import{s as nl,n as sl,o as ol}from"../chunks/scheduler.f9cad8c1.js";import{S as il,i as al,e as i,s,c as m,h as ml,a,d as t,b as o,f as ll,g as p,j as M,k as tl,l as pl,m as n,n as U,t as r,o as T,p as J}from"../chunks/index.6e06ef99.js";import{C as Ml,H as K,E as Ul}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.c4302a62.js";import{C as g}from"../chunks/CodeBlock.72594161.js";import{D as rl}from"../chunks/DocNotebookDropdown.77f2c2c1.js";function Tl(Oe){let f,ee,Y,le,d,te,w,ne,V,se,I,ve=`<code>smolagents</code>는 다양한 프로바이더의 여러 언어 모델을 사용할 수 있는 유연한 프레임워크를 제공합니다.
이 가이드는 에이전트와 함께 다양한 모델 유형을 사용하는 방법을 보여줍니다.`,oe,$,ie,y,Se="<code>smolagents</code>는 기본적으로 여러 모델 유형을 지원합니다:",ae,u,xe='<li><a href="/docs/smolagents/main/ko/reference/models#smolagents.InferenceClientModel">InferenceClientModel</a>: Hugging Face의 추론 API를 사용하여 모델에 접근</li> <li><a href="/docs/smolagents/main/ko/reference/models#smolagents.TransformersModel">TransformersModel</a>: 🤗 Transformers 라이브러리를 사용하여 로컬에서 모델 실행</li> <li><a href="/docs/smolagents/main/ko/reference/models#smolagents.VLLMModel">VLLMModel</a>: 최적화된 서빙으로 빠른 추론을 위해 vLLM 사용</li> <li><a href="/docs/smolagents/main/ko/reference/models#smolagents.MLXModel">MLXModel</a>: MLX를 사용하여 Apple Silicon 디바이스에 최적화</li> <li><a href="/docs/smolagents/main/ko/reference/models#smolagents.LiteLLMModel">LiteLLMModel</a>: LiteLLM을 통해 수백 개의 대규모 언어 모델에 접근 제공</li> <li><a href="/docs/smolagents/main/ko/reference/models#smolagents.LiteLLMRouterModel">LiteLLMRouterModel</a>: 여러 모델 간에 요청을 분산</li> <li><a href="/docs/smolagents/main/ko/reference/models#smolagents.OpenAIModel">OpenAIModel</a>: OpenAI 호환 API를 구현하는 모든 프로바이더에 접근 제공</li> <li><a href="/docs/smolagents/main/ko/reference/models#smolagents.AzureOpenAIModel">AzureOpenAIModel</a>: Azure의 OpenAI 서비스 사용</li> <li><a href="/docs/smolagents/main/ko/reference/models#smolagents.AmazonBedrockModel">AmazonBedrockModel</a>: AWS Bedrock의 API에 연결</li>',me,k,Fe=`모든 모델 클래스는 인스턴스화 시점에 추가 키워드 인수들(<code>temperature</code>, <code>max_tokens</code>, <code>top_p</code> 등)을 직접 전달하는 것을 지원합니다.
이러한 매개변수들은 자동으로 기본 모델의 완성 호출로 전달되어, 창의성, 응답 길이, 샘플링 전략과 같은 모델 동작을 구성할 수 있게 해줍니다.`,pe,b,Me,Q,Pe=`Google Gemini API 문서(<a href="https://ai.google.dev/gemini-api/docs/openai)%EC%97%90%EC%84%9C" rel="nofollow">https://ai.google.dev/gemini-api/docs/openai)에서</a> 설명한 바와 같이,
Google은 Gemini 모델에 대해 OpenAI 호환 API를 제공하므로, 적절한 베이스 URL을 설정하여
<a href="/docs/smolagents/main/ko/reference/models#smolagents.OpenAIModel">OpenAIModel</a>을 Gemini 모델과 함께 사용할 수 있습니다.`,Ue,c,We="먼저, 필요한 의존성을 설치합니다:",re,E,Te,R,Ge='그다음, <a href="https://ai.google.dev/gemini-api/docs/api-key" rel="nofollow">Gemini API 키를 얻고</a> 코드에서 설정합니다:',Je,A,fe,C,Ne=`이제 <code>OpenAIModel</code> 클래스를 사용하고 <code>api_base</code> 매개변수를 Gemini API 베이스 URL로 설정하여
Gemini 모델을 초기화할 수 있습니다:`,ge,h,de,L,we,B,He=`OpenRouter는 통합된 OpenAI 호환 API를 통해 다양한 언어 모델에 대한 접근을 제공합니다.
