import{s as ps,o as gs,n as W}from"../chunks/scheduler.6062bdaf.js";import{S as ms,i as _s,g as d,s as o,r as _,A as fs,h,f as a,c as i,j as H,u as f,x as g,k as U,y as r,a as l,v as u,d as b,t as v,w as $}from"../chunks/index.4bca734e.js";import{T as Ue}from"../chunks/Tip.b9ac1f03.js";import{D as ue}from"../chunks/Docstring.81ba5e3c.js";import{C as Ut}from"../chunks/CodeBlock.fa01571c.js";import{E as Ht}from"../chunks/ExampleCodeBlock.30e7542f.js";import{H as R,E as us}from"../chunks/EditOnGithub.74ab2baa.js";function bs(M){let t,m=`If one of the model’s tensor is bigger than <code>max_shard_size</code>, it will end up in its own shard which will have a
size greater than <code>max_shard_size</code>.`;return{c(){t=d("p"),t.innerHTML=m},l(n){t=h(n,"P",{"data-svelte-h":!0}),g(t)!=="svelte-16sq104"&&(t.innerHTML=m)},m(n,p){l(n,t,p)},p:W,d(n){n&&a(t)}}}function vs(M){let t,m="Example:",n,p,w;return p=new Ut({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMHNhdmVfdG9yY2hfbW9kZWwlMEFtb2RlbCUyMCUzRCUyMC4uLiUyMCUyMyUyMEElMjBQeVRvcmNoJTIwbW9kZWwlMEElMEFzYXZlX3RvcmNoX21vZGVsKG1vZGVsJTJDJTIwJTIycGF0aCUyRnRvJTJGZm9sZGVyJTIyKSUwQSUwQWZyb20lMjBodWdnaW5nZmFjZV9odWIlMjBpbXBvcnQlMjBsb2FkX3RvcmNoX21vZGVsJTIwJTIwJTIzJTIwVE9ETyUwQWxvYWRfdG9yY2hfbW9kZWwobW9kZWwlMkMlMjAlMjJwYXRoJTJGdG8lMkZmb2xkZXIlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> save_torch_model
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ... <span class="hljs-comment"># A PyTorch model</span>

<span class="hljs-comment"># Save state dict to &quot;path/to/folder&quot;. The model will be split into shards of 5GB each and saved as safetensors.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>save_torch_model(model, <span class="hljs-string">&quot;path/to/folder&quot;</span>)

<span class="hljs-comment"># Load model back</span>
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> load_torch_model  <span class="hljs-comment"># TODO</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>load_torch_model(model, <span class="hljs-string">&quot;path/to/folder&quot;</span>)
&gt;&gt;&gt;`,wrap:!1}}),{c(){t=d("p"),t.textContent=m,n=o(),_(p.$$.fragment)},l(c){t=h(c,"P",{"data-svelte-h":!0}),g(t)!=="svelte-11lpom8"&&(t.textContent=m),n=i(c),f(p.$$.fragment,c)},m(c,x){l(c,t,x),l(c,n,x),u(p,c,x),w=!0},p:W,i(c){w||(b(p.$$.fragment,c),w=!0)},o(c){v(p.$$.fragment,c),w=!1},d(c){c&&(a(t),a(n)),$(p,c)}}}function $s(M){let t,m=`If one of the model’s tensor is bigger than <code>max_shard_size</code>, it will end up in its own shard which will have a
size greater than <code>max_shard_size</code>.`;return{c(){t=d("p"),t.innerHTML=m},l(n){t=h(n,"P",{"data-svelte-h":!0}),g(t)!=="svelte-16sq104"&&(t.innerHTML=m)},m(n,p){l(n,t,p)},p:W,d(n){n&&a(t)}}}function ws(M){let t,m="Example:",n,p,w;return p=new Ut({props:{code:"ZnJvbSUyMGh1Z2dpbmdmYWNlX2h1YiUyMGltcG9ydCUyMHNhdmVfdG9yY2hfc3RhdGVfZGljdCUwQW1vZGVsJTIwJTNEJTIwLi4uJTIwJTIzJTIwQSUyMFB5VG9yY2glMjBtb2RlbCUwQSUwQXN0YXRlX2RpY3QlMjAlM0QlMjBtb2RlbF90b19zYXZlLnN0YXRlX2RpY3QoKSUwQXNhdmVfdG9yY2hfc3RhdGVfZGljdChzdGF0ZV9kaWN0JTJDJTIwJTIycGF0aCUyRnRvJTJGZm9sZGVyJTIyKQ==",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> save_torch_state_dict
<span class="hljs-meta">&gt;&gt;&gt; </span>model = ... <span class="hljs-comment"># A PyTorch model</span>

<span class="hljs-comment"># Save state dict to &quot;path/to/folder&quot;. The model will be split into shards of 5GB each and saved as safetensors.</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>state_dict = model_to_save.state_dict()
<span class="hljs-meta">&gt;&gt;&gt; </span>save_torch_state_dict(state_dict, <span class="hljs-string">&quot;path/to/folder&quot;</span>)`,wrap:!1}}),{c(){t=d("p"),t.textContent=m,n=o(),_(p.$$.fragment)},l(c){t=h(c,"P",{"data-svelte-h":!0}),g(t)!=="svelte-11lpom8"&&(t.textContent=m),n=i(c),f(p.$$.fragment,c)},m(c,x){l(c,t,x),l(c,n,x),u(p,c,x),w=!0},p:W,i(c){w||(b(p.$$.fragment,c),w=!0)},o(c){v(p.$$.fragment,c),w=!1},d(c){c&&(a(t),a(n)),$(p,c)}}}function Ts(M){let t,m=`If one of the model’s tensor is bigger than <code>max_shard_size</code>, it will end up in its own shard which will have a
