import{S as Bs,i as Ss,s as Is,e as i,k as d,w,t as n,M as Ds,c as r,d as t,m as h,a as l,x as g,h as p,b as m,G as s,g as o,y,q as v,o as $,B as k,v as Ls}from"../chunks/vendor-hf-doc-builder.js";import{T as Ns}from"../chunks/Tip-hf-doc-builder.js";import{I as ss}from"../chunks/IconCopyLink-hf-doc-builder.js";import{C as L}from"../chunks/CodeBlock-hf-doc-builder.js";function Cs(Ee){let f,P;return{c(){f=i("p"),P=n("Note that the randomly created model is initialized with \u201Cempty\u201D tensors, which take the space in memory without filling it (thus the random values are whatever was in this chunk of memory at a given time). The random initialization following the appropriate distribution for the kind of model/parameters instatiated (like a normal distribution for instance) is only performed after step 3 on the non-initialized weights, to be as fast as possible!")},l(c){f=r(c,"P",{});var u=l(f);P=p(u,"Note that the randomly created model is initialized with \u201Cempty\u201D tensors, which take the space in memory without filling it (thus the random values are whatever was in this chunk of memory at a given time). The random initialization following the appropriate distribution for the kind of model/parameters instatiated (like a normal distribution for instance) is only performed after step 3 on the non-initialized weights, to be as fast as possible!"),u.forEach(t)},m(c,u){o(c,f,u),s(f,P)},d(c){c&&t(f)}}}function Rs(Ee){let f,P,c,u,he,N,lt,me,pt,Pe,V,dt,Ae,j,fe,ht,mt,ce,ft,ct,ue,ut,ze,A,_t,_e,wt,gt,Te,z,Me,X,yt,qe,x,T,we,C,vt,ge,$t,Be,M,kt,ye,jt,bt,Se,q,xt,ve,Et,Pt,Ie,R,De,B,At,Z,zt,Tt,Le,O,Ne,ee,Mt,Ce,G,Re,b,qt,$e,Bt,St,te,It,Dt,Oe,W,Ge,se,Lt,We,ae,Nt,He,H,Ye,oe,Ct,Je,Y,Ue,S,Rt,ke,Ot,Gt,Fe,J,Ke,_,Wt,ie,Ht,Yt,je,Jt,Ut,re,Ft,Kt,Qe,U,Ve,E,I,be,F,Qt,xe,Vt,Xe,ne,Xt,Ze,K,Zt,le,es,et;return N=new ss({}),z=new Ns({props:{$$slots:{default:[Cs]},$$scope:{ctx:Ee}}}),C=new ss({}),R=new L({props:{code:`from transformers import AutoModel

model = AutoModel.from_pretrained("bert-base-cased")`,highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoModel

model = AutoModel.from_pretrained(<span class="hljs-string">&quot;bert-base-cased&quot;</span>)`}}),O=new L({props:{code:`import os
import tempfile

with tempfile.TemporaryDirectory() as tmp_dir:
    model.save_pretrained(tmp_dir)
    print(sorted(os.listdir(tmp_dir)))`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> os
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> tempfile

