transformers safetensors mistral text-generation merge mergekit lazymergekit mlabonne/NeuralBeagle14-7B base_model:mlabonne/NeuralBeagle14-7B autotrain_compatible endpoints_compatible text-generation-inference region:us
---
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/NeuralBeagle14-7B
base_model:
- mlabonne/NeuralBeagle14-7B
---

# munin-neuralbeagle-7b-density-very-low

munin-neuralbeagle-7b-density-very-low is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/NeuralBeagle14-7B](https://huggingface.co/mlabonne/NeuralBeagle14-7B)

## 🧩 Configuration

```yaml
  models:
  - model: danish-foundation-models/munin-7b-alpha
    # No parameters necessary for base model
  - model: mlabonne/NeuralBeagle14-7B
    parameters:
      density: 0.1
      weight: 0.6
  merge_method: dare_ties
  base_model: danish-foundation-models/munin-7b-alpha
  parameters:
    int8_mask: true
  dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "merge-crew/munin-neuralbeagle-7b-density-very-low"
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"])
```