4 篇文章带有标签 “deepseek-r1”

用户的问题

• Understanding Reasoning LLMs
• Sebastian Raschka：关于DeepSeek R1和推理模型，我有几点看法
• Large Language Models are Zero-Shot Reasoners
• Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
• 04 论文 Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling Model Parameters
• 论文笔记：Scaling LLM Test-Time Compute Optimally can be More Effective than Scaling
• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

GPU 服务器

T4 GPU 服务器，4卡16G。

安装 vLLM

conda create -n deepseek-r1 python=3.12 -y
conda activate deepseek-r1

pip install vllm

• Installation GPU

错误处理

ImportError: undefined symbol: __nvJitLinkComplete_12_4, version libnvJitLink.so.12

• DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning

Abstract（摘要）

We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without super- vised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges with numerous powerful and intriguing reasoning behaviors. However, it encounters challenges such as poor readability, and language mixing.