optimum-neuron/pr_1113/en/_app/immutable/nodes/46.98e60104.js

import{s as fr,n as $r,o as hr}from"../chunks/scheduler.56725da7.js";import{S as vr,i as cr,e as m,s as a,c as i,h as br,a as s,d as r,b as n,f as h,g as p,j as $,k as v,l as o,m as l,n as u,t as d,o as g,p as f}from"../chunks/index.18a26576.js";import{C as _r}from"../chunks/CopyLLMTxtMenu.c5feff19.js";import{D as c}from"../chunks/Docstring.ae0283b4.js";import{H as b}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.0f5f04c9.js";function yr(Ut){let k,He,Se,ze,F,je,E,We,Q,Ht="LoRA (Low-Rank Adaptation) implementation optimized for distributed training on AWS Trainium devices. This module provides efficient parameter-efficient fine-tuning with tensor parallelism and sequence parallelism support.",Be,V,Oe,D,Je,I,R,Xe,K,Ye,G,q,Ze,S,et,U,tt,H,z,rt,j,at,y,W,wt,L,B,Lt,be,zt="Merge the active adapter weights into the base weights.",xt,_e,jt=`This works with distributed parallel linear layers (RowParallelLinear, ColumnParallelLinear).
The merge happens on the sharded weights - each rank merges its own shard.`,Ct,x,O,Pt,ye,Wt="Unmerge all merged adapter layers from the base weights.",Tt,we,Bt=`This works with distributed parallel linear layers (RowParallelLinear, ColumnParallelLinear).
The unmerge happens on the sharded weights - each rank unmerges its own shard.`,nt,J,lt,_,X,Mt,C,Y,Nt,Le,Ot="Compute the delta weights for Q, K, V for the given adapter.",kt,xe,Jt="Returns a dict with keys “q”, “k”, “v” (or “qkv” if fused) containing the delta tensors.",At,P,Z,Ft,Ce,Xt="Merge the active adapter weights into the base Q, K, V weights.",Et,Pe,Yt=`This works with GQAQKVColumnParallelLinear layers.
The merge happens on the sharded weights - each rank merges its own shard.`,Qt,T,ee,Vt,Te,Zt="Unmerge all merged adapter layers from the base Q, K, V weights.",Dt,Me,er=`This works with GQAQKVColumnParallelLinear layers.
The unmerge happens on the sharded weights - each rank unmerges its own shard.`,ot,te,mt,w,re,It,M,ae,Rt,Ne,tr="Merge the active adapter weights into the base embedding weights.",Kt,ke,rr=`This works with ParallelEmbedding layers.
The merge happens on the sharded weights - each rank merges its own shard.`,Gt,N,ne,qt,Ae,ar="Unmerge all merged adapter layers from the base embedding weights.",St,Fe,nr=`This works with ParallelEmbedding layers.
The unmerge happens on the sharded weights - each rank unmerges its own shard.`,st,le,it,oe,pt,me,se,ut,ie,dt,pe,gt,ue,de,ft,ge,$t,fe,lr="The Neuron LoRA implementation supports the following parallel layer types:",ht,$e,or="<li><strong>ColumnParallelLinear</strong>: For layers that split weights along the output dimension</li> <li><strong>RowParallelLinear</strong>: For layers that split weights along the input dimension</li> <li><strong>ParallelEmbedding</strong>: For embedding layers distributed across ranks</li> <li><strong>GQAQKVColumnParallelLinear</strong>: For grouped query attention projections with challenging tensor parallel configurations</li>",vt,he,mr="Each layer type has a corresponding LoRA implementation that maintains the parallelization strategy while adding low-rank adaptation capabilities.",ct,ve,bt,ce,sr="<li><strong>Distributed Training</strong>: Full support for tensor