README.md

metadata

license: apache-2.0
tags:
  - moe
  - merge
  - mergekit
  - lazymergekit
  - sethuiyer/Dr_Samantha_7b_mistral
  - fblgit/UNA-TheBeagle-7b-v1

MedleyMD

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

• sethuiyer/Dr_Samantha_7b_mistral
• fblgit/UNA-TheBeagle-7b-v1

🧩 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"]

💻 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.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"])