Models › Olmo-3 family

Olmo-3-7B-Instruct

allenai Apache 2.0 out 11 months ago

7.30B parameters, text in, text out, 64K tokens of context.

Verified 2026-10-25 Rank #503 in LLMs · score 76.3 method v1 Sources 3 Downloads 30d 279.5k · #224 of 619 tracked

Can I run it?4.4GB at INT4Estimatefits a 16 GB cardmethod
Can I use it?Apache 2.0Permissive; patent grantlicence file2026-10-24
Do people use it?279.5kdownloads, 30 days#224 of 619 we track · top 36%+0 likes, downloads −38% this week159 likesHugging Face2026-10-24

Worth reading

Picked by rule from 9 tracked pieces: the lab's own post, the most shared, a field report, the latest.

Latest · 26 days ago

Reproducing Olmo 3 7B Pre-training

Google Cloud reproduced the Allen Institute for AI's Olmo 3 7B model using MaxText on TPUs, matching the original training loss curve and held-out metrics.

  • The team lost three quarters of capacity mid-run but resumed on a quarter-sized slice with no recipe change.
developers.googleblog.com 2026-09-29 1 HN pts 5.2k words

9 best of 9 tracked

The 1 pick above, plus these 8. Top is the radar's score: discussion, source authority and the lab's own posts, fading with age.

  1. PaperNCP-ArchPreview: 8.9B latent LM matches OLMo-3-7B on 51% of tokensarxiv.org · 2026-09-14 · 2 HN pts
  2. ArticleOlmo 3: Charting a path through the model flow to lead open-source AIallenai.org · 2025-11-20 · 390 HN pts · official
  3. ArticleOlmo 3 is a fully open LLMsimonwillison.net · 2025-11-23 · 5 HN pts
  4. ArticleOlmo 3: America's open reasoning modelsinterconnects.ai · 2025-11-20 · 4 HN pts
  5. CodeOlmo 3 from Scratch (Standalone Notebook)github.com · 2025-11-24 · 1 HN pts
  6. ArticleOlmo 3 and the Open LLM Renaissancecameronrwolfe.substack.com · 2025-12-15 · 4 HN pts
  7. DiscussionAi2 Olmo 3, a new SOTA open LLM (7B and 32B)twitter.com · 2025-11-20 · 2 HN pts
  8. DiscussionWe present Olmo 3, our next family of open, leading language modelstwitter.com · 2025-11-20 · 1 HN pts

Where it's used

Hugging Face does not say who downloads a model or with what. These are the proxies: API providers listing it, quantized builds, fine-tunes/adapters/merges, and Spaces that load it.

No API provider serves it, so its downloads are people running it themselves: 41 quantized builds and 86 Spaces.

  • As a service0no API provider listed
  • On own hardware41quantized builds
  • Built on394344 fine-tunes · 49 adapters · 1 merges
  • In apps86Hugging Face Spaces

Top Spaces running it: pliny-the-prompter/obliteratus ♥ 621 · Kodacoda/obliteratus ♥ 2 · dumbordumber/obliteratus ♥ 2 · dromero14521/obliteratus ♥ 1

Will it fit?

Weights only, an estimate: leave room for the KV cache. Method

Fits a 16 GB card at INT4

About 4.4 GB at INT4, 8.8 GB at FP8, 18 GB at BF16: weights only, so leave room for the KV cache.

16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 4.4 GB of weights (estimate; KV cache and activations extra)INT44.4 GBFP8: about 8.8 GB of weights (estimate; KV cache and activations extra)FP88.8 GBBF16: about 18 GB of weights (estimate; KV cache and activations extra)BF1618 GB
16 GB card64 GB Mac2×80 GB8×80 GBINT4: about 4.4 GB of weights (estimate; KV cache and activations extra)INT44.4 GBFP8: about 8.8 GB of weights (estimate; KV cache and activations extra)FP88.8 GBBF16: about 18 GB of weights (estimate; KV cache and activations extra)BF1618 GB

Estimate params × bytes per param × 1.2 · how we estimate

The story so far

Weights, every dated article, and repo commits on one rail.

Coverage peaked at launch and is still arriving

Teal is the weights (2025-11-19), amber triangles are 9 dated articles (each links to it), grey ticks are repo commits.

