Meta's New Llama 3.1 AI Model: Use Cases & Benchmark in 2025

Meta published model weights for Llama 3.1 which is one of the most advanced language models. This access enables enterprises, researchers or individual developers to finetune and deploy their own Llama-based models.

This is especially important for enterprise generative AI as it allows enterprises to train their own LLMs using sensitive data that they may not want to share with cloud or LLM providers.

See the Meta LlaMa 3.1 models, their use cases, and benchmark to leading models:

Meta LlaMa 3.1

In July 2023, Meta announced LlaMA (Large Language Model Meta Artificial Intelligence). The instruction-tuned large language model (LLM) is trained on 15T tokens, 128K context length (vs original 8K), and various model sizes. 

There are different sizes of LlaMa models and all models have instructed fine-tuned versions. See the model descriptions:

• 8B parameters: Light-weight model best for basic text generation
• 70B parameters: Cost-effective model that enables more complex use cases for medium-scale AI applications.
• 405B parameters: Flagship foundation model for large-scale data analysis and complex problem-solving scenarios.

Table 1: Features for 405B models

Source: Meta¹

*All partners offer real-time inference features.

Availability: Users can use the Llama 3.1either directly through Amazon Bedrock or a deployed endpoint using SageMaker JumpStart

Llama 3 models will soon be available on:

• Cloud platforms: AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake 
• Hardware platforms: AMD, AWS, Dell, Intel, NVIDIA, and Qualcomm.

Real-life use cases of Llama 3.1 to enhance your organization

1. Data analysis

Video 1: Tool usage – Extracting a CSV file and building a time series with LlaMa 3.1

Users can upload and analyze a dataset—prompt model to plot graphs and market data.

Source: Meta²

2. Translation

Video 2: Multi-lingual agents

Users can enter a prompt such as “translate the story of Hansel and Gretel into Spanish.”

Source: Meta³

3. Travel advisor

Video 3: Complex reasoning

For example, users can ask “I have 3 shirts and 5 shorts, and 1 sun dress. I’m traveling for 10 days do I have enough for my vacation?”. LLaMa will break it down and offer the user which items to bring in three categories.

Source: Meta⁴

How Llama 3.1-405B compares to leading models

This evaluation set includes 1,800 prompts for 12 key use cases. Engineers worked to improve assessment prompts to optimize benchmark outputs.

Details of our evals are retrieved from GitHub. ⁵

Here are the key findings from the benchmark table:

General performance:

• All models perform similarly in general tasks, achieving near-identical results on the MMLU Chat (0-shot) benchmark, with Llama 3.1 and GPT-4 Omni both scoring 89, while Claude 3.5 Sonnet is slightly behind at 88.
• In the MMLU PRO (5-shot) benchmark, Claude 3.5 Sonnet leads with 77, while GPT-4 Omni scores 74, and Llama 3.1 comes in at 73.
• For the IFEval benchmark, Llama 3.1 performs best, scoring 89, with Claude 3.5 Sonnet and GPT-4 Omni close behind at 88 and 86, respectively.

Code performance:

• On the HumanEval (0-shot) benchmark, Claude 3.5 Sonnet achieves the highest score at 92, followed by GPT-4 Omni with 90 and Llama 3.1 at 89.
• For MBPP EvalPlus (0-shot), Claude 3.5 Sonnet again leads with 91, while Llama 3.1 and GPT-4 Omni score similarly at 89 and 88, respectively.

Math performance:

• On the GSM8K (8-shot) benchmark, all models perform excellently, with Llama 3.1, GPT-4 Omni, and Claude 3.5 Sonnet all scoring in the range of 96-97.
• On the MATH (0-shot) benchmark, GPT-4 Omni performs best with 77, followed by Llama 3.1 at 74, and Claude 3.5 Sonnet at 71.

Reasoning performance:

• In the ARC Challenge (0-shot), all models show strong reasoning capabilities, scoring 97 across the board.
• For GPQA (0-shot), Claude 3.5 Sonnet leads with 59, while GPT-4 Omni scores 54, and Llama 3.1 trails at 51.

