In this tutorial, we demonstrate the workflow for fine-tuning Mistral 7B using QLoRA with Axolotl, showing how to manage limited GPU resources while customizing the model for new tasks. We’ll install Axolotl, create a small example dataset, configure the LoRA-specific hyperparameters, run the fine-tuning process, and test the resulting model’s performance. Step 1: Prepare the… →

Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly in mathematical problem-solving and coding applications. Research has shown a strong correlation between the length of reasoning chains and improved accuracy in problem-solving outcomes. However, they face significant challenges: while extended reasoning processes enhance problem-solving capabilities, they often lead to inefficient solutions.… →

Large language models (LLMs) are the foundation for multi-agent systems, allowing multiple AI agents to collaborate, communicate, and solve problems. These agents use LLMs to understand tasks, generate responses, and make decisions, mimicking teamwork among humans. However, efficiency lags while executing these types of systems as they are based on fixed designs that do not… →

Brain-computer interfaces (BCIs) have seen significant progress in recent years, offering communication solutions for individuals with speech or motor impairments. However, most effective BCIs rely on invasive methods, such as implanted electrodes, which pose medical risks including infection and long-term maintenance issues. Non-invasive alternatives, particularly those based on electroencephalography (EEG), have been explored, but they… →

As the need for high-quality training data grows, synthetic data generation has become essential for improving LLM performance. Instruction-tuned models are commonly used for this task, but they often struggle to generate diverse outputs, which is crucial for model generalization. Despite efforts such as prompting techniques that encourage variation—like conditioning on past outputs or assuming… →

Large foundation models have demonstrated remarkable potential in biomedical applications, offering promising results on various benchmarks and enabling rapid adaptation to downstream tasks with minimal labeled data requirements. However, significant challenges persist in implementing these models in clinical settings. Even advanced models like GPT-4V show considerable performance gaps in multimodal biomedical applications. Moreover, practical barriers… →

Real-time speech translation presents a complex challenge, requiring seamless integration of speech recognition, machine translation, and text-to-speech synthesis. Traditional cascaded approaches often introduce compounding errors, fail to retain speaker identity, and suffer from slow processing, making them less suitable for real-time applications like live interpretation. Additionally, existing simultaneous translation models struggle to balance accuracy and… →

Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly when operating directly on high-dimensional pixel data. Researchers have been investigating ways to optimize latent space representations to improve efficiency without compromising image quality. A critical problem in diffusion models is… →