The introduction of large language models (LLMs) such as Llama, PaLM, and GPT-4 has transformed the world of natural language processing (NLP), elevating the capabilities for text generation and comprehension. However, a key issue with these models is their tendency to produce hallucinations - generating content that is factually incorrect or inconsistent with the input…
Parisian artist Araminta K is an intriguing creative power who has been innovating in the realm of latent space exploration. Focusing her talent and skills in this vein, she has successfully trained several LoRAs that subsequently released on the Huggingface page for people to interact with and appreciate.
For enthusiasts who prefer a more personal interaction…
In this technology-driven era, a robust and efficient tech stack plays an integral role in the success of marketing endeavors, ranging from email campaigns and CRM systems to social media management. The proper selection and optimization of these key tools can dramatically improve business engagement, conversion rates, and growth. However, choosing the right tools from…
Large Language Models (LLMs) require an appropriate inference backend to function correctly, influencing user experience and operational costs. A recent study conducted by the BentoML Engineering Team has benchmarked various backends to better understand their performance when serving LLMs. The study focused primarily on vLLM, LMDeploy, MLC-LLM, TensorRT-LLM, and Hugging Face TGI. The experiment carried…
Artificial intelligence (AI) research aims to create adaptable and self-learning agents that can handle diverse tasks across different environments. Yet achieving this level of versatility and autonomy is a significant challenge, with current models often requiring extensive human supervision, limiting their scalability.
Past research in this arena includes frameworks like AgentBench, AgentBoard, and AgentOhana, which are…
Deep learning methods exhibit excellent performance in diagnosing cardiovascular diseases from ECGs. Nevertheless, their "black-box" nature contributes to their limited integrations into clinical scenarios because a lack of interpretability hinders their broader adoption. To overcome this limitation, researchers from the Institute of Biomedical Engineering, TU Dresden, developed xECGArch, a deep learning architecture designed specifically for…