import{s as Pt,o as qt,n as we}from"../chunks/scheduler.9bc65507.js";import{S as Jt,i as It,g as d,s as a,r as h,A as Bt,h as c,f as s,c as r,j as B,u as g,x as u,k as W,y as i,a as l,v as _,d as b,t as T,w as C}from"../chunks/index.707bf1b6.js";import{T as Lt}from"../chunks/Tip.c2ecdbf4.js";import{D as R}from"../chunks/Docstring.17db21ae.js";import{C as pt}from"../chunks/CodeBlock.54a9f38d.js";import{E as ct}from"../chunks/ExampleCodeBlock.4f515aa9.js";import{H as Me}from"../chunks/Heading.342b1fa6.js";function Wt(x){let t,y="Example:",p,m,f;return m=new pt({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> CpmAntModel, CpmAntConfig

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a CPMAnt cpm-ant-10b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>configuration = CpmAntConfig()

<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-comment"># Initializing a model from the cpm-ant-10b style configuration</span>
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CpmAntModel(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(){t=d("p"),t.textContent=y,p=a(),h(m.$$.fragment)},l(n){t=c(n,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=y),p=r(n),g(m.$$.fragment,n)},m(n,v){l(n,t,v),l(n,p,v),_(m,n,v),f=!0},p:we,i(n){f||(b(m.$$.fragment,n),f=!0)},o(n){T(m.$$.fragment,n),f=!1},d(n){n&&(s(t),s(p)),C(m,n)}}}function jt(x){let t,y=`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(){t=d("p"),t.innerHTML=y},l(p){t=c(p,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=y)},m(p,m){l(p,t,m)},p:we,d(p){p&&s(t)}}}function Ut(x){let t,y="Example:",p,m,f;return m=new pt({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, CpmAntModel
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openbmb/cpm-ant-10b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CpmAntModel.from_pretrained(<span class="hljs-string">&quot;openbmb/cpm-ant-10b&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;pt&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(){t=d("p"),t.textContent=y,p=a(),h(m.$$.fragment)},l(n){t=c(n,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=y),p=r(n),g(m.$$.fragment,n)},m(n,v){l(n,t,v),l(n,p,v),_(m,n,v),f=!0},p:we,i(n){f||(b(m.$$.fragment,n),f=!0)},o(n){T(m.$$.fragment,n),f=!1},d(n){n&&(s(t),s(p)),C(m,n)}}}function Ft(x){let t,y=`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(){t=d("p"),t.innerHTML=y},l(p){t=c(p,"P",{"data-svelte-h":!0}),u(t)!=="svelte-fincs2"&&(t.innerHTML=y)},m(p,m){l(p,t,m)},p:we,d(p){p&&s(t)}}}function Ht(x){let t,y="Example:",p,m,f;return m=new pt({props:{code:"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",highlighted:`<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">import</span> torch
<span class="hljs-meta">&gt;&gt;&gt; </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, CpmAntForCausalLM

<span class="hljs-meta">&gt;&gt;&gt; </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">&quot;openbmb/cpm-ant-10b&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>model = CpmAntForCausalLM.from_pretrained(<span class="hljs-string">&quot;openbmb/cpm-ant-10b&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;pt&quot;</span>)
<span class="hljs-meta">&gt;&gt;&gt; </span>outputs = model(**inputs, labels=inputs[<span class="hljs-string">&quot;input_ids&quot;</span>])
<span class="hljs-meta">&gt;&gt;&gt; </span>loss = outputs.loss
<span class="hljs-meta">&gt;&gt;&gt; </span>logits = outputs.logits`,wrap:!1}}),{c(){t=d("p"),t.textContent=y,p=a(),h(m.$$.fragment)},l(n){t=c(n,"P",{"data-svelte-h":!0}),u(t)!=="svelte-11lpom8"&&(t.textContent=y),p=r(n),g(m.$$.fragment,n)},m(n,v){l(n,t,v),l(n,p,v),_(m,n,v),f=!0},p:we,i(n){f||(b(m.$$.fragment,n),f=!0)},o(n){T(m.$$.fragment,n),f=!1},d(n){n&&(s(t),s(p)),C(m,n)}}}function Zt(x){let t,y,p,m,f,n,v,xe,Q,mt='CPM-Ant is an open-source Chinese pre-trained language model (PLM) with 10B parameters. It is also the first milestone of the live training process of CPM-Live. The training process is cost-effective and environment-friendly. CPM-Ant also achieves promising results with delta tuning on the CUGE benchmark. Besides the full model, we also provide various compressed versions to meet the requirements of different hardware configurations. <a href="https://github.com/OpenBMB/CPM-Live/tree/cpm-ant/cpm-live" rel="nofollow">See more</a>',Ae,S,ut="Tips:",ze,O,ft='This model was contributed by <a href="https://huggingface.co/openbmb" rel="nofollow">OpenBMB</a>. The original code can be found <a href="https://github.com/OpenBMB/CPM-Live/tree/cpm-ant/cpm-live" rel="nofollow">here</a>.',Le,D,ht="⚙️ Training & Inference",Pe,X,gt='<li>A tutorial on <a href="https://github.com/OpenBMB/CPM-Live/tree/cpm-ant/cpm-live" rel="nofollow">CPM-Live</a>.