README.md

---
tags:
- merge
- mergekit
- lazymergekit
- kaitchup/Mayonnaise-4in1-022
- macadeliccc/WestLake-7B-v2-laser-truthy-dpo
- vanillaOVO/supermario_v2
- FelixChao/WestSeverus-7B-DPO-v2
base_model:
- kaitchup/Mayonnaise-4in1-022
- macadeliccc/WestLake-7B-v2-laser-truthy-dpo
- vanillaOVO/supermario_v2
- FelixChao/WestSeverus-7B-DPO-v2
license: apache-2.0
---

# Wernicke-7B-v8

Wernicke-7B-v8 is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [kaitchup/Mayonnaise-4in1-022](https://huggingface.co/kaitchup/Mayonnaise-4in1-022)
* [macadeliccc/WestLake-7B-v2-laser-truthy-dpo](https://huggingface.co/macadeliccc/WestLake-7B-v2-laser-truthy-dpo)
* [vanillaOVO/supermario_v2](https://huggingface.co/vanillaOVO/supermario_v2)
* [FelixChao/WestSeverus-7B-DPO-v2](https://huggingface.co/FelixChao/WestSeverus-7B-DPO-v2)

## 🧩 Configuration

```yaml
models:
  - model: CultriX/Wernicke-7B-v1
    # No parameters necessary for base model
  - model: kaitchup/Mayonnaise-4in1-022
    parameters:
      density: 0.53
      weight: 0.40
  - model: macadeliccc/WestLake-7B-v2-laser-truthy-dpo
    parameters:
      density: 0.53
      weight: 0.25
  - model: vanillaOVO/supermario_v2
    parameters:
      density: 0.53
      weight: 0.25
  - model: FelixChao/WestSeverus-7B-DPO-v2
    parameters:
      density: 0.53
      weight: 0.20
merge_method: dare_ties
base_model: CultriX/Wernicke-7B-v1
parameters:
  int8_mask: true
dtype: float16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "CultriX/Wernicke-7B-v8"
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"])
```