import{s as Cn,o as zn,n as de}from"../chunks/scheduler.25b97de1.js";import{S as xn,i as Fn,g as d,s,r as h,A as Pn,h as c,f as a,c as r,j,u as f,x as y,k as J,y as i,a as l,v as g,d as _,t as b,w as v}from"../chunks/index.d9030fc9.js";import{T as Le}from"../chunks/Tip.baa67368.js";import{D as le}from"../chunks/Docstring.ffac8efa.js";import{C as On}from"../chunks/CodeBlock.e6cd0d95.js";import{E as Mn}from"../chunks/ExampleCodeBlock.22dfe688.js";import{H as ke,E as qn}from"../chunks/EditOnGithub.91d95064.js";function In(k){let n,u="This model is in maintenance mode only, we don’t accept any new PRs changing its code.",o,m,L=`If you run into any issues running this model, please reinstall the last version that supported this model: v4.31.0.
You can do so by running the following command: <code>pip install -U transformers==4.31.0</code>.`;return{c(){n=d("p"),n.textContent=u,o=s(),m=d("p"),m.innerHTML=L},l(p){n=c(p,"P",{"data-svelte-h":!0}),y(n)!=="svelte-1dwyvn5"&&(n.textContent=u),o=r(p),m=c(p,"P",{"data-svelte-h":!0}),y(m)!=="svelte-1o63f7c"&&(m.innerHTML=L)},m(p,w){l(p,n,w),l(p,o,w),l(p,m,w)},p:de,d(p){p&&(a(n),a(o),a(m))}}}function Wn(k){let n,u='This model differs from the <a href="https://huggingface.co/models?search=openllama" rel="nofollow">OpenLLaMA models</a> on the Hugging Face Hub, which primarily use the <a href="llama">LLaMA</a> architecture.';return{c(){n=d("p"),n.innerHTML=u},l(o){n=c(o,"P",{"data-svelte-h":!0}),y(n)!=="svelte-1kc01gl"&&(n.innerHTML=u)},m(o,m){l(o,n,m)},p:de,d(o){o&&a(n)}}}function Sn(k){let n,u;return n=new On({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> OpenLlamaModel, OpenLlamaConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a Open-Llama open_llama-7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = OpenLlamaConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the open_llama-7b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = OpenLlamaModel(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(n.$$.fragment)},l(o){f(n.$$.fragment,o)},m(o,m){g(n,o,m),u=!0},p:de,i(o){u||(_(n.$$.fragment,o),u=!0)},o(o){b(n.$$.fragment,o),u=!1},d(o){v(n,o)}}}function jn(k){let n,u=`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(){n=d("p"),n.innerHTML=u},l(o){n=c(o,"P",{"data-svelte-h":!0}),y(n)!=="svelte-fincs2"&&(n.innerHTML=u)},m(o,m){l(o,n,m)},p:de,d(o){o&&a(n)}}}function Jn(k){let n,u=`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(){n=d("p"),n.innerHTML=u},l(o){n=c(o,"P",{"data-svelte-h":!0}),y(n)!=="svelte-fincs2"&&(n.innerHTML=u)},m(o,m){l(o,n,m)},p:de,d(o){o&&a(n)}}}function Un(k){let n,u="Example:",o,m,L;return m=new On({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, OpenLlamaForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>model = OpenLlamaForCausalLM.from_pretrained(<span class="hljs-string">&quot;openlm-research/open_llama_7b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openlm-research/open_llama_7b&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(){n=d("p"),n.textContent=u,o=s(),h(m.$$.fragment)},l(p){n=c(p,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=u),o=r(p),f(m.$$.fragment,p)},m(p,w){l(p,n,w),l(p,o,w),g(m,p,w),L=!0},p:de,i(p){L||(_(m.$$.fragment,p),L=!0)},o(p){b(m.$$.fragment,p),L=!1},d(p){p&&(a(n),a(o)),v(m,p)}}}function Bn(k){let n,u=`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(){n=d("p"),n.innerHTML=u},l(o){n=c(o,"P",{"data-svelte-h":!0}),y(n)!=="svelte-fincs2"&&(n.innerHTML=u)},m(o,m){l(o,n,m)},p:de,d(o){o&&a(n)}}}function Hn(k){let n,u,o,m,L,p,w,Me,U,Oe,V,Ce,E,cn="The Open-Llama model was proposed in the open source Open-Llama project by community developer s-JoL.",ze,A,mn=`The model is mainly based on LLaMA with some modifications, incorporating memory-efficient attention from Xformers, stable embedding from Bloom, and shared input-output embedding from PaLM.
