[whatmodel.sh](../../README.md) / LFM2 1.2B
# LFM2 1.2B
Liquid AI · released 2025-07 · 1.17B params · lfm1.0
**score 40.8** ±1.3 · #552 of 732 · from 10 benchmark results across 3 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks LFM2 1.2B among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #274 of 402* 5.7% 1
math ▁▁▃▅●▆▄█▄▂▁▁ #300 of 431* 39% 2
chat & writing · #274 of 402 · 5.7% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md), [Ministral 3 14B](ministral-3-14b.md), [Phi-4 14B](phi-4.md) here, though they rank higher overall. Trails [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IFEval (aggregation unspecified) … 74.9% ▄····█▄···▄● beats 6/6 easy · avg 82% 5.7% at its level
math · #300 of 431 · 39% of its score
Beats [Molmo 2 8B](molmo-2-8b.md), [DeepSeek-Coder-V2 Instruct (0724)](deepseek-coder-v2-0724.md), [Llama 3.1 Nemotron 70B Instruct](llama-3-1-nemotron-70b.md) here, though they rank higher overall. Trails [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Gemma 7B (base)](gemma-7b-base.md), [Mistral 7B v0.1 (base)](mistral-7b-v0-1.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MGSM (maker) 55.0% █···█▄··●··▄ beats 5/6 easy · avg 83% 29% sharp · at its level
GSM8K (maker) 58.3% ▄·█···▄·▄··● beats 5/6 easy · avg 83% 10% sharp · at its level
other tests (7)
test result vs the others in its run difficulty counts why
MMLU (maker) 55.2% █·███·█··●·█ beats 5/6 easy · avg 74% 21% at its level
IFBench (aggregation unspecified)… 20.7% ▄▄·▄····▄·●▄ beats 5/6 medium · avg 38% 8.9% typical
MMMLU (maker) 46.7% ▄···▄·▄▄··▄● beats 6/6 medium · avg 57% 8.8% at its level
GPQA (subset unspecified) (maker) 31.5% █······▄▄█·● beats 6/6 hard · avg 37% 6.6% sharp · too hard for it · noisy
Open PT-LLM Leaderboard average (… 58.7% ▃▁▁▁▁▁▁▁●█▅▁ beats 86/276 medium · avg 72% 4.1% at its level · noisy
EuroEval HellaSwag (English), tes… 29.6% ▃·▁●▂▁▂▅█▇▄▁ beats 12/100 medium · avg 67% 3.0% sharp · at its level · noisy
EuroEval Life in the UK (English … 63.1% ▁▁▁▁▁▁▁●▄█▄▃ beats 33/146 easy · avg 82% 2.9% at its level · noisy
source results share of the score
LiquidAI/LFM2-1.2B model card (submitte… 7 89% ▇▇▇▇▇▇▇▇▇▇▇
EuroEval leaderboards 2 6% ▇
Open Portuguese LLM Leaderboard 1 4% ▇
prior: pull toward the middle 1% ▇
weighted average = 40.8
Licences: LiquidAI/LFM2-1.2B model card ([LFM Open License v1.0](https://huggingface.co/LiquidAI/LFM2-1.2B/blob/40f3da0d0164913923aee9462c23077868b816a3/LICENSE)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
## How to run it
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run LFM2 1.2B (Q4_K_M, needs ~0.73 GB, from [huggingface.co/LiquidAI](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF/tree/5399e76c648f4eb8c053feb1ab747277dea5bf8b))
```sh
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)
```sh
llama serve -hf LiquidAI/LFM2-1.2B-GGUF:Q4_K_M
```
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run LFM2 1.2B (Q4_K_M, needs ~0.73 GB, from [huggingface.co/LiquidAI](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF/tree/5399e76c648f4eb8c053feb1ab747277dea5bf8b))
```sh
lms get https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: 4bit (~0.7 GB, Mac only) `lms get https://huggingface.co/mlx-community/LFM2-1.2B-4bit -y` · 8bit (~1.2 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/LFM2-1.2B-MLX-8bit -y`
### Ollama
1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download)
```sh
# Linux
curl -fsSL https://ollama.com/install.sh | sh
```
2. Run LFM2 1.2B (Q4_K_M, needs ~0.73 GB, from [huggingface.co/LiquidAI](https://huggingface.co/LiquidAI/LFM2-1.2B-GGUF/tree/5399e76c648f4eb8c053feb1ab747277dea5bf8b))
```sh
ollama run hf.co/LiquidAI/LFM2-1.2B-GGUF:Q4_K_M
```
### MLX (Mac)
1. Install MLX (Mac). Apple silicon Macs only, text only.
```sh
# with uv
uv tool install mlx-lm
# or
pip install mlx-lm
```
2. Run LFM2 1.2B (4bit, needs ~0.7 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/LFM2-1.2B-4bit/tree/3843e4ad0fcb8b7ed8a050908ac8f0bb5320d1bf))
```sh
mlx_lm.chat --model mlx-community/LFM2-1.2B-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/LFM2-1.2B-4bit
```
Other versions: 8bit (~1.2 GB, Mac only) `mlx_lm.chat --model lmstudio-community/LFM2-1.2B-MLX-8bit`
Model repository and documentation: [huggingface.co/LiquidAI](https://huggingface.co/LiquidAI/LFM2-1.2B)
[interactive version](https://whatmodel.sh/model/lfm2-1-2b) · [JSON](https://whatmodel.sh/api?format=json&all=1)