{"id":21222,"date":"2024-02-26T10:14:13","date_gmt":"2024-02-26T09:14:13","guid":{"rendered":"https:\/\/ubiops.com\/?p=21222"},"modified":"2025-01-13T17:10:40","modified_gmt":"2025-01-13T16:10:40","slug":"deploy-mixtral","status":"publish","type":"post","link":"https:\/\/ubiops.com\/deploy-mixtral\/","title":{"rendered":"Deploy Mixtral in under 15 minutes using UbiOps"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\"><strong>What can you get out of this guide?<\/strong><\/h2>\n\n\n\n<p>In this guide, we explain how to:<\/p>\n\n\n\n<ul>\n<li>Create a UbiOps account<\/li>\n\n\n\n<li>Create a code environment<\/li>\n\n\n\n<li>Create your Mixtral deployment<\/li>\n\n\n\n<li>Create a deployment version<\/li>\n\n\n\n<li>Make an API call to the model!<\/li>\n<\/ul>\n\n\n\n<p>To successfully complete this guide, make sure you have:<\/p>\n\n\n\n<ul>\n<li>UbiOps account (see below)<\/li>\n\n\n\n<li>Python 3.10 or higher installed<\/li>\n\n\n\n<li>UbiOps Client Library installed<\/li>\n<\/ul>\n\n\n\n<p>You\u2019ll also need the following files:<\/p>\n\n\n\n<ul>\n<li><a href=\"https:\/\/storage.googleapis.com\/ubiops\/blog-deployment\/mixtral\/deployment.py.zip\">Mixtral deployment file<\/a><\/li>\n\n\n\n<li><a href=\"https:\/\/storage.googleapis.com\/ubiops\/blog-deployment\/mixtral\/mixtral-env-files.zip\">Environment package<\/a><\/li>\n<\/ul>\n\n\n\n<p><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What is <\/strong><strong>Mixtral<\/strong><strong>?<\/strong><\/h2>\n\n\n\n<p><a href=\"https:\/\/mistral.ai\/news\/mixtral-of-experts\/\">Mixtral<\/a> is a state-of-the-art model developed by <a href=\"https:\/\/mistral.ai\/\">MistralAI<\/a>. It\u2019s open source and very performant. It bases its architecture on a unique technique called <a href=\"https:\/\/arxiv.org\/pdf\/2208.02813.pdf\">Mixture of Experts<\/a>. This technique was appropriated by MistralAI, leading to <a href=\"https:\/\/arxiv.org\/abs\/2401.04088\">Mixtral of Experts<\/a>. This means that the model\u2019s output is a combination of several \u201cexperts,\u201d which are in fact distinct neural networks. In this case, the smaller <a href=\"https:\/\/huggingface.co\/mistralai\/Mistral-7B-v0.1\">Mistral-7B<\/a> models. Each \u201c\u2018expert\u201d\u2019 is specialized to deal with certain kinds of prompts and is best suited for different cases. Their outputs are combined into a more refined version based on how each expert was specialized. This technique is <a href=\"https:\/\/huggingface.co\/blog\/moe\">purported to<\/a> \u201cachieve the same quality as its dense counterpart much faster during pretraining,\u201d as well as pulling off \u201cmuch faster inference compared to a dense model with the same number of parameters.\u201d<\/p>\n\n\n\n<p>This model, using the architecture defined in the <a href=\"https:\/\/arxiv.org\/abs\/2401.04088\">Mixtral of Experts<\/a> research paper, is called Mixtral-8x7B \u2014 or simply Mixtral \u2014 and it is currently one of the most performant large language models (LLMs) to date. It performs exceptionally well on several benchmarks and surpasses models which are greater in size, i.e., have a higher number of parameters. For instance, as shown in the figures below, Mixtral is superior or equivalent to LLaMa 2 70B in terms of performance, while being much cheaper to train and run.&nbsp;<\/p>\n\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"430\" src=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/Screenshot-from-2024-02-01-11-33-26-1024x430.png\" alt=\"\" class=\"wp-image-21246\" srcset=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/Screenshot-from-2024-02-01-11-33-26-1024x430.png 1024w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/Screenshot-from-2024-02-01-11-33-26-300x126.png 300w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/Screenshot-from-2024-02-01-11-33-26-768x322.png 768w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/Screenshot-from-2024-02-01-11-33-26.png 1335w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure><\/div>\n\n<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img decoding=\"async\" width=\"512\" height=\"170\" src=\"https:\/\/ubiops.com\/wp-content\/uploads\/2025\/01\/unnamed-3.png\" alt=\"\" class=\"wp-image-23672\" srcset=\"https:\/\/ubiops.com\/wp-content\/uploads\/2025\/01\/unnamed-3.png 