import{s as Ur,o as Wr,n as A}from"../chunks/scheduler.25b97de1.js";import{S as Ar,i as Hr,g as i,s as a,r as h,A as Nr,h as l,f as o,c as s,j as T,u as f,x as m,k as w,y as t,a as d,v as _,d as g,t as b,w as k}from"../chunks/index.d9030fc9.js";import{T as ie}from"../chunks/Tip.baa67368.js";import{D as M}from"../chunks/Docstring.e257edda.js";import{C as ze}from"../chunks/CodeBlock.e6cd0d95.js";import{E as So}from"../chunks/ExampleCodeBlock.20db4b6e.js";import{P as jr}from"../chunks/PipelineTag.5f100392.js";import{H as Zo,E as Jr}from"../chunks/EditOnGithub.91d95064.js";function Gr(y){let r,v;return r=new ze({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> LlamaModel, LlamaConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a LLaMA llama-7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = LlamaConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the llama-7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = LlamaModel(configuration)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Accessing the model configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = model.config`,wrap:!1}}),{c(){h(r.$$.fragment)},l(c){f(r.$$.fragment,c)},m(c,u){_(r,c,u),v=!0},p:A,i(c){v||(g(r.$$.fragment,c),v=!0)},o(c){b(r.$$.fragment,c),v=!1},d(c){k(r,c)}}}function Sr(y){let r,v="sequence pair mask has the following format:",c,u,L;return u=new ze({props:{code:"MCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAwJTIwMCUyMDAlMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMjAxJTIwMSUyMDElMEElN0MlMjBmaXJzdCUyMHNlcXVlbmNlJTIwJTIwJTIwJTIwJTdDJTIwc2Vjb25kJTIwc2VxdWVuY2UlMjAlN0M=",highlighted:`0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 0 </span>0<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1<span class="hljs-number"> 1 </span>1 1
| first sequence    | second sequence |`,wrap:!1}}),{c(){r=i("p"),r.textContent=v,c=a(),h(u.$$.fragment)},l(p){r=l(p,"P",{"data-svelte-h":!0}),m(r)!=="svelte-16klr56"&&(r.textContent=v),c=s(p),f(u.$$.fragment,p)},m(p,z){d(p,r,z),d(p,c,z),_(u,p,z),L=!0},p:A,i(p){L||(g(u.$$.fragment,p),L=!0)},o(p){b(u.$$.fragment,p),L=!1},d(p){p&&(o(r),o(c)),k(u,p)}}}function Br(y){let r,v;return r=new ze({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMExsYW1hVG9rZW5pemVyRmFzdCUwQSUwQXRva2VuaXplciUyMCUzRCUyMExsYW1hVG9rZW5pemVyRmFzdC5mcm9tX3ByZXRyYWluZWQoJTIyaGYtaW50ZXJuYWwtdGVzdGluZyUyRmxsYW1hLXRva2VuaXplciUyMiklMEF0b2tlbml6ZXIuZW5jb2RlKCUyMkhlbGxvJTIwdGhpcyUyMGlzJTIwYSUyMHRlc3QlMjIp",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> LlamaTokenizerFast

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = LlamaTokenizerFast.from_pretrained(<span class="hljs-string">&quot;hf-internal-testing/llama-tokenizer&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.encode(<span class="hljs-string">&quot;Hello this is a test&quot;</span>)
[<span class="hljs-number">1</span>, <span class="hljs-number">15043</span>, <span class="hljs-number">445</span>, <span class="hljs-number">338</span>, <span class="hljs-number">263</span>, <span class="hljs-number">1243</span>]`,wrap:!1}}),{c(){h(r.$$.fragment)},l(c){f(r.$$.fragment,c)},m(c,u){_(r,c,u),v=!0},p:A,i(c){v||(g(r.$$.fragment,c),v=!0)},o(c){b(r.$$.fragment,c),v=!1},d(c){k(r,c)}}}function Er(y){let r,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){r=i("p"),r.innerHTML=v},l(c){r=l(c,"P",{"data-svelte-h":!0}),m(r)!=="svelte-fincs2"&&(r.innerHTML=v)},m(c,u){d(c,r,u)},p:A,d(c){c&&o(r)}}}function Dr(y){let r,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){r=i("p"),r.innerHTML=v},l(c){r=l(c,"P",{"data-svelte-h":!0}),m(r)!=="svelte-fincs2"&&(r.innerHTML=v)},m(c,u){d(c,r,u)},p:A,d(c){c&&o(r)}}}function Vr(y){let r,v="Example:",c,u,L;return u=new ze({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, LlamaForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>model = LlamaForCausalLM.from_pretrained(<span class="hljs-string">&quot;meta-llama/Llama-2-7b-hf&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;meta-llama/Llama-2-7b-hf&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>prompt = <span class="hljs-string">&quot;Hey, are you conscious? Can you talk to me?&quot;</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(prompt, return_tensors=<span class="hljs-string">&quot;pt&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Generate</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>generate_ids = model.generate(inputs.input_ids, max_length=<span class="hljs-number">30</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer.batch_decode(generate_ids, skip_special_tokens=<span class="hljs-literal">True</span>, clean_up_tokenization_spaces=<span class="hljs-literal">False</span>)[<span class="hljs-number">0</span>]
<span class="hljs-string">&quot;Hey, are you conscious? Can you talk to me?\\nI&#x27;m not conscious, but I can talk to you.&quot;</span>`,wrap:!1}}),{c(){r=i("p"),r.textContent=v,c=a(),h(u.$$.fragment)},l(p){r=l(p,"P",{"data-svelte-h":!0}),m(r)!=="svelte-11lpom8"&&(r.textContent=v),c=s(p),f(u.$$.fragment,p)},m(p,z){d(p,r,z),d(p,c,z),_(u,p,z),L=!0},p:A,i(p){L||(g(u.$$.fragment,p),L=!0)},o(p){b(u.$$.fragment,p),L=!1},d(p){p&&(o(r),o(c)),k(u,p)}}}function Zr(y){let r,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){r=i("p"),r.innerHTML=v},l(c){r=l(c,"P",{"data-svelte-h":!0}),m(r)!=="svelte-fincs2"&&(r.innerHTML=v)},m(c,u){d(c,r,u)},p:A,d(c){c&&o(r)}}}function Rr(y){let r,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){r=i("p"),r.innerHTML=v},l(c){r=l(c,"P",{"data-svelte-h":!0}),m(r)!=="svelte-fincs2"&&(r.innerHTML=v)},m(c,u){d(c,r,u)},p:A,d(c){c&&o(r)}}}function Or(y){let r,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){r=i("p"),r.innerHTML=v},l(c){r=l(c,"P",{"data-svelte-h":!0}),m(r)!=="svelte-fincs2"&&(r.innerHTML=v)},m(c,u){d(c,r,u)},p:A,d(c){c&&o(r)}}}function Xr(y){let r,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){r=i("p"),r.innerHTML=v},l(c){r=l(c,"P",{"data-svelte-h":!0}),m(r)!=="svelte-fincs2"&&(r.innerHTML=v)},m(c,u){d(c,r,u)},p:A,d(c){c&&o(r)}}}function Qr(y){let r,v=`This example uses a random model as the real ones are all very big. To get proper results, you should use
openlm-research/open_llama_3b_v2 instead of afmck/testing-llama-tiny. If you get out-of-memory when loading that checkpoint, you can try
adding <code>device_map=&quot;auto&quot;</code> in the <code>from_pretrained</code> call.`;return{c(){r=i("p"),r.innerHTML=v},l(c){r=l(c,"P",{"data-svelte-h":!0}),m(r)!=="svelte-29cnl4"&&(r.innerHTML=v)},m(c,u){d(c,r,u)},p:A,d(c){c&&o(r)}}}function Yr(y){let r,v="Example:",c,u,L;return u=new ze({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxLlamaModel

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;afmck/testing-llama-tiny&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxLlamaModel.from_pretrained(<span class="hljs-string">&quot;afmck/testing-llama-tiny&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;jax&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span>last_hidden_states = outputs.last_hidden_state`,wrap:!1}}),{c(){r=i("p"),r.textContent=v,c=a(),h(u.$$.fragment)},l(p){r=l(p,"P",{"data-svelte-h":!0}),m(r)!=="svelte-11lpom8"&&(r.textContent=v),c=s(p),f(u.$$.fragment,p)},m(p,z){d(p,r,z),d(p,c,z),_(u,p,z),L=!0},p:A,i(p){L||(g(u.$$.fragment,p),L=!0)},o(p){b(u.$$.fragment,p),L=!1},d(p){p&&(o(r),o(c)),k(u,p)}}}function Kr(y){let r,v=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
instance afterwards instead of this since the former takes care of running the pre and post processing steps while
the latter silently ignores them.`;return{c(){r=i("p"),r.innerHTML=v},l(c){r=l(c,"P",{"data-svelte-h":!0}),m(r)!=="svelte-fincs2"&&(r.innerHTML=v)},m(c,u){d(c,r,u)},p:A,d(c){c&&o(r)}}}function ei(y){let r,v=`This example uses a random model as the real ones are all very big. To get proper results, you should use
openlm-research/open_llama_3b_v2 instead of afmck/testing-llama-tiny. If you get out-of-memory when loading that checkpoint, you can try
adding <code>device_map=&quot;auto&quot;</code> in the <code>from_pretrained</code> call.`;return{c(){r=i("p"),r.innerHTML=v},l(c){r=l(c,"P",{"data-svelte-h":!0}),m(r)!=="svelte-29cnl4"&&(r.innerHTML=v)},m(c,u){d(c,r,u)},p:A,d(c){c&&o(r)}}}function ti(y){let r,v="Example:",c,u,L;return u=new ze({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, FlaxLlamaForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;afmck/testing-llama-tiny&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = FlaxLlamaForCausalLM.from_pretrained(<span class="hljs-string">&quot;afmck/testing-llama-tiny&quot;</span>)

<span class="hljs-meta">&gt;&gt;&gt; </span>inputs = tokenizer(<span class="hljs-string">&quot;Hello, my dog is cute&quot;</span>, return_tensors=<span class="hljs-string">&quot;np&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs)

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># retrieve logts for next token</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>next_token_logits = outputs.logits[:, -<span class="hljs-number">1</span>]`,wrap:!1}}),{c(){r=i("p"),r.textContent=v,c=a(),h(u.$$.fragment)},l(p){r=l(p,"P",{"data-svelte-h":!0}),m(r)!=="svelte-11lpom8"&&(r.textContent=v),c=s(p),f(u.$$.fragment,p)},m(p,z){d(p,r,z),d(p,c,z),_(u,p,z),L=!0},p:A,i(p){L||(g(u.$$.fragment,p),L=!0)},o(p){b(u.$$.fragment,p),L=!1},d(p){p&&(o(r),o(c)),k(u,p)}}}function oi(y){let r,v,c,u,L,p,z,Ro,Ce,us='The LLaMA model was proposed in <a href="https://arxiv.org/abs/2302.13971" rel="nofollow">LLaMA: Open and Efficient Foundation Language Models</a> by Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, Aurelien Rodriguez, Armand Joulin, Edouard Grave, Guillaume Lample. It is a collection of foundation language models ranging from 7B to 65B parameters.',Oo,Fe,hs="The abstract from the paper is the following:",Xo,Pe,fs="<em>We introduce LLaMA, a collection of foundation language models ranging from 7B to 65B parameters. We train our models on trillions of tokens, and show that it is possible to train state-of-the-art models using publicly available datasets exclusively, without resorting to proprietary and inaccessible datasets. In particular, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA-65B is competitive with the best models, Chinchilla-70B and PaLM-540B. We release all our models to the research community.</em>",Qo,qe,_s='This model was contributed by <a href="https://huggingface.co/zphang" rel="nofollow">zphang</a> with contributions from <a href="https://huggingface.co/BlackSamorez" rel="nofollow">BlackSamorez</a>. The code of the implementation in Hugging Face is based on GPT-NeoX <a href="https://github.com/EleutherAI/gpt-neox" rel="nofollow">here</a>. The original code of the authors can be found <a href="https://github.com/facebookresearch/llama" rel="nofollow">here</a>.',Yo,Ie,Ko,je,gs='<li>Weights for the LLaMA models can be obtained from by filling out <a href="https://docs.google.com/forms/d/e/1FAIpQLSfqNECQnMkycAp2jP4Z9TFX0cGR4uf7b_fBxjY_OjhJILlKGA/viewform?usp=send_form" rel="nofollow">this form</a></li> <li>After downloading the weights, they will need to be converted to the Hugging Face Transformers format using the <a href="https://github.com/huggingface/transformers/blob/main/src/transformers/models/llama/convert_llama_weights_to_hf.py" rel="nofollow">conversion script</a>. The script can be called with the following (example) command:</li>',en,Ue,tn,We,bs="<li>After conversion, the model and tokenizer can be loaded via:</li>",on,Ae,nn,He,ks=`Note that executing the script requires enough CPU RAM to host the whole model in float16 precision (even if the biggest versions
