---
license: apache-2.0
tags:
- moe
- merge
- AdaptLLM/medicine-chat
- microsoft/Orca-2-7b
datasets:
- open-llm-leaderboard/details_Technoculture__Medchator-2x7b
model-index:
- name: Medchator-2x7b
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: 57.59
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 78.14
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 56.13
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 48.77
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 75.3
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
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: 32.83
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Technoculture/Medchator-2x7b
name: Open LLM Leaderboard
---
# Medchator-2x7b
Medchator-2x7b is a Mixure of Experts (MoE) made with the following models:
* [AdaptLLM/medicine-chat](https://huggingface.co/AdaptLLM/medicine-chat)
* [microsoft/Orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b)
## Evaluations
# Open LLM Leaderboard
| Model Name | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
| ------------------ | -------- | --------- | -------- | ---------- | ---------- | -------- |
| Orca-2-7b | **78.4** | 76.1 | 53.7 | **52.4** | 74.2 | **47.2** |
| LLAMA-2-7b | 43.2 | 77.1 | 44.4 | 38.7 | 69.5 | 16 |
| MT7Bi-sft | 54.1 | 75.11 | - | 43.08 | 72.14 | 15.54 |
| MT7bi-dpo | 54.69 | 75.89 | 52.82 | 45.48 | 71.58 | 25.93 |
| Medorca-2x7b | 54.1 | 76.04 | 54.1 | 48.04 | 74.51 | 20.64 |
| Medchator-2x7b | **57.59**| **78.14** | **56.13**| **48.77** | **75.3** | **32.83**|
## 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**
| Dataset | Medchator-2x7b | GPT3.5 | Llama-2 7b | Llama-2 70b |
|----------------------------|----------------|--------|------------|-------------|
| MMLU Anatomy | 56.3 | 60.7 | 48.9 | 62.9 |
| MMLU Clinical Knowledge | 63.0 | 68.7 | 46.0 | 71.7 |
| MMLU College Biology | 63.8 | 72.9 | 47.2 | 84.7 |
| MMLU College Medicine | 50.9 | 63.6 | 42.8 | 64.2 |
| MMLU Medical Genetics | 67.0 | 68.0 | 55.0 | 74.0 |
| MMLU Professional Medicine | 55.1 | 69.8 | 53.6 | 75.0 |
## 🧩 Configuration
```yaml
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
```python
!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"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Technoculture__Medchator-2x7b)
| Metric |Value|
|---------------------------------|----:|
|Avg. |58.13|
|AI2 Reasoning Challenge (25-Shot)|57.59|
|HellaSwag (10-Shot) |78.14|
|MMLU (5-Shot) |56.13|
|TruthfulQA (0-shot) |48.77|
|Winogrande (5-shot) |75.30|
|GSM8k (5-shot) |32.83|