</li>',qe,Y,Je,M,K,Ge,pe,_t=`This is the configuration class to store the configuration of a <a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a>. It is used to instantiate an
CPMAnt 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 CPMAnt
<a href="https://huggingface.co/openbmb/cpm-ant-10b" rel="nofollow">openbmb/cpm-ant-10b</a> architecture.`,Ee,me,bt=`Configuration objects inherit from <a href="/docs/transformers/main/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/main/en/main_classes/configuration#transformers.PretrainedConfig">PretrainedConfig</a> for more information.`,Ve,U,Ie,ee,Be,$,te,Ne,ue,Tt="Construct a CPMAnt tokenizer. Based on byte-level Byte-Pair-Encoding.",Re,J,ne,Qe,fe,Ct=`Build model inputs from a sequence or a pair of sequence for sequence classification tasks by concatenating and
adding special tokens. A CPMAnt sequence has the following format:`,Se,he,yt="<li>single sequence: <code>[BOS] Sequence</code>.</li>",Oe,F,oe,De,ge,vt=`Retrieve sequence ids from a token list that has no special tokens added. This method is called when adding
special tokens using the tokenizer <code>prepare_for_model</code> method.`,We,se,je,w,ae,Xe,_e,kt=`The bare CPMAnt Model outputting raw hidden-states without any specific head on top.
This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,Ye,be,Mt=`Parameters
config (<a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntConfig">~CpmAntConfig</a>): Model configuration class with all the parameters of the
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,Ke,A,re,et,Te,$t='The <a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a> forward method, overrides the <code>__call__</code> special method.',tt,H,nt,Z,Ue,ie,Fe,k,le,ot,Ce,wt="The CPMAnt Model with a language modeling head on top (linear layer with weights tied to the input embeddings).",st,ye,xt=`This model is a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> sub-class. Use
it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and
behavior.`,at,ve,At=`Parameters
config (<a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntConfig">~CpmAntConfig</a>): Model configuration class with all the parameters of the
Initializing with a config file does not load the weights associated with the model, only the
configuration. Check out the <a href="/docs/transformers/main/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,rt,z,de,it,ke,zt='The <a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntForCausalLM">CpmAntForCausalLM</a> forward method, overrides the <code>__call__</code> special method.',lt,G,dt,E,He,$e,Ze;return f=new Me({props:{title:"CPMAnt",local:"cpmant",headingTag:"h1"}}),v=new Me({props:{title:"Overview",local:"overview",headingTag:"h2"}}),Y=new Me({props:{title:"CpmAntConfig",local:"transformers.CpmAntConfig",headingTag:"h2"}}),K=new R({props:{name:"class transformers.CpmAntConfig",anchor:"transformers.CpmAntConfig",parameters:[{name:"vocab_size",val:": int = 30720"},{name:"hidden_size",val:": int = 4096"},{name:"num_attention_heads",val:": int = 32"},{name:"dim_head",val:": int = 128"},{name:"dim_ff",val:": int = 10240"},{name:"num_hidden_layers",val:": int = 48"},{name:"dropout_p",val:": int = 0.0"},{name:"position_bias_num_buckets",val:": int = 512"},{name:"position_bias_max_distance",val:": int = 2048"},{name:"eps",val:": int = 1e-06"},{name:"init_std",val:": float = 1.0"},{name:"prompt_types",val:": int = 32"},{name:"prompt_length",val:": int = 32"},{name:"segment_types",val:": int = 32"},{name:"use_cache",val:": bool = True"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CpmAntConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to 30720) &#x2014;