come in several checkpoints they each contain a part of each weight of the model, so we need to load them all in RAM). For the 65B model, it’s thus 130GB of RAM needed.`,an,Ne,vs='<li>The LLaMA tokenizer is a BPE model based on <a href="https://github.com/google/sentencepiece" rel="nofollow">sentencepiece</a>. One quirk of sentencepiece is that when decoding a sequence, if the first token is the start of the word (e.g. “Banana”), the tokenizer does not prepend the prefix space to the string.</li>',sn,Je,ys='This model was contributed by <a href="https://huggingface.co/zphang" rel="nofollow">zphang</a> with contributions from <a href="https://huggingface.co/BlackSamorez" rel="nofollow">BlackSamorez</a>. The code of the implementation in Hugging Face is based on GPT-NeoX <a href="https://github.com/EleutherAI/gpt-neox" rel="nofollow">here</a>. The original code of the authors can be found <a href="https://github.com/facebookresearch/llama" rel="nofollow">here</a>. The Flax version of the implementation was contributed by <a href="https://huggingface.co/afmck" rel="nofollow">afmck</a> with the code in the implementation based on Hugging Face’s Flax GPT-Neo.',rn,Ge,Ts="Based on the original LLaMA model, Meta AI has released some follow-up works:",ln,Se,ws='<li><strong>Llama2</strong>: Llama2 is an improved version of Llama with some architectural tweaks (Grouped Query Attention), and is pre-trained on 2Trillion tokens. Refer to the documentation of Llama2 which can be found <a href="llama2">here</a>.</li>',dn,Be,cn,Ee,Ls="A list of official Hugging Face and community (indicated by 🌎) resources to help you get started with LLaMA. If you’re interested in submitting a resource to be included here, please feel free to open a Pull Request and we’ll review it! The resource should ideally demonstrate something new instead of duplicating an existing resource.",mn,De,pn,Ve,xs='<li>A <a href="https://colab.research.google.com/github/bigscience-workshop/petals/blob/main/examples/prompt-tuning-sst2.ipynb#scrollTo=f04ba4d2" rel="nofollow">notebook</a> on how to use prompt tuning to adapt the LLaMA model for text classification task. 🌎</li>',un,Ze,hn,Re,Ms='<li><a href="https://huggingface.co/blog/stackllama#stackllama-a-hands-on-guide-to-train-llama-with-rlhf" rel="nofollow">StackLLaMA: A hands-on guide to train LLaMA with RLHF</a>, a blog post about how to train LLaMA to answer questions on <a href="https://stackexchange.com/" rel="nofollow">Stack Exchange</a> with RLHF.</li>',fn,Oe,$s="⚗️ Optimization",_n,Xe,zs='<li>A <a href="https://colab.research.google.com/drive/1SQUXq1AMZPSLD4mk3A3swUIc6Y2dclme?usp=sharing" rel="nofollow">notebook</a> on how to fine-tune LLaMA model using xturing library on GPU which has limited memory. 🌎</li>',gn,Qe,Cs="⚡️ Inference",bn,Ye,Fs='<li>A <a href="https://colab.research.google.com/github/DominguesM/alpaca-lora-ptbr-7b/blob/main/notebooks/02%20-%20Evaluate.ipynb" rel="nofollow">notebook</a> on how to run the LLaMA Model using PeftModel from the 🤗 PEFT library. 🌎</li> <li>A <a href="https://colab.research.google.com/drive/1l2GiSSPbajVyp2Nk3CFT4t3uH6-5TiBe?usp=sharing" rel="nofollow">notebook</a> on how to load a PEFT adapter LLaMA model with LangChain. 🌎</li>',kn,Ke,Ps="🚀 Deploy",vn,et,qs='<li>A <a href="https://colab.research.google.com/github/lxe/simple-llama-finetuner/blob/master/Simple_LLaMA_FineTuner.ipynb#scrollTo=3PM_DilAZD8T" rel="nofollow">notebook</a> on how to fine-tune LLaMA model using LoRA method via the 🤗 PEFT library with intuitive UI. 🌎</li> <li>A <a href="https://github.com/aws/amazon-sagemaker-examples/blob/main/introduction_to_amazon_algorithms/jumpstart-foundation-models/text-generation-open-llama.ipynb" rel="nofollow">notebook</a> on how to deploy Open-LLaMA model for text generation on Amazon SageMaker. 🌎</li>',yn,tt,Tn,B,ot,En,Bt,Is=`This is the configuration class to store the configuration of a <a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaModel">LlamaModel</a>. It is used to instantiate an LLaMA
model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
defaults will yield a similar configuration to that of the LLaMA-7B.`,Dn,Et,js=`Configuration objects inherit from <a href="/docs/transformers/pr_31208/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> and can be used to control the model outputs. Read the
documentation from <a href="/docs/transformers/pr_31208/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Vn,le,wn,nt,Us="## LlamaTokenizer[[transformers.LlamaTokenizer]]",Ln,q,at,Zn,Dt,Ws=`Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding. The default padding token is unset as there is
no padding token in the original model.`,Rn,Vt,st,On,de,rt,Xn,Zt,As=`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> method.`,Qn,D,it,Yn,Rt,Hs="Creates a mask from the two sequences passed to be used in a sequence-pair classification task. An ALBERT",Kn,ce,ea,Ot,Ns="if token_ids_1 is None, only returns the first portion of the mask (0s).",ta,me,lt,oa,Xt,Js="Save the vocabulary and special tokens file to a directory.",xn,dt,Gs="## LlamaTokenizerFast[[transformers.LlamaTokenizerFast]]",Mn,x,ct,na,Qt,Ss="Construct a Llama tokenizer. Based on byte-level Byte-Pair-Encoding.",aa,Yt,Bs="This uses notably ByteFallback and no normalization.",sa,pe,ra,Kt,Es=`If you want to change the <code>bos_token</code> or the <code>eos_token</code>, make sure to specify them when initializing the model, or
call <code>tokenizer.update_post_processor()</code> to make sure that the post-processing is correctly done (otherwise the
values of the first token and final token of an encoded sequence will not be correct). For more details, checkout
[post-processors] (<a href="https://huggingface.co/docs/tokenizers/api/post-processors" rel="nofollow">https://huggingface.co/docs/tokenizers/api/post-processors</a>) documentation.`,ia,eo,Ds=`This tokenizer inherits from <a href="/docs/transformers/pr_31208/en/main_classes/tokenizer#transformers.PreTrainedTokenizerFast">PreTrainedTokenizerFast</a> which contains most of the main methods. Users should
refer to this superclass for more information regarding those methods.`,la,to,mt,da,ue,pt,ca,oo,Vs=`Retrieves sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> or <code>encode_plus</code> methods.`,ma,O,ut,pa,no,Zs=`Create the token type IDs corresponding to the sequences passed. <a href="../glossary#token-type-ids">What are token type
IDs?</a>`,ua,ao,Rs="Should be overridden in a subclass if the model has a special way of building those.",ha,he,ht,fa,so,Os="Updates the underlying post processor with the current <code>bos_token</code> and <code>eos_token</code>.",_a,ro,ft,$n,_t,Xs="## LlamaModel[[transformers.LlamaModel]]",zn,I,gt,ga,io,Qs=`The bare LLaMA Model outputting raw hidden-states without any specific head on top.
This model inherits from <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,ba,lo,Ys=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,ka,co,Ks="Transformer decoder consisting of <em>config.num_hidden_layers</em> layers. Each layer is a <code>LlamaDecoderLayer</code>",va,X,bt,ya,mo,er='The <a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaModel">LlamaModel</a> forward method, overrides the <code>__call__</code> special method.',Ta,fe,Cn,kt,tr="## LlamaForCausalLM[[transformers.LlamaForCausalLM]]",Fn,ae,vt,wa,V,yt,La,po,or='The <a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaForCausalLM">LlamaForCausalLM</a> forward method, overrides the <code>__call__</code> special method.',xa,_e,Ma,ge,Pn,Tt,nr="## LlamaForSequenceClassification[[transformers.LlamaForSequenceClassification]]",qn,C,wt,$a,uo,ar="The LLaMa Model transformer with a sequence classification head on top (linear layer).",za,ho,sr=`<a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> uses the last token in order to do the classification, as other causal models
(e.g. GPT-2) do.`,Ca,fo,rr=`Since it does classification on the last token, it requires to know the position of the last token. If a
<code>pad_token_id</code> is defined in the configuration, it finds the last token that is not a padding token in each row. If
no <code>pad_token_id</code> is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when <code>inputs_embeds</code> are passed instead of <code>input_ids</code>, it does the same (take the last value in
each row of the batch).`,Fa,_o,ir=`This model inherits from <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Pa,go,lr=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,qa,Q,Lt,Ia,bo,dr='The <a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaForSequenceClassification">LlamaForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',ja,be,In,xt,cr="## LlamaForQuestionAnswering[[transformers.LlamaForQuestionAnswering]]",jn,j,Mt,Ua,ko,mr=`The Llama Model transformer with a span classification head on top for extractive question-answering tasks like
SQuAD (a linear layer on top of the hidden-states output to compute <code>span start logits</code> and <code>span end logits</code>).`,Wa,vo,pr=`This model inherits from <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Aa,yo,ur=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Ha,Y,$t,Na,To,hr='The <a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaForQuestionAnswering">LlamaForQuestionAnswering</a> forward method, overrides the <code>__call__</code> special method.',Ja,ke,Un,zt,fr="## LlamaForTokenClassification[[transformers.LlamaForTokenClassification]]",Wn,U,Ct,Ga,wo,_r=`The Llama Model transformer with a token classification head on top (a linear layer on top of the hidden-states
output) e.g. for Named-Entity-Recognition (NER) tasks.`,Sa,Lo,gr=`This model inherits from <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Ba,xo,br=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,Ea,K,Ft,Da,Mo,kr='The <a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaForTokenClassification">LlamaForTokenClassification</a> forward method, overrides the <code>__call__</code> special method.',Va,ve,An,Pt,vr="## FlaxLlamaModel[[transformers.FlaxLlamaModel]]",Hn,F,qt,Za,$o,yr="The bare Llama Model transformer outputting raw hidden-states without any specific head on top.",Ra,zo,Tr=`This model inherits from <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,Oa,Co,wr=`This model is also a Flax Linen
<a href="https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html" rel="nofollow">flax.nn.Module</a> subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.`,Xa,Fo,Lr="Finally, this model supports inherent JAX features such as:",Qa,Po,xr='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',Ya,H,It,Ka,qo,Mr="The <code>FlaxLlamaPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",es,ye,ts,Te,os,we,Nn,jt,$r="## FlaxLlamaForCausalLM[[transformers.FlaxLlamaForCausalLM]]",Jn,P,Ut,ns,Io,zr="The Llama Model transformer with a language modeling head (linear layer) on top.",as,jo,Cr=`This model inherits from <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.FlaxPreTrainedModel">FlaxPreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,ss,Uo,Fr=`This model is also a Flax Linen
<a href="https://flax.readthedocs.io/en/latest/_autosummary/flax.nn.module.html" rel="nofollow">flax.nn.Module</a> subclass. Use it as a
regular Flax Module and refer to the Flax documentation for all matter related to general usage and behavior.`,rs,Wo,Pr="Finally, this model supports inherent JAX features such as:",is,Ao,qr='<li><a href="https://jax.readthedocs.io/en/latest/jax.html#just-in-time-compilation-jit" rel="nofollow">Just-In-Time (JIT) compilation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#automatic-differentiation" rel="nofollow">Automatic Differentiation</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#vectorization-vmap" rel="nofollow">Vectorization</a></li> <li><a href="https://jax.readthedocs.io/en/latest/jax.html#parallelization-pmap" rel="nofollow">Parallelization</a></li>',ls,N,Wt,ds,Ho,Ir="The <code>FlaxLlamaPreTrainedModel</code> forward method, overrides the <code>__call__</code> special method.",cs,Le,ms,xe,ps,Me,Gn,At,Sn,Bo,Bn;return L=new Zo({props:{title:"LLaMA",local:"llama",headingTag:"h1"}}),z=new Zo({props:{title:"Overview",local:"overview",headingTag:"h2"}}),Ie=new Zo({props:{title:"Usage tips",local:"usage-tips",headingTag:"h2"}}),Ue=new ze({props:{code:"cHl0aG9uJTIwc3JjJTJGdHJhbnNmb3JtZXJzJTJGbW9kZWxzJTJGbGxhbWElMkZjb252ZXJ0X2xsYW1hX3dlaWdodHNfdG9faGYucHklMjAlNUMlMEElMjAlMjAlMjAlMjAtLWlucHV0X2RpciUyMCUyRnBhdGglMkZ0byUyRmRvd25sb2FkZWQlMkZsbGFtYSUyRndlaWdodHMlMjAtLW1vZGVsX3NpemUlMjA3QiUyMC0tb3V0cHV0X2RpciUyMCUyRm91dHB1dCUyRnBhdGg=",highlighted:`python src/transformers/models/llama/convert_llama_weights_to_hf.py \\