And the model is pre-trained on both Chinese and English, which gives it better performance on Chinese language tasks.`,xe,Y,pn=`This model was contributed by <a href="https://huggingface.co/s-JoL" rel="nofollow">s-JoL</a>.
The original code was released on GitHub by <a href="https://github.com/s-JoL" rel="nofollow">s-JoL</a>, but is now removed.`,Fe,D,Pe,O,R,Ze,ce,un=`This is the configuration class to store the configuration of a <a href="/docs/transformers/v4.42.4/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a>. It is used to instantiate an
Open-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
<a href="https://huggingface.co/s-JoL/Open-Llama-V1" rel="nofollow">s-JoL/Open-Llama-V1</a>.`,Ne,me,hn=`Configuration objects inherit from <a href="/docs/transformers/v4.42.4/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/v4.42.4/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Ge,B,qe,X,Ie,M,Q,Ve,pe,fn=`The bare Open-Llama Model outputting raw hidden-states without any specific head on top.
This model inherits from <a href="/docs/transformers/v4.42.4/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.)`,Ee,ue,gn=`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.`,Ae,he,_n="Transformer decoder consisting of <em>config.num_hidden_layers</em> layers. Each layer is a <code>OpenLlamaDecoderLayer</code>",Ye,P,K,De,fe,bn='The <a href="/docs/transformers/v4.42.4/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a> forward method, overrides the <code>__call__</code> special method.',Re,H,We,ee,Se,I,ne,Xe,z,te,Qe,ge,vn='The <a href="/docs/transformers/v4.42.4/en/model_doc/open-llama#transformers.OpenLlamaForCausalLM">OpenLlamaForCausalLM</a> forward method, overrides the <code>__call__</code> special method.',Ke,Z,en,N,je,oe,Je,T,ae,nn,_e,yn="The LLaMa Model transformer with a sequence classification head on top (linear layer).",tn,be,Tn=`<a href="/docs/transformers/v4.42.4/en/model_doc/open-llama#transformers.OpenLlamaForSequenceClassification">OpenLlamaForSequenceClassification</a> uses the last token in order to do the classification, as other causal
models (e.g. GPT-2) do.`,on,ve,$n=`Since it does classification on the last token, it requires to know the position of the last token. If a
<code>pad_token_id</code> is defined in the configuration, it finds the last token that is not a padding token in each row. If
no <code>pad_token_id</code> is defined, it simply takes the last value in each row of the batch. Since it cannot guess the
padding tokens when <code>inputs_embeds</code> are passed instead of <code>input_ids</code>, it does the same (take the last value in
each row of the batch).`,an,ye,kn=`This model inherits from <a href="/docs/transformers/v4.42.4/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
etc.)`,sn,Te,wn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
and behavior.`,rn,q,se,ln,$e,Ln='The <a href="/docs/transformers/v4.42.4/en/model_doc/open-llama#transformers.OpenLlamaForSequenceClassification">OpenLlamaForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',dn,G,Ue,re,Be,we,He;return L=new ke({props:{title:"Open-Llama",local:"open-llama",headingTag:"h1"}}),w=new Le({props:{warning:!0,$$slots:{default:[In]},$$scope:{ctx:k}}}),U=new Le({props:{warning:!0,$$slots:{default:[Wn]},$$scope:{ctx:k}}}),V=new ke({props:{title:"Overview",local:"overview",headingTag:"h2"}}),D=new