512w, https:\/\/ubiops.com\/wp-content\/uploads\/2025\/01\/unnamed-3-300x100.png 300w\" sizes=\"(max-width: 512px) 100vw, 512px\" \/><figcaption class=\"wp-element-caption\">Source: Figure 2 in <a href=\"https:\/\/arxiv.org\/pdf\/2401.04088.pdf#page=3\">Mixtral of Experts<\/a><\/figcaption><\/figure><\/div>\n\n\n<p><\/p>\n\n\n\n<p>In terms of the ratio between cost and quality, Mixtral is superior to LLaMa 2 models across most benchmarks. If you want to learn more about how to interpret these results, read <a href=\"https:\/\/ubiops.com\/which-llm-to-choose-for-your-use-case\/\">Which LLM to choose for your use case<\/a>.<\/p>\n\n\n\n<p>The Mixtral model we will be deploying in this guide, mixtral-8x7b-instruct, can be found on <a href=\"https:\/\/huggingface.co\/mistralai\/Mixtral-8x7B-Instruct-v0.1\">Hugging Face<\/a>.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>What is UbiOps?<\/strong><\/h2>\n\n\n\n<p>UbiOps is a powerful AI model serving and orchestration service with unmatched simplicity, speed and scale. <strong>UbiOps minimizes DevOps time and costs<\/strong> to run, train, and manage AI models, and distributes them on any compute infrastructure at scale. It\u2019s built for training, deploying, running and managing production-grade AI in an agile way. It features unique functionality for workflow orchestration (<a href=\"https:\/\/ubiops.com\/docs\/pipelines\/\"><strong>Pipelines<\/strong><\/a>), automatic adaptive scaling in hybrid or multi-cloud environments, as well as key MLOps features. You can learn more about UbiOps features on our <a href=\"http:\/\/ubiops.com\/product\">Product page<\/a>. For the record, we have dedicated guides on how to deploy <a href=\"https:\/\/ubiops.com\/deploy-mistral-in-under-10-minutes\/\">Mistral-7b<\/a>, <a href=\"https:\/\/ubiops.com\/how-to-run-transformer-models-ubiops-hugging-face\/\">BERT<\/a>, <a href=\"https:\/\/ubiops.com\/deploy-llama-2-with-a-customizable-front-end-in-under-15-minutes-using-only-ubiops-python-and-streamlit\/\">LLaMa 2<\/a>, and <a href=\"https:\/\/ubiops.com\/deploy-falcon-7b-instruct-in-under-15-minutes-using-ubiops\/\">Falcon<\/a>, using UbiOps.<\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How to deploy <\/strong><strong>Mixtral<\/strong><strong> on UbiOps<\/strong><\/h2>\n\n\n\n<p>The first step is to <a href=\"https:\/\/app.ubiops.com\/sign-up\/\">create a UbiOps account<\/a>.<\/p>\n\n\n\n<p>In UbiOps you work within an <a href=\"https:\/\/ubiops.com\/docs\/organizations\/\">organization<\/a>, which can contain one or more <a href=\"https:\/\/ubiops.com\/docs\/organizations\/projects\/\">projects<\/a>. Within these projects you can create multiple <a href=\"https:\/\/ubiops.com\/docs\/starter-tutorial\/ui-starter-tutorial\/ui-deployment-starter-tutorial\/\">deployments<\/a>, which are basically your containerized code. You can also chain together deployments to create a <a href=\"https:\/\/ubiops.com\/docs\/starter-tutorial\/ui-starter-tutorial\/ui-pipeline-starter-tutorial\/\">pipeline<\/a>.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Create a project<\/h3>\n\n\n\n<p>Head over to the UbiOps WebApp and click on <strong>\u201cCreate new project.\u201d<\/strong> Your project can give your project a unique name, or let UbiOps generate one for you.<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img decoding=\"async\" width=\"1600\" height=\"888\" src=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-3.png\" alt=\"\" class=\"wp-image-21227\" srcset=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-3.png 1600w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-3-300x167.png 300w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-3-1024x568.png 1024w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-3-768x426.png 768w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-3-1536x852.png 1536w\" sizes=\"(max-width: 1600px) 100vw, 1600px\" \/><\/figure>\n\n\n\n<p>Now that we have our project up and running we can start building the environment that our code will run in.