import{s as Lo,o as Do,n as H}from"../chunks/scheduler.3a17fb72.js";import{S as zo,i as Eo,e as d,s as l,c as _,h as Wo,a as p,d as i,b as a,f as D,g as h,j as y,k as z,l as r,m,n as u,t as v,o as M,p as b}from"../chunks/index.093f8863.js";import{C as Po,H as R,E as Ao}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.992fda32.js";import{D as X}from"../chunks/Docstring.8892a1c9.js";import{C as O}from"../chunks/CodeBlock.36d29455.js";import{E as Y}from"../chunks/ExampleCodeBlock.3dc6e4f8.js";function Zo(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"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",highlighted:`<span class="hljs-comment"># Load from YAML file</span>
config = ModelConfig.from_path(<span class="hljs-string">&quot;model_config.yaml&quot;</span>)

<span class="hljs-comment"># Load from command line arguments</span>
config = ModelConfig.from_args(<span class="hljs-string">&quot;model_name=meta-llama/Llama-3.1-8B-Instruct,system_prompt=&#x27;You are a helpful assistant.&#x27;,generation_parameters={temperature=0.7}&quot;</span>)

<span class="hljs-comment"># Direct instantiation</span>
config = ModelConfig(
    model_name=<span class="hljs-string">&quot;meta-llama/Llama-3.1-8B-Instruct&quot;</span>,
    generation_parameters=GenerationParameters(temperature=<span class="hljs-number">0.7</span>),
    system_prompt=<span class="hljs-string">&quot;You are a helpful assistant.&quot;</span>
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function Vo(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"Y29uZmlnJTIwJTNEJTIwVHJhbnNmb3JtZXJzTW9kZWxDb25maWcoJTBBJTIwJTIwJTIwJTIwbW9kZWxfbmFtZSUzRCUyMm1ldGEtbGxhbWElMkZMbGFtYS0zLjEtOEItSW5zdHJ1Y3QlMjIlMkMlMEElMjAlMjAlMjAlMjBiYXRjaF9zaXplJTNENCUyQyUwQSUyMCUyMCUyMCUyMGR0eXBlJTNEJTIyZmxvYXQxNiUyMiUyQyUwQSUyMCUyMCUyMCUyMGdlbmVyYXRpb25fcGFyYW1ldGVycyUzREdlbmVyYXRpb25QYXJhbWV0ZXJzKCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHRlbXBlcmF0dXJlJTNEMC43JTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwbWF4X25ld190b2tlbnMlM0QxMDAlMEElMjAlMjAlMjAlMjApJTBBKQ==",highlighted:`config = TransformersModelConfig(
    model_name=<span class="hljs-string">&quot;meta-llama/Llama-3.1-8B-Instruct&quot;</span>,
    batch_size=<span class="hljs-number">4</span>,
    dtype=<span class="hljs-string">&quot;float16&quot;</span>,
    generation_parameters=GenerationParameters(
        temperature=<span class="hljs-number">0.7</span>,
        max_new_tokens=<span class="hljs-number">100</span>
    )
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function Bo(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"Y29uZmlnJTIwJTNEJTIwVkxMTU1vZGVsQ29uZmlnKCUwQSUyMCUyMCUyMCUyMG1vZGVsX25hbWUlM0QlMjJtZXRhLWxsYW1hJTJGTGxhbWEtMy4xLThCLUluc3RydWN0JTIyJTJDJTBBJTIwJTIwJTIwJTIwdGVuc29yX3BhcmFsbGVsX3NpemUlM0QyJTJDJTBBJTIwJTIwJTIwJTIwZ3B1X21lbW9yeV91dGlsaXphdGlvbiUzRDAuOCUyQyUwQSUyMCUyMCUyMCUyMG1heF9tb2RlbF9sZW5ndGglM0Q0MDk2JTJDJTBBJTIwJTIwJTIwJTIwZ2VuZXJhdGlvbl9wYXJhbWV0ZXJzJTNER2VuZXJhdGlvblBhcmFtZXRlcnMoJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwdGVtcGVyYXR1cmUlM0QwLjclMkMlMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBtYXhfbmV3X3Rva2VucyUzRDEwMCUwQSUyMCUyMCUyMCUyMCklMEEp",highlighted:`config = VLLMModelConfig(
    model_name=<span class="hljs-string">&quot;meta-llama/Llama-3.1-8B-Instruct&quot;</span>,
    tensor_parallel_size=<span class="hljs-number">2</span>,
    gpu_memory_utilization=<span class="hljs-number">0.8</span>,
    max_model_length=<span class="hljs-number">4096</span>,
    generation_parameters=GenerationParameters(
        temperature=<span class="hljs-number">0.7</span>,
        max_new_tokens=<span class="hljs-number">100</span>
    )
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function Ro(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"Y29uZmlnJTIwJTNEJTIwU0dMYW5nTW9kZWxDb25maWcoJTBBJTIwJTIwJTIwJTIwbW9kZWxfbmFtZSUzRCUyMm1ldGEtbGxhbWElMkZMbGFtYS0zLjEtOEItSW5zdHJ1Y3QlMjIlMkMlMEElMjAlMjAlMjAlMjB0cF9zaXplJTNEMiUyQyUwQSUyMCUyMCUyMCUyMGNvbnRleHRfbGVuZ3RoJTNEODE5MiUyQyUwQSUyMCUyMCUyMCUyMGdlbmVyYXRpb25fcGFyYW1ldGVycyUzREdlbmVyYXRpb25QYXJhbWV0ZXJzKCUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHRlbXBlcmF0dXJlJTNEMC43JTJDJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwbWF4X25ld190b2tlbnMlM0QxMDAlMEElMjAlMjAlMjAlMjApJTBBKQ==",highlighted:`config = SGLangModelConfig(
    model_name=<span class="hljs-string">&quot;meta-llama/Llama-3.1-8B-Instruct&quot;</span>,
    tp_size=<span class="hljs-number">2</span>,
    context_length=<span class="hljs-number">8192</span>,
    generation_parameters=GenerationParameters(
