AI

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

Large Language Models (LLMs) have shown remarkable reasoning functions in mathematical problem solving, logical inference and programming. However, their effectiveness is often conditioned by two approaches: monitored fine tuning (SFT) with human-annoted reasoning chains and Search strategies in inference time Guided by external verifiers. While monitored fine -tuning offers structured reasoning, it requires significant annotation … Read more

The development of transformer-based large language models (LLMs) has significantly advanced AI-driven applications, especially conversation agents. However, these models face inherent limitations due to their fixed context windows, which can lead to loss of relevant information over time. While methods of recycling-augmented generation (RAG) provide external knowledge to supplement LLMs, they are often dependent on … Read more

Large -scale Language Models (LLMS) has put forward the area of ​​artificial intelligence as they are used in many applications. While they can almost perfectly simulate human language, they tend to lose in response diversity. This limitation is particularly problematic in tasks that require creativity, such as synthetic data recovery and storytelling, where different outputs … Read more

In our former tutorial, we built an AI agent who was able to answer questions by surfing the web. But when building funds for longer ongoing tasks, two critical concepts come into play: endurance and Streaming. Persistence allows you to save an agent’s condition at any given time so you can resume from this condition … Read more