size greater than <code>max_shard_size</code>.`;return{c(){t=d("p"),t.innerHTML=m},l(n){t=h(n,"P",{"data-svelte-h":!0}),g(t)!=="svelte-16sq104"&&(t.innerHTML=m)},m(n,p){l(n,t,p)},p:W,d(n){n&&a(t)}}}function ys(M){let t,m=`To save a model state dictionary to the disk, see <a href="/docs/huggingface_hub/main/en/package_reference/serialization#huggingface_hub.save_torch_state_dict">save_torch_state_dict()</a>. This helper uses
<code>split_torch_state_dict_into_shards</code> under the hood.`;return{c(){t=d("p"),t.innerHTML=m},l(n){t=h(n,"P",{"data-svelte-h":!0}),g(t)!=="svelte-6g0ega"&&(t.innerHTML=m)},m(n,p){l(n,t,p)},p:W,d(n){n&&a(t)}}}function Ms(M){let t,m=`If one of the model’s tensor is bigger than <code>max_shard_size</code>, it will end up in its own shard which will have a
size greater than <code>max_shard_size</code>.`;return{c(){t=d("p"),t.innerHTML=m},l(n){t=h(n,"P",{"data-svelte-h":!0}),g(t)!=="svelte-16sq104"&&(t.innerHTML=m)},m(n,p){l(n,t,p)},p:W,d(n){n&&a(t)}}}function xs(M){let t,m="Example:",n,p,w;return p=new Ut({props:{code:"aW1wb3J0JTIwanNvbiUwQWltcG9ydCUyMG9zJTBBZnJvbSUyMHNhZmV0ZW5zb3JzLnRvcmNoJTIwaW1wb3J0JTIwc2F2ZV9maWxlJTIwYXMlMjBzYWZlX3NhdmVfZmlsZSUwQWZyb20lMjBodWdnaW5nZmFjZV9odWIlMjBpbXBvcnQlMjBzcGxpdF90b3JjaF9zdGF0ZV9kaWN0X2ludG9fc2hhcmRzJTBBJTBBZGVmJTIwc2F2ZV9zdGF0ZV9kaWN0KHN0YXRlX2RpY3QlM0ElMjBEaWN0JTVCc3RyJTJDJTIwdG9yY2guVGVuc29yJTVEJTJDJTIwc2F2ZV9kaXJlY3RvcnklM0ElMjBzdHIpJTNBJTBBJTIwJTIwJTIwJTIwc3RhdGVfZGljdF9zcGxpdCUyMCUzRCUyMHNwbGl0X3RvcmNoX3N0YXRlX2RpY3RfaW50b19zaGFyZHMoc3RhdGVfZGljdCklMEElMjAlMjAlMjAlMjBmb3IlMjBmaWxlbmFtZSUyQyUyMHRlbnNvcnMlMjBpbiUyMHN0YXRlX2RpY3Rfc3BsaXQuZmlsZW5hbWVfdG9fdGVuc29ycy5pdGVtcygpJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwc2hhcmQlMjAlM0QlMjAlN0J0ZW5zb3IlM0ElMjBzdGF0ZV9kaWN0JTVCdGVuc29yJTVEJTIwZm9yJTIwdGVuc29yJTIwaW4lMjB0ZW5zb3JzJTdEJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwc2FmZV9zYXZlX2ZpbGUoJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwc2hhcmQlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBvcy5wYXRoLmpvaW4oc2F2ZV9kaXJlY3RvcnklMkMlMjBmaWxlbmFtZSklMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBtZXRhZGF0YSUzRCU3QiUyMmZvcm1hdCUyMiUzQSUyMCUyMnB0JTIyJTdEJTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwKSUwQSUyMCUyMCUyMCUyMGlmJTIwc3RhdGVfZGljdF9zcGxpdC5pc19zaGFyZGVkJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwaW5kZXglMjAlM0QlMjAlN0IlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJtZXRhZGF0YSUyMiUzQSUyMHN0YXRlX2RpY3Rfc3BsaXQubWV0YWRhdGElMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjJ3ZWlnaHRfbWFwJTIyJTNBJTIwc3RhdGVfZGljdF9zcGxpdC50ZW5zb3JfdG9fZmlsZW5hbWUlMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjAlN0QlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjB3aXRoJTIwb3Blbihvcy5wYXRoLmpvaW4oc2F2ZV9kaXJlY3RvcnklMkMlMjAlMjJtb2RlbC5zYWZldGVuc29ycy5pbmRleC5qc29uJTIyKSUyQyUyMCUyMnclMjIpJTIwYXMlMjBmJTNBJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwZi53cml0ZShqc29uLmR1bXBzKGluZGV4JTJDJTIwaW5kZW50JTNEMikp",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">import</span> os
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> safetensors.torch <span class="hljs-keyword">import</span> save_file <span class="hljs-keyword">as</span> safe_save_file
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> huggingface_hub <span class="hljs-keyword">import</span> split_torch_state_dict_into_shards

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">def</span> <span class="hljs-title function_">save_state_dict</span>(<span class="hljs-params">state_dict: <span class="hljs-type">Dict</span>[<span class="hljs-built_in">str</span>, torch.Tensor], save_directory: <span class="hljs-built_in">str</span></span>):
