import{s as Zt,o as gt,n as Me}from"../chunks/scheduler.8c3d61f6.js";import{S as Ct,i as _t,g as h,s as a,r as p,A as Wt,h as J,f as s,c as o,j as Tt,u as d,x as $,k as wt,y as vt,a as l,v as u,d as y,t as c,w as b}from"../chunks/index.da70eac4.js";import{T as st}from"../chunks/Tip.1d9b8c37.js";import{C as R}from"../chunks/CodeBlock.00a903b3.js";import{H as k,E as Rt}from"../chunks/EditOnGithub.1e64e623.js";import{H as Bt,a as Ut}from"../chunks/HfOption.c1483eb1.js";function kt(v){let i,T="Quantizing a model in 8-bit halves the memory-usage:",r,f,m,M,_="By default, all the other modules such as <code>torch.nn.LayerNorm</code> are converted to <code>torch.float16</code>. You can change the data type of these modules with the <code>torch_dtype</code> parameter if you want:",W,U,g,Z,B='Once a model is quantized, you can push the model to the Hub with the <a href="/docs/diffusers/pr_10083/en/api/pipelines/overview#diffusers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> method. The quantization <code>config.json</code> file is pushed first, followed by the quantized model weights. You can also save the serialized 4-bit models locally with <a href="/docs/diffusers/pr_10083/en/api/models/overview#diffusers.ModelMixin.save_pretrained">save_pretrained()</a>.',C;return f=new R({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FluxTransformer2DModel, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_8bit=<span class="hljs-literal">True</span>)

model_8bit = FluxTransformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;black-forest-labs/FLUX.1-dev&quot;</span>, 
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quantization_config
)`,wrap:!1}}),U=new R({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FluxTransformer2DModel, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_8bit=<span class="hljs-literal">True</span>)

model_8bit = FluxTransformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;black-forest-labs/FLUX.1-dev&quot;</span>, 
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quantization_config,
    torch_dtype=torch.float32
)
model_8bit.transformer_blocks.layers[-<span class="hljs-number">1</span>].norm2.weight.dtype`,wrap:!1}}),{c(){i=h("p"),i.textContent=T,r=a(),p(f.$$.fragment),m=a(),M=h("p"),M.innerHTML=_,W=a(),p(U.$$.fragment),g=a(),Z=h("p"),Z.innerHTML=B},l(n){i=J(n,"P",{"data-svelte-h":!0}),$(i)!=="svelte-4djpqq"&&(i.textContent=T),r=o(n),d(f.$$.fragment,n),m=o(n),M=J(n,"P",{"data-svelte-h":!0}),$(M)!=="svelte-kfzkum"&&(M.innerHTML=_),W=o(n),d(U.$$.fragment,n),g=o(n),Z=J(n,"P",{"data-svelte-h":!0}),$(Z)!=="svelte-8ov19u"&&(Z.innerHTML=B)},m(n,w){l(n,i,w),l(n,r,w),u(f,n,w),l(n,m,w),l(n,M,w),l(n,W,w),u(U,n,w),l(n,g,w),l(n,Z,w),C=!0},p:Me,i(n){C||(y(f.$$.fragment,n),y(U.$$.fragment,n),C=!0)},o(n){c(f.$$.fragment,n),c(U.$$.fragment,n),C=!1},d(n){n&&(s(i),s(r),s(m),s(M),s(W),s(g),s(Z)),b(f,n),b(U,n)}}}function zt(v){let i,T="Quantizing a model in 4-bit reduces your memory-usage by 4x:",r,f,m,M,_="By default, all the other modules such as <code>torch.nn.LayerNorm</code> are converted to <code>torch.float16</code>. You can change the data type of these modules with the <code>torch_dtype</code> parameter if you want:",W,U,g,Z,B='Call <a href="/docs/diffusers/pr_10083/en/api/pipelines/overview#diffusers.utils.PushToHubMixin.push_to_hub">push_to_hub()</a> after loading it in 4-bit precision. You can also save the serialized 4-bit models locally with <a href="/docs/diffusers/pr_10083/en/api/models/overview#diffusers.ModelMixin.save_pretrained">save_pretrained()</a>.',C;return f=new R({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FluxTransformer2DModel, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=<span class="hljs-literal">True</span>)