적절한 베이스 URL을 설정하여 <a href="/docs/smolagents/main/ko/reference/models#smolagents.OpenAIModel">OpenAIModel</a>을 사용해 OpenRouter에 연결할 수 있습니다.`,Ve,_,Ze="먼저, 필요한 의존성을 설치합니다:",Ie,j,$e,O,Xe='그다음, <a href="https://openrouter.ai/keys" rel="nofollow">OpenRouter API 키를 얻고</a> 코드에서 설정합니다:',ye,v,ue,S,De="이제 <code>OpenAIModel</code> 클래스를 사용하여 OpenRouter에서 사용 가능한 모든 모델을 초기화할 수 있습니다:",ke,x,be,F,Qe,P,ze='xAI의 Grok 모델은 <a href="/docs/smolagents/main/ko/reference/models#smolagents.LiteLLMModel">LiteLLMModel</a>을 통해 접근할 수 있습니다.',ce,W,Ye=`일부 모델(“grok-4” 및 “grok-3-mini” 등)은 <code>stop</code> 매개변수를 지원하지 않으므로,
API 호출에서 이를 제외하기 위해 <code>REMOVE_PARAMETER</code>를 사용해야 합니다.`,Ee,G,qe="먼저, 필요한 의존성을 설치합니다:",Re,N,Ae,H,Ke='그다음, <a href="https://console.x.ai/" rel="nofollow">xAI API 키를 얻고</a> 코드에서 설정합니다:',Ce,Z,he,X,el="이제 <code>LiteLLMModel</code> 클래스를 사용하여 Grok 모델을 초기화하고 해당되는 경우 <code>stop</code> 매개변수를 제거할 수 있습니다:",Le,D,Be,z,_e,q,je;return d=new Ml({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),w=new rl({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;",options:[{label:"Mixed",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/smolagents_doc/ko/using_different_models.ipynb"},{label:"PyTorch",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/smolagents_doc/ko/pytorch/using_different_models.ipynb"},{label:"TensorFlow",value:"https://colab.research.google.com/github/huggingface/notebooks/blob/main/smolagents_doc/ko/tensorflow/using_different_models.ipynb"},{label:"Mixed",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/smolagents_doc/ko/using_different_models.ipynb"},{label:"PyTorch",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/smolagents_doc/ko/pytorch/using_different_models.ipynb"},{label:"TensorFlow",value:"https://studiolab.sagemaker.aws/import/github/huggingface/notebooks/blob/main/smolagents_doc/ko/tensorflow/using_different_models.ipynb"}]}}),V=new K({props:{title:"다양한 모델 사용하기",local:"using-different-models",headingTag:"h1"}}),$=new K({props:{title:"사용 가능한 모델 유형",local:"available-model-types",headingTag:"h2"}}),b=new K({props:{title:"Google Gemini 모델 사용하기",local:"using-google-gemini-models",headingTag:"h2"}}),E=new g({props:{code:"cGlwJTIwaW5zdGFsbCUyMCdzbW9sYWdlbnRzJTVCb3BlbmFpJTVEJw==",highlighted:'pip install <span class="hljs-string">&#x27;smolagents[openai]&#x27;</span>',wrap:!1}}),A=new g({props:{code:"R0VNSU5JX0FQSV9LRVklMjAlM0QlMjAlM0NZT1VSLUdFTUlOSS1BUEktS0VZJTNF",highlighted:"GEMINI_API_KEY = &lt;YOUR-GEMINI-API-KEY&gt;",wrap:!1}}),h=new g({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> OpenAIModel

