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---
tags:
- merge
- mergekit
- lazymergekit
- BioMistral/BioMistral-7B
- Nexusflow/Starling-LM-7B-beta
base_model:
- BioMistral/BioMistral-7B
- Nexusflow/Starling-LM-7B-beta
license: apache-2.0
---
# BioLing-7B-Dare
[<img src="https://repository-images.githubusercontent.com/104670986/2e728700-ace4-11ea-9cfc-f3e060b25ddf">](http://www.johnsnowlabs.com)
This model is developed by [John Snow Labs](https://www.johnsnowlabs.com/).
This model is available under a [CC-BY-NC-ND](https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en) license and must also conform to this [Acceptable Use Policy](https://huggingface.co/johnsnowlabs). If you need to license this model for commercial use, please contact us at info@johnsnowlabs.com.
## 🧩 Configuration
```yaml
models:
- model: BioMistral/BioMistral-7B
parameters:
density: 0.53
weight: 0.4
- model: Nexusflow/Starling-LM-7B-beta
parameters:
density: 0.53
weight: 0.3
merge_method: dare_ties
base_model: BioMistral/BioMistral-7B
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "johnsnowlabs/BioLing-7B-Dare"
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
Coming Soon! |