base on Olive: Simplify ML Model Finetuning, Conversion, Quantization, and Optimization for CPUs, GPUs and NPUs. <div align="center"> <picture> <source media="(prefers-color-scheme: dark)" srcset="docs/source/images/olive-white-text.png"> <source media="(prefers-color-scheme: light)" srcset="docs/source/images/olive-black-text.png"> <img alt="olive text" src="docs/source/images/olive-black-text.png" height="100" style="max-width: 100%;"> </picture> [](https://pypi.org/project/olive-ai/) [](https://microsoft.github.io/Olive/) ## AI Model Optimization Toolkit for the ONNX Runtime </div> Given a model and targeted hardware, Olive (abbreviation of **O**nnx **LIVE**) composes the best suitable optimization techniques to output the most efficient ONNX model(s) for inferencing on the cloud or edge, while taking a set of constraints such as accuracy and latency into consideration. ## 📰 News Highlights Here are some recent videos, blog articles and labs that highlight Olive: - [ Oct 2025 ] [Exploring Optimal Quantization Settings for Small Language Models with Olive](https://microsoft.github.io/Olive/blogs/quant-slms.html) - [ Sep 2025 ] [Olive examples are relocated to new Olive-recipes repository](https://github.com/microsoft/olive-recipes) - [ Aug 2025 ] [Olive 0.9.2 is released with new quantization algorithms](https://github.com/microsoft/Olive/releases/tag/v0.9.2) - [ May 2025 ] [Olive 0.9.0 is released with support for NPUs](https://github.com/microsoft/Olive/releases/tag/v0.9.0) - [ Mar 2025 ] [Olive 0.8.0 is released with new quantization techniques](https://github.com/microsoft/Olive/releases/tag/v0.8.0) - [ Feb 2025 ] [New Notebook available - Finetune and Optimize DeepSeek R1 with Olive 🐋 ](https://github.com/microsoft/Olive/blob/main/notebooks/olive-deepseek-finetune.ipynb) - [ Nov 2024 ] [Democratizing AI Model optimization with the new Olive CLI](https://onnxruntime.ai/blogs/olive-cli) - [ Nov 2024 ] [Unlocking NLP Potential: Fine-Tuning with Microsoft Olive (Ignite Pre-Day Lab PRE016)](https://github.com/Azure/Ignite_FineTuning_workshop) - [ Nov 2024 ] [Olive supports generating models for MultiLoRA serving on the ONNX Runtime ](https://onnxruntime.ai/blogs/multilora) - [ Oct 2024 ] [Windows Dev Chat: Optimizing models from Hugging Face for the ONNX Runtime (video)](https://www.youtube.com/live/lAc1fq_0ftw?t=775s) - [ May 2024 ] [AI Toolkit - VS Code Extension that uses Olive to fine tune models](https://marketplace.visualstudio.com/items?itemName=ms-windows-ai-studio.windows-ai-studio) For a full list of news and blogs, read the [news archive](./NEWS.md). ## 🚀 Getting Started ### ✨ Quickstart If you prefer using the command line directly instead of Jupyter notebooks, we've outlined the quickstart commands here. #### 1. Install Olive CLI We recommend installing Olive in a [virtual environment](https://docs.python.org/3/library/venv.html) or a [conda environment](https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html). ``` pip install olive-ai[auto-opt] pip install transformers onnxruntime-genai ``` > [!NOTE] > Olive has optional dependencies that can be installed to enable additional features. Please refer to [Olive package config](./olive/olive_config.json) for the list of extras and their dependencies. #### 2. Automatic Optimizer In this quickstart you'll be optimizing [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct), which has many model files in the Hugging Face repo for different precisions that are not required by Olive. Run the automatic optimization: ```bash olive optimize \ --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct \ --precision int4 \ --output_path models/qwen ``` >[!TIP] ><details> ><summary>PowerShell Users</summary> >Line continuation between Bash and PowerShell are not interchangable. If you are using PowerShell, then you can copy-and-paste the following command that uses compatible line continuation. > >```powershell >olive optimize ` > --model_name_or_path Qwen/Qwen2.5-0.5B-Instruct ` > --output_path models/qwen ` > --precision int4 >``` </details> <br> The automatic optimizer will: 1. Acquire the model from the the Hugging Face model repo. 1. Quantize the model to `int4` using GPTQ. 1. Capture the ONNX Graph and store the weights in an ONNX data file. 