parallelism and sequence parallelism</li> <li><strong>Checkpoint Consolidation</strong>: Automatic conversion between sharded and consolidated checkpoints</li> <li><strong>Weight Transformation</strong>: Seamless integration with model weight transformation specs</li> <li><strong>Compatibility</strong>: Works with all supported custom modeling architectures in Optimum Neuron</li>",_t,Ue,yt;return F=new _r({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),E=new b({props:{title:"LoRA for Neuron",local:"lora-for-neuron",headingTag:"h1"}}),V=new b({props:{title:"PEFT Model Classes",local:"peft-model-classes",headingTag:"h2"}}),D=new b({props:{title:"NeuronPeftModel",local:"optimum.neuron.peft.NeuronPeftModel",headingTag:"h3"}}),R=new c({props:{name:"class optimum.neuron.peft.NeuronPeftModel",anchor:"optimum.neuron.peft.NeuronPeftModel",parameters:[{name:"model",val:": PreTrainedModel"},{name:"peft_config",val:": PeftConfig"},{name:"adapter_name",val:": str = 'default'"},{name:"autocast_adapter_dtype",val:": bool = True"},{name:"**kwargs",val:": Any"}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/peft_model.py#L82"}}),K=new b({props:{title:"NeuronPeftModelForCausalLM",local:"optimum.neuron.peft.NeuronPeftModelForCausalLM",headingTag:"h3"}}),q=new c({props:{name:"class optimum.neuron.peft.NeuronPeftModelForCausalLM",anchor:"optimum.neuron.peft.NeuronPeftModelForCausalLM",parameters:[{name:"model",val:": PreTrainedModel"},{name:"peft_config",val:": PeftConfig"},{name:"adapter_name",val:": str = 'default'"},{name:"autocast_adapter_dtype",val:": bool = True"},{name:"**kwargs",val:": Any"}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/peft_model.py#L463"}}),S=new b({props:{title:"LoRA Layer Implementations",local:"lora-layer-implementations",headingTag:"h2"}}),U=new b({props:{title:"Base LoRA Layer",local:"optimum.neuron.peft.tuners.lora.layer.NeuronLoraLayer",headingTag:"h3"}}),z=new c({props:{name:"class optimum.neuron.peft.tuners.lora.layer.NeuronLoraLayer",anchor:"optimum.neuron.peft.tuners.lora.layer.NeuronLoraLayer",parameters:[{name:"base_layer",val:": Module"},{name:"ephemeral_gpu_offload",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L81"}}),j=new b({props:{title:"Parallel Linear LoRA",local:"optimum.neuron.peft.tuners.lora.layer.ParallelLinear",headingTag:"h3"}}),W=new c({props:{name:"class optimum.neuron.peft.tuners.lora.layer.ParallelLinear",anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelLinear",parameters:[{name:"base_layer",val:""},{name:"adapter_name",val:": str"},{name:"r",val:": int = 0"},{name:"lora_alpha",val:": int = 1"},{name:"lora_dropout",val:": float = 0.0"},{name:"fan_in_fan_out",val:": bool = False"},{name:"is_target_conv_1d_layer",val:": bool = False"},{name:"init_lora_weights",val:": bool | str = True"},{name:"use_rslora",val:": bool = False"},{name:"use_dora",val:": bool = False"},{name:"lora_bias",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L232"}}),B=new c({props:{name:"merge",anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelLinear.merge",parameters:[{name:"safe_merge",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"}],parametersDescription:[{anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelLinear.merge.safe_merge",description:"<strong>safe_merge</strong> &#x2014; If