[whatmodel.sh](../../README.md) / Granite 3.0 8B Instruct
# Granite 3.0 8B Instruct
IBM · released 2024-10 · 8.17B params · apache-2.0
**score 45.3** ±0.8 · #468 of 732 · from 22 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 3.0 8B Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃▅●█▇▅▄▃▁ #330 of 469 17% 6
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #327 of 402* 9.3% 2
math ▁▁▃●█▆▄█▄▂▁▁ #347 of 431* 15% 2
coding · #330 of 469 · 17% of its score
Beats [Qwen3-VL 8B Instruct](qwen3-vl-8b-instruct.md), [Qwen3-VL 4B Instruct](qwen3-vl-4b-instruct.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Llama 3.2 11B Vision Instruct](llama-3-2-11b-vision.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval (maker) 64.6% ▄·▄█·▄·▄···● beats 7/7 easy · avg 78% 7.6% sharp · at its level
MBPP (maker) 49.6% ▄···▄·▄▄█··● beats 6/7 medium · avg 61% 4.2% at its level
CodeReviewQA: solution identifica… 25.3% ▃··▁▂▂●▂█▃▄▂ beats 16/47 medium · avg 51% 1.9% sharp · at its level · noisy
CodeReviewQA: change localisation… 22.7% █▂▁▁▂▂▂▃●▅▅▇ beats 22/47 hard · avg 33% 1.7% sharp · noisy
CodeReviewQA: automated code refi… 31.8% ▁▁▁▁▁▁▂▂▃●▅▄ beats 26/47 hard · avg 32% 1.5% typical
CodeReviewQA: change type recogni… 55.9% ▁▁▁······●▃█ beats 8/47 easy · avg 67% 0.1% blunt · at its level · noisy
chat & writing · #327 of 402 · 9.3% of its score
Beats [Gemma 4 E4B](gemma-4-e4b.md), [OpenThinker3 7B](openthinker3-7b.md), [Code Llama 70B Instruct](codellama-70b-instruct.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) 1183.2 ▁▁▁▂●▃▃▆▆▇█▃ beats 60/385 avg 1260 6.8% blunt
IFEval (aggregation unspecified) … 52.3% ▄····█·▄··█● beats 6/7 easy · avg 65% 2.5% at its level
math · #347 of 431 · 15% of its score
Beats [Athene-70B](athene-70b.md), [Gemma 2 9B IT](gemma-2-9b.md), [GPT-3.5 Turbo (0125)](gpt-3-5-turbo-0125.md) here, though they rank higher overall. Trails [Gemma 2B (base)](gemma-2b-base.md), [Stable Code 3B](stable-code-3b.md), [Zephyr 7B beta](zephyr-7b-beta.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH (subset unspecified) (maker) 30.9% ▄···▄▄▄▄··●▄ beats 5/7 medium · avg 49% 7.9% at its level
GSM8K (maker) 69.0% █▄····▄··█▄● beats 7/7 easy · avg 80% 6.6% sharp · at its level
other tests (12)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 51.5% ▃▃···▃··█··● beats 7/7 medium · avg 63% 15% sharp · at its level
GPQA (subset unspecified) (maker) 33.8% ▄·▄··▄··▄█·● beats 7/7 hard · avg 37% 11% sharp
MMLU (maker) 65.8% ▄····▄·▄▄█·● beats 6/7 easy · avg 76% 7.8% at its level
MMLU-Pro (maker) 34.4% ▄·····█··█▄● beats 6/7 medium · avg 44% 6.6% at its level
WinoGrande (maker) 77.5% █···▄▄▄▄···● beats 6/7 medium · avg 81% 5.0% at its level
OpenBookQA (maker) 46.6% ▄▄··▄··▄·█▄● beats 7/7 medium · avg 55% 3.5% at its level
ARC Challenge (maker) 64.2% ▄····█··▄▄▄● beats 7/7 medium · avg 72% 3.1% sharp · at its level
HellaSwag (maker) 82.6% ▄·▄··█▄·▄·▄● beats 7/7 easy · avg 85% 2.8% at its level
BoolQ (maker) 88.6% ▄▄····▄▄·█·● beats 7/7 easy · avg 88% 1.8% at its level
EuroEval Life in the UK (English … 80.6% ▁▁▁▁▁▁▁▂▄●▄▃ beats 70/146 easy · avg 82% 1.0% at its level · noisy
Vectara hallucination leaderboard… 93.5% ▁▁▂▃▄●█▆▃▂▂▁ beats 48/140 very easy · avg 94% 0.5% blunt · maxed out · noisy
Social IQA (maker) 71.2% ▃·····▃··█▃● beats 7/7 medium · avg 66% 0.5% blunt · at its level
source results share of the score
IBM Granite 3.0 Language Models technic… 15 86% ▇▇▇▇▇▇▇▇▇▇
LMArena leaderboard dataset 1 7% ▇
CodeReviewQA: The Code Review Comprehen… 4 5% ▇
EuroEval leaderboards 1 1% ▇
Vectara hallucination leaderboard 1 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 45.3
Licences: IBM Granite 3.0 Language Models technical report ([Apache-2.0](https://github.com/ibm-granite/granite-3.0-language-models/blob/5960b7fb3308588bf5f86348278ec9d6015ab647/LICENSE)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · 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 3.0 8B Instruct (Q4_K_M, needs ~4.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.0-8b-instruct-GGUF/tree/1202e514956ce412b0cc7aca228fa269cbd4d4b0))
```sh
llama cli -hf lmstudio-community/granite-3.0-8b-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-3.0-8b-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~8.7 GB) `llama cli -hf lmstudio-community/granite-3.0-8b-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 3.0 8B Instruct (Q4_K_M, needs ~4.9 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.0-8b-instruct-GGUF/tree/1202e514956ce412b0cc7aca228fa269cbd4d4b0))
```sh
lms get https://huggingface.co/lmstudio-community/granite-3.0-8b-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 (~8.7 GB) `lms get https://huggingface.co/lmstudio-community/granite-3.0-8b-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 3.0 8B Instruct (Q4_K_M, needs ~5.4 GB, from [ollama.com](https://ollama.com/library/granite3-dense/tags))
```sh
ollama run granite3-dense:8b-instruct-q4_K_M
```
Other versions: Q8_0 (~10.3 GB) `ollama run granite3-dense:8b-instruct-q8_0`
Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.0-8b-instruct)
[interactive version](https://whatmodel.sh/model/granite-3-0-8b) · [JSON](https://whatmodel.sh/api?format=json&all=1)