Update README.md

README.md

@@ -10,7 +10,7 @@ license: apache-2.0
 
 # BiMediX: Bilingual Medical Mixture of Experts LLM
 
-Welcome to the official HuggingFace repository for BiMediX, the bilingual medical Large Language Model (LLM) designed for English and Arabic interactions. BiMediX facilitates a broad range of medical interactions, including multi-turn chats, multiple-choice Q&A, and open-ended question answering.
+Welcome to the official HuggingFace repository for BiMediX, the bilingual medical Large Language Model (LLM) designed for English and Arabic interactions. BiMediX facilitates a broad range of **medical interactions**, including multi-turn chats, multiple-choice Q&A, and open-ended question answering.
 
 ## Key Features
 
@@ -18,7 +18,7 @@ Welcome to the official HuggingFace repository for BiMediX, the bilingual medica
 - **BiMed1.3M Dataset**: Unique dataset with 1.3 million bilingual medical interactions across English and Arabic, including 250k synthesized multi-turn doctor-patient chats for instruction tuning.
 - **High-Quality Translation** : Utilizes a semi-automated English-to-Arabic translation pipeline with human refinement to ensure accuracy and quality in translations.
 - **Evaluation Benchmark for Arabic Medical LLMs**: Comprehensive benchmark for evaluating Arabic medical language models, setting a new standard in the field.
-- **State-of-the-Art Performance**: Significantly outperforms existing models in medical benchmarks, while 8-times faster than existing models.
+- **State-of-the-Art Performance**: Outperforms existing models in medical benchmarks, while 8-times faster than comparable existing models.
 
 
 ## Getting Started
@@ -53,18 +53,10 @@ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 ## Limitations and Ethical Considerations
 
-Users are urged to employ BiMediX responsibly, especially when applying its outputs in real-world medical scenarios. 
+**This release, intended for research, is not ready for clinical or commercial use.** Users are urged to employ BiMediX responsibly, especially when applying its outputs in real-world medical scenarios. 
 It is imperative to verify the model's advice with qualified healthcare professionals and not to rely on AI for medical diagnoses or treatment decisions.
 Despite the overall advancements BiMediX brings to the field of medical NLP, it shares common challenges with other language models, 
-including hallucinations, toxicity, and stereotypes.
-
-### Recognized Limitations
-
-- **Hallucinations**: BiMediX, like many advanced LLMs, can sometimes generate information that, while plausible, may not be factually accurate or relevant to the given context.
-- **Toxicity and Stereotypes**: The model may inadvertently propagate stereotypes or produce responses that could be considered toxic, reflecting biases present in the training data.
-- **Accuracy of Medical Diagnoses**: While we have conducted both automatic and qualitative evaluations with medical professionals, we must acknowledge that BiMediX's medical diagnoses and recommendations are not infallible. Users should treat the model's output as informational rather than definitive medical advice.
-
-We are committed to continuous improvement and ethical AI research, and we look forward to collaborating with the community to address these challenges.
+including hallucinations, toxicity, and stereotypes. BiMediX's medical diagnoses and recommendations are not infallible.
 
 ## License and Citation
 
@@ -86,5 +78,4 @@ If you use BiMediX in your research, please cite our work as follows:
 }
 ```
 
-
-Visit our [GitHub](#) for more information and resources.
+Visit our [GitHub](https://github.com/mbzuai-oryx/BiMediX) for more information and resources.