Medmerge-tulu-70b
Medmerge-tulu-70b is a merge of the following models:
Open LLM Leaderboard
| 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
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
!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"])