&nbsp;<\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Create a code environment<\/h3>\n\n\n\n<p>There are two ways to create environment using UbiOps:&nbsp;<\/p>\n\n\n\n<ul>\n<li><strong>Explicitly<\/strong>: by going to the \u201cEnvironments\u201d tab and creating an environment there<\/li>\n\n\n\n<li><strong>Implicitly<\/strong>: by adding environment files (requirements.txt and\/or ubiops.yaml) to your deployment package. When these files are detected, UbiOps will build an environment for you.<\/li>\n<\/ul>\n\n\n\n<p>See our documentation page for more information on <a href=\"https:\/\/ubiops.com\/docs\/environments\/\">environments<\/a>.<\/p>\n\n\n\n<p>For this guide we\u2019ll be building our environment <strong>explicitly<\/strong>. You can do this by going to the <strong>\u201cEnvironments\u201d<\/strong> tab on the left hand side, and clicking on <strong>\u201c+ Custom environment.\u201d<\/strong> Then, fill in the following parameters:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Name<\/strong><\/td><td>mixtral-environment<\/td><\/tr><tr><td><strong>Base environment<\/strong><\/td><td>Ubuntu 22.04 + Python 3.10 + CUDA 11.7.1<\/td><\/tr><tr><td><strong>Custom dependencies<\/strong><\/td><td>Upload the <a href=\"https:\/\/storage.googleapis.com\/ubiops\/blog-deployment\/mixtral\/mixtral-env-files.zip\">environment package<\/a><strong>&nbsp;<\/strong><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>Version labels are optional.&nbsp;<\/p>\n\n\n\n<p>Then click on the <strong>\u201cCreate\u201d<\/strong> button below and UbiOps will start building your environment (this should only take a few minutes).<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"942\" height=\"834\" src=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image.png\" alt=\"\" class=\"wp-image-21224\" srcset=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image.png 942w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-300x266.png 300w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-768x680.png 768w\" sizes=\"(max-width: 942px) 100vw, 942px\" \/><\/figure>\n\n\n\n<h3 class=\"wp-block-heading\">Create your Mixtral deployment<\/h3>\n\n\n\n<p>Now you can navigate to the <strong>\u201cDeployments\u201d<\/strong> tab on the left and click on <strong>\u201cCreate.\u201d<\/strong> In the following menu, you can define the name of the deployment as well as its input(s) and output(s). The input and output fields of the deployment define what data the deployment expects when making a request (i.e. when running the model). For this guide you can use the following:<\/p>\n\n\n\n<figure class=\"wp-block-table\"><table><tbody><tr><td><strong>Name<\/strong><\/td><td>mixtral-8x7b-instruct<\/td><\/tr><tr><td><strong>Input<\/strong><\/td><td>Name: prompt, Data Type: string<\/td><\/tr><tr><td><strong>Output<\/strong><\/td><td>Name: response, Data Type: string<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n<p>After providing that information, UbiOps will generate Python and R deployment code snippets that can be downloaded or copied. These are used to create the deployment package. For this guide, we will be using <strong>Python<\/strong>.<\/p>\n\n\n\n<h3 class=\"wp-block-heading\"><strong><\/strong><\/h3>\n\n\n\n<p>To finish creating your deployment, click <strong>\u201cNext: Create a version.\u201d<\/strong><\/p>\n\n\n\n<h3 class=\"wp-block-heading\">Create a deployment version<\/h3>\n\n\n\n<p>Upload the Mixtral <a href=\"https:\/\/storage.googleapis.com\/ubiops\/blog-deployment\/mixtral\/deployment.py.zip\">deployment file<\/a>, which contains the code that retrieves the <a href=\"https:\/\/huggingface.co\/mistralai\/Mixtral-8x7B-Instruct-v0.1\">Mixtral model from HuggingFace<\/a><strong>.<\/strong> In the environment settings, select mixtral-environment from the <strong>\u201cSelect code environment\u201d<\/strong> dropdown menu.<\/p>\n\n\n\n<p>Then, select the hardware the model will run on. For this deployment, you\u2019ll need at least the \u201876000MB + 11 vCPU + NVIDIA A100 40GB\u2019 instance type. Due to Mixtral\u2019s size, this deployment will be quantised.&nbsp;<\/p>\n\n\n\n<p>When you are happy with your settings, click on <strong>\u201cCreate\u201d<\/strong> and UbiOps will get straight to work building your deployment version.