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 Instruct

OLMo 3.1 32B Instruct is a large language model developed by the Allen Institute for AI (AI2), designed to facilitate open research in language models through comprehensive transparency. This variant is specifically instruction-tuned, making it suitable for conversational AI, agentic applications, and tool-use scenarios. It represents an evolution in the OLMo family, applying a refined instruction-tuning methodology to a larger parameter count to enhance its capabilities in complex interactive tasks. The model's development emphasizes an open science approach, providing access to its training data, code, and intermediate checkpoints, which allows for detailed scrutiny and reproducibility in research endeavors.

Architecturally, OLMo 3.1 32B Instruct is a decoder-only Transformer model, a design frequently employed in autoregressive language generation. The model incorporates Grouped-Query Attention (GQA) with 40 attention heads and 8 key-value heads, which contributes to efficient inference by reducing the memory footprint of the KV cache. Positional encoding is handled by Rotary Position Embeddings (RoPE) with YaRN scaling, enabling the model to effectively process long input sequences up to a context length of 65,536 tokens. The model employs SwiGLU-style activation functions within its feed-forward networks and utilizes RMSNorm for normalization, architectural choices that are common in high-performance language models for improved stability and efficiency.

The primary purpose of OLMo 3.1 32B Instruct is to provide a robust foundation for instruction-following tasks, multi-turn dialogue, and the integration of external tools. Its training involved supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning from Verifiable Rewards (RLVR) on the Dolci-Instruct datasets. This multi-stage post-training pipeline is engineered to improve the model's ability to interpret and execute complex instructions, thereby enhancing its utility in applications requiring precise control and interactive capabilities. The model's fully open nature extends its utility for researchers examining model behavior, data influence, and the efficacy of various training paradigms.

没有可用的 OLMo 3.1 32B Instruct 评估基准。