lighteval/pr_744/en/_app/immutable/nodes/16.bf8a5bf5.js

import{s as vt,o as bt,n as Ae}from"../chunks/scheduler.7da89386.js";import{S as Ut,i as Tt,g as s,s as n,r as p,A as _t,h as r,f as l,c as a,j as yt,u as c,x as d,k as Mt,y as wt,a as o,v as u,d as f,t as h,w as g}from"../chunks/index.20910acc.js";import{T as Be}from"../chunks/Tip.53e22153.js";import{C as ie}from"../chunks/CodeBlock.143bd81e.js";import{H as M,E as Ct}from"../chunks/index.c9cd5e8b.js";function xt(v){let i,$=`We recommend using the <code>--help</code> flag to get more information about the
available options for each command.
<code>lighteval --help</code>`;return{c(){i=s("p"),i.innerHTML=$},l(m){i=r(m,"P",{"data-svelte-h":!0}),d(i)!=="svelte-1gm4bcl"&&(i.innerHTML=$)},m(m,y){o(m,i,y)},p:Ae,d(m){m&&l(i)}}}function kt(v){let i,$=`Both data and pipeline parallelism can be combined by setting
<code>model_parallel=True</code> and using accelerate to distribute the data across the
GPUs.`;return{c(){i=s("p"),i.innerHTML=$},l(m){i=r(m,"P",{"data-svelte-h":!0}),d(i)!=="svelte-2p0bbo"&&(i.innerHTML=$)},m(m,y){o(m,i,y)},p:Ae,d(m){m&&l(i)}}}function Jt(v){let i,$="Nanotron models cannot be evaluated without torchrun.";return{c(){i=s("p"),i.textContent=$},l(m){i=r(m,"P",{"data-svelte-h":!0}),d(i)!=="svelte-xg3kaz"&&(i.textContent=$)},m(m,y){o(m,i,y)},p:Ae,d(m){m&&l(i)}}}function Gt(v){let i,$,m,y,_,re,b,me,w,De="Lighteval can be used with a few different commands.",de,C,Ee=`<li><code>lighteval accelerate</code> : evaluate models on CPU or one or more GPUs using <a href="https://github.com/huggingface/accelerate" rel="nofollow">🤗
Accelerate</a></li> <li><code>lighteval nanotron</code>: evaluate models in distributed settings using <a href="https://github.com/huggingface/nanotron" rel="nofollow">⚡️
Nanotron</a></li> <li><code>lighteval vllm</code>: evaluate models on one or more GPUs using <a href="https://github.com/vllm-project/vllm" rel="nofollow">🚀
VLLM</a></li> <li><code>lighteval endpoint</code><ul><li><code>inference-endpoint</code>: evaluate models on one or more GPUs using <a href="https://huggingface.co/inference-endpoints/dedicated" rel="nofollow">🔗
Inference Endpoint</a></li> <li><code>tgi</code>: evaluate models on one or more GPUs using <a href="https://huggingface.co/docs/text-generation-inference/en/index" rel="nofollow">🔗 Text Generation Inference</a></li> <li><code>openai</code>: evaluate models on one or more GPUs using <a href="https://platform.openai.com/" rel="nofollow">🔗 OpenAI API</a></li></ul></li>`,pe,x,ce,k,Oe=`To evaluate <code>GPT-2</code> on the Truthful QA benchmark with <a href="https://github.com/huggingface/accelerate" rel="nofollow">🤗
Accelerate</a> , run:`,ue,J,fe,G,Ke="Here, we first choose a backend (either <code>accelerate</code>, <code>nanotron</code>, or <code>vllm</code>), and then specify the model and task(s) to run.",he,W,et=`The syntax for the model arguments is <code>key1=value1,key2=value2,etc</code>.
Valid key-value pairs correspond with the backend configuration, and are detailed [below](#Model Arguments).`,ge,H,tt="The syntax for the task specification might be a bit hard to grasp at first. The format is as follows:",$e,L,ye,Z,lt=`If the fourth value is set to 1, lighteval will check if the prompt (including the few-shot examples) is too long for the context size of the task or the model.
