初始化项目，由ModelHub XC社区提供模型

Model: BiMediX/BiMediX-Bi
Source: Original Platform

README.md

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+---
+language:
+- en
+- ar
+metrics:
+- accuracy
+pipeline_tag: text-generation
+tags:
+- medical
+license: cc-by-nc-sa-4.0
+---
+## Model Card for BiMediX-Bilingual
+
+### Model Details
+- **Name:** BiMediX
+- **Version:** 1.0
+- **Type:** Bilingual Medical Mixture of Experts Large Language Model (LLM)
+- **Languages:** English, Arabic
+- **Model Architecture:** [Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
+- **Training Data:** BiMed1.3M, a bilingual dataset with diverse medical interactions.
+
+### Intended Use
+- **Primary Use:** Medical interactions in both English and Arabic.
+- **Capabilities:** MCQA, closed QA and chats.
+
+## Getting Started
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+model_id = "BiMediX/BiMediX-Bi"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id)
+text = "Hello BiMediX! I've been experiencing increased tiredness in the past week."
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=500)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### Training Procedure
+- **Dataset:** BiMed1.3M, 632 million healthcare specialized tokens.
+- **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.
+- **Training Resources:** The model underwent training on approximately 632 million tokens from the Arabic-English corpus, including 288 million tokens exclusively for English.
+
+### Model Performance
+- **Benchmarks:** Outperforms the baseline model and Jais-30B in medical evaluations.
+
+| **Model**                         | **CKG** | **CBio** | **CMed** | **MedGen** | **ProMed** | **Ana** | **MedMCQA** | **MedQA** | **PubmedQA** | **AVG** |
+|-----------------------------------|------------|-----------|-----------|-------------|-------------|---------|-------------|-----------|--------------|---------|
+| Jais-30B | 57.4       | 55.2      | 46.2      | 55.0        | 46.0        | 48.9    | 40.2        | 31.0      | 75.5         | 50.6    |
+| Mixtral-8x7B| 59.1       | 57.6      | 52.6      | 59.5        | 53.3        | 54.4    | 43.2        | 40.6      | 74.7         | 55.0    |
+| **BiMediX (Bilingual)**           | **70.6**   | **72.2**  | **59.3**  | **74.0**    | **64.2**    | **59.6**| **55.8**    | **54.0**  | **78.6**     | **65.4**|
+
+### Safety and Ethical Considerations
+- **Potential issues**: hallucinations, toxicity, stereotypes.
+- **Usage:** Research purposes only.
+
+### Accessibility
+- **Availability:** [BiMediX GitHub Repository](https://github.com/mbzuai-oryx/BiMediX).
+- arxiv.org/abs/2402.13253 
+
+### Authors
+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)**