---
tags:
- merge
- mergekit
- lazymergekit
- FelixChao/WestSeverus-7B-DPO-v2
- jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
- mlabonne/Daredevil-7B
base_model:
- FelixChao/WestSeverus-7B-DPO-v2
- jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
- mlabonne/Daredevil-7B
license: apache-2.0
---

# WONMSeverusDevil-TIES-7B

WONMSeverusDevil-TIES-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)
* [jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B](https://huggingface.co/jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B)
* [mlabonne/Daredevil-7B](https://huggingface.co/mlabonne/Daredevil-7B)



```
# Open-LLM Benchmark Results:
 WONMSeverusDevil-TIES-7B LLM AutoEval📑
|       Model            |AGIEval|GPT4All|TruthfulQA|Bigbench|Average|
|------------------------|------:|------:|---------:|-------:|------:|
|WONMSeverusDevil-TIES-7B|  45.26|  77.07|     72.47|   48.85|  60.91|


```


# 🧩 Configuration

```yaml
models:
  - model: FelixChao/WestSeverus-7B-DPO-v2
    parameters:
      density: [1, 0.7, 0.1] # density gradient
      weight: 1.0
  - model: jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
    parameters:
      density: 0.65
      weight: [0, 0.3, 0.7, 1] # weight gradient
  - model: mlabonne/Daredevil-7B
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  normalize: true
  int8_mask: true
dtype: float16

```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/WONMSeverusDevil-TIES-7B"
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"])
```