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OLMo 2 1B Instruct (0425)

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OLMo 2 1B Instruct (0425) is an open-weights language model from Allen Institute for AI, released April 2025. Its published size is 1.48B parameters and a 4k-token context window. Licence: apache-2.0.

Catalogue identity notes

Maker repo allenai/OLMo-2-0425-1B-Instruct @48d788e: licence tag apache-2.0. 1,484,916,736 parameters from the Hugging Face safetensors metadata. Dense, so active = total. Released 2025-04: month the maker's Hugging Face repo was created (createdAt 2025-04-29).

32.6±1.5#622 of 719 LLMsfrom 9 benchmark results across 2 sources

$ how it compares

The LLMs ranked either side of OLMo 2 1B Instruct (0425), out of 828 with a score, then more from Allen Institute for AI.

#modelscorewhereRAMreleased
717Guanaco 33B University of Washington33±2local~70GB2023-05
718Stable Code 3B Stability AI33±1local~3.3GB2024-01
719OLMo 2 1B Instruct (0425) Allen Institute for AI33±2local~2.4GB2025-04
720DeepSeek-Coder 1.3B Base DeepSeek32±1local~2.4GB2023-10
721Orca 2 13B Microsoft32±2local~9.8GB2023-11
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▁▁▁▃▅▄▅█▆▆▇▃ #336 of 393 * · 12% of its score · 2 tests

Beats OpenThinker3 7B, Gemma 4 E2B, Code Llama 70B Instruct here, though they rank higher overall. Trails Granite 4.0 H 350M, LFM2 350M, Gemma 3 1B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
IFEval prompt-level loose lab70.1% ✎▁▃▁▃▁▄▁▁█▃▃▁ beats 8/22easy · avg 80%▇▇ 6.0%at its level
AlpacaEval 2 length-controlled lab9.1% ✎▃▃·▁▁█▇▅·▁·▁ beats 2/22medium · avg 42%▇▇ 5.6%typical
math▁▂▃▅▆▅▄█▄▁▁▁ #362 of 398 * · 30% of its score · 2 tests

Beats Yi 34B Chat, Llama 3 8B Instruct, Command R (03-2024) here, though they rank higher overall. Trails Gemma 3 1B here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
GSM8K lab68.3% ✎▂▃··▃▂▅█▆▃▂▃ beats 4/22very easy · avg 88%▇▇▇▇▇▇▇▇▇▇ 24%sharp · at its level
MATH (subset unspecified) lab20.7% ✎▃▃█▂▂▆·····▁ beats 1/22medium · avg 61%▇▇ 5.9%typical
other tests5 not in a task · 57% of its score
testresultvs the others in its rundifficultycountswhy
MMLU lab40.0% ✎▁▃·▁·▁▁█·▃▃▅ beats 2/22easy · avg 77%▇▇▇▇▇▇▇▇▇▇ 26%at its level
BIG-Bench Hard lab35.0% ✎▃▄·▁▁·▅█·▄▄▁ beats 0/22easy · avg 75%▇▇▇▇▇▇▇▇ 20%sharp
TruthfulQA MC2 lab48.7% ✎▃▃▃·▃▃·▅█▂▅▂ beats 4/21medium · avg 63%▇▇ 4.7%blunt · at its level
Open PT-LLM Leaderboard average (9 Portuguese tasks)45.0% ✎▃▁▁▁▁▁▃▁▂█▅▁ beats 60/276medium · avg 73%▇ 3.7%at its level · noisy
PopQA lab12.9% ✎▁▃▁▃▃▃▁█▁▃·▄ beats 1/21hard · avg 32%▇ 3.4%blunt

$ 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 1B Instruct (0425) · needs ~0.9 GB · from huggingface.co/allenai
    chat in the terminal
    llama cli -hf allenai/OLMo-2-0425-1B-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-2-0425-1B-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 1B Instruct (0425) · needs ~0.9 GB · from huggingface.co/allenai

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/allenai/OLMo-2-0425-1B-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 1B Instruct (0425) · needs ~0.9 GB · from huggingface.co/allenai
    chat in the terminal
    ollama run hf.co/allenai/OLMo-2-0425-1B-Instruct-GGUF:Q4_K_M

MLX (Mac)

  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 1B Instruct (0425) · needs ~1.4 GB, Mac only · from huggingface.co/ekryski
    chat in the terminal
    mlx_lm.chat --model ekryski/OLMo-2-0425-1B-Instruct-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model ekryski/OLMo-2-0425-1B-Instruct-4bit

Other ways to run it

  1. Run it as published · needs ~4.05 GB, NVIDIA only · from huggingface.co/allenai
    vllm serve allenai/OLMo-2-0425-1B-Instruct

Weights and documentation: huggingface.co/allenai ↗

$ how the score was worked out · 2 OLMo 2 Furious (Team OLMo,… 95%, Open Portuguese LLM Leaderboa… 4%

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▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 95%CC BY 4.0
Open Portuguese LLM Leaderboard (archived)1▇ 4%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 32.6

$ improve this page

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