---
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
---

# WONMSeverusDevilv2-TIES

WONMSeverusDevilv2-TIES 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)

## 🧩 Configuration

```yaml
models:
  - model: FelixChao/WestSeverus-7B-DPO-v2
    parameters:
      density: [1, 0.7, 0.1]
      weight: [0, 0.3, 0.7, 1]
  - model: jsfs11/WestOrcaNeuralMarco-DPO-v2-DARETIES-7B
    parameters:
      density: [1, 0.7, 0.3]
      weight: [0, 0.25, 0.5, 1]
  - model: mlabonne/Daredevil-7B
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: [0.35, 0.65]
        - value: 0
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
parameters:
  int8_mask: true
  normalize: true
  t:
    - filter: lm_head 
      value: [0.55]
    - filter: embed_tokens
      value: [0.7]
    - filter: self_attn
      value: [0.65, 0.35]
    - filter: mlp
      value:  [0.35, 0.65]
    - filter: layernorm
      value: [0.4, 0.6]
    - filter: modelnorm
      value: [0.6]
    - value: 0.5 # fallback for rest of tensors
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/WONMSeverusDevilv2-TIES"
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"])
```