Update README.md

README.md

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 print(outputs[0]["generated_text"][-1])
 ```
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-Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
+# **Intended Use**  
+1. **Mathematical Problem Solving**: Llama-3.2-3B-Math-Oct is designed for solving a wide range of mathematical problems, including arithmetic, algebra, calculus, and probability.  
+2. **Reasoning Enhancement**: It enriches logical reasoning capabilities, helping users understand and solve complex mathematical concepts.  
+3. **Context Understanding**: The model is highly effective in interpreting problem statements, mathematical scenarios, and context-heavy equations.  
+4. **Educational Support**: It serves as a learning tool for students, educators, and enthusiasts, providing step-by-step explanations for mathematical solutions.  
+5. **Scenario Simulation**: The model can role-play specific mathematical scenarios, such as tutoring, creating math problems, or acting as a math assistant.  
+
+# **Limitations**  
+1. **Accuracy Constraints**: While effective in many cases, the model may occasionally provide incorrect solutions, particularly for highly complex or unconventional problems.  
+2. **Parameter Limitation**: Being a 3B-parameter model, it might lack the precision and capacity of larger models for intricate problem-solving.  
+3. **Lack of Domain-Specific Expertise**: The model may struggle with problems requiring niche mathematical knowledge or specialized fields like advanced topology or quantum mechanics.  
+4. **Dependency on Input Clarity**: Ambiguous or poorly worded problem statements might lead to incorrect interpretations and solutions.  
+5. **Inability to Learn Dynamically**: The model cannot improve its understanding or reasoning dynamically without retraining.  
+6. **Non-Mathematical Queries**: While optimized for mathematics, the model may underperform in general-purpose tasks compared to models designed for broader use cases.  
+7. **Computational Resources**: Deploying the model may require significant computational resources for real-time usage.