---
tags:
- merge
- mergekit
- lazymergekit
- kaist-ai/mistral-orpo-beta
- mlabonne/NeuralBeagle14-7B
base_model:
- kaist-ai/mistral-orpo-beta
- mlabonne/NeuralBeagle14-7B
license: apache-2.0
---

# mistral-orpo-beta-NeuralBeagle14-7B-dare-ties

mistral-orpo-beta-NeuralBeagle14-7B-dare-ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [kaist-ai/mistral-orpo-beta](https://huggingface.co/kaist-ai/mistral-orpo-beta)
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)

## 🧩 Configuration

```yaml
models:
  - model: kaist-ai/mistral-orpo-beta
    parameters:
      density: 0.5
      weight: 0.6
    # No parameters necessary for base model
  - model: mlabonne/NeuralBeagle14-7B
    parameters:
      density: 0.5
      weight: 0.4
merge_method: dare_ties
base_model: kaist-ai/mistral-orpo-beta
parameters:
  int8_mask: true
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "saucam/mistral-orpo-beta-NeuralBeagle14-7B-dare-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"])
```

Evaluation results for openllm benchmark via [llm-autoeval](https://github.com/mlabonne/llm-autoeval)

https://gist.github.com/saucam/dcc1f43acce8179f476afc2d91be53ff