Description:

The Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support.\nThe model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures. Phi-3 Mini has 3.8B parameters and is a dense decoder-only Transformer model.

The model is licensed under the MIT license.

Terms of use:

By accessing this model, you are agreeing to the phi3 Terms and Conditions of the MIT License.

References(s):

Phi-3-mini-128k-instruct - Model Card
Phi-3 - Microsoft Blog

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, Ada GPUs

Supported Operating System(s): Windows

Inference:

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

Test Hardware:

RTX 4090

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This dataset includes both synthetic data and filtered publicly available website data, with high-quality properties.","--------------\n\n### Description:\nThe Phi-3-Mini-128K-Instruct is a 3.8 billion-parameter, lightweight, state-of-the-art open model trained using the Phi-3 datasets. This dataset includes both synthetic data and filtered publicly available website data, with an emphasis on high-quality and reasoning-dense properties. The model belongs to the Phi-3 family with the Mini version in two variants 4K and 128K which is the context length (in tokens) that it can support.\nThe model underwent a rigorous enhancement process, incorporating both supervised fine-tuning and direct preference optimization to ensure precise instruction adherence and robust safety measures. Phi-3 Mini has 3.8B parameters and is a dense decoder-only Transformer model.\n\nThe model is licensed under the MIT license.\n\n\n\n### Terms of use:\nBy accessing this model, you are agreeing to the phi3 Terms and Conditions of the [MIT License](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/blob/main/LICENSE).\n\n### References(s):\n\n* Phi-3-mini-128k-instruct - [Model Card](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)\n* Phi-3 - [Microsoft Blog](https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/)\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, Ada GPUs \n\n**Supported Operating System(s):** Windows\n\n### Inference:\n\n[TRT-LLM Inference Engine](https://github.com/NVIDIA/TensorRT-LLM/tree/main/examples/phi) \n[Windows Setup with TRT-LLM](https://github.com/NVIDIA/TensorRT-LLM/tree/main/windows) \n\n### Test Hardware:\nRTX 4090","displayName","Phi-3-mini-128k Instruct Int4 RTX",["D",1723226182703],"publisher","Microsoft","latestVersion","collectionCounts","imageUrl","/api/image/nvidia/-/models/phi-3-mini-128k-instruct-int4-rtx","rawImageUrl","https://developer.download.nvidia.com/images/microsoft-logo.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","","explainability","privacy","safetyAndSecurity","featureLabels",[251],"displayLabels",[238,239],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_206":247,"_241":248,"_242":249,"_244":245},{"_206":240,"_241":240,"_242":243,"_244":245},"NSPECT-566U-2ZOY","value","filter",{"_246":245,"_241":240},"category","label","field","AI","soln_ai",{"_246":250,"_241":248},"technology",{"_206":252,"_241":253,"_242":254,"_244":255},"Runs on RTX","pltfm_runs_on_rtx",{"_246":256,"_241":253},"feature","platform",{"_118":1237,"_120":1238},"ngcApps",[1190],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1171":1172,"_1173":1174,"_1175":1176,"_1177":1178,"_1179":268,"_149":292,"_1180":1181,"_1182":59,"_1183":97,"_1184":97,"_1185":1186,"_1187":97,"_1188":1189},"amplitude",{"_1168":1169,"_805":-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",{"_1045":223,"_1046":1047,"_1048":1049,"_1050":1051},"announcements",[],"consentState",{"_331":1035,"_1036":97,"_1037":34,"_1038":34,"_1039":1040},"consentConfig",{"_313":314,"_315":316,"_317":318,"_319":320,"_321":322,"_323":324,"_325":326,"_327":328,"_329":330,"_331":332},"showContactAdmin",1785183093160,[],[],[306,307,308,309,310,311],{"_48":62,"_50":72,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":69,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":67,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":62,"_50":63,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":60,"_50":61,"_52":113,"_54":-5,"_56":-5,"_58":59},{"_48":49,"_50":51,"_52":53,"_54":55,"_56":312,"_58":59},5,"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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