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 ...

Large-scale language models (LLMs) have advanced the field of artificial intelligence as they are used in many applications. Although they can almost perfectly simulate human language, they tend to ...

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

Mathematical reasoning remains one of the most complex challenges in AI. While AI has advanced in NLP and pattern recognition, its ability to solve ...

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 ...

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

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

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

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 ...

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 ...

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