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PROUDLY PRESENTS
TeTO-MS-8x7b-exl2-rpcal
Quantized using 200 samples of 8192 tokens from an RP-oriented PIPPA dataset.
Branches:
main
--measurement.json
8b8h
-- 8bpw, 8bit lm_head6b6h
-- 6bpw, 6bit lm_head4b6h
-- 4bpw, 6bit lm_head3b6h
-- 3bpw, 6bit lm_head2.25b6h
-- 2.25bpw, 6bit lm_head
Original model link: InferenceIllusionist/TeTO-MS-8x7b
Original model README below.
TeTO-MS-8x7b
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 / General Purpose
- Typhon-Mixtral-v1 / Sao10K / Creative & Story Completion
- Open_Gpt4_8x7B_v0.2 / 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.
- Huge shout out to 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)
- Methodology and merging process was based on the following paper - Model Stock: All we need is just a few fine-tuned models
- 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.
Merge Details
Merge Method
This model was merged using the Model Stock 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:
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