技术规格

系统要求

不同量化方法和上下文大小的显存要求

OLMo 3.1 32B Think

OLMo 3.1 32B Think, developed by the Allen Institute for AI, is a large-scale language model specifically engineered for advanced reasoning and multi-step problem-solving. This variant is a core component of the broader OLMo 3 family, distinguished by its focus on transparent, interpretable intelligence. The model is built upon a decoder-only Transformer architecture, a widely adopted framework in contemporary large language models, and is designed to facilitate detailed logical progression in its outputs.

The training methodology for OLMo 3.1 32B Think involves a multi-stage process that extends beyond foundational pretraining. Following initial pretraining on the extensive Dolma 3 dataset, the model undergoes supervised fine-tuning (SFT), direct preference optimization (DPO), and reinforcement learning from verifiable rewards (RLVR) using specialized Dolci-Think datasets. This post-training regimen is meticulously crafted to cultivate chain-of-thought reasoning capabilities, enabling the model to articulate its problem-solving steps explicitly. The architecture incorporates grouped-query attention to optimize computational efficiency, particularly for inference within single-GPU environments.

As a reasoning-oriented specialist, OLMo 3.1 32B Think demonstrates proficiency across a spectrum of demanding intellectual tasks, including advanced mathematics, complex coding challenges, and intricate logical inference. Its substantial context window supports analysis of extensive documents and multi-step analytical processes. The model's complete openness, encompassing not only its weights but also its training code, data, and detailed methodology, positions it as a robust platform for machine learning research and for applications requiring a verifiable and auditable artificial intelligence system.

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