flower-f839d50b·2 events·first seen Aliases: Flower
Researchers introduce a large-scale reaction mechanism reasoning dataset and FukuyamaBench, a benchmark derived from Fukuyama's Advanced Organic Reaction Mechanism textbook, to evaluate step-by-step chemical mechanism inference in LLMs. A fine-tuned Qwen3-30B-A3B model achieves 8.3% exact pathway match on FukuyamaBench Set A, outperforming the specialized FlowER model at 5.1%. The work argues that mechanism-aware training addresses hallucinations and physical inconsistencies in current chemical LLMs, which typically focus only on coarse-grained product prediction and retrosynthesis.
This Hugging Face blog post describes how to combine the Hugging Face ecosystem with the Flower federated learning framework to train models across distributed, privacy-preserving data silos. It provides a practical walkthrough of integrating Transformers and Datasets libraries with Flower's federated training loop. The post targets practitioners looking to apply federated learning to NLP and other ML tasks without centralizing sensitive data.