metadata

license: cc-by-nc-nd-4.0
tags:
  - moe
  - merge
  - medical
  - mergekit
  - sethuiyer/Dr_Samantha_7b_mistral
  - fblgit/UNA-TheBeagle-7b-v1
language:
  - en
datasets:
  - medmcqa
  - cognitivecomputations/samantha-data
  - jondurbin/bagel-v0.3
library_name: transformers
pipeline_tag: text-generation

MedleyMD

MedleyMD is a Mixure of Experts (MoE) made with the following models using LazyMergekit:

These models were chosen because fblgit/UNA-TheBeagle-7b-v1 has excellent performance for a 7B parameter model and Dr.Samantha has capabilities of a medical knowledge-focused model (trained on USMLE databases and doctor-patient interactions) with the philosophical, psychological, and relational understanding, scoring 68.82% in topics related to clinical domain and psychology.

Benchmark

On a synthetic benchmark of 15 medical diagnosis questions generated by GPT-4, GPT-4 also being an evaluator, MedleyMD scored 94.25/100.

Nous Benchmark numbers shall be available soon.

🧩 Configuration

base_model: OpenPipe/mistral-ft-optimized-1227
gate_mode: hidden
dtype: bfloat16

experts:
  - source_model: sethuiyer/Dr_Samantha_7b_mistral
    positive_prompts: ["differential diagnosis", "Clinical Knowledge", "Medical Genetics", "Human Aging", "Human Sexuality", "College Medicine", "Anatomy", "College Biology", "High School Biology", "Professional Medicine", "Nutrition", "High School Psychology", "Professional Psychology", "Virology"]

  - source_model: fblgit/UNA-TheBeagle-7b-v1
    positive_prompts: ["How do you", "Explain the concept of", "Give an overview of", "Compare and contrast between", "Provide information about", "Help me understand", "Summarize", "Make a recommendation on", "chat", "math", "reason", "mathematics", "solve", "count", "python", "javascript", "programming", "algorithm", "tell me", "assistant"]

GGUF

medleymd.Q4_K_M [7.2GB]
medleymd.Q5_K_M [9.13GB]

Ollama

MedleyMD can be used in ollama by runningollama run stuehieyr/medleymd in your terminal.

If you have limited computing resources, check out this video to learn how to run it on a Google Colab backend.

💻 Usage

!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "sethuiyer/MedleyMD"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.bfloat16, "load_in_4bit": True},
)

generation_kwargs = {
    "max_new_tokens": 512,
    "do_sample": True,
    "temperature": 0.7,
    "top_k": 50,
    "top_p": 95,
}

messages = [{"role":"system", "content":"You are an helpful AI assistant. Please use </s> when you want to end the answer."},
{"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, **generation_kwargs)
print(outputs[0]["generated_text"])

A Mixture of Experts (Mixout) is a neural network architecture that combines the strengths of multiple expert networks to make a more accurate and robust prediction.
It is composed of a topmost gating network that assigns weights to each expert network based on their performance on past input samples.
The expert networks are trained independently, and the gating network learns to choose the best combination of these experts to make the final prediction.
Mixout demonstrates a stronger ability to handle complex data distributions and is more efficient in terms of training time and memory usage compared to a
traditional ensemble approach.