Update README.md

README.md

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 print(outputs[0]["generated_text"][-1])
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+```
+
+# **Intended Use**
+
+1. **Mathematical Problem Solving**:  
+   PyThagorean is designed for solving complex mathematical problems, including algebra, calculus, trigonometry, and more, by leveraging Python-based libraries. It is ideal for educational tools, tutoring platforms, and automated math assistants.  
+
+2. **Python Code Generation**:  
+   The model generates Python code snippets for mathematical computations, simulations, and problem-solving, making it valuable for developers, researchers, and students.  
+
+3. **Multilingual Dialogue Systems**:  
+   With support for multiple languages, PyThagorean can assist users worldwide in understanding and solving mathematical problems through dialogue-based interfaces.  
+
+4. **Instruction-Following Tasks**:  
+   The model excels at adhering to precise mathematical instructions and delivering accurate, step-by-step solutions for problems embedded in text.  
+
+5. **Agent-Based Knowledge Retrieval**:  
+   PyThagorean can retrieve and summarize mathematical concepts or problem-solving techniques, enabling quick access to relevant knowledge for educational and research purposes.  
+
+6. **Educational Content Creation**:  
+   It generates educational content such as example problems, solutions, and Python-based tutorials, aiding teachers and content creators.  
+
+7. **Summarization and Explanation**:  
+   The model provides clear explanations and breakdowns of mathematical solutions, helping users understand the rationale and process behind the answers.
+
+   
+# **Limitations**
+
+1. **Performance on Ambiguous Instructions**:  
+   The model may struggle with ambiguous, vague, or poorly framed mathematical instructions, potentially leading to incorrect or incomplete solutions.  
+
+2. **Edge Cases and Special Scenarios**:  
+   For highly specialized or niche mathematical problems, especially those not commonly encountered in training data, the model's performance may degrade.  
+
+3. **Errors in Multi-Step Reasoning**:  
+   While trained on reasoning datasets, the model may sometimes produce incorrect results for multi-step or highly complex reasoning tasks, particularly if intermediate steps are not explicitly defined.  
+
+4. **Bias Toward Common Solutions**:  
+   The model may favor standard or commonly used approaches to mathematical problems, potentially missing creative or less conventional methods of solution.  
+
+5. **Resource Intensity**:  
+   As a large-scale model, PyThagorean requires significant computational resources, including high-end GPUs, for efficient inference and deployment.  
+
+6. **Context Window Limitations**:  
+   The model's finite context window may lead to incomplete understanding or truncated responses for problems requiring extensive context or lengthy input.  
+
+7. **Handling of Non-Mathematical Queries**:  
+   While capable of engaging in general conversations, its performance for non-mathematical tasks may not match models specifically tuned for broader use cases.  
+
+8. **Dependency on Python Libraries**:  
+   Generated solutions may rely on specific Python libraries or functions, which users must have installed and configured correctly to execute the code successfully.