DeepSeek-R1
DeepSeek-R1 is a reasoning model trained via large-scale reinforcement learning (RL) without the need for supervised fine-tuning (SFT). It demonstrates remarkable performance in reasoning tasks, including self-verification and reflection. The model addresses challenges such as endless repetition and poor readability, and achieves performance comparable to OpenAI-o1 across math, code, and reasoning tasks.
Detaljert introduksjon
DeepSeek-R1 is an advanced reasoning model that leverages large-scale reinforcement learning to achieve significant performance in reasoning tasks. It incorporates cold-start data before RL to enhance reasoning capabilities and address issues like repetition and readability. DeepSeek-R1 is designed to provide high accuracy in reasoning tasks and is suitable for a wide range of applications.
MerAI
DeepSeek-VL2
DeepSeek-VL2 is an advanced series of large Mixture-of-Experts (MoE) Vision-Language Models designed for advanced multimodal understanding. It demonstrates superior capabilities across various tasks, including visual question answering, optical character recognition, document/table/chart understanding, and visual grounding. The model series includes three variants with 1 billion, 2.8 billion, and 4.5 billion activated parameters respectively.
Psy-Insight: Mental Health Counseling Dataset
Psy-Insight is a bilingual, interpretable multi-turn dataset for mental health counseling dialogues. It includes 6,208 rounds of multi-turn counseling dialogues in English and 5,776 rounds in Chinese, annotated with step-by-step reasoning labels and multi-task labels. This dataset is designed to support the application of large language models in mental health and is suitable for tasks such as emotion classification and psychological treatment interpretation.
Mental Health in Tech Survey
This dataset contains survey responses from individuals in the tech industry about their mental health, including questions about treatment, workplace resources, and attitudes towards discussing mental health in the workplace. By analyzing this dataset, we can better understand how prevalent mental health issues are among those who work in the tech sector—and what kinds of resources they rely upon to find help—so that more can be done to create a healthier working environment for all.