merge mergekit #dpo MaximeLabonne #mergeofmerge base_model:CultriX/NeuralTrix-7B-dpo base_model:paulml/OmniBeagleSquaredMBX-v3-7B-v2 license:apache-2.0 region:us
---

tags:
- merge
- mergekit
- '#dpo'
- MaximeLabonne
- '#mergeofmerge'
base_model:
- CultriX/NeuralTrix-7B-dpo
- paulml/OmniBeagleSquaredMBX-v3-7B-v2

license: apache-2.0
---

NOT FOR USE - BUG INSTINSTINSTINSTINSTINST --


# This model was merged, trained, and so on, thanks to the knowledge I gained from reading Maxime Labonne's course. Special thanks to him! 
[Labonne LLM Course](https://github.com/mlabonne/llm-course)

![NeuTrixOmniBe](https://raw.githubusercontent.com/kukedlc87/imagenes/main/DALL%C2%B7E%202023-12-29%2002.13.09%20-%20A%20robot%20with%20a%20unique%20design%20where%20its%20face%20is%20a%20screen%20displaying%20binary%20code.%20The%20robot's%20body%20is%20sleek%20and%20modern%2C%20with%20a%20metallic%20finish%20that%20refl.png)

# NeuTrixOmniBe-DPO

NeuTrix7000-7b-DPO is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):

## 🧩 Configuration

```yaml
MODEL_NAME = "NeuTrix7000-7b-DPO"
yaml_config = """
slices:
  - sources:
      - model: CultriX/NeuralTrix-7B-dpo
        layer_range: [0, 32]
      - model: paulml/OmniBeagleSquaredMBX-v3-7B-v2
        layer_range: [0, 32]
merge_method: slerp
base_model: CultriX/NeuralTrix-7B-dpo
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16
"""
```

It was then trained with DPO using: 
* Intel/orca_dpo_pairs





## 💻 Usage

```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuTrix7000-7b-DPO"
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=128, do_sample=True, temperature=0.5, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])