README.md

metadata

base_model:
  - win10/EVA-QwQ-32B-Preview
  - ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3
  - maldv/Qwentile2.5-32B-Instruct
  - Sao10K/32B-Qwen2.5-Kunou-v1
library_name: transformers
tags:
  - mergekit
  - merge
license: apache-2.0

Info

Trying to make something different, feel free to try like or dislike or leave a feedback, I'm not claiming anything about anything.

I made one quant here (https://huggingface.co./Aryanne/QwentileSwap/blob/main/q4ks_QwentileSwap.gguf)

Also thanks to mradermacher for the other quants: gguf and gguf_imatrix_quants.

merged

This is a merge of pre-trained language models created using my custom method in mergekit.

Merge Details

Merge Method

This model was merged using the task_swapping merge method using win10/EVA-QwQ-32B-Preview as a base.

Models Merged

The following models were included in the merge:

• ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3
• maldv/Qwentile2.5-32B-Instruct
• Sao10K/32B-Qwen2.5-Kunou-v1

Configuration

The following YAML configuration was used to produce this model:

base_model: win10/EVA-QwQ-32B-Preview
dtype: bfloat16
merge_method: task_swapping
slices:
- sources:
  - layer_range: [0, 64]
    model: maldv/Qwentile2.5-32B-Instruct
    parameters:
      diagonal_offset: 2.0 # ignored here
      random_mask: 0.666
      random_mask_seed: 888.0
      weight: 0.5
  - layer_range: [0, 64]
    model: ArliAI/Qwen2.5-32B-ArliAI-RPMax-v1.3
    parameters:
      diagonal_offset: 5.0
      weight: 0.75
  - layer_range: [0, 64]
    model: Sao10K/32B-Qwen2.5-Kunou-v1
    parameters:
      diagonal_offset: 2.0 # ignored here
      random_mask: 0.333
      random_mask_seed: 12347888.0
      weight: 0.5
  - layer_range: [0, 64]
    model: win10/EVA-QwQ-32B-Preview