Vocabulary size of the CPMAnt model. Defines the number of different tokens that can be represented by the
<code>input</code> passed when calling <a href="/docs/transformers/main/en/model_doc/cpmant#transformers.CpmAntModel">CpmAntModel</a>.`,name:"vocab_size"},{anchor:"transformers.CpmAntConfig.hidden_size",description:`<strong>hidden_size</strong> (<code>int</code>, <em>optional</em>, defaults to 4096) &#x2014;
Dimension of the encoder layers.`,name:"hidden_size"},{anchor:"transformers.CpmAntConfig.num_attention_heads",description:`<strong>num_attention_heads</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
Number of attention heads in the Transformer encoder.`,name:"num_attention_heads"},{anchor:"transformers.CpmAntConfig.dim_head",description:`<strong>dim_head</strong> (<code>int</code>, <em>optional</em>, defaults to 128) &#x2014;
Dimension of attention heads for each attention layer in the Transformer encoder.`,name:"dim_head"},{anchor:"transformers.CpmAntConfig.dim_ff",description:`<strong>dim_ff</strong> (<code>int</code>, <em>optional</em>, defaults to 10240) &#x2014;
Dimension of the &#x201C;intermediate&#x201D; (i.e., feed-forward) layer in the Transformer encoder.`,name:"dim_ff"},{anchor:"transformers.CpmAntConfig.num_hidden_layers",description:`<strong>num_hidden_layers</strong> (<code>int</code>, <em>optional</em>, defaults to 48) &#x2014;
Number of layers of the Transformer encoder.`,name:"num_hidden_layers"},{anchor:"transformers.CpmAntConfig.dropout_p",description:`<strong>dropout_p</strong> (<code>float</code>, <em>optional</em>, defaults to 0.0) &#x2014;
The dropout probabilitiy for all fully connected layers in the embeddings, encoder.`,name:"dropout_p"},{anchor:"transformers.CpmAntConfig.position_bias_num_buckets",description:`<strong>position_bias_num_buckets</strong> (<code>int</code>, <em>optional</em>, defaults to 512) &#x2014;
The number of position_bias buckets.`,name:"position_bias_num_buckets"},{anchor:"transformers.CpmAntConfig.position_bias_max_distance",description:`<strong>position_bias_max_distance</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:"position_bias_max_distance"},{anchor:"transformers.CpmAntConfig.eps",description:`<strong>eps</strong> (<code>float</code>, <em>optional</em>, defaults to 1e-06) &#x2014;
The epsilon used by the layer normalization layers.`,name:"eps"},{anchor:"transformers.CpmAntConfig.init_std",description:`<strong>init_std</strong> (<code>float</code>, <em>optional</em>, defaults to 1.0) &#x2014;
Initialize parameters with std = init_std.`,name:"init_std"},{anchor:"transformers.CpmAntConfig.prompt_types",description:`<strong>prompt_types</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The type of prompt.`,name:"prompt_types"},{anchor:"transformers.CpmAntConfig.prompt_length",description:`<strong>prompt_length</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The length of prompt.`,name:"prompt_length"},{anchor:"transformers.CpmAntConfig.segment_types",description:`<strong>segment_types</strong> (<code>int</code>, <em>optional</em>, defaults to 32) &#x2014;
The type of segment.`,name:"segment_types"},{anchor:"transformers.CpmAntConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) &#x2014;
Whether to use cache.`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/cpmant/configuration_cpmant.py#L29"}}),U=new ct({props:{anchor:"transformers.CpmAntConfig.example",$$slots:{default:[Wt]},$$scope:{ctx:x}}}),ee=new Me({props:{title:"CpmAntTokenizer",local:"transformers.CpmAntTokenizer",headingTag:"h2"}}),te=new R({props:{name:"class transformers.CpmAntTokenizer",anchor:"transformers.CpmAntTokenizer",parameters:[{name:"vocab_file",val:""},{name:"bod_token",val:" = '<d>'"},{name:"eod_token",val:" = '</d>'"},{name:"bos_token",val:" = '<s>'"},{name:"eos_token",val:" = '</s>'"},{name:"pad_token",val:" = '<pad>'"},{name:"unk_token",val:" = '<unk>'"},{name:"line_token",val:" = '</n>'"},{name:"space_token",val:" = '</_>'"},{name:"padding_side",val:" = 'left'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CpmAntTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) &#x2014;
Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.CpmAntTokenizer.bod_token",description:`<strong>bod_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;d&gt;&quot;</code>) &#x2014;
