Llama 3.1 70B

Approved Data Classifications

Description

Llama-3.1-70B is a powerful large language model developed by Meta, designed to excel in a wide range of natural language processing tasks. With 70 billion parameters, this model offers significant improvements over its predecessors, particularly in multilingual dialogue and complex reasoning capabilities. It features an extensive context length of up to 128,000 tokens, allowing it to process and generate detailed responses from lengthy text inputs. Llama-3.1-70B is optimized for various applications, including content creation, conversational AI, sentiment analysis, and code generation, making it suitable for both research and enterprise environments. Its advanced training techniques, including supervised fine-tuning and reinforcement learning with human feedback, enhance its ability to generate relevant and contextually appropriate outputs. This model represents a robust solution for developers and businesses looking to leverage cutting-edge AI technology for diverse applications across multiple languages.

Capabilities

Model	Training Data	Input	Output	Context Length	Cost (per 1 million tokens)
llama-3.1-70b-instruct	July 2024	Text	Text	128,000	$0.99/1M input $0.99/1M output

info

• 1M represents 1 Million Tokens
• All prices listed are based on 1 Million Tokens

Availability

Cloud Provider

Usage

curl

curl -X POST https://api.ai.it.ufl.edu/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer <API_TOKEN>" \
-d '{
    "model": "llama-3.1-70b-instruct",
    "messages": [
        {
            "role": "system",
            "content": "You are a helpful assistant."
        },
        {
            "role": "user",
            "content": "Write a haiku about an Alligator."
        }
    ]
}'

python

from openai import OpenAI
  client = OpenAI(
      api_key="your_api_key",
      base_url="https://api.ai.it.ufl.edu/v1"
  )

  response = client.chat.completions.create(
      model="llama-3.1-70b-instruct", # model to send to the proxy
      messages = [
          { role: "system", content: "You are a helpful assistant." },
          {
              "role": "user",
              "content": "Write a haiku about an Alligator."
          }
      ]
  )

  print(response.choices[0].message)

javascript

import OpenAI from 'openai';

const openai = new OpenAI({
  apiKey: 'your_api_key',
  baseURL: 'https://api.ai.it.ufl.edu/v1'
});

const completion = await openai.chat.completions.create({
    model: "llama-3.1-70b-instruct",
    messages: [
        { role: "system", content: "You are a helpful assistant." },
        {
            role: "user",
            content: "Write a haiku about an Alligator.",
        },
    ],
});

print(completion.choices[0].message)