# Olmo

Our fully open language model and complete model flow.

## The Olmo 3 model family

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

### 32B-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

Capable of reasoning through complex problems step by step. A strong platform for RL research and other advanced experiments that need serious horsepower.

### 32B-Instruct

Our most capable fully open chat model to date. An instruction-tuned model built for chat, tool use, and multi-turn dialogue.

### 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.

## Mid-training data

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

## Post-training data

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

## Open-source tools

These are the tools we use to make Olmo.

##### OlmoCore

Our training framework for fast, easy configuration

##### Data preprocessing tools

- **Duplodocus:** Ultra-efficient fuzzy de-duplication
- **Datamap-rs:** For large-scale data cleaning

##### Open Instruct

Our post-training pipeline

##### Model evaluation

- **OLMES:** Utility for reproducible evals
- **Decon:** Helps remove test sets from training data

## Built for research— already making impact

From unlearning to clinical NLP, Olmo is powering discoveries across domains.

### 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.

### Clinical NLP applications

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

### Understanding how LLMs learn

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

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