The beginning of document token.`,name:"bod_token"},{anchor:"transformers.CpmAntTokenizer.eod_token",description:`<strong>eod_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/d&gt;&quot;</code>) &#x2014;
The end of document token.`,name:"eod_token"},{anchor:"transformers.CpmAntTokenizer.bos_token",description:`<strong>bos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;s&gt;&quot;</code>) &#x2014;
The beginning of sequence token.`,name:"bos_token"},{anchor:"transformers.CpmAntTokenizer.eos_token",description:`<strong>eos_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/s&gt;&quot;</code>) &#x2014;
The end of sequence token.`,name:"eos_token"},{anchor:"transformers.CpmAntTokenizer.pad_token",description:`<strong>pad_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;pad&gt;&quot;</code>) &#x2014;
The token used for padding.`,name:"pad_token"},{anchor:"transformers.CpmAntTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;unk&gt;&quot;</code>) &#x2014;
The unknown token.`,name:"unk_token"},{anchor:"transformers.CpmAntTokenizer.line_token",description:`<strong>line_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/n&gt;&quot;</code>) &#x2014;
The line token.`,name:"line_token"},{anchor:"transformers.CpmAntTokenizer.space_token",description:`<strong>space_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>&quot;&lt;/_&gt;&quot;</code>) &#x2014;
The space token.`,name:"space_token"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/cpmant/tokenization_cpmant.py#L88"}}),ne=new R({props:{name:"build_inputs_with_special_tokens",anchor:"transformers.CpmAntTokenizer.build_inputs_with_special_tokens",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.List[int] = None"}],parametersDescription:[{anchor:"transformers.CpmAntTokenizer.build_inputs_with_special_tokens.token_ids_0",description:"<strong>token_ids_0</strong> (<code>List[int]</code>) &#x2014; The first tokenized sequence that special tokens will be added.",name:"token_ids_0"},{anchor:"transformers.CpmAntTokenizer.build_inputs_with_special_tokens.token_ids_1",description:"<strong>token_ids_1</strong> (<code>List[int]</code>) &#x2014; The optional second tokenized sequence that special tokens will be added.",name:"token_ids_1"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/cpmant/tokenization_cpmant.py#L236",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The model input with special tokens.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>List[int]</code></p>
`}}),oe=new R({props:{name:"get_special_tokens_mask",anchor:"transformers.CpmAntTokenizer.get_special_tokens_mask",parameters:[{name:"token_ids_0",val:": typing.List[int]"},{name:"token_ids_1",val:": typing.Optional[typing.List[int]] = None"},{name:"already_has_special_tokens",val:": bool = False"}],parametersDescription:[{anchor:"transformers.CpmAntTokenizer.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.CpmAntTokenizer.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.CpmAntTokenizer.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/main/src/transformers/models/cpmant/tokenization_cpmant.py#L254",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>
`}}),se=new Me({props:{title:"CpmAntModel",local:"transformers.CpmAntModel",headingTag:"h2"}}),ae=new R({props:{name:"class transformers.CpmAntModel",anchor:"transformers.CpmAntModel",parameters:[{name:"config",val:": CpmAntConfig"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/cpmant/modeling_cpmant.py#L603"}}),re=new R({props:{name:"forward",anchor:"transformers.CpmAntModel.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"past_key_values",val:": typing.Optional[typing.Tuple[typing.Tuple[torch.Tensor]]] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CpmAntModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, seq_len)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <code>CPMAntTokenizer</code>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__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.CpmAntModel.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;