    --input_dir /path/to/downloaded/llama/weights --model_size 7B --output_dir /output/path`,wrap:!1}}),Ae=new ze({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMExsYW1hRm9yQ2F1c2FsTE0lMkMlMjBMbGFtYVRva2VuaXplciUwQSUwQXRva2VuaXplciUyMCUzRCUyMExsYW1hVG9rZW5pemVyLmZyb21fcHJldHJhaW5lZCglMjIlMkZvdXRwdXQlMkZwYXRoJTIyKSUwQW1vZGVsJTIwJTNEJTIwTGxhbWFGb3JDYXVzYWxMTS5mcm9tX3ByZXRyYWluZWQoJTIyJTJGb3V0cHV0JTJGcGF0aCUyMik=",highlighted:`<span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> LlamaForCausalLM, LlamaTokenizer

tokenizer = LlamaTokenizer.from_pretrained(<span class="hljs-string">&quot;/output/path&quot;</span>)
model = LlamaForCausalLM.from_pretrained(<span class="hljs-string">&quot;/output/path&quot;</span>)`,wrap:!1}}),Be=new Zo({props:{title:"Resources",local:"resources",headingTag:"h2"}}),De=new jr({props:{pipeline:"text-classification"}}),Ze=new jr({props:{pipeline:"question-answering"}}),tt=new Zo({props:{title:"LlamaConfig",local:"transformers.LlamaConfig",headingTag:"h2"}}),ot=new M({props:{name:"class transformers.LlamaConfig",anchor:"transformers.LlamaConfig",parameters:[{name:"vocab_size",val:" = 32000"},{name:"hidden_size",val:" = 4096"},{name:"intermediate_size",val:" = 11008"},{name:"num_hidden_layers",val:" = 32"},{name:"num_attention_heads",val:" = 32"},{name:"num_key_value_heads",val:" = None"},{name:"hidden_act",val:" = 'silu'"},{name:"max_position_embeddings",val:" = 2048"},{name:"initializer_range",val:" = 0.02"},{name:"rms_norm_eps",val:" = 1e-06"},{name:"use_cache",val:" = True"},{name:"pad_token_id",val:" = None"},{name:"bos_token_id",val:" = 1"},{name:"eos_token_id",val:" = 2"},{name:"pretraining_tp",val:" = 1"},{name:"tie_word_embeddings",val:" = False"},{name:"rope_theta",val:" = 10000.0"},{name:"rope_scaling",val:" = None"},{name:"attention_bias",val:" = False"},{name:"attention_dropout",val:" = 0.0"},{name:"mlp_bias",val:" = False"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.LlamaConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32000) &#x2014;
Vocabulary size of the LLaMA model. Defines the number of different tokens that can be represented by the
<code>inputs_ids</code> passed when calling <a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaModel">LlamaModel</a>`,name:"vocab_size"},{anchor:"transformers.LlamaConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimension of the hidden representations.`,name:"hidden_size"},{anchor:"transformers.LlamaConfig.intermediate_size",description:`<strong>intermediate_size</strong> (<code>int</code>, <em>optional</em>, defaults to 11008) &#x2014;
Dimension of the MLP representations.`,name:"intermediate_size"},{anchor:"transformers.LlamaConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of hidden layers in the Transformer decoder.`,name:"num_hidden_layers"},{anchor:"transformers.LlamaConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of attention heads for each attention layer in the Transformer decoder.`,name:"num_attention_heads"},{anchor:"transformers.LlamaConfig.num_key_value_heads",description:`<strong>num_key_value_heads</strong> (<code>int</code>, <em>optional</em>) &#x2014;
This is the number of key_value heads that should be used to implement Grouped Query Attention. If
<code>num_key_value_heads=num_attention_heads</code>, the model will use Multi Head Attention (MHA), if
<code>num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed by meanpooling all the original heads within that group. For more details checkout [this paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to </code>num_attention_heads\`.`,name:"num_key_value_heads"},{anchor:"transformers.LlamaConfig.hidden_act",description:`<strong>hidden_act</strong> (<code>str</code> or <code>function</code>, <em>optional</em>, defaults to <code>&quot;silu&quot;</code>) &#x2014;
The non-linear activation function (function or string) in the decoder.`,name:"hidden_act"},{anchor:"transformers.LlamaConfig.max_position_embeddings",description:`<strong>max_position_embeddings</strong> (<code>int</code>, <em>optional</em>, defaults to 2048) &#x2014;
The maximum sequence length that this model might ever be used with. Llama 1 supports up to 2048 tokens,
Llama 2 up to 4096, CodeLlama up to 16384.`,name:"max_position_embeddings"},{anchor:"transformers.LlamaConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to 0.02) &#x2014;
The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.LlamaConfig.rms_norm_eps",description:`<strong>rms_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-06) &#x2014;
The epsilon used by the rms normalization layers.`,name:"rms_norm_eps"},{anchor:"transformers.LlamaConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not the model should return the last key/values attentions (not used by all models). Only
relevant if <code>config.is_decoder=True</code>.`,name:"use_cache"},{anchor:"transformers.LlamaConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) &#x2014;
Padding token id.`,name:"pad_token_id"},{anchor:"transformers.LlamaConfig.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Beginning of stream token id.`,name:"bos_token_id"},{anchor:"transformers.LlamaConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>int</code>, <em>optional</em>, defaults to 2) &#x2014;
End of stream token id.`,name:"eos_token_id"},{anchor:"transformers.LlamaConfig.pretraining_tp",description:`<strong>pretraining_tp</strong> (<code>int</code>, <em>optional</em>, defaults to 1) &#x2014;
Experimental feature. Tensor parallelism rank used during pretraining. Please refer to <a href="https://huggingface.co/docs/transformers/main/perf_train_gpu_many#tensor-parallelism" rel="nofollow">this
document</a> to understand more about it. This value is
necessary to ensure exact reproducibility of the pretraining results. Please refer to <a href="https://github.com/pytorch/pytorch/issues/76232" rel="nofollow">this
issue</a>.`,name:"pretraining_tp"},{anchor:"transformers.LlamaConfig.tie_word_embeddings",description:`<strong>tie_word_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to tie weight embeddings`,name:"tie_word_embeddings"},{anchor:"transformers.LlamaConfig.rope_theta",description:`<strong>rope_theta</strong> (<code>float</code>, <em>optional</em>, defaults to 10000.0) &#x2014;
The base period of the RoPE embeddings.`,name:"rope_theta"},{anchor:"transformers.LlamaConfig.rope_scaling",description:`<strong>rope_scaling</strong> (<code>Dict</code>, <em>optional</em>) &#x2014;
Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports two scaling
strategies: linear and dynamic. Their scaling factor must be a float greater than 1. The expected format is
<code>{&quot;type&quot;: strategy name, &quot;factor&quot;: scaling factor}</code>. When using this flag, don&#x2019;t update
<code>max_position_embeddings</code> to the expected new maximum. See the following thread for more information on how
these scaling strategies behave:
<a href="https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/" rel="nofollow">https://www.reddit.com/r/LocalLLaMA/comments/14mrgpr/dynamically_scaled_rope_further_increases/</a>. This is an
experimental feature, subject to breaking API changes in future versions.`,name:"rope_scaling"},{anchor:"transformers.LlamaConfig.attention_bias",description:`<strong>attention_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use a bias in the query, key, value and output projection layers during self-attention.`,name:"attention_bias"},{anchor:"transformers.LlamaConfig.attention_dropout",description:`<strong>attention_dropout</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout ratio for the attention probabilities.`,name:"attention_dropout"},{anchor:"transformers.LlamaConfig.mlp_bias",description:`<strong>mlp_bias</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to use a bias in up_proj, down_proj and gate_proj layers in the MLP layers.`,name:"mlp_bias"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/configuration_llama.py#L29"}}),le=new So({props:{anchor:"transformers.LlamaConfig.example",$$slots:{default:[Gr]},$$scope:{ctx:y}}}),at=new M({props:{name:"class transformers.LlamaTokenizer",anchor:"transformers.LlamaTokenizer",parameters:[{name:"vocab_file",val:""},{name:"unk_token",val:" = '<unk>'"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"pad_token",val:" = None"},{name:"sp_model_kwargs",val:": Optional = None"},{name:"add_bos_token",val:" = True"},{name:"add_eos_token",val:" = False"},{name:"clean_up_tokenization_spaces",val:" = False"},{name:"use_default_system_prompt",val:" = False"},{name:"spaces_between_special_tokens",val:" = False"},{name:"legacy",val:" = None"},{name:"add_prefix_space",val:" = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.LlamaTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.LlamaTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.LlamaTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.`,name:"bos_token"},{anchor:"transformers.LlamaTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.LlamaTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>) &#x2014;
A special token used to make arrays of tokens the same size for batching purpose. Will then be ignored by
attention mechanisms or loss computation.`,name:"pad_token"},{anchor:"transformers.LlamaTokenizer.sp_model_kwargs",description:`<strong>sp_model_kwargs</strong> (<code>Dict[str, Any]</code>, <code>Optional</code>, <em>optional</em>) &#x2014;
Will be passed to the <code>SentencePieceProcessor.__init__()</code> method. The <a href="https://github.com/google/sentencepiece/tree/master/python" rel="nofollow">Python wrapper for
SentencePiece</a> can be used, among other things,
to set:</p>
<ul>
<li>
<p><code>enable_sampling</code>: Enable subword regularization.</p>
</li>
<li>
<p><code>nbest_size</code>: Sampling parameters for unigram. Invalid for BPE-Dropout.</p>
<ul>
<li><code>nbest_size = {0,1}</code>: No sampling is performed.</li>
<li><code>nbest_size &gt; 1</code>: samples from the nbest_size results.</li>
<li><code>nbest_size &lt; 0</code>: assuming that nbest_size is infinite and samples from the all hypothesis (lattice)
using forward-filtering-and-backward-sampling algorithm.</li>
</ul>
</li>
<li>
<p><code>alpha</code>: Smoothing parameter for unigram sampling, and dropout probability of merge operations for
BPE-dropout.</p>
</li>
</ul>`,name:"sp_model_kwargs"},{anchor:"transformers.LlamaTokenizer.add_bos_token",description:`<strong>add_bos_token</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to add an <code>bos_token</code> at the start of sequences.`,name:"add_bos_token"},{anchor:"transformers.LlamaTokenizer.add_eos_token",description:`<strong>add_eos_token</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to add an <code>eos_token</code> at the end of sequences.`,name:"add_eos_token"},{anchor:"transformers.LlamaTokenizer.clean_up_tokenization_spaces",description:`<strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.`,name:"clean_up_tokenization_spaces"},{anchor:"transformers.LlamaTokenizer.use_default_system_prompt",description:`<strong>use_default_system_prompt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the default system prompt for Llama should be used.`,name:"use_default_system_prompt"},{anchor:"transformers.LlamaTokenizer.spaces_between_special_tokens",description:`<strong>spaces_between_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to add spaces between special tokens.`,name:"spaces_between_special_tokens"},{anchor:"transformers.LlamaTokenizer.legacy",description:`<strong>legacy</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not the <code>legacy</code> behavior of the tokenizer should be used. Legacy is before the merge of #24622
and #25224 which includes fixes to properly handle tokens that appear after special tokens.
Make sure to also set <code>from_slow</code> to <code>True</code>.