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir:
<span class="hljs-meta">... </span>    model.save_pretrained(tmp_dir)
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-built_in">sorted</span>(os.listdir(tmp_dir)))
[<span class="hljs-string">&#x27;config.json&#x27;</span>, <span class="hljs-string">&#x27;pytorch_model.bin&#x27;</span>]`}}),G=new L({props:{code:`with tempfile.TemporaryDirectory() as tmp_dir:
    model.save_pretrained(tmp_dir, max_shard_size="200MB")
    print(sorted(os.listdir(tmp_dir)))`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir:
<span class="hljs-meta">... </span>    model.save_pretrained(tmp_dir, max_shard_size=<span class="hljs-string">&quot;200MB&quot;</span>)
<span class="hljs-meta">... </span>    <span class="hljs-built_in">print</span>(<span class="hljs-built_in">sorted</span>(os.listdir(tmp_dir)))
[<span class="hljs-string">&#x27;config.json&#x27;</span>, <span class="hljs-string">&#x27;pytorch_model-00001-of-00003.bin&#x27;</span>, <span class="hljs-string">&#x27;pytorch_model-00002-of-00003.bin&#x27;</span>, <span class="hljs-string">&#x27;pytorch_model-00003-of-00003.bin&#x27;</span>, <span class="hljs-string">&#x27;pytorch_model.bin.index.json&#x27;</span>]`}}),W=new L({props:{code:`with tempfile.TemporaryDirectory() as tmp_dir:
    model.save_pretrained(tmp_dir, max_shard_size="200MB")
    new_model = AutoModel.from_pretrained(tmp_dir)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir:
<span class="hljs-meta">... </span>    model.save_pretrained(tmp_dir, max_shard_size=<span class="hljs-string">&quot;200MB&quot;</span>)
<span class="hljs-meta">... </span>    new_model = AutoModel.from_pretrained(tmp_dir)`}}),H=new L({props:{code:`import json

with tempfile.TemporaryDirectory() as tmp_dir:
    model.save_pretrained(tmp_dir, max_shard_size="200MB")
    with open(os.path.join(tmp_dir, "pytorch_model.bin.index.json"), "r") as f:
        index = json.load(f)

print(index.keys())`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> json

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir:
<span class="hljs-meta">... </span>    model.save_pretrained(tmp_dir, max_shard_size=<span class="hljs-string">&quot;200MB&quot;</span>)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(os.path.join(tmp_dir, <span class="hljs-string">&quot;pytorch_model.bin.index.json&quot;</span>), <span class="hljs-string">&quot;r&quot;</span>) <span class="hljs-keyword">as</span> f:
<span class="hljs-meta">... </span>        index = json.load(f)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-built_in">print</span>(index.keys())
dict_keys([<span class="hljs-string">&#x27;metadata&#x27;</span>, <span class="hljs-string">&#x27;weight_map&#x27;</span>])`}}),Y=new L({props:{code:'index["metadata"]',highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>index[<span class="hljs-string">&quot;metadata&quot;</span>]
{<span class="hljs-string">&#x27;total_size&#x27;</span>: <span class="hljs-number">433245184</span>}`}}),J=new L({props:{code:'index["weight_map"]',highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span>index[<span class="hljs-string">&quot;weight_map&quot;</span>]
{<span class="hljs-string">&#x27;embeddings.LayerNorm.bias&#x27;</span>: <span class="hljs-string">&#x27;pytorch_model-00001-of-00003.bin&#x27;</span>,
 <span class="hljs-string">&#x27;embeddings.LayerNorm.weight&#x27;</span>: <span class="hljs-string">&#x27;pytorch_model-00001-of-00003.bin&#x27;</span>,
 ...`}}),U=new L({props:{code:`from transformers.modeling_utils import load_sharded_checkpoint

with tempfile.TemporaryDirectory() as tmp_dir:
    model.save_pretrained(tmp_dir, max_shard_size="200MB")
    load_sharded_checkpoint(model, tmp_dir)`,highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers.modeling_utils <span class="hljs-keyword">import</span> load_sharded_checkpoint

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">with</span> tempfile.TemporaryDirectory() <span class="hljs-keyword">as</span> tmp_dir:
<span class="hljs-meta">... </span>    model.save_pretrained(tmp_dir, max_shard_size=<span class="hljs-string">&quot;200MB&quot;</span>)
<span class="hljs-meta">... </span>    load_sharded_checkpoint(model, tmp_dir)`}}),F=new ss({}),{c(){f=i("meta"),P=d(),c=i("h1"),u=i("a"),he=i("span"),w(N.$$.fragment),lt=d(),me=i("span"),pt=n("Instantiating a big model"),Pe=d(),V=i("p"),dt=n(`When you want to use a very big pretrained model, one challenge is to minimize the use of the RAM. The usual workflow
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