---
tags:
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- beowolx/CodeNinja-1.0-OpenChat-7B
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- beowolx/CodeNinja-1.0-OpenChat-7B
license: apache-2.0
---


# Hugo-7B-slerp

<img src="https://cdn.openart.ai/stable_diffusion/54be6f0516fee5ce9b3f8a8b68620a05059fc4cf_2000x2000.webp" alt="alt text" class="center" width="300"/>
  


Hugo-7B-slerp is a successful merge of the following models using mergekit:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B)




## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: mistralai/Mistral-7B-Instruct-v0.2
        layer_range: [0, 32]
      - model: beowolx/CodeNinja-1.0-OpenChat-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: mistralai/Mistral-7B-Instruct-v0.2
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
```
## 📈 Performance
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |  
| --- | --- | --- | --- | --- | --- | --- | --- |  
| [paulilioaica/Hugo-7B-slerp](#) | **67.07** | **64.51** | 84.77 | **62.54** | 57.13 | **80.03** | 53.45 |  
| [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 65.71 | 63.14 | 84.88 | 60.78 | 68.26 | 77.19 | 40.03 |  
| [beowolx/CodeNinja-1.0-OpenChat-7B](https://huggingface.co/beowolx/CodeNinja-1.0-OpenChat-7B) | 67.4 | 63.48 | 83.65 | 63.77 | 47.16 | 79.79 | 66.57 |  

With bold one can see the benchmarks where this merge overtakes the basemodel in performance.

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "paulilioaica/Hugo-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "conversational",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(messages, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs)
```

## 🛈 More on megekit
[mergekit](https://huggingface.co/blog/mlabonne/merge-models)