whatmodel.sh / OLMo 2 7B Instruct (1124)

OLMo 2 7B Instruct (1124)

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OLMo 2 7B Instruct (1124) is an open-weights language model from Allen Institute for AI, released December 2024. Its published size is 7.3B parameters and a 4k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo allenai/OLMo-2-1124-7B-Instruct @470b1fb: licence tag apache-2.0. 7,298,617,344 parameters from the Hugging Face safetensors metadata. Dense, so active = total. Released 2024-12: month the maker's Hugging Face repo was created (createdAt 2024-12-18). Ollama tags olmo2:7b-1124-instruct-q4_K_M, olmo2:7b-1124-instruct-q8_0 are the library's instruct/chat builds of this checkpoint (tag names on the pinned tags page); download sizes are estimated from the parameter count.

41.5±1.3#507 of 719 LLMsfrom 14 benchmark results across 5 sources

$ how it compares

The LLMs ranked either side of OLMo 2 7B Instruct (1124), out of 828 with a score, then more from Allen Institute for AI.

#modelscorewhereRAMreleased
597Command R (03-2024) Cohere42±1local~24GB2024-03
598Ministral 3B (2410) Mistral42±1comparison only2024-10
599OLMo 2 7B Instruct (1124) Allen Institute for AI42±1local~5.8GB2024-12
600WizardCoder-15B-V1.0 WizardLM42±1local~12GB2023-06
601Snowflake Arctic Instruct Snowflake41±2local~1007GB2024-04
more from Allen Institute for AI
493OLMo 2 13B Instruct (1124) Allen Institute for AI46±1local~9.6GB2024-12
516Tulu 2 70B Allen Institute for AI46±2local~28GB2023-11
557Code Tulu 2 34B Allen Institute for AI44±2local~72GB2023-11

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #286 of 393 * · 12% of its score · 2 tests

Beats Ministral 3 14B Reasoning 2512, Gemma 4 E4B, OpenThinker3 7B here, though they rank higher overall. Trails Flan-T5 XXL, LFM2 700M, EXAONE 3.5 2.4B Instruct here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
AlpacaEval 2 length-controlled lab29.1% ✎▃▃·▁▁█▇▅·▁·▁ beats 6/22medium · avg 42%▇▇▇ 7.8%typical
IFEval prompt-level loose lab72.3% ✎▁▃▁▃▁▄▁▁█▃▃▁ beats 9/22easy · avg 80%▇▇ 3.8%at its level
math▁▂▃▅▆▅▄█▄▁▁▁ #339 of 398 · 27% of its score · 3 tests

Beats GPT-3.5 Turbo (0125), DBRX Instruct, Command R Plus (04-2024) here, though they rank higher overall. Trails Qwen3 0.6B, Gemma 7B (base), LFM2 700M here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab85.1% ✎▂▃··▃▂▅█▆▃▂▃ beats 13/22very easy · avg 88%▇▇▇▇▇▇ 14%sharp · at its level
MATH (subset unspecified) lab +132.5% ✎▃▃█▂▂▆·····▁ beats 4/22medium · avg 61%▇▇▇▇ 10%at its level · several runs
OMEGA (math generalisation) lab5.2% ✎█···▄·▄·▄··█ beats 1/6hard · avg 20%▇ 3.2%typical
other tests8 not in a task · 61% of its score
testresultvs the others in its rundifficultycountswhy
MMLU lab61.3% ✎▁▂·▁·▁▃█·▃▃▅ beats 5/22easy · avg 77%▇▇▇▇▇▇▇▇▇ 23%at its level
BIG-Bench Hard lab51.4% ✎▁▄·▁▃·▅█·▄▄▁ beats 5/22easy · avg 75%▇▇▇▇▇▇▇▇▇ 22%sharp · at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)63.7% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 105/276medium · avg 73%▇ 3.4%at its level · noisy
PopQA lab23.2% ✎▁▁▁▃▃▃▁█▁▃·▄ beats 8/21hard · avg 32%▇ 3.4%blunt
TruthfulQA MC2 lab56.5% ✎▃▃▂·▃▃·▅█▂▅▂ beats 8/21medium · avg 63%▇ 3.3%blunt · at its level
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy64.1% ✎▃·▁▁▂▁▂▅█▇▄▁ beats 38/100medium · avg 68%▇ 2.5%at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy79.3% ✎▁▁▁▁▁▁▁▂▄█▄▃ beats 62/146easy · avg 83%▇ 2.3%at its level · noisy
Vectara hallucination leaderboard, 2025 dataset (HHEM-2.3, factual consistency rate)88.9% ✎▁▁▂▃▄▄█▆▃▂▂▁ beats 21/140very easy · avg 94%▇ 1.3%blunt · noisy

$ where to run it

llama.cpp · recommended

version

  1. Install llama.cpp · Homebrew and winget installs name the commands llama-cli and llama-server instead.
    macOS / Linux
    curl -LsSf https://llama.app/install.sh | sh
    Windows
    irm https://llama.app/install.ps1 | iex

    other ways to install ↗

  2. Run OLMo 2 7B Instruct (1124) · needs ~4.5 GB · from huggingface.co/allenai
    chat in the terminal
    llama cli -hf allenai/OLMo-2-1124-7B-Instruct-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf allenai/OLMo-2-1124-7B-Instruct-GGUF:Q4_K_M

LM Studio

version

  1. Install LM Studio · Open the app once before using the lms command.

    download it (macOS / Windows / Linux) ↗

  2. Run OLMo 2 7B Instruct (1124) · needs ~4.5 GB · from huggingface.co/allenai

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/allenai/OLMo-2-1124-7B-Instruct-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

version

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run OLMo 2 7B Instruct (1124) · needs ~4.88 GB · from ollama.com
    chat in the terminal
    ollama run olmo2:7b-1124-instruct-q4_K_M

MLX (Mac)

version

  1. Install MLX (Mac) · Apple silicon Macs only, text only.
    with uv
    uv tool install mlx-lm
    or
    pip install mlx-lm
  2. Run OLMo 2 7B Instruct (1124) · needs ~4.1 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/OLMo-2-1124-7B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/OLMo-2-1124-7B-Instruct-4bit

Weights and documentation: huggingface.co/allenai ↗

$ how the score was worked out · 2 OLMo 2 Furious (Team OLMo,… 83%, allenai/Olmo-3-7B-Instruct mo… 7%, EuroEval leaderboards (result… 5% and 2 more

Each result implies a score on its own, after adjusting for how hard its test is and who ran it. The score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its counts column above; here it's added up by who ran the tests. How it works

sourceresultsshare of the scorelicence
2 OLMo 2 Furious (Team OLMo, Ai2) — authors' own evaluations of OLMo 2 Instruct and peer instruct models · lab-reported8▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 83%CC BY 4.0
allenai/Olmo-3-7B-Instruct model card · lab-reported2▇▇ 7%Apache-2.0
EuroEval leaderboards (results collection), English generative leaderboard2▇ 5%MIT
Open Portuguese LLM Leaderboard (archived)1▇ 3%Apache-2.0
Vectara hallucination leaderboard (vectara/hallucination-leaderboard repository)1▇ 1%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 41.5

$ improve this page

Update model informationAdd a place to run itAdd a test resultPropose a benchmarkCorrect a benchmark scoreEdit data on GitHub

Use a short GitHub form or edit the data through a pull request. Every number needs a source; here's which ones we can use.

data updated · sources and licences · json