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OLMo-3-7B

OLMo-3-7B

3 ยท 7Bย ยทย Family: OLMo
A fully open 7B language model from Ai2 (open weights, Dolma 3 data, code, and recipes). Base/Think/Instruct/RL variants; strong reasoning at 7B scale. Apache 2.0.
โœ“ Activeโœ“ Public accessโš– Open sourceLLMReasoning model๐Ÿ“ OLMo
Parameters
7B
parameters
Release date
1 November 2025
Access:DownloadHostedDeployment:๐Ÿ’ป Localโ˜ Cloud

Overview

OLMo-3-7B is a 7B-parameter language model from the OLMo family (Allen Institute for AI, Ai2). It is fully open: weights, training data (Dolma 3), code (the OLMoCore framework and Open Instruct pipeline), and complete training recipes are all publicly available, ensuring full reproducibility.

It comes in several variants: 7B-Base (a foundation model optimized for efficient inference), 7B-Think (reasoning-focused, surfacing intermediate thinking steps), 7B-Instruct (tuned for chat, tool use, and multi-turn dialogue), and an RL Zero track. The model delivers strong reasoning while remaining highly efficient at 7B scale.

The full training pipeline covers pretraining on Dolma 3 data, midtraining, and post-training (supervised fine-tuning, DPO, and reinforcement learning). The weights are released under the Apache 2.0 license.

Classification
LLMReasoning model
Family: OLMo
Access & deployment
DownloadHosted
LocalCloud
Weights: Open source
Key parameters
๐Ÿงฉ Parameters: 7B
โœ“ Toolsย ยทย โœ“ Fine-tuning
๐Ÿ“ฅ Input: text

Technical specification

Parameters
7B
parameters
License
Apache 2.0
Hardware requirements
7B model; Base/Think/Instruct/RL variants. OLMoCore framework + Open Instruct pipeline.
Features:โœ“ Tool useโœ“ Fine-tuning
Modalities
โฌ‡ Input
text
โฌ† Output
textcode

Capabilities and applications

Native model capabilities
Language modeling
Ability to predict subsequent tokens and generate coherent natural-language text based on the preceding context.
Category: language
Reasoning
The model's ability to reason logically and solve complex problems.
Category: reasoning
Mathematical reasoning
The model's ability to solve mathematical tasks requiring multi-step reasoning โ€” equations, proofs, combinatorics, geometry, calculus and competition-level problems.
Category: reasoning
Coding
Generating, analysing and modifying code in many programming languages. Covers writing functions, debugging, refactoring, code review, and creating tests. Measured by benchmarks such as HumanEval and SWE-bench.
Category: coding
Instruction following
Precisely following instructions contained in the prompt: response format, length, style, constraints (e.g. 'reply in six words'). GPT-5.1 significantly improved this capability compared to GPT-5.
Category: language
Tool use
The model's ability to call external functions, APIs and tools during a conversation: calculator, search engine, code editor, database. The model decides when and how to use a tool and interprets its result.
Category: planning

Technical architecture

Core Architecture