Technoculture_-_Medchator-2x7b-gguf

Quantization made by Richard Erkhov.

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Medchator-2x7b - GGUF

• Model creator: https://huggingface.co/Technoculture/
• Original model: https://huggingface.co/Technoculture/Medchator-2x7b/

Original model description:

license: apache-2.0 tags:

• moe
• merge
• AdaptLLM/medicine-chat
• microsoft/Orca-2-7b datasets:
• open-llm-leaderboard/details_Technoculture__Medchator-2x7b

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Medchator-2x7b

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

• AdaptLLM/medicine-chat
• microsoft/Orca-2-7b

Evaluations

Open LLM Leaderboard

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

🧩 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"])