Model Overview

Description:

CodeGemma is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text decoder-only models. This is a 7 billion parameter instruction-tuned varient for code chat and instruction.

Terms of use:

Use of this model is governed by the NVIDIA AI Foundation Models Community License. ADDITIONAL INFORMATION: Gemma Terms of Use and Google Prohibited Use Policy.

References(s):

CodeGemma Model Card

Model Architecture:

Architecture Type: Transformer

Input:

Input Format: Text

Input Parameters: None

Output:

Output Format: Text

Output Parameters: None

Software Integration:

Supported Hardware Platform(s): RTX 4090

Supported Operating System(s): Windows

Inference:

TRT-LLM Inference Engine
\nWindows Setup with TRT-LLM

Test Hardware:

RTX 4090

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CodeGemma models are text-to-text decoder-only models. This is a 7 billion parameter instruction-tuned varient for code chat and instruction.","Model Overview\n--------------\n\n### Description:\nCodeGemma is a collection of lightweight open code models built on top of Gemma. CodeGemma models are text-to-text decoder-only models. This is a 7 billion parameter instruction-tuned varient for code chat and instruction. \n\n\n### Terms of use:\nUse of this model is governed by the [NVIDIA AI Foundation Models Community License](https://developer.download.nvidia.com/ai-foundation-models/nvidia-ai-foundation-models-license-10Nov2023.pdf). ADDITIONAL INFORMATION: [Gemma Terms of Use](https://ai.google.dev/gemma/terms) and [Google Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).\n\n### References(s):\n\n* CodeGemma [Model Card](https://ai.google.dev/gemma/docs/codegemma) \n\n### Model Architecture:\n\n**Architecture Type:** Transformer \n\n### Input:\n**Input Format:** Text \n\n**Input Parameters:** None \n\n### Output:\n\n**Output Format:** Text\n\n**Output Parameters:** None\n\n### Software Integration:\n\n**Supported Hardware Platform(s):** RTX 4090\n\n**Supported Operating System(s):** Windows\n\n### Inference:\n\n[TRT-LLM Inference Engine](https://github.com/NVIDIA/TensorRT-LLM/tree/gemma/examples/gemma) \n[Windows Setup with TRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/gemma/windows) \n\n### Test Hardware:\nRTX 4090","displayName","CodeGemma-7B-IT-INT4-RTX",["D",1724062401043],"publisher","Google","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/codegemma7bitint4","rawImageUrl","https://developer.download.nvidia.com/images/Google_logo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[251],"displayLabels",[238,239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_207":247,"_241":248,"_242":249,"_244":245},{"_207":240,"_241":240,"_242":243,"_244":245},"NSPECT-1BX7-0LFE","value","filter",{"_246":245,"_241":240},"category","label","field","AI","soln_ai",{"_246":250,"_241":248},"technology",{"_207":252,"_241":253,"_242":254,"_244":255},"Runs on RTX","pltfm_runs_on_rtx",{"_246":256,"_241":253},"feature","platform",{"_121":1237,"_123":1238},"ngcApps",[1190],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1171":1172,"_1173":1174,"_1175":1176,"_1177":1178,"_1179":268,"_152":292,"_1180":1181,"_1182":59,"_1183":100,"_1184":100,"_1185":1186,"_1187":100,"_1188":1189},"amplitude",{"_1168":1169,"_806":-7,"_1170":34},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmVuYWJsZUNzYVdpZGdldCUyMiUzQWZhbHNlJTJDJTIyZW5hYmxlUHJvZHVjdEdyb3VwaW5nQnlTdWJzY3JpcHRpb24lMjIlM0FmYWxzZSUyQyUyMmNvbnRhaW5lclNjYW5uaW5nUmVkZXNpZ25NMSUyMiUzQXRydWUlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1162":1163,"_1164":1165,"_1166":1167},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.10.0","defaultSearchSuggestions",{"_1046":374,"_1047":1048,"_1049":1050,"_1051":1052},"announcements",[],"consentState",{"_331":1036,"_1037":34,"_1038":34,"_1039":100,"_1040":1041},"consentConfig",{"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332},"showContactAdmin",1785049576921,[],[],[306,307,308,309,310,311],{"_48":63,"_50":74,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":70,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":68,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":63,"_50":64,"_52":116,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":53,"_54":55,"_56":312,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":57,"_58":59},3,"mainInfoText","We and our third-party partners (including social media, advertising, and analytics partners) use cookies and other tracking technologies to collect, store, monitor, and process certain information about you when you visit our website. 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users logged in across NVIDIA domains.","api.ngc.nvidia.com","This cookie is managed by AWS and is used for load balancing.","bm_so","This cookie name is used to load the site as quickly as possible and to recognize bots using Akamai.","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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It is set when you click on one of our links and used to let the advertiser and us know the website from which you came.","This cookie is associated with Akamai and is used to differentiate between traffic from humans and bots.","nv_co","__uxq412__ses.88c1","learn.learn.nvidia.com","This cookie is associated with the Amazon Web Services Elastic Load Balancing functionality for routing client request on the server.","pctrk","Used to count page views by unauthenticated users against license usage.","bm_s","bpazaws52gukakzc__ctrl0_ctl36_uccaptcha","bm_sv","store.nvidia.com","The __cf_bm cookie is a cookie necessary to support Cloudflare Bot Management, currently in private beta. 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