README.md

---
tags:
- merge
- mergekit
- lazymergekit
- mistralai/Mistral-7B-Instruct-v0.2
- teknium/OpenHermes-2.5-Mistral-7B
base_model:
- mistralai/Mistral-7B-Instruct-v0.2
- teknium/OpenHermes-2.5-Mistral-7B
license: apache-2.0
---

# Nero-7B-slerp

<p align="center"> 
<img src="https://i.postimg.cc/28Pc5XT1/output-1.jpg" alt="alt text" class="center" width="300"/>
  </p>

Nero-7B-slerp is a merge of the following models using mergekit:
* [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2)
* [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)

## 📈 Performance
| Model | AGIEval | GPT4All | TruthfulQA | Bigbench | Average |  
| --- | --- | --- | --- | --- | --- |  
| [teodortita/Nero-7B-slerp](#) | 41.73 | **73.37** | 58.66 | **43.03** | 54.2 |  
| [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) | 38.68 | 71.64 | 66.85 | 42.28 | 54.86 |  
| [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) | 42.82 | 73.04 | 53.02 | 40.99 | 52.47 |  

Observe the metrics in bold to see the benchmarks where this merged model overtakes the base models in performance.

## 🧩 Configuration

```yaml
slices:
  - sources:
      - model: mistralai/Mistral-7B-Instruct-v0.2
        layer_range: [0, 32]
      - model: teknium/OpenHermes-2.5-Mistral-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
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "teodortita/Nero-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"])
```