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.
 
+---
+Model details:
+-
+GWQ2 - Gemma with Questions Prev is a family of lightweight, 
+state-of-the-art open models from Google, built using the same research 
+and technology employed to create the Gemini models. These models are 
+text-to-text, decoder-only large language models, available in English, 
+with open weights for both pre-trained and instruction-tuned variants. 
+Gemma models are well-suited for a variety of text generation tasks, 
+including question answering, summarization, and reasoning. GWQ is 
+fine-tuned on the Chain of Continuous Thought Synthetic Dataset, built 
+upon the Gemma2forCasualLM architecture.
+
+
+
+	
+		
+	
+
+		Running GWQ Demo
+	
+
+
+
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/GWQ-9B-Preview2")
+model = AutoModelForCausalLM.from_pretrained(
+    "prithivMLmods/GWQ-9B-Preview2",
+    device_map="auto",
+    torch_dtype=torch.bfloat16,
+)
+
+input_text = "Write me a poem about Machine Learning."
+input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=32)
+print(tokenizer.decode(outputs[0]))
+
+
+
+You can ensure the correct chat template is applied by using tokenizer.apply_chat_template as follows:
+
+
+messages = [
+    {"role": "user", "content": "Write me a poem about Machine Learning."},
+]
+input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True).to("cuda")
+
+outputs = model.generate(**input_ids, max_new_tokens=256)
+print(tokenizer.decode(outputs[0]))
+
+
+
+
+	
+		
+	
+
+		Key Architecture
+	
+
+
+
+Transformer-Based Design:
+Gemma 2 leverages 
+the transformer architecture, utilizing self-attention mechanisms to 
+process input text and capture contextual relationships effectively.
+
+
+Lightweight and Efficient:
+It is designed to 
+be computationally efficient, with fewer parameters compared to larger 
+models, making it ideal for deployment on resource-constrained devices 
+or environments.
+
+
+Modular Layers:
+The architecture consists of 
+modular encoder and decoder layers, allowing flexibility in adapting the
+ model for specific tasks like text generation, summarization, or 
+classification.
+
+
+Attention Mechanisms:
+Gemma 2 employs 
+multi-head self-attention to focus on relevant parts of the input text, 
+improving its ability to handle long-range dependencies and complex 
+language structures.
+
+
+Pre-training and Fine-Tuning:
+The model is 
+pre-trained on large text corpora and can be fine-tuned for specific 
+tasks, such as markdown processing in ReadM.Md, to enhance its 
+performance on domain-specific data.
+
+
+Scalability:
+The architecture supports scaling up or down based on the application's requirements, balancing performance and resource usage.
+
+
+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 GWQ2 (Gemma with Questions2)
+	
+
+
+
+Question Answering:
+The model excels in generating concise and relevant answers to user-provided queries across various domains.
+
+
+Summarization:
+It can be used to summarize 
+large bodies of text, making it suitable for news aggregation, academic 
+research, and report generation.
+
+
+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.
+
+
+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)