README.md

---
license: apache-2.0
tags:
- merge
- mergekit
- wanglab/ClinicalCamel-70B
- epfl-llm/meditron-70b
- allenai/tulu-2-dpo-70b
base_model:
- NousResearch/Llama-2-70b-hf
- allenai/tulu-2-dpo-70b
---

# Medmerge-tulu-70b

Medmerge-tulu-70b is a merge of the following models:
* [wanglab/ClinicalCamel-70B](https://huggingface.co/wanglab/ClinicalCamel-70B)
* [epfl-llm/meditron-70b](https://huggingface.co/epfl-llm/meditron-70b)
* [allenai/tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b)

## 🧩 Configuration

```yaml
models:
  - model: NousResearch/Llama-2-70b-hf
    # no parameters necessary for base model
  - model: wanglab/ClinicalCamel-70B
    parameters:
      weight: 0.08
      density: 0.45
  - model: epfl-llm/meditron-70b
    parameters:
      weight: 0.08
      density: 0.45
  - model: allenai/tulu-2-dpo-70b
    parameters:
      weight: 0.08
      density: 0.45
merge_method: dare_ties
base_model: NousResearch/Llama-2-70b-hf
parameters:
  int8_mask: true
dtype: bfloat16
```

## 💻 Usage

```python
!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Technoculture/Medmerge-tulu-70b"
messages = [{"role": "user", "content": "I am feeling sleepy these days"}]

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"])
```