If so, the number of few shot examples is automatically reduced.`,Me,P,ot=`All officially supported tasks can be found at the <a href="available-tasks">tasks_list</a> and in the
<a href="https://github.com/huggingface/lighteval/tree/main/src/lighteval/tasks/extended" rel="nofollow">extended folder</a>.
Moreover, community-provided tasks can be found in the
<a href="https://github.com/huggingface/lighteval/tree/main/community_tasks" rel="nofollow">community</a> folder.
For more details on the implementation of the tasks, such as how prompts are constructed, or which metrics are used, you can have a look at the
<a href="https://github.com/huggingface/lighteval/blob/main/src/lighteval/tasks/default_tasks.py" rel="nofollow">file</a>
implementing them.`,ve,j,nt=`Running multiple tasks is supported, either with a comma-separated list, or by specifying a file path.
The file should be structured like <a href="https://github.com/huggingface/lighteval/blob/main/examples/tasks/recommended_set.txt" rel="nofollow">examples/tasks/recommended_set.txt</a>.
When specifying a path to file, it should start with <code>./</code>.`,be,Y,Ue,z,Te,N,_e,Q,at="To evaluate a model on one or more GPUs, first create a multi-gpu config by running.",we,R,Ce,q,it="You can then evaluate a model using data parallelism on 8 GPUs like follows:",xe,F,ke,I,st=`Here, <code>--override_batch_size</code> defines the batch size per device, so the effective
batch size will be <code>override_batch_size * num_gpus</code>.`,Je,V,Ge,X,rt="To evaluate a model using pipeline parallelism on 2 or more GPUs, run:",We,S,He,B,mt="This will automatically use accelerate to distribute the model across the GPUs.",Le,U,Ze,A,Pe,D,dt=`The <code>model-args</code> argument takes a string representing a list of model
argument. The arguments allowed vary depending on the backend you use (vllm or
accelerate).`,je,E,Ye,O,pt=`<li><strong>pretrained</strong> (str):
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.</li> <li><strong>tokenizer</strong> (Optional[str]): HuggingFace Hub tokenizer ID that will be
used for tokenization.</li> <li><strong>multichoice_continuations_start_space</strong> (Optional[bool]): Whether to add a
space at the start of each continuation in multichoice generation.
For example, context: “What is the capital of France?” and choices: “Paris”, “London”.
Will be tokenized as: “What is the capital of France? Paris” and “What is the capital of France? London”.
True adds a space, False strips a space, None does nothing</li> <li><strong>subfolder</strong> (Optional[str]): The subfolder within the model repository.</li> <li><strong>revision</strong> (str): The revision of the model.</li> <li><strong>max_gen_toks</strong> (Optional[int]): The maximum number of tokens to generate.</li> <li><strong>max_length</strong> (Optional[int]): The maximum length of the generated output.</li> <li><strong>add_special_tokens</strong> (bool, optional, defaults to True): 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.</li> <li><strong>model_parallel</strong> (bool, optional, defaults to None):
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’s smaller =&gt; model-parallelism, else not.</li> <li><strong>dtype</strong> (Union[str, torch.dtype], optional, defaults to None):):
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’s weights.</li> <li><strong>device</strong> (Union[int, str]): device to use for model training.</li> <li><strong>quantization_config</strong> (Optional[BitsAndBytesConfig]): 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.</li> <li><strong>trust_remote_code</strong> (bool): Whether to trust remote code during model
loading.</li>`,ze,K,Ne,ee,ct="<li><strong>pretrained</strong> (str): HuggingFace Hub model ID name or the path to a pre-trained model to load.</li> <li><strong>gpu_memory_utilization</strong> (float): The fraction of GPU memory to use.</li> <li><strong>batch_size</strong> (int): The batch size for model training.</li> <li><strong>revision</strong> (str): The revision of the model.</li> <li><strong>dtype</strong> (str, None): The data type to use for the model.</li> <li><strong>tensor_parallel_size</strong> (int): The number of tensor parallel units to use.</li> <li><strong>data_parallel_size</strong> (int): The number of data parallel units to use.</li> <li><strong>max_model_length</strong> (int): The maximum length of the model.</li> <li><strong>swap_space</strong> (int): The CPU swap space size (GiB) per GPU.</li> <li><strong>seed</strong> (int): The seed to use for the model.</li> <li><strong>trust_remote_code</strong> (bool): Whether to trust remote code during model loading.</li> <li><strong>use_chat_template</strong> (bool): Whether to use the chat template or not.</li> <li><strong>add_special_tokens</strong> (bool): Whether to add special tokens to the input sequences.</li> <li><strong>multichoice_continuations_start_space</strong> (bool): Whether to add a space at the start of each continuation in multichoice generation.</li> <li><strong>subfolder</strong> (Optional[str]): The subfolder within the model repository.</li>",Qe,te,Re,le,ut="To evaluate a model trained with nanotron on a single gpu.",qe,T,Fe,oe,Ie,ne,ft=`The <code>nproc-per-node</code> argument should match the data, tensor and pipeline
parallelism confidured in the <code>lighteval_config_template.yaml</code> file.
