lighteval/pr_476/en/_app/immutable/nodes/13.ddffdd48.js

import{s as Ca,n as La,o as ka}from"../chunks/scheduler.7da89386.js";import{S as wa,i as Ta,g as r,s as o,r as i,A as Na,h as s,f as n,c as a,j as v,u as d,x as _,k as f,y as t,a as h,v as m,d as g,t as p,w as c}from"../chunks/index.0b7befd3.js";import{D as u}from"../chunks/Docstring.80026b50.js";import{H as M,E as Da}from"../chunks/EditOnGithub.0cb2bc8e.js";function Ia(Uo){let N,At,Pt,Gt,ee,Ut,te,Ht,ne,Wt,b,oe,Rn,z,ae,Yn,it,Ho="Clean up operations if needed, such as closing an endpoint.",jn,O,le,Jn,dt,Wo="Generates responses using a greedy decoding strategy until certain ending conditions are met.",Kn,V,re,Qn,mt,So="Generates responses using a greedy decoding strategy until certain ending conditions are met.",Xn,B,se,Zn,gt,Ro=`Tokenize the context and continuation and compute the log likelihood of those
tokenized sequences.`,eo,P,ie,to,pt,Yo="This function is used to compute the log likelihood of the context for perplexity metrics.",no,F,de,oo,ct,jo=`Tokenize the context and continuation and compute the log likelihood of those
tokenized sequences.`,ao,w,me,lo,ht,Jo="Encodes a context, continuation pair by taking care of the spaces in between.",ro,vt,Ko=`The advantage of pairwise is:
1) It better aligns with how LLM predicts tokens
2) Works in case len(tok(context,cont)) != len(tok(context)) + len(tok(continuation)).
E.g this can happen for chinese if no space is used between context/continuation`,St,ge,Rt,pe,Yt,k,ce,so,ft,Qo="Base configuration class for models.",io,ut,Xo=`Methods:
<strong>post_init</strong>(): Performs post-initialization checks on the configuration.
_init_configs(model_name, env_config): Initializes the model configuration.
init_configs(env_config): Initializes the model configuration using the environment configuration.
get_model_sha(): Retrieves the SHA of the model.`,jt,$,he,mo,A,ve,go,_t,Zo="Generates responses using a greedy decoding strategy until certain ending conditions are met.",po,G,fe,co,bt,ea="Compute all the parameters related to model_parallel",ho,U,ue,vo,$t,ta=`Tokenize the context and continuation and compute the log likelihood of those
tokenized sequences.`,fo,H,_e,uo,xt,na=`Tokenize the context and continuation and compute the log likelihood of those
tokenized sequences.`,_o,W,be,bo,yt,oa="Pads the <code>output_tensor</code> to the maximum length and gathers the lengths across processes.",$o,S,$e,xo,Mt,aa=`Tokenize a batch of inputs and return also the length, truncations and padding.
This step is done manually since we tokenize log probability inputs together with their continuation,
to manage possible extra spaces added at the start by tokenizers, see tok_encode_pair.`,Jt,xe,Kt,ye,Me,Qt,Ce,Le,Xt,ke,Zt,we,Te,en,Ne,De,tn,Ie,nn,Ee,on,D,qe,yo,R,ze,Mo,Ct,la="Load configuration for inference endpoint model from YAML file path.",an,Oe,Ve,ln,I,Be,Co,Lt,ra=`InferenceEndpointModels can be used both with the free inference client, or with inference
endpoints, which will use text-generation-inference to deploy your model for the duration of the evaluation.`,rn,Pe,sn,E,Fe,Lo,Y,Ae,ko,kt,sa="Load configuration for TGI endpoint model from YAML file path.",dn,Ge,Ue,mn,He,gn,We,Se,pn,Re,cn,Ye,hn,x,je,wo,j,Je,To,wt,ia="Gather together tensors of (possibly) various size spread on separate GPUs (first exchange the lengths and then pad and gather)",No,J,Ke,Do,Tt,da="Greedy generation until a stop token is generated.",Io,K,Qe,Eo,Nt,ma=`Ending conditions are submitted in several possible formats.
