Ai2

nonprofit

usa · Founded 2014

The Allen Institute for AI (Ai2) is a nonprofit research institute founded in 2014 by Microsoft co-founder Paul Allen, headquartered in Seattle. Funded by the Paul Allen estate ($3.1B foundation) with annual budget exceeding $100M, plus $152M from NSF and NVIDIA for the OMAI scientific-AI initiative (2025). ~300 researchers.

Ai2's defining contribution is the OLMo (Open Language Model) ecosystem — the first competitive LLM to release everything: weights, training data, code, logs, and thousands of intermediate checkpoints under Apache 2.0. OLMo 1 (Feb 2024) established the paradigm; OLMo 2 (Dec 2024, up to 32B/6T tokens) was the first fully open model to outperform GPT-3.5 Turbo; OLMo 3 (Dec 2025, 32B, 65K context, Think variants) introduced 3-stage training with mid-training and long-context phases. The latest OLMo Hybrid (Mar 2026) combines Gated DeltaNet + Transformer at 3:1 ratio, reaching OLMo 3 quality in 35% fewer tokens.

The OLMo stack includes Dolma (3T+ token open pretraining corpus), Tülu (post-training framework introducing RLVR — Reinforcement Learning with Verifiable Rewards), OLMoE (first fully open MoE LLM, 6.9B/1.3B active, 64 experts), and OLMES (evaluation standard). In March 2026, Ai2 lost CEO Ali Farhadi and several key researchers to Microsoft's Superintelligence team, though all 2026 programs remain funded.

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Outputs (16)

OLMo Hybrid

model

<p>First fully open hybrid Transformer + linear RNN model at scale. <strong>Gated DeltaNet + Transformer attention at 3:1 ratio</strong> (3 DeltaNet l…

OLMix

paper

Framework for data mixing across the full LM development lifecycle. Solves the "evolving domain problem" where domains are added, removed, or revised…

Molmo 2

model

<p>Open VLM family expanding Molmo's image strengths to <strong>video and multi-image understanding</strong>. 4B (Qwen 3), 8B (Qwen 3), and 7B-O (OLMo…

OLMo 3

model

<p>7B and 32B dense Transformers with <strong>65K native context</strong> (up from 4K). 3-stage training: main pretraining (~95%), mid-training (100-2…

FlexOlmo

model

<p>New paradigm for LLM training via <strong>data collaboration</strong>. Data owners independently train MoE expert modules on their private data, th…

DataDecide

paper

Most extensive open suite of controlled pretraining experiments over data and scale: 25 corpora (Dolma, DCLM, RefinedWeb, C4, FineWeb), sizes up to 1B…

WebOrganizer

paper

Framework for constructing meaningful domain taxonomies over web corpora. Introduces topic and format taxonomies (24 categories each), distilling Llam…

OLMo 2

model

<p>"2 OLMo 2 Furious." Dense Transformers at 7B (4T tokens), 13B (5T), and 32B (6T tokens, 1.5 epochs). 4K context. Introduces <strong>Dolmino Mix</st…

Language Model Ladders

paper

Develops task-specific scaling laws using small "ladder" models (costing 1% of target compute) to predict large model performance within 2 percentage…

Tülu 3

model

<p>Post-training framework introducing <strong>RLVR (Reinforcement Learning with Verifiable Rewards)</strong>. Applied to Llama 3.1 at 8B, 70B, and 40…

Molmo

model

Open multimodal VLM family with fully open training data (PixMo). Pioneered image pointing capabilities. Trained in two stages: dense captioning pre-t…

OLMoE

model

<p>First <strong>fully open MoE LLM</strong>: weights, training data (OLMoE-Mix), code, and logs. 6.9B total / 1.3B active per token with 64 experts p…

SciRIFF

dataset

137K instruction-following demonstrations across 54 scientific literature tasks (extraction, summarization, QA, claim verification, classification). S…

OLMo

model

<p>First competitive LLM to release <strong>everything</strong>: weights, training data (Dolma), training code, logs, and 500+ intermediate checkpoint…

Dolma

dataset

Largest fully open pretraining corpus at release: ~3T tokens from Common Crawl (2,180B), GitHub code (342B), Reddit (80B), Semantic Scholar/peS2o (57B…

Paloma

dataset

Perplexity-based benchmark measuring language model fit across 585 textual domains from 18 sources, rather than assuming perplexity on one distributio…