        temperature=<span class="hljs-number">0.7</span>,
        max_new_tokens=<span class="hljs-number">100</span>
    )
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function Xo(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"Y29uZmlnJTIwJTNEJTIwRHVtbXlNb2RlbENvbmZpZyglMEElMjAlMjAlMjAlMjBtb2RlbF9uYW1lJTNEJTIybXlfZHVtbXklMjIlMkMlMEElMjAlMjAlMjAlMjBzZWVkJTNEMTIzJTJDJTBBKQ==",highlighted:`config = DummyModelConfig(
    model_name=<span class="hljs-string">&quot;my_dummy&quot;</span>,
    seed=<span class="hljs-number">123</span>,
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function Fo(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"Y29uZmlnJTIwJTNEJTIwSW5mZXJlbmNlUHJvdmlkZXJzTW9kZWxDb25maWcoJTBBJTIwJTIwJTIwJTIwbW9kZWxfbmFtZSUzRCUyMmRlZXBzZWVrLWFpJTJGRGVlcFNlZWstUjEtMDUyOCUyMiUyQyUwQSUyMCUyMCUyMCUyMHByb3ZpZGVyJTNEJTIydG9nZXRoZXIlMjIlMkMlMEElMjAlMjAlMjAlMjBwYXJhbGxlbF9jYWxsc19jb3VudCUzRDUlMkMlMEElMjAlMjAlMjAlMjBnZW5lcmF0aW9uX3BhcmFtZXRlcnMlM0RHZW5lcmF0aW9uUGFyYW1ldGVycyglMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjB0ZW1wZXJhdHVyZSUzRDAuNyUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMG1heF9uZXdfdG9rZW5zJTNEMTAwJTBBJTIwJTIwJTIwJTIwKSUwQSk=",highlighted:`config = InferenceProvidersModelConfig(
    model_name=<span class="hljs-string">&quot;deepseek-ai/DeepSeek-R1-0528&quot;</span>,
    provider=<span class="hljs-string">&quot;together&quot;</span>,
    parallel_calls_count=<span class="hljs-number">5</span>,
    generation_parameters=GenerationParameters(
        temperature=<span class="hljs-number">0.7</span>,
        max_new_tokens=<span class="hljs-number">100</span>
    )
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function So(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"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",highlighted:`config = InferenceEndpointModelConfig(
    model_name=<span class="hljs-string">&quot;microsoft/DialoGPT-medium&quot;</span>,
    instance_type=<span class="hljs-string">&quot;nvidia-a100&quot;</span>,
    instance_size=<span class="hljs-string">&quot;x1&quot;</span>,
    vendor=<span class="hljs-string">&quot;aws&quot;</span>,
    region=<span class="hljs-string">&quot;us-east-1&quot;</span>,
    dtype=<span class="hljs-string">&quot;float16&quot;</span>,
    generation_parameters=GenerationParameters(
        temperature=<span class="hljs-number">0.7</span>,
        max_new_tokens=<span class="hljs-number">100</span>
    )
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function qo(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"Y29uZmlnJTIwJTNEJTIwU2VydmVybGVzc0VuZHBvaW50TW9kZWxDb25maWcoJTBBJTIwJTIwJTIwJTIwbW9kZWxfbmFtZSUzRCUyMm1ldGEtbGxhbWElMkZMbGFtYS0zLjEtOEItSW5zdHJ1Y3QlMjIlMkMlMEElMjAlMjAlMjAlMjBnZW5lcmF0aW9uX3BhcmFtZXRlcnMlM0RHZW5lcmF0aW9uUGFyYW1ldGVycyglMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjB0ZW1wZXJhdHVyZSUzRDAuNyUyQyUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMG1heF9uZXdfdG9rZW5zJTNEMTAwJTBBJTIwJTIwJTIwJTIwKSUwQSk=",highlighted:`config = ServerlessEndpointModelConfig(
    model_name=<span class="hljs-string">&quot;meta-llama/Llama-3.1-8B-Instruct&quot;</span>,
    generation_parameters=GenerationParameters(
        temperature=<span class="hljs-number">0.7</span>,
        max_new_tokens=<span class="hljs-number">100</span>
    )
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function Qo(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"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",highlighted:`config = TGIModelConfig(
    inference_server_address=<span class="hljs-string">&quot;http://localhost:8080&quot;</span>,
    inference_server_auth=<span class="hljs-string">&quot;your-auth-token&quot;</span>,
    model_name=<span class="hljs-string">&quot;meta-llama/Llama-3.1-8B-Instruct&quot;</span>,
    generation_parameters=GenerationParameters(
        temperature=<span class="hljs-number">0.7</span>,
        max_new_tokens=<span class="hljs-number">100</span>
    )
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function Yo(C){let n,x="Example:",c,o,g;return o=new O({props:{code:"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",highlighted:`config = LiteLLMModelConfig(
    model_name=<span class="hljs-string">&quot;gpt-4&quot;</span>,
    provider=<span class="hljs-string">&quot;openai&quot;</span>,
    base_url=<span class="hljs-string">&quot;https://api.openai.com/v1&quot;</span>,
    concurrent_requests=<span class="hljs-number">5</span>,
    generation_parameters=GenerationParameters(
        temperature=<span class="hljs-number">0.7</span>,
        max_new_tokens=<span class="hljs-number">100</span>
