--- license: cc-by-nc-4.0 tags: - conversational - mixtral - merge - mergekit --- ``` e88 88e d8 d888 888b 8888 8888 ,"Y88b 888 8e d88 C8888 8888D 8888 8888 "8" 888 888 88b d88888 Y888 888P Y888 888P ,ee 888 888 888 888 "88 88" "88 88" "88 888 888 888 888 b 8b, e88'Y88 d8 888 d888 'Y ,"Y88b 888,8, d88 ,e e, 888 C8888 "8" 888 888 " d88888 d88 88b 888 Y888 ,d ,ee 888 888 888 888 , 888 "88,d88 "88 888 888 888 "YeeP" 888 PROUDLY PRESENTS ``` ## TeTO-MS-8x7b-iMat-GGUF Weighted quants were made using the full precision fp16 model and groups_merged_enhancedV3. Tesoro + Typhon + OpenGPT 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.

Methodology

> [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 (From [arXiv:2403.19522](https://arxiv.org/pdf/2403.19522)) * 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 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* These results were calculated via perplexity.exe from llama.cpp using the following params: `.\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)