ke({props:{title:"OpenLlamaConfig",local:"transformers.OpenLlamaConfig",headingTag:"h2"}}),R=new le({props:{name:"class transformers.OpenLlamaConfig",anchor:"transformers.OpenLlamaConfig",parameters:[{name:"vocab_size",val:" = 100000"},{name:"hidden_size",val:" = 4096"},{name:"intermediate_size",val:" = 11008"},{name:"num_hidden_layers",val:" = 32"},{name:"num_attention_heads",val:" = 32"},{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:" = 0"},{name:"bos_token_id",val:" = 1"},{name:"eos_token_id",val:" = 2"},{name:"tie_word_embeddings",val:" = False"},{name:"use_memory_efficient_attention",val:" = True"},{name:"hidden_dropout_prob",val:" = 0.1"},{name:"attention_dropout_prob",val:" = 0.1"},{name:"use_stable_embedding",val:" = True"},{name:"shared_input_output_embedding",val:" = True"},{name:"rope_theta",val:" = 10000.0"},{name:"rope_scaling",val:" = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.OpenLlamaConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 32000) &#x2014;
Vocabulary size of the Open-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/v4.42.4/en/model_doc/open-llama#transformers.OpenLlamaModel">OpenLlamaModel</a>`,name:"vocab_size"},{anchor:"transformers.OpenLlamaConfig.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.OpenLlamaConfig.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.OpenLlamaConfig.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 encoder.`,name:"num_hidden_layers"},{anchor:"transformers.OpenLlamaConfig.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 encoder.`,name:"num_attention_heads"},{anchor:"transformers.OpenLlamaConfig.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.OpenLlamaConfig.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. Typically set this to something large
just in case (e.g., 512 or 1024 or 2048).`,name:"max_position_embeddings"},{anchor:"transformers.OpenLlamaConfig.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.OpenLlamaConfig.rms_norm_eps",description:`<strong>rms_norm_eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-12) &#x2014;
The epsilon used by the rms normalization layers.`,name:"rms_norm_eps"},{anchor:"transformers.OpenLlamaConfig.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.OpenLlamaConfig.tie_word_embeddings(bool,",description:`<strong>tie_word_embeddings(<code>bool</code>,</strong> <em>optional</em>, defaults to <code>False</code>) &#x2014;
Whether to tie weight embeddings`,name:"tie_word_embeddings(bool,"},{anchor:"transformers.OpenLlamaConfig.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.OpenLlamaConfig.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.</p>
<p>Example &#x2014;`,name:"rope_scaling"}],source:"https://github.com/huggingface/transformers/blob/v4.42.4/src/transformers/models/deprecated/open_llama/configuration_open_llama.py#L29"}}),B=new Mn({props:{anchor:"transformers.OpenLlamaConfig.example",$$slots:{default:[Sn]},$$scope:{ctx:k}}}),X=new ke({props:{title:"OpenLlamaModel",local:"transformers.OpenLlamaModel",headingTag:"h2"}}),Q=new le({props:{name:"class transformers.OpenLlamaModel",anchor:"transformers.OpenLlamaModel",parameters:[{name:"config",val:": OpenLlamaConfig"}],parametersDescription:[{anchor:"transformers.OpenLlamaModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.42.4/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</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/v4.42.4/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.