    )
)`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-11lpom8"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function Ho(C){let n,x="Example usage:",c,o,g;return o=new O({props:{code:"JTIzJTIwRGVmaW5lJTIwY29uZmlnJTBBY29uZmlnJTIwJTNEJTIwQ3VzdG9tTW9kZWxDb25maWcoJTBBJTIwJTIwJTIwJTIwbW9kZWwlM0QlMjJteS1jdXN0b20tbW9kZWwlMjIlMkMlMEElMjAlMjAlMjAlMjBtb2RlbF9kZWZpbml0aW9uX2ZpbGVfcGF0aCUzRCUyMnBhdGglMkZ0byUyRm15X21vZGVsLnB5JTIyJTBBKSUwQSUwQSUyMyUyMEV4YW1wbGUlMjBjdXN0b20lMjBtb2RlbCUyMGZpbGUlMjAobXlfbW9kZWwucHkpJTNBJTBBZnJvbSUyMGxpZ2h0ZXZhbC5tb2RlbHMuYWJzdHJhY3RfbW9kZWwlMjBpbXBvcnQlMjBMaWdodGV2YWxNb2RlbCUwQSUwQWNsYXNzJTIwTXlDdXN0b21Nb2RlbChMaWdodGV2YWxNb2RlbCklM0ElMEElMjAlMjAlMjAlMjBkZWYlMjBfX2luaXRfXyhzZWxmJTJDJTIwY29uZmlnJTJDJTIwZW52X2NvbmZpZyklM0ElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBzdXBlcigpLl9faW5pdF9fKGNvbmZpZyUyQyUyMGVudl9jb25maWcpJTBBJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIwJTIzJTIwQ3VzdG9tJTIwaW5pdGlhbGl6YXRpb24uLi4lMEElMEElMjAlMjAlMjAlMjBkZWYlMjBncmVlZHlfdW50aWwoc2VsZiUyQyUyMGRvY3MlM0ElMjBsaXN0JTVCRG9jJTVEKSUyMC0lM0UlMjBsaXN0JTVCTW9kZWxSZXNwb25zZSU1RCUzQSUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMyUyMEN1c3RvbSUyMGdlbmVyYXRpb24lMjBsb2dpYy4uLiUwQSUyMCUyMCUyMCUyMCUyMCUyMCUyMCUyMHBhc3MlMEElMEElMjAlMjAlMjAlMjBkZWYlMjBsb2dsaWtlbGlob29kKHNlbGYlMkMlMjBkb2NzJTNBJTIwbGlzdCU1QkRvYyU1RCklMjAtJTNFJTIwbGlzdCU1Qk1vZGVsUmVzcG9uc2UlNUQlM0ElMEElMjAlMjAlMjAlMjAlMjAlMjAlMjAlMjBwYXNz",highlighted:`<span class="hljs-comment"># Define config</span>
config = CustomModelConfig(
    model=<span class="hljs-string">&quot;my-custom-model&quot;</span>,
    model_definition_file_path=<span class="hljs-string">&quot;path/to/my_model.py&quot;</span>
)

<span class="hljs-comment"># Example custom model file (my_model.py):</span>
<span class="hljs-keyword">from</span> lighteval.models.abstract_model <span class="hljs-keyword">import</span> LightevalModel

<span class="hljs-keyword">class</span> <span class="hljs-title class_">MyCustomModel</span>(<span class="hljs-title class_ inherited__">LightevalModel</span>):
    <span class="hljs-keyword">def</span> <span class="hljs-title function_">__init__</span>(<span class="hljs-params">self, config, env_config</span>):
        <span class="hljs-built_in">super</span>().__init__(config, env_config)
        <span class="hljs-comment"># Custom initialization...</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">greedy_until</span>(<span class="hljs-params">self, docs: <span class="hljs-built_in">list</span>[Doc]</span>) -&gt; <span class="hljs-built_in">list</span>[ModelResponse]:
        <span class="hljs-comment"># Custom generation logic...</span>
        <span class="hljs-keyword">pass</span>

    <span class="hljs-keyword">def</span> <span class="hljs-title function_">loglikelihood</span>(<span class="hljs-params">self, docs: <span class="hljs-built_in">list</span>[Doc]</span>) -&gt; <span class="hljs-built_in">list</span>[ModelResponse]:
        <span class="hljs-keyword">pass</span>`,wrap:!1}}),{c(){n=d("p"),n.textContent=x,c=l(),_(o.$$.fragment)},l(t){n=p(t,"P",{"data-svelte-h":!0}),y(n)!=="svelte-1ni337v"&&(n.textContent=x),c=a(t),h(o.$$.fragment,t)},m(t,f){m(t,n,f),m(t,c,f),u(o,t,f),g=!0},p:H,i(t){g||(v(o.$$.fragment,t),g=!0)},o(t){M(o.$$.fragment,t),g=!1},d(t){t&&(i(n),i(c)),b(o,t)}}}function Oo(C){let n,x,c,o,g,t,f,$t,ge,Qn=`The model configs are used to define the model and its parameters. All the parameters can be
set in the <code>model-args</code> or in the model yaml file (see example
<a href="https://github.com/huggingface/lighteval/blob/main/examples/model_configs/vllm_model_config.yaml" rel="nofollow">here</a>).`,Tt,ce,wt,J,fe,en,Ae,Yn="Base configuration class for all model types in Lighteval.",tn,Ze,Hn=`This is the foundation class that all specific model configurations inherit from.
It provides common functionality for parsing configuration from files and command-line arguments,
as well as shared attributes that are used by all models like generation parameters and system prompts.`,nn,Ve,On=`Methods:
from_path(path: str):
Load configuration from a YAML file.
from_args(args: str):
Parse configuration from a command-line argument string.
_parse_args(args: str):
Static method to parse argument strings into configuration dictionaries.`,on,te,Jt,_e,It,he,kt,I,ue,ln,Be,Kn="Configuration class for HuggingFace Transformers models.",an,Re,eo="This configuration is used to load and configure models from the HuggingFace Transformers library.",sn,ne,rn,Xe,to=`Note:
This configuration supports quantization (4-bit and 8-bit) through the dtype parameter.
