[whatmodel.sh](../../README.md) / Granite 3.1 1B-A400M Instruct # Granite 3.1 1B-A400M Instruct IBM · released 2024-12 · 1.3B params (0.4B active) · apache-2.0 **score 31.1** ±1.7 · #655 of 732 · from 6 benchmark results across 1 source · † no independently run results verified 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 1B-A400M Instruct among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
chat & writing  ▁▁▁▃●▄▆█▆▇▇▃   #354 of 402*    9.1%      1
math            ▁▁●▅█▆▄█▄▂▁▁   #388 of 431*     36%      1
chat & writing · #354 of 402 · 9.1% 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) …   46.9%  ▁●▄▂▂▄▆██▅▂▁ beats 6/145  easy · avg 77%    9.1%  at its level
math · #388 of 431 · 36% 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-2 XL (1.5B)](gpt-2-xl.md), [GPT-Neo 1.3B](gpt-neo-1-3b.md) here, though they rank lower overall. 
test           result  vs the others in its run   difficulty      counts  why
GSM8K (maker)   33.9%  ▂▂▂▂▃▄●▇██▆▄ beats 65/206  easy · avg 85%     36%  sharp · at its level
other tests (4) 
test                   result  vs the others in its run   difficulty        counts  why
ARC Challenge (maker)   42.7%  ▄▁▁▂▁▄●▇▃▂▃▂ beats 62/140  easy · avg 77%       22%  sharp
WinoGrande (maker)      62.4%  ▁▁▁▁▂▁▁▅●█▃▁ beats 46/131  medium · avg 80%     12%  at its level
HellaSwag (maker)       66.0%  ▁▁▁▂▂▂▄●█▇▄▂ beats 57/135  easy · avg 87%       12%  at its level
MMLU (maker)            26.1%  ▁●▁▁▁▂▂▅██▆▄ beats 5/214   medium · avg 74%    8.5%  typical
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
ibm-granite/granite-3.1-3b-a800m-instru…        6   99% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 31.1
Licences: 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)) ## 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 1B-A400M Instruct (Q4_K_M, needs ~0.8 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.1-1b-a400m-instruct-GGUF/tree/20c8444f497d3470eb373d8978159d9e2acfc38f)) ```sh llama cli -hf lmstudio-community/granite-3.1-1b-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-3.1-1b-a400m-instruct-GGUF:Q4_K_M ``` Other versions: Q8_0 (~1.4 GB) `llama cli -hf lmstudio-community/granite-3.1-1b-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 3.1 1B-A400M Instruct (Q4_K_M, needs ~0.8 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/granite-3.1-1b-a400m-instruct-GGUF/tree/20c8444f497d3470eb373d8978159d9e2acfc38f)) ```sh lms get https://huggingface.co/lmstudio-community/granite-3.1-1b-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-3.1-1b-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 3.1 1B-A400M Instruct (Q8_0, needs ~1.4 GB, from [ollama.com](https://ollama.com/library/granite3.1-moe/tags)) ```sh ollama run granite3.1-moe:1b ``` Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3.1-1b-a400m-instruct) ## Published speed measurements Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine. 
hardware                      precision        speed  engine and settings
Intel CoffeeLake-S GT2 [UHD…  q8_0       19.73 tok/s  ollama (version not recorded) · l…
Each speed links to its source, which has the full settings. [interactive version](https://whatmodel.sh/model/granite-3-1-1b-a400m) · [JSON](https://whatmodel.sh/api?format=json&all=1)