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Finetune Mistral 7B using NVIDIA NeMo and PEFT

In this notebook, we will use NVIDIA's NeMo framework to finetune the Mistral 7B LLM. Finetuning is the process of adjusting the weights of a pre-trained foundation model with custom data. Considering that foundation models can be significantly large, a variant of fine-tuning has gained traction recently, known as parameter-efficient fine-tuning (PEFT). PEFT encompasses several methods, including P-Tuning, LoRA, Adapters, and IA3. For those interested in a deeper understanding of these methods, we have included a list of additional resources below.

Deploy now

To streamline your experience and jump directly into a GPU-accelerated environment with this notebook and NeMo pre-installed, click the badge below. Our 1-click deploys are powered by Brev.dev.

$\"$

Getting started

Use the 1-click deploy link above to set up a machine with NeMo installed. Once the VM is ready, use the Access Notebook button to enter the Jupyter Lab instance

Model

For this notebook, we use the Mistral-7B parameter model and the NeMo framework. We will be finetuning on the PubMedQA dataset and training our model to respond with simple yes/no answer. PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts.

NeMo

NVIDIA NeMo framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). NeMo provides a scalable framework to easily design, implement, and scale new AI models using existing pre-trained models and a simple API for configuration.

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Finetuning can be done using Brev quick deploy option.","# Finetune Mistral 7B using NVIDIA NeMo and PEFT\n\nIn this notebook, we will use NVIDIA's [NeMo framework](https://github.com/NVIDIA/NeMo) to finetune the Mistral 7B LLM. Finetuning is the process of adjusting the weights of a pre-trained foundation model with custom data. Considering that foundation models can be significantly large, a variant of fine-tuning has gained traction recently, known as parameter-efficient fine-tuning (PEFT). PEFT encompasses several methods, including P-Tuning, LoRA, Adapters, and IA3. For those interested in a deeper understanding of these methods, we have included a list of additional resources below.\n\n## Deploy now\nTo streamline your experience and jump directly into a GPU-accelerated environment with this notebook and NeMo pre-installed, click the badge below. Our 1-click deploys are powered by [Brev.dev](https://brev.dev/). \n\n[![ Click here to deploy.](https://uohmivykqgnnbiouffke.supabase.co/storage/v1/object/public/landingpage/brevdeploynavy.svg)](https://console.brev.dev/launchable/deploy?userID=p2mzt91a8&orgID=jnj0c501d&name=Fine-tune+Mistral-7b+using+NVIDA+NeMO&instance=A100%40a2-ultragpu-1g%3Anvidia-a100-80gb%3A1&diskStorage=350&cloudID=GCP&python=3.10&cuda=12.0.1&file=https%3A%2F%2Fgithub.com%2Fbrevdev%2Fnotebooks%2Fblob%2Fmain%2Fmistral-finetune-nemo.ipynb&launchableID=env-2hTEKRr8tLl58Ju1FCRXy3sshtt) \n\n## Getting started \nUse the 1-click deploy link above to set up a machine with NeMo installed. Once the VM is ready, use the Access Notebook button to enter the Jupyter Lab instance\n\n### Model\nFor this notebook, we use the Mistral-7B parameter model and the NeMo framework. We will be finetuning on the PubMedQA dataset and training our model to respond with simple yes/no answer. PubMedQA is a novel biomedical question answering (QA) dataset collected from PubMed abstracts. \n\n### NeMo\nNVIDIA NeMo framework is a generative AI framework built for researchers and pytorch developers working on large language models (LLMs), multimodal models (MM), automatic speech recognition (ASR), and text-to-speech synthesis (TTS). NeMo provides a scalable framework to easily design, implement, and scale new AI models using existing pre-trained models and a simple API for configuration.","displayName","Finetune Mistral 7B Using Brev.dev Quick Deploy",["D",1723568420324],"publisher","NVIDIA","latestVersion","versions","collectionCounts","imageUrl","/api/image/nvidia/csp_launcher/resources/finetune_mistral7b","rawImageUrl","https://developer.download.nvidia.com/images/brev-nvidia-dark.png","canGuestDownload","latestVersionSizeInBytes","licenseTerms","isMultinodeEnabled","isMultiArchitecture","bias","explainability","privacy","safetyAndSecurity","featureLabels",[],"displayLabels",[233,234,235,236,237,238],"policyLabels",[],"systemLabels",[],"products",[],"hasNimMetadata",{"_201":260,"_240":261,"_241":262,"_243":244},{"_201":257,"_240":258,"_241":259,"_243":244},{"_201":253,"_240":254,"_241":255,"_243":244},{"_201":251,"_240":251,"_241":252,"_243":244},{"_201":247,"_240":248,"_241":249,"_243":244},{"_201":239,"_240":239,"_241":242,"_243":244},"NeMo","value","filter",{"_245":246,"_240":239},"category","label","field","framework","Language