October 17, 2025

Optimize Model Quantization for Large Language Models on AI Devices

By Alireza Pourmahdavi October 17, 2025

Introduction Model quantization is a powerful technique that optimizes large language models for deployment on AI devices, such as smartphones and edge devices. By reducing the precision of machine learning model parameters, model quantization significantly decreases memory usage and enhances processing speed, making sophisticated AI applications more accessible on resource-constrained devices. This technique, including methods […]

Optimize Model Quantization for Large Language Models on Edge Devices

By Alireza Pourmahdavi October 17, 2025

Introduction Model quantization is a game-changing technique for optimizing large language models (LLMs) and deploying them efficiently on edge devices, smartphones, and IoT devices. By reducing the size and computational demands of machine learning models, model quantization enables AI to perform faster, with lower power consumption and minimal sacrifice to accuracy. This process involves adjusting […]

Optimize NLP Models with Backtracking: Enhance Summarization, NER, and Tuning

By Alireza Pourmahdavi October 17, 2025

Introduction Backtracking algorithms are a key tool for optimizing NLP models, helping navigate complex solution spaces and improve tasks like text summarization, named entity recognition (NER), and hyperparameter tuning. While these algorithms offer an exhaustive search for the best solution, they can be computationally expensive. However, techniques like constraint propagation, heuristic search, and dynamic reordering […]

Master Python Map Function: Use Lambda & User-Defined Functions

By Alireza Pourmahdavi October 17, 2025

Introduction The Python map() function is an essential tool for processing data iterables efficiently. By applying a given function to each item in an iterable, it returns an iterator with the results, making it a powerful asset for handling large datasets. Whether you’re using lambda functions, user-defined functions, or built-in functions, map() helps streamline the […]

Optimize NLP Models with Backtracking, Text Summarization, and More

By Alireza Pourmahdavi October 17, 2025

Introduction Optimizing NLP models requires a strategic approach, and backtracking is one of the most effective techniques for improving performance. By systematically exploring potential solutions and discarding ineffective paths, backtracking helps in tasks like text summarization, Named Entity Recognition, and hyperparameter tuning. With its ability to evaluate and refine model configurations, this method is a […]

Alireza Pourmahdavi

I’m Alireza Pourmahdavi, a founder, CEO, and builder with a background that combines deep technical expertise with practical business leadership. I’ve launched and scaled companies like Caasify and AutoVM, focusing on cloud services, automation, and hosting infrastructure. I hold VMware certifications, including VCAP-DCV and VMware NSX. My work involves constructing multi-tenant cloud platforms on VMware, optimizing network virtualization through NSX, and integrating these systems into platforms using custom APIs and automation tools. I’m also skilled in Linux system administration, infrastructure security, and performance tuning. On the business side, I lead financial planning, strategy, budgeting, and team leadership while also driving marketing efforts, from positioning and go-to-market planning to customer acquisition and B2B growth.

Optimize Model Quantization for Large Language Models on AI Devices

Optimize Model Quantization for Large Language Models on AI Devices

Optimize Model Quantization for Large Language Models on Edge Devices

Optimize NLP Models with Backtracking: Enhance Summarization, NER, and Tuning

Master Python Map Function: Use Lambda & User-Defined Functions

Optimize NLP Models with Backtracking, Text Summarization, and More

Alireza Pourmahdavi

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