metadata

license: cc-by-nc-4.0
tags:
  - conversational
  - mixtral
  - merge
  - mergekit

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

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