Update README.md

README.md

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 ---
 base_model: unsloth/qwen2.5-coder-1.5b-bnb-4bit
 library_name: peft
+license: mit
 ---
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
+This model is a fine-tuned version of unsloth/qwen2.5-coder-1.5b-bnb-4bit, specifically adapted to solve coding problems using the CodeAlpaca-20k dataset. The model has been optimized for generating high-quality solutions to programming questions across various languages. It leverages the benefits of low-bit quantization for efficient inference while maintaining competitive performance.
 
 
 
 ## Model Details
 
+
 ### Model Description
 
-<!-- Provide a longer summary of what this model is. -->
+Architecture: The model is based on QWen-2.5, a 1.5-billion parameter model optimized using 4-bit quantization via Bits and Bytes. This allows for reduced memory usage and faster inference while maintaining the model’s effectiveness.
+Fine-tuning Process: The model was fine-tuned on the CodeAlpaca-20k dataset, a large corpus of coding-related prompts and solutions that span multiple programming languages. The goal of the fine-tuning was to improve the model’s ability to solve real-world coding problems and generate accurate, executable code.
+Max Sequence Length: 2048 tokens to accommodate larger input sizes.
+Quantization: The use of 4-bit quantization significantly reduces the memory footprint without sacrificing much on model performance, making it ideal for deployment in environments with limited resources
 
 
 
-- **Developed by:** [More Information Needed]
+- **Developed by:** Bidhan Acharya
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
 - **Model type:** [More Information Needed]
 - **Language(s) (NLP):** [More Information Needed]
 - **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+- **Finetuned from model [optional]:** Qwen/Qwen2.5-Coder-1.5B
 
 ### Model Sources [optional]