[whatmodel.sh](../../README.md) / Granite 3B Code Base (2K) # Granite 3B Code Base (2K) IBM · released 2024-04 · 3.48B params · apache-2.0 **score 35.0** ±1.3 · #628 of 732 · from 8 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at _{● marks Granite 3B Code Base (2K) among every model on that task or test, worst to best.}

task    vs the others         rank  counts  tests
coding  ▁▁▁●▅▇█▇▅▄▃▁   #421 of 469     82%      6

coding · #421 of 469 · 82% 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) here, though they rank lower overall.

test                                result  vs the others in its run  difficulty        counts  why
HumanEvalSynthesize (HumanEvalPac…   33.1%  ▁·▂▁▂●▅█▄▄▁▂ beats 11/35  medium · avg 62%     24%  at its level
MBPP (maker)                         36.0%  █·▅●▃▃▅▃▃▅·▅ beats 5/15   easy · avg 67%       15%  at its level
HumanEvalExplain (HumanEvalPack, …   21.9%  ▄▇█▃●▇▄▁▇▃·▁ beats 18/35  medium · avg 46%     12%  typical
CRUXEval-I (maker)                   30.6%  ▃▅●▃▃▅·▃▃▅·▅ beats 3/15   medium · avg 56%     11%  at its level
HumanEvalFix (HumanEvalPack, 6-la…   19.3%  ▁·▁▁▄██●█▁▅▅ beats 18/35  medium · avg 51%     10%  sharp
CRUXEval-O (maker)                   31.4%  ▃·●██▅▃·▃▃▅▃ beats 1/15   medium · avg 54%    9.3%  blunt · at its level

other tests (2)

test                                result  vs the others in its run  difficulty        counts  why
BigCodeBench (Complete)              20.2%  ●▁▁▄▂▅▇█▆▂▂▂ beats 0/58   medium · avg 48%     15%  at its level
EuroEval Life in the UK (English …   33.4%  ▁▁▁▁●▁▁▂▄█▄▃ beats 8/146  easy · avg 82%      2.8%  at its level · noisy

## 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
Granite Code Models: A Family of Open F…        6   82% ▇▇▇▇▇▇▇▇▇▇
BigCodeBench paper                              1   15% ▇▇
EuroEval leaderboards                           1    3% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 35.0

_{Licences: Granite Code Models: A Family of Open Foundation Models for Code Intelligence ([CC BY 4.0](../../licenses/paper-2405.04324v1-CC-BY-4.0.txt)) · BigCodeBench paper ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · 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 3B Code Base (2K) (Q4_K_M, needs ~2.13 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3b-code-base-2k-GGUF/tree/798a3d731dc15be27c453935842b53310a7c6dc9)) ```sh llama cli -hf ibm-granite/granite-3b-code-base-2k-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf ibm-granite/granite-3b-code-base-2k-GGUF:Q4_K_M ``` ### 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 3B Code Base (2K) (Q4_K_M, needs ~2.13 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3b-code-base-2k-GGUF/tree/798a3d731dc15be27c453935842b53310a7c6dc9)) ```sh lms get https://huggingface.co/ibm-granite/granite-3b-code-base-2k-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name ### 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 3B Code Base (2K) (Q4_K_M, needs ~2.13 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3b-code-base-2k-GGUF/tree/798a3d731dc15be27c453935842b53310a7c6dc9)) ```sh ollama run hf.co/ibm-granite/granite-3b-code-base-2k-GGUF:Q4_K_M ``` Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-3b-code-base-2k) [interactive version](https://whatmodel.sh/model/granite-3b-code-base-2k) · [JSON](https://whatmodel.sh/api?format=json&all=1)