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 ...

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 ...

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 ...

Aligning large language models (LLMs) with human values remains difficult due to unclear goals, weak training signals, and the complexity of human intent. Direct Alignment Algorithms (DAAs) offer a ...

Real-time speech translation presents a complex challenge, requiring seamless integration of speech recognition, machine translation, and text-to-speech synthesis. Traditional cascaded approaches ...

Large foundation models have demonstrated remarkable potential in biomedical applications, offering promising results on various benchmarks and enabling rapid adaptation to downstream tasks with ...

Databases are essential for storing and retrieving structured data supporting business intelligence, research, and enterprise applications. Querying databases typically requires SQL, which varies ...

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 ...

Diffusion models generate images by progressively refining noise into structured representations. However, the computational cost associated with these models remains a key challenge, particularly ...

Large Language Models (LLMs) such as GPT, Gemini, and Claude utilize vast training datasets and complex architectures to generate high-quality responses. However, optimizing their inference-time ...

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 ...

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 ...