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---
tags:
- merge
- mergekit
- lazymergekit
- Kukedlc/NeuralFusion-7b-Dare-Ties
- paulml/OmniBeagleSquaredMBX-v3-7B-v2
- macadeliccc/MBX-7B-v3-DPO
- Kukedlc/Fasciculus-Arcuatus-7B-slerp
- liminerity/Neurotic-Jomainotrik-7b-slerp
base_model:
- Kukedlc/NeuralFusion-7b-Dare-Ties
- paulml/OmniBeagleSquaredMBX-v3-7B-v2
- macadeliccc/MBX-7B-v3-DPO
- Kukedlc/Fasciculus-Arcuatus-7B-slerp
- liminerity/Neurotic-Jomainotrik-7b-slerp
---
# Neural-4-Wino-7b
Neural-4-Wino-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/NeuralFusion-7b-Dare-Ties](https://huggingface.co/Kukedlc/NeuralFusion-7b-Dare-Ties)
* [paulml/OmniBeagleSquaredMBX-v3-7B-v2](https://huggingface.co/paulml/OmniBeagleSquaredMBX-v3-7B-v2)
* [macadeliccc/MBX-7B-v3-DPO](https://huggingface.co/macadeliccc/MBX-7B-v3-DPO)
* [Kukedlc/Fasciculus-Arcuatus-7B-slerp](https://huggingface.co/Kukedlc/Fasciculus-Arcuatus-7B-slerp)
* [liminerity/Neurotic-Jomainotrik-7b-slerp](https://huggingface.co/liminerity/Neurotic-Jomainotrik-7b-slerp)
## 🧩 Configuration
```yaml
models:
- model: liminerity/Neurotic-Jomainotrik-7b-slerp
# No parameters necessary for base model
- model: Kukedlc/NeuralFusion-7b-Dare-Ties
parameters:
density: 0.66
weight: 0.2
- model: paulml/OmniBeagleSquaredMBX-v3-7B-v2
parameters:
density: 0.55
weight: 0.2
- model: macadeliccc/MBX-7B-v3-DPO
parameters:
density: 0.55
weight: 0.2
- model: Kukedlc/Fasciculus-Arcuatus-7B-slerp
parameters:
density: 0.44
weight: 0.2
- model: liminerity/Neurotic-Jomainotrik-7b-slerp
parameters:
density: 0.66
weight: 0.2
merge_method: dare_ties
base_model: liminerity/Neurotic-Jomainotrik-7b-slerp
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/Neural-4-Wino-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"])
``` |