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1
- ---
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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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-
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- ## Model Card for BiMediX-Bilingual
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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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-
23
- ### Intended Use
24
- - **Primary Use:** Medical interactions in both English and Arabic.
25
- - **Capabilities:** MCQA, closed QA and chats.
26
-
27
- ## Getting Started
28
-
29
- ```python
30
- from transformers import AutoModelForCausalLM, AutoTokenizer
31
-
32
- model_id = "BiMediX/BiMediX-Bi"
33
-
34
- tokenizer = AutoTokenizer.from_pretrained(model_id)
35
- model = AutoModelForCausalLM.from_pretrained(model_id)
36
-
37
- text = "Hello BiMediX! I've been experiencing increased tiredness in the past week."
38
- inputs = tokenizer(text, return_tensors="pt")
39
-
40
- outputs = model.generate(**inputs, max_new_tokens=500)
41
- print(tokenizer.decode(outputs[0], skip_special_tokens=True))
42
- ```
43
-
44
- ### Training Procedure
45
- - **Dataset:** BiMed1.3M, 632 million healthcare specialized tokens.
46
- - **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.
47
- - **Training Resources:** The model underwent training on approximately 632 million tokens from the Arabic-English corpus, including 288 million tokens exclusively for English.
48
-
49
- ### Model Performance
50
- - **Benchmarks:** Outperforms the baseline model and Jais-30B in medical evaluations.
51
-
52
- | **Model** | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** |
53
- |-----------------------------------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------|
54
- | Jais-30B | 57.4 | 55.2 | 46.2 | 55.0 | 46.0 | 48.9 | 40.2 | 31.0 | 75.5 | 50.6 |
55
- | Mixtral-8x7B| 59.1 | 57.6 | 52.6 | 59.5 | 53.3 | 54.4 | 43.2 | 40.6 | 74.7 | 55.0 |
56
- | **BiMediX (Bilingual)** | **70.6** | **72.2** | **59.3** | **74.0** | **64.2** | **59.6**| **55.8** | **54.0** | **78.6** | **65.4**|
57
-
58
- ### Safety and Ethical Considerations
59
- - **Potential issues**: hallucinations, toxicity, stereotypes.
60
- - **Usage:** Research purposes only.
61
-
62
- ### Accessibility
63
- - **Availability:** [BiMediX GitHub Repository](https://github.com/mbzuai-oryx/BiMediX).
64
- - arxiv.org/abs/2402.13253
65
-
66
- ### Authors
67
- Sara Pieri, Sahal Shaji Mullappilly, Fahad Shahbaz Khan, Rao Muhammad Anwer Salman Khan, Timothy Baldwin, Hisham Cholakkal
68
- **Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)**
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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