model_4bit = FluxTransformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;black-forest-labs/FLUX.1-dev&quot;</span>, 
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quantization_config
)`,wrap:!1}}),U=new R({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FluxTransformer2DModel, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=<span class="hljs-literal">True</span>)

model_4bit = FluxTransformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;black-forest-labs/FLUX.1-dev&quot;</span>, 
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quantization_config,
    torch_dtype=torch.float32
)
model_4bit.transformer_blocks.layers[-<span class="hljs-number">1</span>].norm2.weight.dtype`,wrap:!1}}),{c(){i=h("p"),i.textContent=T,r=a(),p(f.$$.fragment),m=a(),M=h("p"),M.innerHTML=_,W=a(),p(U.$$.fragment),g=a(),Z=h("p"),Z.innerHTML=B},l(n){i=J(n,"P",{"data-svelte-h":!0}),$(i)!=="svelte-i07c71"&&(i.textContent=T),r=o(n),d(f.$$.fragment,n),m=o(n),M=J(n,"P",{"data-svelte-h":!0}),$(M)!=="svelte-kfzkum"&&(M.innerHTML=_),W=o(n),d(U.$$.fragment,n),g=o(n),Z=J(n,"P",{"data-svelte-h":!0}),$(Z)!=="svelte-144sb79"&&(Z.innerHTML=B)},m(n,w){l(n,i,w),l(n,r,w),u(f,n,w),l(n,m,w),l(n,M,w),l(n,W,w),u(U,n,w),l(n,g,w),l(n,Z,w),C=!0},p:Me,i(n){C||(y(f.$$.fragment,n),y(U.$$.fragment,n),C=!0)},o(n){c(f.$$.fragment,n),c(U.$$.fragment,n),C=!1},d(n){n&&(s(i),s(r),s(m),s(M),s(W),s(g),s(Z)),b(f,n),b(U,n)}}}function jt(v){let i,T,r,f;return i=new Ut({props:{id:"bnb",option:"8-bit",$$slots:{default:[kt]},$$scope:{ctx:v}}}),r=new Ut({props:{id:"bnb",option:"4-bit",$$slots:{default:[zt]},$$scope:{ctx:v}}}),{c(){p(i.$$.fragment),T=a(),p(r.$$.fragment)},l(m){d(i.$$.fragment,m),T=o(m),d(r.$$.fragment,m)},m(m,M){u(i,m,M),l(m,T,M),u(r,m,M),f=!0},p(m,M){const _={};M&2&&(_.$$scope={dirty:M,ctx:m}),i.$set(_);const W={};M&2&&(W.$$scope={dirty:M,ctx:m}),r.$set(W)},i(m){f||(y(i.$$.fragment,m),y(r.$$.fragment,m),f=!0)},o(m){c(i.$$.fragment,m),c(r.$$.fragment,m),f=!1},d(m){m&&s(T),b(i,m),b(r,m)}}}function Ft(v){let i,T="Training with 8-bit and 4-bit weights are only supported for training <em>extra</em> parameters.";return{c(){i=h("p"),i.innerHTML=T},l(r){i=J(r,"P",{"data-svelte-h":!0}),$(i)!=="svelte-of9sym"&&(i.innerHTML=T)},m(r,f){l(r,i,f)},p:Me,d(r){r&&s(i)}}}function Gt(v){let i,T='Learn more about the details of 8-bit quantization in this <a href="https://huggingface.co/blog/hf-bitsandbytes-integration" rel="nofollow">blog post</a>!';return{c(){i=h("p"),i.innerHTML=T},l(r){i=J(r,"P",{"data-svelte-h":!0}),$(i)!=="svelte-1bb05fp"&&(i.innerHTML=T)},m(r,f){l(r,i,f)},p:Me,d(r){r&&s(i)}}}function qt(v){let i,T='Learn more about its details in this <a href="https://huggingface.co/blog/4bit-transformers-bitsandbytes" rel="nofollow">blog post</a>.';return{c(){i=h("p"),i.innerHTML=T},l(r){i=J(r,"P",{"data-svelte-h":!0}),$(i)!