Update README.md

README.md

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 This model was converted to GGUF format from [`prithivMLmods/GWQ-9B-Preview2`](https://huggingface.co/prithivMLmods/GWQ-9B-Preview2) 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/GWQ-9B-Preview2) for more details on the model.
 
+---
+
+Chain of Continuous Thought Synthetic Dataset, which enhances its 
+ability to perform reasoning, multi-step problem solving, and logical 
+inferences.
+
+
+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.
+
+
+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.
+
+
+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 GWQ2
+	
+
+
+
+Resource Requirements:
+Although lightweight 
+compared to larger models, the 9B parameter size still requires 
+significant computational resources, including GPUs with large memory 
+for inference.
+
+
+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.
+
+
+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.
+
+
+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.
+
+
+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.
+
+
+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.
+
+
+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.
+
+---
 ## Use with llama.cpp
 Install llama.cpp through brew (works on Mac and Linux)