README.md

---
tags:
- merge
- mergekit
- lazymergekit
- aaditya/Llama3-OpenBioLLM-8B
- johnsnowlabs/JSL-MedLlama-3-8B-v1.0
- winninghealth/WiNGPT2-Llama-3-8B-Base
license: other
license_name: llama3
base_model:
- aaditya/Llama3-OpenBioLLM-8B
- johnsnowlabs/JSL-MedLlama-3-8B-v1.0
- winninghealth/WiNGPT2-Llama-3-8B-Base
---

# Llama-3-OpenBioMed-13B-dare-ties

Llama-3-OpenBioMed-13B-dare-ties is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [aaditya/Llama3-OpenBioLLM-8B](https://huggingface.co/aaditya/Llama3-OpenBioLLM-8B)
* [johnsnowlabs/JSL-MedLlama-3-8B-v1.0](https://huggingface.co/johnsnowlabs/JSL-MedLlama-3-8B-v1.0)
* [winninghealth/WiNGPT2-Llama-3-8B-Base](https://huggingface.co/winninghealth/WiNGPT2-Llama-3-8B-Base)

## 🧩 Configuration

```yaml
models:
  - model: meta-llama/Meta-Llama-3-8B-Instruct
    # No parameters necessary for base model
  - model: aaditya/Llama3-OpenBioLLM-8B
    parameters:
      density: 0.53
      weight: 0.5
  - model: johnsnowlabs/JSL-MedLlama-3-8B-v1.0
    parameters:
      density: 0.53
      weight: 0.3
  - model: winninghealth/WiNGPT2-Llama-3-8B-Base
    parameters:
      density: 0.53
      weight: 0.2
merge_method: dare_ties
base_model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
  int8_mask: true
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "abhinand/Llama-3-OpenBioMed-13B-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"])
```