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const { OpenAI } = require('openai');
const api = new OpenAI({
baseURL: 'https://api.ai.cc/v1',
apiKey: '',
});
const main = async () => {
const result = await api.chat.completions.create({
model: 'meta-llama/llama-4-scout',
messages: [
{
role: 'system',
content: 'You are an AI assistant who knows everything.',
},
{
role: 'user',
content: 'Tell me, why is the sky blue?'
}
],
});
const message = result.choices[0].message.content;
console.log(`Assistant: ${message}`);
};
main();
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ai.cc/v1",
api_key="",
)
response = client.chat.completions.create(
model="meta-llama/llama-4-scout",
messages=[
{
"role": "system",
"content": "You are an AI assistant who knows everything.",
},
{
"role": "user",
"content": "Tell me, why is the sky blue?"
},
],
)
message = response.choices[0].message.content
print(f"Assistant: {message}")
-
AI Playground
Test all API models in the sandbox environment before you integrate.
We provide more than 300 models to integrate into your app.
Product Detail
Llama 4 Scout: A Lightweight, Multimodal AI Powerhouse
Llama 4 Scout is an advanced, lightweight multimodal AI model engineered to seamlessly process both text and image data. Designed for optimal efficiency, it can operate on a single Nvidia H100 GPU and boasts an impressive context window of up to 1 million tokens, making it ideal for highly complex and data-intensive tasks.
Key Features
🚀Massive 1M Token Context Window: Supports up to 1 million tokens, unlocking capabilities for extensive multi-document summarization and sophisticated long-form code reasoning.
⚡Optimized GPU Performance: Runs efficiently on just one Nvidia H100 GPU, ensuring high performance with minimal resource consumption.
💡Versatile Multimodal Processing: Seamlessly handles both text and image inputs, significantly expanding its application versatility across various domains.
Intended Use Cases
- 📄Advanced Multi-Document Summarization: Efficiently condenses information from numerous documents, providing coherent and precise summaries.
- 💻Comprehensive Code Analysis: Facilitates in-depth understanding, reasoning, and analysis of large and complex codebases.
- 🧠Intelligent Content Parsing: Processes vast amounts of text and image data for various applications, from data extraction to knowledge graph construction.
Technical Specifications
Architecture
Llama 4 Scout utilizes Meta's cutting-edge Mixture-of-Experts (MoE) framework, featuring an active parameter count of 109 billion. It leverages 16 specialized experts for dynamic, task-specific activation, enhancing efficiency and performance.
Training Data
Trained on meticulously curated datasets, encompassing diverse multilingual corpora, extensive image datasets, and sophisticated synthetic reasoning examples to ensure robust and versatile performance across various tasks.
Usage and Guidelines
Code Samples
Developers can integrate Llama 4 Scout using provided code samples. Below is a placeholder for common API interactions:
API Documentation
For comprehensive technical specifications and integration guides, refer to the Llama 4 Scout API Documentation, where detailed information is readily available.
Ethical Guidelines
🔒Llama 4 Scout is developed with robust safeguards to prevent misuse, including mechanisms against generating harmful content and ensuring user privacy during tool integrations. Our commitment is to foster responsible AI deployment.
Licensing
⚖️Llama 4 Scout operates under a Custom Llama 4 Community License, promoting responsible and collaborative use within the developer community.
Frequently Asked Questions (FAQ)
1. What is Llama 4 Scout?
Llama 4 Scout is a lightweight, multimodal AI model developed to efficiently process both text and image data, featuring a large context window and optimized performance on a single GPU.
2. What are the key technical specifications of Llama 4 Scout?
It supports up to a 1 million token context window, runs on a single Nvidia H100 GPU, and utilizes Meta's Mixture-of-Experts (MoE) framework with 109 billion active parameters.
3. What are the primary applications of Llama 4 Scout?
Its main applications include multi-document summarization, comprehensive code analysis, and intelligent content parsing for text and image data.
4. How does Llama 4 Scout address ethical concerns?
Llama 4 Scout incorporates robust safeguards to prevent harmful content generation and protect user privacy during tool integrations, reflecting a strong commitment to ethical AI.
5. Where can I find detailed API documentation?
Detailed API documentation is available here, providing comprehensive guides for integration and usage.
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