DataGemma Google Data Common

 #DataGemma is an experimental set of #open #models designed to ground responses in #realworld #statistical #data from numerous #public #sources ranging from census and health bureaus to the #UN, resulting in more factual and trustworthy AI.

By integrating with Google’s #Data Commons, DataGemma’s early research advancements attempt to address the issue of #hallucination—a key challenge faced by language models #llm.

What is the Data Commons?

Google Data Commons: A Knowledge Graph for Public Data

Google Data Commons is a public knowledge graph that integrates and harmonizes data from various sources, making it easier to explore and analyze. It's designed to provide a unified view of the world's information, enabling users to discover insights and trends across different domains.

Key Features and Benefits:

Unified Dataset: Data Commons combines data from over 200 sources, including government statistics, academic research, and private sector data. This creates a comprehensive and interconnected dataset.

Knowledge Graph: The data is organized as a knowledge graph, where entities (e.g., countries, cities, people) are connected by relationships (e.g., location, affiliation). This structure makes it easier to explore data and discover connections.

Natural Language Queries: Users can query the data using natural language, making it accessible to a wider audience, even those without technical expertise.

Visualization Tools: Data Commons provides tools for visualizing data, such as charts and maps, making it easier to understand complex information.

API Access: Developers can access the data through an API, allowing them to integrate it into their applications and workflows.

Use Cases:

Research: Researchers can use Data Commons to explore trends, identify patterns, and test hypotheses.

Policy Making: Governments and policymakers can use the data to inform decisions and develop effective policies.

Journalism: Journalists can use Data Commons to investigate stories and uncover hidden trends.

Business: Businesses can use the data to understand their customers, identify market opportunities, and optimize their operations.

In essence, Google Data Commons is a valuable resource for anyone looking to explore and analyze public data. By providing a unified and accessible platform, it empowers users to discover insights and make informed decisions.

#datascience #machinelearning #artificialintelligence #google #knowledge

Reducing the size of an LLM

 

                                    image: wikimedia

Understanding the Trade-off: Size Reduction vs. Performance

Reducing the size of an LLM often involves a trade-off with performance. Key factors to consider include:

• Model Architecture: The underlying structure of the LLM determines its capacity and efficiency. Simpler architectures can lead to smaller models but might compromise performance.
• Parameter Quantization: Reducing the precision of numerical values in the model can significantly decrease its size, but it may also impact accuracy.
• Knowledge Distillation: Transferring knowledge from a larger model to a smaller one can help maintain performance while reducing size, but it's not always perfect.
• Pruning: Removing unnecessary connections or neurons can streamline the model, but it requires careful selection to avoid degrading performance.

Techniques for LLM Size Reduction

Here are some specific methods to achieve size reduction:

Model Architecture Simplification

• Reducing the number of layers: Fewer layers generally mean a smaller model, but performance might suffer.
• Decreasing the number of neurons per layer: This can reduce model size but might impact its ability to capture complex patterns.
• Exploring simpler architectures: Consider alternatives to transformers, such as RNNs or CNNs, which can be smaller but might have limitations.

Parameter Quantization

• Reducing bit precision: Storing weights with fewer bits (e.g., 8-bit instead of 32-bit) can significantly reduce model size.
• Quantization techniques: Explore methods like uniform quantization, dynamic quantization, or post-training quantization.

Knowledge Distillation

• Training a smaller model: Use a larger, more complex model as a teacher to train a smaller, student model.
• Transferring knowledge: The student model learns to mimic the teacher's output, capturing essential information.

Pruning

• Identifying unimportant connections: Analyze the model to find weights or neurons with minimal impact.
• Removing connections: Pruning can reduce the number of parameters without significantly affecting performance.
• Iterative pruning: Combine pruning with retraining for better results.

Other Considerations

• Data Efficiency: Use techniques like data augmentation or curriculum learning to improve model performance with less data.
• Hardware Optimization: Leverage specialized hardware or software for efficient model execution.

Balancing Size Reduction and Performance

• Experimentation: Test different techniques and combinations to find the optimal balance.
• Evaluation Metrics: Use appropriate metrics to assess the impact of size reduction on performance.
• Iterative Process: Continuously refine the model and evaluation process.

It's important to note that the best approach depends on the specific LLM, its intended use case, and the desired level of performance. Carefully consider the trade-offs and experiment with different methods to achieve the desired outcome.

Recently NVIDIA reduced the size of Meta's Llama opensource LLM using structured weight pruning and knowledge distillation, the NVIDIA research team refined Llama 3.1 8B into a new Llama-3.1-Minitron 4B. They're releasing the new models on Hugging Face and shared a deep dive into their approach details here