Medchator-2x7b
Medchator-2x7b is a Mixure of Experts (MoE) made with the following models:
Evaluations
Open LLM Leaderboard
| Model Name | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|
| Orca-2-7b | 78.4 | 76.1 | 53.7 | 52.4 | 74.2 | 47.2 |
| LLAMA-2-7b | 43.2 | 77.1 | 44.4 | 38.7 | 69.5 | 16 |
| MT7Bi-sft | 54.1 | 75.11 | - | 43.08 | 72.14 | 15.54 |
| MT7bi-dpo | 54.69 | 75.89 | 52.82 | 45.48 | 71.58 | 25.93 |
| Medorca-2x7b | 54.1 | 76.04 | 54.1 | 48.04 | 74.51 | 20.64 |
| Medchator-2x7b | 57.59 | 78.14 | 56.13 | 48.77 | 75.3 | 32.83 |
Medical Performance
Clinical Camel demonstrates competitive performance on medical benchmarks.
Table: Five-Shot Performance of GPT3.5, llama-2-7b and Llama-2-70b on Various Medical Datasets
| Dataset | Medchator-2x7b | GPT3.5 | Llama-2 7b | Llama-2 70b |
|---|---|---|---|---|
| MMLU Anatomy | 56.3 | 60.7 | 48.9 | 62.9 |
| MMLU Clinical Knowledge | 63.0 | 68.7 | 46.0 | 71.7 |
| MMLU College Biology | 63.8 | 72.9 | 47.2 | 84.7 |
| MMLU College Medicine | 50.9 | 63.6 | 42.8 | 64.2 |
| MMLU Medical Genetics | 67.0 | 68.0 | 55.0 | 74.0 |
| MMLU Professional Medicine | 55.1 | 69.8 | 53.6 | 75.0 |
🧩 Configuration
base_model: microsoft/Orca-2-7b
gate_mode: hidden
dtype: bfloat16
experts:
- source_model: AdaptLLM/medicine-chat
positive_prompts:
- "How does sleep affect cardiovascular health?"
- "Could a plant-based diet improve arthritis symptoms?"
- "A patient comes in with symptoms of dizziness and nausea"
- "When discussing diabetes management, the key factors to consider are"
- "The differential diagnosis for a headache with visual aura could include"
negative_prompts:
- "Recommend a good recipe for a vegetarian lasagna."
- "Give an overview of the French Revolution."
- "Explain how a digital camera captures an image."
- "What are the environmental impacts of deforestation?"
- "The recent advancements in artificial intelligence have led to developments in"
- "The fundamental concepts in economics include ideas like supply and demand, which explain"
- source_model: microsoft/Orca-2-7b
positive_prompts:
- "Here is a funny joke for you -"
- "When considering the ethical implications of artificial intelligence, one must take into account"
- "In strategic planning, a company must analyze its strengths and weaknesses, which involves"
- "Understanding consumer behavior in marketing requires considering factors like"
- "The debate on climate change solutions hinges on arguments that"
negative_prompts:
- "In discussing dietary adjustments for managing hypertension, it's crucial to emphasize"
- "For early detection of melanoma, dermatologists recommend that patients regularly check their skin for"
- "Explaining the importance of vaccination, a healthcare professional should highlight"
💻 Usage
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Technoculture/Medchator-2x7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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"])