By default in lighteval we pass them as tuples (stop sequence, max number of items).
In the harness they sometimes are passed as dicts {“until”: .., “max_length”: …} or
as only ending conditions, either lists or strings.
Here, we convert all these formats to a tuple containing a list of ending conditions,
and a float for the max length allowed.`,qo,Q,Xe,zo,Dt,ga=`Tokenize the context and continuation and compute the log likelihood of those
tokenized sequences.`,Oo,X,Ze,Vo,It,pa="Gather together tensors of (possibly) various size spread on separate GPUs (first exchange the lengths and then pad and gather)",Bo,T,et,Po,Et,ca="Tokenize a batch of inputs and return also the length, truncations and padding",Fo,qt,ha=`We truncate to keep only at most <code>max_context</code> tokens
We pad to <code>padding_length</code> tokens`,vn,tt,fn,nt,un,ot,at,_n,q,lt,Ao,Z,rt,Go,zt,va="Generates responses using a greedy decoding strategy until certain ending conditions are met.",bn,st,$n,Ft,xn;return ee=new M({props:{title:"Models",local:"models",headingTag:"h1"}}),te=new M({props:{title:"Model",local:"model",headingTag:"h2"}}),ne=new M({props:{title:"LightevalModel",local:"lighteval.models.abstract_model.LightevalModel",headingTag:"h3"}}),oe=new u({props:{name:"class lighteval.models.abstract_model.LightevalModel",anchor:"lighteval.models.abstract_model.LightevalModel",parameters:[],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/abstract_model.py#L57"}}),ae=new u({props:{name:"cleanup",anchor:"lighteval.models.abstract_model.LightevalModel.cleanup",parameters:[],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/abstract_model.py#L62"}}),le=new u({props:{name:"greedy_until",anchor:"lighteval.models.abstract_model.LightevalModel.greedy_until",parameters:[{name:"requests",val:": list"},{name:"override_bs",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"lighteval.models.abstract_model.LightevalModel.greedy_until.requests",description:"<strong>requests</strong> (list[Request]) &#x2014; list of requests containing the context and ending conditions.",name:"requests"},{anchor:"lighteval.models.abstract_model.LightevalModel.greedy_until.disable_tqdm",description:"<strong>disable_tqdm</strong> (bool, optional) &#x2014; Whether to disable the progress bar. Defaults to False.",name:"disable_tqdm"},{anchor:"lighteval.models.abstract_model.LightevalModel.greedy_until.override_bs",description:"<strong>override_bs</strong> (int, optional) &#x2014; Override the batch size for generation. Defaults to None.",name:"override_bs"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/abstract_model.py#L105",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>list of generated responses.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>list[GenerativeResponse]</p>
`}}),re=new u({props:{name:"greedy_until_multi_turn",anchor:"lighteval.models.abstract_model.LightevalModel.greedy_until_multi_turn",parameters:[{name:"requests",val:": list"},{name:"override_bs",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/abstract_model.py#L99"}}),se=new u({props:{name:"loglikelihood",anchor:"lighteval.models.abstract_model.LightevalModel.loglikelihood",parameters:[{name:"requests",val:": list"},{name:"override_bs",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/abstract_model.py#L124"}}),ie=new u({props:{name:"loglikelihood_rolling",anchor:"lighteval.models.abstract_model.LightevalModel.loglikelihood_rolling",parameters:[{name:"requests",val:": list"},{name:"override_bs",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/abstract_model.py#L133"}}),de=new u({props:{name:"loglikelihood_single_token",anchor:"lighteval.models.abstract_model.LightevalModel.loglikelihood_single_token",parameters:[{name:"requests",val:": list"},{name:"override_bs",val:": typing.Optional[int] = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/abstract_model.py#L140"}}),me=new u({props:{name:"tok_encode_pair",anchor:"lighteval.models.abstract_model.LightevalModel.tok_encode_pair",parameters:[{name:"context",val:""},{name:"continuation",val:""},{name:"pairwise",val:": bool = False"}],parametersDescription:[{anchor:"lighteval.models.abstract_model.LightevalModel.tok_encode_pair.context",description:"<strong>context</strong> (str) &#x2014; The context string to be encoded.",name:"context"},{anchor:"lighteval.models.abstract_model.LightevalModel.tok_encode_pair.continuation",description:"<strong>continuation</strong> (str) &#x2014; The continuation string to be encoded.",name:"continuation"},{anchor:"lighteval.models.abstract_model.LightevalModel.tok_encode_pair.pairwise",description:`<strong>pairwise</strong> (bool) &#x2014;
If True, encode context and continuation separately.
