[whatmodel.sh](../../README.md) / Granite 3.2 2B Instruct
# Granite 3.2 2B Instruct
IBM · released 2025-02 · 2.53B params · apache-2.0
**score 38.5** ±1.3 · #587 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 Granite 3.2 2B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #340 of 402* 13% 2
math ▁▁▃●█▆▄█▄▂▁▁ #359 of 431 45% 3
chat & writing · #340 of 402 · 13% of its score
Beats [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md) here, though they rank higher overall. Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IFEval (aggregation unspecified) … 61.6% ●▄·▄······▄█ beats 0/5 easy · avg 78% 6.8% at its level
Arena-Hard (version/judge unspeci… 24.9% ●·▄··▄·····█ beats 1/5 medium · avg 55% 5.8% at its level
math · #359 of 431 · 45% of its score
Beats [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md), [DBRX Instruct](dbrx-instruct.md), [Command R Plus (04-2024)](command-r-plus-04-2024.md) here, though they rank higher overall. Trails [OPT-2.7B](opt-2-7b.md), [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 67.0% ●··▄·····▄·█ beats 0/5 very easy · avg 89% 30% sharp · at its level
MATH-500 (maker) 35.5% ●···▄·▄▄···▄ beats 1/5 easy · avg 70% 7.7% at its level
AIME 2024 (maker) 0.9% ●···▄▄▄····▄ beats 0/5 very hard · avg 8.9% 6.8% sharp · too hard for it
other tests (5)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 61.4% ●·▄·····▄▄·▄ beats 0/5 easy · avg 81% 18% sharp · at its level
MMLU (maker) 57.2% ▄●········▄█ beats 2/5 easy · avg 76% 15% at its level
PopQA (maker) 20.6% ▄·●······▄·█ beats 2/5 hard · avg 33% 5.5% typical
Vectara hallucination leaderboard… 83.5% ▁▁●▃▄▄█▆▃▂▂▁ beats 9/140 very easy · avg 94% 1.7% blunt · noisy
DROP (maker) 23.8% ▃●·····▃···█ beats 1/5 medium · avg 54% 0.8% blunt · at its level
source results share of the score
ibm-granite/granite-3.3-8b-instruct mod… 9 98% ▇▇▇▇▇▇▇▇▇▇▇▇
Vectara hallucination leaderboard 1 2% ▇
prior: pull toward the middle 1% ▇
weighted average = 38.5
Licences: ibm-granite/granite-3.3-8b-instruct model card ([Apache-2.0](../../licenses/ibm-granite--granite-3.3-8b-instruct.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
## 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 Granite 3.2 2B Instruct (Q4_K_M, needs ~1.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.2-2b-instruct-GGUF/tree/69f48b80a7ca30f91a70e54ad844827b73f5a00c))
```sh
llama cli -hf lmstudio-community/granite-3.2-2b-instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/granite-3.2-2b-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~2.7 GB) `llama cli -hf lmstudio-community/granite-3.2-2b-instruct-GGUF:Q8_0`
### 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 Granite 3.2 2B Instruct (Q4_K_M, needs ~1.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.2-2b-instruct-GGUF/tree/69f48b80a7ca30f91a70e54ad844827b73f5a00c))
```sh
lms get https://huggingface.co/lmstudio-community/granite-3.2-2b-instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~2.7 GB) `lms get https://huggingface.co/lmstudio-community/granite-3.2-2b-instruct-GGUF@Q8_0 -y` · 4bit (~1.4 GB, Mac only) `lms get https://huggingface.co/alexgusevski/granite-3.2-2b-instruct-q4-mlx -y` · 8bit (~2.7 GB, Mac only) `lms get https://huggingface.co/alexgusevski/granite-3.2-2b-instruct-q8-mlx -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 Granite 3.2 2B Instruct (Q4_K_M, needs ~2.02 GB, from [ollama.com](https://ollama.com/library/granite3.2/tags))
```sh
ollama run granite3.2:2b-instruct-q4_K_M
```
Other versions: Q8_0 (~3.54 GB) `ollama run granite3.2:2b-instruct-q8_0`
### 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 Granite 3.2 2B Instruct (4bit, needs ~1.4 GB, Mac only, from [huggingface.co/alexgusevski](https://huggingface.co/alexgusevski/granite-3.2-2b-instruct-q4-mlx/tree/707890a1785cec3ab2d4d942f2242ffc00915408))
```sh
mlx_lm.chat --model alexgusevski/granite-3.2-2b-instruct-q4-mlx
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model alexgusevski/granite-3.2-2b-instruct-q4-mlx
```
Other versions: 8bit (~2.7 GB, Mac only) `mlx_lm.chat --model alexgusevski/granite-3.2-2b-instruct-q8-mlx`
Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.2-2b-instruct)
[interactive version](https://whatmodel.sh/model/granite-3-2-2b) · [JSON](https://whatmodel.sh/api?format=json&all=1)