A simple example:</p>
<ul>
<li><code>legacy=True</code>:</li>
</ul>`,name:"legacy"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/tokenization_llama.py#L56"}}),st=new M({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.LlamaTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:""},{name:"token_ids_1",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/tokenization_llama.py#L333"}}),rt=new M({props:{name:"get_special_tokens_mask",anchor:"transformers.LlamaTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.LlamaTokenizer.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of IDs.`,name:"token_ids_0"},{anchor:"transformers.LlamaTokenizer.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"},{anchor:"transformers.LlamaTokenizer.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/tokenization_llama.py#L344",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of integers in the range [0, 1]: 1 for a special token, 0 for a sequence token.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),it=new M({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.LlamaTokenizer.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.LlamaTokenizer.create_token_type_ids_from_sequences.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of ids.`,name:"token_ids_0"},{anchor:"transformers.LlamaTokenizer.create_token_type_ids_from_sequences.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
Optional second list of IDs for sequence pairs.`,name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/tokenization_llama.py#L381",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List of <a href="../glossary#token-type-ids">token type IDs</a> according to the given sequence(s).</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),ce=new So({props:{anchor:"transformers.LlamaTokenizer.create_token_type_ids_from_sequences.example",$$slots:{default:[Sr]},$$scope:{ctx:y}}}),lt=new M({props:{name:"save_vocabulary",anchor:"transformers.LlamaTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:""},{name:"filename_prefix",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.LlamaTokenizer.save_vocabulary.save_directory",description:`<strong>save_directory</strong> (<code>str</code>) &#x2014;
The directory in which to save the vocabulary.`,name:"save_directory"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/tokenization_llama.py#L306",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Paths to the files saved.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>Tuple(str)</code></p>
`}}),ct=new M({props:{name:"class transformers.LlamaTokenizerFast",anchor:"transformers.LlamaTokenizerFast",parameters:[{name:"vocab_file",val:" = None"},{name:"tokenizer_file",val:" = None"},{name:"clean_up_tokenization_spaces",val:" = False"},{name:"unk_token",val:" = '<unk>'"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"add_bos_token",val:" = True"},{name:"add_eos_token",val:" = False"},{name:"use_default_system_prompt",val:" = False"},{name:"legacy",val:" = None"},{name:"add_prefix_space",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.LlamaTokenizerFast.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
<a href="https://github.com/google/sentencepiece" rel="nofollow">SentencePiece</a> file (generally has a .model extension) that
contains the vocabulary necessary to instantiate a tokenizer.`,name:"vocab_file"},{anchor:"transformers.LlamaTokenizerFast.tokenizer_file",description:`<strong>tokenizer_file</strong> (<code>str</code>, <em>optional</em>) &#x2014;
<a href="https://github.com/huggingface/tokenizers" rel="nofollow">tokenizers</a> file (generally has a .json extension) that
contains everything needed to load the tokenizer.`,name:"tokenizer_file"},{anchor:"transformers.LlamaTokenizerFast.clean_up_tokenization_spaces",description:`<strong>clean_up_tokenization_spaces</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to cleanup spaces after decoding, cleanup consists in removing potential artifacts like
extra spaces.`,name:"clean_up_tokenization_spaces"},{anchor:"transformers.LlamaTokenizerFast.unk_token",description:`<strong>unk_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
token instead.`,name:"unk_token"},{anchor:"transformers.LlamaTokenizerFast.bos_token",description:`<strong>bos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The beginning of sequence token that was used during pretraining. Can be used a sequence classifier token.`,name:"bos_token"},{anchor:"transformers.LlamaTokenizerFast.eos_token",description:`<strong>eos_token</strong> (<code>str</code> or <code>tokenizers.AddedToken</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.LlamaTokenizerFast.add_bos_token",description:`<strong>add_bos_token</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether or not to add an <code>bos_token</code> at the start of sequences.`,name:"add_bos_token"},{anchor:"transformers.LlamaTokenizerFast.add_eos_token",description:`<strong>add_eos_token</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not to add an <code>eos_token</code> at the end of sequences.`,name:"add_eos_token"},{anchor:"transformers.LlamaTokenizerFast.use_default_system_prompt",description:`<strong>use_default_system_prompt</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the default system prompt for Llama should be used`,name:"use_default_system_prompt"},{anchor:"transformers.LlamaTokenizerFast.legacy",description:`<strong>legacy</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not the <code>legacy</code> behavior of the tokenizer should be used. Legacy is before the merge of #24622
and #25224 which includes fixes to properly handle tokens that appear after special tokens.
Make sure to also set <code>from_slow</code> to <code>True</code>.
A simple example:</p>
<ul>
<li><code>legacy=True</code>:</li>
</ul>`,name:"legacy"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/tokenization_llama_fast.py#L49"}}),pe=new So({props:{anchor:"transformers.LlamaTokenizerFast.example",$$slots:{default:[Br]},$$scope:{ctx:y}}}),mt=new M({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.LlamaTokenizerFast.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:""},{name:"token_ids_1",val:" = None"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/tokenization_llama_fast.py#L300"}}),pt=new M({props:{name:"get_special_tokens_mask",anchor:"transformers.LlamaTokenizerFast.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.LlamaTokenizerFast.get_special_tokens_mask.token_ids_0",description:`<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014;
List of ids of the first sequence.`,name:"token_ids_0"},{anchor:"transformers.LlamaTokenizerFast.get_special_tokens_mask.token_ids_1",description:`<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014;
List of ids of the second sequence.`,name:"token_ids_1"},{anchor:"transformers.LlamaTokenizerFast.get_special_tokens_mask.already_has_special_tokens",description:`<strong>already_has_special_tokens</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether or not the token list is already formatted with special tokens for the model.`,name:"already_has_special_tokens"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/tokenization_utils_base.py#L3918",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>1 for a special token, 0 for a sequence token.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A list of integers in the range [0, 1]</p>
`}}),ut=new M({props:{name:"create_token_type_ids_from_sequences",anchor:"transformers.LlamaTokenizerFast.create_token_type_ids_from_sequences",parameters:[{name:"token_ids_0",val:": List"},{name:"token_ids_1",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.LlamaTokenizerFast.create_token_type_ids_from_sequences.token_ids_0",description:"<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014; The first tokenized sequence.",name:"token_ids_0"},{anchor:"transformers.LlamaTokenizerFast.create_token_type_ids_from_sequences.token_ids_1",description:"<strong>token_ids_1</strong> (<code>List[int]</code>, <em>optional</em>) &#x2014; The second tokenized sequence.",name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/tokenization_utils_base.py#L3448",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The token type ids.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),ht=new M({props:{name:"update_post_processor",anchor:"transformers.LlamaTokenizerFast.update_post_processor",parameters:[],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/tokenization_llama_fast.py#L180"}}),ft=new M({props:{name:"save_vocabulary",anchor:"transformers.LlamaTokenizerFast.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": Optional = None"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/tokenization_llama_fast.py#L224"}}),gt=new M({props:{name:"class transformers.LlamaModel",anchor:"transformers.LlamaModel",parameters:[{name:"config",val:": LlamaConfig"}],parametersDescription:[{anchor:"transformers.LlamaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.
config &#x2014; LlamaConfig`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L857"}}),bt=new M({props:{name:"forward",anchor:"transformers.LlamaModel.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"past_key_values",val:": Union = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"},{name:"cache_position",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.LlamaModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.LlamaModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>input_ids</code> have to be input (see
<code>past_key_values</code>).</p>
<p>If you want to change padding behavior, you should read <code>modeling_opt._prepare_decoder_attention_mask</code>
and modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the paper</a> for more
information on the default strategy.</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"attention_mask"},{anchor:"transformers.LlamaModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.LlamaModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Cache</code> or <code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>) &#x2014;
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the <code>past_key_values</code>
returned by the model at a previous stage of decoding, when <code>use_cache=True</code> or <code>config.use_cache=True</code>.</p>
<p>Two formats are allowed:</p>
<ul>
<li>a <a href="/docs/transformers/pr_31208/en/internal/generation_utils#transformers.Cache">Cache</a> instance;</li>
<li>Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of
shape <code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>). This is also known as the legacy
cache format.</li>
</ul>
<p>The model will output the same cache format that is fed as input. If no <code>past_key_values</code> are passed, the
legacy cache format will be returned.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>input_ids</code> (those that don&#x2019;t
have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all <code>input_ids</code>
of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.LlamaModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.LlamaModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.LlamaModel.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.LlamaModel.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.LlamaModel.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.LlamaModel.forward.cache_position",description:`<strong>cache_position</strong> (<code>torch.LongTensor</code> of shape <code>(sequence_length)</code>, <em>optional</em>) &#x2014;
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to <code>position_ids</code>,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.`,name:"cache_position"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L890"}}),fe=new ie({props:{$$slots:{default:[Er]},$$scope:{ctx:y}}}),vt=new M({props:{name:"class transformers.LlamaForCausalLM",anchor:"transformers.LlamaForCausalLM",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L1084"}}),yt=new M({props:{name:"forward",anchor:"transformers.LlamaForCausalLM.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"past_key_values",val:": Union = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"},{name:"cache_position",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.LlamaForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.LlamaForCausalLM.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>input_ids</code> have to be input (see
<code>past_key_values</code>).</p>
<p>If you want to change padding behavior, you should read <code>modeling_opt._prepare_decoder_attention_mask</code>
and modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the paper</a> for more
information on the default strategy.</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"attention_mask"},{anchor:"transformers.LlamaForCausalLM.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.LlamaForCausalLM.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Cache</code> or <code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>) &#x2014;
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the <code>past_key_values</code>
returned by the model at a previous stage of decoding, when <code>use_cache=True</code> or <code>config.use_cache=True</code>.</p>
<p>Two formats are allowed:</p>
<ul>
<li>a <a href="/docs/transformers/pr_31208/en/internal/generation_utils#transformers.Cache">Cache</a> instance;</li>
<li>Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of
shape <code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>). This is also known as the legacy
cache format.</li>
</ul>
<p>The model will output the same cache format that is fed as input. If no <code>past_key_values</code> are passed, the
legacy cache format will be returned.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>input_ids</code> (those that don&#x2019;t
have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all <code>input_ids</code>
of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.LlamaForCausalLM.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.LlamaForCausalLM.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.LlamaForCausalLM.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.LlamaForCausalLM.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.LlamaForCausalLM.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.LlamaForCausalLM.forward.cache_position",description:`<strong>cache_position</strong> (<code>torch.LongTensor</code> of shape <code>(sequence_length)</code>, <em>optional</em>) &#x2014;
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to <code>position_ids</code>,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.</p>
<p>Args &#x2014;
labels (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>):
Labels for computing the masked language modeling loss. Indices should either be in <code>[0, ..., config.vocab_size]</code> or -100 (see <code>input_ids</code> docstring). Tokens with indices set to <code>-100</code> are ignored
(masked), the loss is only computed for the tokens with labels in <code>[0, ..., config.vocab_size]</code>.`,name:"cache_position"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L1114",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_31208/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast"
>transformers.modeling_outputs.CausalLMOutputWithPast</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaConfig"
>LlamaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Language modeling loss (for next-token prediction).</p>
</li>
<li>
<p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>past_key_values</strong> (<code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of shape
<code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>)</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
<code>past_key_values</code> input) to speed up sequential decoding.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_31208/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast"
>transformers.modeling_outputs.CausalLMOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),_e=new ie({props:{$$slots:{default:[Dr]},$$scope:{ctx:y}}}),ge=new So({props:{anchor:"transformers.LlamaForCausalLM.forward.example",$$slots:{default:[Vr]},$$scope:{ctx:y}}}),wt=new M({props:{name:"class transformers.LlamaForSequenceClassification",anchor:"transformers.LlamaForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.LlamaForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L1293"}}),Lt=new M({props:{name:"forward",anchor:"transformers.LlamaForSequenceClassification.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"past_key_values",val:": Union = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.LlamaForSequenceClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.LlamaForSequenceClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>input_ids</code> have to be input (see
<code>past_key_values</code>).</p>
<p>If you want to change padding behavior, you should read <code>modeling_opt._prepare_decoder_attention_mask</code>
and modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the paper</a> for more
information on the default strategy.</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"attention_mask"},{anchor:"transformers.LlamaForSequenceClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.LlamaForSequenceClassification.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Cache</code> or <code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>) &#x2014;
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the <code>past_key_values</code>
returned by the model at a previous stage of decoding, when <code>use_cache=True</code> or <code>config.use_cache=True</code>.</p>
<p>Two formats are allowed:</p>
<ul>
<li>a <a href="/docs/transformers/pr_31208/en/internal/generation_utils#transformers.Cache">Cache</a> instance;</li>
<li>Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of
shape <code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>). This is also known as the legacy
cache format.</li>
</ul>
<p>The model will output the same cache format that is fed as input. If no <code>past_key_values</code> are passed, the
legacy cache format will be returned.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>input_ids</code> (those that don&#x2019;t
have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all <code>input_ids</code>
of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.LlamaForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.LlamaForSequenceClassification.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.LlamaForSequenceClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.LlamaForSequenceClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.LlamaForSequenceClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.LlamaForSequenceClassification.forward.cache_position",description:`<strong>cache_position</strong> (<code>torch.LongTensor</code> of shape <code>(sequence_length)</code>, <em>optional</em>) &#x2014;
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to <code>position_ids</code>,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.`,name:"cache_position"},{anchor:"transformers.LlamaForSequenceClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L1324"}}),be=new ie({props:{$$slots:{default:[Zr]},$$scope:{ctx:y}}}),Mt=new M({props:{name:"class transformers.LlamaForQuestionAnswering",anchor:"transformers.LlamaForQuestionAnswering",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.LlamaForQuestionAnswering.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L1416"}}),$t=new M({props:{name:"forward",anchor:"transformers.LlamaForQuestionAnswering.forward",parameters:[{name:"input_ids",val:": Optional = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"past_key_values",val:": Union = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"start_positions",val:": Optional = None"},{name:"end_positions",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.LlamaForQuestionAnswering.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.LlamaForQuestionAnswering.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>input_ids</code> have to be input (see
<code>past_key_values</code>).</p>
<p>If you want to change padding behavior, you should read <code>modeling_opt._prepare_decoder_attention_mask</code>
and modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the paper</a> for more
information on the default strategy.</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"attention_mask"},{anchor:"transformers.LlamaForQuestionAnswering.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.LlamaForQuestionAnswering.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Cache</code> or <code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>) &#x2014;
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the <code>past_key_values</code>
returned by the model at a previous stage of decoding, when <code>use_cache=True</code> or <code>config.use_cache=True</code>.</p>
<p>Two formats are allowed:</p>
<ul>
<li>a <a href="/docs/transformers/pr_31208/en/internal/generation_utils#transformers.Cache">Cache</a> instance;</li>
<li>Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of
shape <code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>). This is also known as the legacy
cache format.</li>
</ul>
<p>The model will output the same cache format that is fed as input. If no <code>past_key_values</code> are passed, the
legacy cache format will be returned.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>input_ids</code> (those that don&#x2019;t
have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all <code>input_ids</code>
of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.LlamaForQuestionAnswering.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.LlamaForQuestionAnswering.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.LlamaForQuestionAnswering.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.LlamaForQuestionAnswering.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.LlamaForQuestionAnswering.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.LlamaForQuestionAnswering.forward.cache_position",description:`<strong>cache_position</strong> (<code>torch.LongTensor</code> of shape <code>(sequence_length)</code>, <em>optional</em>) &#x2014;
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to <code>position_ids</code>,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.`,name:"cache_position"},{anchor:"transformers.LlamaForQuestionAnswering.forward.start_positions",description:`<strong>start_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the start of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"start_positions"},{anchor:"transformers.LlamaForQuestionAnswering.forward.end_positions",description:`<strong>end_positions</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for position (index) of the end of the labelled span for computing the token classification loss.
