https://flex.ai/assets/FineTuning-DNQiMzWa.js

1import{r as e}from"./rolldown-runtime-hePW80VL.js";import{d as t,n,u as r}from"./react-vendor-Bx0rRGkx.js";import{a as i,d as a,l as o,n as s,t as c}from"./SEOHead-D2_Na5KX.js";import{t as l}from"./createLucideIcon-DtIHWjV7.js";import{i as u,t as d}from"./TestimonialsSection-BQSUftu7.js";import{t as f}from"./circle-check-C1EcirS2.js";import{t as p}from"./dollar-sign-e-ZtcqVU.js";import{t as m}from"./receipt-DCDgfjJo.js";import{t as h}from"./sparkles-DKjAq69o.js";import{t as g}from"./target-g5ExMtXW.js";import{t as _}from"./trending-up-bk6dCMZu.js";import{r as v}from"./app-dvUn509z.js";import{c as y,n as b,r as x,s as S}from"./section-helpers-CiGrD-f0.js";import{l as C}from"./seo-CCvDaH4G.js";import{t as w}from"./HeroBackground-Cl1d2p_Q.js";import{t as T}from"./OutcomeCards-aKFYPJ1W.js";import{t as E}from"./RuntimeFactsSection-_MElSbJE.js";var D={name:`settings-2`,size:24,node:[[`path`,{d:`M14 17H5`,key:`gfn3mx`}],[`path`,{d:`M19 7h-9`,key:`6i9tg`}],[`circle`,{cx:`17`,cy:`17`,r:`3`,key:`18b49y`}],[`circle`,{cx:`7`,cy:`7`,r:`3`,key:`dfmy0x`}]]};D.node;var O=l(D),k=e(r(),1),A=`/assets/finetuning-console-D9QQZk0b.png`,j=t(),M=({className:e=``})=>(0,j.jsx)(`div`,{className:`absolute rounded-full blur-[120px] pointer-events-none ${e}`});function N({children:e}){return(0,j.jsx)(`span`,{className:`inline-flex items-center rounded-full border border-primary/20 bg-secondary/60 px-3 py-1 text-xs font-medium text-secondary-foreground shadow-xs`,children:e})}function P(){let[e,t]=(0,k.useState)(`accuracy`),n=[{key:`accuracy`,title:`Accuracy`,subtitle:`Model quality first`,desc:`Fine tuning built for accuracy. 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Deep adaptation.`}),(0,j.jsxs)(`div`,{className:`mt-3 space-y-2 text-sm text-muted-foreground`,children:[(0,j.jsxs)(`div`,{className:`flex gap-2`,children:[(0,j.jsx)(f,{className:`mt-0.5 h-4 w-4 flex-none text-primary`}),(0,j.jsx)(`span`,{children:`Update all model weights for maximum domain alignment`})]}),(0,j.jsxs)(`div`,{className:`flex gap-2`,children:[(0,j.jsx)(f,{className:`mt-0.5 h-4 w-4 flex-none text-primary`}),(0,j.jsx)(`span`,{children:`Multi GPU parallelized execution with stable synchronization`})]}),(0,j.jsxs)(`div`,{className:`flex gap-2`,children:[(0,j.jsx)(f,{className:`mt-0.5 h-4 w-4 flex-none text-primary`}),(0,j.jsx)(`span`,{children:`Managed checkpoints with automatic versioning and rollback`})]}),(0,j.jsxs)(`div`,{className:`flex gap-2`,children:[(0,j.jsx)(f,{className:`mt-0.5 h-4 w-4 flex-none text-primary`}),(0,j.jsx)(`span`,{children:`Best for foundational shifts in model behavior and knowledge`})]})]})]})]})]})]})}function F(){let e=(0,k.useMemo)(()=>[{title:`Fine-Tune and Deploy an LLM with LlamaFactory`,meta:`Fine-tune and deploy any LLM with LlamaFactory on FlexAI: managed checkpoints, train and serve in one platform`,pills:[`llama-factory`,`sft`,`deploy`],href:`https://docs.flex.ai/blueprints/llama-factory`},{title:`Fine-Tune LLMs with Axolotl on Domain-Specific Data`,meta:`Fine-tune language models on your own data with Axolotl, then deploy as a production inference endpoint`,pills:[`axolotl`,`fsdp`,`deploy`],href:`https://docs.flex.ai/blueprints/axolotl`},{title:`Reinforcement Learning Fine-Tuning with EasyR1`,meta:`Fine-tune language models with reinforcement learning using GRPO and DAPO algorithms for enhanced reasoning`,pills:[`reinfor

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