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.`,name:"past_key_values"},{anchor:"transformers.CpmAntModel.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.CpmAntModel.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.`,name:"output_attentions"},{anchor:"transformers.CpmAntModel.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.`,name:"output_hidden_states"},{anchor:"transformers.CpmAntModel.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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.`,name:"return_dict"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/cpmant/modeling_cpmant.py#L641",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast"
>transformers.modeling_outputs.BaseModelOutputWithPast</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/main/en/model_doc/cpmant#transformers.CpmAntConfig"
>CpmAntConfig</a>) and inputs.</p>
<ul>
<li>
<p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</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>
<p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</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>) and optionally if
<code>config.is_encoder_decoder=True</code> 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 optionally if
<code>config.is_encoder_decoder=True</code> in the cross-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/main/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast"
>transformers.modeling_outputs.BaseModelOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),H=new Lt({props:{$$slots:{default:[jt]},$$scope:{ctx:x}}}),Z=new ct({props:{anchor:"transformers.CpmAntModel.forward.example",$$slots:{default:[Ut]},$$scope:{ctx:x}}}),ie=new Me({props:{title:"CpmAntForCausalLM",local:"transformers.CpmAntForCausalLM",headingTag:"h2"}}),le=new R({props:{name:"class transformers.CpmAntForCausalLM",anchor:"transformers.CpmAntForCausalLM",parameters:[{name:"config",val:": CpmAntConfig"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/cpmant/modeling_cpmant.py#L750"}}),de=new R({props:{name:"forward",anchor:"transformers.CpmAntForCausalLM.forward",parameters:[{name:"input_ids",val:": typing.Optional[torch.Tensor] = None"},{name:"past_key_values",val:": typing.Union[typing.List[typing.Tuple[torch.Tensor, torch.Tensor]], NoneType] = None"},{name:"use_cache",val:": typing.Optional[bool] = None"},{name:"output_attentions",val:": typing.Optional[bool] = None"},{name:"output_hidden_states",val:": typing.Optional[bool] = None"},{name:"labels",val:": typing.Optional[torch.Tensor] = None"},{name:"return_dict",val:": typing.Optional[bool] = None"},{name:"attention_mask",val:": typing.Optional[torch.Tensor] = None"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CpmAntForCausalLM.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.Tensor</code> of shape <code>(batch_size, seq_len)</code>) &#x2014;
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <code>CPMAntTokenizer</code>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__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.CpmAntForCausalLM.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;
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.`,name:"past_key_values"},{anchor:"transformers.CpmAntForCausalLM.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.CpmAntForCausalLM.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.`,name:"output_attentions"},{anchor:"transformers.CpmAntForCausalLM.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.`,name:"output_hidden_states"},{anchor:"transformers.CpmAntForCausalLM.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/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.</p>
<p>Args &#x2014;
input_ids (<code>torch.Tensor</code> of shape <code>(batch_size, seq_len)</code>):
Indices of input sequence tokens in the vocabulary.</p>
<p>Indices can be obtained using <code>CPMAntTokenizer</code>. See <a href="/docs/transformers/main/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
<a href="/docs/transformers/main/en/model_doc/perceiver#transformers.PerceiverTokenizer.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
<p><a href="../glossary#input-ids">What are input IDs?</a>
past_key_values (<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>):
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.