Positions are clamped to the length of the sequence (<code>sequence_length</code>). Position outside of the sequence
are not taken into account for computing the loss.`,name:"end_positions"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L1441"}}),ke=new ie({props:{$$slots:{default:[Rr]},$$scope:{ctx:y}}}),Ct=new M({props:{name:"class transformers.LlamaForTokenClassification",anchor:"transformers.LlamaForTokenClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.LlamaForTokenClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a>) &#x2014;
Model configuration class with all the parameters of the model. Initializing with a config file does not
load the weights associated with the model, only the configuration. Check out the
<a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L1515"}}),Ft=new M({props:{name:"forward",anchor:"transformers.LlamaForTokenClassification.forward",parameters:[{name:"input_ids",val:": LongTensor = None"},{name:"attention_mask",val:": Optional = None"},{name:"position_ids",val:": Optional = None"},{name:"past_key_values",val:": Optional = None"},{name:"inputs_embeds",val:": Optional = None"},{name:"labels",val:": Optional = None"},{name:"use_cache",val:": Optional = None"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.LlamaForTokenClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.LlamaForTokenClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>input_ids</code> have to be input (see
<code>past_key_values</code>).</p>
<p>If you want to change padding behavior, you should read <code>modeling_opt._prepare_decoder_attention_mask</code>
and modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the paper</a> for more
information on the default strategy.</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"attention_mask"},{anchor:"transformers.LlamaForTokenClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.LlamaForTokenClassification.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>Cache</code> or <code>tuple(tuple(torch.FloatTensor))</code>, <em>optional</em>) &#x2014;
Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used to speed up sequential decoding. This typically consists in the <code>past_key_values</code>
returned by the model at a previous stage of decoding, when <code>use_cache=True</code> or <code>config.use_cache=True</code>.</p>
<p>Two formats are allowed:</p>
<ul>
<li>a <a href="/docs/transformers/pr_31208/en/internal/generation_utils#transformers.Cache">Cache</a> instance;</li>
<li>Tuple of <code>tuple(torch.FloatTensor)</code> of length <code>config.n_layers</code>, with each tuple having 2 tensors of
shape <code>(batch_size, num_heads, sequence_length, embed_size_per_head)</code>). This is also known as the legacy
cache format.</li>
</ul>
<p>The model will output the same cache format that is fed as input. If no <code>past_key_values</code> are passed, the
legacy cache format will be returned.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>input_ids</code> (those that don&#x2019;t
have their past key value states given to this model) of shape <code>(batch_size, 1)</code> instead of all <code>input_ids</code>
of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.LlamaForTokenClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) &#x2014;
Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
model&#x2019;s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.LlamaForTokenClassification.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.LlamaForTokenClassification.forward.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.LlamaForTokenClassification.forward.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.LlamaForTokenClassification.forward.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.LlamaForTokenClassification.forward.cache_position",description:`<strong>cache_position</strong> (<code>torch.LongTensor</code> of shape <code>(sequence_length)</code>, <em>optional</em>) &#x2014;
Indices depicting the position of the input sequence tokens in the sequence. Contrarily to <code>position_ids</code>,
this tensor is not affected by padding. It is used to update the cache in the correct position and to infer
the complete sequence length.`,name:"cache_position"},{anchor:"transformers.LlamaForTokenClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) &#x2014;
Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
<code>config.num_labels &gt; 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_llama.py#L1545"}}),ve=new ie({props:{$$slots:{default:[Or]},$$scope:{ctx:y}}}),qt=new M({props:{name:"class transformers.FlaxLlamaModel",anchor:"transformers.FlaxLlamaModel",parameters:[{name:"config",val:": LlamaConfig"},{name:"input_shape",val:": Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxLlamaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxLlamaModel.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code>, or
<code>jax.numpy.bfloat16</code>.</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_flax_llama.py#L646"}}),It=new M({props:{name:"__call__",anchor:"transformers.FlaxLlamaModel.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"position_ids",val:" = None"},{name:"params",val:": dict = None"},{name:"past_key_values",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlaxLlamaModel.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, input_ids_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxLlamaModel.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).</p>
<p>If you want to change padding behavior, you should read <code>modeling_opt._prepare_decoder_attention_mask</code>
and modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the paper</a> for more
information on the default strategy.</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"attention_mask"},{anchor:"transformers.FlaxLlamaModel.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.FlaxLlamaModel.__call__.past_key_values",description:`<strong>past_key_values</strong> (<code>Dict[str, np.ndarray]</code>, <em>optional</em>, returned by <code>init_cache</code> or when passing previous <code>past_key_values</code>) &#x2014;
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape <em>[batch_size, max_length]</em>.`,name:"past_key_values"},{anchor:"transformers.FlaxLlamaModel.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxLlamaModel.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxLlamaModel.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_flax_llama.py#L470",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_31208/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput"
>transformers.modeling_flax_outputs.FlaxBaseModelOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaConfig"
>LlamaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_31208/en/main_classes/output#transformers.modeling_flax_outputs.FlaxBaseModelOutput"
>transformers.modeling_flax_outputs.FlaxBaseModelOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),ye=new ie({props:{$$slots:{default:[Xr]},$$scope:{ctx:y}}}),Te=new ie({props:{warning:!0,$$slots:{default:[Qr]},$$scope:{ctx:y}}}),we=new So({props:{anchor:"transformers.FlaxLlamaModel.__call__.example",$$slots:{default:[Yr]},$$scope:{ctx:y}}}),Ut=new M({props:{name:"class transformers.FlaxLlamaForCausalLM",anchor:"transformers.FlaxLlamaForCausalLM",parameters:[{name:"config",val:": LlamaConfig"},{name:"input_shape",val:": Tuple = (1, 1)"},{name:"seed",val:": int = 0"},{name:"dtype",val:": dtype = <class 'jax.numpy.float32'>"},{name:"_do_init",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.FlaxLlamaForCausalLM.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaConfig">LlamaConfig</a>) &#x2014; Model configuration class with all the parameters of the model.
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.FlaxPreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"},{anchor:"transformers.FlaxLlamaForCausalLM.dtype",description:`<strong>dtype</strong> (<code>jax.numpy.dtype</code>, <em>optional</em>, defaults to <code>jax.numpy.float32</code>) &#x2014;
The data type of the computation. Can be one of <code>jax.numpy.float32</code>, <code>jax.numpy.float16</code>, or
<code>jax.numpy.bfloat16</code>.</p>
<p>This can be used to enable mixed-precision training or half-precision inference on GPUs or TPUs. If
specified all the computation will be performed with the given <code>dtype</code>.</p>
<p><strong>Note that this only specifies the dtype of the computation and does not influence the dtype of model
parameters.</strong></p>
<p>If you wish to change the dtype of the model parameters, see <a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.FlaxPreTrainedModel.to_fp16">to_fp16()</a> and
