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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-maverick',
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-maverick",
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
Introducing Llama 4 Maverick: A Next-Generation AI Model
Llama 4 Maverick stands out as a powerful and efficient AI model, designed to push the boundaries of large language models. It employs an innovative Mixture-of-Experts (MoE) architecture to deliver unparalleled performance in complex reasoning and coding tasks, often surpassing leading models like GPT-4o and Gemini 2.0.
With a total of approximately 400 billion parameters, Llama 4 Maverick intelligently activates only 17 billion active parameters per token. This design ensures remarkable efficiency while maintaining immense computational power, making it ideal for cutting-edge multimodal applications and advanced problem-solving.
✨ Key Features & Capabilities
- Mixture-of-Experts (MoE) Architecture: Utilizes 128 specialized experts for enhanced performance, dynamically engaging relevant knowledge for each task.
- Multimodal Support: Seamlessly processes both text and images across 12 languages, enabling richer interactions and broader application possibilities.
- Cost-Effective Deployment: Optimized with FP8 quantization, ensuring efficient resource utilization and lower operational costs without sacrificing performance.
💡 Intended Applications
- Complex Problem Solving: Expertly handles advanced reasoning tasks, making it ideal for scientific research, data analysis, and strategic planning.
- Code Generation & Analysis: Excels in creating, debugging, and understanding intricate code structures across various programming languages.
- Diverse Multimodal Applications: Powers multilingual assistants, creative content generation (e.g., visual storytelling), and advanced coding applications.
⚙️ Technical Specifications
Architecture: Built on Meta’s robust Mixture-of-Experts (MoE) framework, featuring a massive pool of 128 experts. This allows for dynamic, task-specific activation of parameters from the total 400 billion parameter count.
Training Data: Trained on meticulously curated datasets that include extensive multilingual corpora, diverse image datasets, and sophisticated synthetic reasoning examples to ensure broad capability and robustness.
🚀 Usage & Integration
Code Samples: Developers can integrate Llama 4 Maverick into their projects using familiar API structures. Below is an example snippet:
import llama_maverick as lm
client = lm.LlamaMaverickClient(api_key="YOUR_API_KEY")
response = client.chat.completions.create(
model="meta-llama/llama-4-maverick",
messages=[
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Explain quantum entanglement simply."}
]
)
print(response.choices[0].message.content)
API Documentation: For comprehensive details on integration, endpoints, and advanced functionalities, please refer to our API Documentation.
🔒 Ethical Guidelines & Licensing
Ethical Use: Llama 4 Maverick incorporates robust safeguards to prevent misuse, including mechanisms against generating harmful content and ensuring user privacy during tool integrations. Our commitment is to responsible AI deployment.
Licensing: Llama 4 Maverick operates under a Custom Llama 4 Community License, fostering broad access and collaborative development within the AI community.
❓ Frequently Asked Questions (FAQ)
Q: What is the primary advantage of Llama 4 Maverick's Mixture-of-Experts architecture?
A: The MoE architecture allows Llama 4 Maverick to activate only a subset of its 400 billion parameters (17 billion per token) for a given task, making it significantly more efficient and performant than models that engage all parameters at once, especially for complex reasoning and coding.
Q: Does Llama 4 Maverick support multiple languages for multimodal tasks?
A: Yes, Llama 4 Maverick is designed to process both text and images across 12 different languages, enabling truly global multimodal applications such as multilingual assistants and visual storytelling.
Q: How does Llama 4 Maverick ensure cost-effectiveness?
A: It achieves cost-effectiveness through FP8 quantization, a technique that reduces the precision of numerical values in the model. This leads to lower memory usage and faster computation without significant degradation in performance, optimizing deployment costs.
Q: What kind of applications is Llama 4 Maverick best suited for?
A: It excels in complex problem-solving, advanced code generation and analysis, and diverse multimodal applications. This includes creative content generation, intelligent multilingual assistants, and sophisticated coding applications that require deep understanding and generation capabilities.
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