config &#x2014; OpenLlamaConfig`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/v4.42.4/src/transformers/models/deprecated/open_llama/modeling_open_llama.py#L508"}}),K=new le({props:{name:"forward",anchor:"transformers.OpenLlamaModel.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:"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.OpenLlamaModel.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/v4.42.4/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.42.4/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.42.4/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.OpenLlamaModel.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/v4.42.4/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.42.4/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.42.4/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.OpenLlamaModel.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.OpenLlamaModel.forward.past_key_values",description:`<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>) &#x2014;
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>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_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>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.OpenLlamaModel.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.OpenLlamaModel.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.OpenLlamaModel.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.OpenLlamaModel.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.OpenLlamaModel.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/v4.42.4/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.42.4/src/transformers/models/deprecated/open_llama/modeling_open_llama.py#L543"}}),H=new Le({props:{$$slots:{default:[jn]},$$scope:{ctx:k}}}),ee=new ke({props:{title:"OpenLlamaForCausalLM",local:"transformers.OpenLlamaForCausalLM",headingTag:"h2"}}),ne=new le({props:{name:"class transformers.OpenLlamaForCausalLM",anchor:"transformers.OpenLlamaForCausalLM",parameters:[{name:"config",val:""}],source:"https://github.com/huggingface/transformers/blob/v4.42.4/src/transformers/models/deprecated/open_llama/modeling_open_llama.py#L670"}}),te=new le({props:{name:"forward",anchor:"transformers.OpenLlamaForCausalLM.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.OpenLlamaForCausalLM.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/v4.42.4/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.42.4/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.42.4/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.OpenLlamaForCausalLM.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/v4.42.4/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.42.4/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.42.4/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.OpenLlamaForCausalLM.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.OpenLlamaForCausalLM.forward.past_key_values",description:`<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>) &#x2014;
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>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_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>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.OpenLlamaForCausalLM.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.OpenLlamaForCausalLM.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.OpenLlamaForCausalLM.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.OpenLlamaForCausalLM.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.OpenLlamaForCausalLM.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/v4.42.4/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.</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:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/v4.42.4/src/transformers/models/deprecated/open_llama/modeling_open_llama.py#L700",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/v4.42.4/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/v4.42.4/en/model_doc/open-llama#transformers.OpenLlamaConfig"
