---
base_model:
- mergekit-community/Qwen2.5-7B-della
- mergekit-community/Qwen2.5-7B-ties
- Qwen/Qwen2.5-7B-Instruct
- Qwen/Qwen2.5-7B-Instruct-1M
- mergekit-community/Qwen2.5-7B-ties-1M
- Qwen/Qwen2.5-7B
- mergekit-community/Qwen2.5-7B-della-1M
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
language:
- en
- zh
pipeline_tag: text-generation
---
# Achieve the Optimal Merged Model by Using One Basic Model and Two Fine-tuned Models!

*What is the best way to merge **one base model** and **two fine-tuned models**?*

## This might be the best answer at the present stage!

[Qwen2.5-7B-YOYO-super](https://huggingface.co/YOYO-AI/Qwen2.5-7B-YOYO-super)

[Qwen2.5-14B-YOYO-super](https://huggingface.co/YOYO-AI/Qwen2.5-14B-YOYO-super)

*This is not a whim release, but the optimal result of countless merging experiments!*

*Here is the formula for the **previous generation**:*
```yaml
models:  
  - model: Qwen/Qwen2.5-7B-Instruct  
    parameters:  
      density: 1  
      weight: 1  
      lambda: 0.9
  - model: Qwen/Qwen2.5-7B-Instruct-1M  
    parameters:  
      density: 1  
      weight: 1  
      lambda: 0.9  
merge_method: della  
base_model: Qwen/Qwen2.5-7B  
parameters:  
  density: 1  
  weight: 1  
  lambda: 0.9  
  normalize: true  
  int8_mask: true  
dtype: bfloat16  
tokenizer_source: base
```
*It was widely used in the merging process of the **previous generation of models***.

*However, there are some **deficiencies***:
 
*1.There is relatively little retention of knowledge of the basic model.*

*2.The mathematical and coding abilities have declined.*

## And here is the formula for this generation:
```yaml
models:
  - model: Qwen/Qwen2.5-7B-instruct
    parameters:
      density: 1 
      weight: 1
      lambda: 0.9
merge_method: della
base_model: Qwen/Qwen2.5-7B
parameters:
  density: 1
  weight: 1
  lambda: 0.9
  normalize: true
  int8_mask: true
dtype: float16
tokenizer_source: base
name: Qwen2.5-7B-della
```
```yaml
models:
  - model: Qwen/Qwen2.5-7B-instruct-1M
    parameters:
      density: 1 
      weight: 1
      lambda: 0.9
merge_method: della
base_model: Qwen/Qwen2.5-7B
parameters:
  density: 1
  weight: 1
  lambda: 0.9
  normalize: true
  int8_mask: true
dtype: float16
tokenizer_source: base
name: Qwen2.5-7B-della-1M
```
```yaml
models:
  - model: Qwen/Qwen2.5-7B-instruct
    parameters:
      density: 1 
      weight: 1
merge_method: ties
base_model: Qwen/Qwen2.5-7B
parameters:
  density: 1
  weight: 1
  normalize: true
  int8_mask: true
dtype: float16
tokenizer_source: base
name: Qwen2.5-7B-ties
```
```yaml
models:
  - model: Qwen/Qwen2.5-7B-instruct-1M
    parameters:
      density: 1 
      weight: 1
merge_method: ties
base_model: Qwen/Qwen2.5-7B
parameters:
  density: 1
  weight: 1
  normalize: true
  int8_mask: true
dtype: float16
tokenizer_source: base
name: Qwen2.5-7B-ties-1M
```
```yaml
merge_method: model_stock
base_model: Qwen/Qwen2.5-7B
models:
  - model: mergekit-community/Qwen2.5-7B-della
  - model: mergekit-community/Qwen2.5-7B-della-1M
  - model: mergekit-community/Qwen2.5-7B-ties
  - model: mergekit-community/Qwen2.5-7B-ties-1M
  - model: Qwen/Qwen2.5-7B-instruct-1M
  - model: Qwen/Qwen2.5-7B-instruct
tokenizer_source: base
int8_mask: true
normalize: true
dtype: float16
```
*Except for a slight decrease in instruction following, **significant improvements** have been achieved in all other aspects.*

*This formula will also be used in the development of **the next generation of YOYO models**.*

***YOYO-AI** not only releases merged models with excellent performance but also publishes a **complete and high-quality model merging formula**, hoping to promote the progress of model merging technology in the open-source community with this!*

### If you can use this formula when merging models, it will be the greatest support for YOYO-AI!