TeTO-MS-8x7b-exl2-rpcal

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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_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

Original model README below.

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<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 / 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.

<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)</i>

• 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

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

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*

<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