Update README.md

Spelling correction.

README.md

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----
-license: cc-by-nc-4.0
----
+---
+license: cc-by-nc-4.0
+tags:
+- conversational
+- mixtral
+- merge
+- mergekit
+---
+
+<img src="https://files.catbox.moe/zdxyzv.png" width="400"/>
+
+## TeTO-MS-8x7b-iMat-GGUF
+<i>Weighted quants were made using the full precision fp16 model and groups_merged_enhancedV3.</i> 
+
+<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
+
+# 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)