That is: <code>nproc-per-node = data_parallelism * tensor_parallelism * pipeline_parallelism</code>.`,Ve,ae,Xe,se,Se;return _=new M({props:{title:"Quicktour",local:"quicktour",headingTag:"h1"}}),b=new Be({props:{warning:!1,$$slots:{default:[xt]},$$scope:{ctx:v}}}),x=new M({props:{title:"Basic usage",local:"basic-usage",headingTag:"h2"}}),J=new ie({props:{code:"bGlnaHRldmFsJTIwYWNjZWxlcmF0ZSUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMCUyMm1vZGVsX25hbWUlM0RvcGVuYWktY29tbXVuaXR5JTJGZ3B0MiUyMiUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMCUyMmxlYWRlcmJvYXJkJTdDdHJ1dGhmdWxxYSUzQW1jJTdDMCU3QzAlMjI=",highlighted:`lighteval accelerate \\
     <span class="hljs-string">&quot;model_name=openai-community/gpt2&quot;</span> \\
     <span class="hljs-string">&quot;leaderboard|truthfulqa:mc|0|0&quot;</span>`,wrap:!1}}),L=new ie({props:{code:"JTdCc3VpdGUlN0QlN0MlN0J0YXNrJTdEJTdDJTdCbnVtX2Zld19zaG90JTdEJTdDJTdCMCUyMGZvciUyMHN0cmljdCUyMCU2MG51bV9mZXdfc2hvdHMlNjAlMkMlMjBvciUyMDElMjB0byUyMGFsbG93JTIwYSUyMHRydW5jYXRpb24lMjBpZiUyMGNvbnRleHQlMjBzaXplJTIwaXMlMjB0b28lMjBzbWFsbCU3RA==",highlighted:"{suite}|{task}|{num_few_shot}|{0 for strict `num_few_shots`, or 1 to allow a truncation if context size is too small}",wrap:!1}}),Y=new ie({props:{code:"bGlnaHRldmFsJTIwYWNjZWxlcmF0ZSUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMCUyMm1vZGVsX25hbWUlM0RvcGVuYWktY29tbXVuaXR5JTJGZ3B0MiUyMiUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMC4lMkZwYXRoJTJGdG8lMkZsaWdodGV2YWwlMkZleGFtcGxlcyUyRnRhc2tzJTJGcmVjb21tZW5kZWRfc2V0LnR4dCUwQSUyMyUyMG9yJTJDJTIwZS5nLiUyQyUyMCUyMmxlYWRlcmJvYXJkJTdDdHJ1dGhmdWxxYSUzQW1jJTdDMCU3QzAlN0MlMkNsZWFkZXJib2FyZCU3Q2dzbThrJTdDMyU3QzElMjI=",highlighted:`lighteval accelerate \\
     <span class="hljs-string">&quot;model_name=openai-community/gpt2&quot;</span> \\
     ./path/to/lighteval/examples/tasks/recommended_set.txt
<span class="hljs-comment"># or, e.g., &quot;leaderboard|truthfulqa:mc|0|0|,leaderboard|gsm8k|3|1&quot;</span>`,wrap:!1}}),z=new M({props:{title:"Evaluate a model on one or more GPUs",local:"evaluate-a-model-on-one-or-more-gpus",headingTag:"h2"}}),N=new M({props:{title:"Data parallelism",local:"data-parallelism",headingTag:"h4"}}),R=new ie({props:{code:"YWNjZWxlcmF0ZSUyMGNvbmZpZw==",highlighted:"accelerate config",wrap:!1}}),F=new