Modeling","uscs_language_modeling",{"_245":250,"_240":248},"application","Jupyter Notebook",{"_245":73,"_240":251},"DL","soln_dl",{"_245":256,"_240":254},"technology","Automatic Speech Recognition","uscs_automatic_speech_recognition",{"_245":250,"_240":258},"AI","soln_ai",{"_245":256,"_240":261},{"_107":1448,"_109":1449},"ngcApps",[1401],"csaApiBaseUrl","https://www.nvidia.com/ai-agents/csa-agent/dcsm","datadogRum",{"_1383":1384,"_1385":1386,"_1387":1388,"_1389":1390,"_1391":274,"_134":298,"_1392":1393,"_1381":55,"_1394":86,"_1395":86,"_1396":1397,"_1398":86,"_1399":1400},"amplitude",{"_1379":1380,"_1381":55,"_972":-7,"_1382":30},"disableJs","environment","prod","featureFlagsBlob","JTdCJTIydGVzdEZsYWclMjIlM0FmYWxzZSUyQyUyMnNob3dTZWFyY2hMYWJlbFNldEZpbHRlcnMlMjIlM0FmYWxzZSUyQyUyMmNoaW5hTmltRGlzdHJpYnV0b3JzUmVkZXNpZ24lMjIlM0F0cnVlJTJDJTIyZW5hYmxlQ3NhV2lkZ2V0JTIyJTNBZmFsc2UlMkMlMjJlbmFibGVQcm9kdWN0R3JvdXBpbmdCeVN1YnNjcmlwdGlvbiUyMiUzQWZhbHNlJTJDJTIyY29udGFpbmVyU2Nhbm5pbmdSZWRlc2lnbk0xJTIyJTNBZmFsc2UlN0Q=","featureFlagsGuarded","isDevLikeEnv","isPlaywright","isMobileDevice","categorizedOrgs",{"_1373":1374,"_1375":1376,"_1377":1378},"currentOrgContext","currentContextRoles",[],"enterpriseOrgs",[],"locale","en","ngcApiOrigin","ngcHelmOrigin","origin","https://catalog.ngc.nvidia.com","usesMocks","theme","year",2026,"1.4.0","defaultSearchSuggestions",{"_1247":176,"_1248":1249,"_1250":1251,"_1252":1253},"announcements",[],"consentState",{"_336":1237,"_1238":30,"_1239":30,"_1240":86,"_1241":1242},"consentConfig",{"_318":319,"_320":321,"_322":323,"_324":325,"_326":327,"_328":329,"_330":331,"_332":333,"_334":335,"_336":337},"showContactAdmin",1782586253164,[],[],[312,313,314,315,316,317],{"_44":59,"_46":70,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":68,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":66,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":59,"_46":60,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":56,"_46":57,"_48":102,"_50":-5,"_52":-5,"_54":55},{"_44":45,"_46":47,"_48":102,"_50":-5,"_52":-5,"_54":55},"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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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.","sessionid","This is a very generic cookie name that may have different purposes on different sites, but generally it will be some kind of anonymous session identifier.","_csrf","This cookie is used to prevent Cross-Site Request Forgery (CSRF) attacks, ensuring that each request made to the server is from the user's browser and is intentional.","enterprise-support.nvidia.com","Used to apply end-user cookie consent preferences set by our client-side utility.Custom- Short Duration. Unique ID of an Enquiry- used to validate access to case, and load exisiting data.\n","renderCtx","academicgrants.nvidia.com","Used to store site parameters in the session for reuse across requests by a single client for functionality and performance reasons.","ph_phc_yQgAEdJJkVpI24NdSRID2mor1x1leRpDoC9yZ9mfXal_posthog","The PostHog ph_phc_xxx cookie is a session cookie used for identifying and tracking visitors on websites, helping us understand user behavior and website performance.","This cookie is managed by AWS and is used for load balancing.Used to attribute commission to affiliates when you arrive at the website from an affiliate referral link. 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.","OptanonAlertBoxClosed","docs.ngc.nvidia.com","This cookie is set by websites using certain versions of the cookie law compliance solution from OneTrust. It is set after visitors have seen a cookie information notice and in some cases only when they actively close the notice down. It enables the website not to show the message more than once to a user. The cookie has a normal lifespan of one year and contains no personal information.","nv_co","__uxq412__ses.88c1","investor.nvidia.com","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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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.","_devzone_session","This cookie is likely used to maintain a user session on the website by the server. It is common for session identifiers to be used to keep users logged in and track their interaction with the site during a session.","_cs_c","The cookie is used by Content Square to save the user consent to be tracked.","bm_sz","This cookie is used by Akamai to optimize site performance and security. It is typically associated with the delivery of web content and services.","vcalliance.nvidia.com","ASP.NET_SessionId","General purpose platform session cookie, used by sites written with Microsoft .NET based technologies. 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