If False, encode them together and then split.`,name:"pairwise"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/abstract_model.py#L162",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>A tuple containing the encoded context and continuation.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Tuple[TokenSequence, TokenSequence]</p>
`}}),ge=new M({props:{title:"Accelerate and Transformers Models",local:"accelerate-and-transformers-models",headingTag:"h2"}}),pe=new M({props:{title:"BaseModel",local:"lighteval.models.transformers.base_model.BaseModelConfig",headingTag:"h3"}}),ce=new u({props:{name:"class lighteval.models.transformers.base_model.BaseModelConfig",anchor:"lighteval.models.transformers.base_model.BaseModelConfig",parameters:[{name:"pretrained",val:": str"},{name:"accelerator",val:": Accelerator = None"},{name:"tokenizer",val:": typing.Optional[str] = None"},{name:"multichoice_continuations_start_space",val:": typing.Optional[bool] = None"},{name:"pairwise_tokenization",val:": bool = False"},{name:"subfolder",val:": typing.Optional[str] = None"},{name:"revision",val:": str = 'main'"},{name:"batch_size",val:": int = -1"},{name:"max_gen_toks",val:": typing.Optional[int] = 256"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"add_special_tokens",val:": bool = True"},{name:"model_parallel",val:": typing.Optional[bool] = None"},{name:"dtype",val:": typing.Union[str, torch.dtype, NoneType] = None"},{name:"device",val:": typing.Union[int, str] = 'cuda'"},{name:"quantization_config",val:": typing.Optional[transformers.utils.quantization_config.BitsAndBytesConfig] = None"},{name:"trust_remote_code",val:": bool = False"},{name:"use_chat_template",val:": bool = False"},{name:"compile",val:": bool = False"}],parametersDescription:[{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.pretrained",description:`<strong>pretrained</strong> (str) &#x2014;
HuggingFace Hub model ID name or the path to a pre-trained
model to load. This is effectively the <code>pretrained_model_name_or_path</code>
argument of <code>from_pretrained</code> in the HuggingFace <code>transformers</code> API.`,name:"pretrained"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.accelerator",description:"<strong>accelerator</strong> (Accelerator) &#x2014; accelerator to use for model training.",name:"accelerator"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.tokenizer",description:`<strong>tokenizer</strong> (Optional[str]) &#x2014; HuggingFace Hub tokenizer ID that will be
used for tokenization.`,name:"tokenizer"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.multichoice_continuations_start_space",description:`<strong>multichoice_continuations_start_space</strong> (Optional[bool]) &#x2014; Whether to add a
space at the start of each continuation in multichoice generation.
For example, context: &#x201C;What is the capital of France?&#x201D; and choices: &#x201C;Paris&#x201D;, &#x201C;London&#x201D;.
Will be tokenized as: &#x201C;What is the capital of France? Paris&#x201D; and &#x201C;What is the capital of France? London&#x201D;.
True adds a space, False strips a space, None does nothing`,name:"multichoice_continuations_start_space"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.pairwise_tokenization",description:"<strong>pairwise_tokenization</strong> (bool) &#x2014; Whether to tokenize the context and continuation as separately or together.",name:"pairwise_tokenization"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.subfolder",description:"<strong>subfolder</strong> (Optional[str]) &#x2014; The subfolder within the model repository.",name:"subfolder"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.revision",description:"<strong>revision</strong> (str) &#x2014; The revision of the model.",name:"revision"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.batch_size",description:"<strong>batch_size</strong> (int) &#x2014; The batch size for model training.",name:"batch_size"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.max_gen_toks",description:"<strong>max_gen_toks</strong> (Optional[int]) &#x2014; The maximum number of tokens to generate.",name:"max_gen_toks"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.max_length",description:"<strong>max_length</strong> (Optional[int]) &#x2014; The maximum length of the generated output.",name:"max_length"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.add_special_tokens",description:`<strong>add_special_tokens</strong> (bool, optional, defaults to True) &#x2014; Whether to add special tokens to the input sequences.
