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  license: cc-by-nc-sa-4.0
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  ---
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  | **Model** | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** |
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  |-----------------------------------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------|
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  | Jais-30B | 57.4 | 55.2 | 46.2 | 55.0 | 46.0 | 48.9 | 40.2 | 31.0 | 75.5 | 50.6 |
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  | Mixtral-8x7B| 59.1 | 57.6 | 52.6 | 59.5 | 53.3 | 54.4 | 43.2 | 40.6 | 74.7 | 55.0 |
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- | **BiMediX (Bilingual)** | **70.6** | **72.2** | **59.3** | **74.0** | **64.2** | **59.6**| **55.8** | **54.0** | **78.6** | **65.4**|
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  license: cc-by-nc-sa-4.0
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  ---
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+ ## Model Card for BiMediX
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+
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+ ### Model Details
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+ - **Name:** BiMediX
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+ - **Version:** 1.0
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+ - **Type:** Bilingual Medical Mixture of Experts Large Language Model (LLM)
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+ - **Languages:** English, Arabic
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+ - **Model Architecture:** [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
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+ - **Training Data:** BiMed1.3M, a bilingual dataset with diverse medical interactions.
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+
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+ ### Intended Use
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+ - **Primary Use:** Medical interactions in both English and Arabic.
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+ - **Capabilities:** MCQA, closed QA and chats.
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+
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+ ## Getting Started
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+
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ model_id = "BiMediX/BiMediX-Bi"
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+
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+ tokenizer = AutoTokenizer.from_pretrained(model_id)
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+ model = AutoModelForCausalLM.from_pretrained(model_id)
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+
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+ text = "Hello BiMediX! I've been experiencing increased tiredness in the past week."
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+ inputs = tokenizer(text, return_tensors="pt")
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+
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+ outputs = model.generate(**inputs, max_new_tokens=500)
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+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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+ ```
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+
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+ ### Training Procedure
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+ - **Dataset:** Semi-automated English-to-Arabic translation with human refinement.
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+ - **Training Resources:** 632 million healthcare specialized tokens.
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+ - **Evaluation:**
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+
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+ ### Model Performance
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+ - **Benchmarks:** Outperforms the baseline model and Jais-30B in medical evaluations.
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  | **Model** | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** |
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  |-----------------------------------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------|
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  | Jais-30B | 57.4 | 55.2 | 46.2 | 55.0 | 46.0 | 48.9 | 40.2 | 31.0 | 75.5 | 50.6 |
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  | Mixtral-8x7B| 59.1 | 57.6 | 52.6 | 59.5 | 53.3 | 54.4 | 43.2 | 40.6 | 74.7 | 55.0 |
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+ | **BiMediX (Bilingual)** | **70.6** | **72.2** | **59.3** | **74.0** | **64.2** | **59.6**| **55.8** | **54.0** | **78.6** | **65.4**|
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+
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+ ### Limitations
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+ - Potential issues: hallucinations, toxicity, stereotypes.
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+ - Medical diagnoses and recommendations require human evaluation.
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+
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+ ### Safety and Ethical Considerations
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+ - **Usage:** Research purposes only.
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+
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+
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+ ### Accessibility
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+ - **Availability:** [BiMediX GitHub Repository](https://github.com/mbzuai-oryx/BiMediX).
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+
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+ ### Authors
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+ Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan, Rao Muhammad Anwer Salman Khan, Timothy Baldwin, Hisham Cholakkal, Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI).
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+