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--- |
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language: |
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- en |
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- ar |
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metrics: |
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- accuracy |
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pipeline_tag: text-generation |
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tags: |
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- medical |
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license: cc-by-nc-sa-4.0 |
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--- |
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## Model Card for BiMediX-Bilingual |
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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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### 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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## Getting Started |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_id = "BiMediX/BiMediX-Bi" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id) |
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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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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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### Training Procedure |
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- **Dataset:** BiMed1.3M, 632 million healthcare specialized tokens. |
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- **QLoRA Adaptation:** Implements a low-rank adaptation technique, incorporating learnable low-rank adapter weights into the experts and the routing network. This results in training about 4% of the original parameters. |
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- **Training Resources:** The model underwent training on approximately 632 million tokens from the Arabic-English corpus, including 288 million tokens exclusively for English. |
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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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### Safety and Ethical Considerations |
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- **Potential issues**: hallucinations, toxicity, stereotypes. |
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- **Usage:** Research purposes only. |
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### Accessibility |
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- **Availability:** [BiMediX GitHub Repository](https://github.com/mbzuai-oryx/BiMediX). |
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- arxiv.org/abs/2402.13253 |
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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 |
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**Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)** |
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