When using quantization, ensure you have the required dependencies installed
(bitsandbytes for 4-bit/8-bit quantization).`,Ut,ve,Me,Gt,S,be,mn,Fe,no="Configuration class for delta models (weight difference models).",dn,Se,oo=`This configuration is used to load models that represent the difference between a
fine-tuned model and its base model. The delta weights are added to the base model
during loading to reconstruct the full fine-tuned model.`,jt,ye,Lt,k,xe,pn,qe,lo="Configuration class for VLLM inference engine.",gn,Qe,ao=`This configuration is used to load and configure models using the VLLM inference engine,
which provides high-performance inference for large language models with features like
PagedAttention, continuous batching, and efficient memory management.`,cn,Ye,so='vllm doc: <a href="https://docs.vllm.ai/en/v0.7.1/serving/engine_args.html" rel="nofollow">https://docs.vllm.ai/en/v0.7.1/serving/engine_args.html</a>',fn,oe,Dt,Ce,zt,U,Ne,_n,He,ro="Configuration class for SGLang inference engine.",hn,Oe,io=`This configuration is used to load and configure models using the SGLang inference engine,
which provides high-performance inference.`,un,Ke,mo='sglang doc: <a href="https://docs.sglang.ai/index.html#" rel="nofollow">https://docs.sglang.ai/index.html#</a>',vn,le,Et,$e,Wt,E,Te,Mn,et,po="Configuration class for dummy models used for testing and baselines.",bn,tt,go=`This configuration is used to create dummy models that generate random responses
or baselines for evaluation purposes. Useful for testing evaluation pipelines
without requiring actual model inference.`,yn,ae,Pt,we,At,Je,Zt,$,Ie,xn,nt,co="Configuration class for HuggingFace’s inference providers (like Together AI, Anyscale, etc.).",Cn,ot,fo='inference providers doc: <a href="https://huggingface.co/docs/inference-providers/en/index" rel="nofollow">https://huggingface.co/docs/inference-providers/en/index</a>',Nn,se,$n,lt,_o="Note:",Tn,at,ho="<li>Requires HF API keys to be set in environment variable</li> <li>Different providers have different rate limits and pricing</li>",Vt,ke,Bt,N,Ue,wn,st,uo="Configuration class for HuggingFace Inference Endpoints (dedicated infrastructure).",Jn,rt,vo=`This configuration is used to create and manage dedicated inference endpoints
on HuggingFace’s infrastructure. These endpoints provide dedicated compute
resources and can handle larger batch sizes and higher throughput.`,In,it,Mo=`Methods:
model_post_init():
Validates configuration and ensures proper parameter combinations.
get_dtype_args():
Returns environment variables for dtype configuration.
get_custom_env_vars():
Returns custom environment variables for the endpoint.`,kn,re,Un,mt,bo="Note:",Gn,dt,yo="<li>Creates dedicated infrastructure for model inference</li> <li>Supports various quantization methods and hardware configurations</li> <li>Auto-scaling available for optimal resource utilization</li> <li>Requires HuggingFace Pro subscription for most features</li> <li>Endpoints can take several minutes to start up</li> <li>Billed based on compute usage and duration</li>",Rt,W,Ge,jn,pt,xo="Configuration class for HuggingFace Inference API (inference endpoints).",Ln,gt,Co='<a href="https://huggingface.co/inference-endpoints/dedicated" rel="nofollow">https://huggingface.co/inference-endpoints/dedicated</a>',Dn,ie,Xt,je,Ft,G,Le,zn,ct,No="Configuration class for Text Generation Inference (TGI) backend.",En,ft,$o='doc: <a href="https://huggingface.co/docs/text-generation-inference/en/index" rel="nofollow">https://huggingface.co/docs/text-generation-inference/en/index</a>',Wn,_t,To=`This configuration is used to connect to TGI servers that serve HuggingFace models
using the text-generation-inference library. TGI provides high-performance inference
with features like continuous batching and efficient memory management.`,Pn,me,St,De,qt,j,ze,An,ht,wo="Configuration class for LiteLLM unified API client.",Zn,ut,Jo=`This configuration is used to connect to various LLM providers through the LiteLLM
unified API. LiteLLM provides a consistent interface to multiple providers including
OpenAI, Anthropic, Google, and many others.`,Vn,vt,Io='litellm doc: <a href="https://docs.litellm.ai/docs/" rel="nofollow">https://docs.litellm.ai/docs/</a>',Bn,de,Qt,Ee,Yt,T,We,Rn,Mt,ko="Configuration class for loading custom model implementations in Lighteval.",Xn,bt,Uo=`This config allows users to define and load their own model implementations by specifying
a Python file containing a custom model class that inherits from LightevalModel.`,Fn,yt,Go=`The custom model file should contain exactly one class that inherits from LightevalModel.
This class will be automatically detected and instantiated when loading the model.`,Sn,pe,qn,xt,jo="An example of a custom model can be found in <code>examples/custom_models/google_translate_model.py</code>.",Ht,Pe,Ot,Ct,Kt;return g=new Po({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),f=new R({props:{title:"Model Configs",local:"model-configs",headingTag:"h1"}}),ce=new R({props:{title:"Base model config",local:"lighteval.models.abstract_model.ModelConfig",headingTag:"h3"}}),fe=new X({props:{name:"class lighteval.models.abstract_model.ModelConfig",anchor:"lighteval.models.abstract_model.ModelConfig",parameters:[{name:"model_name",val:": str = None"},{name:"generation_parameters",val:": GenerationParameters = GenerationParameters(num_blocks=None, block_size=None, early_stopping=None, repetition_penalty=None, frequency_penalty=None, length_penalty=None, presence_penalty=None, max_new_tokens=None, min_new_tokens=None, seed=None, stop_tokens=None, temperature=0, top_k=None, min_p=None, top_p=None, truncate_prompt=None, cache_implementation=None, response_format=None)"},{name:"system_prompt",val:": str | None = None"},{name:"cache_dir",val:": str = '~/.cache/huggingface/lighteval'"}],parametersDescription:[{anchor:"lighteval.models.abstract_model.ModelConfig.model_name",description:`<strong>model_name</strong> (str) &#x2014;
The model name or unique id`,name:"model_name"},{anchor:"lighteval.models.abstract_model.ModelConfig.generation_parameters",description:`<strong>generation_parameters</strong> (GenerationParameters) &#x2014;
Configuration parameters that control text generation behavior, including
temperature, top_p, max_new_tokens, etc. Defaults to empty GenerationParameters.`,name:"generation_parameters"},{anchor:"lighteval.models.abstract_model.ModelConfig.system_prompt",description:`<strong>system_prompt</strong> (str | None) &#x2014;