What people built on it

435 derivatives on the Hub. Which build to run, by runtime: the most downloaded in each class.

435 derivatives on the Hub, most of them fine-tunes

From the Hugging Face model tree; quantized builds in teal, fine-tunes, merges and adapters in greys.

41 quantized (Hugging Face model tree)344 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)49 adapters (Hugging Face model tree)41 quantized344 fine-tunes1 merge49 adapters
41 quantized (Hugging Face model tree)344 fine-tunes (Hugging Face model tree)1 merge (Hugging Face model tree)49 adapters (Hugging Face model tree)41 quantized344 fine-tunes1 merge49 adapters

All variants →

Reference

Everything else, folded: the full tables, every source.

Benchmarks

35 rows

Benchmarks 35 rows

measured third-party leaderboard rowauthor-reported number from the model card or eval files (35)No confidence intervals unless the source gave them; settings differ between rows, so compare within a benchmark, not across. Date = the benchmark's own date when it gives one, else when we read it.
BenchmarkValueSetting / notesReported byDate
author-reported AGI Eval English59.2Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported AGI Eval English64Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported AGI Eval English64.4Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported AIME 2024 · Reason through hard problems6.7Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported AIME 2024 · Reason through hard problems23.5Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported AIME 2024 · Reason through hard problems44.3Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported AIME 2025 · Reason through hard problems7.2Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported AIME 2025 · Reason through hard problems20.4Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported AIME 2025 · Reason through hard problems32.5Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported BBH · Reason through hard problems51Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported BBH · Reason through hard problems69.3Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported BBH · Reason through hard problems71.2Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported HumanEval · Write code with agents69.8Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported HumanEval · Write code with agents72.9Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported HumanEval · Write code with agents77.2Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported IFBench · Chat and follow instructions27.4Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported IFEval · Chat and follow instructions81.7Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported IFEval · Chat and follow instructions82Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported IFEval · Chat and follow instructions85.6Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported LiveCodeBench · Write code with agents20Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported LiveCodeBench · Write code with agents18.8Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported LiveCodeBench · Write code with agents29.5Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported MATH-500 · Reason through hard problems65.1Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported MATH-500 · Reason through hard problems65.1Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported MATH-500 · Reason through hard problems79.6Olmo 3 Instruct 7B DPOmodel author2026-10-07
Show all 35 rows (10 more)
BenchmarkValueSetting / notesReported byDate
author-reported MATH-500 · Reason through hard problems87.3Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported MBPP · Write code with agents56.5Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported MBPP · Write code with agents55.9Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported MBPP · Write code with agents60.2Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported OMEGA14.4Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported OMEGA22.8Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported OMEGA28.9Olmo 3 Instruct 7Bmodel author2026-10-07
author-reported ZebraLogic18Olmo 3 Instruct 7B SFTmodel author2026-10-07
author-reported ZebraLogic28.4Olmo 3 Instruct 7B DPOmodel author2026-10-07
author-reported ZebraLogic32.9Olmo 3 Instruct 7Bmodel author2026-10-07

Use-case evidence

5 use cases

What it is good at 5 use cases

Labels are evidence, not verdicts: measured a third-party leaderboard row for this model; author-reported a number the model author published in the card or eval files; structural a deterministic signal from the name, config or chat template, or a decision-model estimate. Each chip links to its source. All use cases.

Facts

21 fields

Listed in LLMs

Facts

Pipeline tagtext-generation
Librarytransformers
ArchitecturesOlmo3ForCausalLM
Model typeolmo3
Dtypebfloat16
Params by dtypeBF16: 7,298,011,136
Size bandM
MoEno
Context length65,536 (config.json max_position_embeddings)
Languagesen
PapersarXiv:2512.13961
LicenseApache 2.0 (apache-2.0)
Hub snapshot2026-10-25

Servability

vLLM · SGLang

Servability

vLLM: supported · Olmo3ForCausalLM · 7050ef881ff0835f6890a47db1859a24c65481d6SGLang: not in registry · 773cb938fe9484a8f7e7e46fb1b1fe96412c59ed

vLLM registry (7050ef881ff0835f6890a47db1859a24c65481d6) · SGLang registry (773cb938fe9484a8f7e7e46fb1b1fe96412c59ed) · Matched by architecture class name against each project's registry on main; 'supported' means the architecture is registered, not that this checkpoint was tested.