=="svelte-kpdzjq"&&(i.innerHTML=T)},m(r,f){l(r,i,f)},p:Me,d(r){r&&s(i)}}}function Vt(v){let i,T,r,f,m,M,_,W='<a href="https://huggingface.co/docs/bitsandbytes/index" rel="nofollow">bitsandbytes</a> is the easiest option for quantizing a model to 8 and 4-bit. 8-bit quantization multiplies outliers in fp16 with non-outliers in int8, converts the non-outlier values back to fp16, and then adds them together to return the weights in fp16. This reduces the degradative effect outlier values have on a model’s performance.',U,g,Z='4-bit quantization compresses a model even further, and it is commonly used with <a href="https://hf.co/papers/2305.14314" rel="nofollow">QLoRA</a> to finetune quantized LLMs.',B,C,n="To use bitsandbytes, make sure you have the following libraries installed:",w,q,he,V,lt='Now you can quantize a model by passing a <a href="/docs/diffusers/pr_10083/en/api/quantization#diffusers.BitsAndBytesConfig">BitsAndBytesConfig</a> to <a href="/docs/diffusers/pr_10083/en/api/models/overview#diffusers.ModelMixin.from_pretrained">from_pretrained()</a>. This works for any model in any modality, as long as it supports loading with <a href="https://hf.co/docs/accelerate/index" rel="nofollow">Accelerate</a> and contains <code>torch.nn.Linear</code> layers.',Je,z,$e,j,Te,x,nt="Check your memory footprint with the <code>get_memory_footprint</code> method:",we,Q,Ue,X,it='Quantized models can be loaded from the <a href="/docs/diffusers/pr_10083/en/api/models/overview#diffusers.ModelMixin.from_pretrained">from_pretrained()</a> method without needing to specify the <code>quantization_config</code> parameters:',Ze,E,ge,I,Ce,F,_e,N,at="This section explores some of the specific features of 8-bit models, such as outlier thresholds and skipping module conversion.",We,Y,ve,L,ot="An “outlier” is a hidden state value greater than a certain threshold, and these values are computed in fp16. While the values are usually normally distributed ([-3.5, 3.5]), this distribution can be very different for large models ([-60, 6] or [6, 60]). 8-bit quantization works well for values ~5, but beyond that, there is a significant performance penalty. A good default threshold value is 6, but a lower threshold may be needed for more unstable models (small models or finetuning).",Re,H,rt='To find the best threshold for your model, we recommend experimenting with the <code>llm_int8_threshold</code> parameter in <a href="/docs/diffusers/pr_10083/en/api/quantization#diffusers.BitsAndBytesConfig">BitsAndBytesConfig</a>:',Be,A,ke,S,ze,D,mt='For some models, you don’t need to quantize every module to 8-bit which can actually cause instability. For example, for diffusion models like <a href="../api/pipelines/stable_diffusion/stable_diffusion_3">Stable Diffusion 3</a>, the <code>proj_out</code> module can be skipped using the <code>llm_int8_skip_modules</code> parameter in <a href="/docs/diffusers/pr_10083/en/api/quantization#diffusers.BitsAndBytesConfig">BitsAndBytesConfig</a>:',je,P,Fe,K,Ge,G,qe,O,ft="This section explores some of the specific features of 4-bit models, such as changing the compute data type, using the Normal Float 4 (NF4) data type, and using