use_cache (<code>bool</code>, <em>optional</em>):
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>).
output_attentions (<code>bool</code>, <em>optional</em>):
Whether or not to return the attentions tensors of all attention layers.
output_hidden_states (<code>bool</code>, <em>optional</em>):
Whether or not to return the hidden states of all layers.
labels (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>):
Labels for computing the masked language modeling loss.
return_dict (<code>bool</code>, <em>optional</em>):
Whether or not to return a <a href="/docs/transformers/main/en/main_classes/output#transformers.utils.ModelOutput">ModelOutput</a> instead of a plain tuple.
attention_mask (<code>torch.Tensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>):
CPMAnt will process attention mask automatically, this parameter is a dummy parameter for
text-generation pipeline.</p>
<p>Example &#x2014;`,name:"return_dict"},{anchor:"transformers.CpmAntForCausalLM.forward.Text",description:"<strong>Text</strong> Generation with CpmAntForCausalLM. &#x2014;",name:"Text"}],source:"https://github.com/huggingface/transformers/blob/main/src/transformers/models/cpmant/modeling_cpmant.py#L763",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A <a
  href="/docs/transformers/main/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/main/en/model_doc/cpmant#transformers.CpmAntConfig"
>CpmAntConfig</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/main/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast"
>transformers.modeling_outputs.CausalLMOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
`}}),G=new Lt({props:{$$slots:{default:[Ft]},$$scope:{ctx:x}}}),E=new ct({props:{anchor:"transformers.CpmAntForCausalLM.forward.example",$$slots:{default:[Ht]},$$scope:{ctx:x}}}),{c(){t=d("meta"),y=a(),p=d("p"),m=a(),h(f.$$.fragment),n=a(),h(v.$$.fragment),xe=a(),Q=d("p"),Q.innerHTML=mt,Ae=a(),S=d("p"),S.textContent=ut,ze=a(),O=d("p"),O.innerHTML=ft,Le=a(),D=d("p"),D.textContent=ht,Pe=a(),X=d("ul"),X.innerHTML=gt,qe=a(),h(Y.$$.fragment),Je=a(),M=d("div"),h(K.$$.fragment),Ge=a(),pe=d("p"),pe.innerHTML=_t,Ee=a(),me=d("p"),me.innerHTML=bt,Ve=a(),h(U.$$.fragment),Ie=a(),h(ee.$$.fragment),Be=a(),$=d("div"),h(te.$$.fragment),Ne=a(),ue=d("p"),ue.textContent=Tt,Re=a(),J=d("div"),h(ne.$$.fragment),Qe=a(),fe=d("p"),fe.textContent=Ct,Se=a(),he=d("ul"),he.innerHTML=yt,Oe=a(),F=d("div"),h(oe.$$.fragment),De=a(),ge=d("p"),ge.innerHTML=vt,We=a(),h(se.$$.fragment),je=a(),w=d("div"),h(ae.$$.fragment),Xe=a(),_e=d("p"),_e.innerHTML=kt,Ye=a(),be=d("p"),be.innerHTML=Mt,Ke=a(),A=d("div"),h(re.$$.fragment),et=a(),Te=d("p"),Te.innerHTML=$t,tt=a(),h(H.$$.fragment),nt=a(),h(Z.$$.fragment),Ue=a(),h(ie.$$.fragment),Fe=a(),k=d("div"),h(le.$$.fragment),ot=a(),Ce=d("p"),Ce.textContent=wt,st=a(),ye=d("p"),ye.innerHTML=xt,at=a(),ve=d("p"),ve.innerHTML=At,rt=a(),z=d("div"),h(de.$$.fragment),it=a(),ke=d("p"),ke.innerHTML=zt,lt=a(),h(G.$$.fragment),dt=a(),h(E.$$.fragment),He=a(),$e=d("p"),this.h()},l(e){const 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