Hardware fit

3 estimates

Hardware fit ESTIMATE

Weights-only memory for Olmo-3-7B-Instruct: params × bytes/param × 1.2 overhead. KV cache and activations are extra. How this is computed.

BF16 (2 bytes/param)18 GB · Single GPU (<24 GB)
FP8 (1 byte/param)8.8 GB · Single GPU (<24 GB)
INT4 (0.5 bytes/param)4.4 GB · Single GPU (<24 GB)

Weights only (params × bytes/param × 1.2 overhead); KV cache and activations extra.

All variants

40 variants

Lineage

Fine-tuned from allenai/Olmo-3-7B-Instruct-DPO (same organization) Family: allenai/Olmo-3 435 derivatives on the Hub

Base model: allenai/Olmo-3-7B-Instruct-DPO

finetune: 344quantized: 41adapter: 49merge: 1

Top variants (40 shown, ranked by downloads)

Relation
Quant
Size
40 rows
Variant Relation Quant Params Band License Author Likes Downloads
allenai/Molmo2-O-7B finetune — 7.76B M apache-2.0 allenai 26 823.7k
farbodtavakkoli/OTel-LLM-7B-IT finetune — — — apache-2.0 farbodtavakkoli 0 158.7k
unsloth/Olmo-3-7B-Instruct-GGUF quantized GGUF — — apache-2.0 unsloth 12 56.7k
unsloth/Olmo-3-7B-Instruct finetune — 528K S apache-2.0 unsloth 0 15.8k
bartowski/allenai_Olmo-3-7B-Instruct-GGUF quantized GGUF — — apache-2.0 bartowski 2 11.9k
prithivMLmods/Olmo-3-7B-Instruct-AIO-GGUF quantized GGUF — — apache-2.0 prithivMLmods 1 8.5k
lmstudio-community/Olmo-3-7B-Instruct-GGUF quantized GGUF — — apache-2.0 lmstudio-community 6 7.5k
mradermacher/Olmo-3-7B-Instruct-i1-GGUF quantized GGUF — — apache-2.0 mradermacher 0 7.8k
unsloth/Olmo-3-7B-Instruct-unsloth-bnb-4bit quantized -bnb 7.50B M apache-2.0 unsloth 1 5.8k
Ex0bit/Elbaz-Olmo-3-7B-Instruct-abliterated quantized GGUF — — apache-2.0 Ex0bit 4 5.4k
eaddario/Olmo-3-7B-Instruct-GGUF quantized GGUF — — apache-2.0 eaddario 0 5.4k
ChiKoi7/Olmo-3-7B-Instruct-Heretic-GGUF quantized GGUF — — apache-2.0 ChiKoi7 0 4.6k
mradermacher/Olmo-3-7B-Instruct-GGUF quantized GGUF — — apache-2.0 mradermacher 0 4.4k
lmstudio-community/Olmo-3-7B-Instruct-MLX-8bit quantized MLX 7.30B M apache-2.0 lmstudio-community 1 3.1k
noctrex/Olmo-3-7B-Instruct-abliterated-GGUF quantized GGUF — — — noctrex 1 2k
mlx-community/Olmo-3-7B-Instruct-mxfp4-QAT quantized mxfp4 7.30B M — mlx-community 4 1.4k
mlx-community/Olmo-3-7B-Instruct-8bit quantized -8bit 7.30B M apache-2.0 mlx-community 2 1.4k
mlx-community/Olmo-3-7B-Instruct-4bit quantized -4bit 7.30B M apache-2.0 mlx-community 2 1.3k
lmstudio-community/Olmo-3-7B-Instruct-MLX-4bit quantized MLX 7.30B M apache-2.0 lmstudio-community 1 1.2k
CCATresearch/Olmo-3-7B-Instruct quantized GGUF — — apache-2.0 CCATresearch 0 1.2k
vanta-research/atom-olmo3-7b finetune — 7.30B M apache-2.0 vanta-research 5 877
ermiaazarkhalili/Olmo-3-7B-SFT-Fable5 adapter — 528K S apache-2.0 ermiaazarkhalili 0 1.1k
Tuwhy/Olmo-3-7B-Instruct-OPSA finetune — 7.30B M apache-2.0 Tuwhy 0 951
MaziyarPanahi/Olmo-3-7B-Instruct-GGUF quantized GGUF — — — MaziyarPanahi 0 888
jimfhahn/bibframe-olmo3-v3 quantized GGUF 7.30B M apache-2.0 jimfhahn 0 848
cturan/Olmo-3-7B-Instruct-Q1_0 quantized GGUF — — apache-2.0 cturan 4 592
Goekdeniz-Guelmez/Josiefied-Olmo-3-7B-Instruct-abliterated-v1 finetune — 7.30B M — Goekdeniz-Guelmez 2 518
lmstudio-community/Olmo-3-7B-Instruct-MLX-6bit quantized MLX 7.30B M apache-2.0 lmstudio-community 0 600
Harvard-DCML/ADAPT-Olmo3-4.3B-Instruct finetune — 1.07B S apache-2.0 Harvard-DCML 0 558
EleutherAI/Olmo-3-7B-Instruct-recall-9000 adapter — — — — EleutherAI 0 532
DevQuasar/allenai.Olmo-3-7B-Instruct-GGUF quantized GGUF — — — DevQuasar 0 499
Tuwhy/Olmo-3-7B-Instruct-OPSA-Code finetune — 7.30B M apache-2.0 Tuwhy 0 347
Ex0bit/OLMo-3-7B-Instruct-NVFP4-1M quantized NVFP4 4.06B M apache-2.0 Ex0bit 2 242
ConicCat/Role-mo-V2-7B finetune — 7.30B M — ConicCat 4 139
ermiaazarkhalili/Olmo-3-7B-SFT-Fable5-GGUF quantized GGUF — — apache-2.0 ermiaazarkhalili 0 312
mlx-community/Olmo-3-7B-Instruct-6bit quantized — 7.30B M apache-2.0 mlx-community 0 304
jkim96/Olmo-3-7B-Instruct-DASHQ-Q2-GGUF quantized GGUF — — apache-2.0 jkim96 0 301
mlx-community/Olmo-3-7B-Instruct-bf16 finetune -bf16 7.30B M apache-2.0 mlx-community 0 279
kaitchup/Olmo-3-7B-Instruct-awq-w4a16-asym quantized awq 1.69B S apache-2.0 kaitchup 0 258
h3rb3rn/moe-sovereign-planner-olmo3-7b adapter GGUF — — apache-2.0 h3rb3rn 0 220

