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--- |
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license: cc-by-nc-4.0 |
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tags: |
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- conversational |
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- mixtral |
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- merge |
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- mergekit |
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--- |
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``` |
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e88 88e d8 |
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d888 888b 8888 8888 ,"Y88b 888 8e d88 |
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C8888 8888D 8888 8888 "8" 888 888 88b d88888 |
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Y888 888P Y888 888P ,ee 888 888 888 888 |
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"88 88" "88 88" "88 888 888 888 888 |
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b |
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8b, |
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e88'Y88 d8 888 |
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d888 'Y ,"Y88b 888,8, d88 ,e e, 888 |
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C8888 "8" 888 888 " d88888 d88 88b 888 |
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Y888 ,d ,ee 888 888 888 888 , 888 |
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"88,d88 "88 888 888 888 "YeeP" 888 |
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PROUDLY PRESENTS |
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``` |
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# TeTO-MS-8x7b-exl2-rpcal |
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Quantized using 200 samples of 8192 tokens from an RP-oriented [PIPPA](https://huggingface.co/datasets/royallab/PIPPA-cleaned) dataset. |
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Branches: |
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- `main` -- `measurement.json` |
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- `8b8h` -- 8bpw, 8bit lm_head |
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- `6b6h` -- 6bpw, 6bit lm_head |
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- `4b6h` -- 4bpw, 6bit lm_head |
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- `3b6h` -- 3bpw, 6bit lm_head |
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- `2.25b6h` -- 2.25bpw, 6bit lm_head |
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Original model link: [InferenceIllusionist/TeTO-MS-8x7b](https://huggingface.co/InferenceIllusionist/TeTO-MS-8x7b) |
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Original model README below. |
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----- |
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<img src="https://files.catbox.moe/zdxyzv.png" width="400"/> |
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## TeTO-MS-8x7b |
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<u><b>Te</b></u>soro + <u><b>T</b></u>yphon + <u><b>O</b></u>penGPT |
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Presenting a Model Stock experiment combining the unique strengths from the following 8x7b Mixtral models: |
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* Tess-2.0-Mixtral-8x7B-v0.2 / [migtissera](https://huggingface.co/migtissera) / General Purpose |
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* Typhon-Mixtral-v1 / [Sao10K](https://huggingface.co/Sao10K) / Creative & Story Completion |
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* Open_Gpt4_8x7B_v0.2 / [rombodawg](https://huggingface.co/rombodawg) / Conversational |
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Weighted (iMat) GGUFS: https://huggingface.co/Quant-Cartel/TeTO-MS-8x7b-iMat-GGUF |
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# Recommended Template |
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* Basic: Alpaca Format |
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* Advanced: See context/instruct/sampler settings in [our new Recommended Settings repo](https://huggingface.co/Quant-Cartel/Recommended-Settings/tree/main/Teto-MS-8x7b). |
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* Huge shout out to [rAIfle](https://huggingface.co/rAIfle) for his original work on the Wizard 8x22b templates which were modified for this model. |
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<H2>Methodology</H2> |
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> [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 |
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<i> (From [arXiv:2403.19522](https://arxiv.org/pdf/2403.19522))</i> |
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* 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) |
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* Initial model selection was based on top performing models of Mixtral architecture covering a variety of use cases and skills |
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* Base model (Mixtral Instruct 8x7b v0.1) was chosen after outperforming two other potential base models in terms of MMLU benchmark performance. |
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# Output |
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<img src="https://files.catbox.moe/bw97yg.PNG" width="400"/> |
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This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). |
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## Merge Details |
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### Merge Method |
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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. |
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### Models Merged |
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The following models were included in the merge: |
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* migtissera_Tess-2.0-Mixtral-8x7B-v0.2 |
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* rombodawg_Open_Gpt4_8x7B_v0.2 |
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* Sao10K_Typhon-Mixtral-v1 |
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### Configuration |
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The following YAML configuration was used to produce this model: |
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```yaml |
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models: |
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- model: models/migtissera_Tess-2.0-Mixtral-8x7B-v0.2 |
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- model: models/Sao10K_Typhon-Mixtral-v1 |
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- model: models/rombodawg_Open_Gpt4_8x7B_v0.2 |
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merge_method: model_stock |
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base_model: models/Mixtral-8x7B-v0.1-Instruct |
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dtype: float16 |
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``` |
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## Appendix - Llama.cpp MMLU Benchmark Results* |
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<i>These results were calculated via perplexity.exe from llama.cpp using the following params:</i> |
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`.\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` |
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``` |
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* V0.01 (4 model / Mixtral Base): |
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Final result: 43.3049 +/- 0.4196 |
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Random chance: 25.0000 +/- 0.3667 |
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* V0.02 (3 model / Tess Mixtral Base): |
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Final result: 43.8356 +/- 0.4202 |
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Random chance: 25.0000 +/- 0.3667 |
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* V0.03 (4 model / Mixtral Instruct Base): |
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Final result: 45.7004 +/- 0.4219 |
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Random chance: 25.0000 +/- 0.3667 |
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``` |
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*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) |