nested quantization.",Ve,ee,xe,te,pt='To speedup computation, you can change the data type from float32 (the default value) to bf16 using the <code>bnb_4bit_compute_dtype</code> parameter in <a href="/docs/diffusers/pr_10083/en/api/quantization#diffusers.BitsAndBytesConfig">BitsAndBytesConfig</a>:',Qe,se,Xe,le,Ee,ne,dt='NF4 is a 4-bit data type from the <a href="https://hf.co/papers/2305.14314" rel="nofollow">QLoRA</a> paper, adapted for weights initialized from a normal distribution. You should use NF4 for training 4-bit base models. This can be configured with the <code>bnb_4bit_quant_type</code> parameter in the <a href="/docs/diffusers/pr_10083/en/api/quantization#diffusers.BitsAndBytesConfig">BitsAndBytesConfig</a>:',Ie,ie,Ne,ae,ut="For inference, the <code>bnb_4bit_quant_type</code> does not have a huge impact on performance. However, to remain consistent with the model weights, you should use the <code>bnb_4bit_compute_dtype</code> and <code>torch_dtype</code> values.",Ye,oe,Le,re,yt="Nested quantization is a technique that can save additional memory at no additional performance cost. This feature performs a second quantization of the already quantized weights to save an additional 0.4 bits/parameter.",He,me,Ae,fe,Se,pe,ct="Once quantized, you can dequantize the model to the original precision but this might result in a small quality loss of the model. Make sure you have enough GPU RAM to fit the dequantized model.",De,de,Pe,ue,Ke,ye,bt='<li><a href="https://gist.github.com/sayakpaul/c76bd845b48759e11687ac550b99d8b4" rel="nofollow">End-to-end notebook showing Flux.1 Dev inference in a free-tier Colab</a></li> <li><a href="https://gist.github.com/sayakpaul/05afd428bc089b47af7c016e42004527" rel="nofollow">Training</a></li>',Oe,ce,et,be,tt;return m=new k({props:{title:"bitsandbytes",local:"bitsandbytes",headingTag:"h1"}}),q=new R({props:{code:"cGlwJTIwaW5zdGFsbCUyMGRpZmZ1c2VycyUyMHRyYW5zZm9ybWVycyUyMGFjY2VsZXJhdGUlMjBiaXRzYW5kYnl0ZXMlMjAtVQ==",highlighted:"pip install diffusers transformers accelerate bitsandbytes -U",wrap:!1}}),z=new Bt({props:{id:"bnb",options:["8-bit","4-bit"],$$slots:{default:[jt]},$$scope:{ctx:v}}}),j=new st({props:{warning:!0,$$slots:{default:[Ft]},$$scope:{ctx:v}}}),Q=new R({props:{code:"cHJpbnQobW9kZWwuZ2V0X21lbW9yeV9mb290cHJpbnQoKSk=",highlighted:'<span class="hljs-built_in">print</span>(model.get_memory_footprint())',wrap:!1}}),E=new R({props:{code:"ZnJvbSUyMGRpZmZ1c2VycyUyMGltcG9ydCUyMEZsdXhUcmFuc2Zvcm1lcjJETW9kZWwlMkMlMjBCaXRzQW5kQnl0ZXNDb25maWclMEElMEFxdWFudGl6YXRpb25fY29uZmlnJTIwJTNEJTIwQml0c0FuZEJ5dGVzQ29uZmlnKGxvYWRfaW5fNGJpdCUzRFRydWUpJTBBJTBBbW9kZWxfNGJpdCUyMCUzRCUyMEZsdXhUcmFuc2Zvcm1lcjJETW9kZWwuZnJvbV9wcmV0cmFpbmVkKCUwQSUyMCUyMCUyMCUyMCUyMmhmLWludGVybmFsLXRlc3RpbmclMkZmbHV4LjEtZGV2LW5mNC1wa2clMjIlMkMlMjBzdWJmb2xkZXIlM0QlMjJ0cmFuc2Zvcm1lciUyMiUwQSk=",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FluxTransformer2DModel, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=<span class="hljs-literal">True</span>)

