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Model: Technoculture/Medorca-4x7b Source: Original Platform
2026-08-01 11:11:17 +08:00
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+---
+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"])
+```