Research has shown that large language models (LLMs) tend to overemphasize information at the beginning and end of a document ...

Recently, the Bi-level Knowledge Graph (Bi-level KG) has addressed this issue by modeling facts as nodes ... during the training process. Extensive experiments validate the effectiveness of our data ...

Using 32 AMD Instinct™ MI300X GPUs across four nodes, MangoBoost fine-tuned the Llama2-70B-LoRA model in just 10.91 minutes, setting the fastest multi-node MLPerf benchmark on AMD GPUs to date. The ...

Diversity training is more effective when it's personalized, according to my new research in the peer-reviewed journal Applied Psychology. We found that this personalized approach worked ...

Whatever the data inputs are, the AI models take them and synthesise the data into a mental map of the world before responding based on those inputs. Another part of training AI is labelling ...

Theta EdgeCloud now allows GPU clusters for parallel training, letting devs handle massive AI models across multiple ...

Correspondence to Dr David Jiménez-Pavón, GALENO Research Group and Department of Physical Education, Faculty of Education Sciences, University of Cádiz, Cádiz 11519, Spain; david.jimenez{at}uca.es If ...

This refers to the further training of an AI model to optimize performance for a more specific task or area than was previously a focal point of its training — typically by feeding in new ...

May I ask if your node supports directly loading GGUF-quantized diffusion models and text encoders, such as 'flux1-dev-Q8_0.gguf', 'flux1-fill-dev-Q8_0.gguf', and 't5-v1_1-xxl-encoder-Q8_0.gguf'?