If <code>None</code>, the default value will be set to <code>True</code> for seq2seq models (e.g. T5) and
<code>False</code> for causal models.`,name:"add_special_tokens"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.model_parallel",description:`<strong>model_parallel</strong> (bool, optional, defaults to False) &#x2014;
True/False: force to use or not the <code>accelerate</code> library to load a large
model across multiple devices.
Default: None which corresponds to comparing the number of processes with
the number of GPUs. If it&#x2019;s smaller =&gt; model-parallelism, else not.`,name:"model_parallel"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.dtype",description:`<strong>dtype</strong> (Union[str, torch.dtype], optional, defaults to None) &#x2014;):
Converts the model weights to <code>dtype</code>, if specified. Strings get
converted to <code>torch.dtype</code> objects (e.g. <code>float16</code> -&gt; <code>torch.float16</code>).
Use <code>dtype=&quot;auto&quot;</code> to derive the type from the model&#x2019;s weights.`,name:"dtype"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.device",description:"<strong>device</strong> (Union[int, str]) &#x2014; device to use for model training.",name:"device"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.quantization_config",description:`<strong>quantization_config</strong> (Optional[BitsAndBytesConfig]) &#x2014; quantization
configuration for the model, manually provided to load a normally floating point
model at a quantized precision. Needed for 4-bit and 8-bit precision.`,name:"quantization_config"},{anchor:"lighteval.models.transformers.base_model.BaseModelConfig.trust_remote_code",description:`<strong>trust_remote_code</strong> (bool) &#x2014; Whether to trust remote code during model
loading.`,name:"trust_remote_code"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/base_model.py#L85"}}),he=new u({props:{name:"class lighteval.models.transformers.base_model.BaseModel",anchor:"lighteval.models.transformers.base_model.BaseModel",parameters:[{name:"env_config",val:": EnvConfig"},{name:"config",val:": BaseModelConfig"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/base_model.py#L224"}}),ve=new u({props:{name:"greedy_until",anchor:"lighteval.models.transformers.base_model.BaseModel.greedy_until",parameters:[{name:"requests",val:": list"},{name:"override_bs",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"lighteval.models.transformers.base_model.BaseModel.greedy_until.requests",description:"<strong>requests</strong> (list[Request]) &#x2014; list of requests containing the context and ending conditions.",name:"requests"},{anchor:"lighteval.models.transformers.base_model.BaseModel.greedy_until.override_bs",description:"<strong>override_bs</strong> (int, optional) &#x2014; Override the batch size for generation. Defaults to None.",name:"override_bs"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/base_model.py#L720",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>list of generated responses.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>list[GenerativeResponse]</p>
`}}),fe=new u({props:{name:"init_model_parallel",anchor:"lighteval.models.transformers.base_model.BaseModel.init_model_parallel",parameters:[{name:"model_parallel",val:": bool | None = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/base_model.py#L352"}}),ue=new u({props:{name:"loglikelihood",anchor:"lighteval.models.transformers.base_model.BaseModel.loglikelihood",parameters:[{name:"requests",val:": list"},{name:"override_bs",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"lighteval.models.transformers.base_model.BaseModel.loglikelihood.requests",description:"<strong>requests</strong> (list[Tuple[str, dict]]) &#x2014; <em>description</em>",name:"requests"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/base_model.py#L926",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><em>description</em></p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>list[Tuple[float, bool]]</p>