<a href="/docs/transformers/pr_31208/en/main_classes/model#transformers.FlaxPreTrainedModel.to_bf16">to_bf16()</a>.`,name:"dtype"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_flax_llama.py#L707"}}),Wt=new M({props:{name:"__call__",anchor:"transformers.FlaxLlamaForCausalLM.__call__",parameters:[{name:"input_ids",val:""},{name:"attention_mask",val:" = None"},{name:"position_ids",val:" = None"},{name:"params",val:": dict = None"},{name:"past_key_values",val:": dict = None"},{name:"dropout_rng",val:": PRNGKey = None"},{name:"train",val:": bool = False"},{name:"output_attentions",val:": Optional = None"},{name:"output_hidden_states",val:": Optional = None"},{name:"return_dict",val:": Optional = None"}],parametersDescription:[{anchor:"transformers.FlaxLlamaForCausalLM.__call__.input_ids",description:`<strong>input_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, input_ids_length)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary. Padding will be ignored by default should you provide
it.</p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.FlaxLlamaForCausalLM.__call__.attention_mask",description:`<strong>attention_mask</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
<ul>
<li>1 for tokens that are <strong>not masked</strong>,</li>
<li>0 for tokens that are <strong>masked</strong>.</li>
</ul>
<p><a href="../glossary#attention-mask">What are attention masks?</a></p>
<p>Indices can be obtained using <a href="/docs/transformers/pr_31208/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/pr_31208/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p>If <code>past_key_values</code> is used, optionally only the last <code>decoder_input_ids</code> have to be input (see
<code>past_key_values</code>).</p>
<p>If you want to change padding behavior, you should read <code>modeling_opt._prepare_decoder_attention_mask</code>
and modify to your needs. See diagram 1 in <a href="https://arxiv.org/abs/1910.13461" rel="nofollow">the paper</a> for more
information on the default strategy.</p>
<ul>
<li>1 indicates the head is <strong>not masked</strong>,</li>
<li>0 indicates the head is <strong>masked</strong>.</li>
</ul>`,name:"attention_mask"},{anchor:"transformers.FlaxLlamaForCausalLM.__call__.position_ids",description:`<strong>position_ids</strong> (<code>numpy.ndarray</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) &#x2014;
Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.FlaxLlamaForCausalLM.__call__.past_key_values",description:`<strong>past_key_values</strong> (<code>Dict[str, np.ndarray]</code>, <em>optional</em>, returned by <code>init_cache</code> or when passing previous <code>past_key_values</code>) &#x2014;
Dictionary of pre-computed hidden-states (key and values in the attention blocks) that can be used for fast
auto-regressive decoding. Pre-computed key and value hidden-states are of shape <em>[batch_size, max_length]</em>.`,name:"past_key_values"},{anchor:"transformers.FlaxLlamaForCausalLM.__call__.output_attentions",description:`<strong>output_attentions</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the attentions tensors of all attention layers. See <code>attentions</code> under returned
tensors for more detail.`,name:"output_attentions"},{anchor:"transformers.FlaxLlamaForCausalLM.__call__.output_hidden_states",description:`<strong>output_hidden_states</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return the hidden states of all layers. See <code>hidden_states</code> under returned tensors for
more detail.`,name:"output_hidden_states"},{anchor:"transformers.FlaxLlamaForCausalLM.__call__.return_dict",description:`<strong>return_dict</strong> (<code>bool</code>, <em>optional</em>) &#x2014;
Whether or not to return a <a href="/docs/transformers/pr_31208/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/vr_31208/src/transformers/models/llama/modeling_flax_llama.py#L470",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/pr_31208/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or a tuple of
<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
elements depending on the configuration (<a
  href="/docs/transformers/pr_31208/en/model_doc/llama#transformers.LlamaConfig"
>LlamaConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>logits</strong> (<code>jnp.ndarray</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p>
</li>
<li>
<p><strong>hidden_states</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>jnp.ndarray</code> (one for the output of the embeddings + one for the output of each layer) of shape
<code>(batch_size, sequence_length, hidden_size)</code>.</p>
<p>Hidden-states of the model at the output of each layer plus the initial embedding outputs.</p>
</li>
<li>
<p><strong>attentions</strong> (<code>tuple(jnp.ndarray)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>jnp.ndarray</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p>
<p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
heads.</p>
</li>
</ul>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><a
  href="/docs/transformers/pr_31208/en/main_classes/output#transformers.modeling_flax_outputs.FlaxMaskedLMOutput"
>transformers.modeling_flax_outputs.FlaxMaskedLMOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Le=new ie({props:{$$slots:{default:[Kr]},$$scope:{ctx:y}}}),xe=new ie({props:{warning:!0,$$slots:{default:[ei]},$$scope:{ctx:y}}}),Me=new So({props:{anchor:"transformers.FlaxLlamaForCausalLM.__call__.example",$$slots:{default:[ti]},$$scope:{ctx:y}}}),At=new Jr({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/llama.md"}}),{c(){r=i("meta"),v=a(),c=i("p"),u=a(),h(L.$$.fragment),p=a(),h(z.$$.fragment),Ro=a(),Ce=i("p"),Ce.innerHTML=us,Oo=a(),Fe=i("p"),Fe.textContent=hs,Xo=a(),Pe=i("p"),Pe.innerHTML=fs,Qo=a(),qe=i("p"),qe.innerHTML=_s,Yo=a(),h(Ie.$$.fragment),Ko=a(),je=i("ul"),je.innerHTML=gs,en=a(),h(Ue.$$.fragment),tn=a(),We=i("ul"),We.innerHTML=bs,on=a(),h(Ae.$$.fragment),nn=a(),He=i("p"),He.textContent=ks,an=a(),Ne=i("ul"),Ne.innerHTML=vs,sn=a(),Je=i("p"),Je.innerHTML=ys,rn=a(),Ge=i("p"),Ge.textContent=Ts,ln=a(),Se=i("ul"),Se.innerHTML=ws,dn=a(),h(Be.$$.fragment),cn=a(),Ee=i("p"),Ee.textContent=Ls,mn=a(),h(De.$$.fragment),pn=a(),Ve=i("ul"),Ve.innerHTML=xs,un=a(),h(Ze.$$.fragment),hn=a(),Re=i("ul"),Re.innerHTML=Ms,fn=a(),Oe=i("p"),Oe.textContent=$s,_n=a(),Xe=i("ul"),Xe.innerHTML=zs,gn=a(),Qe=i("p"),Qe.textContent=Cs,bn=a(),Ye=i("ul"),Ye.innerHTML=Fs,kn=a(),Ke=i("p"),Ke.textContent=Ps,vn=a(),et=i("ul"),et.innerHTML=qs,yn=a(),h(tt.$$.fragment),Tn=a(),B=i("div"),h(ot.$$.fragment),En=a(),Bt=i("p"),Bt.innerHTML=Is,Dn=a(),Et=i("p"),Et.innerHTML=js,Vn=a(),h(le.$$.fragment),wn=a(),nt=i("p"),nt.textContent=Us,Ln=a(),q=i("div"),h(at.$$.fragment),Zn=a(),Dt=i("p"),Dt.textContent=Ws,Rn=a(),Vt=i("div"),h(st.$$.fragment),On=a(),de=i("div"),h(rt.$$.fragment),Xn=a(),Zt=i("p"),Zt.innerHTML=As,Qn=a(),D=i("div"),h(it.$$.fragment),Yn=a(),Rt=i("p"),Rt.textContent=Hs,Kn=a(),h(ce.$$.fragment),ea=a(),Ot=i("p"),Ot.textContent=Ns,ta=a(),me=i("div"),h(lt.$$.fragment),oa=a(),Xt=i("p"),Xt.textContent=Js,xn=a(),dt=i("p"),dt.textContent=Gs,Mn=a(),x=i("div"),h(ct.$$.fragment),na=a(),Qt=i("p"),Qt.textContent=Ss,aa=a(),Yt=i("p"),Yt.textContent=Bs,sa=a(),h(pe.$$.fragment),ra=a(),Kt=i("p"),Kt.innerHTML=Es,ia=a(),eo=i("p"),eo.innerHTML=Ds,la=a(),to=i("div"),h(mt.$$.fragment),da=a(),ue=i("div"),h(pt.$$.fragment),ca=a(),oo=i("p"),oo.innerHTML=Vs,ma=a(),O=i("div"),h(ut.$$.fragment),pa=a(),no=i("p"),no.innerHTML=Zs,ua=a(),ao=i("p"),ao.textContent=Rs,ha=a(),he=i("div"),h(ht.$$.fragment),fa=a(),so=i("p"),so.innerHTML=Os,_a=a(),ro=i("div"),h(ft.$$.fragment),$n=a(),_t=i("p"),_t.textContent=Xs,zn=a(),I=i("div"),h(gt.$$.fragment),ga=a(),io=i("p"),io.innerHTML=Qs,ba=a(),lo=i("p"),lo.innerHTML=Ys,ka=a(),co=i("p"),co.innerHTML=Ks,va=a(),X=i("div"),h(bt.$$.fragment),ya=a(),mo=i("p"),mo.innerHTML=er,Ta=a(),h(fe.$$.fragment),Cn=a(),kt=i("p"),kt.textContent=tr,Fn=a(),ae=i("div"),h(vt.$$.fragment),wa=a(),V=i("div"),h(yt.$$.fragment),La=a(),po=i("p"),po.innerHTML=or,xa=a(),h(_e.$$.fragment),Ma=a(),h(ge.$$.fragment),Pn=a(),Tt=i("p"),Tt.textContent=nr,qn=a(),C=i("div"),h(wt.$$.fragment),$a=a(),uo=i("p"),uo.textContent=ar,za=a(),ho=i("p"),ho.innerHTML=sr,Ca=a(),fo=i("p"),fo.innerHTML=rr,Fa=a(),_o=i("p"),_o.innerHTML=ir,Pa=a(),go=i("p"),go.innerHTML=lr,qa=a(),Q=i("div"),h(Lt.$$.fragment),Ia=a(),bo=i("p"),bo.innerHTML=dr,ja=a(),h(be.$$.fragment),In=a(),xt=i("p"),xt.textContent=cr,jn=a(),j=i("div"),h(Mt.$$.fragment),Ua=a(),ko=i("p"),ko.innerHTML=mr,Wa=a(),vo=i("p"),vo.innerHTML=pr,Aa=a(),yo=i("p"),yo.innerHTML=ur,Ha=a(),Y=i("div"),h($t.$$.fragment),Na=a(),To=i("p"),To.innerHTML=hr,Ja=a(),h(ke.$$.fragment),Un=a(),zt=i("p"),zt.textContent=fr,Wn=a(),U=i("div"),h(Ct.$$.fragment),Ga=a(),wo=i("p"),wo.textContent=_r,Sa=a(),Lo=i("p"),Lo.innerHTML=gr,Ba=a(),xo=i("p"),xo.innerHTML=br,Ea=a(),K=i("div"),h(Ft.$$.fragment),Da=a(),Mo=i("p"),Mo.innerHTML=kr,Va=a(),h(ve.$$.fragment),An=a(),Pt=i("p"),Pt.textContent=vr,Hn=a(),F=i("div"),h(qt.$$.fragment),Za=a(),$o=i("p"),$o.textContent=yr,Ra=a(),zo=i("p"),zo.innerHTML=Tr,Oa=a(),Co=i("p"),Co.innerHTML=wr,Xa=a(),Fo=i("p"),Fo.textContent=Lr,Qa=a(),Po=i("ul"),Po.innerHTML=xr,Ya=a(),H=i("div"),h(It.$$.fragment),Ka=a(),qo=i("p"),qo.innerHTML=Mr,es=a(),h(ye.$$.fragment),ts=a(),h(Te.