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README.md

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+
+---
+
+license: gemma
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- gemma2
+- text-generation-inference
+- f16
+
+---
+
+[![QuantFactory Banner](https://lh7-rt.googleusercontent.com/docsz/AD_4nXeiuCm7c8lEwEJuRey9kiVZsRn2W-b4pWlu3-X534V3YmVuVc2ZL-NXg2RkzSOOS2JXGHutDuyyNAUtdJI65jGTo8jT9Y99tMi4H4MqL44Uc5QKG77B0d6-JfIkZHFaUA71-RtjyYZWVIhqsNZcx8-OMaA?key=xt3VSDoCbmTY7o-cwwOFwQ)](https://hf.co/QuantFactory)
+
+
+# QuantFactory/GWQ-9B-Preview-GGUF
+This is quantized version of [prithivMLmods/GWQ-9B-Preview](https://huggingface.co/prithivMLmods/GWQ-9B-Preview) created using llama.cpp
+
+# Original Model Card
+
+![gwq.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/OSjnMjpPnapGr4Kguuh2O.png)
+
+# **GWQ-9B-Preview**
+
+GWQ - Gemma with Questions Prev is a family of lightweight, state-of-the-art open model base 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("prithivMLmods/GWQ-9B-Preview")
+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.
+
+# **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.