[whatmodel.sh](../../README.md) / LFM2.5 1.2B Instruct
# LFM2.5 1.2B Instruct
Liquid AI · released 2026-01 · 1.17B params · lfm1.0
**score 44.5** ±1.7 · #488 of 732 · from 10 benchmark results across 2 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks LFM2.5 1.2B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃●█▄▅▅▄▄▂▁ #260 of 313* 3.1% 1
chat & writing ▁▁▁▃▆▄▆█●▇▇▃ #168 of 402* 10% 1
math ▁▁▃▅●▆▄█▄▂▁▁ #319 of 431 44% 3
agent tasks · #260 of 313 · 3.1% of its score
Beats [DeepSeek R1 0528 Qwen3 8B](deepseek-r1-0528-qwen3-8b.md), [Magistral Small 1.0 (2506)](magistral-small-2506.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank higher overall. Trails [Granite 4.0 H 1B](granite-4-0-h-1b.md), [Granite 4.0 1B](granite-4-0-1b.md), [SmolLM3 3B](smollm3-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
BFCL v3 (maker) 49.1% ▄▄·······●██ beats 2/6 medium · avg 55% 3.1% blunt · at its level
chat & writing · #168 of 402 · 10% of its score
Beats [MiniMax-M2](minimax-m2.md), [MiniCPM5 2B](minicpm5-2b.md), [Granite 4.2 30B](granite-4-2-30b.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
IFEval mean of four measures (mak… 86.2% █·█··█·██·●█ beats 5/6 very easy · avg 86% 10% typical
math · #319 of 431 · 44% of its score
Beats [GPT-4 (0613)](gpt-4-0613.md), [Athene-70B](athene-70b.md), [Gemma 2 27B IT](gemma-2-27b.md) here, though they rank higher overall. Trails [StarCoder2 7B](starcoder2-7b.md), [DeepSeek R1 Distill Qwen 1.5B](deepseek-r1-distill-qwen-1-5b.md), [Tulu 2 DPO 7B](tulu-2-dpo-7b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 64.5% █····●▄▄···█ beats 2/6 very easy · avg 87% 28% sharp
AIME 2025 (maker) 14.0% ▄█··▄···●··█ beats 4/6 hard · avg 30% 9.4% sharp · noisy
MATH-500 (maker) 63.2% ▄··█▄·●···▄▄ beats 4/6 easy · avg 77% 6.5% at its level · noisy
other tests (5)
test result vs the others in its run difficulty counts why
GPQA Diamond (maker) 38.9% █··········● beats 5/5 hard · avg 40% 11% sharp · noisy
MMLU-Pro (maker) 44.4% █···█·██·●██ beats 4/6 medium · avg 52% 11% at its level
IFBench (aggregation unspecified)… 47.3% ▄·█·█······● beats 6/6 medium · avg 42% 9.2% at its level
EuroEval HellaSwag (English), tes… 32.5% ▃·▁▁●▁▂▅█▇▄▁ beats 15/100 medium · avg 67% 5.4% sharp · at its level · noisy
EuroEval Life in the UK (English … 61.2% ▁▁▁▁▁▁▁●▄█▄▃ beats 31/146 easy · avg 82% 4.3% at its level · noisy
source results share of the score
LiquidAI/LFM2.5-1.2B-Thinking model car… 8 89% ▇▇▇▇▇▇▇▇▇▇▇
EuroEval leaderboards 2 10% ▇
prior: pull toward the middle 1% ▇
weighted average = 44.5
Licences: LiquidAI/LFM2.5-1.2B-Thinking model card ([LFM Open License v1.0](../../licenses/cards-gap-NOTICE.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE))
## 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 LFM2.5 1.2B Instruct (Q4_K_M, needs ~0.73 GB, from [huggingface.co/LiquidAI](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF/tree/8ed288026e23958ad9dfa92d53ed773a8eee7125))
```sh
llama cli -hf LiquidAI/LFM2.5-1.2B-Instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf LiquidAI/LFM2.5-1.2B-Instruct-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 LFM2.5 1.2B Instruct (Q4_K_M, needs ~0.73 GB, from [huggingface.co/LiquidAI](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF/tree/8ed288026e23958ad9dfa92d53ed773a8eee7125))
```sh
lms get https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-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/LiquidAI/LFM2.5-1.2B-Instruct-MLX-4bit -y` · 8bit (~1.2 GB, Mac only) `lms get https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-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 LFM2.5 1.2B Instruct (Q4_K_M, needs ~0.73 GB, from [huggingface.co/LiquidAI](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-GGUF/tree/8ed288026e23958ad9dfa92d53ed773a8eee7125))
```sh
ollama run hf.co/LiquidAI/LFM2.5-1.2B-Instruct-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 LFM2.5 1.2B Instruct (4bit, needs ~0.7 GB, Mac only, from [huggingface.co/LiquidAI](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct-MLX-4bit/tree/7ccafdb04c36936f4f1c4685198c6c9a40275932))
```sh
mlx_lm.chat --model LiquidAI/LFM2.5-1.2B-Instruct-MLX-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model LiquidAI/LFM2.5-1.2B-Instruct-MLX-4bit
```
Other versions: 8bit (~1.2 GB, Mac only) `mlx_lm.chat --model LiquidAI/LFM2.5-1.2B-Instruct-MLX-8bit`
Model repository and documentation: [huggingface.co/LiquidAI](https://huggingface.co/LiquidAI/LFM2.5-1.2B-Instruct)
[interactive version](https://whatmodel.sh/model/lfm2-5-1-2b-instruct) · [JSON](https://whatmodel.sh/api?format=json&all=1)