README.md

---
license: apache-2.0
tags:
- moe
- merge
- epfl-llm/meditron-7b
- medalpaca/medalpaca-7b
- chaoyi-wu/PMC_LLAMA_7B_10_epoch
- microsoft/Orca-2-7b
---

# Medorca-4x7b

Mediquad-orca-20B is a Mixure of Experts (MoE) made with the following models:
* [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b)
* [medalpaca/medalpaca-7b](https://huggingface.co/medalpaca/medalpaca-7b)
* [chaoyi-wu/PMC_LLAMA_7B_10_epoch](https://huggingface.co/chaoyi-wu/PMC_LLAMA_7B_10_epoch)
* [microsoft/Orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b)

## Evaluations

[open_llm_leaderboard](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__Mediquad-orca-20B)

| Benchmark | Medorca-4x7b | Orca-2-7b | meditron-7b | meditron-70b |
| --- | --- | --- | --- | --- |
| MedMCQA |  |  |  |  |
| ClosedPubMedQA |  |  |  |  |
| PubMedQA |  |  |  |  |
| MedQA |  |  |  |  |
| MedQA4 |  |  |  |  |
| MedicationQA |  |  |  |  |
| MMLU Medical |  |  |  |  |
| MMLU | 24.28 | 56.37 |  |  |
| TruthfulQA | 48.42 | 52.45 |  |  |
| GSM8K | 0 | 47.2 |  |  |
| ARC | 29.35 | 54.1 |  |  |
| HellaSwag | 25.72 | 76.19 |  |  |
| Winogrande | 48.3 | 73.48 |  |  |

## 🧩 Configuration

```yamlbase_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?"
      - "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: medalpaca/medalpaca-7b
    positive_prompts:
      - "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."
      - "The fundamental concepts in economics include ideas like supply and demand, which explain"
  - source_model: chaoyi-wu/PMC_LLAMA_7B_10_epoch
    positive_prompts:
      - "How does sleep affect cardiovascular health?"
      - "When discussing diabetes management, the key factors to consider are"
    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: 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

```python
!pip install -qU transformers bitsandbytes accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "Technoculture/Mediquad-orca-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"])
```