1. Optimize the ONNX Graph. Olive can automatically optimize popular model *architectures* like Llama, Phi, Qwen, Gemma, etc out-of-the-box - [see detailed list here](https://huggingface.co/docs/optimum/en/exporters/onnx/overview). Also, you can optimize other model architectures by providing details on the input/outputs of the model (`io_config`). #### 3. Inference on the ONNX Runtime The ONNX Runtime (ORT) is a fast and light-weight cross-platform inference engine with bindings for popular programming language such as Python, C/C++, C#, Java, JavaScript, etc. ORT enables you to infuse AI models into your applications so that inference is handled on-device. The sample chat app to run is found as [model-chat.py](https://github.com/microsoft/onnxruntime-genai/blob/main/examples/python/model-chat.py) in the [onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai/) Github repository. ## 🎓 Learn more - [Documentation](https://microsoft.github.io/Olive) - [Recipes](https://github.com/microsoft/olive-recipes) ## 🤝 Contributions and Feedback - We welcome contributions! Please read the [contribution guidelines](./CONTRIBUTING.md) for more details on how to contribute to the Olive project. - For feature requests or bug reports, file a [GitHub Issue](https://github.com/microsoft/Olive/issues). - For general discussion or questions, use [GitHub Discussions](https://github.com/microsoft/Olive/discussions). ## ⚖️ License Copyright (c) Microsoft Corporation. All rights reserved. Licensed under the [MIT](./LICENSE) License. ## Pipeline Status [](https://dev.azure.com/aiinfra/PublicPackages/_build/latest?definitionId=1240) [](https://dev.azure.com/aiinfra/PublicPackages/_build/latest?definitionId=1279) ", Assign "at most 3 tags" to the expected json: {"id":"5573","tags":[]} "only from the tags list I provide: [{"id":77,"name":"3d"},{"id":89,"name":"agent"},{"id":17,"name":"ai"},{"id":54,"name":"algorithm"},{"id":24,"name":"api"},{"id":44,"name":"authentication"},{"id":3,"name":"aws"},{"id":27,"name":"backend"},{"id":60,"name":"benchmark"},{"id":72,"name":"best-practices"},{"id":39,"name":"bitcoin"},{"id":37,"name":"blockchain"},{"id":1,"name":"blog"},{"id":45,"name":"bundler"},{"id":58,"name":"cache"},{"id":21,"name":"chat"},{"id":49,"name":"cicd"},{"id":4,"name":"cli"},{"id":64,"name":"cloud-native"},{"id":48,"name":"cms"},{"id":61,"name":"compiler"},{"id":68,"name":"containerization"},{"id":92,"name":"crm"},{"id":34,"name":"data"},{"id":47,"name":"database"},{"id":8,"name":"declarative-gui "},{"id":9,"name":"deploy-tool"},{"id":53,"name":"desktop-app"},{"id":6,"name":"dev-exp-lib"},{"id":59,"name":"dev-tool"},{"id":13,"name":"ecommerce"},{"id":26,"name":"editor"},{"id":66,"name":"emulator"},{"id":62,"name":"filesystem"},{"id":80,"name":"finance"},{"id":15,"name":"firmware"},{"id":73,"name":"for-fun"},{"id":2,"name":"framework"},{"id":11,"name":"frontend"},{"id":22,"name":"game"},{"id":81,"name":"game-engine "},{"id":23,"name":"graphql"},{"id":84,"name":"gui"},{"id":91,"name":"http"},{"id":5,"name":"http-client"},{"id":51,"name":"iac"},{"id":30,"name":"ide"},{"id":78,"name":"iot"},{"id":40,"name":"json"},{"id":83,"name":"julian"},{"id":38,"name":"k8s"},{"id":31,"name":"language"},{"id":10,"name":"learning-resource"},{"id":33,"name":"lib"},{"id":41,"name":"linter"},{"id":28,"name":"lms"},{"id":16,"name":"logging"},{"id":76,"name":"low-code"},{"id":90,"name":"message-queue"},{"id":42,"name":"mobile-app"},{"id":18,"name":"monitoring"},{"id":36,"name":"networking"},{"id":7,"name":"node-version"},{"id":55,"name":"nosql"},{"id":57,"name":"observability"},{"id":46,"name":"orm"},{"id":52,"name":"os"},{"id":14,"name":"parser"},{"id":74,"name":"react"},{"id":82,"name":"real-time"},{"id":56,"name":"robot"},{"id":65,"name":"runtime"},{"id":32,"name":"sdk"},{"id":71,"name":"search"},{"id":63,"name":"secrets"},{"id":25,"name":"security"},{"id":85,"name":"server"},{"id":86,"name":"serverless"},{"id":70,"name":"storage"},{"id":75,"name":"system-design"},{"id":79,"name":"terminal"},{"id":29,"name":"testing"},{"id":12,"name":"ui"},{"id":50,"name":"ux"},{"id":88,"name":"video"},{"id":20,"name":"web-app"},{"id":35,"name":"web-server"},{"id":43,"name":"webassembly"},{"id":69,"name":"workflow"},{"id":87,"name":"yaml"}]" returns me the "expected json"