README.md

---
tags:
- merge
- mergekit
- lazymergekit
- Kukedlc/NeuTrixOmniBe-7B-model-remix
- PetroGPT/WestSeverus-7B-DPO
- vanillaOVO/supermario_v4
base_model:
- Kukedlc/NeuTrixOmniBe-7B-model-remix
- PetroGPT/WestSeverus-7B-DPO
- vanillaOVO/supermario_v4
---

# MoEv4Config-TestWeightedTIES-7b

MoEv4Config-TestWeightedTIES-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/NeuTrixOmniBe-7B-model-remix](https://huggingface.co/Kukedlc/NeuTrixOmniBe-7B-model-remix)
* [PetroGPT/WestSeverus-7B-DPO](https://huggingface.co/PetroGPT/WestSeverus-7B-DPO)
* [vanillaOVO/supermario_v4](https://huggingface.co/vanillaOVO/supermario_v4)

## 🧩 Configuration

```yaml
models:
  - model: Kukedlc/NeuTrixOmniBe-7B-model-remix
    # No parameters necessary for base model
  - model: Kukedlc/NeuTrixOmniBe-7B-model-remix
    parameters:
      density: [1, 0.7, 0.1]
      weight: [0, 0.3, 0.7, 1]
  - model: PetroGPT/WestSeverus-7B-DPO
    parameters:
      density: [1, 0.7, 0.3]
      weight: [0, 0.25, 0.5, 1]
  - model: vanillaOVO/supermario_v4
    parameters:
      density: 0.33
      weight:
        - filter: mlp
          value: 0.5
        - value: 0
merge_method: ties
base_model: Kukedlc/NeuTrixOmniBe-7B-model-remix
parameters:
  int8_mask: true
  normalize: true
  sparsify:
    - filter: mlp
      value: 0.5
    - filter: self_attn
      value: 0.5
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "jsfs11/MoEv4Config-TestWeightedTIES-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"])
```