$$.fragment),os=a(),h(we.$$.fragment),Nn=a(),jt=i("p"),jt.textContent=$r,Jn=a(),P=i("div"),h(Ut.$$.fragment),ns=a(),Io=i("p"),Io.textContent=zr,as=a(),jo=i("p"),jo.innerHTML=Cr,ss=a(),Uo=i("p"),Uo.innerHTML=Fr,rs=a(),Wo=i("p"),Wo.textContent=Pr,is=a(),Ao=i("ul"),Ao.innerHTML=qr,ls=a(),N=i("div"),h(Wt.$$.fragment),ds=a(),Ho=i("p"),Ho.innerHTML=Ir,cs=a(),h(Le.$$.fragment),ms=a(),h(xe.$$.fragment),ps=a(),h(Me.$$.fragment),Gn=a(),h(At.$$.fragment),Sn=a(),Bo=i("p"),this.h()},l(e){const n=Nr("svelte-u9bgzb",document.head);r=l(n,"META",{name:!0,content:!0}),n.forEach(o),v=s(e),c=l(e,"P",{}),T(c).forEach(o),u=s(e),f(L.$$.fragment,e),p=s(e),f(z.$$.fragment,e),Ro=s(e),Ce=l(e,"P",{"data-svelte-h":!0}),m(Ce)!=="svelte-1mfapnf"&&(Ce.innerHTML=us),Oo=s(e),Fe=l(e,"P",{"data-svelte-h":!0}),m(Fe)!=="svelte-vfdo9a"&&(Fe.textContent=hs),Xo=s(e),Pe=l(e,"P",{"data-svelte-h":!0}),m(Pe)!=="svelte-sl0nly"&&(Pe.innerHTML=fs),Qo=s(e),qe=l(e,"P",{"data-svelte-h":!0}),m(qe)!=="svelte-1ivivso"&&(qe.innerHTML=_s),Yo=s(e),f(Ie.$$.fragment,e),Ko=s(e),je=l(e,"UL",{"data-svelte-h":!0}),m(je)!=="svelte-oqmv1y"&&(je.innerHTML=gs),en=s(e),f(Ue.$$.fragment,e),tn=s(e),We=l(e,"UL",{"data-svelte-h":!0}),m(We)!=="svelte-yjbkch"&&(We.innerHTML=bs),on=s(e),f(Ae.$$.fragment,e),nn=s(e),He=l(e,"P",{"data-svelte-h":!0}),m(He)!=="svelte-3h7jh3"&&(He.textContent=ks),an=s(e),Ne=l(e,"UL",{"data-svelte-h":!0}),m(Ne)!=="svelte-1xmggxn"&&(Ne.innerHTML=vs),sn=s(e),Je=l(e,"P",{"data-svelte-h":!0}),m(Je)!=="svelte-1s478nn"&&(Je.innerHTML=ys),rn=s(e),Ge=l(e,"P",{"data-svelte-h":!0}),m(Ge)!=="svelte-a39vkx"&&(Ge.textContent=Ts),ln=s(e),Se=l(e,"UL",{"data-svelte-h":!0}),m(Se)!=="svelte-y8u21a"&&(Se.innerHTML=ws),dn=s(e),f(Be.$$.fragment,e),cn=s(e),Ee=l(e,"P",{"data-svelte-h":!0}),m(Ee)!=="svelte-h1kz0m"&&(Ee.textContent=Ls),mn=s(e),f(De.$$.fragment,e),pn=s(e),Ve=l(e,"UL",{"data-svelte-h":!0}),m(Ve)!=="svelte-dp296g"&&(Ve.innerHTML=xs),un=s(e),f(Ze.$$.fragment,e),hn=s(e),Re=l(e,"UL",{"data-svelte-h":!0}),m(Re)!=="svelte-ab7kge"&&(Re.innerHTML=Ms),fn=s(e),Oe=l(e,"P",{"data-svelte-h":!0}),m(Oe)!=="svelte-liq2q7"&&(Oe.textContent=$s),_n=s(e),Xe=l(e,"UL",{"data-svelte-h":!0}),m(Xe)!=="svelte-10a5770"&&(Xe.innerHTML=zs),gn=s(e),Qe=l(e,"P",{"data-svelte-h":!0}),m(Qe)!=="svelte-1wntqpp"&&(Qe.textContent=Cs),bn=s(e),Ye=l(e,"UL",{"data-svelte-h":!0}),m(Ye)!=="svelte-1t43wyu"&&(Ye.innerHTML=Fs),kn=s(e),Ke=l(e,"P",{"data-svelte-h":!0}),m(Ke)!=="svelte-lk14e4"&&(Ke.textContent=Ps),vn=s(e),et=l(e,"UL",{"data-svelte-h":!0}),m(et)!=="svelte-162yhfz"&&(et.innerHTML=qs),yn=s(e),f(tt.$$.fragment,e),Tn=s(e),B=l(e,"DIV",{class:!0});var Z=T(B);f(ot.$$.fragment,Z),En=s(Z),Bt=l(Z,"P",{"data-svelte-h":!0}),m(Bt)!=="svelte-1delj0"&&(Bt.innerHTML=Is),Dn=s(Z),Et=l(Z,"P",{"data-svelte-h":!0}),m(Et)!=="svelte-ux0v9v"&&(Et.innerHTML=js),Vn=s(Z),f(le.$$.fragment,Z),Z.forEach(o),wn=s(e),nt=l(e,"P",{"data-svelte-h":!0}),m(nt)!=="svelte-1stkzc"&&(nt.textContent=Us),Ln=s(e),q=l(e,"DIV",{class:!0});var W=T(q);f(at.$$.fragment,W),Zn=s(W),Dt=l(W,"P",{"data-svelte-h":!0}),m(Dt)!=="svelte-qfiu5a"&&(Dt.textContent=Ws),Rn=s(W),Vt=l(W,"DIV",{class:!0});var Eo=T(Vt);f(st.$$.fragment,Eo),Eo.forEach(o),On=s(W),de=l(W,"DIV",{class:!0});var Ht=T(de);f(rt.$$.fragment,Ht),Xn=s(Ht),Zt=l(Ht,"P",{"data-svelte-h":!0}),m(Zt)!=="svelte-1f4f5kp"&&(Zt.innerHTML=As),Ht.forEach(o),Qn=s(W),D=l(W,"DIV",{class:!0});var R=T(D);f(it.$$.fragment,R),Yn=s(R),Rt=l(R,"P",{"data-svelte-h":!0}),m(Rt)!=="svelte-13bfd60"&&(Rt.textContent=Hs),Kn=s(R),f(ce.$$.fragment,R),ea=s(R),Ot=l(R,"P",{"data-svelte-h":!0}),m(Ot)!=="svelte-wtrslu"&&(Ot.textContent=Ns),R.forEach(o),ta=s(W),me=l(W,"DIV",{class:!0});var Nt=T(me);f(lt.$$.fragment,Nt),oa=s(Nt),Xt=l(Nt,"P",{"data-svelte-h":!0}),m(Xt)!=="svelte-1slb66l"&&(Xt.textContent=Js),Nt.forEach(o),W.forEach(o),xn=s(e),dt=l(e,"P",{"data-svelte-h":!0}),m(dt)!=="svelte-6ws73c"&&(dt.textContent=Gs),Mn=s(e),x=l(e,"DIV",{class:!0});var $=T(x);f(ct.$$.fragment,$),na=s($),Qt=l($,"P",{"data-svelte-h":!0}),m(Qt)!=="svelte-15tdcz8"&&(Qt.textContent=Ss),aa=s($),Yt=l($,"P",{"data-svelte-h":!0}),m(Yt)!=="svelte-llhmpa"&&(Yt.textContent=Bs),sa=s($),f(pe.$$.fragment,$),ra=s($),Kt=l($,"P",{"data-svelte-h":!0}),m(Kt)!=="svelte-cnb6q1"&&(Kt.innerHTML=Es),ia=s($),eo=l($,"P",{"data-svelte-h":!0}),m(eo)!=="svelte-19w2g9e"&&(eo.innerHTML=Ds),la=s($),to=l($,"DIV",{class:!0});var Do=T(to);f(mt.$$.fragment,Do),Do.forEach(o),da=s($),ue=l($,"DIV",{class:!0});var Jt=T(ue);f(pt.$$.fragment,Jt),ca=s(Jt),oo=l(Jt,"P",{"data-svelte-h":!0}),m(oo)!=="svelte-1wmjg8a"&&(oo.innerHTML=Vs),Jt.forEach(o),ma=s($),O=l($,"DIV",{class:!0});var se=T(O);f(ut.$$.fragment,se),pa=s(se),no=l(se,"P",{"data-svelte-h":!0}),m(no)!=="svelte-1rw5jlb"&&(no.innerHTML=Zs),ua=s(se),ao=l(se,"P",{"data-svelte-h":!0}),m(ao)!=="svelte-9vptpw"&&(ao.textContent=Rs),se.forEach(o),ha=s($),he=l($,"DIV",{class:!0});var Gt=T(he);f(ht.$$.fragment,Gt),fa=s(Gt),so=l(Gt,"P",{"data-svelte-h":!0}),m(so)!=="svelte-nfci2w"&&(so.innerHTML=Os),Gt.forEach(o),_a=s($),ro=l($,"DIV",{class:!0});var Vo=T(ro);f(ft.$$.fragment,Vo),Vo.forEach(o),$.forEach(o),$n=s(e),_t=l(e,"P",{"data-svelte-h":!0}),m(_t)!=="svelte-1v6i4i0"&&(_t.textContent=Xs),zn=s(e),I=l(e,"DIV",{class:!0});var E=T(I);f(gt.$$.fragment,E),ga=s(E),io=l(E,"P",{"data-svelte-h":!0}),m(io)!=="svelte-1lpe2in"&&(io.innerHTML=Qs),ba=s(E),lo=l(E,"P",{"data-svelte-h":!0}),m(lo)!=="svelte-1ydu1el"&&(lo.innerHTML=Ys),ka=s(E),co=l(E,"P",{"data-svelte-h":!0}),m(co)!=="svelte-eom0yk"&&(co.innerHTML=Ks),va=s(E),X=l(E,"DIV",{class:!0});var re=T(X);f(bt.$$.fragment,re),ya=s(re),mo=l(re,"P",{"data-svelte-h":!0}),m(mo)!=="svelte-1tff782"&&(mo.innerHTML=er),Ta=s(re),f(fe.$$.fragment,re),re.forEach(o),E.forEach(o),Cn=s(e),kt=l(e,"P",{"data-svelte-h":!0}),m(kt)!=="svelte-tmto08"&&(kt.textContent=tr),Fn=s(e),ae=l(e,"DIV",{class:!0});var St=T(ae);f(vt.$$.fragment,St),wa=s(St),V=l(St,"DIV",{class:!0});var $e=T(V);f(yt.$$.fragment,$e),La=s($e),po=l($e,"P",{"data-svelte-h":!0}),m(po)!=="svelte-pspx9u"&&(po.innerHTML=or),xa=s($e),f(_e.$$.fragment,$e),Ma=s($e),f(ge.$$.fragment,$e),$e.forEach(o),St.forEach(o),Pn=s(e),Tt=l(e,"P",{"data-svelte-h":!0}),m(Tt)!=="svelte-143qoye"&&(Tt.textContent=nr),qn=s(e),C=l(e,"DIV",{class:!0});var J=T(C);f(wt.$$.fragment,J),$a=s(J),uo=l(J,"P",{"data-svelte-h":!0}),m(uo)!=="svelte-62must"&&(uo.textContent=ar),za=s(J),ho=l(J,"P",{"data-svelte-h":!0}),m(ho)!=="svelte-w24vkl"&&(ho.innerHTML=sr),Ca=s(J),fo=l(J,"P",{"data-svelte-h":!0}),m(fo)!=="svelte-10ugs3m"&&(fo.innerHTML=rr),Fa=s(J),_o=l(J,"P",{"data-svelte-h":!0}),m(_o)!=="svelte-1qfr7uo"&&(_o.innerHTML=ir),Pa=s(J),go=l(J,"P",{"data-svelte-h":!0}),m(go)!=="svelte-1ydu1el"&&(go.innerHTML=lr),qa=s(J),Q=l(J,"DIV",{class:!0});var No=T(Q);f(Lt.$$.fragment,No),Ia=s(No),bo=l(No,"P",{"data-svelte-h":!0}),m(bo)!=="svelte-1bmchbo"&&(bo.innerHTML=dr),ja=s(No),f(be.$$.fragment,No),No.forEach(o),J.forEach(o),In=s(e),xt=l(e,"P",{"data-svelte-h":!0}),m(xt)!=="svelte-w7re0"&&(xt.textContent=cr),jn=s(e),j=l(e,"DIV",{class:!0});var ee=T(j);f(Mt.$$.fragment,ee),Ua=s(ee),ko=l(ee,"P",{"data-svelte-h":!0}),m(ko)!=="svelte-2ba709"&&(ko.innerHTML=mr),Wa=s(ee),vo=l(ee,"P",{"data-svelte-h":!0}),m(vo)!=="svelte-1qfr7uo"&&(vo.innerHTML=pr),Aa=s(ee),yo=l(ee,"P",{"data-svelte-h":!0}),m(yo)!=="svelte-1ydu1el"&&(yo.innerHTML=ur),Ha=s(ee),Y=l(ee,"DIV",{class:!0});var Jo=T(Y);f($t.$$.fragment,Jo),Na=s(Jo),To=l(Jo,"P",{"data-svelte-h":!0}),m(To)!=="svelte-wgdeai"&&(To.innerHTML=hr),Ja=s(Jo),f(ke.$$.fragment,Jo),Jo.forEach(o),ee.forEach(o),Un=s(e),zt=l(e,"P",{"data-svelte-h":!0}),m(zt)!=="svelte-jv2w3w"&&(zt.textContent=fr),Wn=s(e),U=l(e,"DIV",{class:!0});var te=T(U);f(Ct.$$.fragment,te),Ga=s(te),wo=l(te,"P",{"data-svelte-h":!0}),m(wo)!=="svelte-1hteym"&&(wo.textContent=_r),Sa=s(te),Lo=l(te,"P",{"data-svelte-h":!0}),m(Lo)!=="svelte-1qfr7uo"&&(Lo.innerHTML=gr),Ba=s(te),xo=l(te,"P",{"data-svelte-h":!0}),m(xo)!=="svelte-1ydu1el"&&(xo.innerHTML=br),Ea=s(te),K=l(te,"DIV",{class:!0});var Go=T(K);f(Ft.$$.fragment,Go),Da=s(Go),Mo=l(Go,"P",{"data-svelte-h":!0}),m(Mo)!=="svelte-zcubyy"&&(Mo.innerHTML=kr),Va=s(Go),f(ve.$$.fragment,Go),Go.forEach(o),te.forEach(o),An=s(e),Pt=l(e,"P",{"data-svelte-h":!0}),m(Pt)!=="svelte-1uabxia"&&(Pt.textContent=vr),Hn=s(e),F=l(e,"DIV",{class:!0});var G=T(F);f(qt.$$.fragment,G),Za=s(G),$o=l(G,"P",{"data-svelte-h":!0}),m($o)!=="svelte-1g8y7nc"&&($o.textContent=yr),Ra=s(G),zo=l(G,"P",{"data-svelte-h":!0}),m(zo)!=="svelte-1h440d8"&&(zo.innerHTML=Tr),Oa=s(G),Co=l(G,"P",{"data-svelte-h":!0}),m(Co)!=="svelte-idybz1"&&(Co.innerHTML=wr),Xa=s(G),Fo=l(G,"P",{"data-svelte-h":!0}),m(Fo)!=="svelte-1pplc4a"&&(Fo.textContent=Lr),Qa=s(G),Po=l(G,"UL",{"data-svelte-h":!0}),m(Po)!=="svelte-m6h3rq"&&(Po.innerHTML=xr),Ya=s(G),H=l(G,"DIV",{class:!0});var oe=T(H);f(It.$$.fragment,oe),Ka=s(oe),qo=l(oe,"P",{"data-svelte-h":!0}),m(qo)!=="svelte-9bmwma"&&(qo.innerHTML=Mr),es=s(oe),f(ye.$$.fragment,oe),ts=s(oe),f(Te.$$.fragment,oe),os=s(oe),f(we.$$.fragment,oe),oe.forEach(o),G.forEach(o),Nn=s(e),jt=l(e,"P",{"data-svelte-h":!0}),m(jt)!