From 5e8bc980f0fb4f7fd09da3a24598c9545cb2ce13 Mon Sep 17 00:00:00 2001 From: ulrikenoe9675 Date: Fri, 30 May 2025 21:41:53 +0800 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..2372627 --- /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](https://oros-git.regione.puglia.it) DeepSeek-R1, an LLM fine-tuned with reinforcement [learning](https://www.imdipet-project.eu) (RL) to improve reasoning ability. DeepSeek-R1 attains results on par with OpenAI's o1 design on several benchmarks, including MATH-500 and [SWE-bench](https://andonovproltd.com).
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DeepSeek-R1 is based upon DeepSeek-V3, a mix of specialists (MoE) design just recently open-sourced by [DeepSeek](https://gitlab.optitable.com). This base model is [fine-tuned](https://www.telix.pl) utilizing Group Relative Policy Optimization (GRPO), a [reasoning-oriented variation](https://jvptube.net) of RL. The research study group likewise performed knowledge [distillation](https://hesdeadjim.org) from DeepSeek-R1 to open-source Qwen and Llama designs and [pipewiki.org](https://pipewiki.org/wiki/index.php/User:IvyCano5125640) released a number of variations of each \ No newline at end of file