[whatmodel.sh](../../README.md) / Granite 3.0 3B-A800M Instruct
# Granite 3.0 3B-A800M Instruct
IBM · released 2024-10 · 3.3B params (0.8B active) · apache-2.0
**score 36.9** ±1.0 · #610 of 732 · from 16 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 3.0 3B-A800M Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁▃●▇█▇▅▄▃▁ #418 of 469* 17% 2
chat & writing ▁▁▁▃●▄▆█▆▇▇▃ #355 of 402* 3.6% 1
math ▁▁▃●█▆▄█▄▂▁▁ #383 of 431* 18% 2
coding · #418 of 469 · 17% of its score
Beats [DeepSeek R1 Distill Qwen 7B](deepseek-r1-distill-qwen-7b.md), [Qwen3.5 2B](qwen3-5-2b.md), [DeepSeek R1 Distill Llama 8B](deepseek-r1-distill-llama-8b.md) here, though they rank higher overall. Trails [Llama 3.2 1B Instruct](llama-3-2-1b.md), [SmolLM2 1.7B Instruct](smollm2-1-7b.md), [DeepSeek LLM 7B Chat](deepseek-llm-7b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
HumanEval (maker) 39.6% ▄·▄●·▄·▄···█ beats 2/7 easy · avg 78% 11% sharp · at its level
MBPP (maker) 27.4% ▄···●·▄▄█··█ beats 1/7 medium · avg 61% 5.4% at its level
chat & writing · #355 of 402 · 3.6% of its score
Beats [Code Llama 70B Instruct](codellama-70b-instruct.md), [Gemma 4 E2B](gemma-4-e2b.md), [Phi-3-mini-4k-instruct (June 2024 update)](phi-3-mini-4k-instruct-june-2024.md) here, though they rank higher overall. Trails [Gemma 3 270M IT](gemma-3-270m.md), [SmolLM2 360M Instruct](smollm2-360m.md), [Granite 4.0 H 350M](granite-4-0-h-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IFEval (aggregation unspecified) … 42.5% ▄····●·▄··██ beats 2/7 easy · avg 65% 3.6% at its level
math · #383 of 431 · 18% of its score
Beats [Yi 34B Chat](yi-34b-chat.md), [Llama 3 8B Instruct](llama-3-8b.md), [Command R (03-2024)](command-r-03-2024.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md), [OPT-1.3B](opt-1-3b.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 47.5% █▄····●··█▄▄ beats 3/7 easy · avg 80% 10% sharp · at its level
MATH (subset unspecified) (maker) 19.9% ▄···▄●▄▄··█▄ beats 2/7 medium · avg 49% 8.1% typical
other tests (11)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 37.7% ▃▃···●··█··▅ beats 2/7 medium · avg 63% 14% sharp
MMLU (maker) 50.2% ▄····▄·●▄█·█ beats 2/7 easy · avg 76% 10% at its level
GPQA (subset unspecified) (maker) 26.9% ▄·▄··●··▄█·█ beats 2/7 hard · avg 37% 7.0% sharp · too hard for it
WinoGrande (maker) 67.0% █···●▄▄▄···█ beats 2/7 medium · avg 81% 6.5% at its level
MMLU-Pro (maker) 20.5% ▄·····●··█▄█ beats 2/7 medium · avg 44% 6.3% typical
HellaSwag (maker) 71.9% ▄·▄··●▄·▄·▄▄ beats 3/7 easy · avg 85% 4.8% at its level
ARC Challenge (maker) 50.9% ▄····█··●▄▄█ beats 3/7 medium · avg 72% 3.6% sharp · at its level
OpenBookQA (maker) 40.8% ▄▄··▄··●·█▄▄ beats 3/7 medium · avg 55% 3.5% typical
BoolQ (maker) 78.6% ▄▄····●▄·█·█ beats 2/7 easy · avg 88% 2.9% at its level
EuroEval Life in the UK (English … 55.8% ▁▁▁▁▁▁▁●▄█▄▃ beats 27/146 easy · avg 82% 1.6% at its level · noisy
Social IQA (maker) 60.0% ▃·····▃··●▃▅ beats 3/7 medium · avg 66% 0.7% blunt · at its level
source results share of the score
IBM Granite 3.0 Language Models technic… 15 98% ▇▇▇▇▇▇▇▇▇▇▇▇
EuroEval leaderboards 1 2% ▇
prior: pull toward the middle 0% ▇
weighted average = 36.9
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)) · 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 Granite 3.0 3B-A800M Instruct (Q4_K_M, needs ~2.1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.0-3b-a800m-instruct-GGUF/tree/46f75d55362bc1d5152541ec9579d38381ad7a59))
```sh
llama cli -hf lmstudio-community/granite-3.0-3b-a800m-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-3.0-3b-a800m-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~3.6 GB) `llama cli -hf lmstudio-community/granite-3.0-3b-a800m-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 3B-A800M Instruct (Q4_K_M, needs ~2.1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.0-3b-a800m-instruct-GGUF/tree/46f75d55362bc1d5152541ec9579d38381ad7a59))
```sh
lms get https://huggingface.co/lmstudio-community/granite-3.0-3b-a800m-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 (~3.6 GB) `lms get https://huggingface.co/lmstudio-community/granite-3.0-3b-a800m-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 3B-A800M Instruct (Q4_K_M, needs ~2.1 GB, from [ollama.com](https://ollama.com/library/granite3-moe/tags))
```sh
ollama run granite3-moe:3b
```
Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.0-3b-a800m-instruct)
[interactive version](https://whatmodel.sh/model/granite-3-0-3b-a800m) · [JSON](https://whatmodel.sh/api?format=json&all=1)