`}}),_e=new u({props:{name:"loglikelihood_single_token",anchor:"lighteval.models.transformers.base_model.BaseModel.loglikelihood_single_token",parameters:[{name:"requests",val:": list"},{name:"override_bs",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"lighteval.models.transformers.base_model.BaseModel.loglikelihood_single_token.requests",description:"<strong>requests</strong> (list[Tuple[str, dict]]) &#x2014; <em>description</em>",name:"requests"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/base_model.py#L1185",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><em>description</em></p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>list[Tuple[float, bool]]</p>
`}}),be=new u({props:{name:"pad_and_gather",anchor:"lighteval.models.transformers.base_model.BaseModel.pad_and_gather",parameters:[{name:"output_tensor",val:": Tensor"},{name:"drop_last_samples",val:": bool = True"},{name:"num_samples",val:": int = None"}],parametersDescription:[{anchor:"lighteval.models.transformers.base_model.BaseModel.pad_and_gather.output_tensor",description:"<strong>output_tensor</strong> (torch.Tensor) &#x2014; The output tensor to be padded.",name:"output_tensor"},{anchor:"lighteval.models.transformers.base_model.BaseModel.pad_and_gather.drop_last_samples",description:"<strong>drop_last_samples</strong> (bool, optional) &#x2014; Whether to drop the last samples during gathering.",name:"drop_last_samples"},{anchor:"lighteval.models.transformers.base_model.BaseModel.pad_and_gather.Last",description:`<strong>Last</strong> samples are dropped when the number of samples is not divisible by the number of processes. &#x2014;
Defaults to True.`,name:"Last"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/base_model.py#L1149",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>The padded output tensor and the gathered length tensor.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>torch.Tensor</p>
`}}),$e=new u({props:{name:"prepare_batch_logprob",anchor:"lighteval.models.transformers.base_model.BaseModel.prepare_batch_logprob",parameters:[{name:"batch",val:": list"},{name:"padding_length",val:": int"},{name:"max_context",val:": typing.Optional[int] = None"},{name:"single_token",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/base_model.py#L1088"}}),xe=new M({props:{title:"AdapterModel",local:"lighteval.models.transformers.adapter_model.AdapterModelConfig",headingTag:"h3"}}),Me=new u({props:{name:"class lighteval.models.transformers.adapter_model.AdapterModelConfig",anchor:"lighteval.models.transformers.adapter_model.AdapterModelConfig",parameters:[{name:"pretrained",val:": str"},{name:"accelerator",val:": Accelerator = None"},{name:"tokenizer",val:": typing.Optional[str] = None"},{name:"multichoice_continuations_start_space",val:": typing.Optional[bool] = None"},{name:"pairwise_tokenization",val:": bool = False"},{name:"subfolder",val:": typing.Optional[str] = None"},{name:"revision",val:": str = 'main'"},{name:"batch_size",val:": int = -1"},{name:"max_gen_toks",val:": typing.Optional[int] = 256"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"add_special_tokens",val:": bool = True"},{name:"model_parallel",val:": typing.Optional[bool] = None"},{name:"dtype",val:": typing.Union[str, torch.dtype, NoneType] = None"},{name:"device",val:": typing.Union[int, str] = 'cuda'"},{name:"quantization_config",val:": typing.Optional[transformers.utils.quantization_config.BitsAndBytesConfig] = None"},{name:"trust_remote_code",val:": bool = False"},{name:"use_chat_template",val:": bool = False"},{name:"compile",val:": bool = False"},{name:"base_model",val:": str = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/adapter_model.py#L42"}}),Le=new u({props:{name:"class lighteval.models.transformers.adapter_model.AdapterModel",anchor:"lighteval.models.transformers.adapter_model.AdapterModel",parameters:[{name:"env_config",val:": EnvConfig"},{name:"config",val:": BaseModelConfig"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/adapter_model.py#L60"}}),ke=new M({props:{title:"DeltaModel",local:"lighteval.models.transformers.delta_model.DeltaModelConfig",headingTag:"h3"}}),Te=new