Mediquad-4x7b / README.md
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metadata
license: apache-2.0
tags:
  - moe
  - merge
  - epfl-llm/meditron-7b
  - chaoyi-wu/PMC_LLAMA_7B_10_epoch
  - allenai/tulu-2-dpo-7b
  - microsoft/Orca-2-7b
model-index:
  - name: Mediquad-4x7b
    results:
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: AI2 Reasoning Challenge (25-Shot)
          type: ai2_arc
          config: ARC-Challenge
          split: test
          args:
            num_few_shot: 25
        metrics:
          - type: acc_norm
            value: 27.47
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Mediquad-4x7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: HellaSwag (10-Shot)
          type: hellaswag
          split: validation
          args:
            num_few_shot: 10
        metrics:
          - type: acc_norm
            value: 28.21
            name: normalized accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Mediquad-4x7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: MMLU (5-Shot)
          type: cais/mmlu
          config: all
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 28.66
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Mediquad-4x7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: TruthfulQA (0-shot)
          type: truthful_qa
          config: multiple_choice
          split: validation
          args:
            num_few_shot: 0
        metrics:
          - type: mc2
            value: 49.56
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Mediquad-4x7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: Winogrande (5-shot)
          type: winogrande
          config: winogrande_xl
          split: validation
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 50.51
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Mediquad-4x7b
          name: Open LLM Leaderboard
      - task:
          type: text-generation
          name: Text Generation
        dataset:
          name: GSM8k (5-shot)
          type: gsm8k
          config: main
          split: test
          args:
            num_few_shot: 5
        metrics:
          - type: acc
            value: 0
            name: accuracy
        source:
          url: >-
            https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Mediquad-4x7b
          name: Open LLM Leaderboard

Mediquad-20B

Mediquad-20B is a Mixure of Experts (MoE) made with the following models:

Evaluations

Benchmark Mediquad-4x7b meditron-7b Orca-2-7b meditron-70b
MedMCQA
ClosedPubMedQA
PubMedQA
MedQA
MedQA4
MedicationQA
MMLU Medical
TruthfulQA
GSM8K
ARC
HellaSwag
Winogrande

🧩 Configuration

gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: epfl-llm/meditron-7b
    positive_prompts:
      - "How does sleep affect cardiovascular health?"
      - "When discussing diabetes management, the key factors to consider are"
      - "The differential diagnosis for a headache with visual aura could include"
    negative_prompts:
      - "What are the environmental impacts of deforestation?"
      - "The recent advancements in artificial intelligence have led to developments in"
  - source_model: chaoyi-wu/PMC_LLAMA_7B_10_epoch
    positive_prompts:
      - "How would you explain the importance of hypertension management to a patient?"
      - "Describe the recovery process after knee replacement surgery in layman's terms."
    negative_prompts:
      - "Recommend a good recipe for a vegetarian lasagna."
      - "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: allenai/tulu-2-dpo-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"
  - source_model: microsoft/Orca-2-7b
    positive_prompts:
      - "Given the riddle above,"
      - "Given the above context deduce the outcome:"
      - "The logical flaw in the above paragraph is"
    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/Mediquad-20B"

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"])

Open LLM Leaderboard Evaluation Results

Detailed results can be found here

Metric Value
Avg. 30.74
AI2 Reasoning Challenge (25-Shot) 27.47
HellaSwag (10-Shot) 28.21
MMLU (5-Shot) 28.66
TruthfulQA (0-shot) 49.56
Winogrande (5-shot) 50.51
GSM8k (5-shot) 0.00