&nbsp;<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1036\" height=\"914\" src=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-1.png\" alt=\"\" class=\"wp-image-21225\" srcset=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-1.png 1036w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-1-300x265.png 300w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-1-1024x903.png 1024w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-1-768x678.png 768w\" sizes=\"(max-width: 1036px) 100vw, 1036px\" \/><\/figure>\n\n\n\n<p><strong>Once that\u2019s done, your model will be ready for action!<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>How to run a <\/strong><strong>Mixtral<\/strong><strong> model on UbiOps<\/strong><\/h2>\n\n\n\n<p>Navigate to your Mixtral deployment version and click on the <strong>\u201cCreate Request\u201d<\/strong> button to create your first request. For this model, your input could be \u201ctell me a fun fact about bears.\u201d<\/p>\n\n\n\n<figure class=\"wp-block-image\"><img loading=\"lazy\" decoding=\"async\" width=\"1600\" height=\"799\" src=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-2.png\" alt=\"\" class=\"wp-image-21226\" srcset=\"https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-2.png 1600w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-2-300x150.png 300w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-2-1024x511.png 1024w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-2-768x384.png 768w, https:\/\/ubiops.com\/wp-content\/uploads\/2024\/02\/image-2-1536x767.png 1536w\" sizes=\"(max-width: 1600px) 100vw, 1600px\" \/><\/figure>\n\n\n\n<p><strong>How easy was that?<\/strong><\/p>\n\n\n\n<h2 class=\"wp-block-heading\"><strong>Conclusion<\/strong><\/h2>\n\n\n\n<p><strong>And there we have it!<\/strong><\/p>\n\n\n\n<p>Your very own Mixtral deployment, hosted and served on UbiOps. <strong>All in under 15 minutes, without needing an enterprise-grade data center.<\/strong><\/p>\n\n\n\n<p>Naturally, there are further optimizations that can be made to the code to get your deployment running as fast as possible every time. We left these out of scope for this guide &#8211; but we invite you to iterate and improve your own deployment!<\/p>\n\n\n\n<p>Having completed this guide, you may now be wondering how to <a href=\"https:\/\/ubiops.com\/falcon-llm-fine-tuning\/\"><strong>fine-tune your LLM<\/strong><\/a>, <a href=\"https:\/\/ubiops.com\/implementing-rag-for-your-llm-mistral\/\"><strong>implement RAG<\/strong><\/a><strong>,<\/strong> or <a href=\"https:\/\/ubiops.com\/deploy-llama-2-with-a-customizable-front-end-in-under-15-minutes-using-only-ubiops-python-and-streamlit\/\"><strong>build a chatbot front-end<\/strong><\/a>. For more guides and tutorials, head over to the <a href=\"https:\/\/ubiops.com\/blog\/\">UbiOps blog<\/a>. Or, for guidance on Ubiops features, check out our <a href=\"https:\/\/ubiops.com\/docs\/\">documentation<\/a>.<\/p>\n\n\n\n<p>If you\u2019d like us to write about something specific, just shoot us a message or start a conversation in our <a href=\"https:\/\/join.slack.com\/t\/ubiops-community\/shared_invite\/zt-np02blts-5xyFK0azBOuhJzdRSYwM_w\">Slack community<\/a>. The UbiOps team would love to help you bring your project to life!<\/p>\n\n\n\n<p>Thanks for reading!&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>What can you get out of this guide? In this guide, we explain how to: To successfully complete this guide, make sure you have: You\u2019ll also need the following files: What is Mixtral? Mixtral is a state-of-the-art model developed by MistralAI. It\u2019s open source and very performant. It bases its architecture on a unique technique [&hellip;]<\/p>\n","protected":false},"author":7,"featured_media":21231,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"content-type":"","inline_featured_image":false,"footnotes":""},"categories":[1],"tags":[],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v23.5 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Deploy Mixtral in under 15 minutes using UbiOps - UbiOps - AI model serving, orchestration &amp; training<\/title>\n<meta name=\"description\" content=\"Your very own Mixtral deployment, hosted and served on UbiOps. 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