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import{s as me,n as be,o as pe}from"../chunks/scheduler.8a2cc2fa.js";import{S as le,i as _e,e as r,s as i,c as m,h as ce,a as d,d as e,b as n,f as g,g as b,j as D,k as h,l as o,m as s,n as p,t as l,o as _,p as c}from"../chunks/index.7079e750.js";import{C as ge,H as et,E as he}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.2b7ce466.js";import{D as f}from"../chunks/Docstring.8c9a5003.js";function fe(te){let u,bt,dt,pt,N,lt,E,_t,L,ee='<a href="https://hf.co/papers/1711.05101" rel="nofollow">AdamW</a> is a variant of the <code>Adam</code> optimizer that separates weight decay from the gradient update based on the observation that the weight decay formulation is different when applied to <code>SGD</code> and <code>Adam</code>.',ct,j,ae="bitsandbytes also supports paged optimizers which take advantage of CUDAs unified memory to transfer memory from the GPU to the CPU when GPU memory is exhausted.",gt,I,ht,v,M,Ut,w,V,Ht,at,ie="Base AdamW optimizer.",ft,S,ut,y,G,kt,z,q,Bt,it,ne="8-bit AdamW optimizer.",vt,U,yt,$,H,Ot,P,k,Rt,nt,se="32-bit AdamW optimizer.",$t,B,At,A,O,Jt,T,R,Kt,st,oe="Paged AdamW optimizer.",Wt,J,xt,W,K,Qt,F,Q,Xt,ot,re="Paged 8-bit AdamW optimizer.",wt,X,zt,x,Y,Yt,C,Z,Zt,rt,de="Paged 32-bit AdamW optimizer.",Pt,tt,Tt,mt,Ft;return N=new ge({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),E=new et({props:{title:"AdamW",local:"adamw",headingTag:"h1"}}),I=new et({props:{title:"AdamW",local:"api-class ][ bitsandbytes.optim.AdamW",headingTag:"h2"}}),M=new f({props:{name:"class bitsandbytes.optim.AdamW",anchor:"bitsandbytes.optim.AdamW",parameters:[{name:"params",val:""},{name:"lr",val:" = 0.001"},{name:"betas",val:" = (0.9, 0.999)"},{name:"eps",val:" = 1e-08"},{name:"weight_decay",val:" = 0.01"},{name:"amsgrad",val:" = False"},{name:"optim_bits",val:" = 32"},{name:"args",val:" = None"},{name:"min_8bit_size",val:" = 4096"},{name:"is_paged",val:" = False"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/vr_1908/bitsandbytes/optim/adamw.py#L9"}}),V=new f({props:{name:"__init__",anchor:"bitsandbytes.optim.AdamW.__init__",parameters:[{name:"params",val:""},{name:"lr",val:" = 0.001"},{name:"betas",val:" = (0.9, 0.999)"},{name:"eps",val:" = 1e-08"},{name:"weight_decay",val:" = 0.01"},{name:"amsgrad",val:" = False"},{name:"optim_bits",val:" = 32"},{name:"args",val:" = None"},{name:"min_8bit_size",val:" = 4096"},{name:"is_paged",val:" = False"}],parametersDescription:[{anchor:"bitsandbytes.optim.AdamW.__init__.params",description:`<strong>params</strong> (<code>torch.Tensor</code>) &#x2014;
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