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Ai2

Olmo

Our fully open language model and complete model flow.

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The Olmo 3 model family

Pick a variant to explore weights, code and reports. Every card includes instant links to artifacts.
Read the technical report

32B-Base

Our flagship base. Achieves strong results in programming, reading comprehension, and math problem solving, maintains performance at extended context lengths, and works well with RL setups.

32B-Think

Our flagship post-trained release, capable of reasoning through complex problems step by step. A strong platform for RL research and other advanced experiments that need serious horsepower.

7B-Base

A smaller, lighter-weight base model able to run on a wider range of hardware while delivering competitive performance.

7B-Think

Delivers strong reasoning capabilities at 7B scale, surfacing intermediate thinking steps for complex prompts at high efficiency.

7B-Instruct

Model for efficient inference that handles multi-turn chat, tool use, and more.

A complete model flow

To truly advance open AI development and research, the entire model flow – not just its endpoint – should be accessible and customizable. The model flow is the full lifecycle of an LM, starting with the data.

Olmo 3 Model FlowPretrainingMidtrainingLong contextOlmo 3 BaseInstruct SFTInstruct DPOInstruct RLOlmo 3 InstructThinking SFTThinking DPOThinking RLOlmo 3 ThinkRL ZeroOlmo 3 RL ZeroOlmo 3 Model FlowPretrainingMidtrainingLong contextOlmo 3 BaseInstruct SFTInstruct DPOInstruct RLOlmo 3 InstructThinking SFTThinking DPOThinking RLOlmo 3 ThinkRL ZeroOlmo 3 RL Zero

Explore the Model Flow

Click on any stage to learn more about it and download artifacts.

Pretraining data

The fully open mixture used to train Olmo from scratch—curated web, code, books, and scientific text—deduplicated and quality-filtered.

Standard PoolLong Context Mix
Mid-training data

Targeted continuation sets used to refine the base model mid-course. Higher-quality, domain-focused mixtures.

Download
Post-training data

Corpora used after pretraining for instruction tuning and preference-based optimization where applicable—supervised responses and comparison data.

Download

Open-source tools

These are the tools we use to make Olmo.

OlmoCore

Our training framework for fast, easy configuration

Data preprocessing tools
Open Instruct

Our post-training pipeline

Model evaluation
  • OLMES

    Utility for reproducible evals

  • Decon

    Helps remove test sets from training data

What people are saying

Unlike current open LLMs, which limit access to their training data, architectures, or evaluation methodologies, Olmo stands out by providing full access.

Janakiram MSV
Forbes

“We see Olmo’s architecture as pushing the frontier of open-source model design... Open source is how we drive progress forward!

Simon Mo
Project Co-Lead, vLLM

Olmo is becoming the instrument through which the community builds the next layer of open, foundational intelligence.

Anastasios Angelopoulos
CEO, LMArena

Olmo has led to a wellspring of research and innovation that couldn't have been accomplished without a fully open model... Olmo ensures complete transparency and sets a strong foundation for transformative work.

Clem Delangue
Co-Founder & CEO, Hugging Face

Ai2’s idea is to give the AI community full visibility into a state-of-the-art large language model in order to confront the problems with existing LLMs

Mark Sullivan
Fast Company

Transparency and performance are essential for developers to scale AI with open, U.S.-built models like Olmo 3

Kari Briski
NVIDIA

Built for research— already making impact

From unlearning to clinical NLP, Olmo is powering discoveries across domains. Explore how researchers are using fully-open models.

Machine unlearning with Olmo-7B

Researchers used Olmo-7B as a testbed for developing machine unlearning methods—removing specific data influence without retraining from scratch.

See project

Clinical NLP applications

Healthcare teams leveraged Olmo checkpoints to explore clinical text analysis while preserving transparency around data and methods.

See project

Understanding how LLMs learn

Olmo’s openness—datasets, logs, and checkpoints—enabled fundamental studies into learning dynamics and scaling behaviors.

See project

Deep dive with Olmo lead researchers Hanna Hajishirzi and Noah Smith on how - and why - we built Olmo 3, and what comes next.

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