True, perform merge in a copy and check for NaNs before merging.",name:"safe_merge"},{anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelLinear.merge.adapter_names",description:"<strong>adapter_names</strong> &#x2014; List of adapter names to merge. If None, all active adapters will be merged.",name:"adapter_names"}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L307"}}),O=new c({props:{name:"unmerge",anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelLinear.unmerge",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L361"}}),J=new b({props:{title:"GQA QKV Column Parallel LoRA",local:"optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear",headingTag:"h3"}}),X=new c({props:{name:"class optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear",anchor:"optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear",parameters:[{name:"base_layer",val:""},{name:"adapter_name",val:": str"},{name:"r",val:": int = 0"},{name:"lora_alpha",val:": int = 1"},{name:"lora_dropout",val:": float = 0.0"},{name:"fan_in_fan_out",val:": bool = False"},{name:"is_target_conv_1d_layer",val:": bool = False"},{name:"init_lora_weights",val:": bool | str = True"},{name:"use_rslora",val:": bool = False"},{name:"use_dora",val:": bool = False"},{name:"lora_bias",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L433"}}),Y=new c({props:{name:"get_delta_weight",anchor:"optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear.get_delta_weight",parameters:[{name:"adapter",val:": str"}],parametersDescription:[{anchor:"optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear.get_delta_weight.adapter",description:"<strong>adapter</strong> &#x2014; The name of the adapter for which the delta weight should be computed.",name:"adapter"}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L578",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Dict mapping “q”/“k”/“v” (or “qkv”) to their delta weight tensors (sharded).</p>
`}}),Z=new c({props:{name:"merge",anchor:"optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear.merge",parameters:[{name:"safe_merge",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"}],parametersDescription:[{anchor:"optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear.merge.safe_merge",description:"<strong>safe_merge</strong> &#x2014; If True, perform merge in a copy and check for NaNs before merging.",name:"safe_merge"},{anchor:"optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear.merge.adapter_names",description:"<strong>adapter_names</strong> &#x2014; List of adapter names to merge. If None, all active adapters will be merged.",name:"adapter_names"}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L625"}}),ee=new c({props:{name:"unmerge",anchor:"optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear.unmerge",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L688"}}),te=new b({props:{title:"Parallel Embedding LoRA",local:"optimum.neuron.peft.tuners.lora.layer.ParallelEmbedding",headingTag:"h3"}}),re=new c({props:{name:"class optimum.neuron.peft.tuners.lora.layer.ParallelEmbedding",anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelEmbedding",parameters:[{name:"base_layer",val:": Module"},{name:"adapter_name",val:": str"},{name:"r",val:": int = 0"},{name:"lora_alpha",val:": int = 1"},{name:"lora_dropout",val:": float = 0.0"},{name:"fan_in_fan_out",val:": bool = False"},{name:"init_lora_weights",val:": bool | str = True"},{name:"use_rslora",val:": bool = False"},{name:"use_dora",val:": bool = False"},{name:"lora_bias",val:": bool = False"},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L758"}}),ae=new c({props:{name:"merge",anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelEmbedding.merge",parameters:[{name:"safe_merge",val:": bool = False"},{name:"adapter_names",val:": list[str] | None = None"}],parametersDescription:[{anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelEmbedding.merge.safe_merge",description:"<strong>safe_merge</strong> &#x2014; If True, perform merge in a copy and check for NaNs before merging.",name:"safe_merge"},{anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelEmbedding.merge.adapter_names",description:"<strong>adapter_names</strong> &#x2014; List of adapter names to merge. If None, all active adapters will be merged.",name:"adapter_names"}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L847"}}),ne=new c({props:{name:"unmerge",anchor:"optimum.neuron.peft.tuners.lora.layer.ParallelEmbedding.unmerge",parameters:[],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/layer.py#L885"}}),le=new b({props:{title:"LoRA Model",local:"lora-model",headingTag:"h2"}}),oe=new b({props:{title:"NeuronLoraModel",local:"optimum.neuron.peft.tuners.NeuronLoraModel",headingTag:"h3"}}),se=new c({props:{name:"class optimum.neuron.peft.tuners.NeuronLoraModel",anchor:"optimum.neuron.peft.tuners.NeuronLoraModel",parameters:[{name:"model",val:""},{name:"config",val:""},{name:"adapter_name",val:""},{name:"low_cpu_mem_usage",val:": bool = False"}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/tuners/lora/model.py#L29"}}),ie=new b({props:{title:"Utility Functions",local:"utility-functions",headingTag:"h2"}}),pe=new b({props:{title:"get_peft_model",local:"optimum.neuron.peft.get_peft_model",headingTag:"h3"}}),de=new c({props:{name:"optimum.neuron.peft.get_peft_model",anchor:"optimum.neuron.peft.get_peft_model",parameters:[{name:"model",val:": PreTrainedModel"},{name:"peft_config",val:": PeftConfig"},{name:"adapter_name",val:": str = 'default'"},{name:"mixed",val:": bool = False"},{name:"autocast_adapter_dtype",val:": bool = True"},{name:"revision",val:": str | None = None"},{name:"low_cpu_mem_usage",val:": bool = False"}],source:"https://github.com/huggingface/optimum-neuron/blob/vr_1113/optimum/neuron/peft/mapping_func.py#L45"}}),ge=new b({props:{title:"Architecture Support",local:"architecture-support",headingTag:"h2"}}),ve=new b({props:{title:"Key Features",local:"key-features",headingTag:"h2"}}),{c(){k=m("meta"),He=a(),Se=m("p"),ze=a(),i(F.$$.fragment),je=a(),i(E.$$.fragment),We=a(),Q=m("p"),Q.textContent=Ht,Be=a(),i(V.$$.fragment),Oe=a(),i(D.$$.fragment),Je=a(),I=m("div"),i(R.$$.fragment),Xe=a(),i(K.$$.fragment),Ye=a(),G=m("div"),i(q.$$.fragment),Ze=a(),i(S.$$.fragment),et=a(),i(U.$$.fragment),tt=a(),H=m("div"),i(z.$$.fragment),rt=a(),i(j.$$.fragment),at=a(),y=m("div"),i(W.$$.fragment),wt=a(),L=m("div"),i(B.