llama-4-scout-17b-16e-instruct

Text Generation • Meta

@cf/meta/llama-4-scout-17b-16e-instruct

• Cloudflare-hosted
• Batch
• Function calling
• Vision

Meta's Llama 4 Scout is a 17 billion parameter model with 16 experts that is natively multimodal. These models leverage a mixture-of-experts architecture to offer industry-leading performance in text and image understanding.

Playground

Try out this model with Workers AI LLM Playground. It does not require any setup or authentication and is an instant way to preview and test a model directly in the browser.

Launch the LLM Playground

Usage

export interface Env {
  AI: Ai;
}

export default {
  async fetch(request, env): Promise<Response> {

    const messages = [
      { role: "system", content: "You are a friendly assistant" },
      {
        role: "user",
        content: "What is the origin of the phrase Hello, World",
      },
    ];

    const stream = await env.AI.run("@cf/meta/llama-4-scout-17b-16e-instruct", {
      messages,
      stream: true,
    });

    return new Response(stream, {
      headers: { "content-type": "text/event-stream" },
    });
  },
} satisfies ExportedHandler<Env>;

export interface Env {
  AI: Ai;
}

export default {
  async fetch(request, env): Promise<Response> {

    const messages = [
      { role: "system", content: "You are a friendly assistant" },
      {
        role: "user",
        content: "What is the origin of the phrase Hello, World",
      },
    ];
    const response = await env.AI.run("@cf/meta/llama-4-scout-17b-16e-instruct", { messages });

    return Response.json(response);
  },
} satisfies ExportedHandler<Env>;

import os
import requests

ACCOUNT_ID = "your-account-id"
AUTH_TOKEN = os.environ.get("CLOUDFLARE_AUTH_TOKEN")

prompt = "Tell me all about PEP-8"
response = requests.post(
  f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/@cf/meta/llama-4-scout-17b-16e-instruct",
    headers={"Authorization": f"Bearer {AUTH_TOKEN}"},
    json={
      "messages": [
        {"role": "system", "content": "You are a friendly assistant"},
        {"role": "user", "content": prompt}
      ]
    }
)
result = response.json()
print(result)

curl https://api.cloudflare.com/client/v4/accounts/$CLOUDFLARE_ACCOUNT_ID/ai/run/@cf/meta/llama-4-scout-17b-16e-instruct \
  -X POST \
  -H "Authorization: Bearer $CLOUDFLARE_AUTH_TOKEN" \
  -d '{ "messages": [{ "role": "system", "content": "You are a friendly assistant" }, { "role": "user", "content": "Why is pizza so good" }]}'

OpenAI compatible endpoints

Workers AI also supports OpenAI compatible API endpoints for /v1/chat/completions and /v1/embeddings. For more details, refer to Configurations .

Parameters

<details>

<summary>Synchronous — Send a request and receive a complete response</summary>

prompt

<code>string</code>requiredminLength: 1The input text prompt for the model to generate a response.

guided_json{}

<code>object</code>JSON schema that should be fulfilled for the response.

▶response_format{}

<code>object</code>

raw

<code>boolean</code>default: falseIf true, a chat template is not applied and you must adhere to the specific model's expected formatting.

stream

<code>boolean</code>default: falseIf true, the response will be streamed back incrementally using SSE, Server Sent Events.

max_tokens

<code>integer</code>default: 256The maximum number of tokens to generate in the response.

temperature

<code>number</code>default: 0.15minimum: 0maximum: 5Controls the randomness of the output; higher values produce more random results.

top_p

<code>number</code>minimum: 0maximum: 2Adjusts the creativity of the AI's responses by controlling how many possible words it considers. Lower values make outputs more predictable; higher values allow for more varied and creative responses.

top_k

<code>integer</code>minimum: 1maximum: 50Limits the AI to choose from the top 'k' most probable words. Lower values make responses more focused; higher values introduce more variety and potential surprises.

seed

<code>integer</code>minimum: 1maximum: 9999999999Random seed for reproducibility of the generation.

repetition_penalty

<code>number</code>minimum: 0maximum: 2Penalty for repeated tokens; higher values discourage repetition.

frequency_penalty

<code>number</code>minimum: 0maximum: 2Decreases the likelihood of the model repeating the same lines verbatim.

presence_penalty

<code>number</code>minimum: 0maximum: 2Increases the likelihood of the model introducing new topics.

response

<code>string</code>The generated text response from the model

▶usage{}

<code>object</code>Usage statistics for the inference request

▶tool_calls[]

<code>array</code>An array of tool calls requests made during the response generation

</details>

<details>

<summary>Streaming — Send a request with stream: true and receive server-sent events</summary>

prompt

<code>string</code>requiredminLength: 1The input text prompt for the model to generate a response.

guided_json{}

<code>object</code>JSON schema that should be fulfilled for the response.

▶response_format{}

<code>object</code>

raw

<code>boolean</code>default: falseIf true, a chat template is not applied and you must adhere to the specific model's expected formatting.

stream

<code>boolean</code>default: falseIf true, the response will be streamed back incrementally using SSE, Server Sent Events.

max_tokens

<code>integer</code>default: 256The maximum number of tokens to generate in the response.

temperature

<code>number</code>default: 0.15minimum: 0maximum: 5Controls the randomness of the output; higher values produce more random results.

top_p

<code>number</code>minimum: 0maximum: 2Adjusts the creativity of the AI's responses by controlling how many possible words it considers. Lower values make outputs more predictable; higher values allow for more varied and creative responses.

top_k

<code>integer</code>minimum: 1maximum: 50Limits the AI to choose from the top 'k' most probable words. Lower values make responses more focused; higher values introduce more variety and potential surprises.

seed

<code>integer</code>minimum: 1maximum: 9999999999Random seed for reproducibility of the generation.

repetition_penalty

<code>number</code>minimum: 0maximum: 2Penalty for repeated tokens; higher values discourage repetition.

frequency_penalty

<code>number</code>minimum: 0maximum: 2Decreases the likelihood of the model repeating the same lines verbatim.

presence_penalty

<code>number</code>minimum: 0maximum: 2Increases the likelihood of the model introducing new topics.

type

<code>string</code>

contentType

<code>text/event-stream</code>

format

<code>binary</code>

</details>

<details>

<summary>Batch — Send multiple requests in a single API call</summary>

▶requests[]

<code>array</code>required

response

<code>string</code>The generated text response from the model

▶usage{}

<code>object</code>Usage statistics for the inference request

▶tool_calls[]

<code>array</code>An array of tool calls requests made during the response generation

</details>

API Schemas (Raw)

SynchronousInput Open Download

SynchronousOutput Open Download

StreamingInput Open Download

StreamingOutput Open Download

BatchInput Open Download

BatchOutput Open Download

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description: Llama Guard 3 is a Llama-3.1-8B pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM inputs (prompt classification) and in LLM responses (response classification). It acts as an LLM – it generates text in its output that indicates whether a given prompt or response is safe or unsafe, and if unsafe, it also lists the content categories violated. title: llama-guard-3-8b image: https://developers.cloudflare.com/og-docs.png

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