>OpenLlamaConfig</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/v4.42.4/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast"
>transformers.modeling_outputs.CausalLMOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),Z=new Le({props:{$$slots:{default:[Jn]},$$scope:{ctx:k}}}),N=new Mn({props:{anchor:"transformers.OpenLlamaForCausalLM.forward.example",$$slots:{default:[Un]},$$scope:{ctx:k}}}),oe=new ke({props:{title:"OpenLlamaForSequenceClassification",local:"transformers.OpenLlamaForSequenceClassification",headingTag:"h2"}}),ae=new le({props:{name:"class transformers.OpenLlamaForSequenceClassification",anchor:"transformers.OpenLlamaForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.OpenLlamaForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/v4.42.4/en/model_doc/open-llama#transformers.OpenLlamaConfig">OpenLlamaConfig</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/v4.42.4/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/v4.42.4/src/transformers/models/deprecated/open_llama/modeling_open_llama.py#L844"}}),se=new le({props:{name:"forward",anchor:"transformers.OpenLlamaForSequenceClassification.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.OpenLlamaForSequenceClassification.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/v4.42.4/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.42.4/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.42.4/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.OpenLlamaForSequenceClassification.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/v4.42.4/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/v4.42.4/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/v4.42.4/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.OpenLlamaForSequenceClassification.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.OpenLlamaForSequenceClassification.forward.past_key_values",description:`<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>) &#x2014;
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>) and 2 additional tensors of shape
<code>(batch_size, num_heads, encoder_sequence_length, embed_size_per_head)</code>.</p>
<p>Contains pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
blocks) that can be used (see <code>past_key_values</code> input) to speed up sequential decoding.</p>
<p>If <code>past_key_values</code> are used, the user can optionally input only the last <code>decoder_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>decoder_input_ids</code> of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.OpenLlamaForSequenceClassification.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.OpenLlamaForSequenceClassification.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.OpenLlamaForSequenceClassification.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.OpenLlamaForSequenceClassification.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.OpenLlamaForSequenceClassification.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/v4.42.4/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"},{anchor:"transformers.OpenLlamaForSequenceClassification.