$$.fragment),Lt=a(),be=m("p"),be.textContent=zt,xt=a(),_e=m("p"),_e.textContent=jt,Ct=a(),x=m("div"),i(O.$$.fragment),Pt=a(),ye=m("p"),ye.textContent=Wt,Tt=a(),we=m("p"),we.textContent=Bt,nt=a(),i(J.$$.fragment),lt=a(),_=m("div"),i(X.$$.fragment),Mt=a(),C=m("div"),i(Y.$$.fragment),Nt=a(),Le=m("p"),Le.textContent=Ot,kt=a(),xe=m("p"),xe.textContent=Jt,At=a(),P=m("div"),i(Z.$$.fragment),Ft=a(),Ce=m("p"),Ce.textContent=Xt,Et=a(),Pe=m("p"),Pe.textContent=Yt,Qt=a(),T=m("div"),i(ee.$$.fragment),Vt=a(),Te=m("p"),Te.textContent=Zt,Dt=a(),Me=m("p"),Me.textContent=er,ot=a(),i(te.$$.fragment),mt=a(),w=m("div"),i(re.$$.fragment),It=a(),M=m("div"),i(ae.$$.fragment),Rt=a(),Ne=m("p"),Ne.textContent=tr,Kt=a(),ke=m("p"),ke.textContent=rr,Gt=a(),N=m("div"),i(ne.$$.fragment),qt=a(),Ae=m("p"),Ae.textContent=ar,St=a(),Fe=m("p"),Fe.textContent=nr,st=a(),i(le.$$.fragment),it=a(),i(oe.$$.fragment),pt=a(),me=m("div"),i(se.$$.fragment),ut=a(),i(ie.$$.fragment),dt=a(),i(pe.$$.fragment),gt=a(),ue=m("div"),i(de.$$.fragment),ft=a(),i(ge.$$.fragment),$t=a(),fe=m("p"),fe.textContent=lr,ht=a(),$e=m("ul"),$e.innerHTML=or,vt=a(),he=m("p"),he.textContent=mr,ct=a(),i(ve.$$.fragment),bt=a(),ce=m("ul"),ce.innerHTML=sr,_t=a(),Ue=m("p"),this.h()},l(e){const t=br("svelte-u9bgzb",document.head);k=s(t,"META",{name:!0,content:!0}),t.forEach(r),He=n(e),Se=s(e,"P",{}),h(Se).forEach(r),ze=n(e),p(F.$$.fragment,e),je=n(e),p(E.$$.fragment,e),We=n(e),Q=s(e,"P",{"data-svelte-h":!0}),$(Q)!=="svelte-okn7ne"&&(Q.textContent=Ht),Be=n(e),p(V.$$.fragment,e),Oe=n(e),p(D.$$.fragment,e),Je=n(e),I=s(e,"DIV",{class:!0});var ir=h(I);p(R.$$.fragment,ir),ir.forEach(r),Xe=n(e),p(K.$$.fragment,e),Ye=n(e),G=s(e,"DIV",{class:!0});var pr=h(G);p(q.$$.fragment,pr),pr.forEach(r),Ze=n(e),p(S.$$.fragment,e),et=n(e),p(U.$$.fragment,e),tt=n(e),H=s(e,"DIV",{class:!0});var ur=h(H);p(z.$$.fragment,ur),ur.forEach(r),rt=n(e),p(j.$$.fragment,e),at=n(e),y=s(e,"DIV",{class:!0});var Ee=h(y);p(W.$$.fragment,Ee),wt=n(Ee),L=s(Ee,"DIV",{class:!0});var Qe=h(L);p(B.$$.fragment,Qe),Lt=n(Qe),be=s(Qe,"P",{"data-svelte-h":!0}),$(be)!=="svelte-17iyydo"&&(be.textContent=zt),xt=n(Qe),_e=s(Qe,"P",{"data-svelte-h":!0}),$(_e)!=="svelte-1ehbrnx"&&(_e.textContent=jt),Qe.forEach(r),Ct=n(Ee),x=s(Ee,"DIV",{class:!0});var Ve=h(x);p(O.$$.fragment,Ve),Pt=n(Ve),ye=s(Ve,"P",{"data-svelte-h":!0}),$(ye)!=="svelte-g5136u"&&(ye.textContent=Wt),Tt=n(Ve),we=s(Ve,"P",{"data-svelte-h":!0}),$(we)!=="svelte-rq75fv"&&(we.textContent=Bt),Ve.forEach(r),Ee.forEach(r),nt=n(e),p(J.$$.fragment,e),lt=n(e),_=s(e,"DIV",{class:!0});var A=h(_);p(X.$$.fragment,A),Mt=n(A),C=s(A,"DIV",{class:!0});var De=h(C);p(Y.$$.fragment,De),Nt=n(De),Le=s(De,"P",{"data-svelte-h":!0}),$(Le)!=="svelte-2b2w8s"&&(Le.textContent=Ot),kt=n(De),xe=s(De,"P",{"data-svelte-h":!0}),$(xe)!=="svelte-1rp6b5t"&&(xe.textContent=Jt),De.forEach(r),At=n(A),P=s(A,"DIV",{class:!0});var Ie=h(P);p(Z.$$.fragment,Ie),Ft=n(Ie),Ce=s(Ie,"P",{"data-svelte-h":!0}),$(Ce)!=="svelte-1hhbwu6"&&(Ce.textContent=Xt),Et=n(Ie),Pe=s(Ie,"P",{"data-svelte-h":!0}),$(Pe)!=="svelte-713olw"&&(Pe.textContent=Yt),Ie.forEach(r),Qt=n(A),T=s(A,"DIV",{class:!0});var Re=h(T);p(ee.$$.fragment,Re),Vt=n(Re),Te=s(Re,"P",{"data-svelte-h":!0}),$(Te)!=="svelte-wmk44g"&&(Te.textContent=Zt),Dt=n(Re),Me=s(Re,"P",{"data-svelte-h":!0}),$(Me)!