---
tags:
- merge
- mergekit
- lazymergekit
- MaziyarPanahi/Calme-7B-Instruct-v0.9
- yam-peleg/Experiment26-7B
base_model:
- MaziyarPanahi/Calme-7B-Instruct-v0.9
- yam-peleg/Experiment26-7B
license: cc-by-nc-4.0
---

# Myriad-7B-Slerp
![image/png](https://cdn-uploads.huggingface.co/production/uploads/64fc6d81d75293f417fee1d1/zQLKL_Bf6M6zgajds_Ap6.png)
Myriad-7B-Slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [MaziyarPanahi/Calme-7B-Instruct-v0.9](https://huggingface.co/MaziyarPanahi/Calme-7B-Instruct-v0.9)
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
Special thanks to Charles Goddard for the quick implementation!

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: MaziyarPanahi/Calme-7B-Instruct-v0.9
        layer_range: [0, 32]
      - model: yam-peleg/Experiment26-7B
        layer_range: [0, 32]
merge_method: slerp
base_model: yam-peleg/Experiment26-7B
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: float16


```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Muhammad2003/Myriad-7B-Slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
## 🏆 Evaluation
| Task          | Score   |
|---------------|---------|
| ARC           | 73.38   |
| Hellaswag     | 89.05   |
| MMLU          | 64.32   |
| TruthfulQA    | 77.95   |
| Winogrande    | 84.85   |
| GSM8k         | 70.28   |