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…
Efficient long-context inference with LLMs requires managing substantial GPU memory due to the high storage demands of key-value (KV) caching. Traditional KV cache compression techniques reduce memory usage by selectively pruning less significant tokens, often based on attention scores. However, existing methods assess token importance independently, overlooking the crucial dependencies among tokens for preserving semantic…
As deep learning models continue to grow, the quantization of machine learning models becomes essential, and the need for effective compression techniques has become increasingly relevant. Low-bit quantization is a method that reduces model size while attempting to retain accuracy. Researchers have been determining the best bit-width for maximizing efficiency without compromising performance. Various studies…
Time series forecasting presents a fundamental challenge due to its intrinsic non-determinism, making it difficult to predict future values accurately. Traditional methods generally employ point forecasting, providing a single deterministic value that cannot describe the range of possible values. Although recent deep learning methods have improved forecasting precision, they require task-specific training and do not…
In this tutorial, we demonstrate how to efficiently fine-tune the Llama-2 7B Chat model for Python code generation using advanced techniques such as QLoRA, gradient checkpointing, and supervised fine-tuning with the SFTTrainer. Leveraging the Alpaca-14k dataset, we walk through setting up the environment, configuring LoRA parameters, and applying memory optimization strategies to train a model…
Logical reasoning remains a crucial area where AI systems struggle despite advances in processing language and knowledge. Understanding logical reasoning in AI is essential for improving automated systems in areas like planning, decision-making, and problem-solving. Unlike common-sense reasoning, logical reasoning requires precise rule-based deductions, making it more challenging for LLMs to master. A major obstacle…
Code generation models have made remarkable progress through increased computational power and improved training data quality. State-of-the-art models like Code-Llama, Qwen2.5-Coder, and DeepSeek-Coder show exceptional capabilities across various programming tasks. These models undergo pre-training and supervised fine-tuning (SFT) using extensive coding data from web sources. However, the application of reinforcement learning (RL) in code generation…
The integration of visual and textual data in 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 comprehension, which are crucial for applications in data analysis, information retrieval, and decision-making. As organizations increasingly…