model = OpenAIModel(
    model_id=<span class="hljs-string">&quot;gemini-2.0-flash&quot;</span>,
    <span class="hljs-comment"># Google Gemini OpenAI 호환 API 베이스 URL</span>
    api_base=<span class="hljs-string">&quot;https://generativelanguage.googleapis.com/v1beta/openai/&quot;</span>,
    api_key=GEMINI_API_KEY,
)`,wrap:!1}}),L=new K({props:{title:"OpenRouter 모델 사용하기",local:"using-openrouter-models",headingTag:"h2"}}),j=new g({props:{code:"cGlwJTIwaW5zdGFsbCUyMCdzbW9sYWdlbnRzJTVCb3BlbmFpJTVEJw==",highlighted:'pip install <span class="hljs-string">&#x27;smolagents[openai]&#x27;</span>',wrap:!1}}),v=new g({props:{code:"T1BFTlJPVVRFUl9BUElfS0VZJTIwJTNEJTIwJTNDWU9VUi1PUEVOUk9VVEVSLUFQSS1LRVklM0U=",highlighted:"OPENROUTER_API_KEY = &lt;YOUR-OPENROUTER-API-KEY&gt;",wrap:!1}}),x=new g({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> OpenAIModel

model = OpenAIModel(
    <span class="hljs-comment"># OpenRouter에서 사용 가능한 모든 모델 ID를 사용할 수 있습니다</span>
    model_id=<span class="hljs-string">&quot;openai/gpt-4o&quot;</span>,
    <span class="hljs-comment"># OpenRouter API 베이스 URL</span>
    api_base=<span class="hljs-string">&quot;https://openrouter.ai/api/v1&quot;</span>,
    api_key=OPENROUTER_API_KEY,
)`,wrap:!1}}),F=new K({props:{title:"xAI의 Grok 모델 사용하기",local:"using-xais-grok-models",headingTag:"h2"}}),N=new g({props:{code:"cGlwJTIwaW5zdGFsbCUyMHNtb2xhZ2VudHMlNUJsaXRlbGxtJTVE",highlighted:"pip install smolagents[litellm]",wrap:!1}}),Z=new g({props:{code:"WEFJX0FQSV9LRVklMjAlM0QlMjAlM0NZT1VSLVhBSS1BUEktS0VZJTNF",highlighted:"XAI_API_KEY = &lt;YOUR-XAI-API-KEY&gt;",wrap:!1}}),D=new g({props:{code:"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",highlighted:`<span class="hljs-keyword">from</span> smolagents <span class="hljs-keyword">import</span> LiteLLMModel, REMOVE_PARAMETER

<span class="hljs-comment"># Grok-4 사용</span>
model = LiteLLMModel(
    model_id=<span class="hljs-string">&quot;xai/grok-4&quot;</span>,
    api_key=XAI_API_KEY,
    stop=REMOVE_PARAMETER,  <span class="hljs-comment"># grok-4 모델이 이를 지원하지 않으므로 stop 매개변수 제거</span>
    temperature=<span class="hljs-number">0.7</span>
)

<span class="hljs-comment"># 또는 Grok-3-mini 사용</span>
model_mini = LiteLLMModel(
    model_id=<span class="hljs-string">&quot;xai/grok-3-mini&quot;</span>,
    api_key=XAI_API_KEY,
    stop=REMOVE_PARAMETER,  <span class="hljs-comment"># grok-3-mini 모델이 이를 지원하지 않으므로 stop 매개변수 제거</span>
    max_tokens=<span class="hljs-number">1000</span>
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