transformers safetensors mixtral text-generation moe merge epfl-llm/meditron-7b medalpaca/medalpaca-7b chaoyi-wu/PMC_LLAMA_7B_10_epoch allenai/tulu-2-dpo-7b license:apache-2.0 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
license: apache-2.0
tags:
- moe
- merge
- epfl-llm/meditron-7b
- medalpaca/medalpaca-7b
- chaoyi-wu/PMC_LLAMA_7B_10_epoch
- allenai/tulu-2-dpo-7b
---

# Mediquad-tulu-20B

Mediquad-tulu-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)
* [allenai/tulu-2-dpo-7b](https://huggingface.co/allenai/tulu-2-dpo-7b)

## Evaluations

| Benchmark | Mediquad-tulu-20B | meditron-7b | Orca-2-7b | meditron-70b |
| --- | --- | --- | --- | --- |
| MedMCQA |  |  |  |  |
| ClosedPubMedQA |  |  |  |  |
| PubMedQA |  |  |  |  |
| MedQA |  |  |  |  |
| MedQA4 |  |  |  |  |
| MedicationQA |  |  |  |  |
| MMLU Medical |  |  |  |  |
| TruthfulQA |  |  |  |  |
| GSM8K |  |  |  |  |
| ARC |  |  |  |  |
| HellaSwag |  |  |  |  |
| Winogrande |  |  |  |  |

## 🧩 Configuration

```yamlbase_model: allenai/tulu-2-dpo-7b
gate_mode: hidden
dtype: bfloat16
experts:
  - source_model: epfl-llm/meditron-7b
    positive_prompts:
      - "What are the latest guidelines for managing type 2 diabetes?"
      - "Best practices for post-operative care in cardiac surgery are"
    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 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"

```

## 💻 Usage

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

from transformers import AutoTokenizer
import transformers
import torch

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