=="svelte-fxotoe"&&(jt.textContent=$r),Jn=s(e),P=l(e,"DIV",{class:!0});var S=T(P);f(Ut.$$.fragment,S),ns=s(S),Io=l(S,"P",{"data-svelte-h":!0}),m(Io)!=="svelte-1as0j1x"&&(Io.textContent=zr),as=s(S),jo=l(S,"P",{"data-svelte-h":!0}),m(jo)!=="svelte-1h440d8"&&(jo.innerHTML=Cr),ss=s(S),Uo=l(S,"P",{"data-svelte-h":!0}),m(Uo)!=="svelte-idybz1"&&(Uo.innerHTML=Fr),rs=s(S),Wo=l(S,"P",{"data-svelte-h":!0}),m(Wo)!=="svelte-1pplc4a"&&(Wo.textContent=Pr),is=s(S),Ao=l(S,"UL",{"data-svelte-h":!0}),m(Ao)!=="svelte-m6h3rq"&&(Ao.innerHTML=qr),ls=s(S),N=l(S,"DIV",{class:!0});var ne=T(N);f(Wt.$$.fragment,ne),ds=s(ne),Ho=l(ne,"P",{"data-svelte-h":!0}),m(Ho)!=="svelte-9bmwma"&&(Ho.innerHTML=Ir),cs=s(ne),f(Le.$$.fragment,ne),ms=s(ne),f(xe.$$.fragment,ne),ps=s(ne),f(Me.$$.fragment,ne),ne.forEach(o),S.forEach(o),Gn=s(e),f(At.$$.fragment,e),Sn=s(e),Bo=l(e,"P",{}),T(Bo).forEach(o),this.h()},h(){w(r,"name","hf:doc:metadata"),w(r,"content",ni),w(B,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Vt,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(de,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(D,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(me,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(to,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ue,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(he,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ro,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(X,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(ae,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(C,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(Y,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(K,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(U,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(H,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(F,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(N,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),w(P,"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,n){t(document.head,r),d(e,v,n),d(e,c,n),d(e,u,n),_(L,e,n),d(e,p,n),_(z,e,n),d(e,Ro,n),d(e,Ce,n),d(e,Oo,n),d(e,Fe,n),d(e,Xo,n),d(e,Pe,n),d(e,Qo,n),d(e,qe,n),d(e,Yo,n),_(Ie,e,n),d(e,Ko,n),d(e,je,n),d(e,en,n),_(Ue,e,n),d(e,tn,n),d(e,We,n),d(e,on,n),_(Ae,e,n),d(e,nn,n),d(e,He,n),d(e,an,n),d(e,Ne,n),d(e,sn,n),d(e,Je,n),d(e,rn,n),d(e,Ge,n),d(e,ln,n),d(e,Se,n),d(e,dn,n),_(Be,e,n),d(e,cn,n),d(e,Ee,n),d(e,mn,n),_(De,e,n),d(e,pn,n),d(e,Ve,n),d(e,un,n),_(Ze,e,n),d(e,hn,n),d(e,Re,n),d(e,fn,n),d(e,Oe,n),d(e,_n,n),d(e,Xe,n),d(e,gn,n),d(e,Qe,n),d(e,bn,n),d(e,Ye,n),d(e,kn,n),d(e,Ke,n),d(e,vn,n),d(e,et,n),d(e,yn,n),_(tt,e,n),d(e,Tn,n),d(e,B,n),_(ot,B,null),t(B,En),t(B,Bt),t(B,Dn),t(B,Et),t(B,Vn),_(le,B,null),d(e,wn,n),d(e,nt,n),d(e,Ln,n),d(e,q,n),_(at,q,null),t(q,Zn),t(q,Dt),t(q,Rn),t(q,Vt),_(st,Vt,null),t(q,On),t(q,de),_(rt,de,null),t(de,Xn),t(de,Zt),t(q,Qn),t(q,D),_(it,D,null),t(D,Yn),t(D,Rt),t(D,Kn),_(ce,D,null),t(D,ea),t(D,Ot),t(q,ta),t(q,me),_(lt,me,null),t(me,oa),t(me,Xt),d(e,xn,n),d(e,dt,n),d(e,Mn,n),d(e,x,n),_(ct,x,null),t(x,na),t(x,Qt),t(x,aa),t(x,Yt),t(x,sa),_(pe,x,null),t(x,ra),t(x,Kt),t(x,ia),t(x,eo),t(x,la),t(x,to),_(mt,to,null),t(x,da),t(x,ue),_(pt,ue,null),t(ue,ca),t(ue,oo),t(x,ma),t(x,O),_(ut,O,null),t(O,pa),t(O,no),t(O,ua),t(O,ao),t(x,ha),t(x,he),_(ht,he,null),t(he,fa),t(he,so),t(x,_a),t(x,ro),_(ft,ro,null),d(e,$n,n),d(e,_t,n),d(e,zn,n),d(e,I,n),_(gt,I,null),t(I,ga),t(I,io),t(I,ba),t(I,lo),t(I,ka),t(I,co),t(I,va),t(I,X),_(bt,X,null),t(X,ya),t(X,mo),t(X,Ta),_(fe,X,null),d(e,Cn,n),d(e,kt,n),d(e,Fn,n),d(e,ae,n),_(vt,ae,null),t(ae,wa),t(ae,V),_(yt,V,null),t(V,La),t(V,po),t(V,xa),_(_e,V,null),t(V,Ma),_(ge,V,null),d(e,Pn,n),d(e,Tt,n),d(e,qn,n),d(e,C,n),_(wt,C,null),t(C,$a),t(C,uo),t(C,za),t(C,ho),t(C,Ca),t(C,fo),t(C,Fa),t(C,_o),t(C,Pa),t(C,go),t(C,qa),t(C,Q),_(Lt,Q,null),t(Q,Ia),t(Q,bo),t(Q,ja),_(be,Q,null),d(e,In,n),d(e,xt,n),d(e,jn,n),d(e,j,n),_(Mt,j,null),t(j,Ua),t(j,ko),t(j,Wa),t(j,vo),t(j,Aa),t(j,yo),t(j,Ha),t(j,Y),_($t,Y,null),t(Y,Na),t(Y,To),t(Y,Ja),_(ke,Y,null),d(e,Un,n),d(e,zt,n),d(e,Wn,n),d(e,U,n),_(Ct,U,null),t(U,Ga),t(U,wo),t(U,Sa),t(U,Lo),t(U,Ba),t(U,xo),t(U,Ea),t(U,K),_(Ft,K,null),t(K,Da),t(K,Mo),t(K,Va),_(ve,K,null),d(e,An,n),d(e,Pt,n),d(e,Hn,n),d(e,F,n),_(qt,F,null),t(F,Za),t(F,$o),t(F,Ra),t(F,zo),t(F,Oa),t(F,Co),t(F,Xa),t(F,Fo),t(F,Qa),t(F,Po),t(F,Ya),t(F,H),_(It,H,null),t(H,Ka),t(H,qo),t(H,es),_(ye,H,null),t(H,ts),_(Te,H,null),t(H,os),_(we,H,null),d(e,Nn,n),d(e,jt,n),d(e,Jn,n),d(e,P,n),_(Ut,P,null),t(P,ns),t(P,Io),t(P,as),t(P,jo),t(P,ss),t(P,Uo),t(P,rs),t(P,Wo),t(P,is),t(P,Ao),t(P,ls),t(P,N),_(Wt,N,null),t(N,ds),t(N,Ho),t(N,cs),_(Le,N,null),t(N,ms),_(xe,N,null),t(N,ps),_(Me,N,null),d(e,Gn,n),_(At,e,n),d(e,Sn,n),d(e,Bo,n),Bn=!0},p(e,[n]){const Z={};n&2&&(Z.$$scope={dirty:n,ctx:e}),le.$set(Z);const W={};n&2&&(W.$$scope={dirty:n,ctx:e}),ce.$set(W);const Eo={};n&2&&(Eo.$$scope={dirty:n,ctx:e}),pe.$set(Eo);const Ht={};n&2&&(Ht.$$scope={dirty:n,ctx:e}),fe.$set(Ht);const R={};n&2&&(R.$$scope={dirty:n,ctx:e}),_e.$set(R);const Nt={};n&2&&(Nt.$$scope={dirty:n,ctx:e}),ge.$set(Nt);const $={};n&2&&($.$$scope={dirty:n,ctx:e}),be.$set($);const Do={};n&2&&(Do.$$scope={dirty:n,ctx:e}),ke.$set(Do);const Jt={};n&2&&(Jt.$$scope={dirty:n,ctx:e}),ve.$set(Jt);const se={};n&2&&(se.$$scope={dirty:n,ctx:e}),ye.$set(se);const Gt={};n&2&&(Gt.$$scope={dirty:n,ctx:e}),Te.$set(Gt);const Vo={};n&2&&(Vo.$$scope={dirty:n,ctx:e}),we.$set(Vo);const E={};n&2&&(E.$$scope={dirty:n,ctx:e}),Le.$set(E);const re={};n&2&&(re.$$scope={dirty:n,ctx:e}),xe.$set(re);const St={};n&2&&(St.$$scope={dirty:n,ctx:e}),Me.$set(St)},i(e){Bn||(g(L.$$.fragment,e),g(z.$$.fragment,e),g(Ie.$$.fragment,e),g(Ue.$$.fragment,e),g(Ae.$$.fragment,e),g(Be.$$.fragment,e),g(De.$$.fragment,e),g(Ze.$$.fragment,e),g(tt.$$.fragment,e),g(ot.$$.fragment,e),g(le.$$.fragment,e),g(at.$$.fragment,e),g(st.$$.fragment,e),g(rt.$$.fragment,e),g(it.$$.fragment,e),g(ce.$$.fragment,e),g(lt.$$.fragment,e),g(ct.$$.fragment,e),g(pe.$$.fragment,e),g(mt.$$.fragment,e),g(pt.$$.fragment,e),g(ut.$$.fragment,e),g(ht.$$.fragment,e),g(ft.$$.fragment,e),g(gt.$$.fragment,e),g(bt.$$.fragment,e),g(fe.$$.fragment,e),g(vt.$$.fragment,e),g(yt.$$.fragment,e),g(_e.$$.fragment,e),g(ge.$$.fragment,e),g(wt.$$.fragment,e),g(Lt.$$.fragment,e),g(be.$$.fragment,e),g(Mt.$$.fragment,e),g($t.$$.fragment,e),g(ke.$$.fragment,e),g(Ct.$$.fragment,e),g(Ft.$$.fragment,e),g(ve.$$.fragment,e),g(qt.$$.fragment,e),g(It.$$.fragment,e),g(ye.$$.fragment,e),g(Te.$$.fragment,e),g(we.$$.fragment,e),g(Ut.$$.fragment,e),g(Wt.$$.fragment,e),g(Le.$$.fragment,e),g(xe.$$.fragment,e),g(Me.$$.fragment,e),g(At.$$.fragment,e),Bn=!0)},o(e){b(L.$$.fragment,e),b(z.$$.fragment,e),b(Ie.$$.fragment,e),b(Ue.$$.fragment,e),b(Ae.$$.fragment,e),b(Be.$$.fragment,e),b(De.$$.fragment,e),b(Ze.$$.fragment,e),b(tt.$$.fragment,e),b(ot.$$.fragment,e),b(le.$$.fragment,e),b(at.$$.fragment,e),b(st.$$.fragment,e),b(rt.$$.fragment,e),b(it.$$.fragment,e),b(ce.$$.fragment,e),b(lt.$$.fragment,e),b(ct.$$.fragment,e),b(pe.$$.fragment,e),b(mt.$$.fragment,e),b(pt.$$.fragment,e),b(ut.$$.fragment,e),b(ht.$$.fragment,e),b(ft.$$.fragment,e),b(gt.$$.fragment,e),b(bt.$$.fragment,e),b(fe.$$.fragment,e),b(vt.$$.fragment,e),b(yt.$$.fragment,e),b(_e.$$.fragment,e),b(ge.$$.fragment,e),b(wt.$$.fragment,e),b(Lt.$$.fragment,e),b(be.$$.fragment,e),b(Mt.$$.fragment,e),b($t.$$.fragment,e),b(ke.$$.fragment,e),b(Ct.$$.fragment,e),b(Ft.$$.fragment,e),b(ve.$$.fragment,e),b(qt.$$.fragment,e),b(It.$$.fragment,e),b(ye.$$.fragment,e),b(Te.$$.fragment,e),b(we.$$.fragment,e),b(Ut.$$.fragment,e),b(Wt.$$.fragment,e),b(Le.$$.fragment,e),b(xe.$$.fragment,e),b(Me.$$.fragment,e),b(At.$$.fragment,e),Bn=!1},d(e){e&&(o(v),o(c),o(u),o(p),o(Ro),o(Ce),o(Oo),o(Fe),o(Xo),o(Pe),o(Qo),o(qe),o(Yo),o(Ko),o(je),o(en),o(tn),o(We),o(on),o(nn),o(He),o(an),o(Ne),o(sn),o(Je),o(rn),o(Ge),o(ln),o(Se),o(dn),o(cn),o(Ee),o(mn),o(pn),o(Ve),o(un),o(hn),o(Re),o(fn),o(Oe),o(_n),o(Xe),o(gn),o(Qe),o(bn),o(Ye),o(kn),o(Ke),o(vn),o(et),o(yn),o(Tn),o(B),o(wn),o(nt),o(Ln),o(q),o(xn),o(dt),o(Mn),o(x),o($n),o(_t),o(zn),o(I),o(Cn),o(kt),o(Fn),o(ae),o(Pn),o(Tt),o(qn),o(C),o(In),o(xt),o(jn),o(j),o(Un),o(zt),o(Wn),o(U),o(An),o(Pt),o(Hn),o(F),o(Nn),o(jt),o(Jn),o(P),o(Gn),o(Sn),o(Bo)),o(r),k(L,e),k(z,e),k(Ie,e),k(Ue,e),k(Ae,e),k(Be,e),k(De,e),k(Ze,e),k(tt,e),k(ot),k(le),k(at),k(st),k(rt),k(it),k(ce),k(lt),k(ct),k(pe),k(mt),k(pt),k(ut),k(ht),k(ft),k(gt),k(bt),k(fe),k(vt),k(yt),k(_e),k(ge),k(wt),k(Lt),k(be),k(Mt),k($t),k(ke),k(Ct),k(Ft),k(ve),k(qt),k(It),k(ye),k(Te),k(we),k(Ut),k(Wt),k(Le),k(xe),k(Me),k(At,e)}}}const ni='{"title":"LLaMA","local":"llama","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Usage tips","local":"usage-tips","sections":[],"depth":2},{"title":"Resources","local":"resources","sections":[],"depth":2},{"title":"LlamaConfig","local":"transformers.LlamaConfig","sections":[],"depth":2}],"depth":1}';function ai(y){return Wr(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ui extends Ar{constructor(r){super(),Hr(this,r,ai,oi,Ur,{})}}export{ui as component};