Repository changelog

21 commits

Repository changelog 21 commits

DateCommitSHA
2026-06-25 Update the number of parameters (#13) 6e5971d
2026-01-05 Add OLMo 3 citation 096bb54
2025-12-10 Update inference examples to use the correct chat template (#7) c34eee9
2025-11-26 fix (#5) 51c1f0b
2025-11-24 Update README.md 84e56fc
2025-11-24 Update README.md 37b7955
2025-11-24 Upload olmo-instruct.png cdf1ec0
2025-11-24 Update README.md 3497a82
2025-11-21 Update README.md 14975fe
2025-11-21 Update README.md 7d4fa93
2025-11-21 Update generation_config.json 48683df
2025-11-21 Add `transformers` as the library name (#2) 8b6b19f
2025-11-20 Update README.md 14a937e
2025-11-20 Update README.md 5498984
2025-11-20 Update README.md e00f0d2
2025-11-20 Update README.md d5287fe
2025-11-20 Update README.md ca644da
2025-11-20 Update README.md 1280663
2025-11-20 Update README.md d93ec82
2025-11-20 Upload checkpoint from step 450 d42dc9d
2025-11-19 initial commit 3b160f9

Articles and coverage

9 pieces

Ranked by source signals (HN, Lobsters, authority domains, official posts) and recency. Summaries are generated from a stored copy of the article text; every sentence cites a span of that text, and nothing is shown that failed the citation check.