Optional system prompt to be used with chat models. This prompt sets the
behavior and context for the model during evaluation.`,name:"system_prompt"},{anchor:"lighteval.models.abstract_model.ModelConfig.cache_dir",description:`<strong>cache_dir</strong> (str) &#x2014;
Directory to cache the model. Defaults to &#x201D;~/.cache/huggingface/lighteval&#x201D;.`,name:"cache_dir"}],source:"https://github.com/huggingface/lighteval/blob/vr_1066/src/lighteval/models/abstract_model.py#L41"}}),te=new Y({props:{anchor:"lighteval.models.abstract_model.ModelConfig.example",$$slots:{default:[Zo]},$$scope:{ctx:C}}}),_e=new R({props:{title:"Local Models",local:"local-models",headingTag:"h2"}}),he=new R({props:{title:"Transformers Model",local:"lighteval.models.transformers.transformers_model.TransformersModelConfig",headingTag:"h3"}}),ue=new X({props:{name:"class lighteval.models.transformers.transformers_model.TransformersModelConfig",anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig",parameters:[{name:"model_name",val:": str"},{name:"generation_parameters",val:": GenerationParameters = GenerationParameters(num_blocks=None, block_size=None, early_stopping=None, repetition_penalty=None, frequency_penalty=None, length_penalty=None, presence_penalty=None, max_new_tokens=None, min_new_tokens=None, seed=None, stop_tokens=None, temperature=0, top_k=None, min_p=None, top_p=None, truncate_prompt=None, cache_implementation=None, response_format=None)"},{name:"system_prompt",val:": str | None = None"},{name:"cache_dir",val:": str = '~/.cache/huggingface/lighteval'"},{name:"tokenizer",val:": str | None = None"},{name:"subfolder",val:": str | None = None"},{name:"revision",val:": str = 'main'"},{name:"batch_size",val:": typing.Optional[typing.Annotated[int, Gt(gt=0)]] = None"},{name:"max_length",val:": typing.Optional[typing.Annotated[int, Gt(gt=0)]] = None"},{name:"model_loading_kwargs",val:": dict = <factory>"},{name:"add_special_tokens",val:": bool = True"},{name:"skip_special_tokens",val:": bool = True"},{name:"model_parallel",val:": bool | None = None"},{name:"dtype",val:": str | None = None"},{name:"device",val:": typing.Union[int, str] = 'cuda'"},{name:"trust_remote_code",val:": bool = False"},{name:"compile",val:": bool = False"},{name:"multichoice_continuations_start_space",val:": bool | None = None"},{name:"pairwise_tokenization",val:": bool = False"},{name:"continuous_batching",val:": bool = False"},{name:"override_chat_template",val:": bool = None"}],parametersDescription:[{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.model_name",description:`<strong>model_name</strong> (str) &#x2014;
HuggingFace Hub model ID or path to a pre-trained model. This corresponds to the
<code>pretrained_model_name_or_path</code> argument in HuggingFace&#x2019;s <code>from_pretrained</code> method.`,name:"model_name"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.tokenizer",description:`<strong>tokenizer</strong> (str | None) &#x2014;
Optional HuggingFace Hub tokenizer ID. If not specified, uses the same ID as model_name.
Useful when the tokenizer is different from the model (e.g., for multilingual models).`,name:"tokenizer"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.subfolder",description:`<strong>subfolder</strong> (str | None) &#x2014;
Subfolder within the model repository. Used when models are stored in subdirectories.`,name:"subfolder"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.revision",description:`<strong>revision</strong> (str) &#x2014;
Git revision of the model to load. Defaults to &#x201C;main&#x201D;.`,name:"revision"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.batch_size",description:`<strong>batch_size</strong> (PositiveInt | None) &#x2014;
Batch size for model inference. If None, will be automatically determined.`,name:"batch_size"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.max_length",description:`<strong>max_length</strong> (PositiveInt | None) &#x2014;
Maximum sequence length for the model. If None, uses model&#x2019;s default.`,name:"max_length"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.model_loading_kwargs",description:`<strong>model_loading_kwargs</strong> (dict) &#x2014;
Additional keyword arguments passed to <code>from_pretrained</code>. Defaults to empty dict.`,name:"model_loading_kwargs"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.add_special_tokens",description:`<strong>add_special_tokens</strong> (bool) &#x2014;
Whether to add special tokens during tokenization. Defaults to True.`,name:"add_special_tokens"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.skip_special_tokens",description:`<strong>skip_special_tokens</strong> (bool) &#x2014;
Whether the tokenizer should output special tokens back during generation. Needed for reasoning models. Defaults to True`,name:"skip_special_tokens"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.model_parallel",description:`<strong>model_parallel</strong> (bool | None) &#x2014;
Whether to use model parallelism across multiple GPUs. If None, automatically
determined based on available GPUs and model size.`,name:"model_parallel"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.dtype",description:`<strong>dtype</strong> (str | None) &#x2014;
Data type for model weights. Can be &#x201C;float16&#x201D;, &#x201C;bfloat16&#x201D;, &#x201C;float32&#x201D;, &#x201C;auto&#x201D;, &#x201C;4bit&#x201D;, &#x201C;8bit&#x201D;.
If &#x201C;auto&#x201D;, uses the model&#x2019;s default dtype.`,name:"dtype"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.device",description:`<strong>device</strong> (Union[int, str]) &#x2014;
Device to load the model on. Can be &#x201C;cuda&#x201D;, &#x201C;cpu&#x201D;, or GPU index. Defaults to &#x201C;cuda&#x201D;.`,name:"device"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.trust_remote_code",description:`<strong>trust_remote_code</strong> (bool) &#x2014;
Whether to trust remote code when loading models. Defaults to False.`,name:"trust_remote_code"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.compile",description:`<strong>compile</strong> (bool) &#x2014;
Whether to compile the model using torch.compile for optimization. Defaults to False.`,name:"compile"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.multichoice_continuations_start_space",description:`<strong>multichoice_continuations_start_space</strong> (bool | None) &#x2014;
Whether to add a space before multiple choice continuations. If None, uses model default.
