Update README.md

README.md

@@ -60,4 +60,53 @@ generated_ids = [
 ]
 
 response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
-```
+```
+# **Intended Use**  
+1. **Reasoning and Context Understanding**:  
+   Designed to assist with complex reasoning tasks, contextual understanding, and solving problems requiring logical deduction and critical thinking.  
+
+2. **Mathematical Problem-Solving**:  
+   Specialized for performing advanced mathematical reasoning and calculations, making it suitable for educational, scientific, and research-oriented applications.  
+
+3. **Code Generation and Debugging**:  
+   Offers robust support for coding tasks, including writing, debugging, and optimizing code in various programming languages, ideal for developers and software engineers.  
+
+4. **Structured Data Analysis**:  
+   Excels in processing and analyzing structured data, such as tables and JSON, and generating structured outputs, which is useful for data analysts and automation workflows.  
+
+5. **Multilingual Applications**:  
+   Supports over 29 languages, making it versatile for global applications like multilingual chatbots, content generation, and translations.  
+
+6. **Extended Content Generation**:  
+   Capable of generating long-form content (over 8K tokens), useful for writing reports, articles, and creating detailed instructional guides.  
+
+7. **Interactive Role-Playing and Chatbots**:  
+   Enhanced capabilities for role-playing and condition-setting, making it ideal for interactive chatbots, virtual assistants, and entertainment purposes.  
+
+8. **Large-Context Tasks**:  
+   With a context window of up to 128K tokens, it is ideal for analyzing or generating large documents, books, or datasets in a single session.  
+
+# **Limitations**  
+1. **Hardware Requirements**:  
+   Due to its 20B parameter size and support for long-context inputs, running the model requires significant computational resources, including high-memory GPUs or TPUs.  
+
+2. **Potential Bias in Multilingual Outputs**:  
+   While it supports 29 languages, the quality and accuracy of outputs may vary depending on the language, especially for less-resourced languages.  
+
+3. **Inconsistent Outputs for Creative Tasks**:  
+   The model may occasionally produce inconsistent or repetitive results in creative writing, storytelling, or highly subjective tasks.  
+
+4. **Limited Real-World Awareness**:  
+   It lacks real-time knowledge of current events beyond its training cutoff, which may limit its ability to respond accurately to the latest information.  
+
+5. **Error Propagation in Long-Text Outputs**:  
+   In generating long texts, minor errors in early outputs can sometimes propagate, reducing the overall coherence and accuracy of the response.  
+
+6. **Dependency on High-Quality Prompts**:  
+   Performance may depend on the quality and specificity of the input prompt, requiring users to carefully design queries for optimal results.  
+
+7. **Sensitivity to Adversarial Inputs**:  
+   The model may struggle with adversarial or ambiguous inputs, leading to incorrect or irrelevant outputs.  
+
+8. **Ethical and Safety Concerns**:  
+   Potential misuse in generating misleading, harmful, or offensive content remains a concern, and guardrails must be implemented to ensure responsible use.