[whatmodel.sh](../../README.md) / Granite 3.1 2B Instruct
# Granite 3.1 2B Instruct
IBM · released 2024-12 · 2.53B params · apache-2.0
**score 38.6** ±0.9 · #584 of 732 · from 18 benchmark results across 5 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Granite 3.1 2B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #332 of 402 18% 3
math ▁▁▃●█▆▄█▄▂▁▁ #358 of 431 35% 3
chat & writing · #332 of 402 · 18% 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 [LFM2 350M](lfm2-350m.md), [Gemma 3 1B](gemma-3-1b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1178.9 ▁▁▁▂●▃▃▆▆▇█▃ beats 55/385 avg 1260 8.7% blunt
IFEval (aggregation unspecified) … 63.6% ▄●·▄······▄█ beats 1/5 easy · avg 78% 6.0% at its level · several runs
Arena-Hard (version/judge unspeci… 23.3% ●·▄··▄·····█ beats 0/5 medium · avg 55% 3.1% at its level
math · #358 of 431 · 35% 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) 67.5% ●··▄·····▄·█ beats 1/5 very easy · avg 89% 27% sharp · at its level · several runs
MATH-500 (maker) 35.1% ●···▄·▄▄···▄ beats 0/5 easy · avg 70% 4.1% at its level
AIME 2024 (maker) 0.9% ●···▄▄▄····▄ beats 1/5 very hard · avg 8.9% 3.6% sharp · too hard for it
other tests (9)
test result vs the others in its run difficulty counts why
MMLU (maker) 57.1% ▄●········▄█ beats 1/5 easy · avg 76% 11% at its level · several runs
BIG-Bench Hard (maker) 61.8% ●·▄·····▄▄·▄ beats 1/5 easy · avg 81% 10% sharp · at its level
ARC Challenge (maker) 54.6% ▄▁▁▂▁▄█●▃▂▃▂ beats 97/140 easy · avg 77% 7.9% sharp · at its level
CPTUB average (SpeakLeash, 0-5 ju… 38.9% ▁●▂▂▃█▃▆▅▄▂▃ beats 7/95 medium · avg 65% 7.1% at its level
WinoGrande (maker) 67.5% ▁▁▁▁▂▁▁▅●█▃▁ beats 75/131 medium · avg 80% 3.9% at its level
HellaSwag (maker) 75.1% ▁▁▁▂▂▂▄▆█●▄▂ beats 91/135 easy · avg 87% 2.9% at its level
PopQA (maker) 20.5% ▄·●······▄·█ beats 1/5 hard · avg 33% 2.9% typical
Vectara hallucination leaderboard… 84.3% ▁▁●▃▄▄█▆▃▂▂▁ beats 13/140 very easy · avg 94% 0.9% blunt · noisy
DROP (maker) 21.0% ●▃·····▃···█ beats 0/5 medium · avg 54% 0.4% blunt · at its level
source results share of the score
ibm-granite/granite-3.3-8b-instruct mod… 9 52% ▇▇▇▇▇▇
ibm-granite/granite-3.1-3b-a800m-instru… 6 31% ▇▇▇▇
LMArena leaderboard dataset 1 9% ▇
SpeakLeash CPTUB 1 7% ▇
Vectara hallucination leaderboard 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 38.6
Licences: ibm-granite/granite-3.3-8b-instruct model card ([Apache-2.0](../../licenses/ibm-granite--granite-3.3-8b-instruct.txt)) · ibm-granite/granite-3.1-3b-a800m-instruct model card ([Apache-2.0](https://huggingface.co/ibm-granite/granite-3.1-3b-a800m-instruct/blob/a02780686e08a03fe0d2679a293b5c74a90efa89/README.md)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.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 3.1 2B Instruct (Q4_K_M, needs ~1.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.1-2b-instruct-GGUF/tree/b1c08369dcf04fb238a116f647fba35a9a03f978))
```sh
llama cli -hf lmstudio-community/granite-3.1-2b-instruct-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf lmstudio-community/granite-3.1-2b-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~2.7 GB) `llama cli -hf lmstudio-community/granite-3.1-2b-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 3.1 2B Instruct (Q4_K_M, needs ~1.5 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.1-2b-instruct-GGUF/tree/b1c08369dcf04fb238a116f647fba35a9a03f978))
```sh
lms get https://huggingface.co/lmstudio-community/granite-3.1-2b-instruct-GGUF@Q4_K_M -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: Q8_0 (~2.7 GB) `lms get https://huggingface.co/lmstudio-community/granite-3.1-2b-instruct-GGUF@Q8_0 -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 3.1 2B Instruct (Q4_K_M, needs ~2.02 GB, from [ollama.com](https://ollama.com/library/granite3.1-dense/tags))
```sh
ollama run granite3.1-dense:2b-instruct-q4_K_M
```
Other versions: Q8_0 (~3.54 GB) `ollama run granite3.1-dense:2b-instruct-q8_0`
Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.1-2b-instruct)
[interactive version](https://whatmodel.sh/model/granite-3-1-2b) · [JSON](https://whatmodel.sh/api?format=json&all=1)