whatmodel.sh / LFM2 1.2B

LFM2 1.2B

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

LFM2 1.2B is an open-weights language model from Liquid AI, released July 2025. Its published size is 1.17B parameters and a 33k-token context window. Licence: lfm1.0.

Catalogue identity notes

LiquidAI/LFM2-1.2B @40f3da0 (created 2025-07-10, licence lfm1.0). Liquid AI LFM2 hybrid (conv + attention) instruct model, July-Sept 2025; earlier generation than the catalogued LFM2.5 models. Lab rows from the LFM2-1.2B card's own LFM2 columns (3-model runs) plus independent EuroEval rows.

Model information sources

Release date ↗ · Weights licence ↗

HF safetensors metadata 1,170,340,608 parameters (LiquidAI/LFM2-1.2B @40f3da0).

39.7±1.5#535 of 719 LLMsfrom 10 benchmark results across 3 sources

$ how it compares

The LLMs ranked either side of LFM2 1.2B, out of 828 with a score, then more from Liquid AI.

#modelscorewhereRAMreleased
625Code Llama 34B (base) Meta40±1local~23GB2023-08
626Starling-LM 7B beta Nexusflow40±1local~6GB2024-03
627LFM2 1.2B Liquid AI40±2local~2.2GB2025-07
628Code Tulu 2 13B Allen Institute for AI40±2local~29GB2023-11
629LLaVA-v1.6-34B LLaVA40±2local~24GB2024-01
more from Liquid AI
370LFM2.5 2.6B Liquid AI53±1local~3.2GB2026-07
375LFM2.5 8B-A1B Liquid AI52±1local~6.5GB2026-05
398LFM2 24B-A2B Liquid AI51±1local~16GB2026-02

see all LLMs · find the ones that fit your machine

$ what it's good at

chat & writing▁▁▁▃▅▄▅█▆▆▇▃ #266 of 393 * · 6.8% of its score · 1 test

Beats Ministral 3 14B Reasoning 2512, Ministral 3 14B, Phi-4 14B here, though they rank higher overall. Trails Flan-T5 XXL here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
IFEval (aggregation unspecified) lab74.9% ✎▄····█▄···▄█ beats 6/6easy · avg 82%▇▇▇ 6.8%at its level
math▁▂▃▅▆▅▄█▄▁▁▁ #296 of 398 * · 31% of its score · 2 tests

Beats GPT-4 (0613), Athene-70B, Gemma 2 27B IT here, though they rank higher overall. Trails Gemma 7B (base), DeepSeek R1 Distill Qwen 1.5B, CodeGemma 7B (v1.0) here, though they rank lower overall.

testresultvs the others in its rundifficultycountswhy
MGSM lab55.0% ✎█···█▄··▄··▄ beats 5/6easy · avg 77%▇▇▇▇▇▇▇▇ 21%at its level
GSM8K lab58.3% ✎▄·█···▄·▄··█ beats 5/6easy · avg 83%▇▇▇▇ 10%sharp · at its level
other tests7 not in a task · 61% of its score
testresultvs the others in its rundifficultycountswhy
MMLU lab55.2% ✎█·███·█··█·█ beats 5/6easy · avg 75%▇▇▇▇▇▇▇▇▇ 22%at its level
MMMLU lab46.7% ✎▄···▄·▄▄··▄█ beats 6/6medium · avg 59%▇▇▇▇ 11%at its level
IFBench (aggregation unspecified) lab20.7% ✎▄▄·▄····▄·█▄ beats 5/6medium · avg 40%▇▇▇▇ 10%typical
GPQA (subset unspecified) lab31.5% ✎█······▄▄█·▄ beats 6/6hard · avg 39%▇▇▇ 8.1%sharp · too hard for it · noisy
Open PT-LLM Leaderboard average (9 Portuguese tasks)58.7% ✎▃▁▁▁▁▁▁▁▃█▅▁ beats 86/276medium · avg 73%▇▇ 4.3%at its level · noisy
EuroEval HellaSwag (English), test split, 5-shot generative MC, accuracy29.6% ✎▃·▁▃▂▁▂▅█▇▄▁ beats 12/100medium · avg 68%▇ 3.1%at its level · noisy
EuroEval Life in the UK (English knowledge MC), test split, 5-shot, accuracy63.1% ✎▁▁▁▁▁▁▁▃▄█▄▃ beats 33/146easy · avg 83%▇ 3.1%at its level · noisy

$ where to run it

llama.cpp · recommended

  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 LFM2 1.2B · needs ~0.73 GB · from huggingface.co/LiquidAI (official)
    chat in the terminal
    llama cli -hf LiquidAI/LFM2-1.2B-GGUF:Q4_K_M
  3. Or start a local server and open the address it prints (http://localhost:9931)
    llama serve -hf LiquidAI/LFM2-1.2B-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 LFM2 1.2B · needs ~0.73 GB · from huggingface.co/LiquidAI (official)

    open in LM Studio ↗

  3. Or download it in the terminal
    lms get https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF@Q4_K_M -y
  4. Then run lms ls to see its name, and lms chat followed by that name

Ollama

  1. Install Ollama

    download it (macOS / Windows) ↗

    Linux
    curl -fsSL https://ollama.com/install.sh | sh
  2. Run LFM2 1.2B · needs ~0.73 GB · from huggingface.co/LiquidAI (official)
    chat in the terminal
    ollama run hf.co/LiquidAI/LFM2-1.2B-GGUF: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 LFM2 1.2B · needs ~0.7 GB, Mac only · from huggingface.co/mlx-community
    chat in the terminal
    mlx_lm.chat --model mlx-community/LFM2-1.2B-4bit
  3. Or start an OpenAI-compatible server at http://localhost:8080/v1
    mlx_lm.server --model mlx-community/LFM2-1.2B-4bit

Official weights and documentation: huggingface.co/LiquidAI ↗

$ how the score was worked out · LiquidAI/LFM2-1.2B model card 88%, EuroEval leaderboards (result… 6%, 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
LiquidAI/LFM2-1.2B model card · lab-reported7▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇▇ 88%LFM Open License v1.0
EuroEval leaderboards (results collection), English generative leaderboard2▇▇ 6%MIT
Open Portuguese LLM Leaderboard (archived)1▇ 4%Apache-2.0
prior · pull toward the middle▇ 1%
weighted average= 39.7

$ 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