Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/PocketThinker-QwQ-3B-Instruct`](https://huggingface.co/prithivMLmods/PocketThinker-QwQ-3B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
 Refer to the [original model card](https://huggingface.co/prithivMLmods/PocketThinker-QwQ-3B-Instruct) for more details on the model.
 
+---
+PocketThinker-QwQ-3B-Instruct
+-
+
+PocketThinker-QwQ-3B-Instruct is based on the Qwen2.5-3B-Instruct architecture, designed as a lightweight and efficient reasoning 
+assistant. It serves as the pocket-sized version of QwQ-LCoT-7B-Instruct, optimized for fast inference while maintaining 
+strong problem-solving and computational capabilities. This model is fine-tuned for enhanced structured reasoning, minimal token wastage, and high-quality technical responses.
+
+Key Improvements
+-
+
+Optimized for Coding: Specializes in generating structured, efficient code with minimal redundancy for smooth execution.  
+
+Compact yet Powerful: Maintains strong problem-solving capabilities within a smaller 3B parameter architecture, ensuring accessibility on resource-limited devices.  
+
+Advanced Reasoning Capabilities: Excels in algorithmic problem-solving, mathematical reasoning, and structured technical explanations.  
+
+Efficient Memory Utilization: Reduces computational overhead while maintaining high-quality outputs.  
+
+Focused Output Generation: Avoids unnecessary token generation, ensuring concise and relevant responses.
+
+
+Intended Use
+-
+Code Generation & Optimization:
+Supports developers in writing, refining, and optimizing code across multiple programming languages.  
+
+Algorithm & Mathematical Problem Solving:
+Delivers precise solutions and structured explanations for complex problems.  
+
+Technical Documentation & Explanation:
+Assists in generating well-structured documentation for libraries, APIs, and coding concepts.  
+
+Debugging Assistance:
+Helps identify and correct errors in code snippets.  
+
+Educational Support:
+Simplifies programming topics for students and learners with clear explanations.  
+
+Structured Data Processing:
+Generates structured outputs like JSON, XML, and tables for data science applications.
+
+Limitations
+-
+
+Hardware Constraints:
+Although lighter than larger models, still requires a moderately powerful GPU or TPU for optimal performance.  
+
+Potential Bias in Responses:
+Outputs may reflect biases present in training data.  
+
+Limited Creativity:
+May generate variable results in non-technical, creative tasks.  
+
+No Real-Time Awareness:
+Lacks access to real-world events beyond its training cutoff.  
+
+Error Propagation in Long Responses:
+Minor mistakes in early outputs may affect overall coherence in lengthy responses.  
+
+Prompt Sensitivity:
+The effectiveness of responses depends on well-structured prompts.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)