=="svelte-15mcsvm"&&(Me.textContent=er),Re.forEach(r),A.forEach(r),ot=n(e),p(te.$$.fragment,e),mt=n(e),w=s(e,"DIV",{class:!0});var Ke=h(w);p(re.$$.fragment,Ke),It=n(Ke),M=s(Ke,"DIV",{class:!0});var Ge=h(M);p(ae.$$.fragment,Ge),Rt=n(Ge),Ne=s(Ge,"P",{"data-svelte-h":!0}),$(Ne)!=="svelte-zhgjq7"&&(Ne.textContent=tr),Kt=n(Ge),ke=s(Ge,"P",{"data-svelte-h":!0}),$(ke)!=="svelte-rklxrj"&&(ke.textContent=rr),Ge.forEach(r),Gt=n(Ke),N=s(Ke,"DIV",{class:!0});var qe=h(N);p(ne.$$.fragment,qe),qt=n(qe),Ae=s(qe,"P",{"data-svelte-h":!0}),$(Ae)!=="svelte-13yefkv"&&(Ae.textContent=ar),St=n(qe),Fe=s(qe,"P",{"data-svelte-h":!0}),$(Fe)!=="svelte-1kko8xh"&&(Fe.textContent=nr),qe.forEach(r),Ke.forEach(r),st=n(e),p(le.$$.fragment,e),it=n(e),p(oe.$$.fragment,e),pt=n(e),me=s(e,"DIV",{class:!0});var dr=h(me);p(se.$$.fragment,dr),dr.forEach(r),ut=n(e),p(ie.$$.fragment,e),dt=n(e),p(pe.$$.fragment,e),gt=n(e),ue=s(e,"DIV",{class:!0});var gr=h(ue);p(de.$$.fragment,gr),gr.forEach(r),ft=n(e),p(ge.$$.fragment,e),$t=n(e),fe=s(e,"P",{"data-svelte-h":!0}),$(fe)!=="svelte-1de8olr"&&(fe.textContent=lr),ht=n(e),$e=s(e,"UL",{"data-svelte-h":!0}),$($e)!=="svelte-6frt93"&&($e.innerHTML=or),vt=n(e),he=s(e,"P",{"data-svelte-h":!0}),$(he)!=="svelte-t4pq7i"&&(he.textContent=mr),ct=n(e),p(ve.$$.fragment,e),bt=n(e),ce=s(e,"UL",{"data-svelte-h":!0}),$(ce)!=="svelte-xv3lt8"&&(ce.innerHTML=sr),_t=n(e),Ue=s(e,"P",{}),h(Ue).forEach(r),this.h()},h(){v(k,"name","hf:doc:metadata"),v(k,"content",wr),v(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(G,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(C,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(T,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(_,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(M,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(w,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),v(ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,t){o(document.head,k),l(e,He,t),l(e,Se,t),l(e,ze,t),u(F,e,t),l(e,je,t),u(E,e,t),l(e,We,t),l(e,Q,t),l(e,Be,t),u(V,e,t),l(e,Oe,t),u(D,e,t),l(e,Je,t),l(e,I,t),u(R,I,null),l(e,Xe,t),u(K,e,t),l(e,Ye,t),l(e,G,t),u(q,G,null),l(e,Ze,t),u(S,e,t),l(e,et,t),u(U,e,t),l(e,tt,t),l(e,H,t),u(z,H,null),l(e,rt,t),u(j,e,t),l(e,at,t),l(e,y,t),u(W,y,null),o(y,wt),o(y,L),u(B,L,null),o(L,Lt),o(L,be),o(L,xt),o(L,_e),o(y,Ct),o(y,x),u(O,x,null),o(x,Pt),o(x,ye),o(x,Tt),o(x,we),l(e,nt,t),u(J,e,t),l(e,lt,t),l(e,_,t),u(X,_,null),o(_,Mt),o(_,C),u(Y,C,null),o(C,Nt),o(C,Le),o(C,kt),o(C,xe),o(_,At),o(_,P),u(Z,P,null),o(P,Ft),o(P,Ce),o(P,Et),o(P,Pe),o(_,Qt),o(_,T),u(ee,T,null),o(T,Vt),o(T,Te),o(T,Dt),o(T,Me),l(e,ot,t),u(te,e,t),l(e,mt,t),l(e,w,t),u(re,w,null),o(w,It),o(w,M),u(ae,M,null),o(M,Rt),o(M,Ne),o(M,Kt),o(M,ke),o(w,Gt),o(w,N),u(ne,N,null),o(N,qt),o(N,Ae),o(N,St),o(N,Fe),l(e,st,t),u(le,e,t),l(e,it,t),u(oe,e,t),l(e,pt,t),l(e,me,t),u(se,me,null),l(e,ut,t),u(ie,e,t),l(e,dt,t),u(pe,e,t),l(e,gt,t),l(e,ue,t),u(de,ue,null),l(e,ft,t),u(ge,e,t),l(e,$t,t),l(e,fe,t),l(e,ht,t),l(e,$e,t),l(e,vt,t),l(e,he,t),l(e,ct,t),u(ve,e,t),l(e,bt,t),l(e,ce,t),l(e,_t,t),l(e,Ue,t),yt=!0},p:$r,i(e){yt||(d(F.$$.fragment,e),d(E.$$.fragment,e),d(V.$$.fragment,e),d(D.$$.fragment,e),d(R.$$.fragment,e),d(K.$$.fragment,e),d(q.