[whatmodel.sh](../../README.md) / Granite 34B Code Instruct (8K) # Granite 34B Code Instruct (8K) IBM · released 2024-04 · 33.72B params · apache-2.0 **score 47.1** ±1.1 · #433 of 732 · from 11 benchmark results across 4 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 Instruct (8K) among every model on that task or test, worst to best. 
task    vs the others         rank  counts  tests
coding  ▁▁▁▃▅●█▇▅▄▃▁   #308 of 469     65%      7
coding · #308 of 469 · 65% of its score Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty        counts  why
HumanEvalSynthesize (HumanEvalPac…   54.8%  ▁·▂▁▂▆▅█▄●▁▂ beats 32/35  medium · avg 62%     22%  at its level
HumanEvalExplain (HumanEvalPack, …   41.9%  ▄▇█▃▄▇▄▁▇●·▁ beats 33/35  medium · avg 46%     16%  at its level
HumanEvalFix (HumanEvalPack, 6-la…   50.2%  ▁·▁▁▄██▄█▁▅● beats 32/35  medium · avg 51%     16%  sharp · at its level
CodeReviewQA: solution identifica…   19.3%  ▃··▁▂▂●▂█▃▄▂ beats 13/47  medium · avg 51%    4.0%  sharp · at its level · noisy
CodeReviewQA: change localisation…   20.7%  █▂▁▁▂▂▂●▅▅▅▇ beats 20/47  hard · avg 33%      3.8%  sharp · noisy
CodeReviewQA: automated code refi…   35.4%  ▁▁▁▁▁▁▂▂▃█●▄ beats 33/47  hard · avg 32%      3.1%  at its level
CodeReviewQA: change type recogni…   72.3%  ▁▁▁······▁▃● beats 22/47  easy · avg 67%      0.1%  blunt · at its level · noisy
other tests (4) 
test                                result  vs the others in its run   difficulty        counts  why
BigCodeBench (Instruct)              35.8%  ▃▂·▆▆█●▆▅▃▃▃ beats 18/33   medium · avg 39%     15%  at its level
BigCodeBench (Complete)              43.8%  ▂▁▁▄▂▅▇●▆▂▂▂ beats 40/58   medium · avg 48%     15%  at its level
EuroEval HellaSwag (English), tes…   34.1%  ▃·▁▁●▁▂▅█▇▄▁ beats 18/100  medium · avg 67%    2.6%  sharp · at its level · noisy
EuroEval Life in the UK (English …   51.5%  ▁▁▁▁▁▁●▂▄█▄▃ beats 20/146  easy · avg 82%      1.8%  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…        3   54% ▇▇▇▇▇▇▇
BigCodeBench paper                              2   30% ▇▇▇▇
CodeReviewQA: The Code Review Comprehen…        4   11% ▇
EuroEval leaderboards                           2    4% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 47.1
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/)) · CodeReviewQA: The Code Review Comprehension Assessment for Large Language Models ([CC-BY-4.0](https://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 34B Code Instruct (8K) (Q4_K_M, needs ~21.38 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-34b-code-instruct-8k-GGUF/tree/4e7bb6bc0b242b2cb32f296765fa5b78cf8c827c)) ```sh llama cli -hf ibm-granite/granite-34b-code-instruct-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-instruct-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 Instruct (8K) (Q4_K_M, needs ~21.38 GB, from [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-34b-code-instruct-8k-GGUF/tree/4e7bb6bc0b242b2cb32f296765fa5b78cf8c827c)) ```sh lms get https://huggingface.co/ibm-granite/granite-34b-code-instruct-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 Instruct (8K) (Q4_0, needs ~19 GB, from [ollama.com](https://ollama.com/library/granite-code/tags)) ```sh ollama run granite-code:34b ``` Other versions: Q4_0 (~19 GB) `ollama run granite-code:34b` Model repository and documentation: [huggingface.co/ibm-granite](https://huggingface.co/ibm-granite/granite-34b-code-instruct-8k) [interactive version](https://whatmodel.sh/model/granite-34b-code-instruct-8k) · [JSON](https://whatmodel.sh/api?format=json&all=1)