import{s as Le,n as Me,o as we}from"../chunks/scheduler.8a2cc2fa.js";import{S as Be,i as Te,e as s,s as i,c as d,h as ze,a as r,d as t,b as o,f as x,g as l,j as G,k as A,l as m,m as n,n as c,t as p,o as b,p as _}from"../chunks/index.7079e750.js";import{C as ke,H as ce,E as Fe}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.b2e4ed49.js";import{D as H}from"../chunks/Docstring.7dc3c45d.js";function We(fe){let g,R,I,U,L,O,M,J,w,ve='<a href="https://hf.co/papers/1904.00962" rel="nofollow">LAMB (Layerwise adaptive large batch optimization)</a> is an adaptive optimizer designed for training with large batch sizes to accelerate training, combining ideas from <code>LARS</code> and <code>Adam</code> to automatically scale the learning rate for each layer:',K,B,ye="<li>calculates a <em>trust ratio</em> between the weight and gradient norm in a layer and clips the ratio to prevent overly large or small updates</li> <li>updates weights with the first and second-moments</li>",Q,T,X,h,z,pe,v,k,be,N,$e="Base LAMB optimizer.",Y,F,Z,u,W,_e,y,C,ge,S,xe="8-bit LAMB optimizer.",ee,E,te,f,D,he,$,P,ue,j,Ae="32-bit LAMB optimizer.",ae,q,ne,V,ie;return L=new ke({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),M=new ce({props:{title:"LAMB",local:"lamb",headingTag:"h1"}}),T=new ce({props:{title:"LAMB",local:"api-class ][ bitsandbytes.optim.LAMB",headingTag:"h2"}}),z=new H({props:{name:"class bitsandbytes.optim.LAMB",anchor:"bitsandbytes.optim.LAMB",parameters:[{name:"params",val:""},{name:"lr",val:" = 0.001"},{name:"bias_correction",val:" = True"},{name:"betas",val:" = (0.9, 0.999)"},{name:"eps",val:" = 1e-08"},{name:"weight_decay",val:" = 0"},{name:"amsgrad",val:" = False"},{name:"adam_w_mode",val:" = True"},{name:"optim_bits",val:" = 32"},{name:"args",val:" = None"},{name:"min_8bit_size",val:" = 4096"},{name:"percentile_clipping",val:" = 100"},{name:"block_wise",val:" = False"},{name:"max_unorm",val:" = 1.0"}],source:"https://github.com/bitsandbytes-foundation/bitsandbytes/blob/main/bitsandbytes/optim/lamb.py#L8"}}),k=new H({props:{name:"__init__",anchor:"bitsandbytes.optim.LAMB.__init__",parameters:[{name:"params",val:""},{name:"lr",val:" = 0.001"},{name:"bias_correction",val:" = True"},{name:"betas",val:" = (0.9, 0.999)"},{name:"eps",val:" = 1e-08"},{name:"weight_decay",val:" = 0"},{name:"amsgrad",val:" = False"},{name:"adam_w_mode",val:" = True"},{name:"optim_bits",val:" = 32"},{name:"args",val:" = None"},{name:"min_8bit_size",val:" = 4096"},{name:"percentile_clipping",val:" = 100"},{name:"block_wise",val:" = False"},{name:"max_unorm",val:" = 1.0"}],parametersDescription:[{anchor:"bitsandbytes.optim.LAMB.__init__.params",description:`<strong>params</strong> (<code>torch.tensor</code>) &#x2014;
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The learning rate.`,name:"lr"},{anchor:"bitsandbytes.optim.LAMB.__init__.bias_correction",description:`<strong>bias_correction</strong> (<code>bool</code>, defaults to <code>True</code>) &#x2014;
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