True forces adding space, False removes leading space if present.`,name:"multichoice_continuations_start_space"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.pairwise_tokenization",description:`<strong>pairwise_tokenization</strong> (bool) &#x2014;
Whether to tokenize context and continuation separately or together. Defaults to False.`,name:"pairwise_tokenization"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.continuous_batching",description:`<strong>continuous_batching</strong> (bool) &#x2014;
Whether to use continuous batching for generation. Defaults to False.`,name:"continuous_batching"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.override_chat_template",description:`<strong>override_chat_template</strong> (bool) &#x2014;
If True, we force the model to use a chat template. If alse, we prevent the model from using
a chat template. If None, we use the default (true if present in the tokenizer, false otherwise)`,name:"override_chat_template"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.generation_parameters",description:`<strong>generation_parameters</strong> (GenerationParameters, optional, defaults to empty GenerationParameters) &#x2014;
Configuration parameters that control text generation behavior, including
temperature, top_p, max_new_tokens, etc.`,name:"generation_parameters"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.system_prompt",description:`<strong>system_prompt</strong> (str | None, optional, defaults to None) &#x2014; Optional system prompt to be used with chat models.
This prompt sets the behavior and context for the model during evaluation.`,name:"system_prompt"},{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.cache_dir",description:"<strong>cache_dir</strong> (str, optional, defaults to &#x201D;~/.cache/huggingface/lighteval&#x201D;) &#x2014; Directory to cache the model.",name:"cache_dir"}],source:"https://github.com/huggingface/lighteval/blob/vr_1066/src/lighteval/models/transformers/transformers_model.py#L70"}}),ne=new Y({props:{anchor:"lighteval.models.transformers.transformers_model.TransformersModelConfig.example",$$slots:{default:[Vo]},$$scope:{ctx:C}}}),Me=new X({props:{name:"class lighteval.models.transformers.adapter_model.requires.<locals>.inner_fn.<locals>.Placeholder",anchor:"lighteval.models.transformers.adapter_model.requires.<locals>.inner_fn.<locals>.Placeholder",parameters:[{name:"*args",val:""},{name:"**kwargs",val:""}],source:"https://github.com/huggingface/lighteval/blob/vr_1066/src/lighteval/models/transformers/adapter_model.py#L43"}}),be=new X({props:{name:"class lighteval.models.transformers.delta_model.DeltaModelConfig",anchor:"lighteval.models.transformers.delta_model.DeltaModelConfig",parameters:[{name:"model_name",val:": str"},{name:"generation_parameters",val:": GenerationParameters = GenerationParameters(num_blocks=None, block_size=None, early_stopping=None, repetition_penalty=None, frequency_penalty=None, length_penalty=None, presence_penalty=None, max_new_tokens=None, min_new_tokens=None, seed=None, stop_tokens=None, temperature=0, top_k=None, min_p=None, top_p=None, truncate_prompt=None, cache_implementation=None, response_format=None)"},{name:"system_prompt",val:": str | None = None"},{name:"cache_dir",val:": str = '~/.cache/huggingface/lighteval'"},{name:"tokenizer",val:": str | None = None"},{name:"subfolder",val:": str | None = None"},{name:"revision",val:": str = 'main'"},{name:"batch_size",val:": typing.Optional[typing.Annotated[int, Gt(gt=0)]] = None"},{name:"max_length",val:": typing.Optional[typing.Annotated[int, Gt(gt=0)]] = None"},{name:"model_loading_kwargs",val:": dict = <factory>"},{name:"add_special_tokens",val:": bool = True"},{name:"skip_special_tokens",val:": bool = True"},{name:"model_parallel",val:": bool | None = None"},{name:"dtype",val:": str | None = None"},{name:"device",val:": typing.Union[int, str] = 'cuda'"},{name:"trust_remote_code",val:": bool = False"},{name:"compile",val:": bool = False"},{name:"multichoice_continuations_start_space",val:": bool | None = None"},{name:"pairwise_tokenization",val:": bool = False"},{name:"continuous_batching",val:": bool = False"},{name:"override_chat_template",val:": bool = None"},{name:"base_model",val:": str"}],parametersDescription:[{anchor:"lighteval.models.transformers.delta_model.DeltaModelConfig.base_model",description:`<strong>base_model</strong> (str) &#x2014;
HuggingFace Hub model ID or path to the base model. This is the original
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HuggingFace Hub model ID or path to the model to load.`,name:"model_name"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.tokenizer",description:`<strong>tokenizer</strong> (str | None) &#x2014;
HuggingFace Hub model ID or path to the tokenizer to load.`,name:"tokenizer"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.revision",description:`<strong>revision</strong> (str) &#x2014;
Git revision of the model. Defaults to &#x201C;main&#x201D;.`,name:"revision"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.dtype",description:`<strong>dtype</strong> (str) &#x2014;
Data type for model weights. Defaults to &#x201C;bfloat16&#x201D;. Options: &#x201C;float16&#x201D;, &#x201C;bfloat16&#x201D;, &#x201C;float32&#x201D;.`,name:"dtype"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.tensor_parallel_size",description:`<strong>tensor_parallel_size</strong> (PositiveInt) &#x2014;
Number of GPUs to use for tensor parallelism. Defaults to 1.`,name:"tensor_parallel_size"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.data_parallel_size",description:`<strong>data_parallel_size</strong> (PositiveInt) &#x2014;
Number of GPUs to use for data parallelism. Defaults to 1.`,name:"data_parallel_size"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.pipeline_parallel_size",description:`<strong>pipeline_parallel_size</strong> (PositiveInt) &#x2014;
Number of GPUs to use for pipeline parallelism. Defaults to 1.`,name:"pipeline_parallel_size"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.gpu_memory_utilization",description:`<strong>gpu_memory_utilization</strong> (NonNegativeFloat) &#x2014;
Fraction of GPU memory to use. Lower this if running out of memory. Defaults to 0.9.`,name:"gpu_memory_utilization"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.enable_prefix_caching",description:`<strong>enable_prefix_caching</strong> (bool) &#x2014;
Whether to enable prefix caching to speed up generation. May use more memory. Should be disabled for LFM2. Defaults to True.`,name:"enable_prefix_caching"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.max_model_length",description:`<strong>max_model_length</strong> (PositiveInt | None) &#x2014;
Maximum sequence length for the model. If None, automatically inferred.
