transformers safetensors llama text-generation merge mergekit epfl-llm/meditron-70b allenai/tulu-2-dpo-70b license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
license: apache-2.0
tags:
- merge
- mergekit
- epfl-llm/meditron-70b
- 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)

# Open LLM Leaderboard

![image/png](https://cdn-uploads.huggingface.co/production/uploads/63486df1f8f01fcc4b23e97d/ajm6Z9cCmd74ERdz4xdHs.png)

| Model Name           | ARC      | HellaSwag | MMLU   | TruthfulQA | Winogrande | GSM8K    |
| -------------------- | -------- | --------- | ------ | ---------- | ---------- | -------- |
| tulu-2-dpo-70b       | 72.1     | 88.99     | 69.84  | 65.78      | 83.27      | 62.62    |
| Medmerge-tulu-70b    | 67.81    | 87.46     | 70.1   | 47.89      | 83.43      | 56.56    |

## Performance

Clinical Camel demonstrates competitive performance on medical benchmarks.

**Table: Five-Shot Performance of Clinical Camel-70B (C70), GPT3.5, GPT4, and Med-PaLM 2 on Various Medical Datasets**

| Dataset                     | Medmerge-tulu-70b | ClinicalCamel-70B | GPT3.5 | GPT4  | Med-PaLM 2   |
|-----------------------------|-------------------|-------------------|--------|-------|--------------|
| MMLU Anatomy                | 66.6              | 65.2              | 60.7   | 80.0  | 77.8         |
| MMLU Clinical Knowledge     | 72.0              | 72.8              | 68.7   | 86.4  | 88.3         |
| MMLU College Biology        | 84.7              | 81.2              | 72.9   | 93.8  | 94.4         |
| MMLU College Medicine       | 64.2              | 68.2              | 63.6   | 76.3  | 80.9         |
| MMLU Medical Genetics       | 76.0              | 69.0              | 68.0   | 92.0  | 90.0         |
| MMLU Professional Medicine  | 75.7              | 75.0              | 69.8   | 93.8  | 95.2         |
| MedMCQA                     |               | 54.2              | 51.0   | 72.4  | 71.3         |
| MedQA (USMLE)               |               | 60.7              | 53.6   | 81.4  | 79.7         |
| PubMedQA                    |               | 77.9              | 60.2   | 74.4  | 79.2         |
| USMLE Sample Exam           |               | 64.3              | 58.5   | 86.6  | -            |

## 🧩 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"])
```