u({props:{name:"class lighteval.models.transformers.delta_model.DeltaModelConfig",anchor:"lighteval.models.transformers.delta_model.DeltaModelConfig",parameters:[{name:"pretrained",val:": str"},{name:"accelerator",val:": Accelerator = None"},{name:"tokenizer",val:": typing.Optional[str] = None"},{name:"multichoice_continuations_start_space",val:": typing.Optional[bool] = None"},{name:"pairwise_tokenization",val:": bool = False"},{name:"subfolder",val:": typing.Optional[str] = None"},{name:"revision",val:": str = 'main'"},{name:"batch_size",val:": int = -1"},{name:"max_gen_toks",val:": typing.Optional[int] = 256"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"add_special_tokens",val:": bool = True"},{name:"model_parallel",val:": typing.Optional[bool] = None"},{name:"dtype",val:": typing.Union[str, torch.dtype, NoneType] = None"},{name:"device",val:": typing.Union[int, str] = 'cuda'"},{name:"quantization_config",val:": typing.Optional[transformers.utils.quantization_config.BitsAndBytesConfig] = None"},{name:"trust_remote_code",val:": bool = False"},{name:"use_chat_template",val:": bool = False"},{name:"compile",val:": bool = False"},{name:"base_model",val:": str = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/delta_model.py#L39"}}),De=new u({props:{name:"class lighteval.models.transformers.delta_model.DeltaModel",anchor:"lighteval.models.transformers.delta_model.DeltaModel",parameters:[{name:"env_config",val:": EnvConfig"},{name:"config",val:": BaseModelConfig"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/transformers/delta_model.py#L56"}}),Ie=new M({props:{title:"Endpoints-based Models",local:"endpoints-based-models",headingTag:"h2"}}),Ee=new M({props:{title:"InferenceEndpointModel",local:"lighteval.models.endpoints.endpoint_model.InferenceEndpointModelConfig",headingTag:"h3"}}),qe=new u({props:{name:"class lighteval.models.endpoints.endpoint_model.InferenceEndpointModelConfig",anchor:"lighteval.models.endpoints.endpoint_model.InferenceEndpointModelConfig",parameters:[{name:"endpoint_name",val:": str = None"},{name:"model_name",val:": str = None"},{name:"reuse_existing",val:": bool = False"},{name:"accelerator",val:": str = 'gpu'"},{name:"model_dtype",val:": str = None"},{name:"vendor",val:": str = 'aws'"},{name:"region",val:": str = 'us-east-1'"},{name:"instance_size",val:": str = None"},{name:"instance_type",val:": str = None"},{name:"framework",val:": str = 'pytorch'"},{name:"endpoint_type",val:": str = 'protected'"},{name:"add_special_tokens",val:": bool = True"},{name:"revision",val:": str = 'main'"},{name:"namespace",val:": str = None"},{name:"image_url",val:": str = None"},{name:"env_vars",val:": dict = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/endpoints/endpoint_model.py#L91"}}),ze=new u({props:{name:"from_path",anchor:"lighteval.models.endpoints.endpoint_model.InferenceEndpointModelConfig.from_path",parameters:[{name:"path",val:": str"}],parametersDescription:[{anchor:"lighteval.models.endpoints.endpoint_model.InferenceEndpointModelConfig.from_path.path",description:"<strong>path</strong> (<code>str</code>) &#x2014; Path of the model configuration YAML file.",name:"path"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/endpoints/endpoint_model.py#L120",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Configuration for inference endpoint model.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>InferenceEndpointModelConfig</code></p>
`}}),Ve=new u({props:{name:"class lighteval.models.endpoints.endpoint_model.ServerlessEndpointModelConfig",anchor:"lighteval.models.endpoints.endpoint_model.ServerlessEndpointModelConfig",parameters:[{name:"model_name",val:": str"},{name:"add_special_tokens",val:": bool = True"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/endpoints/endpoint_model.py#L77"}}),Be=new u({props:{name:"class lighteval.models.endpoints.endpoint_model.InferenceEndpointModel",anchor:"lighteval.models.endpoints.endpoint_model.InferenceEndpointModel",parameters:[{name:"config",val:": typing.Union[lighteval.models.endpoints.endpoint_model.InferenceEndpointModelConfig, lighteval.models.endpoints.endpoint_model.ServerlessEndpointModelConfig]"},{name:"env_config",val:": EnvConfig"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/endpoints/endpoint_model.py#L155"}}),Pe=new