Official2

developers.googleblog.com2026-09-29authorityHN 1 pts · 0 comments5,194 words
Google Cloud reproduced the Allen Institute for AI's Olmo 3 7B model using MaxText on TPUs, matching the original training loss curve and held-out metrics. The team converted the PyTorch architecture to JAX, implementing a reordered-norm block, QK-norm, and 3:1 sliding/global attention while catching a data-loader bug that caused memorization. The run tracked the full ~5.93T-token / 1.41M-step budget, sustaining 57.4% MFU on v5p devices during stage 2.written, reviewed, cited
  • The team lost three quarters of capacity mid-run but resumed on a quarter-sized slice with no recipe change.
allenai.org2025-11-20officialauthorityHN front pageabout the Olmo 3 familyHN 390 pts · 125 comments3,773 words
AllenAI released Olmo 3.1 Think 32B and Olmo 3.1 Instruct 32B, the most performant checkpoints to date. The Think variant extended the best reinforcement learning run with an additional 21 days on 224 GPUs, yielding improvements of 5+ points on AIME, 4+ points on ZebraLogic, 4+ points on IFEval, and 20+ points on IFBench. The Instruct variant applies the recipe behind Olmo 3 Instruct 7B to a larger model for chat and tool use.written, reviewed, cited
  • Olmo 3-Think (32B) narrows the gap to top open models while training on roughly 6x fewer tokens than competitors.
  • The model flow allows users to swap in domain-specific data during mid-training or adjust post-training for specific use cases.
  • Olmo 3-Base (32B) achieves competitive performance with Qwen 2.5 and Gemma 3 while maintaining full openness of weights and code.

Analysis5

arxiv.org2026-09-14authorityHN 2 pts · 0 comments436 words
NCP-ArchPreview is a latent-space language model that pushes autoregressive pretraining beyond standard next-token prediction by learning to predict discrete concepts spanning multiple tokens. The 8.9B parameter model trains on 5.73T tokens from the Dolma-3 dataset, consuming only 51.3% of total training tokens to match OLMo-3-7B's loss while outperforming it by 2.45 points on the downstream macro-average and 5.99 points on GSM8K. Updating just the 17M-parameter VQ module yields a novel, lightweight interface for domain adaptation.written, reviewed, cited
simonwillison.net2025-11-23authorityabout the Olmo 3 familyHN 5 pts · 2 comments1,220 words
Olmo 3 is a fully open LLM series from Ai2 that releases training data, processes, and checkpoints alongside model weights. The 32B-scale model is pretrained on a ~9.3-trillion-token corpus and trains on roughly 6x fewer tokens than competing models. During testing, the 32B Think model took 14 minutes 43 seconds to generate 8,437 tokens for a simple SVG request.written, reviewed, cited
  • The 7B Instruct model download size is 4.16GB.
  • OlmoTrace currently relies on phrase matching to find training documents.
interconnects.ai2025-11-20authorityabout the Olmo 3 familyHN 4 pts · 0 comments1,814 words
Olmo 3 introduces a new family of fully open language models available today, featuring 7B and 32B variants. This family represents the first 32B (or larger) fully open reasoning model and the best 7B Western-origin thinking and instruct models. The 32B Thinker scores get within 1-2 points overall with their respective Qwen3 8/32B models.written, reviewed, cited
  • Olmo 3 Instruct models represent the best 7B scale model from a Western or American company.
  • The 32B range is largely devoid of non-Qwen competition for reasoning models.
github.com2025-11-24authorityabout the Olmo 3 familyHN 1 pts · 0 comments323 words
cameronrwolfe.substack.com2025-12-15about the Olmo 3 familyHN 4 pts · 0 comments13,013 words

Community2

twitter.com2025-11-20about the Olmo 3 familyHN 2 pts · 0 comments
twitter.com2025-11-20about the Olmo 3 familyHN 1 pts · 0 comments

Papers

1 paper

Papers 1

arXiv links found in the model card and its tags. "Peer-reviewed" is shown only with evidence: a published venue in OpenAlex (measured) or the authors' own journal reference on arXiv (reported).

Similar models

3 models

Similar models

  • Llama-3.1-8B-Instruct · 8.03B
    same size band (M), same task (text-generation), more providers
  • Qwen2.5-7B-Instruct · 7.62B
    same size band (M), same task (text-generation), more providers, permissive license
  • Mistral-7B-Instruct-v0.2 · 7.24B
    same size band (M), same task (text-generation), more providers, permissive license

Sources

3 sources

Sources

Hub snapshot: 2026-10-25 · HF API: 2026-10-24 21:39 UTC · Providers: 2026-10-24 21:44 UTC · Articles: 2026-10-24 21:46 UTC · Summary: 2026-10-07 15:10 UTC · Last verified 2026-10-25

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