Medorca-2x7b

Medorca-2x7b

Medorca-2x7b is a Mixure of Experts (MoE) made with the following models:

• epfl-llm/meditron-7b
• microsoft/Orca-2-7b

Evaluations

More details on the Open LLM Leaderboard evaluation results can be found here

🧩 Configuration

base_model: microsoft/Orca-2-7b
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: epfl-llm/meditron-7b
    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/Medorca-2x7b"

tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    model_kwargs={"torch_dtype": torch.float16},
)

messages = [{"role": "user", "content": "Why am i feeling so tired this month?"}]
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"])