AI

Quantization is a crucial technique for deep learning for reducing calculation costs and improving model efficiency. Large language models require significant treatment power, making quantization important to minimize memory consumption and improve the rate of inference. By converting high precision weights to lower bit formats, such as Int8, Int4 or InT2, quantization reduces storage requirements. … Read more

In recent years, the rapid scaling of large language models (LLMs) has led to extraordinary improvements in natural language understanding and reasoning. However, this progress comes with a significant warning: the inference process – generating response one token at a time – supports a calculation bottleneck. As LLMS grows in size and complexity, latency and … Read more

Artificial intelligence is increasingly integrated into different sectors, yet there is limited empirical evidence of its real world application across industries. Traditional research methods – such as predictable modeling and user surveys – struggle to capture AI’s evolving role in workplaces. This makes it difficult to assess its influence on productivity, labor markets and economic … Read more

Transformer-based models have significantly advanced Natural Language Processing (NLP), which is distinguished in different tasks. However, they are struggling with reasoning for long contexts, multi-stage inference and numeric reasoning. These challenges arise from their square complexity in self -perception, making them ineffective for expanded sequences and their lack of explicit memory, limiting their ability to … Read more

Mathematical reasoning is still one of the most complex challenges in AI. While AI is advanced in NLP and pattern recognition, its ability to solve complex mathematical problems with human -like logic and reasoning still hangs. Many AI models struggle with structured problem solving, symbolic reasoning and understand the deep relations between mathematical concepts. Tackling … Read more

In this tutorial we demonstrate the workflow of fine -tuning Mistral 7B using Qlora with Axolotlshowing how to manage limited GPU resources while adjusting the model to new tasks. We install Axolotl, create a small sample data set, configure the LORA-specific hyperparameters, run the fine-tuning process and test the resulting model performance. Step 1: Prepare … Read more

Real-time speech translation poses a complex challenge that requires trouble-free integration of speech recognition, machine translation and text-to-speech synthesis. Traditional cascaded approaches often introduce composite errors, fail to preserve the speaker identity and suffer from slow treatment, making them less suitable for real -time applications such as live interpretation. In addition, existing contemporary translation models … Read more

The integration of visual and textual data into artificial intelligence presents a complex challenge. Traditional models often struggle to interpret structured visual documents such as tables, charts, infographics and diagrams with precision. This limitation affects automated content extraction and understanding, which is crucial to applications in data analysis, obtaining information and decision making. As organizations … Read more

Large language models (LLMs) such as GPT, Gemini and Claude use huge training data sets and complex architectures to generate high quality answers. However, optimization of their inference time calculation remains challenging as rising model size leads to higher calculation costs. Researchers continue to explore strategies that maximize efficiency while maintaining or improving model performance. … Read more

Language models (LMS) are significant progress through increased calculation power during training, primarily through large -scale self -monitored pre -entering. While this approach has provided powerful models, a new paradigm called test time scaling has emerged focusing on improving performance by increasing the calculation at inference time. Openai’s O1 model has validated this approach showing … Read more