--- 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.