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

python

# 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:

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

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.