<span class="hljs-meta">... </span>    state_dict_split = split_torch_state_dict_into_shards(state_dict)
<span class="hljs-meta">... </span>    <span class="hljs-keyword">for</span> filename, tensors <span class="hljs-keyword">in</span> state_dict_split.filename_to_tensors.items():
<span class="hljs-meta">... </span>        shard = {tensor: state_dict[tensor] <span class="hljs-keyword">for</span> tensor <span class="hljs-keyword">in</span> tensors}
<span class="hljs-meta">... </span>        safe_save_file(
<span class="hljs-meta">... </span>            shard,
<span class="hljs-meta">... </span>            os.path.join(save_directory, filename),
<span class="hljs-meta">... </span>            metadata={<span class="hljs-string">&quot;format&quot;</span>: <span class="hljs-string">&quot;pt&quot;</span>},
<span class="hljs-meta">... </span>        )
<span class="hljs-meta">... </span>    <span class="hljs-keyword">if</span> state_dict_split.is_sharded:
<span class="hljs-meta">... </span>        index = {
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;metadata&quot;</span>: state_dict_split.metadata,
<span class="hljs-meta">... </span>            <span class="hljs-string">&quot;weight_map&quot;</span>: state_dict_split.tensor_to_filename,
<span class="hljs-meta">... </span>        }
<span class="hljs-meta">... </span>        <span class="hljs-keyword">with</span> <span class="hljs-built_in">open</span>(os.path.join(save_directory, <span class="hljs-string">&quot;model.safetensors.index.json&quot;</span>), <span class="hljs-string">&quot;w&quot;</span>) <span class="hljs-keyword">as</span> f:
<span class="hljs-meta">... </span>            f.write(json.dumps(index, indent=<span class="hljs-number">2</span>))`,wrap:!1}}),{c(){t=d("p"),t.textContent=m,n=o(),_(p.$$.fragment)},l(c){t=h(c,"P",{"data-svelte-h":!0}),g(t)!=="svelte-11lpom8"&&(t.textContent=m),n=i(c),f(p.$$.fragment,c)},m(c,x){l(c,t,x),l(c,n,x),u(p,c,x),w=!0},p:W,i(c){w||(b(p.$$.fragment,c),w=!0)},o(c){v(p.$$.fragment,c),w=!1},d(c){c&&(a(t),a(n)),$(p,c)}}}function js(M){let t,m=`If one of the model’s tensor is bigger than <code>max_shard_size</code>, it will end up in its own shard which will have a
size greater than <code>max_shard_size</code>.`;return{c(){t=d("p"),t.innerHTML=m},l(n){t=h(n,"P",{"data-svelte-h":!0}),g(t)!=="svelte-16sq104"&&(t.innerHTML=m)},m(n,p){l(n,t,p)},p:W,d(n){n&&a(t)}}}function Js(M){let t,m,n,p,w,c,x,Rt="<code>huggingface_hub</code> contains helpers to help ML libraries serialize models weights in a standardized way. This part of the lib is still under development and will be improved in future releases. The goal is to harmonize how weights are serialized on the Hub, both to remove code duplication across libraries and to foster conventions on the Hub.",We,Y,De,Q,Wt='The main helper of the <code>serialization</code> module takes a torch <code>nn.Module</code> as input and saves it to disk. It handles the logic to save shared tensors (see <a href="https://huggingface.co/docs/safetensors/torch_shared_tensors" rel="nofollow">safetensors explanation</a>) as well as logic to split the state dictionary into shards, using <a href="/docs/huggingface_hub/main/en/package_reference/serialization#huggingface_hub.split_torch_state_dict_into_shards">split_torch_state_dict_into_shards()</a> under the hood. At the moment, only <code>torch</code> framework is supported.',Se,O,Dt='If you want to save a state dictionary (e.g. a mapping between layer names and related tensors) instead of a <code>nn.Module</code>, you can use <a href="/docs/huggingface_hub/main/en/package_reference/serialization#huggingface_hub.save_torch_state_dict">save_torch_state_dict()</a> which provides the same features. This is useful for example if you want to apply custom logic to the state dict before saving it.',Ve,T,K,lt,be,St="Saves a given torch model to disk, handling sharding and shared tensors issues.",ct,ve,Vt='See also <a href="/docs/huggingface_hub/main/en/package_reference/serialization#huggingface_hub.save_torch_state_dict">save_torch_state_dict()</a> to save a state dict with more flexibility.',dt,$e,Pt='For more information about tensor sharing, check out <a href="https://huggingface.co/docs/safetensors/torch_shared_tensors" rel="nofollow">this guide</a>.',ht,we,Et=`The model state dictionary is split into shards so that each shard is smaller than a given size. The shards are
