From ab0e9647d2e08e434be9842ae9728466df5e99cd Mon Sep 17 00:00:00 2001 From: kimknoll756725 Date: Fri, 4 Apr 2025 01:58:23 +0000 Subject: [PATCH] Add 'DeepSeek Open-Sources DeepSeek-R1 LLM with Performance Comparable To OpenAI's O1 Model' --- ...R1-LLM-with-Performance-Comparable-To-OpenAI%27s-O1-Model.md | 2 ++ 1 file changed, 2 insertions(+) create mode 100644 DeepSeek-Open-Sources-DeepSeek-R1-LLM-with-Performance-Comparable-To-OpenAI%27s-O1-Model.md diff --git a/DeepSeek-Open-Sources-DeepSeek-R1-LLM-with-Performance-Comparable-To-OpenAI%27s-O1-Model.md b/DeepSeek-Open-Sources-DeepSeek-R1-LLM-with-Performance-Comparable-To-OpenAI%27s-O1-Model.md new file mode 100644 index 0000000..ef090c5 --- /dev/null +++ b/DeepSeek-Open-Sources-DeepSeek-R1-LLM-with-Performance-Comparable-To-OpenAI%27s-O1-Model.md @@ -0,0 +1,2 @@ +
[DeepSeek open-sourced](http://47.75.109.82) DeepSeek-R1, an LLM fine-tuned with support learning (RL) to enhance reasoning capability. DeepSeek-R1 attains outcomes on par with OpenAI's o1 design on a number of standards, [including](http://101.43.129.2610880) MATH-500 and [garagesale.es](https://www.garagesale.es/author/jonathanfin/) SWE-bench.
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DeepSeek-R1 is based on DeepSeek-V3, a mixture of experts (MoE) design recently open-sourced by DeepSeek. This [base design](http://connect.lankung.com) is fine-tuned utilizing Group Relative Policy Optimization (GRPO), a [reasoning-oriented variant](https://www.ojohome.listatto.ca) of RL. The research study group likewise [performed understanding](https://zeustrahub.osloop.com) distillation from DeepSeek-R1 to open-source Qwen and Llama models and launched a number of variations of each \ No newline at end of file