OLMo 3 7B Instruct
Parameters
7B
Context Length
65.536K
Modality
Text
Architecture
Dense
License
Apache 2.0
Release Date
25 Oct 2025
Knowledge Cutoff
Dec 2024
Technical Specifications
Attention Structure
Multi-Head Attention
Hidden Dimension Size
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Number of Layers
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Attention Heads
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Key-Value Heads
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Activation Function
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Normalization
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Position Embedding
Absolute Position Embedding
System Requirements
VRAM requirements for different quantization methods and context sizes
OLMo 3 7B Instruct
OLMo 3 7B Instruct is an instruction-tuned variant developed by the Allen Institute for AI (AI2). It is optimized for chat, tool use, multi-turn dialogue, and function calling. This model is part of the broader OLMo 3 family, which aims to enable the scientific study of language models through complete transparency.
This model employs a Transformer-style autoregressive architecture with 7 billion parameters. Its post-training methodology includes supervised fine-tuning (SFT), Direct Preference Optimization (DPO), and Reinforcement Learning from Verifiable Rewards (RLVR) on the Dolci datasets. The base OLMo 3 models are initially trained on the Dolma 3 dataset, which integrates diverse text modalities such as web content, scientific publications, and code repositories, supporting a context length of up to 65,536 tokens.
OLMo 3 7B Instruct demonstrates capabilities in instruction-following, coding, and knowledge-based tasks. It is suitable for applications such as conversational AI systems, code development tools, and educational platforms. A defining aspect of the OLMo project is its transparency, offering public access to training data, model code, intermediate checkpoints, and training logs, which facilitates research and further development by the AI community.
About OLMo 3
OLMo (Open Language Model) is a series of fully open language models designed to enable the science of language models. Released by the Allen Institute for AI (Ai2), OLMo 3 provides complete access to training data (Dolma 3), code, checkpoints, logs, and evaluation methodologies. The family includes Base models for pretraining research, Instruct variants for chat and tool use, and Think variants with chain-of-thought reasoning capabilities. All models are trained with staged approach including pretraining, mid-training, and long-context phases.
Other OLMo 3 Models
Evaluation Benchmarks
Ranking is for Local LLMs.
No evaluation benchmarks for OLMo 3 7B Instruct available.
Rankings
Overall Rank
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Coding Rank
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GPU Requirements
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Context Size: 1,024 tokens