saved in the <code>save_directory</code> with the given <code>filename_pattern</code>. If the model is too big to fit in a single shard,
an index file is saved in the <code>save_directory</code> to indicate where each tensor is saved. This helper uses
<a href="/docs/huggingface_hub/main/en/package_reference/serialization#huggingface_hub.split_torch_state_dict_into_shards">split_torch_state_dict_into_shards()</a> under the hood. If <code>safe_serialization</code> is <code>True</code>, the shards are saved as
safetensors (the default). Otherwise, the shards are saved as pickle.`,pt,Te,qt="Before saving the model, the <code>save_directory</code> is cleaned from any previous shard files.",gt,D,mt,S,Pe,y,ee,_t,ye,Xt="Save a model state dictionary to the disk, handling sharding and shared tensors issues.",ft,Me,Ft='See also <a href="/docs/huggingface_hub/main/en/package_reference/serialization#huggingface_hub.save_torch_model">save_torch_model()</a> to directly save a PyTorch model.',ut,xe,Nt='For more information about tensor sharing, check out <a href="https://huggingface.co/docs/safetensors/torch_shared_tensors" rel="nofollow">this guide</a>.',bt,je,Yt=`The model state dictionary is split into shards so that each shard is smaller than a given size. The shards are
saved in the <code>save_directory</code> with the given <code>filename_pattern</code>. If the model is too big to fit in a single shard,
an index file is saved in the <code>save_directory</code> to indicate where each tensor is saved. This helper uses
<a href="/docs/huggingface_hub/main/en/package_reference/serialization#huggingface_hub.split_torch_state_dict_into_shards">split_torch_state_dict_into_shards()</a> under the hood. If <code>safe_serialization</code> is <code>True</code>, the shards are saved as
safetensors (the default). Otherwise, the shards are saved as pickle.`,vt,Je,Qt="Before saving the model, the <code>save_directory</code> is cleaned from any previous shard files.",$t,V,wt,P,Ee,te,qe,se,Ot="The <code>serialization</code> module also contains low-level helpers to split a state dictionary into several shards, while creating a proper index in the process. These helpers are available for <code>torch</code> and <code>tensorflow</code> tensors and are designed to be easily extended to any other ML frameworks.",Xe,ae,Fe,G,ne,Tt,ze,Kt="Split a model state dictionary in shards so that each shard is smaller than a given size.",yt,Ie,es=`The shards are determined by iterating through the <code>state_dict</code> in the order of its keys. There is no optimization
made to make each shard as close as possible to the maximum size passed. For example, if the limit is 10GB and we
have tensors of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not
[6+2+2GB], [6+2GB], [6GB].`,Mt,E,Ne,oe,Ye,z,ie,xt,Ge,ts="Split a model state dictionary in shards so that each shard is smaller than a given size.",jt,ke,ss=`The shards are determined by iterating through the <code>state_dict</code> in the order of its keys. There is no optimization
made to make each shard as close as possible to the maximum size passed. For example, if the limit is 10GB and we
have tensors of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not
