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--- |
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license: apache-2.0 |
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tags: |
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- moe |
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- merge |
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- AdaptLLM/medicine-chat |
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- microsoft/Orca-2-7b |
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datasets: |
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- open-llm-leaderboard/details_Technoculture__Medchator-2x7b |
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--- |
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# Medchator-2x7b |
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Medchator-2x7b is a Mixure of Experts (MoE) made with the following models: |
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* [AdaptLLM/medicine-chat](https://huggingface.co/AdaptLLM/medicine-chat) |
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* [microsoft/Orca-2-7b](https://huggingface.co/microsoft/Orca-2-7b) |
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## Evaluations |
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# Open LLM Leaderboard |
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| Model Name | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K | |
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| ------------------ | -------- | --------- | -------- | ---------- | ---------- | -------- | |
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| Orca-2-7b | **78.4** | 76.1 | 53.7 | **52.4** | 74.2 | **47.2** | |
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| LLAMA-2-7b | 43.2 | 77.1 | 44.4 | 38.7 | 69.5 | 16 | |
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| MT7Bi-sft | 54.1 | 75.11 | - | 43.08 | 72.14 | 15.54 | |
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| MT7bi-dpo | 54.69 | 75.89 | 52.82 | 45.48 | 71.58 | 25.93 | |
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| Medorca-2x7b | 54.1 | 76.04 | 54.1 | 48.04 | 74.51 | 20.64 | |
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| Medchator-2x7b | **57.59**| **78.14** | **56.13**| **48.77** | **75.3** | **32.83**| |
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## Medical Performance |
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Clinical Camel demonstrates competitive performance on medical benchmarks. |
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**Table: Five-Shot Performance of GPT3.5, llama-2-7b and Llama-2-70b on Various Medical Datasets** |
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| Dataset | Medchator-2x7b | GPT3.5 | Llama-2 7b | Llama-2 70b | |
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|----------------------------|----------------|--------|------------|-------------| |
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| MMLU Anatomy | 56.3 | 60.7 | 48.9 | 62.9 | |
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| MMLU Clinical Knowledge | 63.0 | 68.7 | 46.0 | 71.7 | |
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| MMLU College Biology | 63.8 | 72.9 | 47.2 | 84.7 | |
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| MMLU College Medicine | 50.9 | 63.6 | 42.8 | 64.2 | |
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| MMLU Medical Genetics | 67.0 | 68.0 | 55.0 | 74.0 | |
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| MMLU Professional Medicine | 55.1 | 69.8 | 53.6 | 75.0 | |
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## 🧩 Configuration |
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```yaml |
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base_model: microsoft/Orca-2-7b |
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gate_mode: hidden |
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dtype: bfloat16 |
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experts: |
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- source_model: AdaptLLM/medicine-chat |
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positive_prompts: |
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- "How does sleep affect cardiovascular health?" |
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- "Could a plant-based diet improve arthritis symptoms?" |
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- "A patient comes in with symptoms of dizziness and nausea" |
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- "When discussing diabetes management, the key factors to consider are" |
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- "The differential diagnosis for a headache with visual aura could include" |
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negative_prompts: |
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- "Recommend a good recipe for a vegetarian lasagna." |
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- "Give an overview of the French Revolution." |
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- "Explain how a digital camera captures an image." |
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- "What are the environmental impacts of deforestation?" |
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- "The recent advancements in artificial intelligence have led to developments in" |
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- "The fundamental concepts in economics include ideas like supply and demand, which explain" |
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- source_model: microsoft/Orca-2-7b |
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positive_prompts: |
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- "Here is a funny joke for you -" |
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- "When considering the ethical implications of artificial intelligence, one must take into account" |
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- "In strategic planning, a company must analyze its strengths and weaknesses, which involves" |
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- "Understanding consumer behavior in marketing requires considering factors like" |
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- "The debate on climate change solutions hinges on arguments that" |
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negative_prompts: |
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- "In discussing dietary adjustments for managing hypertension, it's crucial to emphasize" |
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- "For early detection of melanoma, dermatologists recommend that patients regularly check their skin for" |
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- "Explaining the importance of vaccination, a healthcare professional should highlight" |
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``` |
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## 💻 Usage |
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```python |
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!pip install -qU transformers bitsandbytes accelerate |
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from transformers import AutoTokenizer |
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import transformers |
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import torch |
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model = "Technoculture/Medchator-2x7b" |
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tokenizer = AutoTokenizer.from_pretrained(model) |
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pipeline = transformers.pipeline( |
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"text-generation", |
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model=model, |
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model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True}, |
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) |
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messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}] |
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prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) |
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print(outputs[0]["generated_text"]) |
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``` |
