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Llama 3 70B Instruct Reference

Llama 3 70B Instruct: Advanced language model by Meta, offering superior reasoning, code generation, and instruction-following capabilities for various applications.

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Javascript

Python

                                        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-3-70b-chat-hf',
    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-3-70b-chat-hf",
    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}")

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Llama 3 70B Instruct Reference

Product Detail

🚀 Llama 3 70B Instruct: A New Era in Language AI

Model Name: Llama 3 70B Instruct

Developer/Creator: Meta

Release Date: April 18, 2024

Version: 3

Model Type: Large Language Model (LLM)

Llama 3 70B Instruct represents a significant advancement in large language models. Designed for sophisticated assistant-like chat and various natural language generation tasks, this state-of-the-art model from Meta offers substantial improvements over its predecessor, Llama 2, particularly in areas like reasoning, code generation, and instruction following.

✨ Key Features Setting Llama 3 Apart:

Advanced Reasoning and Code Generation: Enhanced capabilities for complex problem-solving and programming tasks.
Improved Instruction Following and Alignment: More accurately interprets and executes user instructions.
Reduced False Refusal Rates: Less likely to incorrectly decline valid requests.
Increased Diversity in Model Responses: Generates a wider range of creative and relevant outputs.
Enhanced Steerable Outputs: Offers greater control over the nature and style of generated content.

🎯 Intended Use & Language Support:

The Llama 3 70B Instruct model is primarily developed for commercial and research applications in English. Its core focus lies in powering assistant-like chat systems and various natural language generation tasks.

While optimized for English, developers have the flexibility to fine-tune the model for other languages. This must be done in strict compliance with the Llama 3 Community License and its Acceptable Use Policy.

⚙️ Technical Deep Dive

Architecture Overview:

Decoder-only Transformer: A robust architecture optimized for generative tasks.
70 Billion Parameters: A massive parameter count enabling complex understanding and generation.
Grouped Query Attention (GQA): Significantly improves inference efficiency, crucial for large models.
128K Token Vocabulary: Facilitates more efficient and nuanced language encoding.
8,192 Token Context Window: Allows the model to process and maintain context over longer conversations and documents.

Training Data Insights:

Trained on an unprecedented up to 15 trillion tokens.
Remarkable performance improvements were observed even after training on two orders of magnitude more data than the Chinchilla-optimal amount.
Utilizes a diverse dataset specifically curated to enhance overall model performance and mitigate potential biases.

📊 Performance Metrics:

MMLU Score: 0.82

Quality Index Across Evaluations: 62

Output Speed: 54.3 tokens per second

Time to First Token (TTFT): 0.44 seconds

Comparison to Other Models:

Llama 3 70B Instruct sets a new state-of-the-art benchmark for LLM models at the 70B parameter scale.
Human annotator evaluations confirm that it outperforms competing models of comparable size in real-world scenarios, demonstrating superior utility and performance.

💡 Usage Guidelines

Code Samples:

# Example Python usage (conceptual) from llama3_api import Llama3Client client = Llama3Client(model="meta-llama/Llama-3-70b-chat-hf") response = client.chat_completion(prompt="Hello, what can you do?") print(response.choices[0].message.content)

Note: This is a placeholder for actual API code snippets. Refer to official Meta Llama 3 documentation for precise integration examples.

✅ Ethical Guidelines:

Llama 3 70B Instruct is built with a strong commitment to ethical considerations, upholding core principles such as:

Promoting Openness, Inclusivity, and Helpfulness: Designed to assist a broad user base.
Respecting User Dignity and Autonomy: Ensures user interactions are respectful and empowering.
Avoiding Unnecessary Judgment or Normativity: Strives for neutrality and impartiality in responses.
Following a Responsible Use Guide: Comprehensive guidelines are in place to mitigate potential misuse and critical risks.

📄 Licensing Information:

License Type: Llama 3 Community License

The license permits both commercial and research use of the model.
Usage is strictly prohibited in ways that violate applicable laws or regulations.
For complete details, please refer to the official Llama 3 Community License and Acceptable Use Policy.

❓ Frequently Asked Questions (FAQ)

Q1: What is Llama 3 70B Instruct primarily designed for?

A1: It's primarily designed for assistant-like chat applications and various natural language generation tasks, intended for commercial and research use in English.

Q2: How does Llama 3 70B Instruct compare to its predecessor, Llama 2?

A2: Llama 3 70B Instruct offers significant improvements in reasoning, code generation, instruction following, reduced false refusal rates, and increased diversity in responses.

Q3: Can Llama 3 70B Instruct be used for languages other than English?

A3: While primarily designed for English, developers can fine-tune the model for other languages, provided they comply with the Llama 3 Community License and Acceptable Use Policy.

Q4: What are the ethical considerations for using Llama 3 70B Instruct?

A4: It adheres to strict ethical guidelines, promoting openness, inclusivity, helpfulness, respecting user dignity, avoiding unnecessary judgment, and following a responsible use guide to mitigate risks.

Q5: Is Llama 3 70B Instruct suitable for commercial applications?

A5: Yes, the Llama 3 Community License explicitly allows for commercial use, alongside research use, provided all terms and conditions of the license are followed.

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