Reduce this if encountering OOM issues (4096 is usually sufficient).`,name:"max_model_length"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.quantization",description:`<strong>quantization</strong> (str | None) &#x2014;
Quantization method.`,name:"quantization"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.load_format",description:`<strong>load_format</strong> (str | None) &#x2014;
The format of the model weights to load. choices: auto, pt, safetensors, npcache, dummy, tensorizer, sharded_state, gguf, bitsandbytes, mistral, runai_streamer.`,name:"load_format"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.swap_space",description:`<strong>swap_space</strong> (PositiveInt) &#x2014;
CPU swap space size in GiB per GPU. Defaults to 4.`,name:"swap_space"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.seed",description:`<strong>seed</strong> (NonNegativeInt) &#x2014;
Random seed for reproducibility. Defaults to 1234.`,name:"seed"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.trust_remote_code",description:`<strong>trust_remote_code</strong> (bool) &#x2014;
Whether to trust remote code when loading models. Defaults to False.`,name:"trust_remote_code"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.add_special_tokens",description:`<strong>add_special_tokens</strong> (bool) &#x2014;
Whether to add special tokens during tokenization. Defaults to True.`,name:"add_special_tokens"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.multichoice_continuations_start_space",description:`<strong>multichoice_continuations_start_space</strong> (bool) &#x2014;
Whether to add a space before multiple choice continuations. Defaults to True.`,name:"multichoice_continuations_start_space"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.pairwise_tokenization",description:`<strong>pairwise_tokenization</strong> (bool) &#x2014;
Whether to tokenize context and continuation separately for loglikelihood evals. Defaults to False.`,name:"pairwise_tokenization"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.max_num_seqs",description:`<strong>max_num_seqs</strong> (PositiveInt) &#x2014;
Maximum number of sequences per iteration. Controls batch size at prefill stage. Defaults to 128.`,name:"max_num_seqs"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.max_num_batched_tokens",description:`<strong>max_num_batched_tokens</strong> (PositiveInt) &#x2014;
Maximum number of tokens per batch. Defaults to 2048.`,name:"max_num_batched_tokens"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.subfolder",description:`<strong>subfolder</strong> (str | None) &#x2014;
Subfolder within the model repository. Defaults to None.`,name:"subfolder"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.is_async",description:`<strong>is_async</strong> (bool) &#x2014;
Whether to use the async version of VLLM. Defaults to False.`,name:"is_async"},{anchor:"lighteval.models.vllm.vllm_model.VLLMModelConfig.override_chat_template",description:`<strong>override_chat_template</strong> (bool) &#x2014;
If True, we force the model to use a chat template. If alse, we prevent the model from using
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Configuration parameters that control text generation behavior, including
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HuggingFace Hub model ID or path to the model to load.`,name:"model_name"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.load_format",description:`<strong>load_format</strong> (str) &#x2014;
The format of the model weights to load. choices: auto, pt, safetensors, npcache, dummy, tensorizer, sharded_state, gguf, bitsandbytes, mistral, runai_streamer.`,name:"load_format"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.dtype",description:`<strong>dtype</strong> (str) &#x2014;
Data type for model weights. Defaults to &#x201C;auto&#x201D;. Options: &#x201C;auto&#x201D;, &#x201C;float16&#x201D;, &#x201C;bfloat16&#x201D;, &#x201C;float32&#x201D;.`,name:"dtype"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.tp_size",description:`<strong>tp_size</strong> (PositiveInt) &#x2014;
Number of GPUs to use for tensor parallelism. Defaults to 1.`,name:"tp_size"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.dp_size",description:`<strong>dp_size</strong> (PositiveInt) &#x2014;
Number of GPUs to use for data parallelism. Defaults to 1.`,name:"dp_size"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.context_length",description:`<strong>context_length</strong> (PositiveInt | None) &#x2014;
Maximum context length for the model.`,name:"context_length"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.random_seed",description:`<strong>random_seed</strong> (PositiveInt | None) &#x2014;
Random seed for reproducibility. Defaults to 1234.`,name:"random_seed"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.trust_remote_code",description:`<strong>trust_remote_code</strong> (bool) &#x2014;
Whether to trust remote code when loading models. Defaults to False.`,name:"trust_remote_code"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.device",description:`<strong>device</strong> (str) &#x2014;
Device to load the model on. Defaults to &#x201C;cuda&#x201D;.`,name:"device"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.skip_tokenizer_init",description:`<strong>skip_tokenizer_init</strong> (bool) &#x2014;
Whether to skip tokenizer initialization. Defaults to False.`,name:"skip_tokenizer_init"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.kv_cache_dtype",description:`<strong>kv_cache_dtype</strong> (str) &#x2014;
Data type for key-value cache. Defaults to &#x201C;auto&#x201D;.`,name:"kv_cache_dtype"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.add_special_tokens",description:`<strong>add_special_tokens</strong> (bool) &#x2014;
Whether to add special tokens during tokenization. Defaults to True.`,name:"add_special_tokens"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.pairwise_tokenization",description:`<strong>pairwise_tokenization</strong> (bool) &#x2014;
Whether to tokenize context and continuation separately for loglikelihood evals. Defaults to False.`,name:"pairwise_tokenization"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.sampling_backend",description:`<strong>sampling_backend</strong> (str | None) &#x2014;
Sampling backend to use. If None, uses default.`,name:"sampling_backend"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.attention_backend",description:`<strong>attention_backend</strong> (str | None) &#x2014;
Attention backend to use. If None, uses default.`,name:"attention_backend"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.mem_fraction_static",description:`<strong>mem_fraction_static</strong> (PositiveFloat) &#x2014;
Fraction of GPU memory to use for static allocation. Defaults to 0.8.`,name:"mem_fraction_static"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.chunked_prefill_size",description:`<strong>chunked_prefill_size</strong> (PositiveInt) &#x2014;
Size of chunks for prefill operations. Defaults to 4096.`,name:"chunked_prefill_size"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.override_chat_template",description:`<strong>override_chat_template</strong> (bool) &#x2014;
If True, we force the model to use a chat template. If alse, we prevent the model from using
a chat template. If None, we use the default (true if present in the tokenizer, false otherwise)`,name:"override_chat_template"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.generation_parameters",description:`<strong>generation_parameters</strong> (GenerationParameters, optional, defaults to empty GenerationParameters) &#x2014;
Configuration parameters that control text generation behavior, including
temperature, top_p, max_new_tokens, etc.`,name:"generation_parameters"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.system_prompt",description:`<strong>system_prompt</strong> (str | None, optional, defaults to None) &#x2014; Optional system prompt to be used with chat models.
