Machine Learning

This post is co-written with Gordon Campbell, Charles Guan, and Hendra Suryanto from RDC.  The mission of Rich Data Co…

Text-to-speech (TTS) technology has made significant strides in recent years, but challenges remain in creating natural, expressive, and high-fidelity speech…

Large Language Models (LLMs) have demonstrated remarkable capabilities in complex reasoning tasks, particularly in mathematical problem-solving and coding applications. Research…

In this tutorial, we demonstrate the workflow for fine-tuning Mistral 7B using QLoRA with Axolotl, showing how to manage limited…

Large foundation models have demonstrated remarkable potential in biomedical applications, offering promising results on various benchmarks and enabling rapid adaptation…

As the need for high-quality training data grows, synthetic data generation has become essential for improving LLM performance. Instruction-tuned models…

Brain-computer interfaces (BCIs) have seen significant progress in recent years, offering communication solutions for individuals with speech or motor impairments.…

Large language models (LLMs) are the foundation for multi-agent systems, allowing multiple AI agents to collaborate, communicate, and solve problems.…

Efficient long-context inference with LLMs requires managing substantial GPU memory due to the high storage demands of key-value (KV) caching.…

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

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

Code generation models have made remarkable progress through increased computational power and improved training data quality. State-of-the-art models like Code-Llama,…

Logical reasoning remains a crucial area where AI systems struggle despite advances in processing language and knowledge. Understanding logical reasoning…

In this tutorial, we demonstrate how to efficiently fine-tune the Llama-2 7B Chat model for Python code generation using advanced…

Time series forecasting presents a fundamental challenge due to its intrinsic non-determinism, making it difficult to predict future values accurately.…

As deep learning models continue to grow, the quantization of machine learning models becomes essential, and the need for effective…

Aligning large language models (LLMs) with human values remains difficult due to unclear goals, weak training signals, and the complexity…

Reinforcement learning (RL) for large language models (LLMs) has traditionally relied on outcome-based rewards, which provide feedback only on the…

After the success of large language models (LLMs), the current research extends beyond text-based understanding to multimodal reasoning tasks. These…

The integration of visual and textual data in artificial intelligence presents a complex challenge. Traditional models often struggle to interpret…