model_4bit = FluxTransformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;hf-internal-testing/flux.1-dev-nf4-pkg&quot;</span>, subfolder=<span class="hljs-string">&quot;transformer&quot;</span>
)`,wrap:!1}}),I=new k({props:{title:"8-bit (LLM.int8() algorithm)",local:"8-bit-llmint8-algorithm",headingTag:"h2"}}),F=new st({props:{$$slots:{default:[Gt]},$$scope:{ctx:v}}}),Y=new k({props:{title:"Outlier threshold",local:"outlier-threshold",headingTag:"h3"}}),A=new R({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> FluxTransformer2DModel, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=<span class="hljs-literal">True</span>, llm_int8_threshold=<span class="hljs-number">10</span>,
)

model_8bit = FluxTransformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;black-forest-labs/FLUX.1-dev&quot;</span>,
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quantization_config,
)`,wrap:!1}}),S=new k({props:{title:"Skip module conversion",local:"skip-module-conversion",headingTag:"h3"}}),P=new R({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> SD3Transformer2DModel, BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=<span class="hljs-literal">True</span>, llm_int8_skip_modules=[<span class="hljs-string">&quot;proj_out&quot;</span>],
)

model_8bit = SD3Transformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-3-medium-diffusers&quot;</span>,
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=quantization_config,
)`,wrap:!1}}),K=new k({props:{title:"4-bit (QLoRA algorithm)",local:"4-bit-qlora-algorithm",headingTag:"h2"}}),G=new st({props:{$$slots:{default:[qt]},$$scope:{ctx:v}}}),ee=new k({props:{title:"Compute data type",local:"compute-data-type",headingTag:"h3"}}),se=new R({props:{code:"aW1wb3J0JTIwdG9yY2glMEFmcm9tJTIwZGlmZnVzZXJzJTIwaW1wb3J0JTIwQml0c0FuZEJ5dGVzQ29uZmlnJTBBJTBBcXVhbnRpemF0aW9uX2NvbmZpZyUyMCUzRCUyMEJpdHNBbmRCeXRlc0NvbmZpZyhsb2FkX2luXzRiaXQlM0RUcnVlJTJDJTIwYm5iXzRiaXRfY29tcHV0ZV9kdHlwZSUzRHRvcmNoLmJmbG9hdDE2KQ==",highlighted:`<span class="hljs-keyword">import</span> torch
<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(load_in_4bit=<span class="hljs-literal">True</span>, bnb_4bit_compute_dtype=torch.bfloat16)`,wrap:!1}}),le=new k({props:{title:"Normal Float 4 (NF4)",local:"normal-float-4-nf4",headingTag:"h3"}}),ie=new R({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> BitsAndBytesConfig

nf4_config = BitsAndBytesConfig(
    load_in_4bit=<span class="hljs-literal">True</span>,
    bnb_4bit_quant_type=<span class="hljs-string">&quot;nf4&quot;</span>,
)

model_nf4 = SD3Transformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-3-medium-diffusers&quot;</span>,
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=nf4_config,
)`,wrap:!1}}),oe=new k({props:{title:"Nested quantization",local:"nested-quantization",headingTag:"h3"}}),me=new R({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> BitsAndBytesConfig

double_quant_config = BitsAndBytesConfig(
    load_in_4bit=<span class="hljs-literal">True</span>,
    bnb_4bit_use_double_quant=<span class="hljs-literal">True</span>,
)

double_quant_model = SD3Transformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-3-medium-diffusers&quot;</span>,
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=double_quant_config,
)`,wrap:!1}}),fe=new k({props:{title:"Dequantizing bitsandbytes models",local:"dequantizing-bitsandbytes-models",headingTag:"h2"}}),de=new R({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> diffusers <span class="hljs-keyword">import</span> BitsAndBytesConfig

double_quant_config = BitsAndBytesConfig(
    load_in_4bit=<span class="hljs-literal">True</span>,
    bnb_4bit_use_double_quant=<span class="hljs-literal">True</span>,
)

