[whatmodel.sh](../../README.md) / Granite 34B Code Base (8K) # Granite 34B Code Base (8K) IBM · released 2024-04 · 33.72B params · apache-2.0 **score 43.3** ±1.2 · #512 of 732 · from 7 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 34B Code Base (8K) among every model on that task or test, worst to best. 
task    vs the others         rank  counts  tests
coding  ▁▁▁▃▅●█▇▅▄▃▁   #360 of 469     85%      6
coding · #360 of 469 · 85% of its score Beats [LFM2.5 2.6B](lfm2-5-2-6b.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [Qwen3-VL 2B Thinking](qwen3-vl-2b-thinking.md) here, though they rank higher overall. Trails [Code Tulu 2 13B](code-tulu-2-13b.md), [Phi-3-mini-128k-instruct (Apr 2024)](phi-3-mini-128k-instruct.md), [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
HumanEvalSynthesize (HumanEvalPac…   49.1%  ▁·▂▁▂▆▅█●▄▁▂ beats 28/35  medium · avg 62%     23%  at its level
HumanEvalExplain (HumanEvalPack, …   34.1%  ▄▇█▃▄▇▄●▇▃·▁ beats 27/35  medium · avg 46%     15%  at its level
HumanEvalFix (HumanEvalPack, 6-la…   29.5%  ▁·▁▁▄██▄●▁▅▅ beats 25/35  medium · avg 51%     15%  sharp · at its level
MBPP (maker)                         47.2%  █·▅▃▃▃▅▃▃●·▅ beats 12/15  easy · avg 67%       13%  at its level
CRUXEval-I (maker)                   43.3%  ▃▅█▃▃▅·▃▃●·▅ beats 13/15  medium · avg 56%     10%  at its level
CRUXEval-O (maker)                   44.8%  ▃·▃██▅▃·▃▃●▃ beats 14/15  medium · avg 54%    8.3%  blunt · at its level
other tests (1) 
test                     result  vs the others in its run  difficulty        counts  why
BigCodeBench (Complete)   38.5%  ▂▁▁▄▂▅●█▆▂▂▂ beats 24/58  medium · avg 48%     15%  at its level
## 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   85% ▇▇▇▇▇▇▇▇▇▇
BigCodeBench paper                              1   15% ▇▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 43.3
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/)) ## 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 34B Code Base (8K) (Q4_K_M, needs ~21.38 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-34b-code-base-8k-GGUF/tree/db10e16bac727ffd53037253b50c04a7f5b8fca5)) ```sh llama cli -hf ibm-granite/granite-34b-code-base-8k-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-34b-code-base-8k-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 34B Code Base (8K) (Q4_K_M, needs ~21.38 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-34b-code-base-8k-GGUF/tree/db10e16bac727ffd53037253b50c04a7f5b8fca5)) ```sh lms get https://huggingface.co/ibm-granite/granite-34b-code-base-8k-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 34B Code Base (8K) (Q4_K_M, needs ~21.38 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-34b-code-base-8k-GGUF/tree/db10e16bac727ffd53037253b50c04a7f5b8fca5)) ```sh ollama run hf.co/ibm-granite/granite-34b-code-base-8k-GGUF:Q4_K_M ``` Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-34b-code-base-8k) [interactive version](https://whatmodel.sh/model/granite-34b-code-base-8k) · [JSON](https://whatmodel.sh/api?format=json&all=1)