Update README.md

README.md

@@ -43,7 +43,10 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ## Model Details
 
 
-The BiMediX model, built on a Mixture of Experts (MoE) architecture, leverages the Mixtral-8x7B base network. This approach enables the model to scale significantly by utilizing a sparse operation method, where only a subset of its 47 billion parameters are active during inference, enhancing efficiency. It features a sophisticated router network to allocate tasks to the most relevant experts, each being a specialized feedforward block within the model. The training utilized the BiMed1.3M dataset, focusing on bilingual medical interactions in both English and Arabic, with a substantial corpus of over 632 million healthcare-specialized tokens. The model's fine-tuning process includes a low-rank adaptation technique (QLoRA) to efficiently adapt the model to specific tasks while keeping computational demands manageable. 
+The BiMediX model, built on a Mixture of Experts (MoE) architecture, leverages the [Mixtral-8x7B](https://huggingface.co/mistralai/Mixtral-8x7B-v0.1) base model. It features a sophisticated router network to allocate tasks to the most relevant experts, each being a specialized feedforward blocks within the model.
+This approach enables the model to scale significantly by utilizing a sparse operation method, where less than 13 billion parameters are active during inference, enhancing efficiency. 
+The training utilized the BiMed1.3M dataset, focusing on bilingual medical interactions in both English and Arabic, with a substantial corpus of over 632 million healthcare-specialized tokens. 
+The model's fine-tuning process includes a low-rank adaptation technique (QLoRA) to efficiently adapt the model to specific tasks while keeping computational demands manageable. 
 
 ## Dataset
 
@@ -51,7 +54,20 @@ The BiMediX model, built on a Mixture of Experts (MoE) architecture, leverages t
 
 ## Benchmarks and Performance
 
-(Details about benchmarks and results.)
+The BiMediX model was evaluated across several benchmarks, demonstrating its effectiveness in medical language understanding and question answering in both English and Arabic.
+
+1. **Medical Benchmarks Used for Evaluation:**
+   - **PubMedQA**: A dataset for question answering from biomedical research papers, requiring reasoning over biomedical contexts.
+   - **MedMCQA**: Multiple-choice questions from Indian medical entrance exams, covering a wide range of medical subjects.
+   - **MedQA**: Questions from US and other medical board exams, testing specific knowledge and patient case understanding.
+   - **Medical MMLU**: A compilation of questions from various medical subjects, requiring broad medical knowledge.
+
+2. **Results and Comparisons:**
+   - **Bilingual Evaluation**: BiMediX showed superior performance in bilingual (Arabic-English) evaluations, outperforming both the Mixtral-8x7B base model and Jais-30B, a model designed for Arabic. It demonstrated more than 10 and 15 points higher average accuracy, respectively.
+   - **Arabic Benchmark**: In Arabic-specific evaluations, BiMediX outperformed Jais-30B in all categories, highlighting the effectiveness of the BiMed1.3M dataset and bilingual training.
+   - **English Benchmark**: BiMediX also excelled in English medical benchmarks, surpassing other state-of-the-art models like Med42-70B and Meditron-70B in terms of average performance and efficiency.
+
+These results underscore BiMediX's advanced capability in handling medical queries and its significant improvement over existing models in both languages, leveraging its unique bilingual dataset and training approach.
 
 ## Limitations and Ethical Considerations