This prompt sets the behavior and context for the model during evaluation.`,name:"system_prompt"},{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.cache_dir",description:"<strong>cache_dir</strong> (str, optional, defaults to &#x201D;~/.cache/huggingface/lighteval&#x201D;) &#x2014; Directory to cache the model.",name:"cache_dir"}],source:"https://github.com/huggingface/lighteval/blob/vr_1066/src/lighteval/models/sglang/sglang_model.py#L54"}}),le=new Y({props:{anchor:"lighteval.models.sglang.sglang_model.SGLangModelConfig.example",$$slots:{default:[Ro]},$$scope:{ctx:C}}}),$e=new R({props:{title:"Dummy Model",local:"lighteval.models.dummy.dummy_model.DummyModelConfig",headingTag:"h3"}}),Te=new X({props:{name:"class lighteval.models.dummy.dummy_model.DummyModelConfig",anchor:"lighteval.models.dummy.dummy_model.DummyModelConfig",parameters:[{name:"model_name",val:": str = 'dummy'"},{name:"generation_parameters",val:": GenerationParameters = GenerationParameters(num_blocks=None, block_size=None, early_stopping=None, repetition_penalty=None, frequency_penalty=None, length_penalty=None, presence_penalty=None, max_new_tokens=None, min_new_tokens=None, seed=None, stop_tokens=None, temperature=0, top_k=None, min_p=None, top_p=None, truncate_prompt=None, cache_implementation=None, response_format=None)"},{name:"system_prompt",val:": str | None = None"},{name:"cache_dir",val:": str = '~/.cache/huggingface/lighteval'"},{name:"seed",val:": int = 42"}],parametersDescription:[{anchor:"lighteval.models.dummy.dummy_model.DummyModelConfig.model_name",description:`<strong>model_name</strong> (str) &#x2014;
Name of your choice - &#x201C;dummy&#x201D; by default`,name:"model_name"},{anchor:"lighteval.models.dummy.dummy_model.DummyModelConfig.seed",description:`<strong>seed</strong> (int) &#x2014;
Random seed for reproducible dummy responses. Defaults to 42.
This seed controls the randomness of the generated responses and log probabilities.`,name:"seed"}],source:"https://github.com/huggingface/lighteval/blob/vr_1066/src/lighteval/models/dummy/dummy_model.py#L35"}}),ae=new Y({props:{anchor:"lighteval.models.dummy.dummy_model.DummyModelConfig.example",$$slots:{default:[Xo]},$$scope:{ctx:C}}}),we=new R({props:{title:"Endpoints-based Models",local:"endpoints-based-models",headingTag:"h2"}}),Je=new R({props:{title:"Inference Providers Model",local:"lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig",headingTag:"h3"}}),Ie=new X({props:{name:"class lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig",anchor:"lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig",parameters:[{name:"model_name",val:": str"},{name:"generation_parameters",val:": GenerationParameters = GenerationParameters(num_blocks=None, block_size=None, early_stopping=None, repetition_penalty=None, frequency_penalty=None, length_penalty=None, presence_penalty=None, max_new_tokens=None, min_new_tokens=None, seed=None, stop_tokens=None, temperature=0, top_k=None, min_p=None, top_p=None, truncate_prompt=None, cache_implementation=None, response_format=None)"},{name:"system_prompt",val:": str | None = None"},{name:"cache_dir",val:": str = '~/.cache/huggingface/lighteval'"},{name:"provider",val:": str"},{name:"timeout",val:": int | None = None"},{name:"proxies",val:": typing.Optional[typing.Any] = None"},{name:"org_to_bill",val:": str | None = None"},{name:"parallel_calls_count",val:": typing.Annotated[int, Ge(ge=0)] = 10"}],parametersDescription:[{anchor:"lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig.model_name",description:`<strong>model_name</strong> (str) &#x2014;
Name or identifier of the model to use.`,name:"model_name"},{anchor:"lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig.provider",description:`<strong>provider</strong> (str) &#x2014;
Name of the inference provider. Examples: &#x201C;together&#x201D;, &#x201C;anyscale&#x201D;, &#x201C;runpod&#x201D;, etc.`,name:"provider"},{anchor:"lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig.timeout",description:`<strong>timeout</strong> (int | None) &#x2014;
Request timeout in seconds. If None, uses provider default.`,name:"timeout"},{anchor:"lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig.proxies",description:`<strong>proxies</strong> (Any | None) &#x2014;
Proxy configuration for requests. Can be a dict or proxy URL string.`,name:"proxies"},{anchor:"lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig.org_to_bill",description:`<strong>org_to_bill</strong> (str | None) &#x2014;
Organization to bill for API usage. If None, bills the user&#x2019;s account.`,name:"org_to_bill"},{anchor:"lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig.parallel_calls_count",description:`<strong>parallel_calls_count</strong> (NonNegativeInt) &#x2014;
Number of parallel API calls to make. Defaults to 10.
Higher values increase throughput but may hit rate limits.`,name:"parallel_calls_count"},{anchor:"lighteval.models.endpoints.inference_providers_model.InferenceProvidersModelConfig.generation_parameters",description:`<strong>generation_parameters</strong> (GenerationParameters, optional, defaults to empty GenerationParameters) &#x2014;
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