Llama 4 Maverick 17B 128E

🚀 Llama 4

The Llama 4 collection of models are natively multimodal AI models that enable text and multimodal experiences, leveraging a mixture-of-experts architecture for industry-leading performance in text and image understanding.

🚀 Quick Start

Please, make sure you have transformers v4.51.0 installed, or upgrade using pip install -U transformers.

from transformers import pipeline
import torch

model_id = "meta-llama/Llama-4-Maverick-17B-128E"

pipe = pipeline(
    "text-generation",
    model=model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

output = pipe("Roses are red,", max_new_tokens=200)

✨ Features

• Multimodal Capability: The Llama 4 models are natively multimodal, enabling text and multimodal experiences, and offering industry-leading performance in text and image understanding.
• Mixture-of-Experts Architecture: Leveraging a mixture-of-experts architecture to enhance performance.
• Multiple Model Variants: Two efficient models in the Llama 4 series, Llama 4 Scout and Llama 4 Maverick, are launched.
• Supported Languages: Supports multiple languages including Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.

📦 Installation

Make sure you have transformers v4.51.0 installed. You can upgrade it using the following command:

pip install -U transformers

💻 Usage Examples

Basic Usage

from transformers import pipeline
import torch

model_id = "meta-llama/Llama-4-Maverick-17B-128E"

pipe = pipeline(
    "text-generation",
    model=model_id,
    device_map="auto",
    torch_dtype=torch.bfloat16
)

output = pipe("Roses are red,", max_new_tokens=200)

📚 Documentation

Model Information

• Model developer: Meta
• Model Architecture: The Llama 4 models are auto-regressive language models that use a mixture-of-experts (MoE) architecture and incorporate early fusion for native multimodality.

• Supported languages: Arabic, English, French, German, Hindi, Indonesian, Italian, Portuguese, Spanish, Tagalog, Thai, and Vietnamese.
• Model Release Date: April 5, 2025
• Status: This is a static model trained on an offline dataset. Future versions of the tuned models may be released as we improve model behavior with community feedback.
• License: A custom commercial license, the Llama 4 Community License Agreement, is available at: https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE
• Where to send questions or comments about the model: Instructions on how to provide feedback or comments on the model can be found in the Llama README. For more technical information about generation parameters and recipes for how to use Llama 4 in applications, please go here.

Intended Use

• Intended Use Cases: Llama 4 is intended for commercial and research use in multiple languages. Instruction tuned models are intended for assistant-like chat and visual reasoning tasks, whereas pretrained models can be adapted for natural language generation. For vision, Llama 4 models are also optimized for visual recognition, image reasoning, captioning, and answering general questions about an image. The Llama 4 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 4 Community License allows for these use cases.
• Out-of-scope: Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 4 Community License. Use in languages or capabilities beyond those explicitly referenced as supported in this model card.

Hardware and Software

• Training Factors: We used custom training libraries, Meta's custom built GPU clusters, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
• Training Energy Use: Model pre-training utilized a cumulative of 7.38M GPU hours of computation on H100-80GB (TDP of 700W) type hardware. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
• Training Greenhouse Gas Emissions: Estimated total location-based greenhouse gas emissions were 1,999 tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with clean and renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.

The methodology used to determine training energy use and greenhouse gas emissions can be found here. Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.

Training Data

• Overview: Llama 4 Scout was pretrained on ~40 trillion tokens and Llama 4 Maverick was pretrained on ~22 trillion tokens of multimodal data from a mix of publicly available, licensed data and information from Meta’s products and services. This includes publicly shared posts from Instagram and Facebook and people’s interactions with Meta AI.
• Data Freshness: The pretraining data has a cutoff of August 2024.

Benchmarks

Pre-trained models

Instruction tuned models

^reported numbers for MMMU Pro is the average of Standard and Vision tasks

Quantization

The Llama 4 Scout model is released as BF16 weights, but can fit within a single H100 GPU with on-the-fly int4 quantization; the Llama 4 Maverick model is released as both BF16 and FP8 quantized weights. The FP8 quantized weights fit on a single H100 DGX host while still maintaining quality. We provide code for on-the-fly int4 quantization which minimizes performance degradation as well.

Safeguards

As part of our release approach, we followed a three-pronged strategy to manage risks:

• Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
• Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
• Provide protections for the community to help prevent the misuse of our models.

Llama is a foundational technology designed for use in a variety of use cases; examples on how Meta’s Llama models have been deployed can be found in our Community Stories webpage. Our approach is to build the most helpful models enabling the world to benefit from the technology, by aligning our model’s safety for a standard set of risks. Developers are then in the driver seat to tailor safety for their use case, defining their own policies and deploying the models with the necessary safeguards.

📄 License

A custom commercial license, the Llama 4 Community License Agreement, is available at: https://github.com/meta-llama/llama-models/blob/main/models/llama4/LICENSE

⚠️ Important Note

The information you provide will be collected, stored, processed and shared in accordance with the Meta Privacy Policy.

💡 Usage Tip

Developers may fine-tune Llama 4 models for languages beyond the 12 supported languages provided they comply with the Llama 4 Community License and the Acceptable Use Policy. Also, if leveraging additional image understanding capabilities beyond 5 input images, developers are responsible for ensuring that their deployments are mitigated for risks and should perform additional testing and tuning tailored to their specific applications.