Multilingual performance: 

• The models perform well in the multilingual MGSM benchmark, with Claude 3.5 Sonnet and Llama 3.1 scoring 92, and GPT-4 Omni slightly behind at 91.

Note: While evaluating the Meata Llama 3.1 405B to other foundation models, performance metrics aren’t the only thing to consider. 

Unlike closed-source peers, API-only accessible models Llama 3.1-405B can be built on, modified, and even run on-premises, which increases the level of control and predictability.

How to effectively use Llama 3.1 405B

Beyond direct usage of the model for inference and text generation, the 405B can be used for:

• Synthetic data generation: When data for pre-training, and fine-tuning,  is limited, synthetic data can fill the gap. Llama 405B can provide task-specific synthetic data to train another LLM.
  
  NVIDIA 340B exemplifies this by updating LLMs using synthetic data while maintaining the model’s existing knowledge.⁶
  
  
• Knowledge distillation: Llama 405B model’s knowledge and emergent skills may be distilled into a smaller model, combining the capabilities of a large model with the cost-effective model (such as an 8B or 70B).
  
  For example, Alpaca was fine-tuned from a smaller LLaMA model (7B parameters) using 52,000 instruction-following examples. This knowledge distillation helped the Alpaca training process cost $500 less in large-scale model development.⁷
  
  
• Unbiased evaluation: Evaluating LLMs can be subjective due to human preferences, but larger models can serve as evaluators of other models’ outputs.
  
  This is demonstrated in the LlaMA 2 research paper, where larger models like the 405B variant were used to judge the response quality of smaller models during fine-tuning.⁸
  
  This technique helps ensure consistency and objectivity in determining the best responses, bypassing some of the inherent subjectivity in human feedback.
  
  
• Domain-specific fine-tuning: Meta has made LLaMA 3.1-405B fully available for fine-tuning on specific domains.
  
  Meta’s LLaMA 3.1 8B can be fine-tuned using platforms like IBM’s Watsonx Tuning Studio or using Llama 3.1 405B as an alternative to human annotation to generate labels for the dataset.
  
  For example, some machine learning specialists provided a dataset from Hugging Face to LLaMA 3.1 8B to see how well Llama 3 8B can solve the following logical problems.
  
  
  This solution is almost accurate, but not quite. The correct measurement is 31 inches long.
  
  To improve the Llama 3 8B model’s logical question-answering capacity, engineers fine-tuned it using the Llama 3.1 405B model to create verbal responses to the questions and then utilized that dataset to fine-tune the Llama 3 8B model.⁹

How is LlaMa trained?

Similar to other large language models, LlaMA operates by receiving a string of words as input and anticipating the next word to iteratively produce text.

The training of this language model prioritized text from the top 20 languages with the highest number of speakers, particularly those using the Latin and Cyrillic scripts.

The training data of Meta LlaMa is mostly from large public websites and forums such as :

• Webpages scraped by CommonCrawl
• Open source repositories of source code from GitHub
• Wikipedia in 20 different languages
• Public domain books from Project Gutenberg
• The LaTeX source code for scientific papers uploaded to ArXiv

External Links

• 1. ”Meet Llama 3.1”. Meta. 2024. Retrieved on September 4, 2024.
• 2. ”Meet Llama 3.1”. Meta. 2024. Retrieved on September 4, 2024.
• 3. ”Meet Llama 3.1”. Meta. 2024. Retrieved on September 4, 2024.
• 4. ”Meet Llama 3.1”. Meta. 2024. Retrieved on September 4, 2024.
• 5. llama-models/models/llama3_1/eval_details.md at main · meta-llama/llama-models · GitHub.
• 6. Creating Synthetic Data Using Llama 3.1 405B | NVIDIA Technical Blog. NVIDIA Technical Blog
• 7. Stanford CRFM.
• 8. Llama 2: Open Foundation and Fine-Tuned Chat Models | Research - AI at Meta.
• 9. Use Llama 3.1 405B to generate synthetic data for fine-tuning tasks – HKU SPACE AI Hub.