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/v4.42.4/src/transformers/models/deprecated/open_llama/modeling_open_llama.py#L875"}}),G=new Le({props:{$$slots:{default:[Bn]},$$scope:{ctx:k}}}),re=new qn({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/open-llama.md"}}),{c(){n=d("meta"),u=s(),o=d("p"),m=s(),h(L.$$.fragment),p=s(),h(w.$$.fragment),Me=s(),h(U.$$.fragment),Oe=s(),h(V.$$.fragment),Ce=s(),E=d("p"),E.textContent=cn,ze=s(),A=d("p"),A.textContent=mn,xe=s(),Y=d("p"),Y.innerHTML=pn,Fe=s(),h(D.$$.fragment),Pe=s(),O=d("div"),h(R.$$.fragment),Ze=s(),ce=d("p"),ce.innerHTML=un,Ne=s(),me=d("p"),me.innerHTML=hn,Ge=s(),h(B.$$.fragment),qe=s(),h(X.$$.fragment),Ie=s(),M=d("div"),h(Q.$$.fragment),Ve=s(),pe=d("p"),pe.innerHTML=fn,Ee=s(),ue=d("p"),ue.innerHTML=gn,Ae=s(),he=d("p"),he.innerHTML=_n,Ye=s(),P=d("div"),h(K.$$.fragment),De=s(),fe=d("p"),fe.innerHTML=bn,Re=s(),h(H.$$.fragment),We=s(),h(ee.$$.fragment),Se=s(),I=d("div"),h(ne.$$.fragment),Xe=s(),z=d("div"),h(te.$$.fragment),Qe=s(),ge=d("p"),ge.innerHTML=vn,Ke=s(),h(Z.$$.fragment),en=s(),h(N.$$.fragment),je=s(),h(oe.$$.fragment),Je=s(),T=d("div"),h(ae.$$.fragment),nn=s(),_e=d("p"),_e.textContent=yn,tn=s(),be=d("p"),be.innerHTML=Tn,on=s(),ve=d("p"),ve.innerHTML=$n,an=s(),ye=d("p"),ye.innerHTML=kn,sn=s(),Te=d("p"),Te.innerHTML=wn,rn=s(),q=d("div"),h(se.$$.fragment),ln=s(),$e=d("p"),$e.innerHTML=Ln,dn=s(),h(G.$$.fragment),Ue=s(),h(re.$$.fragment),Be=s(),we=d("p"),this.h()},l(e){const t=Pn("svelte-u9bgzb",document.head);n=c(t,"META",{name:!0,content:!0}),t.forEach(a),u=r(e),o=c(e,"P",{}),j(o).forEach(a),m=r(e),f(L.$$.fragment,e),p=r(e),f(w.$$.fragment,e),Me=r(e),f(U.$$.fragment,e),Oe=r(e),f(V.$$.fragment,e),Ce=r(e),E=c(e,"P",{"data-svelte-h":!0}),y(E)!=="svelte-ycxpq0"&&(E.textContent=cn),ze=r(e),A=c(e,"P",{"data-svelte-h":!0}),y(A)!=="svelte-1snb5sc"&&(A.textContent=mn),xe=r(e),Y=c(e,"P",{"data-svelte-h":!0}),y(Y)!=="svelte-glizh3"&&(Y.innerHTML=pn),Fe=r(e),f(D.$$.fragment,e),Pe=r(e),O=c(e,"DIV",{class:!0});var x=j(O);f(R.$$.fragment,x),Ze=r(x),ce=c(x,"P",{"data-svelte-h":!0}),y(ce)!=="svelte-5su8xn"&&(ce.innerHTML=un),Ne=r(x),me=c(x,"P",{"data-svelte-h":!0}),y(me)!=="svelte-q5f6v7"&&(me.innerHTML=hn),Ge=r(x),f(B.$$.fragment,x),x.forEach(a),qe=r(e),f(X.$$.fragment,e),Ie=r(e),M=c(e,"DIV",{class:!0});var C=j(M);f(Q.$$.fragment,C),Ve=r(C),pe=c(C,"P",{"data-svelte-h":!0}),y(pe)!=="svelte-1b8z7cf"&&(pe.innerHTML=fn),Ee=r(C),ue=c(C,"P",{"data-svelte-h":!0}),y(ue)!=="svelte-hswkmf"&&(ue.innerHTML=gn),Ae=r(C),he=c(C,"P",{"data-svelte-h":!0}),y(he)!=="svelte-aj729g"&&(he.innerHTML=_n),Ye=r(C),P=c(C,"DIV",{class:!0});var W=j(P);f(K.$$.fragment,W),De=r(W),fe=c(W,"P",{"data-svelte-h":!0}),y(fe)!=="svelte-1nr1omw"&&(fe.innerHTML=bn),Re=r(W),f(H.$$.fragment,W),W.forEach(a),C.forEach(a),We=r(e),f(ee.$$.fragment,e),Se=r(e),I=c(e,"DIV",{class:!0});var ie=j(I);f(ne.$$.fragment,ie),Xe=r(ie),z=c(ie,"DIV",{class:!0});var F=j(z);f(te.$$.fragment,F),Qe=r(F),ge=c(F,"P",{"data-svelte-h":!0}),y(ge)!=="svelte-1tfkylc"&&(ge.innerHTML=vn),Ke=r(F),f(Z.$$.fragment,F),en=r(F),f(N.$$.fragment,F),F.forEach(a),ie.forEach(a),je=r(e),f(oe.$$.fragment,e),Je=r(e),T=c(e,"DIV",{class:!0});var $=j(T);f(ae.$$.fragment,$),nn=r($),_e=c($,"P",{"data-svelte-h":!0}),y(_e)!=="svelte-62must"&&(_e.textContent=yn),tn=r($),be=c($,"P",{"data-svelte-h":!0}),y(be)!=="svelte-144c5jz"&&(be.innerHTML=Tn),on=r($),ve=c($,"P",{"data-svelte-h":!0}),y(ve)!=="svelte-10ugs3m"&&(ve.innerHTML=$n),an=r($),ye=c($,"P",{"data-svelte-h":!0}),y(ye)!=="svelte-mhvelr"&&(ye.innerHTML=kn),sn=r($),Te=c($,"P",{"data-svelte-h":!0}),y(Te)!=="svelte-hswkmf"&&(Te.innerHTML=wn),rn=r($),q=c($,"DIV",{class:!0});var S=j(q);f(se.$$.fragment,S),ln=r(S),$e=c(S,"P",{"data-svelte-h":!0}),y($e)!