co/Quant-Cartel/TeTO-MS-8x7b-iMat-GGUF

EXL2 rpcal courtesy of Quant Cartel: https://huggingface.co/Quant-Cartel/TeTO-MS-8x7b-exl2-rpcal

# Recommended Template
* Basic: Alpaca Format
* Advanced: See context/instruct/sampler settings in [our new Recommended Settings repo](https://huggingface.co/Quant-Cartel/Recommended-Settings/tree/main/Teto-MS-8x7b). 
* Huge shout out to [rAIfle](https://huggingface.co/rAIfle) for his original work on the Wizard 8x22b templates which were modified for this model. 

<H2>Methodology</H2>

> [I]nnovative layer-wise weight averaging technique surpasses state-of-the-art model methods such as Model Soup, utilizing only two fine-tuned models. This strategy can be aptly coined Model Stock, highlighting its reliance on selecting a minimal number of models to draw a more optimized-averaged model
<i> (From [arXiv:2403.19522](https://arxiv.org/pdf/2403.19522))</i>


* Methodology and merging process was based on the following paper - [Model Stock: All we need is just a few fine-tuned models](https://arxiv.org/abs/2403.19522)
* Initial model selection was based on top performing models of Mixtral architecture covering a variety of use cases and skills
* Base model (Mixtral Instruct 8x7b v0.1) was chosen after outperforming two other potential base models in terms of MMLU benchmark performance.

# Output

<img src="https://files.catbox.moe/bw97yg.PNG" width="400"/>

This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

## Merge Details
### Merge Method

This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using Mixtral-8x7B-v0.1-Instruct as a base.

### Models Merged

The following models were included in the merge:
* migtissera_Tess-2.0-Mixtral-8x7B-v0.2
* rombodawg_Open_Gpt4_8x7B_v0.2
* Sao10K_Typhon-Mixtral-v1

### Configuration

The following YAML configuration was used to produce this model:

```yaml
models:
  - model: models/migtissera_Tess-2.0-Mixtral-8x7B-v0.2
  - model: models/Sao10K_Typhon-Mixtral-v1
  - model: models/rombodawg_Open_Gpt4_8x7B_v0.2 
merge_method: model_stock
base_model: models/Mixtral-8x7B-v0.1-Instruct
dtype: float16
```


## Appendix - Llama.cpp MMLU Benchmark Results*

<i>These results were calculated via perplexity.exe from llama.cpp using the following params:</i>

`.\perplexity -m .\models\TeTO-8x7b-MS-v0.03\TeTO-MS-8x7b-Q6_K.gguf -bf .\evaluations\mmlu-test.bin --multiple-choice -c 8192 -t 23 -ngl 200`


```
* V0.01 (4 model / Mixtral Base):
	Final result: 43.3049 +/- 0.4196
	Random chance: 25.0000 +/- 0.3667


* V0.02 (3 model / Tess Mixtral Base):
	Final result: 43.8356 +/- 0.4202
	Random chance: 25.0000 +/- 0.3667


* V0.03 (4 model / Mixtral Instruct Base):
    Final result: 45.7004 +/- 0.4219
    Random chance: 25.0000 +/- 0.3667
```

*Please be advised metrics above are not representative of final HF benchmark scores for reasons given [here](https://github.com/ggerganov/llama.cpp/pull/5047)