transformers safetensors qwen2 text-generation mergekit merge conversational license:mit autotrain_compatible text-generation-inference endpoints_compatible region:us
---
library_name: transformers
license: mit
tags:
- mergekit
- merge
---
# Cheng-1: Multi-Specialty Merged Language Model

## Model Overview
**Cheng-1** is a high-performance language model created through strategic merging of top-tier, pre-existing fine-tuned models. It excels in **coding, math, translation, and roleplay** without requiring additional fine-tuning. The final model was built using the **model_stock** method with a restore model to maintain strong instruction-following and mathematical abilities.

## Development Process

### 1. Foundation Model - "Yell-Qwen2.5-7B-1M"
- **Base Merge:** Combined `Qwen2.5-7B-Instruct-1M` with `Qwen2.5-7B` using **SCE merging**.
- **Purpose:** Established a strong general-purpose foundation for later merges.

#### **Merge Code:**
```yaml
merge_method: sce  
models:
  - model: Qwen/Qwen2.5-7B-Instruct-1M
  - model: Qwen/Qwen2.5-7B  
base_model: Qwen/Qwen2.5-7B-Instruct-1M  
parameters:  
  select_topk: 1  
dtype: bfloat16  
tokenizer_source: base  
normalize: true  
int8_mask: true  
name: Yell-Qwen2.5-7B-1M  
```

### 2. Domain-Specific Merges
- **Coding:** Merged `AceCoder-Qwen2.5-7B-Ins-Rule` with Yell-Qwen2.5-7B-1M.
- **Translation:** Merged `DRT-7B` with Yell-Qwen2.5-7B-1M.
- **Math:** Merged `AceMath-7B-Instruct` with Yell-Qwen2.5-7B-1M.
- **Method:** All three were merged using **della merging**, producing three intermediate models.

#### **Merge Code:**
```yaml
merge_method: della  
base_model: marcuscedricridia/Yell-Qwen2.5-7B-1M 
models:  
  - model: TIGER-Lab/AceCoder-Qwen2.5-7B-Ins-Rule 
    parameters:  
      density: 1  
      weight: 1  
      lambda: 0.9  
  - model: Krystalan/DRT-7B
    parameters:  
      density: 1  
      weight: 1  
      lambda: 0.9  
  - model: nvidia/AceMath-7B-Instruct
    parameters:  
      density: 1  
      weight: 1  
      lambda: 0.9  
parameters:  
  density: 1  
  weight: 1  
  lambda: 0.9  
  normalize: true  
  int8_mask: true  
dtype: bfloat16  
tokenizer_source: base  
name: Cheng-1
```

### 3. Final Model Stock Merge
- **Models Combined:**
  - `mergekit-della-wpunuct`
  - `mergekit-della-phphmhr`
  - `mergekit-della-qejrhsk`
  - `Hush-Qwen2.5-7B-RP-v1.2-1M` (Roleplay model)
- **Base Model:** `YOYO-AI/Qwen2.5-7B-it-restore`
- **Final Method:** Used **model_stock merging** to integrate all models into Cheng-1.

#### **Merge Code:**
```yaml
merge_method: model_stock  
base_model: YOYO-AI/Qwen2.5-7B-it-restore  
models:  
  - model: marcuscedricridia/mergekit-della-wpunuct
  - model: marcuscedricridia/mergekit-della-phphmhr
  - model: marcuscedricridia/mergekit-della-qejrhsk
  - model: marcuscedricridia/Hush-Qwen2.5-7B-RP-v1.2-1M  
dtype: bfloat16  
tokenizer_source: base  
int8_mask: true  
normalize: true  
name: Cheng-1
```

## Benchmarks
```
Model: marcuscedricridia/Cheng-1
Precision: torch.bfloat16
Revision: cd8c9dd37c67c2e1b7c683fdd5e72b7f08c074b9

Average: 36.06
IFEval: 77.89
BBH: 36.54
MATH: 48.94
GPQA: 6.15
MUSR: 9.62
MMLU-PRO: 37.21
```

## Conclusion
Cheng-1 is a versatile model optimized for multiple domains. By merging top-performing models in coding, math, translation, and roleplay, it achieves balanced and strong benchmark results without direct fine-tuning.