=="svelte-1yyoq4e"&&($e.innerHTML=Ln),dn=r(S),f(G.$$.fragment,S),S.forEach(a),$.forEach(a),Ue=r(e),f(re.$$.fragment,e),Be=r(e),we=c(e,"P",{}),j(we).forEach(a),this.h()},h(){J(n,"name","hf:doc:metadata"),J(n,"content",Zn),J(O,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(P,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(M,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(I,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(q,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),J(T,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,t){i(document.head,n),l(e,u,t),l(e,o,t),l(e,m,t),g(L,e,t),l(e,p,t),g(w,e,t),l(e,Me,t),g(U,e,t),l(e,Oe,t),g(V,e,t),l(e,Ce,t),l(e,E,t),l(e,ze,t),l(e,A,t),l(e,xe,t),l(e,Y,t),l(e,Fe,t),g(D,e,t),l(e,Pe,t),l(e,O,t),g(R,O,null),i(O,Ze),i(O,ce),i(O,Ne),i(O,me),i(O,Ge),g(B,O,null),l(e,qe,t),g(X,e,t),l(e,Ie,t),l(e,M,t),g(Q,M,null),i(M,Ve),i(M,pe),i(M,Ee),i(M,ue),i(M,Ae),i(M,he),i(M,Ye),i(M,P),g(K,P,null),i(P,De),i(P,fe),i(P,Re),g(H,P,null),l(e,We,t),g(ee,e,t),l(e,Se,t),l(e,I,t),g(ne,I,null),i(I,Xe),i(I,z),g(te,z,null),i(z,Qe),i(z,ge),i(z,Ke),g(Z,z,null),i(z,en),g(N,z,null),l(e,je,t),g(oe,e,t),l(e,Je,t),l(e,T,t),g(ae,T,null),i(T,nn),i(T,_e),i(T,tn),i(T,be),i(T,on),i(T,ve),i(T,an),i(T,ye),i(T,sn),i(T,Te),i(T,rn),i(T,q),g(se,q,null),i(q,ln),i(q,$e),i(q,dn),g(G,q,null),l(e,Ue,t),g(re,e,t),l(e,Be,t),l(e,we,t),He=!0},p(e,[t]){const x={};t&2&&(x.$$scope={dirty:t,ctx:e}),w.$set(x);const C={};t&2&&(C.$$scope={dirty:t,ctx:e}),U.$set(C);const W={};t&2&&(W.$$scope={dirty:t,ctx:e}),B.$set(W);const ie={};t&2&&(ie.$$scope={dirty:t,ctx:e}),H.$set(ie);const F={};t&2&&(F.$$scope={dirty:t,ctx:e}),Z.$set(F);const $={};t&2&&($.$$scope={dirty:t,ctx:e}),N.$set($);const S={};t&2&&(S.$$scope={dirty:t,ctx:e}),G.$set(S)},i(e){He||(_(L.$$.fragment,e),_(w.$$.fragment,e),_(U.$$.fragment,e),_(V.$$.fragment,e),_(D.$$.fragment,e),_(R.$$.fragment,e),_(B.$$.fragment,e),_(X.$$.fragment,e),_(Q.$$.fragment,e),_(K.$$.fragment,e),_(H.$$.fragment,e),_(ee.$$.fragment,e),_(ne.$$.fragment,e),_(te.$$.fragment,e),_(Z.$$.fragment,e),_(N.$$.fragment,e),_(oe.$$.fragment,e),_(ae.$$.fragment,e),_(se.$$.fragment,e),_(G.$$.fragment,e),_(re.$$.fragment,e),He=!0)},o(e){b(L.$$.fragment,e),b(w.$$.fragment,e),b(U.$$.fragment,e),b(V.$$.fragment,e),b(D.$$.fragment,e),b(R.$$.fragment,e),b(B.$$.fragment,e),b(X.$$.fragment,e),b(Q.$$.fragment,e),b(K.$$.fragment,e),b(H.$$.fragment,e),b(ee.$$.fragment,e),b(ne.$$.fragment,e),b(te.$$.fragment,e),b(Z.$$.fragment,e),b(N.$$.fragment,e),b(oe.$$.fragment,e),b(ae.$$.fragment,e),b(se.$$.fragment,e),b(G.$$.fragment,e),b(re.$$.fragment,e),He=!1},d(e){e&&(a(u),a(o),a(m),a(p),a(Me),a(Oe),a(Ce),a(E),a(ze),a(A),a(xe),a(Y),a(Fe),a(Pe),a(O),a(qe),a(Ie),a(M),a(We),a(Se),a(I),a(je),a(Je),a(T),a(Ue),a(Be),a(we)),a(n),v(L,e),v(w,e),v(U,e),v(V,e),v(D,e),v(R),v(B),v(X,e),v(Q),v(K),v(H),v(ee,e),v(ne),v(te),v(Z),v(N),v(oe,e),v(ae),v(se),v(G),v(re,e)}}}const Zn='{"title":"Open-Llama","local":"open-llama","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"OpenLlamaConfig","local":"transformers.OpenLlamaConfig","sections":[],"depth":2},{"title":"OpenLlamaModel","local":"transformers.OpenLlamaModel","sections":[],"depth":2},{"title":"OpenLlamaForCausalLM","local":"transformers.OpenLlamaForCausalLM","sections":[],"depth":2},{"title":"OpenLlamaForSequenceClassification","local":"transformers.OpenLlamaForSequenceClassification","sections":[],"depth":2}],"depth":1}';function Nn(k){return zn(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class Xn extends xn{constructor(n){super(),Fn(this,n,Nn,Hn,Cn,{})}}export{Xn as component};
