[whatmodel.sh](../../README.md) / Granite 3.0 1B-A400M Instruct
# Granite 3.0 1B-A400M Instruct
IBM · released 2024-10 · 1.3B params (0.4B active) · apache-2.0
**score 29.5** ±1.2 · #668 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 1B-A400M Instruct among every model on that task or test, worst to best.
task vs the others rank counts tests
coding ▁▁▁●▅▇█▇▅▄▃▁ #442 of 469* 18% 2
chat & writing ▁▁▁●▆▄▆█▆▇▇▃ #383 of 402* 4.8% 1
math ▁▁●▅█▆▄█▄▂▁▁ #403 of 431* 18% 2
coding · #442 of 469 · 18% of its score
Beats [Qwen3.5 2B](qwen3-5-2b.md), [Molmo 7B-D (0924)](molmo-7b-d.md), [Tulu 2 7B](tulu-2-7b.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
HumanEval (maker) 30.2% ●·▄█·▄·▄···█ beats 0/7 easy · avg 78% 12% sharp
MBPP (maker) 15.4% ●···▄·▄▄█··█ beats 0/7 medium · avg 61% 6.0% typical
chat & writing · #383 of 402 · 4.8% of its score
Beats [Phi-2 (2.7B)](phi-2.md), [Qwen2.5 1.5B Instruct](qwen2-5-1-5b.md), [Nous-Hermes-13b](nous-hermes-13b.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 350M](granite-4-0-350m.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
IFEval (aggregation unspecified) … 32.4% ●····█·▄··██ beats 0/7 easy · avg 65% 4.8% at its level
math · #403 of 431 · 18% of its score
Beats [Command R (03-2024)](command-r-03-2024.md), [Mistral 7B Instruct v0.3](mistral-7b-instruct-v0-3.md), [Llama-3-Instruct-8B-SimPO-v0.2](llama-3-instruct-8b-simpo-v0-2.md) here, though they rank higher overall. Trails [GPT-Neo 125M](gpt-neo-125m.md), [OPT-125M](opt-125m.md), [GPT-2 XL (1.5B)](gpt-2-xl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
GSM8K (maker) 26.3% ●▄····▄··█▄▄ beats 1/7 easy · avg 80% 11% sharp
MATH (subset unspecified) (maker) 10.9% ●···▄▄▄▄··█▄ beats 0/7 medium · avg 49% 7.2% typical
other tests (11)
test result vs the others in its run difficulty counts why
BIG-Bench Hard (maker) 31.8% ●▃···▃··█··▅ beats 0/7 medium · avg 63% 11% sharp · too hard for it
MMLU (maker) 32.0% ●····▄·▄▄█·█ beats 0/7 easy · avg 76% 10% typical
WinoGrande (maker) 61.2% ●···▄▄▄▄···█ beats 1/7 medium · avg 81% 7.6% typical
HellaSwag (maker) 65.6% ▄·●··█▄·▄·▄▄ beats 1/7 easy · avg 85% 7.3% at its level
MMLU-Pro (maker) 12.2% ●·····█··█▄█ beats 0/7 medium · avg 44% 5.6% typical
GPQA (subset unspecified) (maker) 23.1% ●·▄··▄··▄█·█ beats 0/7 hard · avg 37% 4.4% sharp · too hard for it
BoolQ (maker) 70.1% ▄●····▄▄·█·█ beats 1/7 easy · avg 88% 4.3% at its level
ARC Challenge (maker) 41.2% ▄····●··▄▄▄█ beats 1/7 medium · avg 72% 3.3% sharp
OpenBookQA (maker) 38.4% ▄▄··●··▄·█▄▄ beats 2/7 medium · avg 55% 3.3% typical
EuroEval Life in the UK (English … 34.8% ▁▁▁▁●▁▁▂▄█▄▃ beats 10/146 easy · avg 82% 2.0% at its level · noisy
Social IQA (maker) 47.5% ▃·····●··█▃▅ beats 1/7 medium · avg 66% 0.9% blunt · at its level
source results share of the score
IBM Granite 3.0 Language Models technic… 15 97% ▇▇▇▇▇▇▇▇▇▇▇▇
EuroEval leaderboards 1 2% ▇
prior: pull toward the middle 1% ▇
weighted average = 29.5
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 1B-A400M Instruct (Q4_K_M, needs ~0.8 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.0-1b-a400m-instruct-GGUF/tree/dc947e9138189efb3295fe3a2bb91b9ca9087bdb))
```sh
llama cli -hf lmstudio-community/granite-3.0-1b-a400m-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-3.0-1b-a400m-instruct-GGUF:Q4_K_M
```
Other versions: Q8_0 (~1.4 GB) `llama cli -hf lmstudio-community/granite-3.0-1b-a400m-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 1B-A400M Instruct (Q4_K_M, needs ~0.8 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.0-1b-a400m-instruct-GGUF/tree/dc947e9138189efb3295fe3a2bb91b9ca9087bdb))
```sh
lms get https://huggingface.co/lmstudio-community/granite-3.0-1b-a400m-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 (~1.4 GB) `lms get https://huggingface.co/lmstudio-community/granite-3.0-1b-a400m-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 1B-A400M Instruct (Q4_K_M, needs ~0.822 GB, from [ollama.com](https://ollama.com/library/granite3-moe/tags))
```sh
ollama run granite3-moe:1b
```
Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.0-1b-a400m-instruct)
[interactive version](https://whatmodel.sh/model/granite-3-0-1b-a400m) · [JSON](https://whatmodel.sh/api?format=json&all=1)