Update README.md

README.md

@@ -69,3 +69,45 @@ print(tokenizer.decode(outputs[0]))
 7. **Open-Source and Customizable**:  
    Being open-source, Gemma 2 allows developers to modify and extend its architecture to suit specific use cases, such as integrating it into tools like ReadM.Md for markdown-related tasks.
 
+# **Intended Use of GWQ (Gemma with Questions)**
+
+1. **Question Answering:**  
+   The model excels in generating concise and relevant answers to user-provided queries across various domains.
+   
+2. **Summarization:**  
+   It can be used to summarize large bodies of text, making it suitable for news aggregation, academic research, and report generation.
+
+3. **Reasoning Tasks:**  
+   GWQ is fine-tuned on the Chain of Continuous Thought Synthetic Dataset, which enhances its ability to perform reasoning, multi-step problem solving, and logical inferences.
+
+4. **Text Generation:**  
+   The model is ideal for creative writing tasks such as generating poems, stories, and essays. It can also be used for generating code comments, documentation, and markdown files.
+
+5. **Instruction Following:**  
+   GWQ’s instruction-tuned variant is suitable for generating responses based on user instructions, making it useful for virtual assistants, tutoring systems, and automated customer support.
+
+6. **Domain-Specific Applications:**  
+   Thanks to its modular design and open-source nature, the model can be fine-tuned for specific tasks like legal document summarization, medical record analysis, or financial report generation.
+
+## **Limitations of GWQ**
+
+1. **Resource Requirements:**  
+   Although lightweight compared to larger models, the 9B parameter size still requires significant computational resources, including GPUs with large memory for inference.
+
+2. **Knowledge Cutoff:**  
+   The model’s pre-training data may not include recent information, making it less effective for answering queries on current events or newly developed topics.
+
+3. **Bias in Outputs:**  
+   Since the model is trained on publicly available datasets, it may inherit biases present in those datasets, leading to potentially biased or harmful outputs in sensitive contexts.
+
+4. **Hallucinations:**  
+   Like other large language models, GWQ can occasionally generate incorrect or nonsensical information, especially when asked for facts or reasoning outside its training scope.
+
+5. **Lack of Common-Sense Reasoning:**  
+   While GWQ is fine-tuned for reasoning, it may still struggle with tasks requiring deep common-sense knowledge or nuanced understanding of human behavior and emotions.
+
+6. **Dependency on Fine-Tuning:**  
+   For optimal performance on domain-specific tasks, fine-tuning on relevant datasets is required, which demands additional computational resources and expertise.
+
+7. **Context Length Limitation:**  
+   The model’s ability to process long documents is limited by its maximum context window size. If the input exceeds this limit, truncation may lead to loss of important information.