metadata

base_model:
  - TareksLab/MO-MODEL3-V0.2-LLaMa-70B
  - TareksLab/MO-MODEL5-V0.3-LLaMa-70B
  - TareksLab/MO-MODEL2-V0.2-LLaMa-70B
  - TareksLab/MO-MODEL1-V1-LLaMa-70B
  - TareksLab/MO-MODEL6-V0.1-LLaMa-70B
  - TareksLab/MO-MODEL4-V0.1-LLaMa-70B
library_name: transformers
tags:
  - mergekit
  - merge
license: llama3.3

Formerly known as MO-MODEL-Fused-V0.6-LLaMa-70B, This model is part of my ongoing experiments with merging specialized curated models. For this one, I started experimenting with gradients, to give myself more finetuned control of how the models influence the final merge.

Recommended sampler settings:

Temp 1.0
Min P 0.02

Because of the nature of this sort of 'Hyper Multi Model Merge', my recommendation is not to run this on anything lower than a Q5 quant.

If you enjoy my work, please consider supporting me, It helps me make more models like this! Support on KO-FI <3

MERGE2

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using TareksLab/MO-MODEL6-V0.1-LLaMa-70B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: TareksLab/MO-MODEL6-V0.1-LLaMa-70B
    parameters:
      weight: [0.1, 0.1, 0.1, 0.2, 0.5]
      density: 0.5
  - model: TareksLab/MO-MODEL4-V0.1-LLaMa-70B
    parameters:
      weight: [0.1, 0.1, 0.2, 0.4, 0.2]
      density: 0.5
  - model: TareksLab/MO-MODEL5-V0.3-LLaMa-70B
    parameters:
      weight: [0.1, 0.2, 0.4, 0.2, 0.1]
      density: 0.5
  - model: TareksLab/MO-MODEL3-V0.2-LLaMa-70B
    parameters:
      weight: [0.2, 0.4, 0.2, 0.1, 0.1]
      density: 0.5
  - model: TareksLab/MO-MODEL2-V0.2-LLaMa-70B
    parameters:
      weight: [0.5, 0.2, 0.1, 0.1, 0.1]
      density: 0.5
  - model: TareksLab/MO-MODEL1-V1-LLaMa-70B
    parameters:
      weight: 0.10
      density: 0.5
merge_method: dare_ties
base_model: TareksLab/MO-MODEL6-V0.1-LLaMa-70B
parameters:
  normalize: false
  int8_mask: true
dtype: float32
out_dtype: bfloat16
chat_template: llama3
tokenizer:
 source: base