M({props:{title:"TGI ModelClient",local:"lighteval.models.endpoints.tgi_model.TGIModelConfig",headingTag:"h3"}}),Fe=new u({props:{name:"class lighteval.models.endpoints.tgi_model.TGIModelConfig",anchor:"lighteval.models.endpoints.tgi_model.TGIModelConfig",parameters:[{name:"inference_server_address",val:": str"},{name:"inference_server_auth",val:": str"},{name:"model_id",val:": str"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/endpoints/tgi_model.py#L48"}}),Ae=new u({props:{name:"from_path",anchor:"lighteval.models.endpoints.tgi_model.TGIModelConfig.from_path",parameters:[{name:"path",val:": str"}],parametersDescription:[{anchor:"lighteval.models.endpoints.tgi_model.TGIModelConfig.from_path.path",description:"<strong>path</strong> (<code>str</code>) &#x2014; Path of the model configuration YAML file.",name:"path"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/endpoints/tgi_model.py#L54",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>Configuration for TGI endpoint model.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p><code>TGIModelConfig</code></p>
`}}),Ue=new u({props:{name:"class lighteval.models.endpoints.tgi_model.ModelClient",anchor:"lighteval.models.endpoints.tgi_model.ModelClient",parameters:[{name:"address",val:""},{name:"auth_token",val:" = None"},{name:"model_id",val:" = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/endpoints/tgi_model.py#L73"}}),He=new M({props:{title:"Open AI Models",local:"lighteval.models.endpoints.openai_model.OpenAIClient",headingTag:"h3"}}),Se=new u({props:{name:"class lighteval.models.endpoints.openai_model.OpenAIClient",anchor:"lighteval.models.endpoints.openai_model.OpenAIClient",parameters:[{name:"config",val:""},{name:"env_config",val:""}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/endpoints/openai_model.py#L67"}}),Re=new M({props:{title:"Nanotron Model",local:"nanotron-model",headingTag:"h2"}}),Ye=new M({props:{title:"NanotronLightevalModel",local:"lighteval.models.nanotron.nanotron_model.NanotronLightevalModel",headingTag:"h3"}}),je=new u({props:{name:"class lighteval.models.nanotron.nanotron_model.NanotronLightevalModel",anchor:"lighteval.models.nanotron.nanotron_model.NanotronLightevalModel",parameters:[{name:"checkpoint_path",val:": str"},{name:"nanotron_config",val:": FullNanotronConfig"},{name:"parallel_context",val:": ParallelContext"},{name:"max_gen_toks",val:": typing.Optional[int] = 256"},{name:"max_length",val:": typing.Optional[int] = None"},{name:"add_special_tokens",val:": typing.Optional[bool] = True"},{name:"dtype",val:": typing.Union[str, torch.dtype, NoneType] = None"},{name:"trust_remote_code",val:": bool = False"},{name:"debug_one_layer_model",val:": bool = False"},{name:"model_class",val:": typing.Optional[typing.Type] = None"},{name:"env_config",val:": EnvConfig = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/nanotron/nanotron_model.py#L87"}}),Je=new u({props:{name:"gather",anchor:"lighteval.models.nanotron.nanotron_model.NanotronLightevalModel.gather",parameters:[{name:"output_tensor",val:": Tensor"},{name:"process_group",val:": dist.ProcessGroup = None"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/nanotron/nanotron_model.py#L597"}}),Ke=new u({props:{name:"greedy_until",anchor:"lighteval.models.nanotron.nanotron_model.NanotronLightevalModel.greedy_until",parameters:[{name:"requests",val:": typing.List[lighteval.tasks.requests.GreedyUntilRequest]"},{name:"disable_tqdm",val:": bool = False"},{name:"override_bs",val:": int = -1"},{name:"num_dataset_splits",val:": int = 1"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/nanotron/nanotron_model.py#L1098"}}),Qe=new u({props:{name:"homogeneize_ending_conditions",anchor:"lighteval.models.nanotron.nanotron_model.NanotronLightevalModel.homogeneize_ending_conditions",parameters:[{name:"ending_condition",val:": tuple | dict | list | str"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/nanotron/nanotron_model.py#L348"}}),Xe=new u({props:{name:"loglikelihood_single_token",anchor:"lighteval.models.nanotron.nanotron_model.NanotronLightevalModel.loglikelihood_single_token",parameters:[{name:"requests",val:": typing.List[typing.Tuple[str, dict]]"},{name:"override_bs",val:" = 0"}],parametersDescription:[{anchor:"lighteval.models.nanotron.nanotron_model.NanotronLightevalModel.loglikelihood_single_token.requests",description:"<strong>requests</strong> (List[Tuple[str, dict]]) &#x2014; <em>description</em>",name:"requests"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/nanotron/nanotron_model.py#L402",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p><em>description</em></p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>List[Tuple[float, bool]]</p>
`}}),Ze=new u({props:{name:"pad_and_gather",anchor:"lighteval.models.nanotron.nanotron_model.NanotronLightevalModel.pad_and_gather",parameters:[{name:"output_tensor",val:": Tensor"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/nanotron/nanotron_model.py#L607"}}),et=new u({props:{name:"prepare_batch",anchor:"lighteval.models.nanotron.nanotron_model.NanotronLightevalModel.prepare_batch",parameters:[{name:"batch",val:": typing.List[str]"},{name:"padding_length",val:": int"},{name:"max_context",val:": typing.Optional[int] = None"},{name:"full_attention_masks",val:": bool = False"},{name:"pad_on_left",val:": bool = False"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/nanotron/nanotron_model.py#L480"}}),tt=new M({props:{title:"VLLM Model",local:"vllm-model",headingTag:"h2"}}),nt=new M({props:{title:"VLLMModel",local:"lighteval.models.vllm.vllm_model.VLLMModelConfig",headingTag:"h3"}}),at=new u({props:{name:"class lighteval.models.vllm.vllm_model.VLLMModelConfig",anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig",parameters:[{name:"pretrained",val:": str"},{name:"gpu_memory_utilisation",val:": float = 0.9"},{name:"revision",val:": str = 'main'"},{name:"dtype",val:": str | None = None"},{name:"tensor_parallel_size",val:": int = 1"},{name:"pipeline_parallel_size",val:": int = 1"},{name:"data_parallel_size",val:": int = 1"},{name:"max_model_length",val:": int | None = None"},{name:"swap_space",val:": int = 4"},{name:"seed",val:": int = 1234"},{name:"trust_remote_code",val:": bool = False"},{name:"use_chat_template",val:": bool = False"},{name:"add_special_tokens",val:": bool = True"},{name:"multichoice_continuations_start_space",val:": bool = True"},{name:"pairwise_tokenization",val:": bool = False"},{name:"subfolder",val:": typing.Optional[str] = None"},{name:"temperature",val:": float = 0.6"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/vllm/vllm_model.py#L69"}}),lt=new u({props:{name:"class lighteval.models.vllm.vllm_model.VLLMModel",anchor:"lighteval.models.vllm.vllm_model.VLLMModel",parameters:[{name:"config",val:": VLLMModelConfig"},{name:"env_config",val:": EnvConfig"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/vllm/vllm_model.py#L93"}}),rt=new u({props:{name:"greedy_until",anchor:"lighteval.models.vllm.vllm_model.VLLMModel.greedy_until",parameters:[{name:"requests",val:": list"},{name:"override_bs",val:": typing.Optional[int] = None"}],parametersDescription:[{anchor:"lighteval.models.vllm.vllm_model.VLLMModel.greedy_until.requests",description:"<strong>requests</strong> (list[Request]) &#x2014; list of requests containing the context and ending conditions.",name:"requests"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModel.greedy_until.override_bs",description:"<strong>override_bs</strong> (int, optional) &#x2014; Override the batch size for generation. Defaults to None.",name:"override_bs"}],source:"https://github.com/huggingface/lighteval/blob/vr_476/src/lighteval/models/vllm/vllm_model.py#L199",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


<p>list of generated responses.</p>
`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


<p>list[GenerateReturn]</p>
`}}),st=new 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