Update README.md

README.md

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 # **GWQ-9B-Preview**
 
-GWQ - 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.
+GWQ - 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**
+
+```python
+# pip install accelerate
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+tokenizer = AutoTokenizer.from_pretrained("google/gemma-2-9b-it")
+model = AutoModelForCausalLM.from_pretrained(
+    "prithivMLmods/GWQ-9B-Preview",
+    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:
+```python
+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**
+
+1. **Transformer-Based Design**:  
+   Gemma 2 leverages the transformer architecture, utilizing self-attention mechanisms to process input text and capture contextual relationships effectively.
+
+2. **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.
+
+3. **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.
+
+4. **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.
+
+5. **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.
+
+6. **Scalability**:  
+   The architecture supports scaling up or down based on the application's requirements, balancing performance and resource usage.
+
+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.
+