import{s as mt,n as lt,o as pt}from"../chunks/scheduler.8a2cc2fa.js";import{S as bt,i as ct,e as r,s as i,c as m,h as _t,a as d,d as t,b as n,f as g,g as l,j as C,k as h,l as s,m as o,n as p,t as b,o as c,p as _}from"../chunks/index.7079e750.js";import{C as gt,H as te,E as ht}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.ac36d8ed.js";import{D as f}from"../chunks/Docstring.c96e8772.js";function ft(et){let u,le,de,pe,D,be,N,ce,E,tt='<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>.',_e,L,at="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.",ge,q,he,v,j,Ge,x,I,Ue,ae,it="Base AdamW optimizer.",fe,M,ue,y,V,He,z,S,Be,ie,nt="8-bit AdamW optimizer.",ve,G,ye,$,U,Oe,T,H,Re,ne,ot="32-bit AdamW optimizer.",$e,B,Ae,A,O,Je,P,R,Ke,oe,st="Paged AdamW optimizer.",We,J,we,W,K,Qe,k,Q,Xe,se,rt="Paged 8-bit AdamW optimizer.",xe,X,ze,w,Y,Ye,F,Z,Ze,re,dt="Paged 32-bit AdamW optimizer.",Te,ee,Pe,me,ke;return D=new gt({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),N=new te({props:{title:"AdamW",local:"adamw",headingTag:"h1"}}),q=new te({props:{title:"AdamW",local:"api-class ][ bitsandbytes.optim.AdamW",headingTag:"h2"}}),j=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:"percentile_clipping",val:" = 100"},{name:"block_wise",val:" = True"},{name:"is_paged",val:" = False"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/adamw.py#L9"}}),I=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:"percentile_clipping",val:" = 100"},{name:"block_wise",val:" = True"},{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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