[6+2+2GB], [6+2GB], [6GB].`,Jt,q,zt,X,It,F,Qe,re,Oe,le,as='This is the underlying factory from which each framework-specific helper is derived. In practice, you are not expected to use this factory directly except if you need to adapt it to a framework that is not yet supported. If that is the case, please let us know by <a href="https://github.com/huggingface/huggingface_hub/issues/new" rel="nofollow">opening a new issue</a> on the <code>huggingface_hub</code> repo.',Ke,k,ce,Gt,Be,ns="Split a model state dictionary in shards so that each shard is smaller than a given size.",kt,Ce,os=`The shards are determined by iterating through the <code>state_dict</code> in the order of its keys. There is no optimization
made to make each shard as close as possible to the maximum size passed. For example, if the limit is 10GB and we
have tensors of sizes [6GB, 6GB, 2GB, 6GB, 2GB, 2GB] they will get sharded as [6GB], [6+2GB], [6+2+2GB] and not
[6+2+2GB], [6+2GB], [6GB].`,Bt,N,et,de,tt,he,st,B,pe,Ct,Ae,is="Return unique identifier to a tensor storage.",At,Ze,rs=`Multiple different tensors can share the same underlying storage. This identifier is
guaranteed to be unique and constant for this tensor’s storage during its lifetime. Two tensor storages with
non-overlapping lifetimes may have the same id.
In the case of meta tensors, we return None since we can’t tell if they share the same storage.`,Zt,Le,ls='Taken from <a href="https://github.com/huggingface/transformers/blob/1ecf5f7c982d761b4daaa96719d162c324187c64/src/transformers/pytorch_utils.py#L278" rel="nofollow">https://github.com/huggingface/transformers/blob/1ecf5f7c982d761b4daaa96719d162c324187c64/src/transformers/pytorch_utils.py#L278</a>.',at,ge,nt,L,me,Lt,He,cs='Taken from <a href="https://github.com/huggingface/safetensors/blob/08db34094e9e59e2f9218f2df133b7b4aaff5a99/bindings/python/py_src/safetensors/torch.py#L31C1-L41C59" rel="nofollow">https://github.com/huggingface/safetensors/blob/08db34094e9e59e2f9218f2df133b7b4aaff5a99/bindings/python/py_src/safetensors/torch.py#L31C1-L41C59</a>',ot,_e,it,Re,rt;return w=new R({props:{title:"Serialization",local:"serialization",headingTag:"h1"}}),Y=new R({props:{title:"Save torch state dict",local:"huggingface_hub.save_torch_model",headingTag:"h2"}}),K=new ue({props:{name:"huggingface_hub.save_torch_model",anchor:"huggingface_hub.save_torch_model",parameters:[{name:"model",val:": torch.nn.Module"},{name:"save_directory",val:": typing.Union[str, pathlib.Path]"},{name:"filename_pattern",val:": typing.Optional[str] = None"},{name:"force_contiguous",val:": bool = True"},{name:"max_shard_size",val:": typing.Union[int, str] = '5GB'"},{name:"metadata",val:": typing.Optional[typing.Dict[str, str]] = None"},{name:"safe_serialization",val:": bool = True"},{name:"is_main_process",val:": bool = True"}],parametersDescription:[{anchor:"huggingface_hub.save_torch_model.model",description:`<strong>model</strong> (<code>torch.nn.Module</code>) &#x2014;
The model to save on disk.`,name:"model"},{anchor:"huggingface_hub.save_torch_model.save_directory",description:`<strong>save_directory</strong> (<code>str</code> or <code>Path</code>) &#x2014;
The directory in which the model will be saved.`,name:"save_directory"},{anchor:"huggingface_hub.save_torch_model.filename_pattern",description:`<strong>filename_pattern</strong> (<code>str</code>, <em>optional</em>) &#x2014;
The pattern to generate the files names in which the model will be saved. Pattern must be a string that
can be formatted with <code>filename_pattern.format(suffix=...)</code> and must contain the keyword <code>suffix</code>
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<p><code>StateDictSplit</code></p>
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<p><code>StateDictSplit</code></p>
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<p>A <code>StateDictSplit</code> object containing the shards and the index to retrieve them.</p>
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<p><code>StateDictSplit</code></p>
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