$$.fragment,e),d(S.$$.fragment,e),d(U.$$.fragment,e),d(z.$$.fragment,e),d(j.$$.fragment,e),d(W.$$.fragment,e),d(B.$$.fragment,e),d(O.$$.fragment,e),d(J.$$.fragment,e),d(X.$$.fragment,e),d(Y.$$.fragment,e),d(Z.$$.fragment,e),d(ee.$$.fragment,e),d(te.$$.fragment,e),d(re.$$.fragment,e),d(ae.$$.fragment,e),d(ne.$$.fragment,e),d(le.$$.fragment,e),d(oe.$$.fragment,e),d(se.$$.fragment,e),d(ie.$$.fragment,e),d(pe.$$.fragment,e),d(de.$$.fragment,e),d(ge.$$.fragment,e),d(ve.$$.fragment,e),yt=!0)},o(e){g(F.$$.fragment,e),g(E.$$.fragment,e),g(V.$$.fragment,e),g(D.$$.fragment,e),g(R.$$.fragment,e),g(K.$$.fragment,e),g(q.$$.fragment,e),g(S.$$.fragment,e),g(U.$$.fragment,e),g(z.$$.fragment,e),g(j.$$.fragment,e),g(W.$$.fragment,e),g(B.$$.fragment,e),g(O.$$.fragment,e),g(J.$$.fragment,e),g(X.$$.fragment,e),g(Y.$$.fragment,e),g(Z.$$.fragment,e),g(ee.$$.fragment,e),g(te.$$.fragment,e),g(re.$$.fragment,e),g(ae.$$.fragment,e),g(ne.$$.fragment,e),g(le.$$.fragment,e),g(oe.$$.fragment,e),g(se.$$.fragment,e),g(ie.$$.fragment,e),g(pe.$$.fragment,e),g(de.$$.fragment,e),g(ge.$$.fragment,e),g(ve.$$.fragment,e),yt=!1},d(e){e&&(r(He),r(Se),r(ze),r(je),r(We),r(Q),r(Be),r(Oe),r(Je),r(I),r(Xe),r(Ye),r(G),r(Ze),r(et),r(tt),r(H),r(rt),r(at),r(y),r(nt),r(lt),r(_),r(ot),r(mt),r(w),r(st),r(it),r(pt),r(me),r(ut),r(dt),r(gt),r(ue),r(ft),r($t),r(fe),r(ht),r($e),r(vt),r(he),r(ct),r(bt),r(ce),r(_t),r(Ue)),r(k),f(F,e),f(E,e),f(V,e),f(D,e),f(R),f(K,e),f(q),f(S,e),f(U,e),f(z),f(j,e),f(W),f(B),f(O),f(J,e),f(X),f(Y),f(Z),f(ee),f(te,e),f(re),f(ae),f(ne),f(le,e),f(oe,e),f(se),f(ie,e),f(pe,e),f(de),f(ge,e),f(ve,e)}}}const wr='{"title":"LoRA for Neuron","local":"lora-for-neuron","sections":[{"title":"PEFT Model Classes","local":"peft-model-classes","sections":[{"title":"NeuronPeftModel","local":"optimum.neuron.peft.NeuronPeftModel","sections":[],"depth":3},{"title":"NeuronPeftModelForCausalLM","local":"optimum.neuron.peft.NeuronPeftModelForCausalLM","sections":[],"depth":3}],"depth":2},{"title":"LoRA Layer Implementations","local":"lora-layer-implementations","sections":[{"title":"Base LoRA Layer","local":"optimum.neuron.peft.tuners.lora.layer.NeuronLoraLayer","sections":[],"depth":3},{"title":"Parallel Linear LoRA","local":"optimum.neuron.peft.tuners.lora.layer.ParallelLinear","sections":[],"depth":3},{"title":"GQA QKV Column Parallel LoRA","local":"optimum.neuron.peft.tuners.lora.layer.GQAQKVColumnParallelLinear","sections":[],"depth":3},{"title":"Parallel Embedding LoRA","local":"optimum.neuron.peft.tuners.lora.layer.ParallelEmbedding","sections":[],"depth":3}],"depth":2},{"title":"LoRA Model","local":"lora-model","sections":[{"title":"NeuronLoraModel","local":"optimum.neuron.peft.tuners.NeuronLoraModel","sections":[],"depth":3}],"depth":2},{"title":"Utility Functions","local":"utility-functions","sections":[{"title":"get_peft_model","local":"optimum.neuron.peft.get_peft_model","sections":[],"depth":3}],"depth":2},{"title":"Architecture Support","local":"architecture-support","sections":[],"depth":2},{"title":"Key Features","local":"key-features","sections":[],"depth":2}],"depth":1}';function Lr(Ut){return hr(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Nr extends vr{constructor(k){super(),cr(this,k,Lr,yr,fr,{})}}export{Nr as component};