ie({props:{code:"YWNjZWxlcmF0ZSUyMGxhdW5jaCUyMC0tbXVsdGlfZ3B1JTIwLS1udW1fcHJvY2Vzc2VzJTNEOCUyMC1tJTIwJTVDJTBBJTIwJTIwJTIwJTIwbGlnaHRldmFsJTIwYWNjZWxlcmF0ZSUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMm1vZGVsX25hbWUlM0RvcGVuYWktY29tbXVuaXR5JTJGZ3B0MiUyMiUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMmxlYWRlcmJvYXJkJTdDdHJ1dGhmdWxxYSUzQW1jJTdDMCU3QzAlMjI=",highlighted:`accelerate launch --multi_gpu --num_processes=8 -m \\
    lighteval accelerate \\
    <span class="hljs-string">&quot;model_name=openai-community/gpt2&quot;</span> \\
    <span class="hljs-string">&quot;leaderboard|truthfulqa:mc|0|0&quot;</span>`,wrap:!1}}),V=new M({props:{title:"Pipeline parallelism",local:"pipeline-parallelism",headingTag:"h4"}}),S=new ie({props:{code:"bGlnaHRldmFsJTIwYWNjZWxlcmF0ZSUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMm1vZGVsX25hbWUlM0RvcGVuYWktY29tbXVuaXR5JTJGZ3B0MiUyQ21vZGVsX3BhcmFsbGVsJTNEVHJ1ZSUyMiUyMCU1QyUwQSUyMCUyMCUyMCUyMCUyMmxlYWRlcmJvYXJkJTdDdHJ1dGhmdWxxYSUzQW1jJTdDMCU3QzAlMjI=",highlighted:`lighteval accelerate \\
    <span class="hljs-string">&quot;model_name=openai-community/gpt2,model_parallel=True&quot;</span> \\
    <span class="hljs-string">&quot;leaderboard|truthfulqa:mc|0|0&quot;</span>`,wrap:!1}}),U=new Be({props:{warning:!1,$$slots:{default:[kt]},$$scope:{ctx:v}}}),A=new M({props:{title:"Backend configuration",local:"backend-configuration",headingTag:"h2"}}),E=new M({props:{title:"Accelerate",local:"accelerate",headingTag:"h3"}}),K=new M({props:{title:"VLLM",local:"vllm",headingTag:"h3"}}),te=new M({props:{title:"Nanotron",local:"nanotron",headingTag:"h2"}}),T=new Be({props:{warning:!0,$$slots:{default:[Jt]},$$scope:{ctx:v}}}),oe=new ie({props:{code:"JTIwdG9yY2hydW4lMjAtLXN0YW5kYWxvbmUlMjAtLW5ub2RlcyUzRDElMjAtLW5wcm9jLXBlci1ub2RlJTNEMSUyMCUyMCU1QyUwQSUyMHNyYyUyRmxpZ2h0ZXZhbCUyRl9fbWFpbl9fLnB5JTIwbmFub3Ryb24lMjAlNUMlMEElMjAtLWNoZWNrcG9pbnQtY29uZmlnLXBhdGglMjAuLiUyRm5hbm90cm9uJTJGY2hlY2twb2ludHMlMkYxMCUyRmNvbmZpZy55YW1sJTIwJTVDJTBBJTIwLS1saWdodGV2YWwtY29uZmlnLXBhdGglMjBleGFtcGxlcyUyRm5hbm90cm9uJTJGbGlnaHRldmFsX2NvbmZpZ19vdmVycmlkZV90ZW1wbGF0ZS55YW1s",highlighted:` torchrun --standalone --nnodes=1 --nproc-per-node=1  \\
 src/lighteval/__main__.py nanotron \\
 --checkpoint-config-path ../nanotron/checkpoints/10/config.yaml \\
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