double_quant_model = SD3Transformer2DModel.from_pretrained(
    <span class="hljs-string">&quot;stabilityai/stable-diffusion-3-medium-diffusers&quot;</span>,
    subfolder=<span class="hljs-string">&quot;transformer&quot;</span>,
    quantization_config=double_quant_config,
)
model.dequantize()`,wrap:!1}}),ue=new k({props:{title:"Resources",local:"resources",headingTag:"h2"}}),ce=new Rt({props:{source:"https://github.com/huggingface/diffusers/blob/main/docs/source/en/quantization/bitsandbytes.md"}}),{c(){i=h("meta"),T=a(),r=h("p"),f=a(),p(m.$$.fragment),M=a(),_=h("p"),_.innerHTML=W,U=a(),g=h("p"),g.innerHTML=Z,B=a(),C=h("p"),C.textContent=n,w=a(),p(q.$$.fragment),he=a(),V=h("p"),V.innerHTML=lt,Je=a(),p(z.$$.fragment),$e=a(),p(j.$$.fragment),Te=a(),x=h("p"),x.innerHTML=nt,we=a(),p(Q.$$.fragment),Ue=a(),X=h("p"),X.innerHTML=it,Ze=a(),p(E.$$.fragment),ge=a(),p(I.$$.fragment),Ce=a(),p(F.$$.fragment),_e=a(),N=h("p"),N.textContent=at,We=a(),p(Y.$$.fragment),ve=a(),L=h("p"),L.textContent=ot,Re=a(),H=h("p"),H.innerHTML=rt,Be=a(),p(A.$$.fragment),ke=a(),p(S.$$.fragment),ze=a(),D=h("p"),D.innerHTML=mt,je=a(),p(P.$$.fragment),Fe=a(),p(K.$$.fragment),Ge=a(),p(G.$$.fragment),qe=a(),O=h("p"),O.textContent=ft,Ve=a(),p(ee.$$.fragment),xe=a(),te=h("p"),te.innerHTML=pt,Qe=a(),p(se.$$.fragment),Xe=a(),p(le.$$.fragment),Ee=a(),ne=h("p"),ne.innerHTML=dt,Ie=a(),p(ie.$$.fragment),Ne=a(),ae=h("p"),ae.innerHTML=ut,Ye=a(),p(oe.$$.fragment),Le=a(),re=h("p"),re.textContent=yt,He=a(),p(me.$$.fragment),Ae=a(),p(fe.$$.fragment),Se=a(),pe=h("p"),pe.textContent=ct,De=a(),p(de.$$.fragment),Pe=a(),p(ue.$$.fragment),Ke=a(),ye=h("ul"),ye.innerHTML=bt,Oe=a(),p(ce.$$.fragment),et=a(),be=h("p"),this.h()},l(e){const 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xt='{"title":"bitsandbytes","local":"bitsandbytes","sections":[{"title":"8-bit (LLM.int8() algorithm)","local":"8-bit-llmint8-algorithm","sections":[{"title":"Outlier threshold","local":"outlier-threshold","sections":[],"depth":3},{"title":"Skip module conversion","local":"skip-module-conversion","sections":[],"depth":3}],"depth":2},{"title":"4-bit (QLoRA algorithm)","local":"4-bit-qlora-algorithm","sections":[{"title":"Compute data type","local":"compute-data-type","sections":[],"depth":3},{"title":"Normal Float 4 (NF4)","local":"normal-float-4-nf4","sections":[],"depth":3},{"title":"Nested quantization","local":"nested-quantization","sections":[],"depth":3}],"depth":2},{"title":"Dequantizing bitsandbytes models","local":"dequantizing-bitsandbytes-models","sections":[],"depth":2},{"title":"Resources","local":"resources","sections":[],"depth":2}],"depth":1}';function Qt(v){return gt(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Ht extends Ct{constructor(i){super(),_t(this,i,Qt,Vt,Zt,{})}}export{Ht as component};
