Meta: Llama 3.3 70B Instruct

Meta's Llama 3.3 70B Instruct is a powerful language model designed for text generation. It features 70 billion parameters and is optimized for multilingual dialogue. This model excels in conversational tasks and outperforms many existing models. It supports eight languages, including English and Spanish. Llama 3.3 is built using advanced transformer architecture for enhanced performance. The model is available under a community license, promoting responsible use. It is trained on a vast dataset of over 15 trillion tokens. Developers can use it for various applications, including chatbots and coding assistants.

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "meta-llama/llama-3.3-70b-instruct",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="meta-llama/llama-3.3-70b-instruct",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

Meta: Llama 3.3 70B Instruct

Context131072

Input$0.13 / M

Output$0.4 / M

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Description

Meta launched the Llama 3.3 70B Instruct model on December 6, 2024. This model is a large language model with 70 billion parameters. It is especially good at generating text in multiple languages. The model supports eight languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Llama 3.3 is designed for dialogue and does better than many other models in tests. It can manage a context length of 128,000 tokens, which helps it process large amounts of information. The training data includes around 15 trillion tokens from public datasets.

The model uses a transformer architecture, which boosts its ability to create clear and coherent text. It uses supervised fine-tuning and reinforcement learning with human feedback to align with what users want. The model has impressive performance metrics, scoring 86.0 on the MMLU benchmark.

Additionally, Llama 3.3 excels at following instructions, with a score of 92.1 on the IF eval metric. Its coding skills are also strong, achieving an 88.4 score on the HumanEval benchmark, showing its usefulness for software development.

Moreover, the model has safety features to ensure responsible AI use. These features help reduce risks when using AI. For developers wanting to use advanced AI, Llama 3.3 provides a good mix of performance and efficiency. Use our AIAPILAB services to integrate this model and enjoy competitive pricing.

Model API Use Case

Meta's Llama 3.3 Instruct API is great for many tasks. It shines in text generation and works in multiple languages. With 70 billion parameters, it can manage a lot of information. This allows it to handle up to 128,000 tokens, which is useful for detailed conversations. 

One key use is in customer support chatbots. Many businesses can create chat systems that work in different languages. For example, a company with 1 million users each month could save a lot of money. Llama 3.3 charges only $0.1 for every million input tokens and $0.4 for output tokens, which is much cheaper than other options.

Research groups can also use Llama 3.3 to create synthetic data. This helps improve how models are trained. The model has been pre-trained with over 15 trillion tokens, giving it a vast knowledge base. It can generate high-quality answers and insights.

Overall, Llama 3.3 is a cost-effective and powerful tool for developers. They can access advanced AI features without needing a lot of hardware. Its strong performance on tests like MMLU and Humaneval shows how effective it is in real-life situations.

Model Review

Pros

1. Llama 3.3 generates engaging text across eight languages, enhancing global communication. 2. The model processes 128,000 tokens, efficiently managing vast information in conversations. 3. It excels in instruction-following, providing accurate responses to user prompts. 4. Llama 3.3 showcases strong coding skills, aiding developers with complex programming tasks. 5. Safety features mitigate risks, ensuring responsible and ethical AI deployment.

Cons

1. Llama 3.3 may produce biased outputs, reflecting flaws in its training data. 2. It struggles with nuanced prompts, leading to vague or irrelevant responses. 3. The model's heavy resource demands can hinder accessibility for smaller developers.

Comparison

Feature/Aspect	Meta Llama 3.3 70B Instruct	Rubra Llama 3.3 70B Instruct	TechxGenus Llama 3.3 70B Instruct-GPTQ
Model Size	70 billion parameters	70 billion parameters	70 billion parameters
Key Strengths	Strong performance in multilingual dialogue and coding tasks	Enhanced ability for complex interactions and function calling	Optimized for text generation and coding tasks
Training Data	15 trillion tokens from publicly available sources	Proprietary dataset for enhanced instruction-following	New mix of publicly available data
Context Length	128,000 tokens	8,192 tokens	8,192 tokens
Supported Languages	English, German, French, Italian, Portuguese, Hindi, Spanish, Thai	English	English

API

import OpenAI from "openai"

const openai = new OpenAI({
  baseURL: "https://api.aiapilab.com/v1",
  apiKey: $AIAPILAB_API_KEY
})

async function main() {
  const completion = await openai.chat.completions.create({
    model: "meta-llama/llama-3.3-70b-instruct",
    messages: [
      {
        "role": "user",
        "content": "Write a blog about cat."
      }
    ]
  })

  console.log(completion.choices[0].message)
}
main()

from openai import OpenAI

client = OpenAI(
  base_url="https://api.aiapilab.com/v1",
  api_key="$AIAPILAB_API_KEY",
)

completion = client.chat.completions.create(
  model="meta-llama/llama-3.3-70b-instruct",
  messages=[
    {
      "role": "user",
      "content": "Write a blog about cat."
    }
  ]
)
print(completion.choices[0].message.content)

FAQ

Q1: What is Llama 3.3?  
A1: Llama 3.3 is a 70 billion parameter language model optimized for text generation.

Q2: What languages does Llama 3.3 support?  
A2: Llama 3.3 supports English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.

Q3: How can I use the Llama 3.3 API?  
A3: Use the API by sending requests with messages to generate text responses.

Q4: What are the key features of Llama 3.3?  
A4: Llama 3.3 features a 128k token context window, multilingual support, and ethical alignment.

Q5: How is Llama 3.3 trained?  
A5: Llama 3.3 is trained on 15 trillion tokens using supervised fine-tuning and human feedback.